Using solar power to drive ground sensors in a UAV-IoT MEC system deployed in inaccessible or hazardous areas provides a sustainable solution to battery replacement for IoT sensors. Nevertheless, this approach faces two critical challenges. Firstly, terrain variations and landscape shadowing cause uneven light distribution, leading to significant disparities in solar energy harvesting among nodes, which subsequently affects system throughput fairness due to unequal energy availability for data computation and task offloading; Secondly, atmospheric attenuation dynamics introduce stochastic variations in solar panel output, resulting in energy conversion instability and potential temporal battery outages. These challenges are further aggravated by the randomness of data arrival, which can destabilize the data queue. To address these difficulties, in this paper, we first design an α-fairness utility function to tackle the throughput fairness issue. After that, to handle the randomness of energy and data arrivals, we employ a Lyapunov-based optimization approach to maximize the long-term system utility function, formulating the problem as a multi-stage online stochastic optimization, with time average constraints on solar energy supply, data queue stability, and energy consumption of the sensor. We then decompose the original problem into a series of deterministic per-slot optimization problems to decouple control solutions across slots. Afterward, we iteratively optimize the data admission control, communication and computation resource allocations, and the UAV’s trajectory in each slot. The proposed scheme has low computation complexity for online execution. Extensive simulations demonstrate its effectiveness in achieving application-specific throughput fairness while maintaining energy and data queue stability under fluctuating working conditions. In addition, compared with benchmark algorithms, our scheme achieves higher system throughput through more judicious resource management and trajectory control strategies.

This paper studies UAV detection by multiple collaborative base stations in an integrated sensing and communication (ISAC) manner. In particular, we propose a computationally efficient UAV 3D localization and velocity estimation approach based on matched filtering (MF) to process the orthogonal frequency division multiplexing (OFDM) sensing signals. The proposed method consists of two main steps: a MF-based preprocessing step at each single base station to efficiently estimate distance, velocity, and angle parameters, and a symbol-level fusion step using a grid searching approach to integrate results from multiple base stations. Compared with traditional multiple signal classification (MUSIC)-based fusion techniques, our approach reduces the overall computational complexity by more than 98.5%. Meanwhile, it demonstrates significantly higher robustness in low SNR conditions (SNR ≤ 0 dB), as evidenced by a reduction in localization error from meter-level to centimeter-level accuracy. In positive SNR conditions (SNR > 0 dB), it also improves the localization and velocity estimation accuracy by approximately 33.5% and 26.3%, respectively. These results demonstrate the practical advantage of the proposed method in real-time UAV sensing application.

Using solar power to drive ground sensors in a UAV-assisted IoT MEC system deployed in inaccessible or hazardous areas offers a sustainable solution to battery replacement for IoT sensors. However, the uneven distribution of solar power leads to unbalanced throughput among sensors. Additionally, fluctuations in solar energy and the stochastic nature of data arrivals destabilize the energy and data queues. To address these issues, we first design an α-fairness utility function to ensure throughput fairness. Then, to stabilize the system queues, we employ Lyapunov optimization to maximizing the utility by formulating it as a multi-stage online stochastic optimization problem. We decompose the original problem into a series of deterministic per-slot optimizations and iteratively optimize data admission control, resource allocation, and the UAV’s trajectory in each time slot. The proposed scheme achieves the desired level of throughput fairness in time-varying environments. Moreover, compared to benchmark algorithms, it attains higher system throughput and energy efficiency.

In this paper, we consider reusing a rotary-wing UAV as both an airborne base station (BS) and radar to provide integrated sensing and communication (ISAC) wideband service to a ground mobile user. Specifically, the UAV transmits orthogonal frequency-division multiplexing (OFDM) signals where a part of the sub-carriers are assigned for communication purposes. We formulate an online optimization problem that jointly optimizes the UAV trajectory and power allocation of the OFDM sub-carriers to provide a balanced communication and localization service to the ground user. The problem is very challenging because of the non-convex localization accuracy metric with respect to the trajectory and transmit power. For this, we decouple the original problem into a sub-carrier power allocation sub-problem and a trajectory design sub-problem, and propose efficient algorithms to solve them respectively. Simulation results show that the proposed algorithm reduces the localization error by more than 66% at the cost of affordable decrease of communication rate compared to the representative benchmark method considered.

This article considers deploying a dual-functional unmanned aerial vehicle (UAV) as both an aerial data collector and aerial anchor node (AN) to assist the ground base stations in providing integrated localization and communication (ILAC) service in urban areas. A major challenge to the urban ILAC service quality lies in the severe blockage of ground-to-air links by densely located buildings. To improve the service quality, we leverage the recent advance in urban physical environment map (PEM), also known as the three-dimensional (3-D) city map, to aid in optimizing the 3-D deployment of UAV. This allows a UAV to avoid blockages and establish strong acrlong LoS links to all target ground users. We propose a PEM-aided ILAC service model and formulate a UAV 3-D deployment optimization problem. The aim is to maximize the sum communication rate of ground users while satisfying individual localization accuracy and communication rate constraints. The problem is very challenging to solve mainly because the localization accuracy and blockage-avoiding constraints are both nonconvex with respect to UAV position. To tackle the problem, we first adopt a new localization accuracy metric and subsequently derive a convex expression of the localization constraint. Then, we convert the blockage-avoiding constraints into an equivalent and analytically tractable form and propose an efficient iterative algorithm to solve the UAV deployment optimization problem. Simulation results show that the proposed method achieves close-to-optimal performance under dense urban blockage setups, while significantly reducing the computational complexity.

