Autonomous aerial vehicles (AAVs) have the potential to greatly aid Internet of Things (IoT) networks in mission-critical data collection, thanks to their flexibility and cost-effectiveness. However, challenges arise due to the AAV’s limited onboard energy and the unpredictable status updates from sensor nodes (SNs), which impact the freshness of collected data. In this paper, we investigate the energy-efficient and timely data collection in IoT networks through the use of a solar-powered AAV. Each SN generates status updates at stochastic intervals, while the AAV collects and subsequently transmits these status updates to a central data center. Furthermore, the AAV harnesses solar energy from the environment to maintain its energy level above a predetermined threshold. To minimize both the average age of information (AoI) for SNs and the energy consumption of the AAV, we jointly optimize the AAV trajectory, SN scheduling, and offloading strategy. Then, we formulate this problem as a Markov decision process (MDP) and propose a meta-reinforcement learning algorithm to enhance the generalization capability. Specifically, the compound-action deep reinforcement learning (CADRL) algorithm is proposed to handle the discrete decisions related to SN scheduling and the AAV’s offloading policy, as well as the continuous control of AAV flight. Moreover, we incorporate meta-learning into CADRL to improve the adaptability of the learned policy to new tasks. To validate the effectiveness of our proposed algorithms, we conduct extensive simulations and demonstrate their superiority over other baseline algorithms.

The unmanned aerial vehicle (UAV) can act as the edge server in delay-sensitive monitoring for data collection and processing in the Internet of things (IoT) networks due to its flexibility and low operational cost. One of its major disadvantages is the limited battery level. This paper focuses on a problem with the rechargeable UAV-assisted energy-efficient and fresh data collection in the IoT networks. In particular, the UAV takes off from the initial position to collect data packets from sensor nodes (SNs) in the IoT networks and needs to reach the final position at a given time. Some charging stations (CSs) are in the IoT networks, which can recharge the UAV by the wireless power transfer technique to keep the UAV’s energy level from falling below the threshold energy. To minimize the weighted sum of the average age of information (AoI) and the average recharging price, we design a Markov Decision Process (MDP) to determine the UAV’s flight trajectory, the scheduling of SNs, and energy recharging. The MDP is then solved using a rechargeable UAV-assisted data collection algorithm based on dueling double deep Q-networks (D3QN). Numerous simulations show that the proposed D3QN algorithm can reduce the weighted sum of the average AoI and the average recharging price more effectively than the baseline algorithms.

Thanks to their high-flexibility and low-operational cost, unmanned aerial vehicles (UAVs) can be used to support mission-critical applications in the Internet of Things (IoT). However, due to the limited onboard energy, it is difficult for UAVs to provide continuous data collection. In this article, we study the problem of rechargeable-UAV-aided timely data collection in IoT networks, where the UAV collects status updates from multiple sensors and gets recharged from the charging stations (CSs) to keep its energy level above a threshold. To tradeoff the information freshness and energy consumption, we formulate a Markov decision process (MDP) with the objective of minimizing the weighted sum of the average total Age of Information and average recharging price. Under the dynamics and uncertainty of the environment, we propose a multitask transfer deep reinforcement learning method to jointly optimize the UAV ’ s flight trajectory, transmission scheduling, and battery recharging. To enable the application of the learned policy to new environments with similar settings and avoid starting from scratch, we develop a multitask network made up of common knowledge layers and task-specific knowledge layers. It specifically makes it possible for the transfer of common knowledge between environments with different network scales (e.g., different numbers of sensors/CSs) and/or topologies (e.g., different locations of sensors/CSs). Simulation results demonstrate that the proposed algorithm can adapt to new environments and achieve superior performance compared to the baseline algorithms.

Asynchronous or partially synchronous Byzantine fault-tolerant (BFT) protocols can tolerate up to one-third Byzantine faults, while synchronous Byzantine fault-tolerant protocols can tolerate up to one-half Byzantine faults. The existing synchronous Byzantine fault-tolerant protocols are leader-based protocols, which may lead to unbalanced load between nodes, and constitute a bottleneck that influences the performance of the whole system. In addition, the synchronous BFT protocols rely on a strong assumption: every message sent by an honest node will arrive at its destination within a known bounded time. In this work, in order to obtain better fairness and allow some degree of asynchrony while tolerating one-half faults, we designed LWSBFT, a leaderless BFT protocol in a weakly synchronous model where the synchronous assumption does not have to hold for all nodes all the time. The leaderless BFT protocol is comprised of the weakly synchronous Byzantine reliable broadcast (RBC) protocol and the weakly synchronous binary Byzantine agreement (BA) protocol, both of which can tolerate one-half faults. The weakly synchronous RBC protocol is used by each node to propose its input, and the weakly synchronous binary BA protocol is used to make a decision for each proposed value. The proposed leaderless BFT protocol can ensure both safety and liveness in a weakly synchronous model. We implement our consensus mechanism in Go, and evaluate the proposed consensus mechanism’s performance in terms of throughput and latency.

