In this paper, we propose a generalizable deep neural network model for indoor pathloss radio map prediction (termed as IPP-Net). IPP-Net is based on a UNet architecture and learned from both large-scale ray tracing simulation data and a modified 3GPP indoor hotspot model. The performance of IPP-Net is evaluated in the First Indoor Pathloss Radio Map Prediction Challenge in ICASSP 2025. The evaluation results show that IPP-Net achieves a weighted root mean square error of 9.501 dB on three competition tasks and obtains the second overall ranking.

The coexistence of ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services in 5G-based industrial wireless networks (IWNs) poses significant resource slicing challenges due to their inherent performance requirement conflicts. To address this challenge, this paper proposes a puncturing method that uses a model-aided deep reinforcement learning (DRL) algorithm for URLLC over eMBB services in uplink 5G networks. First, a puncturing-based optimization problem is formulated to maximize the eMBB accumulated rate under strict URLLC latency and reliability constraints. Next, we design a random repetition coding-based contention (RRCC) scheme for sporadic URLLC traffic and derive its analytical reliability model. To jointly optimize the scheduling parameters of URLLC and eMBB, a DRL solution based on the reliability model is developed, which is capable of dynamically adapting to changing environments. The accelerated convergence of the model-aided DRL algorithm is demonstrated using simulations, and the superiority in resource efficiency of the proposed method over existing approaches is validated.

Wireless communications are susceptible to eaves-dropping, and intelligent reflecting surface (IRS) as a relay capable of reconfiguring the propagation environment to extend the range of covert communication. In this paper, we investigate the covert communication in which the ground transmitter secretly delivers information to the full-duplex receiver through a two-way IRS, avoiding detection by the warden. Furthermore, the error detection probability is determined with an optimal threshold at a warden, which is the worst case for covert transmission. We aim to maximize expected error detection probability of warden subject to the covertness constraint. To this end, we alternately optimize the prior probability and the transmit power of artificial noise while satisfying the outage probability and covertness requirement. Numerical results demonstrate the effectiveness of the proposed scheme for covert communications via the two-way IRS.

Industrial wireless sensor networks for factory automation are globally considered as an integral part of smart manufacturing. Wireless networks for industrial automation-factory automation (WIA-FA) is the first and only international standard specifying industrial wireless sensor networks for time-sensitive automation applications. In this article, a channel-hopping-based retransmission (CHR) scheme for WIA-FA networks is proposed. To take advantage of strictly limited communication resources, we design CHR as a hybrid scheduling-based retransmission scheme that is composed of a cyclic reservation diversity retransmission (CRDR) scheme and an on-demand retransmission (ODR) scheme. For device fairness and channel diversity, we propose to combine time-slotted channel hopping with retransmission schemes. Furthermore, we perform the reliability analysis to CHR, guiding the dynamic selection of retransmission schemes according to the actual traffic and available communication resources. Simulation results demonstrate that CHR outperforms existing retransmission schemes in transmission reliability and worst case latency.

Due to the open accessibility of wireless networks with severe privacy risks, covert communication has gained increasing attention, where the effective range is limited by the low transmit power. Fortunately, employing intelligent reflecting surface (IRS) as a relay has become an appealing solution to extend the range of covert communication. To this end, we investigate the covert communication in which the ground transmitter secretly delivers information to the full-duplex receiver through a two-way IRS, avoiding detection by the warden. Then, the error detection probability is determined with an optimal threshold at a warden, which is the worst case for covert transmission. Moreover, we analyze the closed-form expression of outage probability. To improve the covertness, artificial noise is generated by the receiver to interfere with the adversarial monitoring. Thus, considering the optimal prior probability, we maximize the expected error detection probability of warden subject to the covertness constraint. Specifically, we alternately optimize the prior probability and the transmit power of artificial noise while satisfying the outage probability and covertness requirement. Numerical results demonstrate the effectiveness of the proposed scheme for covert communications via the two-way IRS.

The practical industrial operation systems are not ideally immune to the effect of unmodeled dynamics and the industrial processes generally are operated at multitime-scales, which cause troubles for optimizing the industrial operation. The novelty of this article is that a self-learning composite compensation control method is developed for two-time-scale optimal operation systems, with well dealing with unmodeled dynamics, unknown operation process and input constraints. First, the two-time scales system is decomposed into fast and slow subsystems based on singular perturbation theory. Then, the critic-only reinforcement learning technique and H$\infty$control are employed for designing the composite controller. Finally, the efficacy is verified by an industrial mixed separation thickening process and a numerical example.

