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See detailThroughput Enhancement in FD- and SWIPT-enabled IoT Networks over Non-Identical Rayleigh Fading Channel
Nguyen, Nhat Tan; Tran Dinh, Hieu UL; Chatzinotas, Symeon UL et al

in IEEE Internet of Things Journal (2021)

Simultaneous wireless information and power transfer (SWIPT) and full-duplex (FD) communications have emerged as prominent technologies in overcoming the limited energy resources in Internet-of-Things ... [more ▼]

Simultaneous wireless information and power transfer (SWIPT) and full-duplex (FD) communications have emerged as prominent technologies in overcoming the limited energy resources in Internet-of-Things (IoT) networks and improving their spectral efficiency (SE). The article investigates the outage and throughput performance for a decode-and-forward (DF) relay SWIPT system, which consists of one source, multiple relays, and one destination. The relay nodes in this system can harvest energy from the source’s signal and operate in FD mode. A suboptimal, low-complexity, yet efficient relay selection scheme is also proposed. Specifically, a single relay is selected to convey information from a source to a destination so that it achieves the best channel from the source to the relays. An analysis of outage probability (OP) and throughput performed on two relaying strategies, termed static power splitting-based relaying (SPSR) and optimal dynamic power splitting-based relaying (ODPSR), is presented. Notably, we considered independent and non-identically distributed (i.n.i.d.) Rayleigh fading channels, which pose new challenges in obtaining analytical expressions. In this context, we derived exact closed-form expressions of the OP and throughput of both SPSR and ODPSR schemes. We also obtained the optimal power splitting ratio of ODPSR for maximizing the achievable capacity at the destination. Finally, we present extensive numerical and simulation results to confirm our analytical findings. Both simulation and analytical results show the superiority of ODPSR over SPSR. [less ▲]

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See detailEfficient Federated Learning Algorithm for Resource Allocation in Wireless IoT Networks
Nguyen, van Dinh UL; Sharma, Shree Krishna UL; Vu, Thang Xuan UL et al

in IEEE Internet of Things Journal (2021), 8(5), 3394-3409

Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning model without having to transfer their raw data to a centralized server, thus reducing communication ... [more ▼]

Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning model without having to transfer their raw data to a centralized server, thus reducing communication overhead. However, FL still faces a number of challenges such as non-iid distributed data and heterogeneity of user equipments (UEs). Enabling a large number of UEs to join the training process in every round raises a potential issue of the heavy global communication burden. To address these issues, we generalize the current state-of-the-art Federated Averaging (FedAvg) by adding a weight-based proximal term to the local loss function. The proposed FL algorithm runs stochastic gradient descent in parallel on a sampled subset of the total UEs with replacement during each global round. We provide a convergence upper bound characterizing the trade-off between convergence rate and global rounds, showing that a small number of active UEs per round still guarantees convergence. Next, we employ the proposed FL algorithm in wireless Internet-of-Things (IoT) networks to minimize either total energy consumption or completion time of FL, where a simple yet efficient path-following algorithm is developed for its solutions. Finally, numerical results on unbalanced datasets are provided to demonstrate the performance improvement and robustness on the convergence rate of the proposed FL algorithm over FedAvg. They also reveal that the proposed algorithm requires much less training time and energy consumption than the FL algorithm with full user participation. These observations advocate the proposed FL algorithm for a paradigm shift in bandwidth- constrained learning wireless IoT networks. [less ▲]

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See detailBackscatter-Assisted Data Offloading inOFDMA-based Wireless Powered Mobile EdgeComputing for IoT Networks
Nguyen, Xuan Phu; Tran Dinh, Hieu UL; Onireti, ‪Oluwakayode et al

in IEEE Internet of Things Journal (2021)

Mobile edge computing (MEC) has emerged as a prominent technology to overcome sudden demands on computation-intensive applications of the Internet of Things (IoT) with finite processing capabilities ... [more ▼]