As a nonverbal form of communication, gestures convey information through bodily movements and postures. Gesture recognition provides a more intuitive and natural human-computer interaction (HCI) experience, making it an integral component of the field of HCI. Recently, Wi-Fi-based gesture recognition has become a popular direction in research and applications due to its low cost, privacy-friendly nature, and convenience. However, due to the differences in the distribution of gesture data between the known and target environments, deploying the gesture recognition model in a new environment may induce high costs in annotating data labels and retraining the model. To achieve a cost-effective transferable gesture recognition model, we propose an efficient Wi-Fi-based gesture recognition domain-adaptive method [label-free domain-adaptive wireless gesture recognition (LAGER)] that can maintain high recognition accuracy in a new environment without the need for labeled samples. Specifically, LAGER divides the cross-domain Wi-Fi gesture recognition problem into two interrelated subproblems, where we iteratively apply a pseudo-label-guided feature alignment and feature augmentation method in a latent space by leveraging the wisdom of unsupervised domain adaptation. To minimize the negative impact of erroneous pseudo-labels in the early training stage, we introduce a preheated training technique that separates the training process into two parts associated with different training strategies. We evaluate our method on the Widar3.0 data set and compare the performance under various cross-domain settings with several representative benchmark methods. The proposed LAGER evidently outperforms all the benchmark methods. In particular, compared to the cross-domain recognition method used in Widar3.0, the proposed LAGER achieves 6.89%–7.83% higher average accuracy in different cross-domain experiments considered.

Due to its agility, reusability, and programmability, the unmanned aerial vehicle (UAV) can be utilized as a flying base station in mobile edge computing (MEC) systems, providing cost-effective computation services to distributed ground devices in the absence of terrestrial infrastructure. A defect of traditional UAVs is that they rely heavily on the onboard limited battery for the power supply, severely restricting UAVs’ operating endurance and flying range. To tackle this problem, we consider using a solar-powered UAV as the edge server for sensing data collection and processing. However, owing to the atmospheric absorption, the amount of harvested solar energy increases with the flying altitude, resulting in a non-trivial tradeoff between energy harvesting and communication performance. In addition, the dynamics of the moving clouds also make energy harvesting exhibit stochastic variations in the solar panel’s output, rendering the instability of the energy conversion. In this paper, given the randomness of energy and data arrivals, we propose a Lyapunov-based method to maximize the long-term system throughput, subject to the time average constraints on the solar power supply, the data queue stability, and the energy consumption of the devices. Specifically, without knowing the future system knowledge, we formulate the problem as a multi-stage online stochastic optimization and decompose the original problem into per-slot deterministic optimization problems. In each slot, we iteratively optimize the data sensing rate, the computation offloading, the communication resource allocation, and the 3D trajectory of the UAV. The proposed algorithm can adaptively adjust the UAV’s altitude according to its residual energy, thus striking a balance between energy harvesting and system throughput. Furthermore, it has low complexity which makes it suitable for online implementation. Extensive simulations have demonstrated the effectiveness of the algorithm, in that, it significantly outperforms the benchmark schemes in the system throughput, while satisfying the prescribed time average constraints at the same time.

By separating huge dimensional matrix-matrix multiplication at a single computing node into parallel small matrix multiplications (with appropriate encoding) at parallel worker nodes, coded distributed computing (CDC) tackles the straggler problem and hence speeds up the computation significantly. Existing CDC encoding schemes are based on linear combination (LC), which have two drawbacks: First, heavy computational burden is introduced to both encoding and decoding phases. Second, large numerical error occurs in the decoding phase. To relieve these two effects, a fresh new 2D-SAZD-CDC framework that non-trivially generalizes 1D-SAZD-CDC is proposed, where D is short for dimension, the operation for encoding and decoding is implemented by shift-and-add (SA) and zigzag decoding (ZD) that replaces LC and matrix inversion, respectively. The non-trivial generalization lies in joint design of the operations in 2D are needed in both the encoding and the decoding phases, so as to ensure possesion of combination property (CP) and ZD from 2D viewpoint. More specifically, 2D-SA encoding is designed, 2D-ZD decoding (alternates intermittently between 2D) is proposed, and a proof for satisfying CP and ZD from 2D viewpoint is also given. Numerical studies show that 2D-SAZD-CDC significantly improves the numerical stability and computational load performance over existing LC based schemes.

In future B5G/6G broadband communication systems, non-linear signal distortion caused by the impairment of transmit power amplifier (PA) can severely degrade the communication performance, especially when uplink users share the wireless medium using non-orthogonal multiple access (NOMA) schemes. This is because the successive interference cancellation (SIC) decoding technique, used in NOMA, is incapable of eliminating the interference caused by PA distortion. Consequently, each user’s decoding process suffers from the cumulative distortion noise of all uplink users. In this paper, we establish a new and tractable DPD-PA distortion signal model based on real-world measurements, where the distortion noise power is a polynomial function of PA transmit power diverging from the oversimplified linear function commonly employed in existing studies. Applying the proposed signal model, we characterize the capacity rate region of multi-user uplink NOMA by optimizing the user transmit power. Our findings reveal a significant contraction in the capacity region of NOMA, attributable to polynomial distortion noise power. For practical engineering applications, we formulate a general weighted sum rate maximization (WSRMax) problem under individual user rate constraints. We further propose an efficient power control algorithm to attain the optimal performance. Numerical results show that the optimal power control policy under the proposed non-linear PA model achieves on average 13% higher throughput compared to the policies assuming an ideal linear PA model. Overall, our findings demonstrate the importance of accurate PA distortion modeling to the performance of NOMA and provide efficient optimal power control method accordingly.

