Uncrewed aerial vehicles (UAVs), due to their inherent flexibility and autonomous operation, are widely used in Internet of Things (IoT) networks for collecting data to facilitate real-time evaluation and monitoring applications. This article investigates a UAV-assisted IoT network, in which the UAV sequentially accesses IoT devices (IoTDs). During hovering, the UAV works on a full-duplex mode while collecting data from target devices, taking into account actual propulsion power consumption. In order to quantify the freshness of devices data, we introduce the concept of Age of Information (AoI). A multiobjective optimization method is proposed to jointly optimize three objectives: 1) maximization of the data rate; 2) minization of AoI; and 3) minization of the UAV energy consumption over a particular mission period. These three objectives partially conflict with each other and provide weight parameters to describe their importance. Since the data uploaded by IoTDs is dynamically changing, the trajectory planning of the UAV is required. Considering that the UAV has no prior knowledge of the network environment, the optimization problem is reformulated as a Markov decision process. Aiming at the learning problem of the UAV control strategies over multiple objectives, a deep reinforcement learning algorithm for multiobjective collaborative optimization is proposed. While training, the agent collects data in time according to the devices priority, and generates optimal strategies under the conditions of giving weights. Experimental results demonstrate that the proposed multiobjective twin delayed deep deterministic policy gradient algorithm jointly optimizes three objectives and can adjust the optimal policy based on the weight parameters of each objective.

In this paper, we study uplink covert communication in a space-air system, where an unmanned aerial vehicle (UAV) transmits sensitive data to a Geosynchronous Earth Orbit (GEO) satellite while preventing the transmission action from being discovered by a warden. We derive the optimal decision threshold of the warden. We investigate the 3-dimensional (3D) beamformer and 3D trajectory design for the transmitter UAV against this optimum warden to maximize the covert transmission rate in the presence of imperfect channel state information and uncertain noise. Due to the non-convex structure and dependence between beamforming vectors and locations of the transmitter UAV, we develop a decoupling method that specifies a feasible flight region of the transmitter UAV at each time slot, enabling the decomposition of the original optimization problem into two sub-problems that optimize the trajectory and beamforming vectors individually. We design an iterative algorithm with a new initialization method to solve the sub-problems alternately with the semi-definite relaxation (SDR) and the successive convex approximation (SCA) technique. Numerical results show that the average covert rate of our design approaches the ideal case without the warden and increases by about 102.3% and 19.1% compared with benchmark schemes that do not employ beamforming or design 2D trajectory, respectively.

Most of the existing OFDM backscatter systems adopt phase-modulated schemes to embed tag data, suffering from symbol-level modulation limitation, heavy synchronization accuracy reliance, and small tolerability to symbol time offset (STO) / carrier frequency (CFO) offset. We introduce SubScatter, the first subcarrier-level frequency-modulated OFDM backscatter which is able to tolerate bigger synchronization errors, STO, and CFO. The unique feature of SubScatter is our subcarrier shift keying (SSK) modulation. This method pushes the modulation granularity to the subcarrier by encoding and mapping tag data into different subcarrier patterns. We also design a tandem frequency shift (TFS) scheme that enables SSK with low cost and low power. Furthermore, we design SubScatter+ that shows these advantages while providing an even higher throughput without requiring more subcarrier patterns. We prototype and test SubScatter and SubScatter+, and the results show that our systems outperforms prior works in terms of effectiveness and robustness. Specifically, SubScatter has 743 kbps throughput that is 3.1 times and 14.9 times higher than RapidRider and MOXcatter, respectively. It also has a lower BER under noise and interferences which is over 6 times better than RapidRider or MOXcatter. Moreover, our proposed SubScatter+ could increase the throughput of SubScatter by 30%.

With the rapid advancement of Internet of Things (IoT), there is a growing demand for intelligent applications with varying requirements (e.g. delay, reliability, and energy). Mobile Edge Computing (MEC) can enhance the responsiveness of these applications by caching specific computing services on edge servers. However, the efficiency of task offloading and service caching at MEC servers is often hindered by the diversity of user preferences and privacy across different edge node service regions, especially in heterogeneous networks. To this end, we introduce a personalized computation offloading and service caching method that integrates Deep Reinforcement Learning (DRL) with Personalized Federated Learning (PFL), termed DPFL, aimed at optimizing computation performances in a distributed and privacy-preserving manner. The DPFL employs DRL to jointly optimize computation offloading and service caching placement, reducing task latency and energy consumption. Simultaneously, it leverages personalized federated learning to develop local service prediction models, offering tailored service caching policies for users in heterogeneous regions while safeguarding data privacy. Simulation results demonstrate that our algorithm surpasses existing policies in reducing application delay, energy consumption, and improving system cache hit ratio.

In response to the high computational performance demands of emerging compute-intensive applications for uncertain internet of things (IoT) scenarios, this paper proposes a task offloading and resource allocation algorithm combined with deep reinforcement learning. Firstly, under the constraints of computing resources of mobile devices (MDs) as well as IoT terminals and latency computing tasks, a mixed-integer nonlinear programming (MINLP) problem for joint task offloading and resource allocation is established. Due to the time varying nature of dynamic MEC scenarios and the fact that single agent could only access partial envirionmental state, the original problem is transformed into a partially observable markov decision process (POMDP). However, in dense and uncertain scenarios, each MD has distinct requirements in terms of latency and the computational capacity of Edge Servers (ES) during offloading. To this end, a reliability-enhanced MADDPG (RE-MADDPG) algorithm is proposed to generate the user offloading strategy according to the delay and reliability without knowing the information of the MEC server in the current slot. The simulation results show that the proposed algorithm can effectively reduce the delay and provide users with more efficient services.

Millimeter-wave (mmWave) communication is one of the effective technologies for the next generation of wireless communications due to the enormous amount of available spectrum resources. Rate splitting multiple access (RSMA) is a powerful multiple access, interference management, and multiuser strategy for designing future wireless networks. In this work, a multiple-input-single-output mmWave RSMA system is considered wherein a base station serves two users in the presence of a passive eavesdropper. Different eavesdropping scenarios are considered corresponding to the overlapped resolvable paths between the main and the wiretap channels under the considered transmission schemes. The analytical expressions for the secrecy outage probability (SOP) are derived respectively through the Gaussian–Chebyshev quadrature method. Monte Carlo simulation results are presented to validate the correctness of the derived analytical expressions and demonstrate the effects of system parameters on the SOP of the considered mmWave RSMA systems.