Industrial Wireless Sensor Networks (IWSNs) have been widely used in industrial applications that require highly reliable and real-time wireless transmission. A lot of works have been done to optimize the Relay Node Placement (RNP), which determines the underlying topology of IWSNs and hence impacts the network performance. However, existing RNP algorithms use a fixed communication radius to compute the deployment result at once offline, while ignoring that the radio environment may vary drastically across different locations, also known as radio irregularity. To address this limitation, we propose a Voronoi diagram and Q-learning based RNP (VQRNP) method in this article. Instead of using a fixed communication radius, VQRNP employs the Q-learning algorithm to dynamically update the radio environment of measured areas, uses a Voronoi diagram based method to estimate the radio environment of unmeasured areas, and proposes a coverage extension location selection algorithm to place RNs so as to extend the coverage of the deployed network based on the results estimated by Voronoi diagram based Graph Generating (VGG). In this way, the VQRPN method can adapt itself well to the variation of radio environment and largely speed up the deployment process. Extensive simulations verify that VQRNP significantly outperforms existing RNP algorithms in terms of reliability.

Industrial Wireless Networks (IWNs) are expected to guarantee the Ultra-Reliable Low-Latency Communication (URLLC) in future manufacturing systems. However, due to limited resources and unstable radio environment, it is an inherent challenge to improve transmission reliability while maintaining low latency. This paper considers the uplink URLLC of mixed traffic (deterministic traffic and sporadic traffic) in IWNs with multiple channels. For deterministic traffic, an Automatic On-demand Retransmission scheme based on NACK REpetitions (AOR-NRE) is proposed. For sporadic traffic, a Flexible Repetition Coding-based Contention scheme (FRCC) is proposed. Then, the adaptive decision for mixed traffic on a time slicing based scheme and a frequency slicing based scheme is explored based on reliability analysis of AOR-NRE and FRCC. Finally, the advantages of the proposed retransmission schemes over existing works and the significance of adaptive decision are demonstrated via numerical results.

Facing the dual challenges of massive access and time-sensitive traffics, grant-free non-orthogonal multiple access (GF-NOMA) emerges as a promising technology for implementing massive ultra-reliable and low-latency communications (mURLLC). In this paper, we propose a differentiated power level access (DPLA) policy that exploits the correlations among power levels of GF-NOMA, and implement DPLA by a dynamically distributed GF-NOMA framework. Further, a closed-form expression to the reliability of DPLA is analytically derived and the optimal framework parameters to maximize reliability are obtained. Finally, considering the traffic variation over time, we propose a dynamically-distributed differentiated-layered transmission $(\mathrm{D}^{3}$ LT) algorithm to improve the reliability online. Simulation results show that the proposed scheme in this work outweighs existing schemes in transmission reliability.

We formulate and investigate the problem of distributed channel rendezvous in collision-prone wireless networks. Existing researches on this topic are mainly devoted to designing channel hopping sequences, each pair of which can overlap on a common channel within bounded delay. However, this overlap-based canonical rendezvous design does not take into account channel collision, which may render existing rendezvous algorithms fail to achieve bounded delay in collision-prone environment. Motivated by this observation, we formulate and investigate the collision-aware channel rendezvous problem in a generic scenario, where a collision occurs if more than$C$packets overlap in time on a same channel. Our generic formulation allows to model both the baseline single packet reception model with$C=1$and the more sophisticated multiple packet reception model with$C > 1$. We further abstract the collision-aware rendezvous problem as the problem of constructing a robust rendezvous system. We establish the theoretical limit of the problem, guided by which we design a collision-resilient distributed rendezvous algorithm with truly bounded rendezvous delay. We then demonstrate the performance of our rendezvous algorithm both analytically and numerically.