In future B5G/6G wideband communication systems, non-linear signal distortion caused by the impairment of transmit power amplifier (PA) can severely degrade the communication performance. The performance impact is especially significant when uplink users share the wireless medium using Non-orthogonal Multiple Access (NOMA) scheme. This is because the successive interference cancellation (SIC) information decoding technique of NOMA cannot eliminate the interference caused by the PA non-linear distortion, such that the decoding of each user will suffer from the aggregate distortion noise of all the uplink users. In this paper, we study the impact of PA non-linear distortion on the performance of uplink NOMA. In particular, we first establish a new PA distortion signal model based on real-world measurements, where the distortion noise power is a polynomial function of PA transmit power, instead of a simplified linear function in most existing studies. Under the proposed signal model, we then accurately characterize the capacity region of a two-user uplink NOMA by optimizing the user transmit power. We show that the polynomial distortion noise power significantly shrinks the achievable capacity region of NOMA. This indicates that existing studies may have overestimated the communication performance of NOMA in practical wideband systems. Besides, the non-linear noise power also leads to a rather different optimal power allocation strategy to attain maximum throughput. Simulation results show that, for a PA following the polynomial distortion noise power model, the proposed optimal power allocation method achieves on average 12.2% higher sum throughput than that obtained from ideal PA model. Overall, our results demonstrate the importance of accurate PA distortion modeling to the performance of NOMA and provide an efficient power allocation method to attain the optimal performance.

Accurate indoor crowd counting (ICC) is a key enabler to many smart home/office applications. Recent development of the WiFi-based ICC technology relies on detecting the variation of wireless channel state information (CSI) caused by human motions and has gained increasing popularity due to its low hardware cost, reliability under all lighting conditions, and privacy preservation in sensing data processing. To attain high estimation accuracy, existing WiFi-based ICC methods often require a large amount of labeled CSI training data samples for each application domain, i.e., a particular WiFi transceiver or background deployment. This makes large-scale deployment of the WiFi-based ICC technology across dissimilar domains extremely difficult and costly. In this article, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains. DASECount leverages the wisdom of the few-shot learning (FSL) paradigm consisting of two major stages: 1) source domain meta training and 2) target domain meta testing. Specifically, in the meta-training stage, we design and train two separate convolutional neural network (CNN) modules on the source domain data set to fully capture the implicit amplitude and phase features of CSI measurements related to human activities. A subsequent knowledge distillation procedure is designed to iteratively update the CNN parameters for better generalization performance. In the meta-testing stage, we use the partial CNN modules to extract low-dimension features out of the high-dimension input target domain CSI data. With the obtained low-dimension CSI features, we can even use very few amounts of target domain data samples (e.g., 5-shot samples) to train a lightweight logistic regression (LR) classifier, and attain very high cross-domain ICC accuracy. Experiment results show that the proposed DASECount method achieves over 92.68%, and on average 96.37% detection accuracy in a 0–8 people counting task under various domain setups, which significantly outperforms the other representative benchmark methods considered.

Under the outbreak of the COVID-19 pandemic, respiration state monitoring plays an important role in assisting respiratory disease diagnosis and treatment. Thanks to the nonintrusive nature and low deployment cost, Wi-Fi-based wireless respiration state monitoring methods have gained increasing popularity. By analyzing the variation of channel state information (CSI) of Wi-Fi signals, the respiration states of a target person under the wireless coverage, such as cough, sneeze, and yawn, can be accurately detected. A major problem of the current wireless respiration state monitoring methods is being overly domain-dependent. That is, a sensing algorithm fine-tuned to a specific device placement and background setting (i.e., a domain) can result in drastic drop in detection accuracy when applied to a dissimilar new domain. To enhance the robustness of wireless sensing and reduce the sensing cost across different domains, we propose in this article a domain-adaptive respiration state monitoring system (ResMon) that achieves highly accurate cross-domain detection performance while requiring very limited labeled samples in the new domain. In a nutshell, the proposed ResMon consists of a source domain meta-training stage and a target domain meta-testing stage. In the meta-training stage, we leverage the rich source domain labeled data set to train an embedding model as a feature extractor of high-dimensional CSI data measurements. In particular, we apply the statistical Bayesian deep learning technique to improve the generalization performance of the embedding model in cross-domain applications. In the meta-testing stage, we combine the embedding model with a few-shot learning technique to train a domain-specific classifier using very limited labeled samples in the target domain. Experiment results show that the proposed ResMon can achieve on average 87.26% cross-domain detection accuracy in a 4-class respiration state classification task using only five labeled samples per class, which significantly outperforms the considered benchmark methods.

Linear combination (LC) based coded distributed computing (CDC) suffers from the problem of poor numerical stability. Therefore, LC-CDC based model parallel (MP) training for a deep nueral network (DNN) may have poor accuracy. To enhance accuracy, we propose to replace LC by shift-and-addition (SA) and replace matrix inversion by zigzag decoding (ZD) in the encoding and decoding process of each layer, respectively, and call the scheme Naive SAZD-CDC based MP training (N-SAZD-CDC-MP-T). However, N-SAZD-CDC-MP-T encounters the problem of bottleneck at the master node, which is caused by frequent encoding/decoding at the master node and frequent huge volume of data delivery between master and worker node. This bottleneck problem may pull down the training speed significantly. To alleviate this bottleneck problem, we further design an enhanced version, by offloading certain processing from master node to distributed encoding and updating (DEU) at the worker nodes and call it DEU-SAZD-CDC-MP-T. A proof that DEU-SAZD-CDC-MP-T automatically maitains the code structure during each iteration is provided. Extensive numerical studies show that the prediction accuracy of SAZD-CDC-MP-T improves significantly over that of Poly (which is representative of LC) based scheme. In addition, the training speed of DEU-SAZD-CDC-MP-T over N-SAZD-CDC-MP-T is improved significantly.