The lightweight and energy-efficient Frame Slotted Aloha (FSA) protocol has become a promising MAC protocol in large-scale IoT systems. Existing work on minimizing the age of information (AoI) of FSA protocol cannot significantly benefit from frequent packet generations when the packet generation rate$\lambda$exceeds its throughput$e^{-1}$. To fill this gap, this paper proposes two age threshold-based algorithms to reduce the AoI of FSA systems for$\lambda > e^{-1}$, namely TF and TF+. Their core ideas are to only allow the nodes with age gain over the configured thresholds to send their packets so that the FSA systems are slimmed to a stable one with$\lambda < e^{-1}$and a polling system, respectively. Technically, we design the threshold configuration rules for the two algorithms and characterize the normalized average AoI. We also conduct simulation and the results show that TF and TF+ achieve lower AoI than the prior works.

The multi-access edge computing (MEC) and ultra-dense network (UDN) are regarded as essential and complementary technologies in the age of Internet of Things (IoT). Deploying MEC servers at the macro-cell and small-cell stations can significantly improve user experience as well as increase network capacity. Nevertheless, there still remain many obstacles in practical MEC-enabled UDNs. Among them, a unique challenge is how to coordinate computing and networking to fit the diverse offloading demands of IoT applications in dynamic network environments. To this end, this paper first investigates a distributed delay-constrained computation offloading methodology based on computing and networking coordination in the UDN. An extended game-theoretic approach based on the Lyapunov optimization theory is designed to achieve adaptive task offloading and computing power management in time-varying environments. Furthermore, considering the uncertainty in users' mobility and limited edge resources, distributed two-stage and multi-stage stochastic programming algorithms under various uncertainties are proposed. The proposed algorithms take posterior recourse actions to compensate for inaccurate predicted network information. Extensive simulations validate the effectiveness and rationality of the proposed algorithms and their superior performance over several benchmark schemes.

Applying blockchain technology to the Internet of Things (IoT) environment can significantly enhance the privacy protection of information and enable trusted sharing of data. However, the classic Proof-based blockchain systems have problems, such as unsatisfactory system throughput, low-consensus efficiency, and excessive computing resource consumption, making it difficult to directly apply them to the IoT environment. The recently proposed distributed ledger technology (DLT) represented by Tangle is designed based on the logical structure of the directed acyclic graph (DAG), which significantly improves the transaction confirmation delay and system throughput, making it more suitable for IoT scenarios. This article focuses on the performance and security of Tangle DLT in IoT applications with network instability. Such an unstable network environment can be caused by the predefined node working mode or signal interference, which leads to the status switching between online (work-mode) and offline (sleep-mode) of nodes or unstable data transmission. First, a Markov-chain-based model is established to analyze the time delay of block propagation in the underlying network under different status switching rates, different generation rates of blocks, different nodes’ average uplink bandwidths, and different number of neighbor nodes. Next, the impact of block propagation delay on the consensus efficiency and system throughput is analyzed. Furthermore, we also analyze the influence of block propagation delay on the attacker’s decision to conduct a double-spending attack under different network load, and deduce the suitable moment for the attacker to broadcast the parasite chain to maximize the probability of successful attack. Finally, through detailed numerical simulations, the experimental results comprehensively reveal the performance and security of the Tangle system in the unstable network environment. This article provides a valuable reference for the deployment and performance improvement of Tangle DLT in practical scenarios.

Mobile edge computing (MEC) enables various services to be cached in close proximity to the user equipments (UEs), thereby reducing the service delay of many emerging applications. However, the limitation of storage, computation, and radio resources, the dynamics of the decentralized MEC environment, and the complex spatial relationships of service request types and wireless network states between edge nodes make it difficult to realize efficient edge computing services. To address these challenges, this paper integrates the digital twin (DT) technology with a multi-cell MEC network to study an intelligent cooperative task offloading and service caching scheme, aiming at maximizing a quality of services (QoE)-based system utility. Specifically, we first construct a digital twin edge network (DITEN) to reflect the physical MEC system in real-time and provide data for training. With the help of DT technology, it is easy to access data resources in the DITEN to improve the simulation ability and reduce the communication cost. Then, we propose a graph attention-based multi-agent reinforcement learning (GatMARL) algorithm to learn the optimal task offloading and service caching strategies in the DITEN. The GatMARL employs a graph attention-based value decomposition network to capture the potential spatial relationships between edge nodes to learn better attentive cooperation policy. Simulation results demonstrate that the proposed GatMARL algorithm exhibits an effective performance improvement compared with state-of-the-art benchmarks.

Accurate spatial-temporal traffic modeling and prediction play an important role in intelligent transportation systems (ITS). Recently, various deep learning methods such as graph convolutional networks (GCNs) and recurrent neural networks (RNNs) have been widely adopted in traffic prediction tasks to extract spatial-temporal dependencies based on a large volume of high-quality training data. However, there exist data scarcity problems in some transportation networks, and in these cases, the performance of traditional GCNs and RNNs based approaches will degrade sharply. To address this problem, this paper proposes an adversarial domain adaptation with spatial-temporal graph convolutional network (Ada-STGCN) model to predict traffic indicators for a data-scarce target road network by transferring the knowledge from a data-sufficient source road network. Specifically, Ada-STGCN first develops a spatial-temporal graph convolutional network that combines the GCN and gated recurrent unit (GRU) to extract spatial-temporal dependencies from source and target road networks. Then, the technique of adversarial domain adaptation is integrated with the spatial-temporal graph convolutional network to learn discriminative and domain-invariant features to facilitate knowledge transfer. Experimental results on the real-world traffic datasets in the traffic flow prediction task demonstrate that our model yields the best prediction performance compared to state-of-the-art baseline methods.

As the demand for mobile data traffic continues to grow, offloading data traffic to unlicensed spectrum is a promising approach that can relieve the pressure on cellular systems. Therefore, it is an urgent need to propose an unlicensed spectrum access method to guarantee the harmonious and efficient coexistence between cellular network technologies such as LTE and incumbent users such as WiFi in the unlicensed spectrum. However, existing coexistence schemes such as licensed assisted access (LAA) and LTE-unlicensed (LTE-U) still suffer from inefficient spectrum utilization and unsatisfactory fairness. In the paper, we formulate the optimization problem of the unlicensed spectrum access among multiple small bases (SBSs) as a game, and then solve the Nash Equilibrium (NE) with cooperative and distributed multi-agent deep reinforcement learning (MADRL). Specifically, a two level access framework for the coexistence scenario, which consists of feedback cycle and executive cycle, is first proposed, and then the key elements of MADRL including state, action, reward and Q-network are designed in detail based on the proposed access framework. To overcome the problems of learning divergence and prohibitive computation overhead in the coexistence scenario with multiple SBSs due to the non-stability phenomena, we adopt the mean field technology to solve the NE, which can simplify the process of solving NE by converting the interaction of an agent with the remaining multiple agents into an action with the average effect of them. Simulation results show that 1) the proposed algorithm can overcome the learning divergence problem and converge to the NE quickly, and 2) the proposed algorithm can achieve the bi-objective optimization of total throughput and fairness of the coexistence network, and can achieve better performance in terms of throughput and fairness compared with the baseline methods such as Cat-4 LBT, Cooperative LBT and Random schemes.

