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.

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.

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.

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.

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.

Efficient data delivery in vehicular named data networks (VNDNs) can immensely enhance the safety and entertainment for drivers. For this purpose, in-network caching is used to expedite data delivery. In this work, a distributed probability-based caching with content-location-awareness (DPC-CLA) is proposed for efficient data delivery in VNDNs, where the roadside-units (RSUs) with caching capabilities can accurately access the relatively popular contents of the received packets by normalizing the reciprocal sum of the request hops in an indefinite period. In addition, the RSUs can also perceive the surrounding cache locations using the weighted recursive sum of the neighbouring cache intervals. Simulation results show that the proposed DPC-CLA performs better than four existing caching mechanisms in terms of the average number of hops and the cache hit ratio.

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.

In this paper, we design and implement the programmable nodes in software-defined sensor networks. Combining the emerging software defined networking (SDN) and the existing wireless sensor networks (WSNs), we propose an SDN-based sensor network architecture, called as software defined sensor networks (SDSN), which is composed of one centralized controller node, named as SDSN programmable controller node, on the control plane and several software-defined sensor nodes, named as SDSN programmable sensor nodes, on the data plane. The centralized controller node is configured for network control, and the sensor nodes are utilized to transfer general-purpose packets. The behavior rules of the programmable nodes in the SDSN can be reconfigured through wireless communication. Hence, the goal of this paper is to design and implement SDSN programmable controller node and SDSN programmable sensor node whose behavior can be reconfigured. We develop the programmable controller node using NanoPi and CC2530 modules, and the programmable sensor nodes using CC2530 modules. Finally, the experimental results show data acquisition and transmission in the data plane, and network centralized management in the control plane. Moreover, the experimental results demonstrate that the programmable nodes have been designed own greater flexibility and scalability compared to traditional WSN nodes.

In this paper, a positioning system is designed based on the visible light communication (VLC) technology for the tracking of mobile target in the indoor environment. The practical received signal strength (RSS) can be measured for subsequent positioning algorithm by using the designed system. Based on the practical measurements, an empirical equation is proposed to estimate the target's distances relative to the referenced LEDs. Then, taking advantage of the relative distances, the location estimation can be obtained by the trilateration method. Furthermore, Kalman Filter is used for improving the positioning result computed by trilateration method. Finally, experimental results show that the average location errors by adopting the derived empirical formula and trilateration can reach to 3.4cm, and the accuracy of positioning can be promoted to 2.6cm by using Kalman Filter in a real wireless environment.

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.

In this paper, a three-dimensional (3-D) positioning system using fingerprinting and K-Nearest neighbor is proposed. The implementation of the proposed positioning system based on visible light communication (VLC) is discussed. On-off keying (OOK) modulation, Manchester coding and time division multiplexing (TDM) are utilized to obtain both lossless data and accurate received signal strength indications (RSSIs) without sacrificing the comfort of indoor illumination. The proposed 3-D positioning algorithm is conducted in three processes. Firstly, we quickly traverse all sampling points in the fingerprint database to find the nearest neighbor and choose candidate sampling points around the nearest neighbor. Secondly, we conduct an iterative search among candidate sampling points to find K-Nearest neighbors. Finally, we obtain estimated coordinates using weighted average method. Experimental result shows that, the proposed system possesses fast location and high accuracy.

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 paper, we propose a two-phase based user differentiation scheme (UDS) for slot allocation in vehicular networks. Vehicular nodes in our proposed scheme are divided into two types: the backbone nodes and the ordinary nodes. In the first phase, every vehicular node tries to occupy one time slot with the same behavior, and at the end of the first phase based on the received broadcast packets during one frame vehicular nodes who have more neighbors can be upgraded to backbone nodes. During the second phase the backbone nodes carry out a greedy behavior to contend for more time slots while ordinary nodes give up slot contention. The slot occupation period and the dynamically backbone node selection strategy are given in detail. Moreover, the performance of average channel utilization and broadcast delay have been analyzed. Analysis and simulation results show that the proposed UDS has a better performance in the two indexes listed above compared to the one-to- one scheme without user differentiation.

