The Internet of Things (IoT) possesses enormous potential for a variety of smart agriculture use cases such as pest control, soil management, and autonomous irrigation. Due to the complex terrain and cost constraints, traditional infrastructure-based ubiquitous communication network has poor scalability and cost-efficiency. In such contexts, finding alternative economic and sustainable data collection mechanism becomes paramount. In this paper, the predefined trajectory of UAV equipped with storage capability is opportunistically utilized as a delay-tolerant data transportation channel. We propose an air-ground collaborative data delivery framework and maximize the end-to-end (E2E) data transmission efficiency through jointly optimizing the terrestrial subnet transmission scheduling strategy, subnet resource allocation strategy, subnet formation strategy, and flight speed control of opportunistic UAV. On account of the heterogeneous transmission demands and the task-oriented mobility, the terrestrial IoTs actively pre-network and aggregate the environmental information towards the cluster heads with the position advantage, to improve the data uploading efficiency. We derive the closed-form subnet transmission scheduling and subnet resource allocation strategy. The subnet formation sub-problem is constructed as a coalition formation game, which can be efficiently solved by the better response method. Considering the limited sojourn time in the farmland, the opportunistic UAV dynamically adjusts the flight speed to strike a balance between the traffic distribution, data uploading capability, and data downloading capability. We derive the closed-form solution of the flight speed control strategy. Numerical simulations demonstrate that the proposed algorithm can expand the E2E data delivery volume and outperform the benchmark algorithms.

In this paper, by integrating the advantages of synchronous federated learning (SFL) and asynchronous federated learning (AFL), an efficient federated learning (FL) framework with hybrid aggregation is proposed for the connected and autonomous vehicles (CAVs) over multiple base stations (BSs). Specifically, to cope with the stragglers caused by traffic accidents, extreme weather or other uncontrollable factors, the AFL with periodic aggregation is proposed to perform edge model aggregation within a single BS. Furtherly, taking the freshness of local model updates and the training data distribution into account, a novel weighting strategy is designed correspondingly. Moreover, to reconcile the contradiction between the scarce wireless communication resources and the enormous communication overhead caused by frequent exchanges of model parameters, an effective model compression mechanism is constructed based on the inherent statistical property of FL. In addition, considering a relatively small number of autonomous vehicles (AVs) within the limited coverage of a single BS and the instant guidance required for the CAVs, the SFL based on FedAvg is introduced to aggregate edge models trained from multiple BSs at network edge instead of remote cloud. The superior performance of the proposed method is verified by various simulations on real dataset.

ABSTRACT In this paper, we investigate the anti‐jamming communication challenge for unmanned aerial vehicles (UAVs) in urban environments with strong jammers. Jamming power often far exceeds the UAVs' inherent anti‐jamming capability threshold, causing anti‐jamming measures to fail and even interrupt normal communication. To address this challenge, we propose an innovative strategy that leverages the natural shielding effect of urban buildings to enhance the anti‐jamming performance of UAV communication links. The core of this strategy lies in leveraging multiple UAVs working collaboratively to form an end‐to‐end anti‐jamming communication network in the urban environment. Specifically, we first introduce a UAV formation control mechanism for end‐to‐end collaboration—‘resonant motion’ transmission. Second, we propose an anti‐jamming algorithm for urban environments with strong jammers, combining ‘resonant motion’ transmission with the artificial potential field (APF) algorithm and rapidly exploring random tree star (RRT*) to develop a novel anti‐jamming path planning algorithm. Finally, we leverage prior knowledge of jammers, UAV formation and urban environment to enable UAV formation to evade obstacles and strong jammers in urban environment, find optimal communication positions and thereby build more robust communication links. The anti‐jamming strategy proposed in this paper provides a practical new approach to addressing the technical challenge of difficult UAV communication in urban environment with strong jammers. Simulation experiments demonstrate that UAVs can effectively address the challenge of UAV formation in urban environment through collaborative operations and intelligent algorithms, achieving reliable end‐to‐end transmission for UAV formation, outperforming traditional algorithms in both anti‐jamming performance and energy consumption.