This paper studies the non-orthogonal multicast and unicast coordinated beamforming design for integrated terrestrial and satellite networks (ITSN), when the channel state information at the transmitter (CSIT) is imperfect. In order to mitigate the interference induced by simultaneous multicast and unicast links along with the spectrum coexisting mechanism for integrated terrestrial and satellite transmissions, we consider a two-layer layered division multiplexing (LDM) structure where the mul-ticast and unicast services are provided in different layers. We formulate a coordinated beamforming problem with the objective to minimize the transmit power under individual quality of service (QoS) constraints. With regard to the unknown convexity of the transmit power minimization problem, we transform the original infeasible optimization into a deterministic optimization form with linear matrix inequality (LMI) by utilizing S-procedure and semi-definite relaxation (SDR) methods. Then, we introduce a penalty function and propose an iterative algorithm with guaranteed convergence to obtain optimal solutions. Simulation results demonstrate the superiority of the proposed coordinated beamforming scheme, especially for the case of imperfect CSIT, while our LDM based coordinated beamforming scheme signifi-cantly outperforms the conventional ones in terms of sum rate.

Mobile edge computing (MEC) enables various services to be cached in close proximity to the user equipments (UEs), thereby reducing the computing delay of many emerging applications. Nevertheless, The limited storage capacity of edge servers requires judicious design of service caching as well as task offloading to maximize edge computing performances. In this paper, we formulate a cooperative task offloading, service caching, and transmit power allocation problem to minimize the cost of computing delay and energy consumption of UEs. To address this problem, we propose a graph attention based multi-agent deep deterministic policy gradient (GAT-MADDPG) algorithm, in which a multi-headed graph attention mechanism is incorporated into the centralized critic network to learn the attentive cooperation policies. Simulation results show that the proposed GAT-MADDPG algorithm exhibits an effective performance improvement.

Road icing has become one of the most critical factors threatening traffic safety. This article proposes an Internet of Things (IoT)-enabled road icing detection and prediction system. In the proposed system, we first design a low-power icing sensor equipped with IoT function to periodically collect current road status and transmit the sampled data to IoT gateway through Long Range Radio (LoRa). Then, we design a simple but effective algorithm deployed on IoT gateway to identify road icing in time. The algorithm is proposed based on the change trend of the sampled data of the road state, and can be adapted to the icing recognition on the road covered with various impurities. Furthermore, we put forward a newly designed deep neural network model called Trans-CGAN to achieve accurate road icing prediction even the positive and negative samples are imbalanced. Through a real system deployment and experiments, the results show that our proposed system can detect the formation of road icing effectively and timely, and shows better prediction performance of road icing than several representative models.

In order to improve mobile users’ service experience, mobile cloud computing (MCC) is promoted. Although MCC can alleviate the burdens of Smart mobile devices (SMDs) by offloading computation-intensive applications to the cloud, it also aggravates computing and storage overheads in cloud centers and bandwidth overhead on wireless links for offloading workloads of mobile users. Therefore, we should carefully design the offloading policy to decrease these overheads while easing the burdens of SMDs. To this end, we investigate the offloading policy in heterogeneous wireless networks. In this paper, a queue model is built to formulate the mobile users’ workload offloading problem and Lyapunov optimization framework is proposed to make trade-off between system offloading utility and queue backlog. For deterministic WiFi connections, a Lagrangian optimization method is proposed to decide the optimal offloading workloads. Furthermore, considering random WiFi connection durations, a multi-stage stochastic programming method is proposed. The experimental results show effectiveness of the Lagrangian optimization offloading method for deterministic WiFi connection and the multi-stage stochastic programming method for random WiFi connection.

With the rapid advancement of the Internet of Vehicles and artificial intelligence (AI) technologies, the cooperative intelligent transportation system (C-ITS) has drawn great attention in recent years. To provide an ultra-reliable, low-latency computation experience of C-ITS, computation offloading is deemed indispensable by working with edge-cloud servers. In this paper, we first investigate a distributed dynamic computation offloading model for multi-access edge computing (MEC) enabled C-ITS under a heterogeneous road network, in which the multiple and heterogeneous computing power sources cooperatively provide computation offloading services for vehicles. Considering the autonomous offloading manner of the vehicles, we formulate the task offloading and computing power allocation as a distributed Stackelberg game, where the MEC servers as the leader to allocate computing resources and manage local energy, and the vehicles as the followers to offload local computation task. Since the observable states in the game is incomplete, the problem of resolving the optimal strategies for each game player is modeled as a partially observable Markov decision process (POMDP) to maximize the long-term cumulative reward. Then we develop a computation offloading algorithm using Stackelberg game-based multi-agent deep deterministic policy gradient (SG-MADDPG), which uses a centralized training and decentralized execution method to learn the optimal computing power allocation and computation offloading policies. Finally, extensive simulations are carried out and show the rationality and effectiveness of the proposed algorithm.

Deploying small cell base stations (SBS) under the coverage area of a macro base station (MBS), and caching popular contents at the SBSs in advance, are effective means to provide high-speed and low-latency services in next generation mobile communication networks. In this paper, we investigate the problem of content caching (CC) and user association (UA) for edge computing. A joint CC and UA optimization problem is formulated to minimize the content download latency. We prove that the joint CC and UA optimization problem is NP-hard. Then, we propose a CC and UA algorithm (JCC-UA) to reduce the content download latency. JCC-UA includes a smart content caching policy (SCCP) and dynamic user association (DUA). SCCP utilizes the exponential smoothing method to predict content popularity and cache contents according to prediction results. DUA includes a rapid association (RA) method and a delayed association (DA) method. Simulation results demonstrate that the proposed JCC-UA algorithm can effectively reduce the latency of user content downloading and improve the hit rates of contents cached at the BSs as compared to several baseline schemes.

Despite the fact that visible light communication (VLC) systems provide tremendous data rates, employing visible light for the uplink (UL) is unfavourable due to safety concerns, illumination constraints, interference challenges etc. Using infrared (IR) light is a more feasible alternative for the provision of high UL data rates in VLC systems, even though IR may suffer severe intermittent outages just like visible light. For reliability sake, a lower rate WiFi UL can be used to support the vulnerable IR UL via link switching. Therefore, this paper presents a link switching scheme based on Markov decision process (MDP) for hybrid WiFi and IR UL systems with the objective of minimizing the transfer delay of data. The results of simulation, carried out under varying scenarios, prove our scheme's ability to adapt to the dynamic transiency of IR link interruptions leading to relatively low transfer delays.

Visible light communication (VLC) systems are heavily dependent on a line-of-sight (LoS) path between a user device (UD) receiver and the VLC transmitter. As a result of this requirement, the performance of VLC systems is strongly limited by their susceptibility to signal blockage. Towards network management, it is useful to quantify the extent to which these blockages occupy transmission time. This article, therefore, attempts to model the momentary impact of VLC downlink (DL) obstructions caused by human movements. The shape of potential human blockers that could interrupt the DL is approximated by either a rectangular prism or a cylinder. This work obtains the closed-form expression between interruption duration, angle of obstruction, and blocker size. We proceed to carry out simulation to ascertain the effect of dimensions, of the two types of blockers, on the time duration of LoS link obstructions.