Speaker recognition has become very popular in many application scenarios, such as smart homes and smart assistants, due to ease of use for remote control and economic-friendly features. The rapid development of SRSs is inseparable from the advancement of machine learning, especially neural networks. However, previous work has shown that machine learning models are vulnerable to adversarial attacks in the image domain, which inspired researchers to explore adversarial attacks and defenses in Speaker Recognition Systems (SRS). Unfortunately, existing literature lacks a thorough review of this topic. In this paper, we fill this gap by performing a comprehensive survey on adversarial attacks and defenses in SRSs. We first introduce the basics of SRSs and concepts related to adversarial attacks. Then, we propose two sets of criteria to evaluate the performance of attack methods and defense methods in SRSs, respectively. After that, we provide taxonomies of existing attack methods and defense methods, and further review them by employing our proposed criteria. Finally, based on our review, we find some open issues and further specify a number of future directions to motivate the research of SRSs security.

Mobile crowdsensing has emerged as a promising paradigm where location-based sensing tasks are outsourced to mobile participants carrying sensor-equipped devices. A critical issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participants, which requires participants’ precise locations and thus raises privacy concerns. In this paper, we are motivated to develop a privacy preserving participant recruitment scheme for mobile crowdsensing, which maximizes the spatial coverage of the sensing range while protecting participants’ location privacy against an untrusted crowdsensing platform. Briefly, we propose a utility-assured location obfuscation mechanism operated in a hexagonal grid system, which the participants can follow to locally perturb their locations with personalized privacy demands. Given the obfuscated locations, we efficiently solve a coverage-maximized participant recruitment problem with the budget constraint by using a deterministic rounding algorithm. Considering the existence of biased sensing data incurred by location obfuscation, we further develop a fault-aware crowdsensing framework to improve the robustness of the recruitment strategy, where a constant-approximation algorithm is applied to select participants against any number of unqualified sensing results. Extensive simulations on real-world location datasets and Uber’s geospatial indexing system validate the efficacy of our location obfuscation mechanism and participant recruitment schemes in mobile crowdsensing systems.

Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications. Despite the potential benefits, FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training, raising great challenges to battery constrained mobile devices. In this work, we target at improving the energy efficiency of FL over mobile edge networks to accommodate heterogeneous participating devices without sacrificing the learning performance. To this end, we develop a convergence-guaranteed FL algorithm enabling flexible communication compression. Guided by the derived convergence bound, we design a compression control scheme to balance the energy consumption of local computing (i.e., "working") and wireless communication (i.e., "talking") from the long-term learning perspective. In particular, the compression parameters are elaborately chosen for FL participants adapting to their computing and communication environments. Extensive simulations are conducted using various datasets to validate our theoretical analysis, and the results also demonstrate the efficacy of the proposed scheme in energy saving.

Multiaccess edge computing (MEC) empowers service providers (SPs) to run applications on the shared edge platforms in close proximity to mobile users, enabling ultralow latency access to a wide variety of cloud services. However, how to decide the amount of edge computing resources to rent for mobile service provisioning poses great challenges as the service demand is unknown to SPs a priori and may vary across the geographically distributed edge sites spatially and temporally. The resource rental decision also significantly affects SPs' deploying profits since it is critical for service deployment and workload assignment. This article investigates the service provisioning problem in a cooperative edge computing system under service demand uncertainty. We develop a holistic solution to make two-timescale decisions on edge resource rental and workload assignment to maximize SP's deploying profits. Briefly, we exploit historical service demand traces at the edge sites to characterize the uncertainty in a data-driven manner and formulate the edge service provisioning problem into a two-stage risk-averse optimization. To solve the formulated problem without compromising the data privacy, we propose an algorithm integrating Benders decomposition (BD) and alternating direction method of multipliers (ADMMs), which enables each edge site to keep the historical traces locally and participate in the optimization process. Based on real-world data sets, extensive simulations are conducted to validate the efficacy of our scheme.

