With camera-equipped phones becoming essential items carried by people, utilizing multi-perspective views (MPV) captured from the surrounding environment has emerged as a promising approach to achieve pervasive localization in indoor environments. This MPV-based localization requires extensive data, necessitating the use of crowdsourcing for data collection and training. In this distributed process, multiple clients can process and share results, raising concerns about privacy breaches. To address this challenge, this paper introduces federated learning (FL) into MPV-based localization, resulting in theMPVFedLocalgorithm, which facilitates distributed learning without exchanging raw local data. However, FL-based methods often experience reduced accuracy due to data heterogeneity. To overcome this, we propose a model self-supervised federated learning framework withinMPVFedLoc. This framework integrates self-supervised learning at the model level and incorporates a model self-supervised loss into the local training objective to mitigate the bias between the global and local models. To evaluate the performance of MPV-based localization, we construct a benchmark dataset named TJF_Building and conduct extensive experiments. Additionally, we compare the performance ofMPVFedLocwith three state-of-the-art FL methods in both homogeneous and heterogeneous settings. Experimental results demonstrate the robustness and effectiveness ofMPVFedLoc, particularly in handling heterogeneous scenarios. Further experiments on two typical image classification datasets also highlight the potential ofMPVFedLocfor diverse tasks.

Satellite Edge Computing (SEC) augments the computational capability of Low Earth Orbit (LEO) satellite networks to support latency-sensitive and computation-intensive services. However, limited onboard resources, dynamic network topology, and the lack of trust among heterogeneous nodes hinder efficient task offloading and collaborative processing. To address these challenges, we propose a blockchain-enabled SEC framework that integrates task offloading, resource allocation, and an incentive mechanism via smart contracts, ensuring trusted and autonomous cooperation. We further develop a distributed optimization algorithm based on Successive Convex Approximation and Distributed Alternating Direction Method of Multipliers (SCA-DADMM), enabling decentralized decision-making with only neighbor-level communication. Simulation results show that the proposed approach achieves up to 32.96% higher system revenue and 15.98% lower task latency compared to baseline methods under varying bandwidth and computing resource conditions, demonstrating its potential to enhance both efficiency and trust in resource-constrained satellite edge environments.

In the routing process of low earth orbit satellite networks, frequent topology changes and complex space environment cause routing interrupted and sudden link failures. Traditional terrestrial routing not only fails to manage this problem but also leads to random network congestion. To address these issues and to achieve high adaptability and stability routing strategy, this paper proposes an intelligent distributed routing algorithm based on multi-agent deep reinforcement learning (MADRL) with Lyapunov optimization. Firstly, we build Lyapunov-based network optimization model and analyze the model stability. Then, we combine the Lyapunov optimization with the MADRL framework to stabilize the network. Each satellite agent selects next hop node according to the queue backlogs and the distance between the next hop node and the destination. Evaluation results show that our proposed Lyapunov optimization aided DRL (LOA-DRL) algorithm has better performance in terms of delivery ratio, average delivery time, throughput and average queue backlogs.

The space-air–ground integrated network (SAGIN) integrates satellites, unmanned aerial vehicles (UAVs), and terrestrial remote clouds to provide seamless network access and high-volume computing services for remote Internet of Things (IoT) devices, thus alleviating geographic and resource constraints. Existing methods typically focus on the network dynamics while overlooking the comprehensive consideration of device dynamics, namely, the time-varying task performance weights, task sizes, and task processing demands. Moreover, the centralized learning-based offloading schemes often lead to substantial signaling overhead. To bridge these gaps, this article proposes a distributed dynamic task offloading mechanism with game-theoretic multiagent stochastic learning (MASL). Technically, a stochastic game is formulated with each device as a player minimizing its weighted sum cost of latency and energy. We prove the existence of Nash equilibrium (NE) for our proposed game and propose a multiagent entropy-enhanced stochastic learning (MESL) algorithm in a fully distributed manner with no information exchange among IoT devices. By introducing the entropy of decision probability for each device, MESL increases decision dimensions, accelerates convergence, and facilitates optimal strategy achievement. Experimental results show that the MESL algorithm significantly reduces the overall cost and greatly enhances the convergence speed in dynamic SAGIN environments compared to existing algorithms.

To fulfill the requirements of human lunar exploration programs and establish bases on the moon in the distant future, lunar sensors (LSs) will inevitably produce a significant amount of data. It is necessary to construct the Earth-Moon metaverse in order to obtain and utilize lunar information more effectively. Due to the long communication distance between Earth and Moon as well as the lack of communication resources, a hybrid Earth-Moon metaverse network with channel model and transmission model is designed. To ensure the efficiency and stability of transmission in the network, LSs transmit lunar data to Earth clients (ECs) through the active and passive intelligent reflecting surface (IRS) deployed at relay satellites. Then, we propose a Stackelberg game model to describe the adversarial relationship between the satellites, LSs and ECs, and optimal strategies are obtained by solving the Nash equilibrium to maximize their utility. Simulations demonstrate that the network can effectively shorten the transmission delay and improve the utility of ECs and satellites.

To solve the sudden inter-satellite link failures of low earth orbit satellite networks (LEO-SNs), a robust routing algorithm against link failures is proposed in this paper. Firstly, we introduce a 2-D Markov model for LEO-SNs to study the problem about how to minimize the probability of encountering link failures in minimum-hop path set. Theoretical results indicate that forwarding in the more-hop direction has a lower probability to encounter link failures. Based on the results, we propose a More-Hop Direction Priority routing algorithm with routing Recovery strategy by Extending Path Area (MHDPREPA). The algorithm consists of three components which are routing preparation, routing calculation and routing recovery. In the routing process, each node aims to avoid encountering link failures when selecting the next hop node. If the node encounters link failures, a routing recovery strategy is adopted to bypass the failed links. Simulation results show that our proposed algorithm can effectively improve delivery ratio and average throughput compared with baseline algorithms.