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.

To overcome the weak points that protocol sequence allocation in vehicular ad hoc networks heavily relies on the help of infrastructures and that a large amount of sequence resource is wasted due to the one-to-one scheme, in this paper we propose a comprehensive framework based on which users could take full use of the sequence resource and at the same time adaptively avoid inter-vehicle interference and hidden terminal problem. The sum sequence and the sequence occupation number which lay a basis for the proposed framework are fully studied. The pure dichotomy scheme is proposed for neighboring nodes to occupy sequences without overlapping. Furthermore, an enhanced scheme is presented to handle the potential problem that two nodes in the network may have the same coordinate along the direction of road extension. It is shown that the proposed framework has a better protocol sequence utilization than the one-to-one scheme and works effectively even when the network load is rather heavy.

In this paper, we propose a probability based slot allocation scheme named p-persistent for vehicular networks. One vehicular node makes its decision on slot occupation according to the ratio of the slot number it has owned to the average slot number. In other words, more time slots a tagged vehicular node has owned, lower probability it will contend for a new slot with. Through this scheme the slot resource could be fully used and the fairness among users could be satisfied. Closed form of average channel utilization, slot number per user and periodic broadcast delay have been obtained. Analysis and simulation results show that the proposed p- persistent scheme has a better performance in all the three indexes listed above compared to the one- to-one scheme which is used in most of the existing studies and the pure greedy (P-G) scheme.

Many applications can be benefit greatly from location- awareness information obtained by positioning and navigation system. Nowadays, a hybrid method combining Ultra-wide bandwidth (UWB) ranging modules and inertial measurement unit (IMU) has been one promising scheme for high precision positioning requirement. However, positioning errors could be terribly affected by the noise of UWB ranging measurements and accumulated errors caused by inertial sensing data. In this paper, a semidefinite programming (SDP) based node localization algorithm is proposed for such hybrid method. The positions of target sensors (TNs) can be determined using the distance estimations from location-aware anchor nodes (ANs) as well as other inertial information (e.g., acceleration and azimuth). Meanwhile, the corresponding Cramer-Rao lower bounds (CRLB) are derived as performance benchmarks. Finally, simulations are provided to illustrate the validity of our hybrid algorithm, which demonstrate that the proposed algorithm achieves superior performance and it could be very promising for high precision positioning service.

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.

Localization technologies in wireless sensor networks have been suffering from great energy consumption problem due to the energy-constrained characteristic of the networks. Existing power allocating solutions are mostly distributed for the lack of global network knowledge. In this paper, we investigate localization algorithm with the support of software-defined network (SDN) technique and propose a localization node selection algorithm based on the cramer-rao lower bound (CRLB). By making use of the global network knowledge provided by the SDN controller, we formulate the issues into a 0-1 programming problem on the premise of energy satisfaction. Simulation results show that significant improvement in localization performance can be achieved with our proposed SDN based algorithm.

In this paper we present a multi-round contention based medium access control (MAC) protocol VMR-MAC to overcome the slot-wastage problem in vehicular ad hoc networks (VANETs). Vehicular nodes in a two-hop set (THS) could acquire more than one time slot in a frame interval and the whole slot resource is almost uniformly allocated to the users through the round-by-round contention. Moreover, at the end of a super frame each vehicular node releases its time slots to avoid slot collision. The round-based time slot acquisition process and the delay performance of VMR-MAC are analyzed. Numerical and simulation results show that VMR-MAC can significantly improve the broadcast delay performance, as compared to VeMAC, a single-round MAC protocol for VANETs.

