Data collection and uploading are among the most fundamental problems in an mobile crowdsensing (MCS) system and involve complicated interactions among the system’s platform, mobile users, data collection tasks, and data relaying devices. In the article, we propose a destination-oriented data collection and uploading (DDCU) problem in MCS to facilitate cooperation among various entities. In DDCU, mobile device users are recruited as workers to complete data collection tasks and aim to reach their destinations after collecting the required data. At the same time, edge servers are recruited as data relaying devices to share their idle network resources and gain extra profit. The DDCU problem aims to help an MCS platform plan efficient sensing paths for workers to maximize the platform’s total profit. We further prove the DDCU problem to be NP-hard and thus require a time-efficient approximation-based method to solve the problem. In Xu and Song (2022), we proposed communication-QMIX-based multiagent deep reinforcement learning (CQDRL) as a decentralized method for an MCS routing problem. Although CQDRL was proven effective with high performance, it explicitly targets graph-based problems with two types of nodes and requires considerable effort to be extended to more heterogeneous graph problems. In this article, we propose CQDRL-S by simplifying the graph processing part of CQDRL to solve the DDCU problem with three types of nodes. More importantly, CQDRL-S can potentially be extended to other heterogeneous graph problems with moderate modifications. Extensive experiments further justify the effectiveness, efficiency, and simplicity of CQDRL-S in dealing with the DDCU problem.

Task assignment is a fundamental research problem in mobile crowdsensing (MCS) since it directly determines an MCS system’s practicality and economic value. Due to the complex dynamics of tasks and workers, task assignment problems are usually NP-hard, and approximation-based methods are preferred to impractical optimal methods. In the literature, a graph neural network-based deep reinforcement learning (GDRL) method is proposed in Xu and Song (2022) to solve routing problems in MCS and shows high performance and time efficiency. However, GDRL, as a centralized method, has to cope with the limitation in scalability and the challenge of privacy protection. In this article, we propose a multi-agent deep reinforcement learning-based method named communication-QMIX-based multi-agent DRL (CQDRL) to solve a task assignment problem in a decentralized fashion. The CQDRL method not only inherits the merits of GDRL over handcrafted heuristic and metaheuristic methods but also exploits computation potentials in mobile devices and protects workers’ privacy with a decentralized decision-making scheme. Our extensive experiments show that the CQDRL method can achieve significantly better performance than other traditional methods and performs fairly close to the centralized GDRL method.

Mobile crowdsensing (MCS), as an alternative to traditional sensor networks, has attracted much research attention because of its flexibility and low deployment fee. Compared with the traditional sensor networks, MCS exploits existing network infrastructures (such as edge servers and user devices) to intelligently cooperate with smart device owners. In this way, MCS combines machine intelligence and human intelligence to perform various sensing tasks more efficiently with a significantly lower cost. A challenging problem in MCS is data uploading, where mobile phone users as workers need to upload collected data to an MCS platform. In Xu and Song (2022), we proposed a heuristic approach to make a data transmission plan between mobile phone users and edge servers, which can help an MCS system leverage network resources in edge servers to facilitate data uploading efficiently. In this article, we reinvestigate the data uploading problem in Xu and Song (2022), analyze the heuristic approach’s drawbacks, and propose a deep reinforcement learning (DRL)-based method to complement these drawbacks. Specifically, we show that the heuristic approach may not sufficiently address heterogeneous cases, although it can achieve high efficiency in the homogeneous scenarios. Furthermore, we find that the heuristic method is not a one-fit-for-all method and cannot adjust itself when facing new scenarios. Instead of making a new fixed heuristic to deal with these new scenarios, we design an adaptive method based on DRL and graph neural networks (GNNs) to learn heuristics, enabling the new method to handle all possible situations in theory. Specifically, we train a DRL agent with a group of data uploading instances and then generalize the agent to other instances. Extensive numerical results show that the DRL-based approach achieves a high approximation ratio and performs stably in all sorts of experiment settings.