Video surveillance in intelligent transportation systems is advancing rapidly, with video analytics technology being used to enhance the security of the Internet of Vehicles (IoV) system. However, the sheer volume of video data from cameras and the computational intensity of video analysis pose significant challenges to the IoV network. To address this, mobile edge computing (MEC) has been introduced to offload video tasks from cameras to mobile edge servers/groups formed by vehicles. However, the resource-constrained nature of edge servers and vehicle groups necessitates the design of effective offloading strategies. Additionally, ensuring the security of user data during transmission and computation is a pressing issue. Moreover, the heterogeneous devices in the IoV system may be reluctant to participate in the collaborative processing of video tasks due to mistrust and lack of incentives. To tackle these challenges, we propose a cooperative computing offloading and resource allocation framework that integrates blockchain and MEC to provide secure and low-latency computing offloading services for the IoV system. We also design an efficient incentive mechanism to promote the collaborative processing of video tasks. Our framework formulates computing offloading and resource allocation as a joint optimization problem to maximize the system revenue, and we propose an algorithm based on the alternating direction method of multipliers (ADMM) to solve the distributed optimization problem with fast convergence and low complexity. Simulation results demonstrate that compared to the typical baselines, our scheme can achieve the maximum system revenue and effectively reduce the system delay.

This paper studies the joint computing offloading and resource allocation problem in an MEC network. We formulate the problem as an optimization problem with the objective to maximize the network throughput while satisfying the delay requirements of as many service requests as possible. Meanwhile, we propose a deep-Q-network (DQN) based joint computing offloading and resource allocation (D-CORAL) algorithm to solve the formulated problem. The proposed D-CORAL algorithm attempts to jointly optimize edge node selection, and spectrum resource and computing resource allocation for each service request by learning online to better adapt to a dynamic environment. Simulation results show the proposed algorithm can achieve larger network throughput than two benchmark algorithms.

This paper considers the spectrum resource and computing resource allocation in an mobile edge computing (MEC) network. The problem is formulated as an optimization problem with an objective to maximize the average successful processing ratio of users’ service requests while satisfying users’ service delay requirements. To solve the formulated problem, a federated learning (FL) and deep-Q-network (DQN) based cooperative resource allocation (CoRA) algorithm is proposed to adaptively select optimal offloading nodes and allocate spectrum and computing resources for users’ service requests by training DQNs in a master node and slave nodes in the form of federated learning. Simulation results show that the proposed CoRA algorithm outperforms two benchmark algorithms in terms of the average successful processing ratio.

With the widespread adoption of resource-intensive mobile applications, mobile edge computing (MEC) has emerged as a solution to enhance the computational power of mobile user equipments (UEs) and minimize their computational delay by offloading tasks to edge servers (ESs). This paper delves into the computing offloading challenge for multiple UEs in dynamic Internet of Things (IoT) networks with partial information-sharing. In such settings, the transmission bandwidth for each UE varies over time, and they can only access the historical data of their peers. Since UEs are self-interested in offloading computational tasks to ESs that possess limited computational resources, we model the UEs’ offloading decision-making in this dynamic, privacy-bound scenario as a game. Subsequently, this game is further formulated as a multi-agent Partially Observable Markov Decision Process (POMDP). To address the POMDP and attain a near-optimal Nash equilibrium (NE) of the structured game, we introduce an algorithm grounded in multi-agent reinforcement learning, integrating Differentiable Neural Computer and Advantage Actor-Critic framework (abbreviated as DNA). Through this method, each UE autonomously decides the optimal computing offloading strategy based on its game history, without obtaining the detailed offloading policies of other UEs. Experimental outcomes reveal that DNA surpasses the state-of-the-art benchmark methods by at least 8.3% in computing offloading utilities and 3.98% in convergence rate, highlighting its effectiveness in a dynamic IoT environment with partial information-sharing between UEs.

With the rapid development of wireless communications, integrated sensing and communication (ISAC) has attracted considerable attentions, which enables both data transmission and target detection simultaneously by spectrum sharing. The adaptive Orthogonal Frequency Division Multiplexing (OFDM) shared waveform design can dynamically adjust power allocation based on the preferences of the radar or communication system, which achieves optimal ISAC performance with given static channel conditions. For the perfect channel state information (CSI) is hard to obtain due to the feedback errors, we then propose a robust OFDM shared waveform design, which achieves better performance under the worst-case channel states. The Karush-Kuhn-Tucker (KKT) conditions are formulated and an improved greedy algorithm is introduced to adjust the bit and power allocation on each subcarrier adaptively. Theoretical analysis and simulation results verify the effectiveness of the proposed algorithm for the joint optimization of both radar and communication systems.

In time-varying software-defined wireless sensor networks (SDWSNs) for Internet of Things (IoT) applications, the topology may change due to the interference or abnormal events, thus leading to network performance degradation. In this article, an energy-efficient topology control (TC) mechanism applied for IoT-oriented SDWSNs is proposed to maximize the network energy efficiency (EE) during the dynamic topology maintenance. First, a hierarchical SDWSN architecture consisting of the cluster-based sensing network and the programmable relay network is presented. Second, two TC algorithms based on the link EE are proposed to apply in the cluster and relay subnetworks of SDWSN, respectively. In the cluster subnetwork, the proposed distributed TC algorithm enables the link interference mitigation by employing power control and rate allocation in each cluster. In the relay subnetwork, the proposed centralized TC algorithm first utilizes a specified model to construct the original topology. During the dynamic topology maintenance, the proposed centralized TC algorithm is realized by the value-iteration learning method based on a Markov decision process (MDP) model, upon which the state-transition probability (STP) of the relay subnetwork is obtained, where the relay-network state is composed of the link, the queue, and the residual energy ratio states for all nodes in the relay subnetwork. Finally, simulation results show that both two TC algorithms can improve the corresponding subnetwork EE of time-varying SDWSN.

