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1.
  • Aboelwafa, Mariam M. N., et al. (författare)
  • A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT
  • 2020
  • Ingår i: IEEE Internet of Things Journal. - Piscataway : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 7:9, s. 8462-8471
  • Tidskriftsartikel (refereegranskat)abstract
    • The accelerated move toward the adoption of the Industrial Internet-of-Things (IIoT) paradigm has resulted in numerous shortcomings as far as security is concerned. One of the IIoT affecting critical security threats is what is termed as the false data injection (FDI) attack. The FDI attacks aim to mislead the industrial platforms by falsifying their sensor measurements. FDI attacks have successfully overcome the classical threat detection approaches. In this article, we present a novel method of FDI attack detection using autoencoders (AEs). We exploit the sensor data correlation in time and space, which in turn can help identify the falsified data. Moreover, the falsified data are cleaned using the denoising AEs (DAEs). Performance evaluation proves the success of our technique in detecting FDI attacks. It also significantly outperforms a support vector machine (SVM)-based approach used for the same purpose. The DAE data cleaning algorithm is also shown to be very effective in recovering clean data from corrupted (attacked) data. © 2014 IEEE.
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2.
  • Ahmad, Sabtain, et al. (författare)
  • Sustainable environmental monitoring via energy and information efficient multi-node placement
  • 2023
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 10:24, s. 22065-22079
  • Tidskriftsartikel (refereegranskat)abstract
    • The Internet of Things is gaining traction for sensing and monitoring outdoor environments such as water bodies, forests, or agricultural lands. Sustainable deployment of sensors for environmental sampling is a challenging task because of the spatial and temporal variation of the environmental attributes to be monitored, the lack of the infrastructure to power the sensors for uninterrupted monitoring, and the large continuous target environment despite the sparse and limited sampling locations. In this paper, we present an environment monitoring framework that deploys a network of sensors and gateways connected through low-power, long-range networking to perform reliable data collection. The three objectives correspond to the optimization of information quality, communication capacity, and sustainability. Therefore, the proposed environment monitoring framework consists of three main components: (i) to maximize the information collected, we propose an optimal sensor placement method based on QR decomposition that deploys sensors at information- and communication-critical locations; (ii) to facilitate the transfer of big streaming data and alleviate the network bottleneck caused by low bandwidth, we develop a gateway configuration method with the aim to reduce the deployment and communication costs; and (iii) to allow sustainable environmental monitoring, an energy-aware optimization component is introduced. We validate our method by presenting a case study for monitoring the water quality of the Ergene River in Turkey. Detailed experiments subject to real-world data show that the proposed method is both accurate and efficient in monitoring a large environment and catching up with dynamic changes.
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3.
  • Ahmed, Tauheed, et al. (författare)
  • FIMBISAE : A Multimodal Biometric Secured Data Access Framework for Internet of Medical Things Ecosystem
  • 2023
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 10:7, s. 6259-6270
  • Tidskriftsartikel (refereegranskat)abstract
    • Information from the Internet of Medical Things (IoMT) domain demands building safeguards against illegitimate access and identification. Existing user identification schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. Significant advancement has been achieved in the domain of biometrics and health informatics. This can take a step ahead with the usage of multimodal biometrics for the identification of healthcare system users. With this aim, the proposed work explores the fingerprint and iris modality to develop a multimodal biometric data identification and access control system for the healthcare ecosystem. In the proposed approach, minutiae-based fingerprint features and a combination of local and global iris features are considered for identification. Further, an index space based on the dimension of the feature vector is created, which gives a 1-D embedding of the high-dimensional feature set. Next, to minimize the impact of false rejection, the approach considers the possible deviation in each element of the feature vector and then stores the data in possible locations using the predefined threshold. Besides, to reduce the false acceptance rate, linking of the modalities has been done for every individual data. The modality linking thus helps in carrying out an efficient search of the queried data, thereby minimizing the false acceptance and rejection rate. Experiments on a chimeric iris and fingerprint bimodal database resulted in an average of 95% reduction in the search space at a hit rate of 98%. The results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.
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4.
  • Angelakis, Vangelis, et al. (författare)
  • Allocation of Heterogeneous Resources of an IoT Device to Flexible Services
  • 2016
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 3:5, s. 691-700
  • Tidskriftsartikel (refereegranskat)abstract
    • Internet-of-Things (IoT) devices can be equipped with multiple heterogeneous network interfaces. An overwhelmingly large amount of services may demand some or all of these interfaces available resources. Herein, we present a precise mathematical formulation of assigning services to interfaces with heterogeneous resources in one or more rounds. For reasonable instance sizes, the presented formulation produces optimal solutions for this computationally hard problem. We prove the NP-completeness of the problem and develop two algorithms to approximate the optimal solution for big instance sizes. The first algorithm allocates the most demanding service requirements first, considering the average cost of interfaces resources. The second one calculates the demanding resource shares and allocates the most demanding of them first by choosing randomly among equally demanding shares. Finally, we provide simulation results giving insight into services splitting over different interfaces for both cases.
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5.
  • Arghavani, Abbas, et al. (författare)
  • Power-Adaptive Communication With Channel-Aware Transmission Scheduling in WBANs
  • 2024
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:9, s. 16087-16102
  • Tidskriftsartikel (refereegranskat)abstract
    • Radio links in wireless body area networks (WBANs) are highly subject to short and long-term attenuation due to the unstable network topology and frequent body blockage. This instability makes it challenging to achieve reliable and energy-efficient communication, but on the other hand, provides a great potential for the sending nodes to dynamically schedule the transmissions at the time with the best expected channel quality. Motivated by this, we propose improved Gilbert-Elliott Markov chain model (IGE), a memory-efficient Markov chain model to monitor channel fluctuations and provide a long-term channel prediction. We then design adaptive transmission power selection (ATPS), a deadline-constrained channel scheduling scheme that enables a sending node to buffer the packets when the channel is bad and schedule them to be transmitted when the channel is expected to be good within a deadline. ATPS can self-learn the pattern of channel changes without imposing a significant computation or memory overhead on the sending node. We evaluate the performance of ATPS through experiments using TelosB motes under different scenarios with different body postures and packet rates. We further compare ATPS with several state-of-the-art schemes, including the optimal scheduling policy, in which the optimal transmission time for each packet is calculated based on the collected received signal strength indicator (RSSI) samples in an off-line manner. The experimental results reveal that ATPS performs almost as efficiently as the optimal scheme in high-date-rate scenarios and has a similar trend on power level usage.
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6.
  • Bai, Jianan, et al. (författare)
  • Multiagent Reinforcement Learning Meets Random Access in Massive Cellular Internet of Things
  • 2021
  • Ingår i: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 8:24, s. 17417-17428
  • Tidskriftsartikel (refereegranskat)abstract
    • Internet of Things (IoT) has attracted considerable attention in recent years due to its potential of interconnecting a large number of heterogeneous wireless devices. However, it is usually challenging to provide reliable and efficient random access control when massive IoT devices are trying to access the network simultaneously. In this article, we investigate methods to introduce intelligent random access management for a massive cellular IoT network to reduce access latency and access failures. Toward this end, we introduce two novel frameworks, namely, local device selection (LDS) and intelligent preamble selection (IPS). LDS enables local communication between neighboring devices to provide cluster-wide cooperative congestion control, which leads to a better distribution of the access intensity under bursty traffics. Taking advantage of the capability of reinforcement learning in developing cooperative multiagent policies, IPS is introduced to enable the optimization of the preamble selection policy in each IoT clusters. To handle the exponentially growing action space in IPS, we design a novel reinforcement learning structure, named branching actor-critic, to ensure that the output size of the underlying neural networks only grows linearly with the number of action dimensions. Simulation results indicate that the introduced mechanism achieves much lower access delays with fewer access failures in various realistic scenarios of interests.
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7.
