| 1. |
- Aboelwafa, Mariam M. N., et al.
(författare)
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A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT
- 2020
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Ingår i: IEEE Internet of Things Journal. - Piscataway : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 7:9, s. 8462-8471
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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)
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Sustainable environmental monitoring via energy and information efficient multi-node placement
- 2023
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 10:24, s. 22065-22079
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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)
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FIMBISAE : A Multimodal Biometric Secured Data Access Framework for Internet of Medical Things Ecosystem
- 2023
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 10:7, s. 6259-6270
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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)
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Allocation of Heterogeneous Resources of an IoT Device to Flexible Services
- 2016
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 3:5, s. 691-700
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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)
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Power-Adaptive Communication With Channel-Aware Transmission Scheduling in WBANs
- 2024
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:9, s. 16087-16102
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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)
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Multiagent Reinforcement Learning Meets Random Access in Massive Cellular Internet of Things
- 2021
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Ingår i: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 8:24, s. 17417-17428
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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)
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Synchronous LoRa Communication by Exploiting Large-Area out-of-band Synchronization
- 2021
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Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 8:10, s. 7912-7924
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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)
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Designing Blockchain-Based Access Control Protocol in IoT-Enabled Smart-Grid System
- 2021
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 8:7, s. 5744-5761
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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)
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Software-Defined Networking for Internet of Things : a Survey
- 2017
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 4:6, s. 1994-2008
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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)
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EE-MSWSN : Energy-Efficient Mobile Sink Scheduling in Wireless Sensor Networks
- 2022
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Ingår i: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 9:19, s. 18360-18377
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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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