This paper studies the joint spectrum and computing resource allocation problem in a mobile edge computing (MEC) network, where mobile users can offload computing tasks to an edge node for processing. We formulate the joint resource allocation problem as an optimization problem with an objective to maximize the system throughput while satisfying the service delay requirements of as many service requests as possible. A novel DQN-based resource allocation algorithm is proposed to solve the formulated problem. The proposed algorithm consists of a resource pre-allocation process and a resource allocation process. The former performs joint spectrum and computing resource allocation for each service request based on a deep-Q-network (DQN) model, which attempts to make an optimal decision on resource allocation for each service request by learning online to better adapt to dynamic traffic arrivals and diverse service requirements. The latter performs resource allocation for each service request according to a resource pre-allocation decision and service requirements. Simulation results show that the proposed DQN-based resource allocation algorithm outperforms three benchmark algorithms in terms of system throughput.

This paper considers the network resource allocation problem in multi-service MEC networks, and in particular considers joint spectrum and computing resource allocation in an edge network service system. The resource allocation problem under consideration is formulated as an optimization problem, which takes into account users’ minimum service requirements in terms of the service delay, transmission rate, and computation rate, with an objective to minimize the average service delay of a user’s service request. To solve the problem, we propose a deep-Q-network (DQN) based joint spectrum and computing resource allocation algorithm, which attempts to optimize the resource allocation for each service request by learning a more efficient allocation scheme so as to better adapt to dynamic traffic arrivals, diverse service requirements, and complex environmental conditions. Simulation results show that the proposed resource allocation algorithm can efficiently reduce the average service delay compared with two benchmark algorithms.

Bluetooth Low Energy (BLE) mesh which is the latest and the most innovative network technology has deeply changed the connection mode among massive BLE devices in Internet of Things (IoT). The study on the architecture and networking is quite necessary for the development of BLE Mesh. In IoT, the BLE devices are often used for voice, audio and data services. To satisfy the demands of diverse services and the mobility of the nodes, a hierarchical BLE mesh network (HBMN) architecture is proposed consisting of Hub Layer, Mesh Layer and User Layer. The HBMN architecture combines the star-network and mesh topology. Besides, the networking process of HBMN from the unprovisioned devices is also investigated in this paper. Moreover, two important performance measures have been analyzed and derived including networking time and single-hop delay. Finally, the access delay of one node is measured on the nRF52832 hardware platform. In addition, the networking time and single-hop delay of HBMN are evaluated and compared with the traditional BLE scatternet. The results show that the average single-hop delay of the BLE scatternet is 1.74 times as long as HBMN averagely and HBMN has a superior performance on the networking time when the network has a large number of devices.

In Mobile Ad hoc Networks (MANETs), the mobility of nodes causes frequent changes in the network topology, which directly affects user's Quality of Service (QoS) performance. Therefore, it is crucial for network operator to implement efficient routing optimization (RO) algorithms for diverse traffic flows. Based on the characteristics of centralized control routing in Software-Defined Networking (SDN), in this paper, we propose a routing optimization problem, and formulate the problem as an integer linear programming (ILP) problem. Then to solve the problem efficiently, we propose the QoS-aware routing optimization algorithm (QoS_ROA), which solves the problem in two stages. In the first stage, we use the Wavelet neural network (WNN) to predict the link quality at the next moment. In the second stage, we transform the proposed routing optimization problem into a 0–1 knapsack problem, and use differential search (DS) to solve it. The simulation results verify that, compared with the traditional routing algorithms, our algorithm can achieve high throughput, low packet drop rate, low delay in SDN-based MANETs.

As a supplement of cloud computing, fog computing has attracted wide attention due to its lower latency in data offloading. At present, most researches can only offload data through fixed nodes or can not effectively reduce the offloading delay of different types of data. And it is still a great challenge to develop an effective mechanism for offloading fog nodes selection. Due to the consumption of bandwidth, storage capacity, power and other resources in the process of data transmission, we must formulate a pricing strategy to ensure the revenue of fog nodes. Game theory is a widely adopted method to analyze the pricing strategy between clients and fog nodes. Therefore, this paper uses Stackelberg game to model the interaction between clients and fog nodes. And put forward the best strategy of clients and fog nodes by seeking their Nash equilibrium. Finally, the simulation results show that this mechanism can effectively reduce the offloading delay of clients and improve the revenue of fog nodes.

Mobile edge computing (MEC) enables terminals to migrate their tasks to edge servers instead of the central cloud for efficient execution. However, most researches on task offloading are limited to binary offloading for atomic tasks with a single edge server, while in practice, serial tasks of computation-intensive applications are more important. Therefore, we jointly study the task offloading and resource allocation for serial tasks in the multi-terminal multi-server scenario. A serial task is divided into multiple sub-tasks that are executed sequentially, leading to a better utilization of fragmented resources. The offloading mechanism of inter-coupled terminals is formulated as a noncooperative stochastic game, with evaluation indexes defined by the joint task priority, average task delay and energy consumption. Aiming at minimizing the long-term cost of the whole system, we adopt a multi-agent reinforcement learning (MARL) algorithm with dynamically adjusted offloading strategies, subchannels, transmit power, and allocated resources with only the partial state information. Simulation results demonstrate the feasibility of the proposed algorithm to solve the formulated problem in a distributed way. Compared with the other five benchmark algorithms, it has better system cost performance, and can schedule delay-sensitive tasks with higher priority earlier on the basis of a lower task failure rate.