The pandemic of the coronavirus (COVID-19) has caused an unprecedented global public health crisis, and most countries in the world are running out of the healthcare resources. A fine-grained COVID-19 vulnerability map will be essential to track the number of people with covid-like symptoms, so that the the potential outbreak communities can be identified and the valuable healthcare resources can proactively and dynamically be allocated. Mobile crowdsourcing based symptom reporting is a promising and convenient option to construct such a map, while it may compromise the location privacy of crowdsourcing participants. In this work, we propose a novel approach to establish the COVID-19 vulnerability map based on the crowdsourced reporting without disclosing the participants' location privacy to a semi-honest crowdsourcing aggregator. Briefly, based on the differentially private geo-indistinguishability, the mobile participants are able to locally perturb their geographic data. With the masked geographic information, we employ the best linear unbiased prediction estimator with spatial smoothing to obtain the reliable vulnerability estimates in the areas of interest and construct the map. Given the fast spreading nature of coronavirus, we integrate the vulnerability estimates with a susceptible-exposed-infected-removed (SEIR) model to build up a future trend map. Extensive simulations based on real-world data verify the effectiveness of the proposed method.

The advance of edge computing pushes computing functionalities to the network edge and brings lucrative opportunities for edge operators (EOs) to cater the users with low latency requirement. Unlike in cloud computing, edge servers have limited computing capacity and require a proper resource planning. To avoid loss of potential profit, a promising way is to outsource cloud resources from a public cloud with additional cost when the edge computing capacity is insufficient to meet the real-time demands. Besides, the uncertainty of future demands also affects EOs' profits. It's essential to consider the interaction among market participants with different risk attitudes. To this end, we study multiple risk-averse EOs with one risk-neutral Cloud Provider (CP) in an edge computing market, where each EO competes to serve the users by determining the optimal resource provision strategies given the demand and the outsource price charged by the CP, and the CP sets the price based on the best responses of the EOs. We model the interaction between EOs and CP as a two stage Stackelberg game, and employ a data-driven optimization approach to characterize the uncertainty. We explore the existence and uniqueness of subgame Nash equilibrium, and find the equilibrium based on the Sample Average Approximation (SAA) method. Extensive simulations using real-world cluster data traces verify the effectiveness of the proposed method.

The platoon-based driving pattern, a cooperative driving pattern for a leader and a number of followers, is a good way to deal with some traditional traffic issues and brings many benefits when performing special platoon tasks. However, it's possible for each platoon member to be fully controlled by attackers, especially the leader, which may cause severe damage to the platoon stability. In this paper, we propose a vehicle platoon leader updating scheme in vehicular networks based on blockchain techniques and reputation management mechanism, so as to ensure that the most trusted platoon member acts as leader. In this scheme, according to received warning messages of traffic events, each platoon member evaluates others' reputations in the form of offsets. Using the designed reputation blockchain with Delegated Proof-of-Stake (DPoS) consensus scheme, miners generate blocks in turn and wait for others' verification. If a block successfully passes the consensus process, it will be formally added to the blockchain, which can reflect all platoon members' current reputation values. In addition, we also have to guarantee that the miner group is composed of several platoon members with high reputation and the leader serves as a miner. In this way, the scheme both meets the real-time requirement of reputation management and saves communication overhead compared with existing schemes. Finally, we analyze the proper functioning and security of our proposed scheme.

Integrating Unmanned Aerial Vehicles (UAVs) into the cellular network as the aerial users is a promising solution to meet their ever-increasing communication demands in a wide range of applications. Compared with the terrestrial users, the aerial users suffer more interference, and the quality of the downlink communication cannot be guaranteed. Due to the advantages of the millimeter-wave massive MIMO technology, we consider applying it to provide downlink communication services for UAVs, and both beam tracking and inter-beam interference cancellation are the keys for UAVs. This paper proposes a two-stage hybrid beamforming technique assisted by location information. In the first stage, the base station selects the appropriate beam codeword according to the location information of the UAVs; in the second stage, the base station obtains the equivalent digital domain channel matrix on the basis of the beam selection result in the first stage, and the digital beamforming is performed based on the ZF criterion. Extensive simulation experiments are carried out on the proposed scheme to verify the performance of our proposed beam tracking and inter-beam interference cancellation techniques.

