| 1. |
- Biabani, Morteza, et al.
(author)
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EE-MSWSN : Energy-Efficient Mobile Sink Scheduling in Wireless Sensor Networks
- 2022
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In: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 9:19, s. 18360-18377
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Journal article (peer-reviewed)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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| 2. |
- Chen, Hao, et al.
(author)
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Federated Learning over Wireless IoT Networks with Optimized Communication and Resources
- 2022
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662 .- 2372-2541. ; 9:17, s. 16592-16605
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Journal article (peer-reviewed)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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| 3. |
- Deng, Dan, et al.
(author)
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Reinforcement Learning Based Optimization on Energy Efficiency in UAV Networks for IoT
- 2022
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In: IEEE Internet of Things Journal. - Piscataway : IEEE. - 2327-4662 .- 2372-2541. ; 10:3, s. 2767-2775
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Journal article (peer-reviewed)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
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| 4. |
- Hakansson, Victor Wattin, et al.
(author)
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Optimal Scheduling of Multiple Spatio-temporally Dependent Observations for Remote Estimation using Age-of-Information
- 2022
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662 .- 2372-2541. ; 9:20, s. 20308-20321
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Journal article (peer-reviewed)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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| 5. |
- Hamdi, Monia, et al.
(author)
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Energy-Efficient Joint Task Assignment and Power Control in Energy-Harvesting D2D Offloading Communications
- 2022
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 9:8, s. 6018-6031
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Journal article (peer-reviewed)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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| 6. |
- Lei, Wanlu, et al.
(author)
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Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge IoT
- 2022
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 9:22, s. 22958-22971
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Journal article (peer-reviewed)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.
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| 7. |
- Lv, Zhihan, Dr. 1984-, et al.
(author)
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Cross-Layer Optimization for Industrial Internet of Things in Real Scene Digital Twins
- 2022
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 9:17, s. 15618-15629
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Journal article (peer-reviewed)abstract
- The development of the Industrial Internet of Things (IIoT) and digital twins (DTs) technology brings new opportunities and challenges to all walks of life. The work aims to study the cross-layer optimization of DTs in IIoT. The specific application scenarios of hazardous gas leakage boundary tracking in the industry is explored. The work proposes an industrial hazardous gas tracking algorithm based on a parallel optimization framework, establishes a three-layer network of distributed edge computing based on IIoT, and develops a two-stage industrial hazardous gas tracking algorithm based on a state transition model. The performance of different algorithms is analyzed. The results indicate that the tracking state transition and target wake-up module can effectively track the gas boundary and reduce the network energy consumption. The task success rate of the parallel optimization algorithm exceeds 0.9 in 5 s. When the number of network nodes in the state transition algorithm is N = 600, the energy consumption is only 2.11 J. The minimum tracking error is 0.31, which is at least 1.33 lower than that of the exact conditional tracking algorithm. Therefore, the three-layer network edge computing architecture proposed here has an excellent performance in industrial gas diffusion boundary tracking.
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| 8. |
- Mouris, Boules Atef, et al.
(author)
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Optimizing Low-Complexity Analog Mappings for Low-Power Sensors with Energy Scheduling Capabilities
- 2022
-
In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662 .- 2372-2541. ; , s. 1-1
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Journal article (peer-reviewed)abstract
- Power consumption is a major challenge for massive deployment of wireless sensors in internet of things (IoT) networks. This paper studies the use of analog joint source-channel (AJSCC) mappings in low-power sensing schemes. In particular, we propose a novel triangular mapping geometry as a low-complexity dimension reduction mapping. The proposed triangular mapping is employed for analog compression of multiple sensor readings into one signal, and thus, limits the need for power-hungry analog-to-digital conversion and processing at the sensing nodes. A comprehensive performance analysis of the proposed triangular mapping in terms of the mean squared error (MSE) performance is provided analytically and verified numerically. The problem of mapping adaptation to different source distributions is also studied. Moreover, the proposed triangular mapping is adopted in an energy scheduling problem in which the sensing nodes schedule their use of the received powers at different time instants and adjust the mapping parameters accordingly with the goal of minimizing the sum distortion at the receiver. We present a fast low-complexity algorithm for optimal energy scheduling and verify its performance in comparison with commercial convex optimization solvers. It is shown that the proposed mapping provides a very good MSE performance compared to the AJSCC benchmarks despite having a much lower complexity circuit implementation.
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| 9. |
- Seo, Eunil, 1970-, et al.