In recent years, with the acceleration process of smart cities, the Artificial Intelligence of Things (AIoT) has become a novel approach for emergency rescue. However, due to small capacity and heterogeneity, AIoT nodes are facing various energy limitations and complex routing challenges. Therefore, emergency AIoT often have issues, such as data transmission failures, energy consumption, reduced network lifespan, and latency. To overcome these problems, this study proposes an energy efficiency emergency air–ground integrated heterogeneous AIoT (E3-HetAIoT) structure to improve the energy management efficiency of smart cities. First, we adopt a cell-based clustering mechanism, and the multiobjective zebra optimization algorithm (M-ZOA) is used to select cluster heads (CHs) and super nodes (UNs). The UN uses an urgency level-driven energy efficient sleep scheduling (U-ESS) mechanism to balance the remaining energy of sensors, especially in scheduling sleep time slots for sensors have energy below typical threshold. Second, an air–ground integrated data transmission mechanism is adopted, in which the generator de bits pseudo aleatorios (GBPA) is used to eliminate redundant data in CH and improve security. Then, the data packets are divided into normal packets and emergency ones. The normal packets wait for transmission of unmanned aerial vehicle (UAV) UAVs as intelligent mobile agents (U-iAgents), the trajectory of U-iAgents are dynamically predicted by dueling double deep Q-Network (Dueling-DDQN), meanwhile emergency data packets are immediately transmitted through inter cluster routing. Simulation results demonstrate that compared to existing algorithms, our proposed E3-HetAIoT framework achieves lower energy consumption and higher network lifetime, respectively.

Emergency Communication Network (ECN) is improving network quality of service (QoS) performance via numerous resource allocation and management technologies, such as network slicing, in order to meet the requirements of heterogeneous users in various types of emergency events. Unmanned aerial vehicles (UAVs) serving as NodeB, a.k.a., UxNB, can assist ground base stations (GBSs) to extend the coverage range and network utility of Heterogeneous ECN (HetECN), but make the resource allocation issues for different slice demands in HetECN more complex. This paper investigates the dynamic resource allocation problem of HetECN with a goal of maximizing traffic efficiency while concurrently guaranteeing the transmission rate and the latency by adopting network slicing. Firstly, in order to model the dynamic and uncertain environment of HetECN, we describe the long-term resource allocation problem as a stochastic game, which is an extension of game theory in Markov decision process-like environment. Subsequently, we develop an independent Q-learning based multi-agent reinforcement learning (IQ-MARL) framework, for which all agents execute decision algorithm independently but share a common structure. Simulation results demonstrate that our proposed IQ-MARL algorithm achieves a good balance between performance gains and information exchange overheads in HetECN, which is superior to those of other benchmark schemes.

Heterogeneous Internet of Things (HetIoT) has received widespread attention due to its provision of various convenient services in fields such as smart cities, intelligent transportation, environmental monitoring and security systems. Due to HetIoT inherently demands high data rates, bandwidth, and low latency, the application of millimeter-wave (mmWave) unmanned aerial vehicle (UAV) as emergency aerial base station (ABS) providing services to HetIoT users has become a low-cost and efficient means. However, due to the sensitivity of mmWave to obstacles and jamming, guaranteeing network performance has become a pressing issue. This paper considers a mmWave UAV-assisted HetIoT under jamming conditions, where auxiliary ABSs serve multiple ground users (GUs) who generate a large amount of sensor data. We establish a coverage maximization problem under the constraints of signal-to-interference ratio (SIR) threshold, maximum power of ABSs and maximum number of GUs that the base station can serve, and propose a novel ABS hovering deployment algorithm M-HiAPSO that combines the artificial potential field (APF) method and the improved particle swarm optimization (PSO) algorithm in a hierarchical manner. Specifically, the multi-element APF method is used to characterize the horizontal force between nodes, combined with the improved hierarchical adaptive PSO algorithm to adjust the horizontal position of the ABS to obtain the optimal UAV hovering position and power allocation strategy. Numerical results show that the coverage rate reached 96.2% when the number of iterations was 283, and it can reach up to 99.6%.

Emergency communication network (ECN) can provide fast, efficient and high-capacity communication services for specific areas by using mmWave transmission and unmanned aerial vehicles (UAVs) serving as aerial base stations (ABSs) or relay nodes. Now, in order to satisfy diverse demands, ECN should support different types of nodes, access methods, and traffic distributions, which is referred to as heterogeneous ECN (HetECN). Therefore, inappropriate trajectory planning and unbalanced traffic loading can lead to UAV flight collisions and network congestion. In this article, we jointly optimize UAV jamming avoidance trajectory and user association strategy aimed to load balancing, to maximize the utilization of HetECN. Specifically, an improved artificial potential field (APF) method along with mmWave beam forming technology is used to obtain the jamming avoidance trajectory of UAVs, and the optimal deployment location of UAVs are determined based on the distribution of ground users (GUs). Subsequently, the matching game and alliance game are comprehensively used to determine the load balancing based GU-UAV associated strategy under various GU demands, thereby ensuring traffic load balancing and resource optimization allocation. In addition, altitude fine-tuning have been made to further power consumption, thereby improving overall network efficiency. Simulation results demonstrate that the proposed method can achieve the expected performance in network utilities such as coverage rate, network capacity, load balancing effect of mmWave UAV-assisted HetECNs.