In this study, the hybrid‐access problem in multi‐femtocell networks, where multiple femtocell registered users (FRUs) and unregistered users (FURUs) coexist, is solved based on the optimal resource allocation using Stackelberg game. The main idea of the authors’ proposed scheme is to have the femtocell base station (FBS) allocating resources to FRUs freely to guarantee their quality of service (QoS), and then obtaining the compensation for offering the hybrid‐access through selling the remaining resources to FURUs with high motivation. Based on the Stackelberg game involving the non‐cooperative competition among the FBS and multiple FURUs, a dynamic hybrid‐access control scheme is proposed and analysed for single‐femtocell access. Then a dynamic multi‐round hybrid‐access scheme (DMRHAS) with post‐DMRHAS is further designed for multi‐femtocell hybrid‐access. Simulation results show that the proposed schemes have the FBS not only protect FRUs' QoS, but also dynamically determine the list of accessible FURUs according to the channel power gains of FURUs and the change of FRUs' QoS demands, through pricing the FURUs.

The multi-hop multi-flow transmission has been proposed as a promising solution to cope with the spectrum scarcity in densely populated user environments. Due to the mutual interference between different flows and different hops of the same flow, the resource allocation for multi-hop multi-flow wireless networks is in general non-convex, and thus cannot be solved by conventional convex optimization techniques. In this paper, we propose an algorithm to effectively solve the resource allocation problem by jointly optimizing the rate control and scheduling. Specifically, we show that the problem can be decomposed into a set of problems that maximizes the weight-sum-flow rate at each slot. Furthermore, to solve the non-convex weighted sum flow rate maximization problem, we exploit its hidden monotonicity and develop a global optimal rate control and scheduling (G-RCS) algorithm based on the theory of monotonic optimization. Our analysis shows that the proposed G-RCS algorithm is guaranteed to converge to an optimal solution in a finite number of iterations. To reduce the complexity, we propose an accelerated algorithm, referred to as the A-G-RCS, based on the inherent symmetry of the optimal solution. Numerical results validate that the proposed algorithms can serve as a performance benchmark for the existing heuristic algorithms.

In this study, we propose a novel dynamic hybrid access algorithm for single femtocell network with two different types of users, the femtocell registered users (FRUs) and unregistered users (FURUs). Considering the selfish nature of the femtocell, how to provide enough incentives to the femtocell base station (FBS) for sharing its resources with the FURUs is challenging. Different from the previous works in this area, we adopt the Stackelberg-game modeling to represent the competition behaviors of the FBS and the users. Specifically, the proposed framework has the FBS to allocate resources to the FURUs through pricing while maintaining the quality of service (QoS) of the FRUs, which is done via the optimal solutions of the maximization of the utility functions of the buyers and the revenue of the seller. Simulation results show that upon the QoS of the FRUs are protected, the FBS dynamically determines the list of the accessible FURUs according to the assigned gain to the FURUs and the change of the FRUs' QoS demands.

Femtocell with the open access mode benefits the macrocell users (MUE) which locate closely to the femtocell base station (FBS). However, how to enhance these users' performance with the femtocell registered users quality of service (QoS) protection becomes a major problem. Deploying relay outside the femtocell networks is an economical and effective option to reduce the cross-tier interference while improving the network capacity and indoor coverage in the cellular networks. In this paper, an optimal power allocation scheme considering the protection of the femtocell registered users is proposed in the open access femtocell network assisted with a relay node, which operates under a novel amplify-and-forward (AF) relaying protocol, for the downlink. The power allocation problem is formulated with the objective of maximizing the sum-transmission-rate of the neighboring macrocell users (NMUEs) which perform the handovers from the macrocell to the femtocell due to the degraded channel QoS of the macrocell. This optimization is subject to the constraints of the required transmission rates of the femtocell registered users (FUEs), the thresholds of the cross-tier interferences to the users kept in the macrocell, and the total transmit powers of the FBS and the relay node, etc. We solve the optimization problem via the dual decomposition method. Simulation results have shown that our proposed scheme can protect the QoS required by the FUEs. It also illustrates the better performance of the proposed optimization methodology in terms of the sum-transmission-rate of the NMUEs than the traditional femtocell method which has no presence of the relay node.