Divergences or their counterpart (dis)similarity measures of two probability distributions play an important role in information theory. Especially, Csiszar divergences have many forms. Among so many forms of Csiszar divergences, we plan to find which divergence is the best suit of the analysis of binary sensor deployments. A binary sensor outputs a binary digit 1 or 0 when detecting an object or not, respectively. Recently, modulators made of opaque materials are utilized to modulate the sensing view of binary sensors to enhance their spatial awareness. In this paper, we construct two probability models of binary sensors modulated by modulators, i.e., an ideal model and an actual deployment model. Moreover, we utilize 13 forms of Csiszar divergences to analyze the distribution of those probability models. Based on the divergence calculation results, we classify the 13 divergences into five classifications. Furthermore, we propose a smoothing method to deal with the events which are absent in the experiments, i.e., occurring with zero probability. Our experiment results show that the smoothing method eliminates the zero probabilities and has little influence on the nonzero probabilities. Finally, we select the best divergence among the 13 divergences to analyze binary sensors modulated with modulators.

Conventional wireless automated guided vehicle (AGV) systems based on WiFi or ZigBee suffer random network performance and fail to guarantee the ordered and reliable transmission in AGV applications. In this article, we study the transmission scheduling with order constraints in WIA-FA-based AGV systems. We first present the transmission process of data packets in the WIA-FA-based AGV system, and design a novel superframe structure to support the ordered data exchange in the AGV system. Then, we design three heuristic rules for timeslot allocation to regulate the device transmissions subject to order constraints. Finally, inspired by the designed rules, we propose a dynamic expected packet loss rate-based timeslot allocation (DELTA) algorithm and prove its time complexity rigorously. The simulation results show that the proposed DELTA algorithm outperforms the existing works in terms of transmission reliability for different channel conditions and network scales.

WIA-FA and IEEE 802.11 are two most widely adopted industrial wireless standards in discrete manufacturing. However, comprehensive performance comparisons between WIA-FA and IEEE 802.11 are still missing and industrial applications urgently need experimental methods to guide the selection of appropriate wireless technologies. To this end, this article performs extensive experiments between WIA-FA and IEEE 802.11 in two practical industrial scenarios, with one ordered scenario defining the transmission order of devices and the other order-free scenario imposing no order constraints to the transmission order of devices. Network performance indices of the WIA-FA and IEEE 802.11 networks, including reliability, delay, jitter, and disorder rate, are compared for different network sizes and data generation periods. Experimental results show that the WIA-FA protocol provides stable network performance, while the network performance of the IEEE 802.11 protocol is random and uncontrollable. Additionally, we perform preliminary comparisons of WIA-FA with IEEE 802.11ax and 5G New Radio.

The increasingly wide utilization of Wireless Sensor Networks (WSNs) in industrial applications outstands the significance of the Delay Constrained Relay Node Placement (DCRNP) problem. Existing algorithms to the DCRNP problem are designed based on the ideal geometric disk wireless channel model, and no real-world deployments are performed to verify the effectiveness of these algorithms. However, the unreliable and unpredictable wireless links in WSNs may lead these algorithms to fail in practice. Therefore, we first conduct extensive real-world deployments under the guidance of existing algorithms to evaluate their performance and to gain some insights for designing practical deployment algorithms. The results exhibit that the WSNs built by existing algorithms have a favorable performance in end-to-end delay but a poor performance in reliability, which is mainly due to the lack of methods ensuring high-quality links. To this end, we first devise a Set-Covering-based Algorithm (SCA) which figures out the DCRNP problem while ensuring the quality of each link better than a given threshold. As our experiments also show that the fault-tolerant topology can significantly improve network reliability, we then design a k-Set-Covering-based Algorithm (kSCA) to build fault-tolerant WSNs based on the methodology of SCA. Furthermore, the elaborate analysis proves that both SCA and kSCA are polynomial-time algorithms, and their approximation ratios are both O(ln n), where n is the number of sensor nodes. Finally, extensive experiments are performed under the guidance of SCA and kSCA to demonstrate the effectiveness of these two algorithms.

In industrial wireless sensor networks (IWSNs), monitoring data generated by field devices are supposed to be delivered to the gateway with low latency and high reliability. However, most of industrial wireless standards are based on IEEE 802.15.4 and offer limited data rates, which prevents their adoption in critical scenarios. Based on IEEE 802.11, WIA-FA is proposed to address higher communication requirements in factory automation. In this paper, we first analyze the drawbacks of the default NACK-based retransmission scheme of WIA-FA, and then propose an automatic on-demand retransmission (AODR) scheme. Finally, we give a detailed reliability analysis of the proposed AODR scheme. Simulation results show that the proposed AODR scheme outperforms existing works in terms of reliability for different scenarios.