Abstract This paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework “sense‐as‐you‐fly” based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes.

To realize the benefits expected and ensure user privacy and data security simultaneously, a cost-efficient edge federated learning (FL) architecture over multiple base stations (BSs) is proposed for the intelligent transportation system (ITS) based on connected and autonomous vehicles (CAVs). Firstly, in the proposed FL architecture, the road side units (RSUs) are designed to train the machine learning (ML) model with the BSs equipped with edge servers collaboratively. In this way, since the autonomous vehicles do not participate in model training, the negative impact of unreliable communication caused by vehicle mobility can be eliminated. Then, considering that the limited amount of data involved within the coverage of a single base station (BS), the FL architecture over multiple BSs at network edge is proposed for better learning performance. Along this line, the joint edge aggregation and association problem is studied, and a set function optimization problem is formulated with the objective of minimizing the costs considering latency and energy consumption comprehensively. Finally, after analyzing the complexity, monotonicity, and modularity of the problem formulated, the NP-hardness optimization problem is further decomposed and transformed, and an innovative solution is proposed. The abundant simulation results demonstrate the superior performance of the cost-efficient FL architecture proposed.

The Connected and Autonomous Vehicles (CAVs) is considered to be a promising technology to improve the traffic congestion. However, to realize its expected benefits, the real-time, accurate and forward-looking guidance message are required. Based on this, considering various practical constraints and the characteristics of the CAVs fully, multiple distributed edge computing servers are deployed at the network edge to provide the real-time storage and computation support in our paper. Furtherly, based on the fruitful deployment for edge computing servers, an efficient traffic flow online prediction model is constructed, which can adaptively modify according to the dynamic changes of the actual road traffic state, thus providing more accurate and forward-looking results. The simulation results based on MATLAB platform show the proposed deployment scheme for edge computing servers only needs more cost than the enumeration method. Moreover, compared with other baseline methods, the online prediction model proposed can improve 37.5%–76.9% in terms of prediction accuracy.

As a way of providing users flexible computing services, networks exist that can make full use of air and ground computing resources. Such networks are called air-ground collaborative mobile edge computing (AGC-MEC) networks. AGC-MEC supports numerous emerging real-time applications for which timely computed results are critical. Researchers have developed a novel metric “age of information (AoI)” that can capture the freshness of computed results. This is the first paper to study the problem of AoI-aware scheduling forAir-groundCollaborative mobileEdge computing (i.e., IACE). So as to minimize the weighted AoI of all the terrestrial user equipments (UEs), we have jointly optimized task scheduling, computing resource allocation, and unmanned aerial vehicle (UAV) trajectory taking into account the constraints on the computing resources and the available energy of the UAV. The formulated problem, which is a challenge to solve, is a mixed-integer nonlinear programming (MINLP) problem. To obtain an effective solution, we propose an iterative algorithm based on the alternating optimization approach, which entails dividing the considered problem into three subproblems. Extensive simulations show that the proposed algorithm can achieve lower weighted AoI than five benchmark algorithms, while satisfying the resource constraints. Furthermore, simulation results demonstrate two interesting insights. First, the introduction of an aerial MEC server facilitates a flexible offloading design of the UEs which is critical to guaranteeing the freshness of computed results. Second, by optimizing the scheduling, the proposed design can unlock performance gains, especially in the resource-limited regime.

The cover image is based on the Research Article Joint Channel and Power Optimization for Multi-user Antijamming Communications: A Dual Mode Q-learning Approach by Xiaobo Zhang et al., https://doi.org/10.1049/cmu2.12339

Abstract In view of the anti‐tracking‐jamming problem, traditional online learning methods usually cannot analyse the jamming behaviour, and find an effective way to prevent the jamming attacks. To cope with these challenges, a novel communication/deception dual mode mechanism is proposed in this paper. Deception users are selected to send high‐power signal for jamming attraction, and form collaborative relationships with communication users. The corresponding collaborative anti‐jamming model is then constructed as a Markov game to analyse the multi‐agent decision. Based on that, a joint channel and power optimisation for multi‐user anti‐jamming communications based on dual mode Q‐learning scheme is proposed. Compared with two traditional online learning algorithms, the proposed DCAJ‐QL algorithm effectively achieves 146.5% and 80.4% higher maximum communication rate under tracking jamming conditions, and achieves 40.7% and 53.6% higher maximum communication rate under fixed jamming conditions.