To meet the requirement of constrained delay and computation resource of the future vehicular networks, it is imperative to develop efficient content caching strategy and computation resource allocation strategy in mobile edge computing (MEC) servers. In the proposed network framework, since the caching capacity and computing resource of each MEC are limited, and the coverage areas of MECs are overlapped, the vehicular networks have to decide what contents to cache, how to offload tasks and how much computing resource needs to be allocated for each task. In this study, in order to jointly tackle these issues, we formulate caching strategy, offloading decision and computing resource allocation coordinately as a mixed integer non‐linear programming (MINLP) problem. To solve the MINLP problem, we divide it into two subproblems. Firstly, we investigate a balanced and efficient caching strategy based on similarity in vehicular networks. Secondly, we apply McCormick Envelopes to convert MINLP problem into LP problem, and then adopt improved branch and bound algorithm to obtain the optimal offloading decision and computing resource allocation strategy. Simulation results indicate that the proposed schemes have a good performance in reducing economic cost under the deadline of each task.

Proactive caching in mobile-edge computing (MEC) networks is promising to handle the ever-increasing demand for wireless video services, and transcoding at MEC servers further improves the flexibility of video content delivery. However, how to effectively conduct caching for adaptive bitrate streaming poses great challenges due to the uncertainty of user preferences. The caching decisions also have a profound impact on the system energy efficiency since they may change the video delivery modes. In this article, by integrating caching, transcoding, and backhaul retrieving in a MEC-enabled adaptive streaming system, we propose a holistic solution to jointly determine the caching of bitrate-aware files and the scheduling of video requests in an energy-efficient manner. Specifically, we leverage a data-driven approach to characterize the uncertainty of real request arrivals. Based on the uncertainty model, we formulate a data-driven risk-averse optimization to derive a robust strategy for caching and delivery scheduling, which is a two-stage stochastic mixed-integer programming (SMIP) with the goal of minimizing the total expected energy consumption. We also develop feasible solutions and conduct extensive simulations on real-world data sets. The results validate the effectiveness of the proposed scheme in both the energy efficiency and the cache hit ratio.

The vehicular reputation management scheme based on blockchain has been studied with the rapidly development of blockchain technology. Existing works mainly focus on provisioning the security of the reputation management scheme by using the blockchain technology. However, the broadcast latency of blocks and the throughput capacity of the blockchain also have significant impacts on the performance of the reputation management scheme. This paper proposes a hierarchical blockchain based reputation management scheme in vehicular networks. Our hierarchical blockchain architecture assures that each vehicle can obtain the latest reputation value of any neighboring vehicle in a short time. Moreover, our designed blockchain structure aims at reducing the confirmation time of reputation values and increasing the capacity of blockchain. We finally conduct simulation experiments to compare our proposed scheme with the existing schemes.

Server and Network Assisted Dynamic adaptive streaming over HTTP (SAND) is a promising technology to cope with the dramatic increase in video streaming traffic. The new emerging Mobile Edge Computing (MEC) paradigm may further facilitate bitrate adaptation and video transcoding in a SAND system with the help of local edge servers. A critical issue in MEC-SAND framework is to guarantee Quality of Experience (QoE) for clients while achieving efficient utilization of edging network resources. In this paper, we aim to develop an adaptive video delivery scheme to minimize the delay in MEC assisted ultra-dense networks. In our scheme, each client is mapped to a server that better fits its requirements and transmission condition, and a bitrate selection mechanism is exploited to decide the best video version for the client. Besides, time-consuming transcoding tasks are carefully scheduled considering edge computing capacity. We formulate a Mix-Integer Non-Linear Programming (MINLP) problem to jointly determine cell association, bitrate adaption, and computing resource allocation. We exploit the generalized Benders decomposition method to reduce the solving complexity of the formulated problem. Numerical results validate the effectiveness and efficiency of the proposed scheme.

Mobile crowdsensing is recognized to be a promising paradigm wherein location-based sensing tasks are outsourced to participants carrying mobile devices. A prominent issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participating devices, which requires the disclosure of participants' locations and leads to potential location privacy threats. In this paper, we aim to develop a privacy-preserving participant recruiting scheme for mobile crowdsensing, which guarantees the crowdsensing coverage while preserving participants' location differential privacy against a semi-honest crowdsensing aggregator. Briefly, based on the differential private geo-indistinguishability method, we enable candidate participants to locally perturb their location data. With the obfuscated location information, we formulate the crowdsensing coverage optimization as an Integer Program (IP), and develop a 1-(1 - 1/f)f-approximation algorithm, which yields a near-optimal participant recruiting solution. Through extensive simulations, we demonstrate the tradeoff between privacy preservation and crowdsensing utility, and show that satisfactory crowdsensing coverage can be achieved while preserving the participants' differential location privacy.

