The State of AI-Empowered Backscatter Communications: A Comprehensive Survey

in IEEE Internet of Things Journal (2023)

The Internet of Things (IoT) is undergoing significant advancements, driven by the emergence of Backscatter Communication (BC) and Artificial Intelligence (AI). BC is an energy-saving and cost-effective ... [more ▼]

The Internet of Things (IoT) is undergoing significant advancements, driven by the emergence of Backscatter Communication (BC) and Artificial Intelligence (AI). BC is an energy-saving and cost-effective communication method where passive backscatter devices communicate by modulating ambient Radio-Frequency (RF) carriers. AI has the potential to transform our way of communicating and interacting and represents a powerful tool for enabling the next generation of IoT devices and networks. By integrating AI with BC, we can create new opportunities for energy-efficient and low-cost communication and open the door to a range of innovative applications that were previously not possible. This paper brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with RIS, UAV, and vehicular networks. Finally, we highlight the challenges and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT. [less ▲]

5G Vehicle-to-Everything at the Cross-Borders: Security Challenges and Opportunities

in IEEE Internet of Things Journal (2022)

5G Vehicle-to-Everything (5G-V2X) communications will play a vital role in the development of the automotive industry. Indeed and thanks to the Network Slicing (NS) concept of 5G and beyond networks (B5G ... [more ▼]

5G Vehicle-to-Everything (5G-V2X) communications will play a vital role in the development of the automotive industry. Indeed and thanks to the Network Slicing (NS) concept of 5G and beyond networks (B5G), unprecedented new vehicular use–cases can be supported on top of the same physical network. NS promises to enable the sharing of common network infrastructure and resources while ensuring strict traffic isolation and providing necessary network resources to each NS. However, enabling NS in vehicular networks brings new security challenges and requirements that automotive or 5G standards have not yet addressed. Attackers can exploit the weakest link in the slicing chain, connected and automated vehicles, to violate the slice isolation and degrade its performance. Furthermore, these attacks can be more powerful, especially if they are produced in cross-border areas of two countries, which require an optimal network transition from one operator to another. Therefore, this article aims to provide an overview of newly enabled 5G-V2X slicing use cases and their security issues while focusing on cross-border slicing attacks. It also presents the open security issues of 5G-V2X slicing and identifies some opportunities. [less ▲]

Outage Constrained Robust BeamformingOptimization for Multiuser IRS-AssistedAnti-Jamming Communications With Incomplete Information

in IEEE Internet of Things Journal (2022), 9(15), 13298-13314

Malicious jamming attacks have been regarded asa serious threat to Internet of Things (IoT) networks, which cansignificantly degrade the quality of service (QoS) of users. Thispaper utilizes an ... [more ▼]

Malicious jamming attacks have been regarded asa serious threat to Internet of Things (IoT) networks, which cansignificantly degrade the quality of service (QoS) of users. Thispaper utilizes an intelligent reflecting surface (IRS) to enhanceanti-jamming performance due to its capability in reconfiguringthe wireless propagation environment via dynamicly adjustingeach IRS reflecting elements. To enhance the communicationperformance against jamming attacks, a robust beamformingoptimization problem is formulated in a multiuser IRS-assistedanti-jamming communications scenario with or without imperfectjammer’s channel state information (CSI). In addition, we furtherconsider the fact that the jammer’s transmit beamforming cannot be known at BS. Specifically, with no knowledge of jammerstransmit beamforming, the total transmit power minimizationproblems are formulated subject to the outage probability re-quirements of legitimate users with the jammer’s statistical CSI,and signal-to-interference-plus-noise ratio (SINR) requirementsof legitimate users without the jammer’s CSI, respectively.By applying the Decomposition-based large deviation inequal-ity (DBLDI), Bernstein-type inequality (BTI), Cauchy-Schwarzinequality, and penalty non-smooth optimization method, weefficiently solve the initial intractable and non-convex problems.Numerical simulations demonstrate that the proposed anti-jamming approaches achieve superior anti-jamming performanceand lower power-consumption compared to the non-IRS schemeand reveal the impact of key parameters on the achievable systemperformance. [less ▲]