In this article, we study the problem of task selection and scheduling in unmanned aerial vehicle (UAV)-enabled multiaccess edge computing for reconnaissance (ASSUMER). Specifically, taking into account the time-varying priorities of reconnaissance tasks, we investigate how to maximize the overall reconnaissance utility by selecting an appropriate set of tasks and scheduling their execution sequence in the multiaccess edge computing server of the UAV. The ASSUMER problem is a mixed-integer nonlinear programming (MINLP) problem, which includes both integer and continuous variables and is proved to be NP-hard. To address this challenging problem, we first model the task scheduling subproblem as a single machine scheduling problem with the deterioration effect. We find out that the optimal task scheduling can be solved efficiently given any task selection variables and propose an optimal scheduling algorithm. Second, using the proposed scheduling algorithm, the ASSUMER problem is equivalent to a binary integer programming problem with respect to the task selection variable only. We prove that the objective function falls into the category of the submodular function and transform the original problem into the problem of maximizing submodular function with the energy constraint. Third, combining the proposed scheduling algorithm with submodularity, we design an effective approximation algorithm for the ASSUMER problem and prove that the algorithm has$(1 - {e^{ - 1}})/2$bicriterion approximation guarantee. Finally, simulation results show that the proposed algorithm can improve the overall reconnaissance utility and energy efficiency compared to five benchmark algorithms.

In this paper, we investigate the effect of Multiple- User Multiple-Input Multiple-Output (MU-MIMO) enabled WiFi Access Point (AP) on the performance of traffic offloading in LTE-Unlicensed (LTE-U) networks. We first derive closed-form expressions for the downlink rates of both cellular user and offloaded user under limited Channel State Information (CSI) feedback, and obtain the sum-rate of the system. Then, we validate our analysis by numerical simulations and evaluate the system performance under different number of offloaded users and various CSI feedback lengths. The evaluation illustrates that there is a trade-off between the number of users offloaded to WiFi AP and the sum-rate of the system, and performance inflection point lies on the SNR conditions. Meanwhile, increasing CSI feedback length for the cellular Base Station (BS) or the WiFi AP alone helps increase the sum-rate of the system and rate of corresponding users, while it sacrifices the rate of users in the other network. These results suggest that adaptive utilization of antenna numbers on the MU-MIMO enabled AP based on SNR, and deciding CSI feedback length according to practical traffic demand of corresponding users are critical to achieve high rate performance for traffic offloading in LTE-U networks.

Due to the agility, low-cost and robustness, UAV (Unmanned Aerial Vehicle) Ad Hoc Networks formed by small UAVs have popular application in the battlefield. Considering the high mobility of UAV which may exit and join in the networks frequently, random access is critical for UAV Ad Hoc Networks. Due to the complex and serious electromagnetic environment in the battlefield, how to identify the MAC protocol when demodulation is unrealistic and switch to this MAC protocol adaptively is challenging. In this paper, we propose Demodulation-free Random Access (DFRA) scheme which can help UAVs join in the UAV ad hoc networks without demodulating the property field of MAC protocol header. First, we propose an adaptive feature extraction algorithm and use it for machine learning based MAC protocol identification. Then, DFRA adopts an adaptive MAC switching framework to access the networks. We implement DFRA with USRP N210 and evaluate the performance by experiments. The results show that DFRA can guarantee access accuracy rate over 95% when demodulation is unrealistic.

In this paper, we intend to study the optimal deployment density of drone small cells (DSCs) to achieve maximum coverage considering the inter-cell interference. Due to the high altitude, the air-to-ground channel of DSCs consist of probabilistic line-of-sight (LoS) and non-line-of-sight (NLoS) links, causing computational difficulties in performance analysis. To accurately analyze coverage performance, we calculate the cumulative inter-cell interference considering both LoS and NLoS links. And we derive an approximate and closed-form expression for it to facilitate the computation of the optimal deployment density in a tractable way. Given the altitude, the optimal deployment density is obtained by determining the optimal coverage radius of a DSC. And numerical results show that, increasing the altitude of DSCs does not necessarily improve coverage performance.

The benefits of network coding on multicast in traditional multihop wireless networks have already been extensively demonstrated in previous works. However, most existing approaches cannot be directly applied to multihop cognitive radio networks (CRNs), given the unpredictable primary user occupancy on licensed channels. Specifically, due to the unpredictable occupancy, the channel's available bandwidth is time-varying and uncertain. Accordingly, the capacity of the link using that channel is also uncertain, which can significantly affect the network coding subgraph optimization and may result in severe throughput loss if not properly handled. In this paper, we study the problem of network coding-based multicast in multihop CRNs while considering the uncertain spectrum availability. To capture the uncertainty of spectrum availability, we first formulate our problem as a chance-constrained program. Given the computational intractability of the above-mentioned program, we then transform the original problem into a tractable convex optimization problem, through appropriate Bernstein approximation with relaxation on link scheduling. We further leverage Lagrangian relaxation-based optimization techniques to propose an efficient distributed algorithm for the original problem. Extensive simulation results show that the proposed algorithm achieves higher multicast rates, compared with a state-of-the-art non-network coding algorithm in multihop CRNs, and a conservative robust network coding algorithm that treats the link capacity as a constant value in the optimization.