  • Beltramelli, Luca, et al. (författare)
  • Synchronous LoRa Communication by Exploiting Large-Area out-of-band Synchronization
  • 2021
  • Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 8:10, s. 7912-7924
  • Tidskriftsartikel (refereegranskat)abstract
    • Many new narrowband low-power wide-area networks (LPWANs) (e.g., LoRaWAN, Sigfox) have opted to use pure ALOHA-like access for its reduced control overhead and asynchronous transmissions. Although asynchronous access reduces the energy consumption of IoT devices, the network performance suffers from high intra-network interference in dense deployments. Contrarily, adopting synchronous access can improve throughput and fairness, however, it requires time synchronization. Unfortunately, maintaining synchronization over the narrowband LPWANs wastes channel time and transmission opportunities. In this paper, we propose the use of out-of-band time-dissemination to relatively synchronize the LoRa devices and thereby facilitate resource-efficient slotted uplink communication. In this respect, we conceptualize and analyze a co-designed synchronization and random access communication mechanism that can effectively exploit technologies providing limited time accuracy, such as FM radio data system (FM-RDS). While considering the LoRa-specific parameters, we derive the throughput of the proposed mechanism, compare it to a generic synchronous random access using in-band synchronization, and design the communication parameters under time uncertainty. We scrutinize the transmission time uncertainty of a device by introducing a clock error model that accounts for the errors in the synchronization source, local clock, propagation delay, and transceiver’s transmission time uncertainty. We characterize the time uncertainty of FM-RDS with hardware measurements and perform simulations to evaluate the proposed solution. The results, presented in terms of success probability, throughput, and fairness for a single-cell scenario, suggest that FM-RDS, despite its poor absolute synchronization, can be used effectively to realize time-slotted communication in LoRa with performance similar to that of more accurate time-dissemination technologies. 
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8.
  • Bera, Basudeb, et al. (författare)
  • Designing Blockchain-Based Access Control Protocol in IoT-Enabled Smart-Grid System
  • 2021
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 8:7, s. 5744-5761
  • Tidskriftsartikel (refereegranskat)abstract
    • We design a new blockchain-based access control protocol in IoT-enabled smart-grid system, called DBACP-IoTSG. Through the proposed DBACP-IoTSG, the data is securely brought to the service providers from their respective smart meters (SMs). The peer-to-peer (P2P) network is formed by the participating service providers, where the peer nodes are responsible for creating the blocks from the gathered data securely from their corresponding SMs and adding them into the blockchain after validation of the blocks using the voting-based consensus algorithm. In our work, the blockchain is considered as private because the data collected from the consumers of the SMs are private and confidential. By the formal security analysis under the random oracle model, nonmathematical security analysis and software-based formal security verification, DBACP-IoTSG is shown to be resistant against various attacks. We carry out the experimental results of various cryptographic primitives that are needed for comparative analysis using the widely used multiprecision integer and rational arithmetic cryptographic library (MIRACL). A detailed comparative study reveals that DBACP-IoTSG supports more functionality features and provides better security apart from its low communication and computation costs as compared to recently proposed relevant schemes. In addition, the blockchain implementation of DBACP-IoTSG has been performed to measure computational time needed for the varied number of blocks addition and also the varied number of transactions per block in the blockchain.
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9.
  • Bera, Samaresh, et al. (författare)
  • Software-Defined Networking for Internet of Things : a Survey
  • 2017
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 4:6, s. 1994-2008
  • Tidskriftsartikel (refereegranskat)abstract
    • Internet of things (IoT) facilitates billions of devices to be enabled with network connectivity to collect and exchange real-time information for providing intelligent services. Thus, IoT allows connected devices to be controlled and accessed remotely in the presence of adequate network infrastructure. Unfortunately, traditional network technologies such as enterprise networks and classic timeout-based transport protocols are not capable of handling such requirements of IoT in an efficient, scalable, seamless, and cost-effective manner. Besides, the advent of software-defined networking (SDN) introduces features that allow the network operators and users to control and access the network devices remotely, while leveraging the global view of the network. In this respect, we provide a comprehensive survey of different SDN-based technologies, which are useful to fulfill the requirements of IoT, from different networking aspects – edge, access, core, and data center networking. In these areas, the utility of SDN-based technologies is discussed, while presenting different challenges and requirements of the same in the context of IoT applications. We present a synthesized overview of the current state of IoT development. We also highlight some of the future research directions and open research issues based on the limitations of the existing SDN-based technologies.
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10.
  • Biabani, Morteza, et al. (författare)
  • EE-MSWSN : Energy-Efficient Mobile Sink Scheduling in Wireless Sensor Networks
  • 2022
  • Ingår i: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 9:19, s. 18360-18377
  • Tidskriftsartikel (refereegranskat)abstract
    • Data gathering using mobile sink (MS) based on rendezvous points (RPs) is a need in several Internet of Things (IoT) applications. However, devising energy-efficient and reliable tour planning strategies for MS is a challenging issue, considering that sensors have finite buffer space and disparate sensing rates. This is even more challenging in delay-tolerant networks, where it is more desirable to select the shortest traveling path. There exist several algorithms on MS scheduling, which are based on hierarchical protocols for data forwarding and data collection. These algorithms are lacking efficient tradeoff between the Quality-of-Service (QoS) requirements in terms of energy efficiency, reliability, and computational cost. Besides, these algorithms have shown high packet losses while jointly performing MS tour planning and buffer overflow management. To address these limitations, we propose EE-MSWSN, an energy-efficient MS wireless sensor network that reliably collects data by implementing efficient buffer management. It forms novel clustered tree-based structures to cover all the network, and select each RP based on 1) hop count; 2) number of accumulated data in each clustered tree; and 3) distance to the stationary sink. The extensive simulation results verify that the EE-MSWSN minimizes tour length for various network configurations and incurs less energy consumption while reliably gathering data without packet losses as compared with existing protocols.
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11.
  • Borah, Jintu, et al. (författare)
  • AiCareBreath : IoT Enabled Location Invariant Novel Unified Model for Predicting Air Pollutants to Avoid Related Respiratory Disease
  • 2024
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 11:8, s. 14625-14633
  • Tidskriftsartikel (refereegranskat)abstract
    • This article presents a location-invariant air pollution prediction model with good geographic generalizability. The model uses a Light GBR as part of a machine-learning framework to capture the spatial identification of air contaminants. Given the dynamic nature of air pollution, the model also uses a Random Forest to capture temporal dependencies in the data. Our model uses a transfer learning strategy to deal with location variability. The algorithm can learn concentration patterns because it has been trained on a vast dataset of air quality measurements from various locations. The trained model is then improved using information from a particular target site, customizing it to the features of the target area. Experiments are carried out on a comprehensive dataset containing air pollution measurements from various places to assess the efficacy of the proposed model. The recommended method performs better than standard models at forecasting air pollution levels, proving its dependability in various geographical settings. An interpretability analysis is also performed to learn about the variables affecting air pollution levels. We identify the geographical patterns associated with high pollutant concentrations by visualizing the learned representations within the model, giving important information for environmental planning and mitigation methods. The observations show that the model outperforms state-of-the-art forecasting based on RNNs and transformer-based models. The suggested methodology for forecasting air contaminants has the potential to improve air quality management and aid in decision-making across numerous regions. This helps safeguard the environment and public health by creating more precise and dependable air pollution forecast systems. 
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12.
  • Cai, Hongming, et al. (författare)
  • IoT-Based Big Data Storage Systems in Cloud Computing : Perspectives and Challenges
  • 2017
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 4:1, s. 75-87
  • Tidskriftsartikel (refereegranskat)abstract
    • Internet of Things (IoT) related applications have emerged as an important field for both engineers and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations especially in cloud computing. This paper first provides a functional framework that identifies the acquisition, management, processing and mining areas of IoT big data, and several associated technical modules are defined and described in terms of their key characteristics and capabilities. Then current research in IoT application is analyzed, moreover, the challenges and opportunities associated with IoT big data research are identified. We also report a study of critical IoT application publications and research topics based on related academic and industry publications. Finally, some open issues and some typical examples are given under the proposed IoT-related research framework
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13.