The centralized computing model in traditional Wireless Sensor/Actuator Networks (WSANs) can lead to large delays and unbalances, which severely restricts the adoption of WSANs in applications requiring high network performance. To address this limitation, the in-network computation model has been proposed, in which the computation capability is distributed among wireless nodes in WSANs, i.e., wireless nodes perform not only data communication but also data processing. Node placement is a primary step to build the underlaying topologies of WSANs. Nevertheless, the problem of node placement to design underlaying network topologies supporting in-network computation is still unexplored. To this end, we propose an In-network-oriented Node Placement Algorithm (INPA) to build WSANs supporting in-network computation. Moreover, we investigate the time complexity of INPA and verify the efficiency of INPA through extensive simulations.

Compressive subspace learning (CSL) with the exploitation of space diversity has found a potential performance improvement for wideband spectrum sensing (WBSS). However, previous works mainly focus on either exploiting antenna auto-correlations or adopting a multiple-input multiple-output (MIMO) channel without considering the spatial correlations, which will degrade their performances. In this paper, we consider a spatially correlated MIMO channel and propose two CSL algorithms (i.e., mCSLSACC and vCSLACC) which exploit antenna cross-correlations, where the mCSLSACC utilizes an antenna averaging temporal decomposition, and the vCSLACC uses a spatial-temporal joint decomposition. For both algorithms, the conditions of statistical covariance matrices (SCMs) without noise corruption are derived. Through establishing the singular value relation of SCMs in statistical sense between the proposed and traditional CSL algorithms, we show the superiority of the proposed CSL algorithms. By further depicting the receiving correlation matrix of MIMO channel with the exponential correlation model, we give important closed-form expressions for the proposed CSL algorithms in terms of the amplification of singular values over traditional CSL algorithms. Such expressions provide a possibility to determine optimal algorithm parameters for high system performances in an analytical way. Simulations validate the correctness of this work and its performance improvement over existing works in terms of WBSS performance.

The limited spectrum resources inevitably incur the spectrum sharing among coexisting industrial wireless networks (IWNs), and multiple coexistence IWNs form a heterogeneous environment. An effective resource allocation thus plays a crucial role in coordinating the efficient operations of multiple IWNs. Existing works only study the constrained coexistence problem among specified types of networks with a limited number of nodes over one single channel. In this paper, we investigate a general coexistence problem over multiple channels among arbitrary types of networks with line topologies, and the number of nodes in each network is also arbitrary. We rigorously analyze theoretical scheduling latency of this general coexistence problem, then we propose an algorithm to attain the optimal result. The presented Coexisting Line topology Networks Resource Allocation (CLNRA) algorithm consists of two phases. In the inter-network resource allocation phase, non-overlapped channels are allocated to each network according to the corresponding transmission priority. While in the intra-network resource allocation phase, we filter out the nodes that may generate continuous empty buffers so as to enhance the resource utilization ratio. We also verify the effectiveness of the CLNRA algorithm through extensive simulations. Evaluation results show that the CLNRA algorithm can attain the theoretical optimal result in 99:3% cases, and it has obvious superiorities on resource utilization ratio and scheduling latency.

In industrial wireless sensor networks (IWSNs), monitoring data generated by field devices should be delivered to the gateway prior to deadlines. Traditional field devices with one radio interface can only work in the half-duplex mode, which may cause severe degradation of the network real- timeliness. Considering the scenarios where each field device is with multiple radio interfaces, we study the joint scheduling of slots, channels and radio interfaces in IWSNs with mesh topologies. Specifically, a new method to calculate the total and remaining resource blocks of each transmission is first given. Then, a two-level priority assignment rule is designed by jointly considering remaining resource blocks and deadlines. Finally, a remaining resource blocks based least laxity first (RRBs-LLF) algorithm based on the above rule is proposed. Simulation results show that the proposed RRBs-LLF algorithm outperforms existing works in terms of schedulable ratio.

Clustering is an effective method to manage communications in cognitive radio sensor networks (CRSNs). This letter proposes a network stability-aware clustering (NSAC) protocol for CRSNs. Spectrum dynamics and energy consumption are for the first time simultaneously integrated into the protocol design of NSAC. Extensive simulations show that the proposed NSAC protocol obviously outperforms existing methods in the aspects of network stability and energy consumption.