Due to the agility and low-cost, small Unmanned Aerial Vehicle (UAV) has recently captured great attention of academia and industry. However, since the capability limitation of single device, an ad hoc network formed by small UAVs is very promising. But compared to ordinary ad hoc networks, because of the unmanned characteristic and the diversity of missions, the protocols of UAV ad hoc networks require higher adaptive ability, i.e., the MAC protocol. In this paper, first, we verify that different MAC protocols have respective performance advantage under various network scenarios during the UAV reconnaissance mission. Then, we propose an adaptive MAC framework which allows multiple MAC protocols to switch mutually based on some kind of information you want. After that, in order to demonstrate this framework we design an adaptive MAC protocol called CT-MAC following the proposed framework. CT-MAC allows UAVs to switch between CSMA and TDMA based on their own positions when performing reconnaissance mission. Finally, we implement CT-MAC with Raspberry Pi and MDS Radio. The experiment results show that CT-MAC can always keep desirable performance compared to single MAC protocol through the fast and transparent MAC switching and the proposed adaptive MAC framework is feasible and effective.

UAVs Ad-Hoc Networks has earned more and more attentions recently. How to design a collision-free MAC protocol which allows UAVs to access the networks rapidly and reliably is a crucial challenge. Many MAC protocols proposed for VANETs or UAVs Ad-Hoc Networks have some limitations, i.e., some ones need full duplex technique which is not very practical now. This paper propose a collision-free MAC protocol CF-MAC which allows the UAVs with half-duplex radio and omnidirectional antenas to rapidly access the networks and utilizes a region marking scheme to reduce the collision probability to near zero. Compare with VeMAC which is an outstanding MAC protocol for VANETs, simulations show that CF-MAC can get a 20% improvement in efficiency of channel access, and more importantly it reduce the collision probability to near zero.

Abstract The past few years have witnessed a surge of wireless mesh networks (WMNs)‐based applications and heterogeneous WMNs are taking advantage of multiple radio interfaces to improve network performance. Although many routing protocols have been proposed for heterogeneous WMNs, most of them mainly relied on hierarchical or cluster techniques, which result in high routing overhead and performance degradation due to low utilization of wireless links. This is because only gateway nodes are aware of all the network resources. In contrast, a unified routing protocol (e.g., optimal link state routing (OLSR)), which treats the nodes and links equally, can avoid the performance bottleneck incurred by gateway nodes. However, OLSR has to pay the price for unification, that is, OLSR introduces a great amount of routing overhead for broadcasting routing message on every interface. In this paper, we propose unified routing protocol (URP), which is based on passive bandwidth measurement for heterogeneous WMNs. Firstly, we use the available bandwidth as a metric of the unification and propose a low‐cost passive available bandwidth estimation method to calculate expected transmission time that can capture the dynamics of wireless link more accurately. Secondly, based on the estimated available bandwidth, we propose a multipoint relays selection algorithm to achieve higher transmission ability and to help accelerate the routing message diffusion. Finally, instead of broadcasting routing message on all channels, nodes running URP transmit routing message on a set of selected high bandwidth channels. Results from extensive simulations show that URP helps improve the network throughput and to reduce the routing overhead compared with OLSR and hierarchical routing. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Network coding (NC) can greatly improve the performance of wireless mesh networks (WMNs) in terms of throughput and reliability, and so on. However, NC generally performs a batch‐based transmission scheme, the main drawback of this scheme is the inevitable increase in average packet delay, that is, a large batch size may achieve higher throughput but also induce larger average packet delay. In this work, we put our focus on the tradeoff between the average throughput and packet delay; in particular, our ultimate goal is to maximize the throughput for real‐time traffic under the premise of diversified and time‐varying delay requirements. To tackle this problem, we propose DCNC, a delay controlled network coding protocol, which can improve the throughput for real‐time traffic by dynamically controlling the delay in WMNs. To define an appropriate control foundation, we first build up a delay prediction model to capture the relationship between the average packet delay and the encoding batch size. Then, we design a novel freedom‐based feedback scheme to efficiently reflect the reception of receivers in a reliable way. Based on the predicted delay and current reception status, DCNC utilizes the continuous encoding batch size adjustment to control delay and further improve the throughput. Extensive simulations show that, when faced with the diversified and time‐varying delay requirements, DCNC can constantly fulfill the delay requirements, for example, achieving over 95% efficient packet delivery ratio (EPDR) in all instances under good channel quality, and also obtains higher throughput than the state‐of‐art protocol. Copyright © 2014 John Wiley & Sons, Ltd.