The coupling of unmanned aerial vehicle (UAV) and Internet of Things (IoT) systems can provide an efficient method to collect ground data for the Sixth Generation (6G) networks. Under this UAV-IoT scenario, an intelligent energy balancing strategy should be designed to achieve tunable energy fairness level among all the IoT devices, such that sensors can differ in their lifespans to meet specific application requirements. In this paper, we propose an intelligent $\alpha-$fairness strategy to balance the energy consumption among IoT sensors. Specifically, the heterogeneities among the sensor nodes, i.e., different data loads, diverse residual energy levels, and distinct channel gains, have been taken into consideration. Based on this, an $\alpha-$utility function is designed. In the maximization of the utility function, the bandwidth allocation, transmission power, and the UAV’s trajectory are jointly optimized. In addition, we also demonstrate how to judiciously tune the $\alpha$ value according to the specific application scenarios, thus to achieve different levels of energy fairness and promote the functional longevity of the system to the best effort.

Abstract Network densification is recognized as the key technology to meet the ever growing demand of data traffic in next generation wireless networks. However the proliferation of small cell base stations (SCBSs) introduces vulnerabilities to information security, as they are prone to eavesdropping attacks. In this paper, it is studied how user association strategies can be specifically tailored to meet this security challenge in a ultra dense heterogeneous cellular network(UDHCN) with a group of colluding eavesdroppers. In particular, the situation is considered where eavesdroppers may have limited capabilities in intercepting and decoding data traffic. In this setting each eavesdropper has to make its own decision on eavesdropping targets, and they are able to take concerted actions to sabotage information security. To address this challenge, a zero sum game framework to reflect the conflicting interests of users and eavesdroppers is proposed and a user association scheme is devised that aims to maximize system sum secrecy rate against these adversaries whose actions intend to minimize sum secrecy rate. The case that all adversaries have limited eavesdropping capabilities is first considered, and it is shown that the corresponding zero sum game is indeed a bilinear game, and its Nash equilibrium solution can be readily approximated using a saddle point Frank Wolfe(SP‐FW)algorithm. Then the framework is extended to the case where adversaries with a diverse configuration of eavesdropping capabilities exist. In this case, it is shown that the underlying minimax optimization problem is indeed a nonsmooth convex‐nonconcave one. A two stage method is proposed by first smoothing the objective function with a Hermite cubic polynomial approximation, and then obtaining a nearly stationary solution via a recently proposed proximal point gradient descent method. Simulation results show that the proposed framework leads to significant increases in sum secrecy rate and secrecy probability against both the capability‐limited adversaries and a hybrid type of adversaries.

With the growing demand for latency-critical and computation-intensive Internet of Things (IoT) services, the IoT-oriented network architecture, mobile-edge computing (MEC), has emerged as a promising technique to reinforce the computation capability of the resource-constrained IoT devices. To exploit the cloud-like functions at the network edge, service caching has been implemented to reuse the computation task input/output data, thus effectively reducing the delay incurred by data retransmissions and repeated execution of the same task. In a multiuser cache-assisted MEC system, users’ preferences for different types of services, possibly dependent on their locations, play an important role in the joint design of communication, computation, and service caching. In this article, we consider multiple representative locations, where users at the same location share the same preference profile for a given set of services. Specifically, by exploiting the location-aware users’ preference profiles, we propose joint optimization of the binary cache placement, the edge computation resource, and the bandwidth (BW) allocation to minimize the expected sum-energy consumption, subject to the BW and the computation limitations as well as the service latency constraints. To effectively solve the mixed-integer nonconvex problem, we propose a deep learning (DL)-based offline cache placement scheme using a novel stochastic quantization-based discrete-action generation method. The proposed hybrid learning framework advocates both benefits from the model-free DL approach and the model-based optimization. The simulations verify that the proposed DL-based scheme saves roughly 33% and 6.69% of energy consumption compared with the greedy caching and the popular caching, respectively, while achieving up to 99.01% of the optimal performance.

The rise of Internet of Things (IoT) systems has enabled us to access real-time information about our surrounding environments. However, IoT data collection in hostile and inaccessible areas without infrastructure supports is a challenging issue due to the inherent physical constraints associated with the tiny sensors. A viable solution to this problem is to use agile and controllable unmanned aerial vehicles (UAVs) to collect the ground data and relay it to the remote cloud for further processing. Under this UAV–IoT scenario, the limited battery supply carried by the sensor must be efficiently utilized so as to prolong the lifetime of the IoT system. Nevertheless, lifetime extension does not merely entail the reduction of the sum energy expenditure of sensors. In this article, we first show that minimizing the sum energy consumption cannot effectively extend the system lifetime due to the imbalance in energy expenditure among sensors, which, in fact, can render early energy depletion for some overburdened sensors. We also reveal a tradeoff between energy efficiency and energy fairness. To tackle this imbalance issue, we then propose an$\alpha $-fairness approach to balance the energy consumption among IoT sensors. Specifically, in our study, the heterogeneities among the sensor nodes—different data loads, diverse residual energy levels, and distinct channel gains, have been taken into consideration. Based on this, an$\alpha $-utility function is designed. In the maximization of the utility function, the bandwidth allocation, transmission power, and the UAV’s trajectory are jointly optimized. In addition, we also demonstrate how to properly set the$\alpha $value according to the specific application scenarios, thus to achieve different levels of energy fairness and promote the functional longevity of the system to the best effort.

Abstract Cognitive radio based cooperative spectrum sensing (CSS) is severely affected when some secondary users maliciously attack it. Two attacks regarded as key adversaries to the success of CSS are spectrum sensing data falsification (SSDF) and primary user emulation attack (PUEA). Defending SSDF and PUEAs has received significant attention in research in the past decade globally. This paper performs a state‐of‐the‐art comprehensive survey of the researches on defending SSDF and PUEAs. First, the preliminaries like Hypothesis testing for detecting the primary user and different models of CSS are discussed briefly. Then a categorization of the defence mechanisms for defending both the attacks has been proposed as active and passive. Active mechanisms are suitable for an immediate defence in a limited time span, while passive mechanisms are suitable for flexible CSS systems that are ready to detect the attacks over a period of time and suppress them permanently by bringing changes in their underlying operations. An in‐depth tutorial on both the defence mechanisms is provided from the perspectives of the secondary users throughput and the interference to the primary user. Finally, a detailed survey on the open research problems in this area and some possible solutions has been performed.