Narrow band internet of thing (NB-IoT) is an emerging internet of things technology that is based on cellular networks. Since there is only 180KHz uplink spectrum resource in NB-IoT, it is necessary to optimize the uplink resource allocation in order to obtain as many successful communication devices as possible. However, there is a lack of an uplink resource allocation algorithm that can cope with various situations in existing allocation methods. Therefore, an adaptive uplink resource control algorithm is proposed in the paper. In the algorithm, the base station first estimates the maximum number of accessible devices through exhaustively calculation of the retransmission times, the number of preambles and the size of the transmission data, and then the base station adjusts uplink resource and controls the number of access devices according to the current network load. The simulation results show that compared with the traditional algorithms, the proposed algorithm can maximize the number of successful communication devices in various situations.

The coexistence performance of LTE-licensed assisted access (LAA) and WiFi networks has been extensively investigated. However, these works ignore capture effect, which is the phenomenon that the strongest signal may still be successfully received when more than two signals are transmitted simultaneously on the same channel, and which may occur more frequently in the coexistence scenario than in the pure WiFi network. This may lead to very large deviation in the coexistence performance evaluation. In the paper, we deeply investigate the coexistence performance of LAA and WiFi networks with the capture effect. More specifically, a capture model for more than two signals is first proposed in the coexistence scenario, and the capture probability is derived. Then the LAA access schemes are modeled as a new two-dimensional discrete Markov model integrating with the capture effect. A large number of simulation and numerical results verify the validity of the proposed Markov chain and capture model. The results also show that the capture effect can not only significantly decrease the collision probability but also increase LAA and WiFi throughput as well as total throughput. All these results prove the necessity of considering the capture effect in coexistence performance evaluation.

The spectrum resources licensed to the mobile operators become increasingly scarce because of the explosive growth of the mobile traffic. Device-to-Device (D2D) communication is thus proposed to be deployed in unlicensed frequency bands, i.e. D2D-Unlicensed (D2D-U). The fixed duty cycle method is generally adopted in the coexistence scenario of D2D and WiFi, which may lead to unfair unlicensed spectrum usage since it cannot adapt the data traffic change. Therefore, a Q-learning (QL) based resource allocation algorithm for D2D-U is proposed in this paper. In the algorithm, the considered cellular base station acts as the agent. The actions of agent are defined as the different combinations of the transmission power and the duty cycle of D2D-U users, and the states of agent are defined as the different combinations of the total throughput, fairness and signal-to-noise ratio (SNR) of cellular users. Based on the proposed QL framework, the agent can always learn the optimal power allocation and duty cycle by interacting with the environment, which can maximize the total throughput and fairness while ensuring the satisfactory SNR of cellular users. The simulation results show that the proposed algorithm can obtain the largest throughput and the best fairness while ensuring the satisfactory SNR of LTE-U users among all traditional algorithms.

3GPP's standardization of the Narrowband Internet of Things (NB-IoT) paved the way to support the use of low-power wide area (LPWA) in cellular networks. The design goal of NB-IoT is to expand coverage, low-power and low-cost devices, and large-scale connections. As a new wireless access technology, this paper establishes a Markov model with the working state of the terminal device as the state variable for the extended discontinuous acceptance (eDRX) mechanism adopted by NB-IoT, and calculates the corresponding power consumption and Time delay model. Given that the previous calculations of power consumption and delay did not consider the impact of random access. in this article, We propose a Markov chain with random access process. The numerical results show that the backoff time of each access failure of the terminal device during the process of accessing the network has a greater impact on the power consumption and delay.

Resources allocation efficiently of wireless network virtualization (WNV) in Internet of Things (IoT) has become a key challenge owing to the conflict between the limited capacity of physical resources and the massive virtual resources requests. Focusing on time-frequency resource allocation in WNV-based IoT, this article proposes a dynamic resource allocation algorithm based on the lifetime priority (LP-DRA) of virtual network requests (VNRs). First, LP-DRA investigates the continuity of physical time-frequency resource blocks by the Karnaugh map approach, then calculates the reallocation impact factor (RIF) of existing virtual networks (VNs), and reallocates the time-frequency resources to the VN with the largest RIF. Finally, the resources are preferentially allocated to the VNs with shorter lifetimes. The performance of LP-DRA is compared with the static resource allocation algorithm and the greedy dynamic resource allocation algorithm. The simulation results show that LP-DRA can utilize physical resources more effectively, improve the acceptance rate of VNRs, and increase the revenue of the physical network.

To meet low latency and real-time monitoring demands of IoT-eHealth, fog computing is envisioned as a key technology to offer elastic computing resource at the edge of networks. In this context, eHealth devices can offload collected healthcare data or computational expensive tasks to a nearby fog server. However, the mobility of the eHealth devices may make the connection between them to fog servers uncertain, resulting in possible migration between fog servers. In order to evaluate the impact of this uncertainty on decision-making for offloading and resource allocation, we formulate the task offloading problem as a Multi-Stage Stochastic Programming (MSSP), with aim of minimizing the total latency of offloading to determine whether to offload or not, how much workload to offload, how much computing resource to allocate, as well as whether to migrate or not. Different from the previous MSSP based work focusing on the workload assignment only, the proposed MSSP examines joint decisions of offloading, resource allocation, and migration, advancing the understanding of the interactions among these decisions. Furthermore, to reduce the computational complexity of MSSP, we design an efficient sub-optimal offloading policy based on Sample Average Approximation, called SAA-MSSP. We conduct extensive simulation experiments to validate the effectiveness of SAA-MSSP. The results show that SAA-MSSP can converge to a near-optimal solution quickly.

With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), computing-intensive and delay-sensitive IoT applications (APPs) are proliferating with an unprecedented speed in recent years. Mobile edge computing (MEC) and energy harvesting (EH) technologies can significantly improve the user experience by offloading computation tasks to edge-cloud servers as well as achieving green and durable operation. Traditional centralized strategies require precise information of system states, which may not be feasible in the era of big data and artificial intelligence. To this end, how to allocate limited edge-cloud computing resource on demand, and how to develop heterogeneous task offloading strategies with EH in a more flexible manner are remaining challenges. In this paper, we investigate an EH-enabled MEC offloading system, and propose an online distributed optimization algorithm based on game theory and perturbed Lyapunov optimization theory. The proposed algorithm works online and jointly determines heterogeneous task offloading, on-demand computing resource allocation, and battery energy management. Furthermore, to reduce the unnecessary communication overhead and improve the processing efficiency, an offloading pre-screening criterion is designed by balancing battery energy level, latency, and revenue. Extensive simulations are carried out to validate the effectiveness and rationality of the proposed approach.