Broadcast is the most basic way to disseminate data from one source node to all the others in Wireless Mesh Networks (WMNs) while overhead can be a vital factor restricting the improvement of broadcast performance, especially under multichannel conditions. We propose a novel broadcast scheme named channel vector based broadcast suitable for multiradio multichannel WMNs. We utilize channel vectors which are much shorter than detailed neighbor and channel information to describe the channels every node supports. By exchanging channel vectors through periodic Hello packets, we can reduce the neighbor discovery overhead significantly while guaranteeing the accuracy of topology information for routing at the same time. Besides, channel vectors can help forwarding nodes calculate the best forwarding channel instead of broadcasting data on all channels, which will significantly reduce redundant transmissions. We conduct extensive simulations to evaluate the performance of our scheme, compared with three other broadcast schemes, random broadcast, CDS based broadcast and Broadcast in Multiradio Multichannel Networks (BMMN). Results show that channel vector based broadcast reduces the broadcast overhead by 63.19%, 50.27% and 23.13% compared with random broadcast, CDS based broadcast and BMMN respectively, and improves throughput by about 26.18% compared with BMMN.

The benefits of network coding on multicast in traditional multi-hop wireless networks have already been demonstrated in previous works. However, most existing approaches cannot be directly applied to multi-hop cognitive radio networks (CRNs), given the unpredictable primary user occupancy on licensed channels. Specifically, due to the unpredictable occupancy, the channel's bandwidth is uncertain and thus the capacity of the link using this channel is also uncertain, which may result in severe throughput loss. In this paper, we study the problem of network coding-based multicast in multi-hop CRNs considering the uncertain spectrum availability. To capture the uncertainty of spectrum availability, we first formulate our problem as a chance-constrained program. Given the computationally intractability of the above program, we transform the original problem into a tractable convex optimization problem, through appropriate Bernstein approximation together with relaxation on link scheduling. We further leverage Lagrangian relaxation-based optimization techniques to propose an efficient distributed algorithm for the original problem. Extensive simulation results show that, the proposed algorithm achieves higher multicast rates, compared to a state-of-the-art non-network coding algorithm in multi-hop CRNs, and a conservative robust algorithm that treats the link capacity as a constant value in the optimization.

We consider a multiuser scenario with a center node for data fusion, where simultaneously transmitting is required in real time and with low probability of interception. We established a novel multiple access scheme, named matrix division multiple access (MDMA), based on OFDM technology. Precoded with different matrices, the signal from different end nodes can be distinguished due to the signal space division. At the receiver, the zero-forcing (ZF) and minimum mean square error (MMSE) decoding schemes are developed. Additionally, we analyze the sum capacity of the MDMA system. Simulations show that the MDMA scheme achieves the better performance compared to the OFDMA.

In cognitive radio networks (CRNs), secondary users (SUs) may employ network coding to pursue higher throughput. However, because SUs should not interfere with high-priority primary users (PUs), the available transmission time of SUs is usually uncertain, i.e., SUs do not know how long the idle state can last. Meanwhile, existing network coding strategies generally adopt a block-based transmission scheme, implying that all packets in the same block can be decoded simultaneously only with enough coded packets collected. Therefore, the gain induced by network coding may be dramatically decreased once a block cannot be decoded due to the arrival of PUs. In this paper, for the first time, we develop an efficient network coding strategy for SUs while considering the uncertain idle durations in CRNs. To handle the uncertainty of SUs' available transmission time, we first consider how to estimate the length of idle duration. For the case where the length of idle duration is stochastic, we employ confidential interval estimation (CIE) method to estimate the expected length of the idle duration. For the non-stochastic case, we utilize multi-armed bandit (MAB) to determine the idle durations sequentially. After obtaining the estimated length, we further adopt systematic network coding (SNC) in the data transmission of SUs. We find that SNC is more suitable for SUs' transmission than the general block-based network coding in the sense that it can reduce average perpacket delay without decreasing the throughput gain. However, the block size (also the proportion of uncoded packets to be sent) of SNC is hard to determine, due to the complicated correlation among the receptions at different receivers. To solve this problem, we propose an optimal block size selection algorithm for SNC (OSNC) to determine the transmission proportion of uncoded packets, under a given idle duration length. Due to its low computational complexity, OSNC can be used to make an online decision on the optimal block size with small delay. Simulation results show that, compared to traditional block-based network coding and plain retransmission schemes, our proposed scheme achieves highest performance for both stochastic and non-stochastic idle durations.