The physical layer security of OFDM communication systems is getting more and more attention. Protecting the channel from eavesdroppers has received some attention. Artificial noise is generally considered to utilize the null space of the intended receiver's channel to interfere with the eavesdropper's channel, but it may not be able to defend against passive eavesdroppers. In this paper, a novel anti-eavesdropping OFDM system is proposed by using artificial pilot noise. Pilots play an important role in demodulating OFDM signal. In our proposed scheme, an auxiliary device called Helper would generate noise signals that overlap on the pilots sent by the user terminal. With the knowledge of the signals sent by the Helper, the receiver can decode the user terminal signal but eavesdroppers cannot. This would prevent eavesdroppers from overhearing the information transmitted between the user terminal and the receiver, so as to provide a secure communication for the user terminal. Finally, we use wireless open access research platform (WARP) v3 to test the feasibility and security of the scheme in practice. Our experimental results show that the proposed scheme can provide a secure communication in the environments with passive eavesdroppers.

Cellular Vehicle-to-Everything (C-V2X) as a wireless communication technology has been widely investigated to guarantee road safety and support autonomous driving. Nevertheless, due to high-mobility of nodes and time-varying topology of networks, C-V2X networks always face many security threats. We identify a new Denial of Service (DoS) attack in which a small number of nodes maliciously reserve the communication resources in a C-V2X network by requesting high priority V2X services frequently. A malicious vehicle detection scheme is then developed to defend against this attack. Different from the detection methods based on traffic analysis, the Mobile Edge Computing (MEC) server only needs to focus on detecting nodes with high malicious probability in our scheme. Thus, we propose a model to estimate the probability that a node is malicious based on its communication resources consumption state. Simulation results demonstrate not only the feasibility of our attack but also desirable performance of the proposed malicious node detection mechanism.

High Efficiency WLAN (HEW) focuses on improving metrics that reflect user experience, such as average per station throughput, the 5th percentile of per station throughput of a group of stations, and area throughput. In order to improve area throughput, Access Points (APs) are densely deployed to increase spatial multiplexing due to the short-range communication links. Thus, Overlapping Basic Service Set (OBSS) interference management is one of the major techniques in HEW. More specifically, in the four-way handshake transmission mode, the transmission of control frames increase interference area, such that some conflict-free transmissions cannot be active at the same time. In this paper, we design an OBSS interference management scheme to increase the spatial reusing ratio. We evaluate the performance of our OBSS interference management scheme under NS3 platform by testing different large-scale network scenarios. We also implement our scheme using Wireless Open-Access Research Platform (WARP). Our experiment results show that the proposed scheme can efficiently improve network area throughput.

In this paper, we study the distributed caching scheme in dense small cell networks, that determines which files each SBS should cache locally. In particular, we consider that the appropriate caching policy would produce more Coordinated Multiple Points Joint Transmission (CoMP-JT) opportunities so as to reduce wireless access resource consumption. Our caching scheme minimizes the wireless resource consumption while satisfying the caching capacity limit. Then we formulate the optimal caching problem into a submodular function subject to matroid constraints, which can be solved by an efficient algorithm. Finally we conduct extensive simulations to demonstrate that our proposed distributed caching scheme with considering the impact of CoMP-JT is effective for reducing wireless resource consumption.

OBSS (overlapping basic service set) interference management is a major issue for designing the next generation WLAN with the dense deployment. DSC (dynamic sensitivity control) was proposed to increase the multiple concurrent transmissions to increase throughput, but it would introduce more hidden terminal problems. In this paper, we explore FFR (fractional frequency reuse) to coordinate interference between neighboring BSSs (basic service set). We study how to apply FFR in the existing WLAN system, and present the user identification approach and sub-channel allocation method. Afterwards, based on NS3 simulator platform, we conducted extensive simulations to evaluate the performance of FFR-based interference coordination scheme in WLAN with dense deployment. Our simulation results show that our proposed interference coordination scheme not only can effectively improve system throughput, but also provides fairness for each user.