The Internet of Vehicles (IoV) aims to perceive, compute, and process environmental data in a collaborative manner. Previous works focus on data sharing between vehicles, but a large amount of data will lead to redundant transmission and network congestion. In addition, security and privacy issues prevent these nodes from participating in the sharing process. Knowledge is extracted from data through machine learning (ML) and shared in the form of small-scale well-trained model parameters, which improves collaborative learning more effectively and relieves network pressure. While traditional ML algorithms are not suitable for distributed IoV with local characteristics. Based on this, this paper first divides the vehicles into multiple regions and proposes a Regional Federated Learning (RFL) framework, in which all regions maintain their own learning models, i.e. knowledge. We design a reputation mechanism to measure the reliability of vehicles participating in RFL. To address the security challenges brought by the untrusted centralized trading market, we propose a blockchain-enhanced knowledge trading framework, in which an authorized market agency coordinates the trading quickly. We model the optimal pricing mechanism as a non-cooperative game, taking into account the competition among all knowledge providers. Numerical simulation shows that the proposed reputation mechanism improves the accuracy of knowledge up to 18%, and the optimal knowledge pricing mechanism effectively increases the utility of market.

Compared with traditional cloud computing technology, fog computing provides lower latency services in the next generation mobile networks. However, the imbalance of workloads and computing resources among Fog Nodes (FNs) restricts the further promotion of the network service quality. In this paper, considering the resource sharing of FNs by cooperation where FNs are as agents, we propose a distributed multi-agent cooperative resource sharing (DMCRS) algorithm to minimize the average service latency of the fog network. Firstly, the multi-agent resource sharing problem of the fog network is formulated as a convex optimization problem and the convexity is proved. Then the coalitional graph game framework is applied to achieve the cooperation among agents and the coalition graph Nash equilibrium is proved. To reduce the complexity, the global optimization problem is decomposed into distributed local optimization sub-problems in the DMCRS algorithm and these sub-problems are transformed into root-finding problems of polynomial equations. Simulation results demonstrate that the DMCRS algorithm can balance the workloads of different FNs, reduce the average service latency remarkably and the convergence result of the algorithm is close to the global optimal solution. In addition, the time complexity of the DMCRS algorithm is significantly lower than existing algorithms.

The problem of cloud resource scheduling in smart grid is one of the hot spots in recent years. Different from most existing studies that focus on the scenario with a single service provider, this paper studies cloud resource scheduling with multiple service providers and multiple residential users. The users in this scenario can make service selection dynamically according to the service price. In turn, the price of the service providers' resource is affected by the users' selection. The interactive decision problem between the users and the service providers is modeled as a hierarchical game. At the lower-level, we use the evolutionary game to simulate the service selection of residential users. At the upper-level, non-cooperative game is used to simulate the competition among service providers. Then, we prove that the upper and lower level can reach the Nash equilibrium and the evolutionary equilibrium, respectively. Furthermore, we design a hierarchical game based cloud resource scheduling algorithm (HCRSA) for the proposed game framework. Simulation results show that both the upper and lower level can converge to their equilibrium after a few iterations. Compared with traditional resource scheduling method, the proposed HCRSA algorithm effectively reduces users' payment and reaches a balance between supply and demand.

In vehicle ad‐hoc networks (VANETs), the full coverage of roadside units (RSUs) is not necessary with the assistance of dead reckoning (DR) for the RSU‐based vehicle localisation. This study proposes an optimisation strategy of RSUs deployment, which seeks an optimal RSU layout ensuring the best localisation accuracy with a minimum number of RSUs. With the assistance of DR, first, the average geometric dilution of precision (GDOP) for a specific localisation region is derived through a non‐linear recursive model. Then the RSUs deployment is formulated into an optimisation problem, and the objective is as a function of the average GDOP and deploying interval. Finally, the optimisation problem is solved by a centre particle swarm optimisation (CPSO) algorithm. The convergence and stability of CPSO are evaluated via simulations. Furthermore, simulations also show that the proposed strategy can optimise the localisation accuracy of RSUs deployment in the VANET scenario.

In this article, a dynamic routing algorithm based on energy-efficient relay selection (RS), referred to as DRA-EERS, is proposed to adapt to the higher dynamics in time-varying software-defined wireless sensor networks (SDWSNs) for the Internet-of-Things (IoT) applications. First, the time-varying features of SDWSNs are investigated from which the state-transition probability (STP) of the node is calculated based on a Markov chain. Second, a dynamic link weight is designed for DRA-EERS by incorporating both the link reward and the link cost, where the link reward is related to the link energy efficiency (EE) and the node STP, while the link cost is affected by the locations of nodes. Moreover, one adjustable coefficient is used to balance the link reward and the link cost. Finally, the energy-efficient routing problem can be formulated as an optimization problem, and DRA-EERS is performed to find the best relay according to the energy-efficient RS criteria derived from the designed link weight. The simulation results demonstrate that the path EE obtained by DRA-EERS through an available coefficient adjustment outperforms that by Dijkstra's shortest path algorithm. Again, a tradeoff between the EE and the throughput can be achieved by adjusting the coefficient of the link weight, i.e., increasing the impact of the link reward to improve the EE, and otherwise, to improve the throughput.

Fog Computing is a paradigm of Mobile Edge Computing (MEC), deploying fog computing nodes in the edge of the network to reduce response delay. However, unbalanced workload and computing resources of each fog node result in large difference of response delay among fog nodes. How to reduce the response delay by means of cooperation among fog nodes is a challenging problem. Therefore, we propose a balanced clustering and joint resources allocation (BCJRA) algorithm to achieve the minimized response delay and energy consumption by the cooperation among adjcent fog nodes. The proposed BCJRA algorithm includes two parts: resource-aware clustering and joint wireless and computational resources allocation algorithm. The resource-aware clustering algorithm generates clusters according to the distance between fog nodes, wireless and computational resources. The joint resources allocation algorithm jointly allocate the wireless and computational resources in each cluster in parallel. The convexity of joint resources allocation problem is proved and the interior point method is used to solve the optimization problem. Finally, the simulation results show that the proposed BCJRA can reduce computational delay and energy consumption significantly compared with the existing algorithms.