Unsegmented network coding (UNC) is a promising technology to overcome poor source information scheduling of segmented network coding (SNC) in large scale networks, where unresponsive feedback slacks the scheduling. However, three unsolved problems limit its applicability. First, UNC employs ACK on witness as the feedback. The frequently triggered witness-ACK will introduce considerable overhead and depress throughput. Second, a new constraint from practical decoding requirement on the slide window has recently been proved. The additional constraint will make UNC much different, which has not been studied. Third, although UNC may outperform SNC in large scale networks, it does not work well in small and moderate-sized networks, exhibiting poor universality. In this paper, we address these problems and propose the Adaptive unsegmented Network Coding (ANC). ANC applies technologies for improvements, which is derived through reinvestigating UNC (solve the second problem), to improve achievable throughput, and save a majority of control overhead (solve the first problem). In addition, ANC incorporates a novel hybrid source packets admission scheme and can well adapt to various network conditions (solve the third problem). Simulation results show that ANC outperforms both SNC and UNC in universal network conditions, and the throughput gain over both can be up to 22%.

Wireless mesh networks (WMNs) are taking advantage of multiple radio interfaces to improve network performance, and numerous routing protocols have been proposed for WMNs. However, these routing protocols can not perform well in the heterogeneous multi-radio environment with distinct bandwidth difference, for little attention has been paid to routing overhead balancing. To address this issue, we present a routing protocol called URP (unified routing protocol) which cooperates with distinct bandwidth difference and makes appropriate use of networks to achieve the best performance. In WMNs, a large number of routing messages spread in different channels are duplicated, considering this nodes running URP select a high bandwidth channel set to forward routing messages rather than broadcasting them in each channel. On the one hand, this helps reduce the routing overhead and economize the resource of low bandwidth channels. On the other hand, the low bandwidth channels can contribute their limited resource to improving the throughput. In addition, an advanced expected transmission time (ETT) estimation method accounting for node mobility is implemented in URP, it helps URP considers both the variation of link quality and channel bandwidth to select a route. Simulations show that URP considerably reduces the routing overhead by 25% in heterogeneous WMNs with distinct bandwidth difference compared with OLSR. Moreover, the reduced resource consumption and the advanced ETT estimation method help improve the network throughput.

IEEE 802.16j has introduced the concept of WiMAX mesh network model, which adds a special type of nodes called Relay Stations (RSs) for Subscriber Stations (SSs). With the help of RSs, a WiMAX mesh network is able to provide larger wireless coverage, higher network capacity and Non-Line-OfSight (NLOS) communications. In this paper, we propose a novel strategy which named Dual-Path Relay Placement (DPRP) to improve the efficiency of relay station placement in WiMAX mesh networks. DPRP is a more reliable model that takes users Spectrum Efficiency into account and thus can achieve efficient spatial reuse by two independent parallel transmission paths.If the distance between Subscriber Station and Base Station is constant, the SS's Spectrum Efficiency has a big influence on the number of relays required by the SS. For a given attenuation coefficient, DPRP needs less RSs than Single-Path Relay Placement (SPRP) when the SS's Spectrum Efficiency exceeds a certain threshold. The analysis proves that the higher the Spectrum Efficiency is, the greater the difference is between the relay's number of two models. If the new model is integrated with the SPRP model, the total number of RSs required per cell will decrease a lot.

Unsegmented network coding was incorporated into opportunistic routing to improve inefficient schedule of segmented network coding due to delayed feedback. However, most unsegmented network coding schemes fail to constrain the size of decoding window. Large decoding window not only challenges limited computational ability and decoding memory in practical environments, but also introduces large decoding delay, which is undesirable in delay sensitive applications. In this paper, we prove that the possibility of unacceptably large decoding window is innegligible, and verify the necessity of handling decoding window. To solve the issue, we argue that the number of unknown packets injected into the network must be strictly constrained at the source. Based on the idea, we improve unsegmented network coding scheme for opportunistic routing. Simulation results shows that the solution is robust to losses. In addition, to achieve optimal throughput, the redundancy factor should be selected larger than the reciprocal of end-to-end delivery ratio.