With the rapid development of Internet of Things (IoT) on e-Health, the role of Mobile Edge Computing (MEC) has been increasingly effective in providing high-performance, lowlatency computing services. In this work, we consider the problem of task offloading and computing resource allocation in dynamic environment, wherein heterogeneous IoT devices or e-Health applications with diverse requirements in latency and energy constraint. Taking into account the different traffic characteristics and spatio-temporally varying distributed environment, we formulate the offloading problem as a dynamic game and a Stackelberg Equilibrium (SE) based distributed online offloading manner is proposed. And then, to allocate computing resource on demand, a dynamic quote price mechanism is designed by invoking Lyapunov optimization. Furthermore, to improve processing efficiency and reduce unnecessary communication overhead, a “first-rank” servers selection criteria is proposed by balancing revenue and latency. Finally, the effectiveness and rationality of the algorithm are verified by experimental simulation.

Integrating massive multiple-input multiple-output (MIMO) into satellite network is regarded as an effective strategy to improve the spectral efficiency as well as the coverage of satellite communication. However, the inevitable intra-system and inter-system interference deteriorate the total performance of system. In this paper, we consider precoding in the 5G Satellite Integrated Network (5GSIN) with the deployment of Massive MIMO and propagation of shared millimeter-wave (mmWave) link. Taking the requirements of both frequency efficiency and energy assumption into account, a hybrid analog and digital pre-coding scheme in the specific scenario of 5GSIN is proposed. We model sum rate maximization problem for both of satellite and terrestrial system that incorporates maximum power constrains and minimum achievable rate requirements and formulate to a convex power allocation problem with Minimum Mean Square Error (MMSE) norm and Logarithmic Linearization method. In order to balance between performance and complexity, we propose an analog and digital separated hybrid precoding algorithm to mitigate intra-system interference. Moreover, an iterative power allocation with interference mitigation algorithm is also devised to mitigate interference from satellite to terrestrial link so that power allocation can be executed by generalized iterative algorithm. Simulation results show that our proposed hybrid precoding algorithm in 5GSIN can improve the overall spectral efficiency with a small amount of iterations.

With the exponentially increasing number of mobile devices, crowdsensing has been a hot topic to use the available resource of neighbor mobile devices to perform sensing tasks cooperatively. However, there still remain three main obstacles to be solved in the practical system. First, since mobile devices are selfish and rational, it is natural to provide cooperation for sensing with a reasonable payment. Meanwhile, due to the arrival and departure of sensing tasks, resource should be allocated and released dynamically when sensing task comes or leaves. To this end, this paper designs a game theoretic approach based incentive mechanism to encourage the “best” neighbor mobile devices to share their own resource for sensing. Next, in order to adjust resource among mobile devices for the better crowdsensing response, an auction based task migration algorithm is proposed, which can guarantee the truthfulness of announced price of auctioneer, individual rationality, profitability, and computational efficiency. Moreover, taking into account the random movement of mobile devices resulting in the stochastic connection, we also use multi-stage stochastic decision to take posterior resource allocation to compensate for inaccurate prediction. The numerical results show the effectiveness and improvement of the proposed multi-stage stochastic programming based distributed game theoretic methodology (SPG) for crowdsensing.

Device-to-device (D2D) communication can realize the direct communication between mobile users with short distance. It is an enabling technology for realizing Internet of Things in the long-term evolution-advanced system to the future fifth-generation mobile communication system. D2D communication multiplexes the licensed spectrum of cellular users (CUs) to D2D users (DUs) to improve resource utilization of cellular networks. In this paper, a cross-cell fractional frequency reuse-based frequency resource multiplexing (CFRM) scheme is proposed for the multicell D2D communication. In the proposed CFRM, each cell is first divided into two regions, and each region is allocated different spectrum resources to reduce the interference between neighboring cells. Then, the uplink resources of CUs are partially multiplexed by DUs, which can decrease the interference of the DUs to the CUs. The simulation results show that CFRM can reduce the interference, guarantee the quality of service of CUs, and increase the throughput of cellular networks.

The resource reservation is one of the key techniques to ensure the quality of service (QoS) of a multimedia application. In mobile cloud computing (MCC), the resource reservation and allocation (RRA) in advance can significantly reduce the total provisioning cost of cloud service providers. However, the uncertain features of mobile users' demands for resources make RRA challengeable. In MCC, the QoS of a mobile application, such as voice IP or video, is determined by both of the radio resource (RR) and the cloud virtual machine resource (VMR) allocated to the mobile application, so we should jointly allocate these two types of resources. In this paper, RRA with uncertain demands of mobile users is formulated as a robust optimization model. Logarithmic utility functions are defined to capture the mobile users' satisfaction, which show how to match the allocations between RRs and VMRs according to the resource demands of the mobile applications. Then, a robust joint resource reservation and allocation algorithm in MCC (JRRA-MCC) is proposed to realize the optimal provisioning of RRs and VMRs. Simulation results show that the proposed JRRA-MCC can minimize the total resource provisioning cost of cloud service providers and enhance the resource utilization efficiently.

Offloading has been widely adopted as an effective technique to overcome the processing and computation limitation in mobile networks. In this work, we consider the problem of offloading in a mobile ad hoc environment in order to improve the processing capability and power efficiency. We formulate this as an incentive-based workload assignment problem. For maximizing the individual utility, the buyer/seller game is formulated to model the interactions among mobile devices for offloading. We derive the Stackelberg Equilibrium solution is to determine the workload assignment and power allocation. Based on this, we design distributed allocation algorithms, and the results from experiment verify the effectiveness of our proposal.

Coordinated Multi-Point (CoMP) is an effective way to improve user performance in next-generation wireless cellular networks, such as 3GPP LTE-Advanced(LTE-A). The base station cooperation can reduce interference, and increase the signal to interference and noise ratio (SINR) of cell-edge users and improve the system capacity. However, the base station cooperation also adds additional power consumption for signal processing and sharing information through back-haul links between cooperative base stations. As such, CoMP may potentially consume more energy. This paper studies such energy consumption issue in CoMP, presents a semi-dynamic CoMP cluster division algorithm based on energy efficiency (SCCD-EE) that can effectively adapt to users' real-time interference, and employs the idea of Maximal Independent Set (MIS) to solve the problem of cluster overlapping. To verify the feasibility of the proposed algorithm, this paper performs comprehensive evaluations in terms of energy efficiency and system capacity. The simulation results show that the proposed semi-dynamic cluster division algorithm can not only improve the system capacity and the quality of service (QoS) of cell-edge users, but also achieve higher network energy efficiency compared with static cluster methods and Non-CoMP approaches. Copyright © 2016 John Wiley & Sons, Ltd.