Considering the scenarios where the localization accuracy of the agent is required to meet a desired requirement rather than achieve the best result, it is not necessary for all nodes to participate in positioning the agent. In this paper, a reference node (RN) selection algorithm for wireless network localization under desired accuracy is proposed. A robust error bound (REB) is derived as the RN selection metric and the concept of equal-REB contour is given, based upon which the searching region (SR) for selecting RNs is defined. In REB, the measurement errors of distances are taken into consideration and modeled as a Gaussian noise whose variance is proportional to the square of the distance. The proposed RN selection strategy selects nodes from the SR instead of the whole network region iteratively until the localization accuracy meets the desired requirement. Simulations show that the RN-selection algorithm can select the RN sets providing better localization accuracy when using REB metric. Moreover, the improved performance in terms of power conservation of the proposed algorithm is evaluated through simulation results.

With the rapid growth of terminal equipments, the data traffic in the network has grown exponentially. In order to relieve the pressure of cloud computing on link delay, congestion and energy consumption, the promising fog computing is proposed. The fog network consists of several fog clusters. We consider a fog cluster in which a fog controller (FC) aims to schedule the idle fog nodes (FNs) to serve the task node (TN) while guaranteeing the quality of service (QoS) requirements of the TN. We design an ascending-bid auction mechanism to achieve this goal. In this mechanism, the FC is the auctioneer with the reward prices as its strategy and the FNs play the role of bidders with the task sizes as their strategies. The FC uses the bid prices to motivate the FNs to process more data for the TN. The utility function of FNs is proposed, considering the payment from the FC, the cost of task computational delay and energy consumption. The FNs determine the data sizes to be processed by maximizing their utilities. Numerical simulations indicate the satisfactory performance and verify the theoretical analysis, thereby our proposed mechanism results in a win-win solution under the condition of meeting the QoS.

In order to minimize the total energy consumption of a cloud-fog network, one of the most essential challenges is the assignment of subtasks from the task node (TN) to suitable fog nodes (FNs). In this paper, we apply a many-to-one matching to deal with this problem. Specifically, we first introduce two concepts, Service Efficiency (SE) and Energy Efficiency (EE), as the indexes of the preference list (PL) of TNs and FNs, respectively. Then a stable matching algorithm for energy-minimized task offloading (SMETO) is proposed, which is comprised of two key components: (i) Deferred Acceptance Algorithm Based On Energy Efficiency (EEDA) and (ii) Energy-Minimized Task Allocation (EMTA). Algorithm (i) is an iterative procedure that matches TNs and helpers based on PLs. Algorithm (ii) minimizes the energy consumption in the network by allocating the subtasks to helpers according to the result of matching. Finally, numerical simulations indicate the stability and energy-minimization of our proposed SMETO.

To address the energy‐efficient (EE) routing problem in software‐defined wireless sensor networks (SDWSNs), in this study, a centralised routing algorithm, namely, the interference‐aware EE routing algorithm (IA‐EERA), is proposed to extend the network lifetime (NL) in SDWSNs. Both the link quality of service requirements and the balance between the link energy loads are considered in the proposed IA‐EERA when selecting the EE relays. Concretely, the IA‐EERA comprises the EE relay selection (RS) and the centralised relay scheduling schemes, which are responsible for generating a valid link set with RS priorities and scheduling the eligible relay nodes with expected link rates from the valid link set, respectively. For supporting the network compatibility and scalability, we propose a hierarchical SDWSN based network architecture, upon which the IA‐EERA can be devoted to solving the EE routing problem in the relay layer of SDWSN. Simulation results show that for one data source without interference, the proposed IA‐EERA can significantly improve the NL compared with the traditional routing algorithm utilising the energy efficiency maximisation. For multiple data sources incurring interference at nodes, the IA‐EERA is able to reduce the NL‐dropping rate by adjusting the interference‐aware parameter that affects the RS priorities

In this paper, we propose an energy-efficient power control algorithm (EPCA) using a game theory approach, which can be exploited for non-orthogonal multiple access (NOMA) in heterogeneous networks (HetNets). Firstly, we formulate the energy efficiency (EE) maximization problems for users in the macrocell and femtocell, respectively. Then, to reduce information exchanged between the base stations and users, we present a centralized implementation of the EPCA based on a noncooperative game. By studying the properties of the derived game, we develop convex optimization problems to deduce the unique Nash equilibrium (NE). Simulation results show that the proposed EPCA could converge to the equilibrium with higher system-level EE and spectrum efficiency.

In this paper, an energy-efficient routing algorithm with joint distributed routing and centralized scheduling for software-defined wireless sensor networks (SDWSNs) is proposed to balance energy efficiency and capacity efficiency. The proposed least energy difference-based routing algorithm called LEDRA is composed of two major components that are neighbor type determination criteria and relay selection criteria. Sensor nodes execute LEDRA based on the prior criteria in a distributed routing way. For given sensor and sink node pair, the controller executes LEDRA based on the latter criteria through centralized scheduling and updates the routing considering energy efficiency and interference mitigation into the flow tables stored at the sensor nodes. Simulation results show that the proposed LEDRA is able to reduce energy consumption while improve capacity efficiency by mitigating interference.

In this paper, an energy-efficient relay selection (RS) algorithm is proposed for line-of-sight (LOS) transmissions in wireless sensor networks (WSNs) with blockage. To find the energy-efficient routing for given sensor and sink node pair, the best RS criteria are employed to approach the optimal relay position (ORP) of the effective relay search region (RSR) in multiple different ways. For example, the minimal distance criterion is used to generate the minimal RSR centered on ORP during the initial selection phase. Subsequently, we enlarge RSR and apply the minimal impact-probability criterion to reduce the impact of obstacles on energy efficiency for LOS transmissions. Lastly, the minimal projection criterion is used to refine the results of RS based on the minimal impact-probability criterion. Simulation results show that the proposed algorithm based on the best RS criteria with composite ways is able to reduce the energy consumption as well as the impact of obstacles when the RSR's radius increment is less than a certain threshold.