Intelligent reflecting surface (IRS) is an emerging technology to enhance the energy efficiency and spectrum efficiency of wireless-powered communication networks (WPCNs). In this article, we investigate an IRS-assisted multiuser multiple-input single-output (MISO) WPCN, where the single-antenna wireless devices (WDs) harvest wireless energy in the downlink (DL) and transmit their information simultaneously in the uplink (UL) to a common hybrid access point (HAP) equipped with multiple antennas. Our goal is to maximize the weighted sum rate (WSR) of all the energy-harvesting users. To make full use of the beamforming gain provided by both the HAP and the IRS, we jointly optimize the active beamforming of the HAP and the reflecting coefficients (passive beamforming) of the IRS in both DL and UL transmissions, as well as the transmit power of the WDs to mitigate the interuser interference at the HAP. To tackle the challenging optimization problem, we first consider fixing the passive beamforming, and converting the remaining joint active beamforming and user transmit power control problem into an equivalent weighted minimum mean-square error problem, where we solve it using an efficient block-coordinate descent method. Then, we fix the active beamforming and user transmit power, and optimize the passive beamforming coefficients of the IRS in both the DL and UL using a semidefinite relaxation method. Accordingly, we apply a block-structured optimization method to update the two sets of variables alternately. The numerical results show that the proposed joint optimization achieves significant performance gain over other representative benchmark methods and effectively improves the throughput performance in multiuser MISO WPCNs.

With the proliferation of Internet of things (IoT) devices and their growing demand for computation-extensive and real-time services, fog computing or mobile edge computing (MEC) has become a promising solution to reduce wireless network costs. To further exploit the cloud-like functions at the network edge, a paradigm shift has taken place from pursuing solely computation-communication tradeoffs to joint design of computation, communication and service caching. In this paper, we consider a multi-user caching-enabled MEC system, where users with their task requests proactively cached and executed at the edge server can directly download the desired results without computation offloading under the assumption of reusable caching. In a frequency-division multiple access (FDMA) setup, cache placement and bandwidth (BW) are jointly optimized to minimize the weighted-sum energy of the edge server and the users subject to the limits of computation, communication and caching capacities as well as the computation latency constraints. To solve this mixed-integer non-convex problem, first, we solve a BW allocation problem given any (feasible) caching decisions leveraging Lagrangian duality and ellipsoid method. Next, we propose a heuristic algorithm to iteratively update the cache placement. To further reduce the complexity, a one-shot mixed-integer linear programming (MILP) is also designed leveraging the optimal solution to the BW allocation problem. The striking performance of task caching has been provided by simulations verifying the effectiveness of the suboptimal caching decisions as well.

Mobile edge computing (MEC) platform allows its subscribers to utilize computational resource in close proximity to reduce the computation latency. In this paper, we consider two users each has a set of computation tasks to execute. In particular, one user is a registered subscriber that can access the computation service of MEC platform, while the other unregistered user cannot directly access the MEC service. In this case, we allow the registered user to receive computation offloading from the unregistered user, compute the received task(s) locally or further offload to the MEC platform, and charge a fee that is proportional to the computation workload. We study from the registered user's perspective to maximize its total utility that balances the monetary income and the cost on execution delay and energy consumption. We formulate a mixed integer non-linear programming (MINLP) problem that jointly decides the execution scheduling of the computation tasks (i.e., the device where each task is executed) and the computation/communication resource allocation. To tackle the problem, we first derive the closed-form solution of the optimal resource allocation given the integer task scheduling decisions. We then propose a reduced-complexity approximate algorithm to optimize the combinatorial computation scheduling decisions. Simulation results show that the proposed collaborative computation scheme effectively improves the utility of the helper user compared with other benchmark methods, and the proposed solution method approaches the optimal solution within 0.1% average performance gap with significantly reduced complexity.

Coded distributed computing (CDC) can overcome the problem that the computation of matrix multiplication with an extremely huge dimension cannot be executed in a single Internet-of-Things (IoT) node. All the encoding of existing CDC schemes are based on the linear combination (LC) to generate independent computation tasks, which introduces a heavy computational load, including a significant volume of expensive multiplications (compared with inexpensive additions) and even more expensive divisions to the encoding and decoding phases. Note that the number of elementwise multiplications of the LC operation during the encoding phase is N times that of the original computation task, where N denotes the number of worker nodes. In this article, to avoid expensive multiplications introduced by LC, a fresh new CDC framework based on shift-and-addition (SA) over the real field is proposed. In addition, to avoid the expensive matrix inverse operation (divisions) in the decoding phase, zigzag decoding (ZD) is incorporated. The proposed scheme, which combines SA and ZD and is hence named SAZD-based CDC, avoids expensive multiplications and divisions in both the encoding and decoding phases. It targets the following simultaneous objectives: an arbitrary K out of N generated computation tasks is independent and can recover the original computation tasks with the ZD algorithm, and the shift distance is small so as to cause a light additional computational load in the computation phase. Both analysis and practical study show that compared to the LC-based CDC, the SAZD-based CDC significantly reduces the computational load.