In traditional wireless network infrastructure, multiple wireless networks with various access points (APs) would be deployed in the same area. Although this deployment can easily provide service for mobile user equipment (MUE), any AP only allows the authorized MUEs to access, and thus some wireless networks might be overloaded and others might be lightly loaded. As a result, resource allocation would be inefficient. Using wireless network virtualization, an infrastructure provider (InP) can deploy only a single physical AP in the same area. This AP, which is controlled by a network operator (NO), is shared by multiple service providers (SPs) coexisting in the same AP. In the framework of wireless network virtualization, NO is in charge of resource allocation for the whole system and SP focuses on the access, connection and resource requirement of MUEs (such as the desired transmission power in downlink). In this paper, a Game theory based Two Steps Power Allocation scheme for wireless network virtualization, called G2SPA, is proposed, which designs a Stacklberg Equilibrium price strategy based on the interactions between SP and MUE, and then performs McAfee based auction to reallocate resource. The numerous experimental simulation results show that the rightness and effectiveness of G2SPA.

Cooperative spectrum sharing increases the spectrum efficiency and improves the performance of primary users (PUs) in cognitive radio domain. This paper proposes an energy-aware dynamic spectrum sharing framework, named Cooperative Spectrum Sharing with Energy-save(CSSE), which maximizes energy saving while ensures communication QoS (i.e. transmission rate) of primary transmitter (PT). In CSSE, the PT leverages a proper set of secondary transmitters (STs) as cooperative relays for its transmission and releases a proportion of bandwidth to the cooperative STs. Under the restriction of energy budget, each ST decides its power density allocation (including relaying power density and transmit power density for its own transmission) to maximize its transmission rate. Taking the users' selfishness and intellectuality into consideration, we formulate the above optimal problem as a Stackelberg game (SG) and prove that a Unique Nash Equilibrium (UNE) point exists among the non-cooperative STs. Theoretical analysis and simulation results show that the PU can obtain maximum benefit. Meanwhile, the relaying STs can get acceptable benefits under CSSE.

Opportunistic networks are a emerging networks characterized by frequent network partitions, high bit error ratio and random topology instability, where the message propagation depends on the cooperation of nodes to fulfill a "store-carry-forward" fashion. Due to the constrained energy, memory and processing capacity, some individual nodes may behave selfishly, or even maliciously, which will introduce damage into the existing routing schemes based on cooperation and degrade the performance (lower delivery ratio, longer latency etc.,) of opportunistic networks greatly. In order to address the above issues, the current price-based incentive strategy, Credit relies on a fixed management-center which is rare in the realistic opportunistic networks with little infrastructure to manage the transaction that the source of messages pays virtual credits to nodes that relay messages for it. So this paper proposes a novel Game based Incentive Strategy (GIS) which utilizes three-time bargaining model based on two-person transaction and allows the sending nodes to pay the relay nodes directly according to the optimal price drawn by game without any third party. GIS stimulates the cooperation of selfish nodes to forward messages effectively, while holds back the deceptive price stemmed from the malicious intermediary nodes to facilitate deals. From the extensive simulations results, GIS can optimize the average latency and the delivery ratio to the greatest extent. Additionally, effectiveness and fairness can be guaranteed.

Both capacity and energy efficiency are crucial for next-generation wireless networks. This paper investigates energy efficiency in cooperative cellular networks. Based on the double auction theory, we model the optimal relay assignment problem, which aims at improving the performance of cell-edge users (CEUs) with energy efficiency optimization. In the proposed auction-based model, the selfish nature of users is taken into consideration, which means users in the idle state are unwilling to relay the information for active CEUs unless they are paid enough. Therefore, we use mark-up to determine the bid and ask. Furthermore, the energy efficiency (EE) is defined and the model for optimizing the EE is built. An energy-efficient maximum weighted matching algorithm (EE-MWM) is proposed to solve the EE optimization problem. Finally, the performance of EE-MWM is evaluated in terms of EE, capacity and social welfare, which shows that EE-MWM can greatly improve the performance of cooperative cellular networks.

Abstract Cooperative communications can obtain spatial diversity, high channel capacity, and reliable transmission without multiple antennas, and thus, it has become a hot topic in recent years. Different from existing research, this paper pays attention on cooperative media access control (MAC) mechanism, which considers both physical gain and MAC overhead caused by cooperation. To this end, a dynamic cooperative MAC mechanism for wireless networks, called DCMAC, is proposed. DCMAC can obtain the useful channel state information through broadcasting characteristic of wireless channel, choose the suitable helpers to relay data with our proposed helpers selection algorithm, and reserve wireless channel efficiently and dynamically. Numerical results show the effectiveness of DCMAC to improve the system performance.

The performance gain of cooperative communications depends heavily on the selection of relay. Most of existing relay selection methods aim at maximizing cooperative gain by selecting appropriate relay, without taking into account the adverse effect brought by cooperative communications: extra interferences introduced by relay transmission (called cooperation interference). Thus the derived performance gain could be inaccurate and/or the selected relay may be not optimal. In this paper, we address the assignment of relays for multiple communication sessions using cooperative communications in a wireless network. We first thoroughly investigate the adverse effect brought by using relays, and derive the cooperation gain with consideration of cooperation interference. Based on the insights of our investigation, we propose a method of assigning relays to individual transmission flows while taking into account cooperation interference in cooperative communications. In order to tradeoff the advantage and adverse effect caused by relay transmissions, we use an auction approach to address relay assignment of cooperative communications. Specifically, we propose a Single round double Auction Scheme (SAS) for centralized wireless network and a Multiple rounds sequential Auction Scheme (MAS) for decentralized wireless network for relay assignment. We conduct extensive simulation experiments to validate the effectiveness of SAS and MAS. The significance of the impact of cooperation interference, improvement of system throughput and energy efficiency are demonstrated by numerical results.

In wireless networks, the retransmission should be performed by the sender to recover lost packets at the receiver. The conventional retransmission scheme only includes one lost packet in a retransmission, which results low retransmission efficiency. However, with the help of broadcasting nature of wireless channel and the network coding (NC) technique, we can encode N packets lost at N destinations in one retransmission to effectively recover the lost packets. Some work has been conducted on using NC to retransmit lost packets in the single-sender multiple-receiver (SSMR) wireless network scenarios. In this paper, we combine NC and retransmission in multiple-sender multiple-receiver scenarios, which is a more realistic wireless network scenario. To this end, a Probabilistic Network Coding Retransmission Mechanism (PNCRM) is proposed for MSMR wireless networks. The analysis and numerical results validate the effectiveness of PNCRM.

In this paper, we propose a relay selection method, Maximum Throughput Relay Selection Algorithm (MTRSA) for wireless networks. Based on the derivation of direct and cooperative communications, MTRSA takes both MAC overhead and collision into consideration for maximizing the system throughput. In addition, the analytical derivation and proposed relay selection algorithm support both amplify-and-forward (AF) and decode-and-forward (DF). Numerical results and simulations are provided to validate the efficiency of our algorithm.