(author)
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Resource-efficient federated learning with Non-IID data: An auction theoretic approach
- 2022
-
In: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 9:24, s. 25506-25524
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Journal article (peer-reviewed)abstract
- Federated learning (FL) has gained significant importance for intelligent applications, following data produced on a massive scale by numerous distributed IoT devices. From an FL perspective, the key aspect is that this data is not identically and independently distributed (IID) across different data sources and locations. This distribution-skewness leads to significant quality degradation. Moreover, an intrinsic consequence of using such non-IID data in decentralized learning is increasing costs that would be mitigated if using IID data. As a remedy, we propose a resource-efficient method for training an FL-based application with non-IID data, effectively minimizing cost through an auction approach and mitigating quality degradation through data sharing. In an experimental evaluation, we investigate the FL performance using real-world non-IID data and use the resulting ground-truth outputs to develop functions for estimating the utility of non-IID data, computation resource costs, and data generation costs. These functions are used to optimize the costs of model training, ensuring resource efficiency. It is further demonstrated that using shared-IID data significantly increases the resource efficiency of FL with local non-IID data. This holds true even when the shared IID data size is less than 1% of the size of the local non-IID data. Moreover, this work demonstrates that the profitability of the stakeholders can be maximized using the proposed auction procedure. The integration of the auction procedure and a resource-efficient training strategy allows FL service providers to create practical trading strategies by minimizing the FL clients’ resources and payments in a machine learning marketplace.
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| 10. |
- Symeonidis, Iraklis, et al.
(author)
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HERMES : Scalable, Secure, and Privacy-Enhancing Vehicular Sharing-Access System
- 2022
-
In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 9:1, s. 129-151
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Journal article (peer-reviewed)abstract
- We propose HERMES, a scalable, secure, and privacy-enhancing system for users to share and access vehicles. HERMES securely outsources operations of vehicle access token (AT) generation to a set of untrusted servers. It builds on an earlier proposal, namely, SePCAR, and extends the system design for improved efficiency and scalability. To cater to system and user needs for secure and private computations, HERMES utilizes and combines several cryptographic primitives with secure multiparty computation (MPC) efficiently. It conceals secret keys of vehicles and transaction details from the servers, including vehicle booking details, AT information, and user and vehicle identities. It also provides user accountability in case of disputes. Besides, we provide semantic security analysis and prove that HERMES meets its security and privacy requirements. Last but not least, we demonstrate that HERMES is efficient and, in contrast to SePCAR, scales to a large number of users and vehicles, making it practical for real-world deployments. We build our evaluations with two different MPC protocols: 1) HtMAC-MiMC and 2) CBC-MAC-AES. Our results demonstrate that HERMES is in the range of milliseconds for generating an AT, whether it operates for a single-vehicle owner or a large rental-company branch with over 1000 vehicles; handling 546 and 84 AT generations per second, respectively. As a result, HERMES is an order of magnitude faster compared to SePCAR. Specifically, it delivers 696 (with HtMAC-MiMC) and 42 (with CBC-MAC-AES) more ATs compared to in SePCAR for a single-vehicle owner AT generation. Furthermore, we show that HERMES is practical on the vehicle side, too, as AT operations performed on a prototype vehicle on-board unit take only approximate to 62 ms.
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| 11. |
- Tanyingyong, Voravit, et al.
(author)
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Scalable IoT Sensing Systems with Dynamic Sinks
- 2022
-
In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 9:10, s. 7211-7227
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Journal article (peer-reviewed)abstract
- A sink is a node in a sensor network that functions as a gateway, and it gathers data from other nodes and sends the data over the Internet for further processing. However, a single sink cannot meet the demand of a sensor network for the Internet of Things (IoT) with heavy traffic. Although deploying multiple sinks can improve scalability and mitigate bottleneck problems, it is still challenging to use multiple sinks while keeping energy consumption down. Previous studies have addressed this issue using optimization techniques based on the assumption that the network converges to a steady state in terms of traffic load. We take a practical approach based on real-time changes in traffic load. This work introduces the concept of dynamic sinks, a sensor device that can serve as an on-demand sink. We identify suitable metrics for decision mechanisms to activate/deactivate dynamic sinks and investigate three decision schemes, namely autonomous, delegated, and centralized schemes. We also develop a protocol to disseminate the decisions. As a proof-of-concept, the dynamic sink is implemented in Contiki. Then, we evaluate trade-offs between packet delivery ratio (PDR) and energy consumption using emulated devices in the Cooja network simulator. The results show that setups with dynamic sinks can reduce energy consumption considerably at the expense of slightly lower PDR when compared to a setup with multiple permanent sinks.