  • Caso, Giuseppe, et al. (författare)
  • Empirical Models for NB-IoT Path Loss in an Urban Scenario
  • 2021
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 8:17, s. 13774-13788
  • Tidskriftsartikel (refereegranskat)abstract
    • The lack of publicly available large scale measurements has hindered the derivation of empirical path loss (PL) models for Narrowband Internet of Things (NB-IoT). Therefore, simulation-based investigations currently rely on models conceived for other cellular technologies, which are characterized, however, by different available bandwidth, carrier frequency, and infrastructure deployment, among others. In this paper, we take advantage of data from a large scale measurement campaign in the city of Oslo, Norway, to provide the first empirical characterization of NB-IoT PL in an urban scenario. For the PL average term, we characterize Alpha-Beta-Gamma (ABG) and Close-In (CI) models. By analyzing multiple NBIoT cells, we propose a statistical PL characterization, i.e., the model parameters are not set to a single, constant value across cells, but are randomly extracted from well-known distributions. Similarly, we define the PL shadowing distribution, correlation over distance, and inter-site correlation. Finally, we give initial insights on outdoor-to-indoor propagation, using measurements up to deep indoor scenarios. The proposed models improve PL estimation accuracy compared to the ones currently adopted in NB-IoT investigations, enabling more realistic simulations of urban scenarios similar to the sites covered by our measurements.
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14.
  • Caso, G., et al. (författare)
  • NB-IoT Random Access : Data-driven Analysis and ML-based Enhancements
  • 2021
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662.
  • Tidskriftsartikel (refereegranskat)abstract
    • In the context of massive Machine Type Communications (mMTC), the Narrowband Internet of Things (NB-IoT) technology is envisioned to efficiently and reliably deal with massive device connectivity. Hence, it relies on a tailored Random Access (RA) procedure, for which theoretical and empirical analyses are needed for a better understanding and further improvements. This paper presents the first data-driven analysis of NB-IoT RA, exploiting a large scale measurement campaign. We show how the RA procedure and performance are affected by network deployment, radio coverage, and operators’ configurations, thus complementing simulation-based investigations, mostly focused on massive connectivity aspects. Comparison with the performance requirements reveals the need for procedure enhancements. Hence, we propose a Machine Learning (ML) approach, and show that RA outcomes are predictable with good accuracy by observing radio conditions. We embed the outcome prediction in a RA enhanced scheme, and show that optimized configurations enable a power consumption reduction of at least 50%. We also make our dataset available for further exploration, toward the discovery of new insights and research perspectives.
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15.
  • Chavhan, Suresh, et al. (författare)
  • Edge-enabled Blockchain-based V2X Scheme for Secure Communication within the Smart City Development
  • 2023
  • Ingår i: IEEE Internet of Things Journal. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 10:24, s. 21282-21293
  • Tidskriftsartikel (refereegranskat)abstract
    • As the high mobility nature of the vehicles results in frequent leaving and joining the transportation network, real-time data must be collected and shared in a timely manner. In such a transportation network, malicious vehicles can disrupt services and create serious issues, such as deadlocks and accidents. The blockchain is a technology that ensures traceability, consistency, and security in transportation networks. In this study, we integrated edge computing and blockchain technology to improve the optimal utilization of resources, especially in terms of computing, communication, security, and storage. We propose a novel, edge-integrated, blockchain-based vehicle platoon security scheme. For the vehicle platoon, we developed the security architecture, implemented smart contracts for practical network scenarios in NS-3, and integrated them with the SUMO TraCI API. We exhaustively simulated all the scenarios and analyzed the communication performance metrics, such as throughput, delay, and jitter, and the security performance metrics, such as mean squared error, communication, and computational cost. The performance results demonstrate that the developed scheme can solve security-related issues more effectively and efficiently in smart cities. © IEEE
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16.
  • Chen, Hao, et al. (författare)
  • Coded Stochastic ADMM for Decentralized Consensus Optimization With Edge Computing
  • 2021
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 8:7, s. 5360-5373
  • Tidskriftsartikel (refereegranskat)abstract
    • Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones, and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to analyze information in a decentralized manner instead of aggregating data to a fusion center. To train large-scale machine learning models, edge/fog computing is often leveraged as an alternative to centralized learning. We consider the problem of learning model parameters in a multiagent system with data locally processed via distributed edge nodes. A class of minibatch stochastic alternating direction method of multipliers (ADMMs) algorithms is explored to develop the distributed learning model. To address two main critical challenges in distributed learning systems, i.e., communication bottleneck and straggler nodes (nodes with slow responses), error-control-coding-based stochastic incremental ADMM is investigated. Given an appropriate minibatch size, we show that the minibatch stochastic ADMM-based method converges in a rate of O(1/root k), where k denotes the number of iterations. Through numerical experiments, it is revealed that the proposed algorithm is communication efficient, rapidly responding, and robust in the presence of straggler nodes compared with state-of-the-art algorithms.
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17.
  • Chen, Hao, et al. (författare)
  • Federated Learning over Wireless IoT Networks with Optimized Communication and Resources
  • 2022
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662 .- 2372-2541. ; 9:17, s. 16592-16605
  • Tidskriftsartikel (refereegranskat)abstract
    • To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of interest to investigate fast responding and accurate FL schemes over wireless systems. Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of things (IoT) networks. To reduce complexity, we divide the overall optimization problem into two sub-problems, i.e., the client scheduling problem and the resource allocation problem. To reduce the communication costs for FL in wireless IoT networks, a new client scheduling policy is proposed by reusing stale local model parameters. To maximize successful information exchange over networks, a Lagrange multiplier method is first leveraged by decoupling variables including power variables, bandwidth variables and transmission indicators. Then a linear-search based power and bandwidth allocation method is developed. Given appropriate hyper-parameters, we show that the proposed communication-efficient federated learning (CEFL) framework converges at a strong linear rate. Through extensive experiments, it is revealed that the proposed CEFL framework substantially boosts both the communication efficiency and learning performance of both training loss and test accuracy for FL over wireless IoT networks compared to a basic FL approach with uniform resource allocation.
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18.
  • Chen, Haizhou, et al. (författare)
  • Measurement Capability Evaluation of Acoustic Emission Sensors in IIoT System for PHM
  • 2024
  • Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; , s. 1-1
  • Tidskriftsartikel (refereegranskat)abstract
    • In the realm of Industry 4.0, Industrial Internet of Things (IIoT) is pivotal for advancing Prognostics and Health Management (PHM) through real-time monitoring of asset conditions. The efficacy of these IIoT systems heavily relies on the precision and reliability of Acoustic Emission (AE) sensor data. Recognizing the critical dependence of IIoT system functionality on AE sensor capability, this study proposes a novel, systematic framework tailored for PHM applications. Our approach expands the application of the Gage Repeatability and Reproducibility (Gage R&R) technique, focusing on enhancing the reliability of IIoT-AE systems. In experimental study, AE sensors are deployed to collect data across various operational contexts, including different fault types, measurement positions, operators, speeds, and trial counts. This comprehensive exploration reveals how different contextual factors impact AE sensor capability, thereby facilitating the strategic selection of contexts for precise data acquisition. Additionally, we introduce an innovative three-region graph comprising key metrics, namely Percentage of Repeatability & Reproducibility (PRR), Precision-to-Tolerance Ratio (PTR), and Signal-to-Noise Ratio (SNR), to provide a clear and intuitive visualization of AE sensor capability and acceptability based on well-defined criteria. Our findings lay the groundwork for ensuring the accuracy and reliability in IIoT systems for PHM, while also provides invaluable guidance for contextual design and practical enhancement of AE sensor, with broader implications for real-time sensor capability evaluations in IoT systems. This work marks a significant step forward in ensuring the reliability of IIoT deployments in PHM, ultimately contributing to the advancement of sensor technology in Industry 4.0 applications.
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19.