Mobile edge computing (MEC) has emerged as a prominent technology to overcome sudden demands on computation-intensive applications of the Internet of Things (IoT) with finite processing capabilities. Nevertheless, the limited energy resources also seriously hinders IoT devices from offloading tasks that consume high power in active RF communications. Despite the development of energy harvesting (EH) techniques, the harvested energy from surrounding environments could be inadequate for power-hungry tasks. Fortunately, Backscatter communications (Backcom) is an intriguing technology to narrow the gap between the power needed for communication and harvested power. Motivated by these considerations, this paper investigates a backscatter-assisted data offloading in OFDMA-based wireless-powered (WP) MEC for IoT systems. Specifically, we aim at maximizing the sum computation rate by jointly optimizing the transmit power at the gateway (GW), backscatter coefficient, time-splitting (TS) ratio, and binary decision-making matrices. This problem is challenging to solve due to its non-convexity. To find solutions, we first simplify the problem by determining the optimal values of transmit power of the GW and backscatter coefficient. Then, the original problem is decomposed into two sub-problems, namely, TS ratio optimization with given offloading decision matrices and offloading decision optimization with given TS ratio. Especially, a closedform expression for the TS ratio is obtained which greatly enhances the CPU execution time. Based on the solutions of the two sub-problems, an efficient algorithm, termed the fast-efficient algorithm (FEA), is proposed by leveraging the block coordinate descent method. Then, it is compared with exhaustive search (ES), bisection-based algorithm (BA), edge computing (EC), and local computing (LC) used as reference methods. As a result, the FEA is the best solution which results in a near-globally-optimal solution at a much lower complexity as compared to benchmark schemes. For instance, the CPU execution time of FEA is about 0.029 second in a 50-user network, which is tailored for ultralow latency applications of IoT networks. [less ▲]

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See detailAuction-based Multi-Channel Cooperative Spectrum Sharing in Hybrid Satellite-Terrestrial IoT Networks
Zhang, Xiaokai; Guo, Daoxing; An, Kang et al

in IEEE Internet of Things Journal (2020)

In this paper, we investigate the multi-channel cooperative spectrum sharing in hybrid satellite-terrestrial internet of things (IoT) networks with the auction mechanism, which is designed to reduce the ... [more ▼]

In this paper, we investigate the multi-channel cooperative spectrum sharing in hybrid satellite-terrestrial internet of things (IoT) networks with the auction mechanism, which is designed to reduce the operational expenditure of the satellitebased IoT (S-IoT) network while alleviating the spectrum scarcity issues of terrestrial-based IoT (T-IoT) network. The cluster heads of selected T-IoT networks assist the primary satellite users transmission through cooperative relaying techniques in exchange for spectrum access. We propose an auction-based optimization problem to maximize the sum transmission rate of all primary S-IoT receivers with the appropriate secondary network selection and corresponding radio resource allocation profile by the distributed implementation while meeting the minimum transmission rate of secondary receivers of each TIoT network. Specifically, the one-shot Vickrey-Clarke-Groves (VCG) auction is introduced to obtain the maximum social welfare, where the winner determination problem is transformed into an assignment problem and solved by the Hungarian algorithm. To further reduce the primary satellite network decision complexity, the sequential Vickrey auction is implemented by sequential fashion until all channels are auctioned. Due to incentive compatibility with those two auction mechanisms, the secondary T-IoT cluster yields the true bids of each channel, where both the non-orthogonal multiple access (NOMA) and time division multiple access (TDMA) schemes are implemented in cooperative communication. Finally, simulation results validate the effectiveness and fairness of the proposed auction-based approach as well as the superiority of the NOMA scheme in secondary relays selection. Moreover, the influence of key factors on the performance of the proposed scheme is analyzed in detail. [less ▲]

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See detailData Augmentation and Dense-LSTM for Human Activity Recognition using WiFi Signal
Zhang, Jin; Wu, Fuxiang Wu; Wei, Bo et al

in IEEE Internet of Things Journal (2020)

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere wireless ... [more ▼]

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere wireless signal propagation, that manifested as unique patterns for activities recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carry substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in certain speed and scale, recent works commonly have moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi based human activity recognition system that synthesize variant activities data through 8 CSI transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep learning model that cater to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data. [less ▲]

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See detailDelay Constrained Resource Allocation for NOMA Enabled Satellite Internet of Things with Deep Reinforcement Learning
Yan, Xiaojuan; An, Kang; Zhang, Qianfeng et al

in IEEE Internet of Things Journal (2020)

With the ever increasing requirement of transferring data from/to smart users within a wide area, satellite internet of things (S-IoT) networks has emerged as a promising paradigm to provide cost ... [more ▼]