Intelligent factory automation systems strongly rely on industrial wireless control networks which have to ensure timely and reliable data exchange among their components. This paper presents a comprehensive survey on recently approved International Electrotechnical Commission standard Wireless networks for Industrial Automation-Factory Automation (WIA-FA). This paper first introduces the system architecture of WIA-FA including network device, network topology, and system management, and then illustrates WIA-FA protocol stack and key technologies. Furthermore, two WIA-FA testbeds are described to demonstrate the high performance of WIA-FA. After that, three examples of practical applications are provided in this paper. One application deploys a WIA-FA network to monitor and control industrial robots in a digital workshop. The second application adopts the deployment of WIA-FA as a real-time wireless network that connects automated guided vehicles (AGVs) in a logistic sorting system. The last application coordinates multiple cooperative AGVs via the WIA-FA network to carry large and complex components. Finally, the open issues and future directions for WIA-FA networks are presented.

Cognitive radio has been widely recognized as a promising solution to reliable and time-efficient wireless sensor networks. However, cognitive capability requires an extra energy consumption in spectrum sensing and spectrum access, which imposes a rather challenging problem to low cost sensors. This paper proposes a short preamble cognitive medium access control (SPC-MAC) protocol which supports reliable and fast spectrum access while addressing the energy conservation problem in cognitive radio sensor networks (CRSNs). The novelty of SPC-MAC lies in the combination of short preamble sampling (for supporting low duty cycling in CRSNs) and the opportunistic forwarding (for reliable and fast transmission). Because of the self-organizing nature, SPC-MAC does not require a common control channel. Extensive simulations demonstrate the advantage of SPC-MAC over existing works in terms of energy consumption and throughput.

Wireless Sensor Networks (WSNs) are gradually employed in many applications requiring real-time data transmission. As hop count is an important factor affecting end-to-end delay, in this paper, we investigate the Hop Constrained Relay Node Placement (HCRNP) problem where at least one path fulfilling the hop constraint is built between each Sensor Node (SN) and the sink. To address this problem, we present a Cover-based Relay Node Placement (CRNP) algorithm which places Relay Nodes (RNs) from SNs to the sink. Through formulating the deployment of RNs in each iteration as a cover problem (the set cover problem for arbitrary settings or the discrete unit disk cover problem for special settings) with respect to hop constraint, the CRNP algorithm iteratively deploys RNs adjacent to the SNs or the previously placed RNs so as to gradually connect SNs to the sink. Through rigorous analysis, we show that the CRNP algorithm has an approximation ratio better than existing algorithms for the HCRNP problem (i.e., O(1) for special settings and O(ln n) for arbitrary settings, where n is the number of SNs). Finally, we conduct extensive simulations to verify the effectiveness of the proposed algorithm.

Heterogeneous cognitive radio networks (H‐CRNs) operating in the same TV white space (TVWS) spectrum cause severe mutual interferences, which heavily degrades the network performance of coexisting H‐CRNs. Inspired by the Lotka–Volterra competition model of different species in a biological ecosystem, this study formulates the coexistence of H‐CRNs over TVWS as an equilibrium assignment problem of a discrete non‐linear control system (DNCS). By using the local linearisation method, the authors can find an approximate linear control system (ALCS) model to the DNCS. Further, they obtain an effective feedback control to the equilibrium assignment of the ALCS via solving a sufficient condition in the form of linear matrix inequalities. Third, they propose a novel ecology‐inspired heterogeneous coexistence algorithm (EHCA) based on the obtained feedback control. It is shown in simulations that the proposed EHCA could guarantee any desired but feasible spectrum share among H‐CRNs.

The lifetime of Wireless Sensor Networks (WSNs) is significantly shortened by the energy hole problem that is caused by the many-to-one communication pattern adopted by most WSNs. Various approaches have been designed to solve the energy hole problem, and this paper considers improving the energy efficiency by deploying additional relays, which is called the Lifetime Constrained Relay Node Placement (LCRNP) problem. To address the NP-hardness of the LCRNP problem, this paper proposes a Cluster-based Approximation Algorithm (CAA) that first groups the sensors into different clusters in which the lifetime constraint can be ignored and sensors are close to each other, and then builds network connectivity for each cluster. Next, the Augmented CAA is designed based on the CAA to further improve network lifetime by building addition paths for the relays prone to suffer heavy traffic loads. Unlike existing works, we prove that the proposed algorithms can guarantee polynomial time complexities and explicit approximation ratios. Finally, the efficiency of the proposed algorithms is verified through extensive simulations.