Small cell network is a promising approach to improve network capacity due to the high link quality and spectrum utility efficiency. Nevertheless, the bandwidth enjoyed by small cell networks is limited by the wireless backhaul capacity and the serious interference from neighbor cells. The backhaul resource needed can be reduced by caching in small cell base stations (SBSs). On the other hand, the inter-cell interference can be mitigated by Coordinated Multi-Point-Joint Transmission (CoMP-JT) which allows multiple SBSs transmit concurrently. Unfortunately, CoMP-JT requires more backhaul resources and caching capacity. This paper jointly considers the transmission mode selection and the caching scheme to minimize wireless backhaul resources consumption. Different from existing works, our work identifies the optimal caching placement while satisfying the spectral efficiency. Our simulation results show that our proposed caching scheme can effectively reduce the backhaul resources consumption.

In a social network, spreading information through the powerful leader of the community is highly valuable. Applying effective methods to identify influential nodes will leverage cellular networks in device‐to‐device local communication. In this study, entropy of betweenness centrality (EBC) combining the degree of the node and the degree of the neighbour node is proposed as a metric to select nodes from large and complex networks to disseminate information easily. EBC describes how the node is essential to connect two regions of the network. In the authors' work, nodes are identified by EBC, which is evaluated by susceptible‐infected (SI) model. Hence, if the source of infection in the SI model is one node and the percentage of infected nodes is the largest, which means that the source node is the most powerful node in the network. Simulation results show that the proposed method performs better than the existing methods to spread information in the mobile social network.

With deployment of small cells, cellular multicast is gaining attractions as it can efficiently deliver large amounts of multimedia data to cellular users. Evolved Multimedia Broadcast Multicast Service (eMBMS) is an efficient broadcast/multicast mechanism to deliver shared content from LTE network to multiple users. It introduces Multimedia Broadcast multicast service Single Frequency Network (MBSFN) for the video sharing among users to guarantee the Quality of Experience (QoE) of users. In MBSFN, each Small cell Base Station (SBS) should determine the appropriate Modulation and Coding Scheme (MCS) for each physical Resource Block (RB), so as to simultaneously maximize the multicast throughput and satisfy the users' experience requirements. However, existing works did not consider the effect of limited wireless backhaul capacity on the multicast throughput. In this paper, we first demonstrate that limited backhaul capacity would affect the system throughput. Then we formulate the problem of MBSFN-based multicast resource allocation with the constraint of limited wireless backhaul capacity. To solve the problem, we develop a near-optimal algorithm. Finally, we conduct extensive simulations to demonstrate that the limited wireless backhaul capacity is important when identifying the optimal multicast resource allocation solution.

The evolving cellular network with centric-deployed Cloud Centers has greatly improved the mobile user experience. However, the advent of vehicular networks with a wide variety of new services and devices changes the existing cellular network landscape, and the cellular-based vehicular networks are confronted with serious challenge in terms of latency reduction and flexible service delivery. In this paper, we propose to deploy the specific server, called Mobile-Edge Computing (MEC) server, within the Radio Access Network, and allow them to connect with a set of base stations alongside roads, so as to provide flexible vehicle-related service and efficiently control the radio network. Then, the vision of MEC- assisted slicing network and a traffic scheduling policy are presented to promote network customization. As an instance of the MEC-based tailored network service, a time-predicted handover mechanism for vehicles is further developed by leveraging available road information and the enhanced capacity of the MEC server to satisfy the demand for high mobility and reliability.

Hybrid wireless network is composed of both ad hoc transmissions and cellular transmissions. Under the L-maximum-hop routing policy, flow is transmitted in the ad hoc mode if its source and destination are within L hops away; otherwise, it is transmitted in the cellular mode. Existing works study the hybrid wireless network capacity as a function of L so as to find the optimal L to maximize the network capacity. In this paper, we consider two more factors: traffic model and base station access mode. Different from existing works, which only consider the uniform traffic model, we consider a traffic model with social behavior. We study the impact of traffic model on the optimal routing policy. Moreover, we consider two different access modes: one-hop access (each node directly communicates with base station) and multi-hop access (node may access base station through multiple hops due to power constraint). We study the impact of access mode on the optimal routing policy. Our results show that: 1) the optimal L does not only depend on traffic pattern, but also the access mode; 2) one-hop access provides higher network capacity than multi-hop access at the cost of increasing transmitting power; and 3) under the one-hop access mode, network capacity grows linearly with the number of base stations; however, it does not hold with the multi-hop access mode, and the number of base stations has different effects on network capacity for different traffic models.