The software‐defined wireless sensor networks (SDWSNs) have been proposed recently to solve the energy limitation of sensor nodes and extend the lifetime of the wireless sensor networks by fast node reconstruction and dynamical resource allocation. In this study, the authors investigate an energy‐efficient resource allocation algorithm in SDWSNs, in which radio resource allocation could be handled at central controllers with powerful storage and computation capacity. In this algorithm, the authors formulate an optimisation problem to minimise the energy consumption, under the individual constraint of quality of service. Then, the initial optimisation problem is transformed using semidefinite relaxation, to achieve centralised adaptive bandwidth and power allocation (CABPA). Additionally, two special cases are derived to reveal the performance of the CABPA. Furthermore, an OpenFlow‐based scheme is proposed for information exchanging and updating to realise the centralised resource allocation. Meanwhile, a distributed scheme with limited information about the whole network is developed to serve as a performance benchmark for the CABPA in the SDWSN. Finally, the simulation results reveal that the proposed CABPA performs better than the other algorithms, and it balances the power and bandwidth utilisation.

In this paper, we propose a game theoretic approach for joint offloading and resource allocation optimization (JORAO) problem in mobile edge computing (MEC) system. This study not only investigates offloading strategy, but also considers cloud and wireless resource allocation. Specially, the concern of the JORAO problem is to minimize the energy consumption and monetary cost from mobile terminals' perspective. However, the JORAO problem is non-convex and NP hard. Therefore, it is formulated as a JORAO game. The existence of Nash equilibrium (NE) is proved for it. To obtain NE, we also concentrate on cloud and wireless resource allocation algorithm (CWRAA), which is the sub- algorithm of the JORAO game. For the CWRAA, on one hand, we take consideration of OFDM sub-channels allocation and uplink power allocation in radio access networks (RAN). On the other hand, the computation resource allocation in MEC is studied. Simulation results show that the distributed JORAO game algorithm can nearly minimize the total cost of all mobile terminals (MTs) with low complexity. In addition, the energy consumption and completion time are less when the size of data becomes larger compared with existing algorithms.

This paper presents an optimal roadside unit (RSU) placement method for vehicle localization in Vehicle Ad-hoc Networks (VANETs). Since the RSU layout can significantly affect the performance of localization algorithms, the proposed method needs to find an optimal K-coverage RSU placement, to ensure the best localization accuracy while using minimum number of RSUs. We adopt the Geometric Dilution of Precision (GDOP) metric to evaluate the accuracy provided by RSU placements, and derive the expression of GDOP towards received signal strength (RSS) and hybrid parameter estimators, respectively. There are two steps contained in the proposed method. Firstly, the optimal elementary pattern is obtained and applied to form the 1-coverage placement. Secondly, the optimal K-coverage placement based on K-layer elementary patterns is found by minimizing the average GDOP of the road area, using asynchronous particle swarm optimization (APSO) algorithm. In simulations the convergence and stability of APSO solutions are verified, then our method is compared with existing uniform placement method, the results show that the proposed method can achieve better positioning performance.

In this paper, we propose a weighted cooperative localization algorithm with the ability to mitigate non-line-of-sight (NLOS) propagations in wireless networks. The link condition indicator (LCI) for each connection is calculated based on the amplitude and delay statistics of channel responses. We partition the ambiguity of link condition into N levels according to the LCI values. With the distance-dependent LOS/NLOS probability suggested by the 3rd Generation Partnership Project (3GPP), the relationship between LOS/NLOS probability and the time-of-arrival (TOA) of inter-node signal transmission is derived. We incorporate this probability into N-level LCI range regions and propose the N probabilistic hard weight (N-PHW) strategy for the cooperative localization, which penalizes the NLOS-induced positive biases by weighting the belief terms introduced by the conventional cooperative localization algorithm, the sum-product algorithm over a wireless network (SPAWN). Simulation results show that the proposed weighted algorithm significantly improves the localization performance in terms of localization accuracy, especially in serious NLOS environments.

Regular zero-forcing (RZF) precoding algorithm is well- known as its low complexity and high performance in massive MIMO systems. However, when the number of transmitting antennas increases, the matrix inversion in RZF leads to high algorithmic complexity. In this paper, we propose an improved Newton iteration to estimate the matrix inversion in RZF precoding. Compared with the traditional Newton iteration, the performance improvement of the proposed algorithm is achieved in both of the fast algorithm convergence and the average user arrival rate in RZF precoding.

This paper presents a vehicle positioning method based on joint estimation of time of arrival (TOA) and direction of arrival (DOA) with Vehicle-to-Roadside (V2R) communications. By analyzing the measured channel frequency response (CFR) between vehicles and the roadside unit (RU), the enhanced two-dimensional matrix pencil (2-D MP) algorithm is implemented to design the parameter estimator, which has lower complexity without forming a covariance matrix. The position coordinates of vehicles can then be calculated from the estimates. To improve the positioning accuracy, the extended Kalman filtering (EKF) is further introduced for mitigating the noise influence and estimating error. Simulation results show that the proposed method can achieve better positioning estimation compared with the Global Positioning System (GPS) and inertial navigation systems (INS) fusion method.