  • Chen, Jialu, et al. (författare)
  • Lightweight Privacy-preserving Training and Evaluation for Discretized Neural Networks
  • 2020
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 7:4, s. 2663-2678
  • Tidskriftsartikel (refereegranskat)abstract
    • Machine learning, particularly the neural network, is extensively exploited in dizzying applications. In order to reduce the burden of computing for resource-constrained clients, a large number of historical private datasets are required to be outsourced to the semi-trusted or malicious cloud for model training and evaluation. To achieve privacy preservation, most of the existing work either exploited the technique of public key fully homomorphic encryption (FHE) resulting in considerable computational cost and ciphertext expansion, or secure multiparty computation (SMC) requiring multiple rounds of interactions between user and cloud. To address these issues, in this paper, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized neural networks is proposed. Firstly, we put forward an efficient single key fully homomorphic data encapsulation mechanism (SFH-DEM) without exploiting public key FHE. Based on SFH-DEM, a series of atomic calculations over the encrypted domain including multivariate polynomial, nonlinear activation function, gradient function and maximum operations are devised as building blocks. Furthermore, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized neural networks is proposed, which can also be extended to convolutional neural network. Finally, we give the formal security proofs for dataset privacy, model training privacy and model evaluation privacy under the semi-honest environment and implement the experiment on real dataset MNIST for recognizing handwritten numbers in discretized neural network to demonstrate the high efficiency and accuracy of our proposed LPTE.
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20.
  • Chen, Zhe, et al. (författare)
  • Toward FPGA Security in IoT : A New Detection Technique for Hardware Trojans
  • 2019
  • Ingår i: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 6:4, s. 7061-7068
  • Tidskriftsartikel (refereegranskat)abstract
    • Nowadays, field programmable gate array (FPGA) has been widely used in Internet of Things (IoT) since it can provide flexible and scalable solutions to various IoT requirements. Meanwhile, hardware Trojan (HT), which may lead to undesired chip function or leak sensitive information, has become a great challenge for FPGA security. Therefore, distinguishing the Trojan-infected FPGAs is quite crucial for reinforcing the security of IoT. To achieve this goal, we propose a clock-tree-concerned technique to detect the HTs on FPGA. First, we present an experimental framework which helps us to collect the electromagnetic (EM) radiation emitted by FPGA clock tree. Then, we propose a Trojan identifying approach which extracts the mathematical feature of obtained EM traces, i.e., 2-D principal component analysis (2DPCA) in this paper, and automatically isolates the Trojan-infected FPGAs from the Trojan-free ones by using a BP neural network. Finally, we perform extensive experiments to evaluate the effectiveness of our method. The results reveal that our approach is valid in detecting HTs on FPGA. Specifically, for the trust-hub benchmarks, we can find out the FPGA with always on Trojans (100% detection rate) while identifying the triggered Trojans with high probability (by up to 92%). In addition, we give a thorough discussion on how the experimental setup, such as probe step size, scanning area, and chip ambient temperature, affects the Trojan detection rate.
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21.
  • Dai, B., et al. (författare)
  • Enhancing Physical Layer Security in Internet of Things via Feedback : A General Framework
  • 2020
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 7:1, s. 99-115
  • Tidskriftsartikel (refereegranskat)abstract
    • In this article, a general framework for enhancing the physical layer security (PLS) in the Internet of Things (IoT) systems via channel feedback is established. To be specific, first, we study the compound wiretap channel (WTC) with feedback, which can be viewed as an ideal model for enhancing the PLS in the downlink transmission of IoT systems via feedback. A novel feedback strategy is proposed and a corresponding lower bound on the secrecy capacity is constructed for this ideal model. Next, we generalize the ideal model (i.e., the compound WTC with feedback) by considering channel states and feedback delay, and this generalized model is called the finite state compound WTC with delayed feedback. The lower bounds on the secrecy capacities of this generalized model with or without delayed channel output feedback are provided, and they are constructed according to variations of the previously proposed feedback scheme for the ideal model. Finally, from a Gaussian fading example, we show that the delayed channel output feedback enhances the achievable secrecy rate of the finite state compound WTC with only delayed state feedback, which implies that feedback helps to enhance the PLS in the downlink transmission of the IoT systems.
  •  
22.
  • Deng, Dan, et al. (författare)
  • Reinforcement Learning Based Optimization on Energy Efficiency in UAV Networks for IoT
  • 2022
  • Ingår i: IEEE Internet of Things Journal. - Piscataway : IEEE. - 2327-4662 .- 2372-2541. ; 10:3, s. 2767-2775
  • Tidskriftsartikel (refereegranskat)abstract
    • The combination of Non-Orthogonal Multiplex Access and Unmanned Aerial Vehicles (UAV) can improve theenergy efficiency (EE) for Internet-of-Things (IoT). On the condition of interference constraint and minimum achievable rate of the secondary users, we propose an iterative optimization algorithm on EE. Firstly, with given UAV trajectory, the Dinkelbach method based fractional programming is adopted to obtain theoptimal transmission power factors. By using the previous power allocation scheme, the successive convex optimization algorithmis adopted in the second stage to update the system parameters. Finally, reinforcement learning based optimization is introducedto obtain the best UAV trajectory. © 2022 IEEE
  •  
23.
  • Derhamy, Hasan, et al. (författare)
  • IoT Interoperability : On-demand and low latency Transparent Multi-protocol Translator
  • 2017
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 4:5, s. 1754-1763
  • Tidskriftsartikel (refereegranskat)abstract
    • In the Industrial Internet of Things there is a clear need for a high level of interoperability between independently developedsystems, often from different vendors. Traditional methods of interoperability including protocol gateways and adapters, are often usedat the network layer. Recent work on application interoperability has emphasized the use of middleware or protocol proxy/gateway.However, middleware tends to move the interoperability problem rather than solving it, and there are scalability issues with increasingthe number of proxies; re-configuration effort, and required bandwidth and processing overheads.This paper proposes a secure, on-demand and transparent protocol translator for the Industrial Internet of Things. Targeting thechallenge of interoperability between IP-based communication protocols, the paper analyses current solutions and develops a set ofrequirements to be met by IoT protocol interoperability. The proposed protocol translator is not a middleware, it is a SOA-basedparticipant, it is used on-demand when needed, it does not introduce design time dependencies, it operates transparently, it supportslow-latency, and it is secured through the use of Arrowhead authorization and authentication.
  •  
24.
  • Derhamy, Hasan (författare)
  • Software Architectural Style for Industrial Internetof Things
  • Ingår i: IEEE Internet of Things Journal. - 2327-4662.
  • Tidskriftsartikel (refereegranskat)abstract
    • As society has progressed through periods of evolutionand revolution, technology has played a key role as anenabler. In the same manner that mechanical machines of the1800’s drove the industrial revolution, now digitalized machinesare driving another industrial revolution. With the recognitionof a fourth industrial revolution the Industry 4.0 initiative wasfounded in Germany in 2011. One of the drivers of Industry4.0 is the Industrial Internet of Things. The Internet of Thingsis a natural step as computing ubiquity and interconnectednessbecome more widely present. Add to this intelligence, delegationand human orientation and the result is software intensiveengineering at almost all layers (excluding the physical andhuman layers). Software development is a competency in communications,information systems, computer science, softwareand computer systems engineering and electrical and electronicengineering. Software solutions are becoming more distributed,not only over processes, but over heterogeneous computing platformsand business domains. These platforms could be physicallyseparated over large distances, or highly mobile platforms withvarying security requirements. All these requirements introducecomplexity on a scale previously unseen in the software industry.
  •  
25.