A multi-hop multi-AP multi-channel cooperation scheme, referred to as MHACWlan, is proposed for the purpose of improving the efficiency and performance of wireless local area network (WLAN) in dense scenarios, which is the top concern of HEW (High Efficiency WLAN), a IEEE 802.11 study group created in 2013. The proposed MHACWlan provides an effective way to solve the performance anomaly problem, interference problem, and the load imbalance problem in dense WLAN. In MHACWlan, we considers two-hop association to the APs for the wireless STAs with poor single-hop links to improve the throughput, assigning different channels for the two-hop links to reduce the interference and increase the throughput, and associating with more than one AP so as to balance the load. The mathematical analysis and simulation results demonstrate the performance benefits of MHACWlan compared to the traditional WLAN.

Hybrid wireless networks are networks that are composed of both ad hoc transmissions and cellular transmissions. Many existing works have analyzed the capacity of hybrid wireless networks. By assuming the uniform traffic model that a source node would select a random node as the destination, the network capacity is a function of number of nodes and number of base stations. Nevertheless, the real network traffic pattern is related to the social behaviors of users. In this work, we study the capacity of hybrid wireless networks with the social traffic model under the L-maximum-hop routing policy. If two nodes are within L hops away, packets will be transmitted in the ad hoc mode; otherwise, packets are transmitted through the base stations. To our best knowledge, we are the first to study this problem and develop the capacity as a function of number of nodes, number of stations, traffic model parameters, and L.

In this paper, we study the propagation of deadline- constrained content over wireless cellular network, in which the base station transmits a content to a certain set of users. The lifetime of a content is consumed in two aspects: waiting in the queue and transmitting. In our system, the base station first transmits the content to users with a certain data rate, such that some users may not directly receive the content. Afterwards, the users who obtained the content would forward the content to the other users. Due to the deadline constraint of each content, we formulate the scheduling problem by using Markov Decision Processing (MDP) with the objective of maximizing the throughput of the whole system. We propose an algorithm based on value iteration. Extensive simulation results are provided to demonstrate that our scheduling algorithm can efficiently improve system throughput.

This paper studies the interference management of D2D communications in cellular networks. We consider the underlay D2D communication, such that the signal quality of cellular user would be affected by D2D users. We explore the application of network coding to mitigate interference. In our proposed interference management scheme, the helper nodes overhears the signals from cellular users, and then, code the received packets and sends to the base station. We design the transmission policy for the helper node, and also describe how to select the helper nodes. Our simulation results show that the proposed interference management can effectively improve the spectrum efficiency and increase system throughput.

In this paper, we consider a disruption tolerant network (DTN) which enables data transmission with intermittent connectivity and in which instantaneous end-to-end path between a source and destination may not exist. We explore routing problems in such networks in consideration of limited storage for each intermediate node. A graph model called the storage enhanced time-varying graph (STVG) is presented, which fits the dynamic topology characteristics and time- varying parameters of DTN. We transform the nodes with finite buffer in DTN into links with limited bandwidth in STVG. Then, a Shortest Path Computation with Flow Guaranteed (SPFG) algorithm is proposed to select a path with minimum overall cost (delay), meanwhile, to guarantee the transfer of a certain flow. Simulation results show that the proposed algorithm based on the STVG model can obtain better performance in delay and delivery ratio (which can be improved to more than 90%) as compared with existing routing algorithms without considering the buffer constraints.

Precomputation of the supported QoS is very important for internet routing. By constructing routing tables before a request arrives, a packet can be forwarded with a simple table lookup. When the QoS information is provided, a node can immediately know whether a certain request can be supported without launching the path finding process. Unfortunately, as the problem of finding a route satisfying two additive constraints is NP-complete, the supported QoS information can only be approximated using a polynomial time mechanism. A good approximation scheme should reduce the error in estimating the actual supported QoS. Nevertheless, existing approaches which determine this error may not truly reflect the performance on admission control, meaning whether a request can be correctly classified as feasible or infeasible. In this paper, we propose using a novel metric, known as distortion area , to evaluate the performance of precomputing the supported QoS. We then analyze the performance of the class of algorithms that approximate the supported QoS through discretizing link metrics. We demonstrate how the performance of these schemes can be enhanced without increasing complexity. Our results serve as a guideline on developing discretization-based approximation algorithms.

Network coding is considered as a promising technique to increase the bandwidth available in a wireless network. Many studies show that network coding can improve flow throughput only if an appropriate routing algorithm is used to identify paths with coding opportunities. Nevertheless, a good routing mechanism is very difficult to develop. Existing solutions either do not estimate the path bandwidth precisely enough or cannot identify the best path in some situations. In this paper, we describe our coding-aware routing protocol that provides a better path bandwidth estimate and is able to identify high throughput paths. Extensive NS2 simulations show that our protocol outperforms existing mechanisms.