Mobile crowdsensing (MCS) is a new paradigm of data collection with large-scale sensing. A group of mobile users are recruited as workers to move around in a specific region and carry out sensing tasks. A challenging problem of MCS is task allocation, especially when the MCS platform needs to assign tasks to selected workers among a large user pool and consider mixed spatial and temporal features, including locations and time windows of tasks, and trajectories and arrival time of workers. In this paper, we take into account these features and study the task allocation problem that assigns tasks to workers over time and guarantees the tasks are accomplished before their deadlines. We consider an offline scenario where the MCS platform is informed of all the information of tasks and workers in advance, and an online scenario where the platform does not know the information of workers before they enter the system. For the offline scenario, we provide a cooperative ant colony algorithm with swarm intelligence to approximate the optimal solution in large-scale cases. For the online scenario with incomplete information, we propose several online algorithms, among which the predictive online algorithm exploits historical records of workers and performs the best. Finally, we conduct simulations and evaluate the differences among the online solutions and offline solutions. The results show that the proposed online solutions can approach the offline optimal solution in small-scale cases, and its approximation obtained by the cooperative offline solution in large-scale cases.

Mobile crowdsensing (MCS) is a new and promising paradigm of data collection in large-scale sensing and computing. A large group of users with mobile devices are recruited in a specific area to accomplish sensing tasks. An essential aspect of an MCS application is task allocation, which aims to efficiently assign sensing tasks to the recruited workers. Due to various resource and quality constraints, the MCS task allocation problem is often an NP-hard optimization problem. Traditional greedy or heuristic approaches are usually subject to performance loss in a certain degree so as to maintain tractability or accommodate special requirements such as incentive constraints. In this paper, we attempt to employ a deep reinforcement learning method to search for a more efficient task allocation solution. Specifically, we use a double deep Q-network (DDQN) to solve the task allocation problem as a path-planning problem with time windows. Our formulated problem takes into account location-dependency and time-sensitivity of sensing tasks, as well as the resource limits of workers in terms of maximum travelling distances. Simulations are conducted to compare the DDQN-based solution with two standard baseline solutions. The results show that our proposed solution outperforms the baseline solutions in terms of the platform's profit and the coverage of tasks.

Mobile Crowdsensing (MCS) is a promising paradigm for the large-scale sensing. In this paper, we aim to build a truthful framework of MCS taking user mobility and incentive into account. The proposed framework is composed of two challenging problems, path planning and incentive mechanism design. In path planning, every user as a worker independently plans a tour to carry out tasks based on its own strategy. In incentive mechanism design, the platform leverages a mechanism to select the winners and determine the payments. To solve these two problems, a heuristic bidirectional searching algorithm is proposed for path planning and an incentive mechanism is designed. The proposed mechanism is proved to be computationally efficient, individually rational, and truthful. Finally, the simulation results show that our proposed heuristic algorithm outperforms the baseline algorithms and approaches the optimal solution in path planning. Our proposed mechanism has a total payment smaller than that of a Vickrey-Clarke-Groves (VCG) mechanism.

While the fifth-generation (5G) mobile networks are evolving to a content-centric paradigm, the surging traffic demands keep stressing the ever-increasing network capacities. To alleviate the traffic pressure, especially from video content distribution, in-network caching and particularly edge caching offer an effective means to save network bandwidths and balance traffic loads. In this paper, we consider an integrated content distribution framework with universal in-network caching and collaboration across domains. Leveraging this integrated framework, we develop a context-aware solution, which effectively exploits cache collaboration and context information to relieve network congestion. We also conduct simulations with real datasets for the network topology and video library. The results show that the proposed solution outperforms the reference schemes in terms of link bandwidth usage and video delivery quality.