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| 12. |
- Wu, Mingming, et al.
(author)
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Time Allocation and Mode Selection for Secure Communications in Internet of Things
- 2022
-
In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 9:5, s. 3743-3755
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Journal article (peer-reviewed)abstract
- A joint optimization scheme of both time allocation and mode selection is proposed for defending smart jammer in the Internet of Things (IoT). Specifically, we employ multiple modes to deal with the potential eavesdropping and jamming attacks in wireless sensor networks (WSNs), respectively, as wireless-powered relaying (WPR), ambient backscatter relaying (ABR), friendly jamming (FJ) modes. Assuming a specific frame structure, time allocation between energy harvesting and transmission duration is first optimized for three modes based on transmission and security data maximization. In addition, considering that a single mode may fail to guarantee the performance demand due to the uncertainty of network conditions and adversary attacks, mode selection is carried out for adapting characteristics of different modes. Moreover, according to the available knowledge of jamming detection, for enhancing the robustness in secure transmission, the process of mode selection is further divided into two phases. Specifically, game theory is also utilized to achieve the Nash equilibrium for mode selection. Finally, numerical results verify the effects and exhibit the advantages of the proposed scheme.
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| 13. |
- Yang, Hui, et al.
(author)
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BrainIoT : Brain-Like Productive Services Provisioning with Federated Learning in Industrial IoT
- 2022
-
In: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 9:3, s. 2014-2024
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Journal article (peer-reviewed)abstract
- The Industrial Internet of Things (IIoT) accommodates a huge number of heterogeneous devices to bring vast services under a distributed computing scenarios. Most productive services in IIoT are closely related to production control and require distributed network support with low delay. However, the resource reservation based on gross traffic prediction ignores the importance of productive services and treats them as ordinary services, so it is difficult to provide stable low delay support for large amounts of productive service requests. For many productions, unexpected communication delays are unacceptable, and the delay may lead to serious production accidents causing great losses, especially when the productive service is security related. In this article, we propose a brain-like productive service provisioning scheme with federated learning (BrainIoT) for IIoT. The BrainIoT scheme is composed of three algorithms, including industrial knowledge graph-based relation mining, federated learning-based service prediction, and globally optimized resource reservation. BrainIoT combines production information into network optimization, and utilizes the interfactory and intrafactory relations to enhance the accuracy of service prediction. The globally optimized resource reservation algorithm suitably reserves resources for predicted services considering various resources. The numerical results show that the BrainIoT scheme utilizes interfactory relation and intrafactory relation to make an accurate service prediction, which achieves 96% accuracy, and improves the quality of service.
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| 14. |
- Yang, Jianfei, et al.
(author)
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EfficientFi : Towards Large-Scale Lightweight WiFi Sensing via CSI Compression
- 2022
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 9:15, s. 13086-13095
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Journal article (peer-reviewed)abstract
- WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access. Recently, besides network services, WiFi sensing is appealing in smart homes since it is device-free, cost-effective and privacy-preserving. Though numerous WiFi sensing methods have been developed, most of them only consider single smart home scenario. Without the connection of powerful cloud server and massive users, large-scale WiFi sensing is still difficult. In this paper, we firstly analyze and summarize these obstacles, and propose an efficient large-scale WiFi sensing framework, namely EfficientFi. The EfficientFi works with edge computing at WiFi APs and cloud computing at center servers. It consists of a novel deep neural network that can compress fine-grained WiFi Channel State Information (CSI) at edge, restore CSI at cloud, and perform sensing tasks simultaneously. A quantized auto-encoder and a joint classifier are designed to achieve these goals in an end-to-end fashion. To the best of our knowledge, the EfficientFi is the first IoT-cloud-enabled WiFi sensing framework that significantly reduces communication overhead while realizing sensing tasks accurately. We utilized human activity recognition and identification via WiFi sensing as two case studies, and conduct extensive experiments to evaluate the EfficientFi. The results show that it compresses CSI data from 1.368Mb/s to 0.768Kb/s with extremely low error of data reconstruction and achieves over 98% accuracy for human activity recognition.
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| 15. |
- Yin, J., et al.