This paper investigates an underlay cognitive radio (CR) that extracts energy from radio-frequency signals of one primary user. We assume that both the wireless energy harvesting and the secondary transmission processes of the CR suffer Rayleigh block fading. Considering the intrinsic harvesting- transmitting tradeoff, we optimize the harvesting time and transmit power of the CR to minimize the outage probability of the secondary transmission, subject to the primary interference and the energy causality constraints. Numerical evaluation validates the correctness of this work.

As the smart meters are vulnerable to physical attacks as well as cyber attacks, electricity theft in smart grids is much easier to commit and more difficult to detect than that in traditional power grids. In this paper, to facilitate the inspection of the malicious meters, a full and complete binary inspection tree whose leaves stand for smart meters is employed as a logical structure. We can logically configure an inspector (a meter for detection) at any node on the tree. By calculating the difference between the inspector’s reading and the summation of the readings reported from the smart meters on the subtree of one node, as well as the difference between the total amount of stolen electricity on the subtrees of an internal node and its left child, we propose a difference-comparison-based inspection algorithm which allows the inspector to skip a large number of nodes on the tree and hence accelerates the detection speed of the malicious meters remarkably. Furthermore, for quickly identifying a complete set of malicious meters, we propose an adaptive reporting mechanism which adopts much shorter reporting periods during the inspection process. Analysis with proofs about the performance bounds of the proposed algorithm in terms of the number of inspection steps is provided. Simulations not only validate the theoretical analysis, but also show the superiority of the proposed algorithm over the existing works in terms of inspection steps, regardless of the ratio and the permutation of malicious meters.

This paper studies a green paradigm for the underlay coexistence of primary users (PUs) and secondary users (SUs) in energy harvesting cognitive radio networks (EH-CRNs), wherein battery-free SUs capture both the spectrum and the energy of PUs to enhance spectrum efficiency and green energy utilization. To lower the transmit powers of SUs, we employ multi-hop transmission with time division multiple access, by which SUs first harvest energy from the RF signals of PUs, and then, transmit data in the allocated time concurrently with PUs, all in the licensed spectrum. In this way, the available transmit energy of each SU mainly depends on the harvested energy before the turn to transmit, namely energy causality. Meanwhile, the transmit powers of SUs must be strictly controlled to protect PUs from harmful interference. Thus, subject to the energy causality constraint and the interference power constraint, we study the end-to-end throughput maximization problem for optimal time and power allocation. To solve this nonconvex problem, we first equivalently transform it into a convex optimization problem and then propose the joint optimal time and power allocation (JOTPA) algorithm that iteratively solves a series of feasibility problems until convergence. Extensive simulations evaluate the performance of EH-CRNs with JOTPA in three typical deployment scenarios and validate the superiority of JOTPA by making comparisons with two other resource allocation algorithms.

As Wireless Sensor Networks (WSNs) are widely used in time-critical applications, e.g., factory automation and smart grid, the importance of Delay Constrained Relay Node Placement (DCRNP) problem is becoming increasingly noticeable. This paper proposes a Set-Covering-based Approximation (SCA) algorithm to solve the DCRNP problem. The SCA deploys relay nodes by levels from the sink to sensor nodes. To avoid the limitation suffering by existing algorithms and ensure a polynomial time complexity, SCA employs a novel approach to formulate the deployment of relay nodes at each level as the set covering problem subject to delay constraints, and based on the classic greedy-set-covering algorithm, a set of relay nodes are placed to connect the nodes (sensor nodes and relay nodes) that are already connected to the sink. Since delay constraints are met at each level, all the sensor nodes will be connected to the sink via feasible paths fulfilling delay constraints. In addition, the elaborated analysis of the time complexity and the approximation ratio of the SCA algorithm is given out. Extensive simulations show that SCA can significantly save deployed relay nodes in comparison to existing algorithms.