Wireless Mesh Network (WMN) has become an important edge network to provide Internet access to remote areas and wireless connections in a metropolitan scale. In this paper, we study the problem of identifying the maximum available bandwidth path, a fundamental issue in supporting quality-of-service in WMNs. Due to interference among links, bandwidth, a well-known bottleneck metric in wired networks, is neither concave nor additive in wireless networks. We propose a new path weight which captures the available path bandwidth information. We formally prove that our hop-by-hop routing protocol based on the new path weight satisfies the consistency and loop-freeness requirements. The consistency property guarantees that each node makes a proper packet forwarding decision, so that a data packet does traverse over the intended path. Our extensive simulation experiments also show that our proposed path weight outperforms existing path metrics in identifying high-throughput paths.

Network coding is known to be a promising technology to increase the bandwidth capacity in wireless networks. To our best knowledge, there is limited work on studying the available bandwidth for a given path with network coding. This paper presents a new method to estimate the available bandwidth of a path that considers network coding and wireless interference simultaneously. We show that our estimated path bandwidth can be easily achieved using a simple scheduling scheme. We also show that path bandwidth estimation is more accurate than other existing method through theoretical analysis and simulation experiments.

In this paper, we propose a new path metric for finding the maximum available bandwidth path in the multi radio multi-channel wireless mesh networks. We formally prove that the path metric is isotonic, which is the necessary and sufficient condition for assuring the proper operation of the routing algorithm. Based on the metric, we develop a routing protocol which jointly considers the path selection and the channel assignment. The time complexity of our routing algorithm is polynomial. We conduct the simulation experiments to compare the proposed metric with the existing metrics for finding the maximum available bandwidth path.

This paper discusses the problem of computing the maximum available bandwidth of a given path in TDMA-based network with network coding, which is a fundamental issue for supporting QoS with bandwidth requirement in wireless networks. We present a new path bandwidth computation mechanism considering physical-layer network coding. To our best knowledge, our work is the first proposal on assigning time slots with consideration of wireless interference and network coding simultaneously. Our simulation experiments show that our approach produces higher throughput than the existing approach.

In a wireless network that supports multiple flows, allocation of bandwidth resource among the flows is one of the critical problems. Different allocation strategies have been developed based on different optimization objectives. Unfortunately, these objectives may not reflect directly the time needed for a flow to transmit what it wants. In this paper, we define a new objective, average waiting time, that reflects the average time needed for the flows to finish their transmissions. For small networks, we develop an optimal scheme that minimizes the average waiting time. We extend the mechanism for general networks, and simulation results show that it can significantly reduce the average waiting time when compared with other existing mechanisms.

In this paper, we study the problem of computing the supported QoS from a source to a destination with multiple additive constraints. The problem has been shown to be NP-complete and many approximation algorithms have been developed. We propose a new approximation algorithm called multi-dimensional relaxation algorithm. We formally prove that our algorithm produces smaller approximation error than the existing algorithms. We further verify the performance by extensive simulations.

In this paper, we consider the problem of routing with two additive constraints in the hierarchical networks, such as the Internet. In order for scalability, the supported QoS information in the hierarchical networks has to be aggregated. We propose a novel method for aggregating the QoS information. To the best of our knowledge, our approach is the first study to use the area-minimization optimization, the de facto optimization problem of the QoS information aggregation. We use a set of real numbers to approximate the supported QoS between different domains. The size of the set is predefined so that advertisement overhead and the space requirement will not grow exponentially as the network size grows. The simulation results show that the proposed method outperforms the existing methods.

This paper presents a distributed polynomial algorithm for finding the maximum bandwidth path in Wireless Mesh Networks (WMNs). Our proposed algorithm can be applied for designing the proactive hop-by-hop routing protocol with bandwidth guarantee. To the best of our knowledge, our work is the first distributed path calculation algorithm in WMNs.

The problem of finding a path that satisfies two additive constraints, such as delay and cost, has been proved to be NP-complete. Many heuristic and approximation algorithms have been developed to identify a path given a certain QoS request. Unfortunately, these algorithms cannot be applied directly in the Internet because routing in the Internet is based on table lookups and routing tables are computed before a request arrives. In this paper, we develop an approximation algorithm for computing the supported QoS going across a domain. We analyze the approximation error of our algorithm and formally prove that the approximation error of our proposed algorithm is smaller than those of the existing approaches. We further verify our performance using extensive simulations.