This study proposes an analytical model and an effective scheme for the periodic broadcast on the control channel in vehicular ad hoc networks (VANETs). An improved Markov model for analysing the performance of the periodic broadcast in VANETs is established. Compared with the traditional two‐dimensional Markov chain models, the improvement of our proposed model is achieved by the considerations of the unsaturated traffic conditions with the deterministic message generation at each node, modelled by a discrete‐time D/M/1 queue and the control mechanism of freezing the backoff‐time counter. In adapting to the change of the vehicle densities, the authors propose to use the dynamic contention window (DCW), instead of the fixed contention window (CW), for the broadcast in the IEEE 802.11p medium access control in VANETs. For a certain vehicle density, a best CW size is chosen to achieve a more effective broadcast. Simulation results show that the proposed DCW‐based broadcast performs better than the traditional fixed‐CW‐size broadcast in terms of the packet collision probability. The results also validate our proposed Markov model and its performance improvement than the scheme without the consideration of freezing the backoff‐time counter.

In this paper, we study optimal transmission scheduling and congestion control in multi-hop device-to-device (D2D) communications underlaying cellular networks. The optimization is not only based on the cellular and D2D link qualities but it also takes the interference situation for each possible mode and the quality of service (QoS) requirements of each user into account. The problem is formulated as an overall time-averaged system throughput maximization with system stability provision. By manipulating the formulation and using the Lyapunov optimization theory, the original problem can be decomposed into an end-to-end rate control problem and a joint routing, channel assignment, and power allocation problem. We provide a lower bound of the system throughput and thus the proposed algorithm can achieve an approximate maximization. Simulation results show that the algorithm simultaneously improves the system throughput and stability compared to the scenario where communication modes are fixed for all sources.

In this study, the authors propose a low complexity user scheduling for multi‐antenna broadcast systems with a large number of users with diverse delay‐quality of service (QoS) assurances. Owing to the exclusive user scheduling constraints, the optimisation is combinatorial. Furthermore, by adopting effective capacity and effective bandwidth to illustrate the behaviours of different traffic characteristics such as different source statistics and queue dynamics, the delay‐bound violation probability constraints can be converted into equivalent minimum data rate constraints. To reduce the computational complexity, they use genetic algorithm (GA) to perform scheduling, instead of a brute‐force exhaustive search (ES) over all possible user subsets. By comparing the complexity of GA and ES, they show that GA is a rapid, although suboptimal, option of performing user scheduling optimisation. Simulation results show that the proposed algorithm can not only maximise the achievable user sum data rate, but also keep the delay‐bound violation probability of each user below a given threshold.

Protocol sequences offer a promising alternative for media access control of mobile ad hoc networks, because they do not require any coordination among the users nor any centralized synchronization. We show that by using suitably designed deterministic scheduling, the delay performance can indeed be much better than using random and pseudo-random sequences. The reported results indicate that protocol sequences can offer practical solutions to complicated multiple-access problems in ad hoc networks, such as vehicular ad hoc networks (VANET). The cumulative distribution function of delay and an upper bound of the individual delay in the cases of protocol sequences are derived.

In this paper, a resource allocation algorithm is proposed for different quality of service (QoS) guarantee, fair transmission and high data rate in orthogonal frequency division multiple access (OFDMA) and wireless local area network (WLAN) integrated system. First, different QoS requirements are converted into different rate requests for simplifying the optimization problem. Second, the sub-channels are assigned to reach certain QoS satisfaction level (QSL) before water-filling based power allocation. The channel resource is not allocated according to highest data rate; users will be assigned channels that best suit the needs. The proposed algorithm is compared with three other algorithms and the simulation results show that it can provide different QoS guarantees for different users while maintaining the system data rate and fair transmission.

In this paper, we propose an adaptive user scheduling and resource management algorithm for multiuser MIMO downlink systems with heterogeneous delay requirements, in order to not only maximize the system throughput but also maintain delay constraints as well. We adopt the concepts of effective capacity and effective bandwidth to illustrate the behaviors of different traffic characteristics such as different source statistics and queue dynamics. It is shown that the scheduling problem can be formulated as a mixed convex and combinational optimization problem, and the proposed scheme is thus a water-filling solution. Simulation results show that the proposed algorithm maintains the delay constraints for the delay sensitive users in the presence of bursty data traffic and maximizes the system throughput by exploiting the multiplexing diversity gain and multiuser selection gain.