With the ever increasing requirement of transferring data from/to smart users within a wide area, satellite internet of things (S-IoT) networks has emerged as a promising paradigm to provide cost-effective solution for remote and disaster areas. Taking into account the diverse link qualities and delay qualityof- service (QoS) requirements of S-IoT devices, we introduce a power domain non-orthogonal multiple access (NOMA) scheme in the downlink S-IoT networks to enhance resource utilization efficiency and employ the concept of effective capacity to show delay-QoS requirements of S-IoT traffics. Firstly, resource allocation among NOMA users is formulated with the aim of maximizing sum effective capacity of the S-IoT while meeting the minimum capacity constraint of each user. Due to the intractability and non-convexity of the initial optimization problem, especially in the case of large-scale user-pair in NOMA enabled S-IoT. This paper employs a deep reinforcement learning (DRL) algorithm for dynamic resource allocation. Specifically, channel conditions and/or delay-QoS requirements of NOMA users are carefully selected as state according to exact closed-form expressions as well as low-SNR and high-SNR approximations, a deep Q network is first adopted to yet reward and output the optimum power allocation coefficients for all users, and then learn to adjust the allocation policy by updating the weights of neural networks using gained experiences. Simulation results are provided to demonstrate that with a proper discount factor, reward design, and training mechanism, the proposed DRL based power allocation scheme can output optimal/near-optimal action in each time slot, and thus, provide superior performance than that achieved with a fixed power allocation strategy and orthogonal multiple access (OMA) scheme. [less ▲]

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See detailEfficient Preamble Detection and Time-of-Arrival Estimation for Single-Tone Frequency Hopping Random Access in NB-IoT
Chougrani, Houcine UL; Kisseleff, Steven UL; Chatzinotas, Symeon UL

in IEEE Internet of Things Journal (2020)

The narrowband internet of things (NB-IoT) standard is a new cellular wireless technology, which has been introduced by the 3rd Generation Partnership Project (3GPP) with the goal to connect massive low ... [more ▼]

The narrowband internet of things (NB-IoT) standard is a new cellular wireless technology, which has been introduced by the 3rd Generation Partnership Project (3GPP) with the goal to connect massive low-cost, low-complexity and long-life IoT devices with extended coverage. In order to improve power efficiency, 3GPP proposed a new Random Access (RA) waveform for NB-IoT based on a single-tone frequencyhopping scheme. RA handles the first connection between user equipments (UEs) and the base station (BS). Through this, UEs can be identified and synchronized with the BS. In this context, receiver methods for the detection of the new waveform should satisfy the requirements on the successful user detection as well as the timing synchronization accuracy. This is not a trivial task, especially in the presence of radio impairments like carrier frequency offset (CFO) which constitutes one of the main radio impairments besides the noise. In order to tackle this problem, we propose a new receiver method for NB-IoT Physical Random Access Channel (NPRACH). The method is designed to eliminate perfectly the CFO without any additional computational complexity and supports all NPRACH preamble formats. The associated performance has been evaluated under 3GPP conditions. We observe a very high performance compared both to 3GPP requirements and to the existing state-of-the-art methods in terms of detection accuracy and complexity. [less ▲]

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See detailByzantine Resilient Protocol for the IoT
Fröhlich, Antônio Augusto; Scheffel, M.Roberto; Kozhaya, David et al

in IEEE Internet of Things Journal (2018)

Wireless sensor networks, often adhering to a single gateway architecture, constitute the communication backbone for many modern cyber-physical systems. Consequently, faulttolerance in CPS becomes a ... [more ▼]

Wireless sensor networks, often adhering to a single gateway architecture, constitute the communication backbone for many modern cyber-physical systems. Consequently, faulttolerance in CPS becomes a challenging task, especially when accounting for failures (potentially malicious) that incapacitate the gateway or disrupt the nodes-gateway communication, not to mention the energy, timeliness, and security constraints demanded by CPS domains. This paper aims at ameliorating the fault-tolerance of WSN based CPS to increase system and data availability. To this end, we propose a replicated gateway architecture augmented with energy-efficient real-time Byzantineresilient data communication protocols. At the sensors level, we introduce FT-TSTP, a geographic routing protocol capable of delivering messages in an energy-efficient and timely manner to multiple gateways, even in the presence of voids caused by faulty and malicious sensor nodes. At the gateway level, we propose a multi-gateway synchronization protocol, which we call ByzCast, that delivers timely correct data to CPS applications, despite the failure or maliciousness of a number of gateways. We show, through extensive simulations, that our protocols provide better system robustness yielding an increased system and data availability while meeting CPS energy, timeliness, and security demands. [less ▲]

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See detailA Lightweight Authentication Mechanism for M2M Communications in Industrial IoT Environment
Esfahani, Alireza UL; Mantas, G.; Matischek, R. et al

in IEEE Internet of Things Journal (2017), 6(1), 288-296

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See detailDecentralized Traffic Aware Scheduling in 6TiSCH Networks: Design and Experimental Evaluation
Accettura, Nicola; Vogli, Elvis; Palattella, Maria Rita UL et al

in IEEE Internet of Things Journal (2015)

Detailed reference viewed: 188 (11 UL)