For one moving Internet of Things (IoT) collector to download files from nearby industrial IoT devices, storing network coded files into multiple IoT devices can achieve good reliability performance if the following (n, k) erasure property (EP) is fulfilled: k source packets are encoded into n packets, and k packets chosen from these n packets in an arbitrary manner can reconstruct all the source packets. Besides, low decoding complexity is desired for time-sensitive applications and energy-limited moving IoT collectors, and binary zigzag decoding (BZD) achieves significantly low decoding complexity. The objective of previous EP-BZD designs is unilateral, which limits its application scenarios. In this article, a novel EP-BZD code is designed, which achieves good tradeoff among largest storage room overhead (SRO), SRO variance, and wide range of (n, k). To implement such a code, the source packets are shifted by several bits, respectively, and then Xored together. The numbers of bits shifted are represented by a matrix, which is obtained from a specially constructed triangle by taking a certain maximal submatrix. The triangle is obtained by a series of steps, including the construction of base vector, parallelogram, trapezoid, etc. The proof that the proposed code possesses EP and BZD simultaneously is also provided. A series of comparisons verify that the proposed code achieves significantly better tradeoff among triple objectives than existing EP-BZD codes.

The proliferation of Internet of Things (IoT) systems provides us a formidable way to monitor a multitude of things by recording field data and delivering it to the faraway controlling center. However, in hostile or inaccessible areas without infrastructure supports, transmission of IoT data is a daunting task due to the limited physical constraints associated with the weak communication unit and tiny battery supply at the ground sensors. A feasible solution to this problem is to use flexible and programmable unmanned aerial vehicles (UAVs) to gather the ground IoT data and then relay it to the end user, forming a UAV-IoT data collection system. Nevertheless, as we reveal in this article, there is a tradeoff between the two performance metrics-system throughput and sensor energy efficiency. Therefore, the data collection for UAV-IoT system should be power-aware, i.e., expending just enough energy to achieve the required system performance. To this end, in this article, by locating the optimal system parameters-the UAV flying speed and altitude, as well as the frame length at the MAC layer, we can strike a balance between the two conflicting metrics, in that, we can maximize the energy efficiency at the ground sensors, while satisfying the required system performance at the same time. In addition, with a cross-layer design, we can adaptively tune the frame length at MAC layer according to the varying UAV flying speed at the PHY layer, thus promptly switching the system between “system-efficient mode” and “energy-efficient mode”.

In the current IaaS cloud market, to achieve profit maximization, the cloud provider offers volume discount and congestion pricing, where cloud brokers dynamically aggregate traffic from tenant consumers using stochastic multiplexing techniques. At the same time, tenant consumers judiciously adjust demands when reserving resources from brokers. Specifically, an interrelated market is formed, where brokers procure resources from the cloud provider and then sell the resources to tenant consumers. In this paper, we propose a practical hierarchical resource pricing model to investigate strategic interactions among tenants, brokers, and the cloud provider in both competitive and oligopoly market scenarios. Optimal demand response of tenants and its impact are scrutinized. We then extend our model to the case where tenants may have delay-tolerant traffic. Our evaluation is conducted using data from Google cluster traces, and reveals insightful observations for both theoretical analysis and practical pricing scheme design.

Energy balancing is an effective technique in enhancing the lifetime of a wireless sensor network WSN. Specifically, balancing the energy consumption among sensors can prevent losing some critical sensors prematurely due to energy exhaustion so that the WSN's coverage can be maintained. However, the heterogeneous hostile operating conditions-different transmission distances, varying fading environments, and distinct residual energy levels-have made energy balancing a highly challenging task. A key issue in energy balancing is to maintain a certain level of energy fairness in the whole WSN. To achieve energy fairness, the transmission load should be allocated among sensors such that, regardless of a sensor's working conditions, no sensor node should be unfairly overburdened. In this paper, we model the transmission load assignment in WSN as a game. With our novel utility function that can capture realistic sensors' behaviors, we have derived the Nash equilibrium NE of the energy balancing game. Most importantly, under the NE, while each sensor can maximize its own payoff, the global objective of energy balancing can also be achieved. Moreover, by incorporating a penalty mechanism, the delivery rate and delay constraints imposed by the WSN application can be satisfied. Through extensive simulations, our game theoretic approach is shown to be effective in that adequate energy balancing is achieved and, consequently, network lifetime is significantly enhanced. Copyright © 2012 John Wiley & Sons, Ltd.

A pair of terminals exchanging information via a layer of parallel relay nodes under slow fading is considered. Two protocols are proposed based on the combination of opportunistic relaying (OR) with analogue network coding (ANC), named ORANC, or with digital network coding (DNC), named ORDNC, respectively. Two schemes/versions of ORDNC, including 2‐phase ORDNC (2P‐ORDNC) and 3‐phase ORDNC (3P‐ORDNC) are proposed. Their outage performances are investigated. ORANC and 2P‐ORDNC are proved to achieve optimal diversity‐multiplexing tradeoff (DMT), whereas 3P‐ORDNC is proved to be suboptimal. However, from diversity viewpoint only, all the above schemes are proven to achieve full diversity order. Simulation results verify the analysis, and show that 3P‐ORDNC and ORANC shows advantage at low‐ and high‐data rate regions, respectively.

ABSTRACT In this paper, we propose a new diversity combining scheme to save power, which is called as the generalized selection combining with double threshold (DT‐GSC). It selects the branch whose SNR is above an input threshold to combine, and this process will keep running until the combined output SNR is larger than an output threshold or until all paths are examined. The values of both thresholds are required to be predetermined on the basis of the practical communication conditions. For comparing the complexity of various combining schemes, we will show the mathematical formulas of the average number of path estimation and the average number of combined branches. Moreover, we will also compare the average bit‐error‐ratio performance of the proposed DT‐GSC with absolute threshold GSC (AT‐GSC) and output threshold MRC (OT‐MRC). Numerical examples and simulation results show that the proposed DT‐GSC leads to a lower complexity than the conventional AT‐GSC and OT‐MRC while it has a satisfactory performance.Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we consider energy fairness problem in wireless sensor networks. However, the heterogeneous hostile operating conditions - different transmission distances, varying fading environments and distinct remained energy levels, have made energy balancing a highly challenging design issue. To tackle this problem, we model the packet transmission of sensor nodes as a game. By properly designing the utility function, we get the Nash equilibrium, in which, while each node can optimize its own payoff, the global objective - energy balancing can also be achieved. In addition, by imposing penalty mechanism on sensors to punish selfish behaviors, the delivery rate and delay constraints are also satisfied. Through extensive simulations, the proposed game theoretical approach is proved to be effective in that the energy consumption is balanced and the energy resources are efficiently utilized, which can significantly improve the network lifetime.