In this study, two relativity‐based access strategies are proposed for frequency hopping system with cognitive ability (C‐FH). Sensing module is added in the receiver to perform real‐time measurement of channel states. The first access strategy is studied in consideration of partial relative FH pattern. The spectrum band is divided into several groups and each C‐FH user can only jump over the channel group authorised by the active user. In the second access strategy, the C‐FH user will adaptively choose an active user as the scheduler to apply for access. The application users may be rejected because of lack of resources or accepted to access the available channels in order of arranged sequence. The chosen scheduler will accept applications according to number of available channels and the spectrum resource will be shared in condition of being temporally correlated. With relativity‐based strategies, FH patterns of users have the minimum Hamming correlation, even to zero. Markov chains are built to model the access processes. The performance analysis and the simulation results show that the relativity‐based access strategy brings lower collision probability, higher bandwidth utilisation and higher normalised average throughput.

In some real-time networked control applications, information between sensors and controllers is exchanged over a shared wireless channel. One key issue is to manage multiple access to the shared medium to accomplish different control tasks. In the paper, a protocol sequence based media access control (MAC) design is presented for networked control systems (NCSs). It is challenging to design an optimal or suboptimal controller since the sensor packets and control packets could be lost in unreliable wireless networks. An ad hoc and efficient control policy is presented. Numerical results illustrate that the cost performance of the protocol sequence based NCS is much better than that of the π-persistent random access based NCS.

A C3G-A network integrating the 3G network and the ad hoc network is proposed. In such C3G-A network, 3G and ad hoc networks adopt the same frequency bands, which result in additional interference for each other. In this study, an interference model of the C3G-A network is presented. Based on this model, the effects of the interference on network capacity are analysed. Meanwhile, the corresponding formulae of network capacity are deduced. It is observed from extensive simulation and numerical analysis that network capacity is seriously affected by the additional interference. In order to suppress the effects of the additional interference, an algorithm based on distance (ABD) is proposed so as to decrease the additional interference and maximise network capacity. The simulation results show that the ABD algorithm can effectively overcome the effects of the additional interference and approximate network capacity in the 3G network and the ad hoc network with different frequency bands.

Abstract In this paper, we present an analytical model for determining the optimal number of clusters, with the objective of minimizing the communication costs in a clustered wireless sensor network (WSN). Sensor nodes are located randomly and distributed according to a homogeneous two‐dimensional Poisson point process. Overall communication costs in the network are calculated analytically based on a practical energy consumption model at node level. The optimal probability of being a cluster head (CH) for each node is deduced by minimizing a cost function taking into account the energy dissipation of nodes. The scenarios with single‐hop and multi‐hop clustered networks are investigated. Simulation results show the existence of the optimum number of clusters which improves the energy efficiency of clustering schemes. We also verify by simulations that the analytical optimal probability of being a CH can provide a good approximation of the actual optimum probability, regardless of the different densities of nodes in the network. Copyright © 2010 John Wiley & Sons, Ltd.

We try to achieve quick and accurate channel access in background of coexistence between Bluetooth system (BT) and frequency hopping system with cognitive ability (C-FH) in ISM band. In this paper, access policies based on frequency relativity (FRA) are proposed. The C-FH user could use frequency relativity (FR) to make fuzzy prediction and access the available sub-channels. With channel states variation Markov models, the performance of new schemes could be verified. Compared with access control without FR, the FRA policies are revealed to get lower collision probability and higher bandwidth utilization in ISM band.