Channel rendezvous is a prerequisite for secondary users (SUs) to set up communications in cognitive radio networks (CRNs). It is expected that the rendezvous can be achieved within a short finite time for delay-sensitive applications and over all available channels to increase the robustness to unstable channels. Some existing works suffer from a small number of rendezvous channels and can only guarantee rendezvous under the undesired requirements such as synchronous clock, homogeneous available channels, predetermined roles and explicit SUs' identifiers (IDs). In this paper, to address these limitations, we employ the notion of Disjoint Set Cover (DSC) and propose a DSC-based Rendezvous (DSCR) algorithm. We first present an approximation algorithm to construct one DSC. The variant permutations of elements in the ingeniously constructed DSC are then utilized to regulate the order of accessing channels, enabling SUs to rendezvous on all available channels within a short duration. We derive the theoretical maximum and expected rendezvous latency and prove the full rendezvous degree of the DSCR algorithm. Extensive simulations show that the DSCR algorithm can significantly reduce the rendezvous latency compared to existing algorithms.

This study formulates a novel optimisation problem for joint spectrum sensing and random access control ( JS 2 RAC ) in cognitive radio sensor networks (CRSNs). The JS 2 RAC is formulated as a network utility maximisation problem, which aims to maximise the sum of utilities over all links in the network but subject to the primary user protection constraint, the energy constraint, and the physical constraint. Due to the non‐separable and non‐convex nature of the JS 2 RAC problem, the authors propose a primal‐decomposition‐based iterative (PDI) algorithm which decomposes the JS 2 RAC problem into a spectrum sensing subproblem and a random access control subproblem, and solve the two subproblems iteratively. Then, the authors prove the convergence of the PDI algorithm and show its distributed implementation in practice. Simulations demonstrate the fast convergence and the near‐optimal nature of the PDI algorithm and show its significant improvement in the network utility of CRSNs in comparison with the method of optimising spectrum sensing and random access separately.

Neighbor discovery is of paramount importance in mobile sensing applications and is particularly challenging if the operating frequencies of mobile devices span multiple channels. In this paper, we formulate the multi-channel neighbor discovery problem and establish a theoretical framework of it, under which we derive the performance bound of any neighbor discovery protocol guaranteeing discovery. We then develop a multi-channel discovery protocol that achieves guaranteed discovery with order-minimum worst-case discovery delay and fine-grained control of energy conservation levels.

To detect the malicious meters committing electricity theft in a neighborhood area smart grid, in this paper, a novel inspection algorithm, termed as the Binary-Coded Grouping-based Inspection (BCGI) algorithm, is proposed. In the proposed algorithm, each meter is identified with a unique binary-coded number. The BCGI algorithm can locate the unique malicious meter (if any) by one inspection step under the assumption that at most one meter becomes malicious in one reporting period. Furthermore, by controlling the reporting periods of meters, we could make the probability of the event that at most one meter becomes malicious in one reporting period arbitrarily close to 1 under some assumptions. We further extend the algorithm into a Generalized BCGI algorithm (G-BCGI) to deal with the case that there are two or more meters which happen to commit the theft of electricity in one reporting period. Simulation results demonstrate the inspection efficiency of the BCGI and G-BCGI algorithms.

In this paper, we explore the malicious meter inspection (MMI) problem in neighborhood area smart grids. By exploiting a binary inspection tree, we propose a Difference-Comparison-based Inspection (DCI) algorithm to quickly target the malicious meters. Different from existing algorithms, the DCI algorithm is designed based on three rules that are derived according to the difference comparison results in each local subtree. An attractive feature of the DCI algorithm is that it manages to skip a large number of nodes on the binary inspection tree and thus accelerates the detection of malicious nodes. Both analysis and simulation results show that DCI outperforms the existing inspection algorithms in terms of inspection speed, regardless of the ratio and permutation of malicious meters.

Rendezvous is a vital step for secondary users who want to initiate a communication in cognitive radio networks. In this paper, we propose a novel Padded-Dyck-Path-based (PDP) rendezvous algorithm that generates channel hopping sequences utilizing global channels. PDP is designed according to the roundabout Dyck path so as to increase rendezvous opportunities. As the global channels may not be shared in distributed environments, we also propose a local PDP (L-PDP) heterogeneous rendezvous algorithm that generates channel hopping sequences utilizing only local available channels. L-PDP can significantly reduce rendezvous latency and allow for distributed implementations. We prove that both PDP and L-PDP can provide guaranteed rendezvous and derive their upper bounds of rendezvous latency. Analytical and simulation results show that PDP and L-PDP outperform existing algorithms in terms of time-to-rendezvous in global and local scenarios, respectively.