  • Eldefrawy, Mohamed, et al. (författare)
  • Key Distribution Protocol for Industrial Internet of Things without Implicit Certificates
  • 2019
  • Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 6:1, s. 906-917
  • Tidskriftsartikel (refereegranskat)abstract
    • The deployment of the Internet of Things (IoT) in industry, called the Industrial IoT (IIoT), is supporting the introduction of very desirable improvements such as increasing production flexibility, self-organization and real-time and quick response to events. However, security and privacy challenges are still to be well addressed. The IIoT requires different properties to achieve secure and reliable systems and these requirements create extra challenges considering the limited processing and communication power available to IIoT field devices. In this research article, we present a key distribution protocol for IIoT that is computationally and communicationally lightweight (requires a single message exchange) and handles node addition and revocation, as well as fast re-keying. The scheme can also resist the consequences of node capture attacks (we assume that captured nodes can be detected by the Gateway and previous works have shown this assumption to be acceptable in practice), server impersonation attacks and provides forward/backward secrecy. We show formally the correctness of our protocol and evaluate its energy consumption under realistic scenarios using a real embedded platform compared to previous state-of-the-art key-exchange protocols, to show our protocol reliability for IIoT.
  •  
26.
  • Esfahani, Alireza, et al. (författare)
  • A Lightweight Authentication Mechanism for M2M Communications in Industrial IoT Environment
  • 2019
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 6:1, s. 288-296
  • Tidskriftsartikel (refereegranskat)abstract
    • In the emerging Industrial IoT era, Machine-to-Machine (M2M) communication technology is considered as a key underlying technology for building Industrial IoT environments where devices (e.g., sensors, actuators, gateways) are enabled to exchange information with each other in an autonomous way without human intervention. However, most of the existing M2M protocols that can be also used in the Industrial IoT domain provide security mechanisms based on asymmetric cryptography resulting in high computational cost. As a consequence, the resource-constrained IoT devices are not able to support them appropriately and thus, many security issues arise for the Industrial IoT environment. Therefore, lightweight security mechanisms are required for M2M communications in Industrial IoT in order to reach its full potential. As a step towards this direction, in this paper, we propose a lightweight authentication mechanism, based only on hash and XOR operations, for M2M communications in Industrial IoT environment. The proposed mechanism is characterized by low computational cost, communication and storage overhead, while achieving mutual authentication, session key agreement, device’s identity confidentiality, and resistance against the following attacks: replay attack, man-in-the-middle attack, impersonation attack, and modification attack.
  •  
27.
  • Farag, Hossam, et al. (författare)
  • REA-6TiSCH : Reliable Emergency-Aware Communication Scheme for 6TiSCH Networks
  • 2021
  • Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 8:3, s. 1871-1882
  • Tidskriftsartikel (refereegranskat)abstract
    • In the perspective of the emerging Industrial Internet of things (IIoT), the 6TiSCH working group has been created with the main goal to integrate the capabilities of the IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) with the IPv6 protocol stack. In order to support time-critical applications in IIoT, reliable real-time communication is a key requirement. Specifically, aperiodic critical traffic, such as emergency alarms, must be reliably delivered to the DODAG root within strict deadline bounds to avoid system failure or safety-critical situations. Currently, there is no mechanism defined in the 6TiSCH architecture for timely and reliably handling of such traffic and its prioritization over the non-critical one. In this paper, we introduce REA-6TiSCH, a reliable emergency-aware communication scheme to support real-time communications of emergency alarms in 6TiSCH networks. In REA-6TiSCH, the aperiodic emergency traffic is opportunistically enabled to hijack transmission cells pre-assigned for the regular periodic traffic in the TSCH schedule. Moreover, we introduce a distributed optimization scheme to improve the probability that an emergency flow is delivered successfully within its deadline bound. To the best of our knowledge, this is the first approach to incorporate emergency alarms in 6TiSCH networks. We evaluate the performance of REA-6TiSCH through extensive simulations and the results show the effectiveness of our proposed method in handling emergency traffic compared to Orchestra scheme. Additionally, we discuss the applicability of REA-6TiSCH and provide guidelines for real implementation in 6TiSCH networks.
  •  
28.
  • Fitzgerald, Emma, et al. (författare)
  • Energy-Optimal Data Aggregation and Dissemination for the Internet of Things
  • 2018
  • Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 5:2, s. 955-969
  • Tidskriftsartikel (refereegranskat)abstract
    • Established approaches to data aggregation in wirelesssensor networks (WSNs) do not cover the variety of new usecases developing with the advent of the Internet of Things. In particular,the current push towards fog computing, in which control,computation, and storage are moved to nodes close to the networkedge, induces a need to collect data at multiple sinks, ratherthan the single sink typically considered in WSN aggregationalgorithms. Moreover, for machine-to-machine communicationscenarios, actuators subscribing to sensor measurements may alsobe present, in which case data should be not only aggregated andprocessed in-network, but also disseminated to actuator nodes. Inthis paper, we present mixed-integer programming formulationsand algorithms for the problem of energy-optimal routing andmultiple-sink aggregation, as well as joint aggregation anddissemination, of sensor measurement data in IoT edge networks.We consider optimisation of the network for both minimal totalenergy usage, and min-max per-node energy usage. We alsoprovide a formulation and algorithm for throughput-optimalscheduling of transmissions under the physical interference modelin the pure aggregation case. We have conducted a numericalstudy to compare the energy required for the two use cases, aswell as the time to solve them, in generated network scenarioswith varying topologies and between 10 and 40 nodes. Althoughaggregation only accounts for less than 15% of total energyusage in all cases tested, it provides substantial energy savings.Our results show more than 13 times greater energy usage for40-node networks using direct, shortest-path flows from sensorsto actuators, compared with our aggregation and disseminationsolutions.
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29.
  • Fontes, Afonso, 1987 (författare)
  • Industrial Internet of Things Security enhanced with Deep Learning Approaches for Smart Cities
  • 2021
  • Ingår i: IEEE Internet of Things Journal. - 2327-4662.
  • Tidskriftsartikel (refereegranskat)abstract
    • The significant evolution of the Internet of Things (IoT) enabled the development of numerous devices able to improve many aspects in various fields in the industry for smart cities where machines have replaced humans. With the reduction in manual work and the adoption of automation, cities are getting more efficient and smarter. However, this evolution also made data even more sensitive, especially in the industrial segment. The latter has caught the attention of many hackers targeting Industrial IoT (IIoT) devices or networks, hence the number of malicious software, i.e., malware, has increased as well. In this article, we present the IIoT concept and applications for smart cities, besides also presenting the security challenges faced by this emerging area. We survey currently available deep learning techniques for IIoT in smart cities, mainly Deep Reinforcement Learning, Recurrent Neural Networks, and Convolutional Neural Networks, and highlight the advantages and disadvantages of security-related methods. We also present insights, open issues, and future trends applying deep learning techniques to enhance IIoT security.
  •  
30.
  • Ghayvat, Hemant, et al. (författare)
  • Healthcare-CT : SoLiD PoD and Blockchain-Enabled Cyber Twin Approach for Healthcare 5.0 Ecosystems
  • 2024
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 11:4, s. 6119-6130
  • Tidskriftsartikel (refereegranskat)abstract
    • The healthcare personals often use stored healthcare data to make crucial decisions, assess risk, and care for patients. The extraction of the required information from the saved healthcare data needs a healthcare ecosystem that can guarantee reliable data delivery. The reliability of cyber-physical data needs to be cross-examined using several sources of data of overlapping nature. The cross-examined data can be saved on blockchain and SOLID POD (SP) to preserve its reliability and privacy. Once the reliable healthcare data is stored on the blockchain and SP, the patients’ medical history can be delivered to data-operated systems to monitor, diagnose, and detect augmented healthcare anomalies. Cyber twins (CT) combine the specific cyber-physical objects with digital tools portraying their actual settings. The creation of a live model for the delivery of healthcare services presents a novel opportunity in patient care comprising better evaluation of risk and assessment without hampering the activities of daily living. The introduction of blockchain technology can improve the notion of CTs by certifying transparency, decentralized data storage, data irreversibility, and person-to-person industrial communication. The storage and exchange of CT data in the healthcare ecosystem depend on disseminated ledgers and decentralized databases for storing and processing data to avoid single point reliance. The present study develops an owner-centric decentralized sharing technique to fulfil the decentralized distribution of CT data.
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31.