Cooperative transmission (CT) is a promising technique to improve transmission rate and throughput in wireless networks, and relay node (RN) which could provide a good two-hop channel plays a key role in CT mode. As a result, most of existing work take the advantage of benefit of RN in CT mode, but do not fully recognize its potential adverse effect. In this paper, we investigate the adverse impact called flow-level cooperation interference (FCI) incurred by RN in CT mode, therefore, CT mode may not be beneficial as expected. To this end, we describe our insight and analyze the reason of FCI in wireless networks. To understand and solve FCI, we formulate this problem with a game-theoretic approach, and thus two methods which are named Nash equilibrium cooperative transmission strategy (NECTS) and Bayesian Nash equilibrium cooperative transmission strategy (BNECTS) are proposed, respectively. Our numerical results validate our analytical approach and demonstrate the effectiveness of our proposed NECTS and BNECTS.

Opportunistic network is a type of challenged network where an end-to-end path between the source and the destination doesn't exist. The dissemination of the data relies on the encounters of nodes. Link duration time is a main factor in determining the transmission capacity between two encounter nodes in the opportunistic network. Besides, inter-contact time plays a key role in forwarding algorithms and has an obvious effect on the delivery delay. In this paper, according to statistical analysis and numerical methods, statistical characteristics of wireless link in random waypoint (RWP) are analyzed from aspects of contact duration time and inter-contact time with different moving speed and transmission radiuses of the nodes. Complementary Cumulative Distribution Functions (CCDF) of the contact duration time and inter-contact time of the nodes are provided by numerical methods.

Opportunistic networks are the emerging networks featured by partitions, long disconnections, and topology instability, where the message propagation depends on the cooperation of nodes to fulfill a “store-carry-and-forward” fashion. But due to constrained energy and buffer, some nodes may behave selfishly, which will involve damage to the existing routing approaches and seriously degrade the performance of opportunistic networks. Aiming at the above problem, this paper proposes a novel bargaining based incentive protocol (BIP) for opportunistic networks, which exploits two-person bargaining model and allows a node to pay and charge according to its state and the attributes of messages. In addition, the proposed BIP protocol can tackle the issue of blind cooperation when the resources are very scarce. Extensive simulation results demonstrate the effectiveness and the practicality of the proposed BIP protocol in terms of high delivery ratio, low energy consumption and small average delay.

Recently, as it can increase the capacity of wireless networks greatly through spatial diversity by taking advantage of antennas on other nodes, cooperative communication (CC) has been obtaining more and more attention. However, as the selfish nature, the wireless node may be unwilling to serve as relay node if they can't get the corresponding reward. In this paper, we constructs a real double-auction scenario between source nodes and relay nodes instead of idealized truthful market which may obtain relatively lower system performance. We consider the system performance involving (1) successful source-relay pairs, (2) system capacity and (3) social welfare (SW). We transform the double auction-based optimal relay assignment problem into Maximum Matching (MM) and Maximum Weighted Matching (MWM) problem respectively and solve them using corresponding algorithms. Extensive experiments show that this mechanism can achieve higher system efficiency than truthful auction.

In cognitive radio networks, the idle spectrum bands that cognitive users sensed are usually discontinuous. Only one idle spectrum band may not be able to fulfill cognitive users' bandwidth requirements. In order to let cognitive users access allocated spectrum bands successfully and further improve the efficiency of spectrum utilization, we propose spectrum aggregation-based graph coloring algorithm (SAGCA), a spectrum allocation algorithm in cognitive radio networks. SAGCA considers bandwidth requirements of cognitive users and limitation of spectrum range that equipment can utilize due to hardware constraint. Numerical results show that the proposed algorithm can achieve greater performance in total bandwidth and percentage of cognitive users that networks can support compared to the original algorithm.

In Long Term Evolution (LTE) networks, Energy-Efficient (EE) and Mobility Load Balancing (MLB) are two important functions to optimize the network performance. In order to deal with the energy consumption in downlink LTE system for unbalanced low load scenarios, we built an effective EE resource allocation optimization model and employed a low complexity method to achieve the goal of the optimization in this paper, named EE-VBEM. Simulation results show that EE-VBEM can reduce the overall downlink energy consumption significantly and enhance the spectrum efficiency effectively in unbalanced low load networks.

Relay node (RN) plays a key role in cooperative communications and RN selection may substantially affects the performance gain. In this paper we address the issue of RN selection while taking into account Medium Access Control (MAC) overhead, which is incurred by not only handshake signaling but also frame retransmissions due to transmission error. We use a theoretical model to analyze the cooperation performance gains of cooperative MAC mechanism, and are thus able to select the optimal relay node. We derive the network saturation throughput of the designed MAC with our RN selection algorithm. Numerical results validate the effectiveness of our analytical model and show that our designed MAC significantly outperforms existing cooperative MAC mechanisms which do not consider retransmission MAC overhead.

The essence of spectrum allocation is to find an appropriate distribution of spectrum bands among users so that they can coexist. Thus, spectrum allocation can avoid interference that caused by two interference SUs using the same channel. Different spectrum allocation can lead to different network topologies and consequently have effect on the network connectivity. The aim of this paper is twofold: 1) based on the Laplacian spectrum of graphs, we represent the cognitive radio Ad-Hoc network connectivity with knowledge of the effects of the different labeling rules of spectrum allocation; 2) we give a deep analysis of the network connectivity and the impact that different parameters have on it. The results show that the different labeling rules of Color-Sensitive Graph Coloring have different influence on the network connectivity. For the different labeling rules of Color-Sensitive Graph Coloring, we can obtain the different relationship between the probability of primary users' activity and the network connectivity.

In order to overcome the phenomenon that the existence of end-to-end path is no longer guaranteed because of intermittent connection in the opportunistic network, it is desirable to employ a special node called ferry, which moves in a specific trace and forwards data for disconnected nodes, to provide communication opportunities. In this paper, Global Ferry Scheme (GFS), which exploits multiple local ferries and a global ferry to deliver messages, is proposed to minimize the average message delivery delay. The performance of GFS is evaluated and compared to the existing ferry-based routing method by analysis and simulation. The results show that GFS can improve the performance of opportunistic networks in terms of average delay and delivery ratio.

Cognitive radio has been suggested as an efficient method for secondary users to promote the efficient utilization of spectrum. Meanwhile, Cooperative relay allows different users or nodes to share resources and to create collaboration through distributed transmission in a wireless networks. The combination of Cognitive radio with cooperative communication could significantly improve the system performance in cognitive radio ad-hoc networks (CRAHNs). In this paper, we discuss how to use cooperative relay to increase the transmission rate in CRAHNs. We first give a new distributed relay selection algorithm, it considers several aspects including channel gain, channel available probability and spectrum heterogeneity of secondary nodes. A cooperative MAC protocol, Cooper-MAC, is then proposed for CRAHNs which enables secondary users to negotiate channels and relays. Simulation results demonstrate the effectiveness of the Cooper-MAC.