Abstract Ultra‐wideband (UWB) system is one of the possible solutions to future short‐range indoor data communications with large frequency bandwidth. However, it must coexist with other narrowband wireless systems that may cause interference to each other, and furthermore a large bandwidth will inevitably result in multi‐path fading. The Rake receiver is applicable to combat multi‐path fading but its performance degrades greatly when the narrowband interference (NBI) is present. Although some optimized Rake receivers were proposed to suppress the NBI, such as the minimum mean square error (MMSE) one, their computational complexities are usually too high to be practically implemented. In this paper, we present a new adaptive Rake receiver which can effectively suppress the NBI, based on the nonlinear Masreliez‐type approximate conditional mean (ACM) technique. Simulation results show that it outperforms the previous schemes and even it achieves almost the same performance as that of a MMSE Rake receiver but with much lower complexity. Copyright © 2010 John Wiley & Sons, Ltd.

Energy conservation is a critical issue in the emerging standard IEEE 802.16e/m WiMAX supporting mobility. To guarantee QoS requirements in real-time services such as VoIP, traditional energy saving strategies adopt constant listen-sleep intervals, ignoring the On-Off characteristics of human speech. However, statistically the silence period can account for nearly 60% of the whole speech in time scale. Therefore, neglecting this fact can lead to unnecessary periodical listening in the silence duration, and, in turn, can result in excessive waste of battery energy. In this paper, we adopt a hybrid energy management for the downlink simplex VoIP. We also give an evaluation model to analyze the performance of the scheme. Guided by this model, we obtain the optimal window adjustment parameters. Extensive simulation results have validated the analytical model, and indicated that, compared with the traditional scheme, the hybrid scheme can achieve as much as 90% reduction in energy dissipation during silence period, while meeting the QoS requirements satisfactorily at the same time.

Abstract There is a plethora of recent research on high performance wireless communications using a cross‐layer approach in that adaptive modulation and coding (AMC) schemes at wireless physical layer are used for combating time varying channel fading and enhance link throughput. However, in a wireless sensor network, transmitting packets over deep fading channel can incur excessive energy consumption due to the usage of stronger forwarding error code (FEC) or more robust modulation mode. To avoid such energy inefficient transmission, a straightforward approach is to temporarily buffer packets when the channel is in deep fading, until the channel quality recovers. Unfortunately, packet buffering may lead to communication latency and buffer overflow, which, in turn, can result in severe degradation in communication performance. Specifically, to improve the buffering approach, we need to address two challenging issues: (1) how long should we buffer the packets? and (2) how to choose the optimum channel transmission threshold above which to transmit the buffered packets? In this paper, by using discrete‐time queuing model, we analyze the effects of Rayleigh fading over AMC‐based communications in a wireless sensor network. We then analytically derive the packet delivery rate and average delay. Guided by these numerical results, we can determine the most energy‐efficient operation modes under different transmission environments. Extensive simulation results have validated the analytical results, and indicates that under these modes, we can achieve as much as 40% reduction in energy dissipation. Copyright © 2008 John Wiley & Sons, Ltd.

The adaptive modulation and coding (AMC) schemes has long been adopted at physical layer to combat time-varying properties of the wireless channel. However, transmitting packet over deep fading channel can render extra energy expenditure, due to the incorporation of more error protection or usage of lower modulation mode, which is unaffordable for energy-limited wireless sensor device. To avoid such inefficient energy usage, a simple approach is to temporally buffer the packet when the channel is in deep fading, until the channel quality recovers. Nevertheless, buffering packet can lead to communication performance degradations - communication latency and packet overflow, which should be taken into consideration in sensing applications with QoS requirements. In this paper, by using previously proposed discrete time queuing model, we analyze the effects of Rayleigh fading on the sensor communication system, and propose a cross-layer design on power aware communication of sensor device. Specifically, in such channel adaptive system, each sensor can judiciously accesses the medium according to the channel condition, traffic load, and buffer variation. Simulation and analytical results indicate that, such cross-layer design can lead to energy conservation by as much as 30-40 per cent.

Minimum disc cover problem which is NP-hard is kernel of node scheduling protocol in wireless sensor networks. Size of disc cover set obtained by approximate algorithm determines performance of node scheduling protocol. But the approximation ratios of present approximate algorithms aren't good. This paper proposes a local Voronoi diagrams-based approximate algorithm which can obtain a minimal disc cover set. Theoretical analyses show that the approximation ratio of this algorithm is less than 3. Experiments show that the size of disc cover set obtained by this algorithm is less than 43% of present algorithms and the average coverage degree is around 2.11 which are 1.7 times of optimal.

Abstract To enhance the lifetime of a sensor network which consists of hundreds or even thousands of resource‐limited devices, energy efficient communication is mandatory. Despite that a plethora of work has been done in designing energy efficient protocols for sensor networks, the time‐varying nature of wireless channel is largely unexplored. Indeed, we believe that a cross‐layer design on power aware communication is necessary to further optimize energy usage. In this paper, we propose a new channel adaptive power aware protocol, called CAEM, which works by dynamically adjusting the data throughput under different channel conditions with the help of an adaptive channel coding and modulation facility. Each sensor device judiciously accesses the wireless medium in that communication activity is reduced for devices under poor channel conditions. Simulation results indicate that the proposed CAEM protocol can lead to energy conservation by as much as 30 per cent. Furthermore, CAEM is also efficient in channel utilization as it generates a higher data throughput even under heavy traffic load. Copyright © 2007 John Wiley & Sons, Ltd.