  • Ghayvat, Hemant, et al. (författare)
  • STRENUOUS : Edge-Line Computing, AI, and IIoT Enabled GPS Spatiotemporal Data-Based Meta-Transmission Healthcare Ecosystem for Virus Outbreaks Discovery
  • 2023
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662 .- 2372-2541. ; 10:4, s. 3285-3294
  • Tidskriftsartikel (refereegranskat)abstract
    • COVID-19 is not the last virus; there would be many others viruses we may face in the future. We already witnessed the loss of economy and daily life through the lockdown. In addition, vaccine, medication, and treatment strategies take clinical trials, so there is a need to tracking and tracing approach. Suitably, exhibiting and computing social evolution is critical for refining the epidemic, but maybe crippled by location data ineptitude of inaccessibility. It is complex and time consuming to identify and detect the chain of virus spread from one person to another through the terabytes of spatiotemporal GPS data. The proposed research aims a HPE edge line computing and big data analytic supported virus outbreak tracing and tracking approach that consumes terabytes of spatiotemporal data. Proposed STRENUOUS system discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus-like COVID-19 epidemic transmission. The method and the mechanical assembly further contained an alert component to demonstrate a suspected case if there was a potential exposure with the confirmed subject. The proposed system tracks location data related to a suspected subject in the confirmed subject route, where the location data expresses one or more geographic locations of each user over a period. It recognizes a subcategory of the suspected subject who is expected to transmit a contagion based on the location data. System measure an exposure level of a carrier to the infection based on contaminated location data and a subset of carriers connected with the second location carrier. They investigated whether the people in the confirmed subject’s cross-path can be infected and suggest quarantine followed by testing. The Proposed STRENUOUS system produces a report specifying that the people have been exposed to the virus.
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32.
  • Gisdakis, Stylianos, et al. (författare)
  • Security, Privacy, and Incentive Provision for Mobile Crowd Sensing Systems
  • 2016
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 3:5, s. 839-853
  • Tidskriftsartikel (refereegranskat)abstract
    • Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of mobile crowd sensing (MCS). The openness of such systems and the richness of data MCS users are expected to contribute to them raise significant concerns for their security, privacy-preservation and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a holistic solution. That is, a secure and accountable MCS system that preserves user privacy, and enables the provision of incentives to the participants. At the same time, we are after an MCS architecture that is resilient to abusive users and guarantees privacy protection even against multiple misbehaving and intelligent MCS entities (servers). In this paper, we meet these challenges and propose a comprehensive security and privacy-preserving architecture. With a full blown implementation, on real mobile devices, and experimental evaluation we demonstrate our system's efficiency, practicality, and scalability. Last but not least, we formally assess the achieved security and privacy properties. Overall, our system offers strong security and privacy-preservation guarantees, thus, facilitating the deployment of trustworthy MCS applications.
  •  
33.
  • Gonzalo Peces, Carlos, et al. (författare)
  • Sleepy Devices Versus Radio Duty Cycling : The Case of Lightweight M2M
  • 2019
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662 .- 2372-2541. ; 6:2, s. 2550-2562
  • Tidskriftsartikel (refereegranskat)abstract
    • Standard protocols for wireless Internet of Things (IoT) communication must be energy-efficient in order to prolong the lifetimes of IoT devices. Two energy-saving strategies for wireless communication are prevalent within the IoT domain: 1) sleepy devices and 2) radio duty cycling. In this paper, we conduct a comprehensive evaluation as to what types of application scenarios benefit the most from either type of energy-saving strategy. We select the lightweight machine to machine (LwM2M) protocol for this purpose because it operates atop the standard constrained application protocol, and has support for sleepy devices through its Queue Mode. We implement the Queue Mode at both the server side and client side, and design enhancements of Queue Mode to further improve the performance. In our experimental evaluation, we compare the performance and characteristics of Queue Mode with that of running LwM2M in a network stack with the standard time-slotted channel hopping as the duty cycling medium access control protocol. By analyzing the results with the support of an empirical model, we find that each energy-saving strategy has different advantages and disadvantages depending on the scenario and traffic pattern. Hence, we also produce guidelines that can help developers to select the appropriate energy-saving strategy based on the application scenario.
  •  
34.
  • Gonzalo Peces, Carlos, et al. (författare)
  • Sleepy Devices Versus Radio Duty Cycling: The Case of Lightweight M2M
  • 2019
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 6:2, s. 2550-2562
  • Tidskriftsartikel (refereegranskat)abstract
    • Standard protocols for wireless Internet of Things (IoT) communication must be energy-efficient in order to prolong the lifetimes of IoT devices. Two energy-saving strategies for wireless communication are prevalent within the IoT domain: 1) sleepy devices and 2) radio duty cycling. In this paper, we conduct a comprehensive evaluation as to what types of application scenarios benefit the most from either type of energy-saving strategy. We select the lightweight machine to machine (LwM2M) protocol for this purpose because it operates atop the standard constrained application protocol, and has support for sleepy devices through its Queue Mode. We implement the Queue Mode at both the server side and client side, and design enhancements of Queue Mode to further improve the performance. In our experimental evaluation, we compare the performance and characteristics of Queue Mode with that of running LwM2M in a network stack with the standard time-slotted channel hopping as the duty cycling medium access control protocol. By analyzing the results with the support of an empirical model, we find that each energy-saving strategy has different advantages and disadvantages depending on the scenario and traffic pattern. Hence, we also produce guidelines that can help developers to select the appropriate energy-saving strategy based on the application scenario.
  •  
35.
  • Guntupalli, Lakshmikanth, et al. (författare)
  • An On-Demand Energy Requesting Scheme for Wireless Energy Harvesting Powered IoT Networks
  • 2018
  • Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 5:4, s. 2868-2879
  • Tidskriftsartikel (refereegranskat)abstract
    • Energy harvesting (EH) delivers a unique technique for replenishing batteries in Internet of Things (IoT) devices. Equipped with an energy harvesting accessory, EH-enabled sensor nodes/IoT devices extract energy from ambient resources such as solar or radio frequency (RF) signals. Relying on residual battery or/and harvested energy, sensor nodes in an IoT network perform data exchange activities. Otherwise, the delivery of sensed data would be delayed until sufficient energy is harvested. In this paper, we propose an on-demand energy requesting (OER) mechanism for improving the delay performance of a wireless EH-powered IoT network. The proposed scheme acquires energy when necessary from an energy transmitter that is capable of transmitting energy via directed RF signals. Furthermore we develop two associated discrete time Markov chain (DTMC) models to analyze the performance of the OER scheme, targeting at a generic synchronous medium access control (MAC) protocol. Using the proposed DTMC models, we evaluate OER with respect to average packet delay, network throughput, packet loss probability, and packet reliability ratio by employing a specific synchronous MAC protocol. Numerical results obtained from both analysis and discrete-event simulations coincide with each other, indicating the accuracy of the models and revealing the behavior of EH based packet transmissions.
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36.
  • Guo, Shize, et al. (författare)
  • Securing IoT Space via Hardware Trojan Detection
  • 2020
  • Ingår i: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 7:11, s. 11115-11122
  • Tidskriftsartikel (refereegranskat)abstract
    • Hardware Trojan (HT) is a malicious modification in the chip circuitry, which may lead to undesired chip function changing or sensitive information leaking once activated. As recently studied, HT has become one of the main threats for Internet-of-Things (IoT) security, and therefore, protecting IoT against the HT attack attracts growing attention from IoT researchers. In this article, we propose an HT detection technique which makes use of chip temporal thermal information and self-organizing map (SOM) neural network to automatically isolate the Trojan-infected chips with the Trojan-free ones, and meanwhile, confirm the Trojan location at the infected chips. The experimental results reveal that our method is effective. Specifically, for the Trust-hub benchmarks, it can detect HTs which increase only 0.02% power consumption of the original design and localize the Trojan positions precisely without any error. In addition, we demonstrate the advantages of our method over two existing HT detection methods, namely, the thermal and power map (TPM) and ring oscillator net (RON), and make a thorough discussion on how the thermal image resolution, chip technology, and clustering algorithm affect the Trojan detection results.