Cognitive radio has been proposed as the means for secondary users to promote the efficient utilization of spectrum. Meanwhile, Cooperative relay is well known as a powerful technology to combat signal fading in a wireless medium. In this paper, we discuss a new relay selection algorithm for cognitive radio ad-hoc networks by utilizing cooperative relay to improve spectrum diversity and transmission rate. We define a cooperation-table which considers several aspects including channel gain, channel available probability and spectrum heterogeneity of secondary nodes. Based on this cooperation-table, a new distributed relay selection algorithm is proposed that choose a relay node to maximize the transmission rate of the source node. Simulation results demonstrate the effectiveness of the relay selection algorithm.

Intermittently Connected Wireless Networks (ICWN) or Delay/Disruption Tolerant Networks (DTN), have attracted attention from researchers because of their inherent characteristics, including long latency, low data rate, and intermittent connectivity. Extensive research has been conducted on ICWN, including the architecture, and routing. However, few researchers have investigated the performance of TCP in ICWN, especially when Epidemic Routing is used. In this paper, we first evaluate the performance of TCP in ICWN with Epidemic Routing. Our results show that the Epidemic Routing in ICWN degrades the performance of TCP because multicopy data packets cause duplicate ACKs, and in turn reduce the transmitting rate of TCP. Then an enhanced algorithm for TCP, named A-TCP/Reno is proposed to solve the above problem. A-TCP/Reno can avoid the duplicate ACK problem which is caused by Epidemic Routing. The simulation results show that A-TCP/Reno outperforms the TCP/Reno in ICWN with Epidemic Routing protocol.

IEEE 802.16 supports both single-hop and multi-hop mesh modes. It defines several traffic and service categories and offers differentiated quality of service (QoS) through scheduling algorithms. In IEEE 802.16, a three-way handshaking mechanism is given to reserve the slots hop-by-hop for mesh networks, which is called Hop-by-hop Bandwidth Reservation Protocol (HBRP). However, HBRP can not assure the End-to-End QoS, and can not optimally schedule the idle slots. In this paper, we provide a QoS assurance method in IEEE 802.16 Mesh networks, named End-to-End Bandwidth Reservation Protocol (EBRP). EBRP includes End-to-End optimal bandwidth calculation and End-to-End resource reservation. The performance analysis shows that the EBRP is more effective than HBRP in the existing Mesh networks.

This paper proposes a novel Cross-layer Cooperation Method for IEEE 802.16 mesh networks (CCM). CCM selects cooperation nodes through cross-layer consideration of both channel state of physical layer and control overhead on MAC layer to maximize throughput. CCM uses the enhanced three-way handshaking to allocate the bandwidths between source node and cooperation nodes. Extensive simulations are conducted to demonstrate that CCM can effectively select optimal cooperation nodes according to dynamic network scenarios, and in turn increase channel capacity and improve system performance.

Cooperative communication can obtain spatial diversity without using multiple antennas, and thus achieve more reliable transmission or consume less power. Accordingly, a new cooperative MAC mechanism in wireless networks, the DCMAC, is proposed in this paper. The DCMAC makes full use of the broadcasting characteristics of wireless channel to obtain channel information, chooses the most suitable cooperative nodes, and reserves wireless channel efficiently. Evaluation results show that DCMAC can choose the most suitable cooperative nodes to improve system performance.

We propose an enhanced Probabilistic Routing Protocol which using History of Encounter and Transitivity (E-PROPHET) routing protocol. The E-PROPHET introduces the contact frequency and contact duration into the routing protocols, which makes sure the message be forwarding to accurate next hop. Results of simulation indicate that the E-PROPHET saves the buffer space, improves the packet delivery ratio, and decreases the End-to-End delay.

Delay-Tolerant Networks are wireless networks where disconnections may occur frequently due to node mobility, power outages and propagation phenomena. In order to achieve date delivery, store-and-forward protocols are used in DTN and routing protocols based on epidemic message dissemination has been proposed, such as Epidemic routing. Under Epidemic routing, packets can be delivered completely between every tow nodes if every node buffer is big enough and the communication time is long enough after one node contacts another one. But congestion will occur easily at a node if the buffer of this node is limited under Epidemic routing in DTN. In order to solve this problem, a congestion control strategy was introduced. If a node buffer is full and it needs to store a new packet, every packet in the node buffer will be checked, in order to find out the packets whose numbers of forwarding are over N and then erase them. If there is no packet whose number of forwarding is over N, the last packet will be erased. The strategy is called N-Drop. Using simulations based on a random waypoint model, the simulation results proved the improvement of our strategy.

While throughput maximization and service differentiation are two critical issues in wireless local area networks (WLANs), both are separately investigated in most existing work. This paper, from a different angle, addresses how to maximize saturation throughput of a WLAN conditioned that bandwidth differentiation is supported too. A novel model is established for this problem assuming IEEE 802.11e is used. We calculate the optimal values of minimum contention window for stations to maximize the saturation throughput and provide differentiated service as well. The simulation results validate our new model.

While most existing work focuses separately on how to improve WLAN saturation throughout and how to provide differentiated service, few attention is put to study their relationship. In this paper, we investigate the impact of service differentiation on saturation throughput maximization in IEEE 802.11e WLANs and theoretically prove that it is contradictory and impossible to achieve both of them simultaneously. In other words, saturation throughput is maximized without service differentiation or service differentiation reduces the maximal achievable saturation throughput more or less.

To be adaptive to the traffic variations in some real-time sensor applications, AC-MAC is proposed by Jin Ai et al. Based on Sensor Medium Access Control (S-MAC), AC-MAC introduces an adaptive duty cycle scheme within the framework of S-MAC. However, frequent transceiver state switches can lead to the increasing consumption of energy. In order to solve this problem, we focus our research on how to reduce the number of transceiver state switch. By combining AC-MAC with the Dynamic Power Management, it brings in a new protocol, an Adaptive Coordinated MAC Protocol based on Dynamic Power Management for Wireless Sensor Networks, AC-MAC/DPM, which not only guarantees low delay or high throughput, but also reduces the potential energy consumption when the traffic load is high.

This paper presents a new model to analyze the channel access delay of 802.11 DCF. Based on this analytical model, the average channel access delay of 802.11 DCF is derived. By means of simulation, the correctness of the analysis is validated, and the channel access delay of 802.11 DCF is further evaluated.

In this paper, we explore how to make RWBO+BEB support service differentiation. An analytical model is proposed to analyze how to choose the minimum contention windows according to the bandwidth ratios of stations. Based on the analysis, a novel service differentiated backoff algorithm for IEEE 802.11 DCF, named DS-RWBO, is proposed. The simulation results indicate that DS-RWBO can allocate the wireless bandwidth according to the bandwidth ratio of each station.