Mobile crowdsensing (MCS) offers a promising paradigm for large-scale sensing with the rapid growth of mobile smart devices. Compared with traditional sensing methods, MCS is more effective and efficient in energy and cost. Task allocation is a key problem in MCS, which has a significant impact on the performance. It is challenging to design a generic solution to the task allocation problem because MCS applications typically consider distinct targets under specific constraints. However, there are many common interests such as data quality, budget, and energy consumption. In this paper, we analyze and formulate the task allocation problem from two perspectives, respectively. First, we focus on data quality and propose a genetic algorithm (GA) to maximize data quality. Then, we take the profit of workers into account and propose a detective algorithm (DA) to improve the profit. In the GA-based solution, only the platform is able to decide the task assignment. However, in the DA-based solution, the workers are allowed to determine and submit their task sets to the platform, which just needs to make a selection from these task sets. In addition, we consider the clustering effect of tasks and the influence caused by different geographic distributions of tasks. To evaluate the performance of the proposed solutions, extensive simulations are conducted. The results demonstrate that our proposed solutions outperform the baseline algorithm and there is a tradeoff between the data quality and the profit of workers.

Nowadays, pervasive mobile devices not only pose new challenges for existing wireless networks to accommodate the surging demands, but also offer new opportunities to support various services. For example, device-to-device (D2D) communications provide a promising paradigm for data dissemination with low resource cost and high energy efficiency. In this paper, we propose a three-phase approach for D2D data dissemination, which exploits social-awareness and opportunistic contacts with user mobility. The proposed approach includes one phase of seed selection and two subsequent phases of data forwarding. In Phase I, we build a social-physical graph model, which combines the social network and the mobile network with opportunistic transmissions. Then we partition the social-physical graph into communities using the Girvan-Newman algorithm based on edge-betweenness, and select seeds for the communities according to vertex-closeness. In Phase II, data forwarding only takes place among socially connected users. In Phase III, the base station intervenes to enable data forwarding among cooperative users. For Phases II and III, we propose the new mechanisms for message selection and cooperation pairing, which take into account both altruistic and selfish behaviors of users. The theoretical analysis for the message selection mechanism proves its truthfulness and approximation ratio in the worst case. Extensive simulation results further demonstrate the effectiveness of the proposed three-phase approach with various synthetic and real tracing datasets.

To offer a large capacity in the fifth-generation (5G) mobile networks, a promising technique is to integrate and utilize the intelligence and resources of smart devices at the mobile edge. In this paper, we investigate how to exploit device collaboration to facilitate data dissemination via device-to-device (D2D) communications. In particular, we focus on a key research problem that aims to effectively pair request devices with cache devices in close proximity. Due to the interference among D2D links, it is computationally hard to obtain an optimal pairing for a large-scale network. Hence, we propose an interference-aware approach that can obtain a near- optimal approximation result efficiently. Specifically, the proposed approach first uses Lagrangian relaxation to find an upper-bound solution, and then derives a feasible solution from the initial pairing and further augments it. Extensive simulation results show that our approach performs closely to the optimal solution and achieves significant performance gain over the existing schemes in terms of the ratio of matched device pairs and total sum rate.

Device-to-device (D2D) communications provide a promising technique for the fifth-generation (5G) wireless networks. It has been considered to support intelligent vehicular communications, such as in LTE-Vehicle (LTE-V). Particularly, it is very challenging to manage simultaneous handover of a massive number of fast-moving devices on the cell edge. In this paper, we propose a joint handover approach that effectively exploits D2D multicast and D2D clusters to address the severe resource contention caused by the high density and high mobility of mobile devices. The handover decision takes into account the received signal strength (RSS) from each candidate base station (BS) and the moving speed toward each BS. The simulation results show that the proposed solution can significantly reduce the communication interruption probability and handover latency of mobile devices in high-density and high-speed scenarios.