In two-tiered Wireless Sensor Networks (WSNs) relay node placement considering resource constraints and high overhead of the relay nodes plays a key role in extending the network lifetime. Therefore, approaches that support fewer relay nodes are desired to cover the WSNs. In this paper, we formulate the relay node placement problem as a Geometric Disc Covering (GDC) problem, and propose a novel local search approximation algorithm (LSAA) to solve the GDC problem. In the proposed LSAA, the sensor nodes are allocated into independent groups and then a Set Cover (SC) for each group is performed. The set of the SC for each group constitutes a SC of the GDC problem. LSAA is extensively investigated and analyzed by rigorous proof and the simulation results presented in this paper clearly demonstrate that the proposed LSAA outperform the approaches reported in literature in the reduction in deployed relay nodes.

Neighbor discovery is of paramount importance in mobile sensing applications that rely heavily on data timely collected and shared among nearby users. Guaranteed discovery with bounded latency and supporting heterogenous duty cycles to provide fine-grained control of energy conservation levels are among the most crucial requirements in the design of efficient neighbor discovery protocols. While simultaneously satisfying these two requirements is non-trivial, the situation is exacerbated if the operating frequencies of mobile devices span multiple channels and discovery occurs only if nodes switch to the same channel. In this paper, we formulate this problem as heterogeneous multi-channel neighbor discovery problem and establish a theoretical framework of the problem, under which we derive the performance bound of any neighbor discovery protocol. Based on the theoretical results, we then develop Mc-Dis (Multi-channel Discovery), a novel multi-channel discovery protocol that (1) achieves guaranteed discovery with order-minimal worst-case discovery delay and (2) supports almost all duty cycles to provide fine-grained control of energy conservation levels.

In this paper we propose a gossip algorithm for average consensus in clustered wireless sensor networks called superposition gossiping, where the nodes in each cluster exploit the natural superposition property of wireless multiple-access channels to significantly decrease local averaging times. More precisely, the considered network is organized into single-hop clusters and in each cluster average values are computed at a designated cluster head via the wireless channel and subsequently broadcasted to update the entire cluster. Since the clusters are activated randomly in a time division multiple-access fashion, we can apply well-established techniques for analyzing gossip algorithms to prove the convergence of the algorithm to the average consensus in the second moment and almost surely, provided that some connectivity condition between clusters is fulfilled. Finally, we follow a semidefinite programming approach to optimize wake up probabilities of cluster heads that further accelerates convergence.

In this paper we formulate a novel utility-cost optimization problem for routing and power control in multi-hop wireless networks. As the problem is non-convex and non-separable (no assumption on high or low SINR regime), we approach it by solving a sequence of convex approximation problems. If the initial convex approximate is feasible, it is shown that the solution sequence converges to a KKT point to the original utility-cost optimization problem. The convex approximation problems are solved recursively by means of primal-dual methods that are shown to be amenable to distributed implementation. The seamless interaction between the successive convex approximation and the primal-dual algorithm constitutes the proposed successive primal-dual convex approximation (SPDCA) algorithm.

In the following paper, we study the tradeoff between network utility and network lifetime for energy-constrained wireless sensor networks (WSNs). By introducing a weighted factor, we combine these two objectives into a single weighted objective, and we consider rate control and routing in this tradeoff framework simultaneously. First, by using a dual decomposition method, we decompose the tradeoff model into two subproblems: the congestion control/routing problem and the network lifetime problem, both of which interact through the dual variables for energy dissipation constraints. Based on the decomposition results, we propose a fully distributed algorithm to solve these two sub-problems and the dual problem by using gradient and sub-gradient projection methods. Second, we propose a fully distributed algorithm by approximating the network lifetime maximization problem by using the network utility maximization (NUM) framework. Third, we extend our distributed algorithm to deal with reliable communication and the real-time requirement. Rigorous analysis and simulations are presented to validate our algorithms.

In the following paper, we study the tradeoff between network lifetime and network utility for energy-constrained wireless sensor networks (WSNs) with the real-time requirement. By introducing a parameter r, we combine these two objectives into a single weighted objective, and consider rate control and routing in this tradeoff framework simultaneously. For real-time requirement, we set up real-time constraints by forcing the end-to-end delay of each route to be bounded by the maximum tolerated delay and incorporate real-time constraints into the tradeoff framework. Consequently, the tradeoff model is formulated nonlinear programming. By using the dual decomposition method and gradient/subgradient algorithms, we propose a distributed algorithm to solve nonlinear programming. Rigorous analysis and simulation are presented in order to validate our algorithm.