Throughput Enhancement in FD- and SWIPT-enabled IoT Networks over Non-Identical Rayleigh Fading

in IEEE Internet of Things Journal (2022), 9(12),

Simultaneous wireless information and power transfer (SWIPT) and full-duplex (FD) have emerged as prominent technologies to overcome limited energy re sources and improve spectral efficiency (SE) in ... [more ▼]

Simultaneous wireless information and power transfer (SWIPT) and full-duplex (FD) have emerged as prominent technologies to overcome limited energy re sources and improve spectral efficiency (SE) in Internet-of Things (IoT) networks. This 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. Herein, the relay nodes can harvest energy from the source’s signal and operate in the FD mode. Further, a sub-optimal, low-complexity, yet efficient relay selection scheme is proposed. Specifically, one relay is selected to convey information from a source to a destination so that it achieves the best channel from source to relays. Then, by considering two relaying strategies, termed static power splitting-based relaying (SPSR) and optimal dynamic power splitting-based relaying (ODPSR), performance analysis in terms of outage probability (OP) and throughput are performed for each one. Notably, the independent and non-identically distributed (i.n.i.d.) Rayleigh fading channels are considered, which poses new challenges for obtaining analytical expressions. In this context, we derive exact closed-form expressions for the OP and throughput of both SPSR and ODPSR schemes. Moreover, the optimal power splitting ratio of ODPSR is obtained to maximize the achievable capacity at the destination. Finally, extensive numerical and simulation results are presented to confirm our analytical findings. Both the simulation and analytical results show the superiority of ODPSR over SPSR. [less ▲]

Throughput Enhancement in FD- and SWIPT-enabled IoT Networks over Non-Identical Rayleigh Fading Channel

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 ▲]

NB-IoT Random Access for Non-Terrestrial Networks: Preamble Detection and Uplink Synchronization

in IEEE Internet of Things Journal (2021)

The satellite component is recognized as a promising solution to complement and extend the coverage of future Internet of things (IoT) terrestrial networks (TNs). In this context, a study item to ... [more ▼]

The satellite component is recognized as a promising solution to complement and extend the coverage of future Internet of things (IoT) terrestrial networks (TNs). In this context, a study item to integrate satellites into narrowband-IoT (NBIoT) systems has been approved within the 3rd Generation Partnership Project (3GPP) standardization body. However, as NBIoT systems were initially conceived for TNs, their basic design principles and operation might require some key modifications when incorporating the satellite component. These changes in NB-IoT systems, therefore, need to be carefully implemented in order to guarantee a seamless integration of both TN and non-terrestrial network (NTN) for a global coverage. This paper addresses this adaptation for the random access (RA) step in NBIoT systems, which is in fact the most challenging aspect in the NTN context, for it deals with multi-user time-frequency synchronization and timing advance for data scheduling. In particular, we propose an RA technique which is robust to typical satellite channel impairments, including long delays, significant Doppler effects, and wide beams, without requiring any modification to the current NB-IoT RA waveform. Performance evaluations demonstrate the proposal’s capability of addressing different NTN configurations recently defined by 3GPP for the 5G new radio system. [less ▲]

Efficient Federated Learning Algorithm for Resource Allocation in Wireless IoT Networks

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 ▲]

Auction-based Multiple Channel Cooperative Spectrum Sharing in Hybrid Satellite-Terrestrial IoT Networks

in IEEE Internet of Things Journal (2021), 8(8), 7009-7023

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

In this paper, we investigate the multiple-channel cooperative spectrum sharing in hybrid satellite-terrestrial internet of things (IoT) networks with 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 T-IoT network. Specifically, the one-shot 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 ▲]

Auction-based Multi-Channel Cooperative Spectrum Sharing in Hybrid Satellite-Terrestrial IoT Networks

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 ▲]

Delay Constrained Resource Allocation for NOMA Enabled Satellite Internet of Things with Deep Reinforcement Learning

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 ▲]