In this study, the authors propose a multi‐round elimination contention‐based multi‐channel medium access control (VEC‐MAC) scheme for vehicular ad hoc networks. In our proposed scheme, the control channel (CCH) interval is divided into three phases: roadside unit broadcast phase (BP), safety message BP (SBP) and service channel (SCH) reservation phase (RP). On the basis of this division, both the demand of safety‐relevant applications and non‐safety service applications can be satisfied. In addition, through the multi‐round elimination contention in the SCH RP collision probability of transmissions significantly decreases and more successful reservations can be provided. Furthermore, the proposed multi‐round elimination contention‐based multi‐channel MAC for VANETs (VEC‐MAC) can adaptively adjust the length of the CCH interval (CCHI) and the value of the round number for the improvement of the system throughput. Theoretical analysis and simulation results exhibit the superiority of the proposed VEC‐MAC in saturated throughput compared with the variable CCHI MAC and the wireless access in vehicular environment MAC.

In this study, the authors propose a semidefinite programming (SDP)‐based localisation and tracking algorithm, which mitigates the non‐line‐of‐sight (NLOS) error of range measurement and calibrates the accumulative error within the inertial sensing data. Both the range measurement in a mixed line‐of‐sight/NLOS environment and the step length estimated from inertial sensing information are approximated parametrically using Gaussian mixture modelling, and a maximum‐likelihood estimator (MLE) is formulated to obtain the optimal position estimation. Since the Gaussian mixture models are non‐linear functions of positions, the MLE is a non‐convex problem, which global optimum is difficult to attain. Then, the non‐convex MLE is transformed into an SDP‐based localisation and tracking problem, relying on Jensen's inequality and semidefinite relaxation. Thus, a sub‐optimal solution to the original MLE can be achieved. Moreover, the Cramer‐Rao lower bound is also derived to serve as a performance indicator for localisation errors. The simulation and experimental results demonstrate the performance of the proposed algorithm. Compared with the existing algorithms, the proposed algorithm owns the best localisation accuracy, and can achieve a sub‐metre level accuracy to a root mean square error of 0.46 m in the real deployments.

In this study, the authors propose an improved localisation algorithm in the software‐defined sensor networks (SDSNs). This algorithm includes a node‐selection strategy under the whole network power constraint, based on the software‐defined networking (SDN) technique for providing the centralised control of the network. The analogous Cramer‐Rao lower bound (A‐CRLB) value is derived for each participating node, which represents a fundamental bound on the variance of the position estimator and is used to evaluate the contribution of each node to localisation accuracy. On the basis of A‐CRLB values, the most helpful nodes for localisation are selected to maximise the sum of the nodes' contributory values to the localisation accuracy. With the global network knowledge provided by the SDN controller in the SDSN, the node‐selection strategy is formulated into a 0‐1 programming problem on the premise of power satisfaction of each node. Furthermore, the proposed node‐selection ‐based localisation algorithm is applied to both noncooperative and cooperative localisation scenarios. Simulation results show that the proposed algorithms provide efficient and effective localisation schemes in SDSNs, and can improve the performance in terms of both the selection convergence speed and the localisation accuracy.

Ultra-wide bandwidth (UWB) and Inertial Navigation (IN) have been adopted in high precision localization and tracking systems. However, the ranging measurements influenced by non-line-of-sight (NLOS) path may degrade the localization accuracy. Besides, inertial measurement errors are within the sensing data and constantly accumulate over the time. To overcome the above problems, we investigate NLOS mitigation for UWB measurements, and calibration for IN estimations. Both ranging measurement and step length estimation are modeled using Gaussian mixture model (GMM), and one maximum likelihood (ML) estimator is developed. Then, the non-convexity estimator is relaxed into a semidefinite programming (SDP), which global minimum can be readily attained. Finally, both simulation and experimental results are provided to illustrate the validity and performance of our proposed SDP-based localization and tracking algorithm.

In this paper, one centralized resource allocation algorithm is proposed to minimize energy consumption in software defined wireless sensor networks (SD-WSNs). The energy consumption problem is formulated as an optimization problem, given quality-of-service (QoS) constraint defined as Signal-to-Interference-plus-Noise Ratio (SINR). Then, the nonconvex optimization problem is relaxed into a semidefinite programming (SDP), which serves as a lower bound. To analyze the tightness of the lower bound, two special cases are introduced. Besides, one distributed approach is also developed to provide a performance benchmark. Furthermore, simulation results are revealed that the proposed centralized algorithm performances better with respect to the energy consumption and bandwidth utilization.

Schemes combining Ultra-wide bandwidth (UWB) ranging technology and Inertial Measurement Unit (IMU) have been proposed for high precision positioning and tracking. However, positioning accuracy can be significantly affected by the non-line-of-sight (NLOS) UWB ranging measurements and cumulative inertial sensing error. In this paper, we model the ranging measurement error and the step length as Gaussian Mixture Model (GMM), respectively. Then, we derived a Suboptimal Importance Density (SID) for particle filters, which could resolve the degeneracy of particles and sample impoverishment. Finally, experimental results illustrate the performance gain of the particle filters with the proposed SID.

Cooperative localization has become a promising solution for location-enabled technologies in Wireless Sensor Networks (WSNs). However, it suffers from great energy consumption problem due to the energy-constrained characteristic of the networks. To alleviate this problem, we propose a cluster nodes selection strategy based on the Cramer-Rao lower bound (CRLB) for the cooperative localization algorithm in WSN. We first define clusters for every agent node by setting the received signal strength (RSS) threshold to screen out some less useful nodes, which greatly saves the energy at a cost of only a slight degradation in accuracy. Then, to improve the localization accuracy, the cluster nodes selection strategy catches the nodes that make the biggest contribution to localization results while discarding the least ones based on the derived analogous-CRLB values. Simulations show that the number of nodes participating in the localization is greatly decreased, which means a substantial reduction in energy consumption. In addition, the localization mean absolute error performance is significantly improved by using the proposed nodes selection algorithm.