In the past few decades, wireless networks based on different standards have been developed quickly, and the coexistence of multiple communication systems has turned into reality. Due to the need for mobile computing or pervasive computing, quick and accurate protocol identification and analysis technology is required. Rather than employing the traditional demodulation-based method that requires to implement all known protocol modules in one single device, in this paper, we fully analyze the features in both time domain and frequency domain of physical (PHY) layer signals that can be used to describe a protocol, and present a new method of identifying and analyzing protocols without demodulation, which uses PHY layer signals only. This method can be used in battlefield and other situations where demodulation is impractical. To validate the feasibility of our method, we implement a system using GNU Radio and Universal Software Radio Peripheral (USRP). The results show that the system can successfully identify three different signals (including Wi-Fi, Bluetooth and Zigbee signals) with frequency domain features, and detect the period of beacons in Wi-Fi networks with the help of time domain features.

A Flow Driven MAC Protocol(FD-MAC) is a slot based MAC protocol which is designed for long distance wireless multihop transmission. With the flow-driven resource reservation mechanism, FD-MAC protocol is especially suitable to be used for wireless ad hoc network with dynamic traffic pattern. We argue that the Flow Classification Threshold(FCTs) of the protocol are the key parameters to the performance of the protocol,which optimal values are not yet been fully investigated. Then the value of the parameters are analyzed theoretically, and the optimal values are given in turn. Simulation result validates our analysis, best performance of the protocol would be anticipated when optimal FCT values are chosen.

WANETs is a recent network architecture where the nodes are spread all over the world but behave exactly as if they are part of a single-hop or multi-hop wireless networks at the PHY and MAC layers. Without distinguishing data packets and noise, the Software Defined Access Point (SoDA) samples the wireless channel for the uplink and multicasts the sampled data via Internet to other SoDAs. This leads to tremendous traffic load on the Internet. In this paper, we use energy detection to address this issue. Specifically, we propose EDDD to aim at reducing the traffic load on the Internet under the condition that dropping data packet as few as possible. Through extensive experiments on IEEE 802.11 and IEEE 802.15.4, we validate the feasibility and effectiveness of EDDD.

In the scenario of dynamic spectrum access application for cognitive radio (CR), the spectrum-sidelobe problem of OFDM (orthogonal frequency division multiplexing) must be considered. A precoding ahead of IDFT (inverse discrete Fourier transformation) is presented to suppress the in-band-out-of-subband (IBOSB) radiation. Its design is based on generalized eigenvalue problem. In order to improve its performance for practical application, one column of the precoding matrix can be inverted to reduce the signal peak-to-average-power-ratio (PAPR) to a lower level. At the receiver, iterative soft detection with interference cancelation is adopted to detect the precoded data and further employs the frequency diversity. Both methods introduce some additional transceiver complexity compared with pure precoding, whereas simulations show that joint design not only provides improved PAPR statistics, but also achieves the markedly gain of Bit-Error-Rate (BER) performance over multipath fading channel.

In wireless networks, routing algorithms need to be tolerable to network dynamics. Existing route selection mechanisms suffer from a lack of considerations of stability and its induced routing overhead. Stabilities on route selection, traffic engineering and transmission schedule are fundamental issues in achieving a mobility-tolerable wireless network. In this study, the authors propose a mobility-tolerable paradigm (named ‘MOTOROLA’) in building a stable route level coordination algorithm for dynamic routing and scheduling. In MOTOROLA, mobility-awareness modules explore the mobility parameters and the link duration time, purely by adaptive beacon messages. Transitory links are mitigated based on threshold value of link duration. The route level resource allocation algorithm is also tolerable to network topology changes and the rescheduling costs are minimised in time scale. Analytical and simulation results show that because of mobility-awareness ability and route stability, MOTOROLA could improve network efficiency by transitory links' mitigation and coordinative route restoration.

Spectrum access scheme is a fundamental component in building efficient wireless networks. Conventional methods such as proactive channel assignment is costly due to large amount of protocol overhead. Also, those algorithms suffer from its inability in dealing with channel dynamics. The opportunistic methods however, spend more time on probing, and suffer from the myopic decisions as well. We present a decision based dynamic spectrum access algorithm (DDSA), which is built upon the Markov decision process (MDP), and could adaptively handle the DSA process for higher throughput. We employ quiet probing and dynamic controlling mechanisms in DDSA, so as to achieve a reduced protocol overhead and improved adaptivity. Different from previous methods, the DDSA is a model driven method, and we use the modeling technique on the IEEE 802.11 DCF for virtual channel state probing. The modeling technique could help us improve the accuracy on channel state, and reduce protocol overhead. Using a heuristic and adaptive algorithm named `hindsight optimization', we solve the hardness in computing the MDP. Moreover, under the feasibility testing and scaling processes, the validated decision can be confidentially applied for a congestion-free DSA.