This paper proposes a Markov model for analyzing the performance of periodic broadcast in vehicular ad hoc networks (VANETs). Since there is no packet retransmission in broadcast, we only consider a one-dimensional Markov model. In the proposed model, we assume that the traffic condition is unsaturated and introduce a state to represent the situation when there is no packet waiting for transmission in the buffer of a node. Meanwhile, the state of a backoff counter changes only when the channel is sensed idle. When the channel is busy, the state of the backoff counter does not change. Moreover, we use a discrete time D/M/1 queue to model the periodic broadcast of each node. Based on this model, we also show numerical results to analyze the performance of the periodic broadcast in VANETs in terms of packet collision probability and average packet delay.

In Cognitive Radio (CR) networks, it is common that the spectrum holes are too narrow to support high-speed communications. Discontinuous Orthogonal Frequency Division Multiplexing (DOFDM) is a good way for a secondary user to access several spectrum fragments simultaneously with one Radio Front (RF). In this paper, a novel Channel Characteristic Aware Spectrum Aggregation (CCASA) algorithm which uses DOFDM to aggregation spectrum fragments with only one radio front is proposed in order to increase the overall throughput of a CR network. By combining Adaptive Modulation and Coding (AMC) and spectrum aggregation, the good subcarriers are assigned to the specific secondary users in CCASA algorithm thus achieving a better channel efficiency. Different bandwidth requirement and aggregation limitation of secondary users are both considered in this algorithm while maintaining a fairly low computational complexity. The simulation results show that CCASA achieves a bigger total throughput than existing aggregation algorithms.

The cognitive cooperative system with coexisting scenario of multiple primary users and one secondary capable of relaying is considered in this study. The primary users transmit packets in orthogonal subchannels. According to the cognitive principle, the secondary activity cannot interfere with the primary performance. Therefore in this study, the secondary user makes use of the spectrum when sensed idle. Based on the proposed media access control (MAC) protocol, the authors derive the stable throughput and delay slots expressions of each primary user with secondary relaying. They also achieve the stable network constraints of relaying probability ɛ, the feasible range of primary arrival rates and the maximum allowed secondary transmitting power which is to make a tradeoff between the stable throughput of the primary and secondary user. Simulation results show that secondary relaying can increase the primary and secondary throughput and also reduce the delay slots when designing an appropriate ɛ.

The concept of cognitive radio is to exploit efficiently the spectrum resources by allowing the coexistence of the primary and secondary users in the same bandwidth without interfering the performance of primary users. Three coexisting models (overlay, underlay and interleave) were presented in recent literature. In this study, the authors propose a hybrid cognitive network model with overlay and underlay models, whereby a primary link leases its fractions of transmission time to the secondary users for their cooperation (i.e. these nodes form as a virtual multiple input multiple output (VMIMO) group) under the outage constraints of the primary and secondary systems. A new cooperative protocol between primary and secondary users is presented. The authors attempt to achieve the maximum transmission capacity of the secondary users under the primary and secondary outage constraints, which depend on the secondary density in the cognitive network. This study gives the upper bound density of the secondary transmitters that are modelled as a homogeneous marked Poisson point process. The maximum density is achieved by computing an optimal set of system parameters such as power control factor of the secondary user and the dirty paper coding (DPC) parameter. Simulation results illustrate that the maximum secondary density obtained using VMIMO is superior to that obtained by direct primary transmission.

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.

The cognitive cooperative system with coexisting scenario of multiple primary users and one secondary capable of relaying is considered. The primary users transmit packets in orthogonal sub-channels. According to the cognitive principle, the secondary activity cannot interfere with the primary performance. Therefore, in this paper, the secondary user makes use of the spectrum when sensed idle. Based on the proposed MAC protocol, we derive the stable throughput expressions of each primary user with secondary relaying. We also achieve the stable network constraints of relaying probability ε, the feasible range of primary arrival rates and the maximum allowed secondary transmitting power which is to make a tradeoff between the stable throughput of the primary and secondary user. Numerical simulations show that secondary relaying can increase the primary and secondary throughput when designing an appropriate ε.

In this paper, C3G-A system (Combining the third generation mobile communication network and Ad hoc network) is presented. Based the system structure, we analyze system capacity and terminal power consumption. Then the performance formulas are developed. Numerical results show that C3G-A system with respect to system performance outperform the conventional TD-SCDMA system.