To efficiently support tetherless applications in ad hoc wireless mobile computing networks, a judicious ad hoc routing protocol is needed. Much research has been done on designing ad hoc routing protocols and some well-known protocols are also being implemented in practical situations. However; one major imperfection in existing protocols is that the time-varying nature of the wireless channels among the mobile-terminals is ignored; let alone exploited. This could be a severe design drawback because the varying channel quality can lead to very poor overall route quality in turn, resulting in low data throughput. Indeed, better performance could be achieved if a routing protocol dynamically changes the routes according to the channel conditions. In this paper, we first propose two channel adaptive routing protocols which work by using an adaptive channel coding and modulation scheme that allows a mobile terminal to dynamically adjust the data throughput via changing the amount of error protection incorporated. We then present a qualitative and quantitative comparison of the two classes of ad hoc routing protocols. Extensive simulation results indicate that channel adaptive ad hoc routing protocols are more efficient in that shorter delays and higher rates are achieved, at the expense of a higher overhead in route set-up and maintenance.

Ad hoc routing is important for mobile devices, when they are out of each others transmission range, to communicate in an IEEE 802.11b based wireless LAN using the distributed coordination function. While traditional table-based or on-demand routing protocols can be used, it is much more efficient to use a routing protocol that is channel-adaptive - judiciously selecting links that can transmit at higher data rates to form a route. However, devising channel-adaptive routing protocols is still largely unexplored. In this paper, we propose a reactive ad hoc routing algorithm, called RICA (receiver-initiated channel-adaptive) protocol, to intelligently utilize the multi-rate services (based on different modulation schemes) provided by the IEEE 802.11b standard. Our NS-2 simulation results show that the RICA protocol is highly effective.

As wireless LAN (WLAN) technologies proliferate, it is becoming common that ad hoc networks, in which mobile devices communicate via temporary links, are built using WLAN products. In the IEEE 802.11b standard, the wired equivalent privacy (WEP) scheme is used as the only measure to enhance data confidentiality against eavesdropping. However, owing to well known pitfalls in initialization vector (IV) attachment in the ciphertext, the underlying 40-bit RC4 encryption mechanism in WEP is unsafe regardless of the key size. On the other hand, solutions involving replacement of RC4 by another cipher are not attractive because that may lead to reconstruction of the whole system and result in high cost as well as redevelopment of the products. In order to enhance the security on the existing development efforts, we propose a novel multipath routing approach to combat the link insecurity problem at a higher protocol layer. This approach does not require the application to use sophisticated encryption technologies that may be too heavy burdens for mobile devices. Based on our suggested confidentiality measurement model, we find that our proposed multipath ad hoc routing technique called secure multipath source routing (SMSR), is highly effective.

We propose an enhancement to the original MAC (multiple access control) protocol in the IEEE 802.11 standard by improving the handshake mechanism and adding one more separate power control channel. With the control channel, the receiver notifies its neighbors about the noise tolerance. Thus, the neighbors can adjust their transmission power levels to avoid packet collision at the receiver. Through extensive simulations on the NS-2 platform, our power control mechanism is found to be effective in that network throughput can be increased by about 10%.

In packet radio networks, especially an ad hoc wireless network using IEEE 802.11 as the MAC (media access control) protocol, power control is a crucial issue. By using a judicious power control mechanism, co-channel interference can be significantly reduced, thus improving the channel spatial reuse and network capacity. However, efficient power control in an IEEE 802.11 system is very challenging because according to the standard, fixed power is used for transmitting packets, and there is only one channel. In this paper, we propose an enhancement to the standard IEEE 802.11 MAC protocol by improving the handshaking mechanisms and adding one separate power control channel. With the control channel, the receiver notifies its neighbors its noise tolerance. Thus, the neighbors can adjust their transmission power levels to avoid packet collisions at the receiver. Through extensive simulations using NS-2, our proposed power control mechanism is found to be effective in that network throughput can be increased by about 10%, and the battery utilization can also be improved at the same time.

To support truly peer-to-peer applications in ad hoc wireless mobile computing networks, a judicious and efficient ad hoc routing protocol is needed. Much research has been done on designing ad hoc routing protocols and some well known protocols are also being implemented in practical situations. However, one major drawback in existing state-of-the-art protocols, such as the AODV routing protocol, is that the time-varying nature of the wireless channels among the mobile terminals is ignored, let alone exploited. This can be a severe design shortcoming because the varying channel quality can lead to very poor overall route quality, in turn result in low data throughput. In this paper, by using a previously proposed adaptive channel coding and modulation scheme which allows a mobile terminal to dynamically adjust the data throughput via changing the amount of error protection incorporated, we devise a new receiver-initiated algorithm for ad hoc routing that dynamically changes the routes according to the channel conditions. Extensive simulation results indicate that our proposed protocol are more efficient in that shorter delays and higher rates are achieved.

To support truly peer-to-peer applications in ad hoc wireless networks, a judicious and efficient ad hoc routing protocol is needed. Much research has been done on designing ad hoc routing protocols and some well known protocols are also being implemented in practical situations. However, one major drawback in existing state-of-the-art protocols, such as the AODV (ad hoc on demand distance vector) routing protocol, is that the time-varying nature of the wireless channels among the mobile terminals is ignored, let alone exploited. In this paper, by using a previously proposed adaptive channel coding and modulation scheme which allows a mobile terminal to dynamically adjust the data throughput via changing the amount of error protection incorporated, we devise a new ad hoc routing algorithm that dynamically changes the routes according to the channel conditions. Extensive simulation results indicate that our proposed protocol is more efficient in that shorter delays and higher rates are achieved.