With the rapid expansion of Internet-of-Things (IoT), tremendous traffic produced by a vast number of IoT devices is being injected into networks and straining their capacity. To address the challenge, device-to-device (D2D) communications offer a promising technique that relieves network overloading by localizing traffic between devices. In this paper, we investigate how to exploit D2D communications to support data dissemination and offload traffic. In particular, we focus on an important problem that aims to effectively pair request devices with cache devices in close proximity. Due to the interference among D2D links, we prove that this device pairing problem is NP-hard and thus requires an approximation algorithm to solve it efficiently. Here, we propose a three-step approach, in which the first step uses Lagrangian relaxation to obtain an upper bound solution, the second step derives a feasible solution from the initial pairing and further augments it, and the last step uses a swapping algorithm to refine the pairing to guarantee its stability. The proposed approach is proved to converge to a two-sided exchange stable matching. Extensive simulation results show that our three-step approach performs closely to the optimal solution and achieves significant performance gain over the existing schemes.

Vehicular networks can enable a variety of services to improve safety and comfort for travelling. To mitigate the high cost for deploying dense roadside units (RSUs), vehicle-to-vehicle (V2V) communications can complement vehicle-to-infrastructure (V2I) communications to compensate for intermittent connectivity and enhance transmission performance. In this paper, we analyze a location-aware opportunistic V2V relay scheme in terms of the transmission success probability for a target destination vehicle and the connectivity probability when the scheme is applied to inter-connect adjacent RSUs. The analytical approach based on stochastic geometry captures the effects of key system parameters such as vehicle density, RSU coverage, and modulation schemes. It can be used to adapt the forwarding probabilities of relay vehicles with the network conditions. The numerical and simulation results demonstrate the accuracy of the analysis and the effectiveness of V2V relay scheme.

As an enabling technology for the fifth-generation (5G) wireless networks, device-to-device (D2D) communications can provide many promising applications such as message dissemination and content distribution. In this paper, we study an important problem for D2D-assisted content distribution, which allocates the message requests to be served by the cache devices via D2D multicast. Aiming to minimize the total transmission cost or maximize the gain in cost saving for the base station (BS), this message allocation problem can be formulated from different perspectives, as a weighted set cover problem (WSCP), a hypergraph matching problem, or a multiple-choice knapsack problem (MCKP). Here, we evaluate three approaches for the formulated problems, including a greedy algorithm, a heuristic algorithm based on Lagrangian relaxation, and a fully polynomial-time approximation scheme (FPTAS), respectively. Simulations are conducted to compare the performance in the static and dynamic scenarios in terms of total cost, unit cost, D2D offload ratio, and service latency. The results show that the MCKP based approach outperforms the other two because the approximation guarantee of the FPTAS results in solutions closest to the optimum.

With the evolution of wireless networks and pervasive mobile devices, device-to-device (D2D) communications have been envisioned as an effective means for data dissemination, e.g., in disaster alerts and event notifications. As mobile devices are battery-powered, it is essential to save power when scheduling D2D links for data dissemination. Also, users are generally more willing to forward data to others with social connections. In this paper, we take into account two important aspects, i.e., D2D users' social incentive constraint and power budget constraint, to enable more practical data dissemination. It is found that it is very difficult to obtain an optimal solution that minimizes the total power consumption while satisfying such constraints. Therefore, we propose a coalitional graph game based approach, which iteratively derives a transmission graph to reach every interested user. Simulations are conducted to compare the proposed approach with the optimal solution and two other reference schemes. The simulation results demonstrate the high performance of our approach in various scenarios with different network scales and social connections.

Device-to-device (D2D) communications are not only featured by high spectral and energy efficiency, but also offer appealing benefits for applications such as content distribution, traffic offloading and coverage expansion. In this paper, we consider a content distribution scenario where a base station (BS) can divert message requests to some source devices to be fulfilled via D2D communications and thereby save the resource cost. To maximize the BS's gain in cost saving, we need to properly assign a broadcast message for each source and decide the payment to incentivize participation. The message selection is an NP-hard problem, while the payment determination is also nontrivial. The payment should be sufficient to compensate for a source device's resource cost and meanwhile, incentivize the source to truthfully declare its private cost. Modeling the problem as a reverse auction, we develop a randomized mechanism which is truthful in expectation, individually rational, and subject to a polynomial computation time. Also, it maintains an approximation guarantee with respect to the fractional optimal solution. The numerical results show the performance of the randomized mechanism in the D2D content distribution scenario.