Next generation wireless communications will likely rely on integrated networks consisting of multiple wireless technologies. In this paper, C3G-A system which combines conventional TD-SCDMA network and Ad hoc network is presented. Based the system structure, we propose a new vertical handover scheme which maintain the connection continuously. Numerical results show that the new handover scheme for C3G-A system outperforms the conventional handover scheme with respect to the performance.

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.

A novel selection cooperation scheme for cognitive nodes (Cog-Sel scheme) in multiple sources cooperative networks is presented and the outage probability is derived. In the proposed scheme, the source chooses a single optimal relay or transmits directly to the destination in terms of the SNR between the source-destination pair with some feedback. Each node can smartly utilize the idle frequency band to transmit signal using cognitive radio technology. Therefore, no extra channel resources are allocated for cooperation and the system encounters no bandwidth losses. The outage probability of the proposed scheme is analyzed and the result reveals that it outperforms the conventional relaying strategies such as simple selection and opportunistic relaying schemes. These benefits contribute to the efficient use of power and channel resources. Theoretic analysis and numerical simulation results are presented to verify our analysis.

Performance analysis for keyhole channel of a new kind of quaternion quasi-orthogonal space time block code is introduced in this paper. First the transmission model is formulated. Then the new code of four transmit antennas is designed, the new code can provide full transmission rate. And zero-forcing linear decoding method is adopted. Lastly the new code is compared with the quaternion quasi-orthogonal space-time block code of four transmit antennas for Rayleigh channel and with the traditional quasi-orthogonal space-time block code of four transmit antennas for keyhole channel. Simulation results show that the new code can increase bit error rate compared with the quaternion quasi-orthogonal space-time block code for Rayleigh channel, and can reduce bit error rate compared with the traditional quasi-orthogonal space-time block code for keyhole channel.

A method to determine the optimal clusterhead number in a clustered wireless sensor network is proposed to improve the energy efficiency of clustering schemes. The wireless sensor network is modeled as points locating randomly on the plane according to a homogeneous spatial Poisson process. This leads to derive the means of some typical cluster parameters accordingly. After that, the total energy cost consumed in the system is calculated based on a practical radio model. As a result, the optimal probability of becoming a clusterhead for each node is deduced via minimizing the system energy cost. Ultimately, the total energy costs are compared in the scenarios that the networks employ the same clustering algorithm with a range of values for the probability of becoming a clusterhead. Simulation results show that the optimal probability we obtained has indeed minimized energy dissipation in the system and is applicable to other clustering algorithms directly.

In this paper, we propose an integrated service-based handoff scheme with (ISBQ) and without queue capability (ISB) for the cellular and WLAN integration. The proposed handoff schemes take into account the different services supporting ability, user mobility and vertical handoffs in the integrated network. The system is modeled using multidimensional Markov chain and a numerical analysis is presented to evaluate the most important performance metrics such as the blocking probability of originating calls, dropping probability and average transfer time. The effects of data user mobility are also studied in the paper. It is shown through comparisons with the conventional WLAN first (WF) handoff scheme that the ISB and ISBQ scheme can provide better quality of service (QoS) for mobile subscribers. Especially, the ISBQ outperforms other schemes in reducing the blocking probabilities of originating data calls and dropping probabilities of data calls, as well as guaranteeing the performance of voice calls.

We present a new method for data gathering that maximizes lifetime for wireless sensor networks. It involves three parts. First, nodes organize themselves into several static clusters by the Hausdorff clustering algorithm based on location, communication efficiency and network connectivity. Second, clusters are formed only once but the role of cluster-head is optimally scheduled among the cluster members. We formulate the cluster-head scheduling that maximizes the network lifetime as an integer programming problem and propose a greedy algorithm for its solution. Third, after cluster-heads are selected, they form a backbone network to periodically collect, aggregate, and forward data to the base station, where a minimum energy (cost) routing is used. Comparing with other known methods, significant lifetime extension is obtained with the use of this method.