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37.
  • Hahm, Oliver, et al. (författare)
  • Operating Systems for Low-End Devices in the Internet of Things : a Survey
  • 2015. - 7
  • Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 3:5, s. 720-734
  • Tidskriftsartikel (refereegranskat)abstract
    • The Internet of Things (IoT) is projected to soon interconnect tens of billions of new devices, in large part also connected to the Internet. IoT devices include both high-end devices which can use traditional go-to operating systems (OSs) such as Linux, and low-end devices which cannot, due to stringent resource constraints, e.g., very limited memory, computational power, and power supply. However, large-scale IoT software development, deployment, and maintenance requires an appropriate OS to build upon. In this paper, we thus analyze in detail the specific requirements that an OS should satisfy to run on low-end IoT devices, and we survey applicable OSs, focusing on candidates that could become an equivalent of Linux for such devices, i.e., a one-size-fits-most, open source OS for low-end IoT devices.
  •  
38.
  • Hakansson, Victor Wattin, et al. (författare)
  • Optimal Scheduling of Multiple Spatio-temporally Dependent Observations for Remote Estimation using Age-of-Information
  • 2022
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662 .- 2372-2541. ; 9:20, s. 20308-20321
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper proposes an optimal scheduling policy for a system where spatio-temporally dependent sensor observations are broadcast to remote estimators over a resource-limited broadcast channel. We consider a system with a measurement-blind network scheduler that transmit observations, and design scheduling schemes that minimize MSE by determining a subset of sensor observations to be broadcast based on their information freshness, as measured by their age-of-information (AoI). By modeling the problem as a finite state-space Markov decision process (MDP), we derive an optimal scheduling policy, with AoI as a state-variable, minimizing the average mean squared error for an infinite time horizon. The resulting policy has a periodic pattern that renders an efficient implementation with low data storage. We further show that for any policy that minimizes the overall AoI, the estimation accuracy depends on how the scheduling order relates to the sensor’s intrinsic spatial correlation. Consequently, the estimation accuracy varies from worse than a randomized scheduling approach to near-optimal. Thus, we present an additional age-minimizing policy with optimal scheduling order. We also present alternative policies for large state spaces that are attainable with less computational effort. Numerical results validate the presented theory.
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39.
  • Hamdi, Monia, et al. (författare)
  • Energy-Efficient Joint Task Assignment and Power Control in Energy-Harvesting D2D Offloading Communications
  • 2022
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 9:8, s. 6018-6031
  • Tidskriftsartikel (refereegranskat)abstract
    • In this article, we investigate the joint task assignment and power control problems for Device-to-Device (D2D) offloading communications with energy harvesting. Exploiting the D2D links for data offloading allows reducing the traffic load of the cellular base stations. The energy consumed by the D2D transmitters for data offloading can be compensated by energy harvesting. The main objective is to maximize the energy efficiency (EE) under energy causality and delay constraints, assuming a harvest-transmit model. Hence, the proposed model results in a nonconvex problem. We first derive an equivalent and more tractable optimization problem by exploiting nonlinear fractional programming, also known as the Dinkelbach method. We propose a layered optimization method by decoupling the EE maximization problem into power allocation and offloading assignment. The first step consists of computing the optimal power values by applying the conjugate gradient method. In the second step, the problem of the D2D pair formation for data offloading amounts to the bipartite graph matching. It can be solved to optimality using the Hungarian algorithm. Extensive simulations were performed on various network scenarios. Numerical results show that the proposed resource allocation scheme achieves remarkable improvements in terms of network EE.
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40.
  •  
41.
  • Hossain, Mohammad Istiak, 1987-, et al. (författare)
  • Techno-Economic Framework for IoT Service Platform: : A Cost-Structure Aspects of IoT Service Provisioning
  • 2024
  • Ingår i: IEEE Internet of Things Journal. - : IEEE Communications Society. - 2327-4662.
  • Tidskriftsartikel (refereegranskat)abstract
    • A plethora of Internet of Things (IoT) platforms are available in the market today. Most of the IoT platforms are used mainly for service prototyping. Cost-efficient service scalability on any platform is still an unresolved concern that, so far, has been addressed qualitatively. A quantitative method for IoT platform economics is missing in the literature. In this paper, we propose a generic framework to address this gap. Our proposed framework covers the dimensioning of the platform's software and hardware to envisage the design, deployment, and operation cost of platform services. Then, we use the framework to perform a quantitative study of platform rollout in three platform business contexts. Our analysis shows the applicability of different deployment and platform integration choices. Our results suggest that storage and energy are the main cost drivers for platforms' hardware scalability, where the main cost driver is the intensity of the sensors' message transmission rate. Additionally, our use-case based study suggests that platform as a service (PaaS) is only beneficial for actors who have limited scale or niche market need.
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42.
  •  
43.
  • Jiang, Zhihan, et al. (författare)
  • Leveraging Machine Learning for Disease Diagnoses based on Wearable Devices : A Survey
  • 2023
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 10:24, s. 21959-21981
  • Tidskriftsartikel (refereegranskat)abstract
    • Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development of machine learning bring new opportunities for the early detection and management of various prevalent diseases, such as cardiovascular diseases, Parkinson’s disease, and diabetes. In this survey, we comprehensively review the articles related to specific diseases or health issues based on small wearable devices and machine learning. More specifically, we first present an overview of the articles selected and classify them according to their targeted diseases. Then, we summarize their objectives, wearable device and sensor data, machine learning techniques, and wearing locations. Based on the literature review, we discuss the challenges and propose future directions from the perspectives of privacy concerns, security concerns, transmission latency and reliability, energy consumption, multi-modality, multi-sensor, multi-devices, evaluation metrics, explainability, generalization and personalization, social influence, and human factors, aiming to inspire researchers in this field.
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44.
  • Karapantelakis, Athanasios, 1982-, et al. (författare)
  • Mobile Operator Collaboration Using Cooperative Multi-Agent Deep Reinforcement Learning
  • Ingår i: IEEE Internet of Things Journal. - 2327-4662.
  • Tidskriftsartikel (refereegranskat)abstract
    • Next generation mobile networks will provide connectivity services for an unprecedented number of mobile devices, used in industry-vertical applications with diverse network requirements as to throughput, latency, and geographical area of coverage. In order to satisfy these requirements at scale, collaboration between multiple operator is oftentimes essential. Current collaborations between network operators are long-term, reactive, and established with human involvement. The dynamic nature of mobile network traffic demands contradicts these collaborations’ rigid nature; thus, resulting in sub-optimal network resource allocation to connectivity service. In this work, we introduce an agent-based architecture for automating collaboration of mobile network operators based on predicted future demand. The architecture uses a multi-agent deep reinforcement learning algorithm, wherein every operator has its own agent suggesting future collaborations, which can change dynamically. Using simulation, we show that our approach outperforms the state of the art operator collaboration approaches and leads to more sustainable growth of mobile networks by reducing capital and operational expenses for mobile network operators.
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45.
  • Khodaei, Mohammad, et al. (författare)
  • Cooperative Location Privacy in Vehicular Networks : Why Simple Mix-zones are not Enough
  • 2021
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers Inc.. - 2327-4662. ; , s. 1-1
  • Tidskriftsartikel (refereegranskat)abstract
    • Vehicular communications disclose rich information about the vehicles and their whereabouts. Pseudonymous authentication secures communication while enhancing user privacy. To enhance location privacy, cryptographic mix-zones were proposed to facilitate vehicles covertly transition to new ephemeral credentials. The resilience to (syntactic and semantic) pseudonym linking (attacks) highly depends on the geometry of the mix-zones, mobility patterns, vehicle density, and arrival rates. We introduce a tracking algorithm for linking pseudonyms before and after a cryptographically protected mix-zone. Our experimental results show that an eavesdropper, leveraging standardized vehicular communication messages and road layout, could successfully link ≈73% of pseudonyms during non-rush hours and ≈62% of pseudonyms during rush hours after vehicles change their pseudonyms in a mix-zone. To mitigate such inference attacks, we present a novel cooperative mix-zone scheme that enhances user privacy regardless of the vehicle mobility patterns, vehicle density, and arrival rate to the mix-zone. A subset of vehicles, termed relaying vehicles, are selected to be responsible for emulating non-existing vehicles. Such vehicles cooperatively disseminate decoy traffic without affecting safety-critical operations: with 50% of vehicles as relaying vehicles, the probability of linking pseudonyms (for the entire interval) drops from ≈68% to ≈18%. On average, this imposes 28 ms extra computation overhead, per second, on the Road-Side Units (RSUs) and 4.67 ms extra computation overhead, per second, on the (relaying) vehicle side; it also introduces 1.46 KB/sec extra communication overhead by (relaying) vehicles and 45 KB/sec by RSUs for the dissemination of decoy traffic. Thus, user privacy is enhanced at the cost of low computation and communication overhead. 