(author)
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SmartDID : A Novel Privacy-preserving Identity based on Blockchain for IoT
- 2022
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; , s. 1-1
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Journal article (peer-reviewed)abstract
- Internet of Things (IoT) applications have penetrated into all aspects of human life. Millions of IoT users and devices, online services and applications combine to create a complex and heterogeneous network, which complicates digital identity management. Distributed identity is a promising paradigm to solve IoT identity problems and allows users to have soverignty over their private data. However, existing state-of-the-art methods are unsuitable for IoT due to continuing issues regarding resource limitations for IoT devices, security and privacy issues, and lack of a systematic proof system. Accordingly, in this paper, we propose SmartDID, a novel blockchain-based distributed identity aimed at establishing a self-sovereign identity and providing strong privacy preservation. First, we configure IoT devices as light nodes and design a Sybil-resistant, unlinkable and supervisable distributed identity that does not rely on central identity providers. We further develop a dual-credential model based on commitment and zero-knowledge proofs to protect the privacy of sensitive attributes, on-chain identity data and linkage of credentials. Moreover, we combine the basic credential proofs to prove the knowledge of solutions to more complex problems and create a systematic proof system. We go on to provide the security analysis of SmartDID. Experimental analysis shows that our scheme achieves better performance in terms of both credential generation and proof generation when compared with CanDID.
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| 16. |
- You, Yang, et al.
(author)
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On Data-Driven Self-Calibration for IoT-Based Gas Concentration Monitoring Systems
- 2022
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662 .- 2372-2541. ; , s. 1-1
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Journal article (peer-reviewed)abstract
- In this paper, data-driven self-calibration algorithms for the Internet-of-Things-based gas concentration monitoring systems embedded with low-cost gas sensors are designed. The measurement errors are assumed to be caused by imperfect compensation for the variation of sensor component behavior. Specifically, the calibration procedure for the non-dispersive infrared CO2 sensors is developed, for which the temperature dependency is the most dominant drift source. For a single sensor, the hidden Markov model is used to characterize the statistical relationship between different quantities introduced by the physical model that builds on the Beer-Lambert law. For the calibration in the Internet-of-Things-based system, sensors first transmit their belief functions of the true gas concentration level to the cloud. Then the cloud fusion center computes a fused belief function according to certain rules. This belief function is then used as reference for calibrating the sensors. To deal with the case where belief functions highly conflict with each other, a Wasserstein distance based weighted average belief function fusion approach is first proposed as networked calibration algorithm. To achieve more long-term stable calibration results, the networked calibration problem is further formulated as a partially observed Markov decision process problem, and the calibration strategies are derived in a sequential manner. Correspondingly, the deep Q-network approach is applied as a computationally efficient method to solve the proposed Markov decision process problem. The performance of practical designs of the proposed self-calibration algorithms is finally illustrated in numerical experiments utilizing real data.
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| 17. |
- Yuan, Yachao, et al.
(author)
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LbSP : Load-Balanced Secure and Private Autonomous Electric Vehicle Charging Framework With Online Price Optimization
- 2022
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In: IEEE Internet of Things Journal. - 2327-4662. ; 9:17, s. 15685-15696
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Journal article (peer-reviewed)abstract
- Nowadays, autonomous electric vehicles (AEVs) are increasingly popular due to low resource consumption, low pollutant emission, and high efficiency. In practice, Vehicle-to-Grid (V2G) networks supply energy power to EVs to ensure the usage of EVs. However, there are still certain security and privacy concerns in V2G connections, such as identity impersonation and message manipulation. Additionally, the widespread usage of EVs brings significant pressure on the power grid, leading to undesirable effects like voltage deviations if EVs' charging is not well coordinated. In this article, to tackle these issues, we design a novel load-balanced secure and private EV charging framework named load-balanced secure and private framework (LbSP) for secure, private, and efficient EV charging with a minimal negative effect on the existing power grid. It assures reliable and efficient charging services by a lightweighted encryption technique. Also, it balances the energy consumption of power grids via an online pricing strategy that minimizes load variance by optimizing energy prices in real time. Moreover, it preserves users' privacy while not affecting online pricing using an advanced differential privacy technique. Furthermore, LbSP deploys on an edge-cloud structure for fast response and more precise pricing, where clouds balance overall load consumption by online price optimization while edges gather data for clouds and respond to charging requests from EVs. The evaluation results show that the proposed framework ensures secure and private EV charging, balances energy load consumption, and preserves users' privacy.
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