Homology theory provides new and powerful solutions to address the coverage problems in wireless sensor networks (WSNs). They are based on algebraic objects, such as Čech complex and Rips complex. Čech complex gives accurate information about coverage quality, but requires a precise knowledge of the relative locations of nodes. This assumption is rather strong and hard to implement in practical deployments. Rips complex provides an approximation of Čech complex. It is easier to build and does not require any knowledge of nodes location. This simplicity is at the expense of accuracy. Rips complex cannot always detect all coverage holes. It is then necessary to evaluate its accuracy. This work proposes to use the proportion of the area of undiscovered coverage holes as performance criteria. Investigations show that it depends on the ratio between communication and sensing radii of a sensor. Closed-form expressions for lower and upper bounds of the accuracy are also derived. For those coverage holes that can be discovered by Rips complex, a homology-based distributed algorithm is proposed to detect them. Simulation results are consistent with the proposed analytical lower bound, with a maximum difference of 0.5%. Upper-bound performance depends on the ratio of communication and sensing radii. Simulations also show that the algorithm can localize about 99% coverage holes in about 99% cases.

In this paper, we proposed a general analytical model for mobile relay scenario using stochastic geometry approach. By applying different parameters into this general model, the Cumulative Distribution Function of the SINR and the average achievable rate on different links in both traditional mode and relay mode can be obtained. According to numerical results, we observed that penetration loss between outdoor and in-vehicle is a key factor to decide whether mobile relay could bring data rate gain into the system. When penetration loss is large, mobile relay could bring considerable data rate gain to embedded UEs inside the bus.

Homology theory has attracted great attention because it can provide novel and powerful solutions to address coverage problems in wireless sensor networks. They usually use an easily computable algebraic object, Rips complex, to detect coverage holes. But Rips complex may miss some coverage holes in some cases. In this paper, we investigate homology-based coverage hole detection for wireless sensor networks on sphere. The case when Rips complex may miss coverage holes is first identified. Then we choose the proportion of the area of coverage holes missed by Rips complex as a metric to evaluate the accuracy of homology-based coverage hole detection approaches. Closed-form expressions for lower and upper bounds of the accuracy are derived. Asymptotic lower and upper bounds are also investigated when the radius of sphere tends to infinity. Simulation results are well consistent with the analytical lower and upper bounds, with maximum differences of 0.5% and 3% respectively. Furthermore, it is shown that the radius of sphere has little impact on the accuracy if it is much larger than communication and sensing radii of each sensor.

Homology theory provides new and powerful solutions to address the coverage problems in wireless sensor networks (WSNs). They are based on algebraic objects, such as Cech complex and Rips complex. Cech complex gives accurate information about coverage quality but requires a precise knowledge of the relative locations of nodes. This assumption is rather strong and hard to implement in practical deployments. Rips complex provides an approximation of Cech complex. It is easier to build and does not require knowledge of nodes location. This simplicity is at the expense of accuracy. Rips complex can not always detect all coverage holes. It is then necessary to evaluate its accuracy. This work proposes to use the area of undiscovered coverage holes per unit of surface as performance criteria. Investigations show that it depends on the ratio of communication and sensing ranges of each sensor. Closed form expressions for lower and upper bounds of the accuracy are also derived. Simulation results are consistent with the proposed analytical lower bound, with a maximum difference of 0.4%. Upper bound performance depends on the ratio of communication and sensing ranges.

Coverage is considered as an important measure of quality of service provided by a wireless sensor network (WSN). Yet, coverage holes may appear in the target field due to random deployment, depletion of sensor power or sensor destruction. Discovering the boundaries of coverage holes is important for patching the sensor network. In this paper, we adopt two types of simplicial complexes called Cech complex and Rips complex to capture coverage holes and classify coverage holes to be triangular and non-triangular. A distributed algorithm with only connectivity information is proposed for non-triangular holes detection. Some hole boundary nodes are found first and some of them initiate the process to detect coverage holes. Simulation results show that the area percentage of triangular holes is always below 0.03% when the ratio between communication radius and sensing radius of a sensor is two. It is also shown that our algorithm can discover most non-triangular coverage holes.

Abstract Node positioning is a useful service for a vehicular communication system. The global positioning system (GPS) is the most popular positioning technique widely used in vehicular communication systems. However, GPS cannot work effectively in many situations, for example, in a tunnel or under a bridge. For accurate positioning in such situations, it is desirable to have more reliable positioning techniques. In this paper, we propose a novel method for node positioning in vehicular communication systems. Unlike existing positioning methods that employ a roadside node as an observation station, the proposed positioning method allows a vehicular node to play the role of an observation station, and estimates the vehicular node's absolute coordinates based on the measurement of the Doppler frequency shift, the velocity of the vehicular node, and the known absolute coordinates of roadside nodes. To improve positioning accuracy, it introduces extended Kalman filtering (EKF) to mitigate the effects of channel noise and multipath interference. Analytical and simulation results show that the proposed positioning method can significantly improve the positioning accuracy compared with differential GPS and reduce the positioning delay. Copyright © 2009 John Wiley & Sons, Ltd.

Positioning plays a central role in location-based services. In designing such a method of highway applications, it is of paramount importance to provide a quick and precise positioning service. Such design considerations can help reduce the probability of accidents and other traffic troubles. This paper presents a novel and practical vehicular node positioning method which can achieve a higher accuracy, more quickness and more reliability than the existing global-positioning-system-based positioning solutions by making use of doppler-shifted frequency measurements taken by vehicular node itself. This positioning method uses infrastructure nodes which are placed on the roadside every several kilometers as radiation sources to estimate the relative distance and correlative angle of the vehicular node to the infrastructure node instantaneously. Through coordinate conversion, we get the absolute coordinates of vehicular node based on known absolute coordinates of infrastructure node. We also analyze the maximum distance of neighbor infrastructure nodes in order to ensure a high accuracy. In addition, simulation results demonstrate that the performance of our method with extended Kalman filtering (EKF) is superior to the method without EKF and DGPS.