  •  
46.
  • Kumar Das, Ashok, et al. (författare)
  • Biometrics-Based Privacy-Preserving User Authentication Scheme for Cloud-Based Industrial Internet of Things Deployment
  • 2018
  • Ingår i: IEEE Internet of Things Journal. - Piscataway, NJ : IEEE. - 2327-4662. ; 5:6, s. 4900-4913
  • Tidskriftsartikel (refereegranskat)abstract
    • Due to the widespread popularity of Internet-enabled devices, Industrial Internet of Things (IIoT) becomes popular in recent years. However, as the smart devices share the information with each other using an open channel, i.e., Internet, so security and privacy of the shared information remains a paramount concern. There exist some solutions in the literature for preserving security and privacy in IIoT environment. However, due to their heavy computation and communication overheads, these solutions may not be applicable to wide category of applications in IIoT environment. Hence, in this paper, we propose a new Biometric-based Privacy Preserving User Authentication (BP2UA) scheme for cloud-based IIoT deployment. BP2UA consists of strong authentication between users and smart devices using pre-established key agreement between smart devices and the gateway node. The formal security analysis of BP2UA using the well-known ROR model is provided to prove its session key security. Moreover, an informal security analysis of BP2UA is also given to show its robustness against various types of known attacks. The computation and communication costs of BP2UA in comparison to the other existing schemes of its category demonstrate its effectiveness in the IIoT environment. Finally, the practical demonstration of BP2UA is also done using the NS2 simulation.
  •  
47.
  • Kumar, Sidharth, et al. (författare)
  • RF Energy Transfer Channel Models for Sustainable IoT
  • 2018
  • Ingår i: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 5:4, s. 2817-2828
  • Tidskriftsartikel (refereegranskat)abstract
    • Self-sustainability of wireless nodes in Internet-of-Things applications can be realized with the help of controlled radio frequency energy transfer (RF-ET). However, due to significant energy loss in wireless dissipation, there is a need for novel schemes to improve the end-to-end RF-ET efficiency. In this paper, first we propose a new channel model for accurately characterizing the harvested dc power at the receiver. This model incorporates the effects of nonline of sight (NLOS) component along with the other factors, such as radiation pattern of transmit and receive antennas, losses associated with different polarization of transmitting field, and efficiency of power harvester circuit. Accuracy of the model is verified via experimental studies in an anechoic chamber (a controlled environment). Supported by experiments in controlled environment, we also formulate an optimization problem by accounting for the effect of NLOS component to maximize the RF-ET efficiency, which cannot be captured by the Friis formula. To solve this nonconvex problem, we present a computationally efficient golden section-based iterative algorithm. Finally, through extensive RF-ET measurements in different practical field environments we obtain the statistical parameters for Rician fading as well as path loss factor associated with shadow fading model, which also asserts the fact that Rayleigh fading is not well suited for RF-ET due to presence of a strong line of sight component.
  •  
48.
  • Lei, Wanlu, et al. (författare)
  • Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge IoT
  • 2022
  • Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 9:22, s. 22958-22971
  • Tidskriftsartikel (refereegranskat)abstract
    • Edge computing provides a promising paradigm to support the implementation of Internet of Things (IoT) by offloading tasks to nearby edge nodes. Meanwhile, the increasing network size makes it impractical for centralized data processing due to limited bandwidth, and consequently a decentralized learning scheme is preferable. Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. For RL in a decentralized setup, edge nodes (agents) connected through a communication network aim to work collaboratively to find a policy to optimize the global reward as the sum of local rewards. However, communication costs, scalability, and adaptation in complex environments with heterogeneous agents may significantly limit the performance of decentralized RL. Alternating direction method of multipliers (ADMM) has a structure that allows for decentralized implementation and has shown faster convergence than gradient descent-based methods. Therefore, we propose an adaptive stochastic incremental ADMM (asI-ADMM) algorithm and apply the asI-ADMM to decentralized RL with edge-computing-empowered IoT networks. We provide convergence properties for the proposed algorithms by designing a Lyapunov function and prove that the asI-ADMM has O(1/k) + O(1/M) convergence rate, where k and M are the number of iterations and batch samples, respectively. Then, we test our algorithm with two supervised learning problems. For performance evaluation, we simulate two applications in decentralized RL settings with homogeneous and heterogeneous agents. The experimental results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability and can well adapt to complex IoT environments. 
  •  
49.
  • Li, Chao, et al. (författare)
  • Federated Hierarchical Trust-based Interaction Scheme for Cross-domain Industrial IoT
  • 2023
  • Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662 .- 2372-2541. ; 10:1, s. 447-457
  • Tidskriftsartikel (refereegranskat)abstract
    • The Industrial Internet of Things (IIoT) is considered to be one of the most promising revolutionary technologies to increase productivity. With the refined development of manufacturing, the entire manufacturing process is split up into several areas of IoT production. Devices from different domains cooperate to perform the same task, which cause security problems in interacted communication among them. Existing authentication methods cause heavy key management overhead or rely on a trusted third party. It is imperative to protect privacy and ensure the credibility of the device during device interaction. This paper proposes a federated hierarchical trust interaction scheme (FHTI) for the cross-domain industrial IoT. It builds a low-privacy network platform through blockchain and protects the data privacy of the IIoT. A hierarchical trust mechanism based on federated detection is designed to realize the unified trust evaluation of cross-domain devices. A trusted cross-domain method based on device trust value is designed to ensure the security and trustworthiness of cross-domain devices. The simulation results show that the FHTI scheme can improve the speed of identity authentication and the detection accuracy of malicious devices.
  •  
50.
  • Li, Huafu, et al. (författare)
  • Performance Analysis and Transmission Block Size Optimization for Massive MIMO Vehicular Network with Spatially and Temporally Correlated Channels
  • 2024
  • Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 11:5, s. 8989-9003
  • Tidskriftsartikel (refereegranskat)abstract
    • We investigate the effect of spatially and temporally correlated channels on the transmission performance of multi-cell multi-user massive multiple-input multiple-output (MIMO) vehicular networks in generic non-isotropic scattering environments. A new channel model is established to evaluate the harmfulness of the non-isotropic-scattered angle-of-departure/angle-of-arrival (AoD/AoA) spread and the high mobility of users on the uplink transmission. We derive the expressions of achievable spectral efficiency (SE), taking into account the effects of line-of-sight propagation, channel aging, and pilot contamination. Specifically, two novel receive combining schemes, namely the aging-aware maximum ratio combining and the aging-aware minimum mean square error combining, are presented to mitigate the SE decline caused by outdated channel state information. A low-complexity pilot assignment algorithm is proposed to suppress pilot contamination. We find that the quasi-static assumption of the channel may be unsafe for the system design of the vehicular networks even within a single transmission block period lasting from hundreds of microseconds to a few milliseconds. We observe that there exists an optimal block size Copt that maximizes area spectral efficiency. Especially, Copt can be expressed as a function of movement speed, AoD spread, and AoA spread. Numerical results are presented to validate the efficacy of the proposed schemes and highlight the importance of correct performance evaluation for practical massive MIMO system designs.
  •  
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