With the high penetration rate of smart mobile devices, it is appealing to exploit device-to-device (D2D) communications for data dissemination, e.g., in disaster alerts and event notifications. The popularity of social networks also offers good opportunities to improve the efficiency of data dissemination. Though there have been some existing works on data dissemination with D2D and mobile social networks, many focus on mitigating the D2D co-channel interference to achieve high resource utilization. As mobile devices are power-limited, it is important to consider the energy efficiency and finishing time in data dissemination. In this paper, we aim at developing an effective solution for D2D data dissemination to balance between total energy consumption and transmission completion time. In particular, we propose novel algorithms for seed selection and transmission scheduling with a single seed or multiple seeds. Simulation results demonstrate that our solution outperforms two reference schemes in total energy consumption while achieving a good balance for transmission completion time.

Device-to-device (D2D) communications provide a promising paradigm for data dissemination with low resource cost and high energy efficiency. In this paper, we propose a three-phase approach for D2D data dissemination, which exploits social-awareness and addresses users' incentive constraints via moneyless mechanisms. The proposed approach includes one phase of seed selection and two subsequent phases of data forwarding. First, we build a social-physical graph and partition it into communities based on edge-betweenness, and then select one seed for each community according to vertex-closeness. In the subsequent two data forwarding phases, we propose new mechanisms for message selection and cooperation pairing which take into account both altruistic and selfish behaviors of users. The theoretical analysis proves truthfulness of the message selection mechanism. Extensive simulation results further demonstrate the effectiveness of the three-phase approach.

Built-in content caching in mobile core networks can help improve quality of service, reduce operation expenses, simplify inter-network cooperation, and thus is a promising approach for more efficient networking architectures. In addition to the complexity of content placement as revealed in the literature, routing video requests remains a challenging issue. Two problems must be addressed: (i) how to distribute video requests among multiple internal servers (i.e., server selection); and (ii) how to route so-generated video flows (i.e., flow routing). In this work, we jointly formulate these two problems with two traffic-engineering objectives considered, namely, minimizing maximum link utilization and minimizing total link cost. We develop fast algorithms to solve the problems with provable approximation guarantees. We then propose a hop-by-hop routing protocol, which implements the optimization solutions by generating a set of flow-splitting and routing decisions for each router/caching node. Simulation results show that our algorithms significantly outperform existing routing schemes under various system settings, reducing up to 68 percent of maximum link utilization and more than 50 percent of link cost, and supporting over 60 percent more of traffic load.

Mobile cloud computing offers an appealing paradigm to relieve the pressure of soaring data demands and augment energy efficiency for future green networks. Cloudlets can provide available resources to nearby mobile devices with lower access overhead and energy consumption. To stimulate service provisioning by cloudlets and improve resource utilization, a feasible and efficient incentive mechanism is required to charge mobile users and reward cloudlets. Although auction has been considered as a promising form for incentive, it is challenging to design an auction mechanism that holds certain desirable properties for the cloudlet scenario. Truthfulness and system efficiency are two crucial properties in addition to computational efficiency, individual rationality and budget balance. In this paper, we first propose a feasible and truthful incentive mechanism (TIM), to coordinate the resource auction between mobile devices as service users (buyers) and cloudlets as service providers (sellers). Further, TIM is extended to a more efficient design of auction (EDA). TIM guarantees strong truthfulness for both buyers and sellers, while EDA achieves a fairly high system efficiency but only satisfies strong truthfulness for sellers. We also show the difficulties for the buyers to manipulate the resource auction in EDA and the high expected utility with truthful bidding.

As a promising way to increase network capacity and reduce expenses, radio access network (RAN) sharing among mobile (virtual) network operators, has attracted extensive recent attention from both industry and academia. Meanwhile, mobile systems are undergoing fast evolution to virtualized infrastructure so as to tackle the ever-growing mobile traffic and the unremitting demand for high data rates. However, existing RAN sharing models intend to expose resource details, e.g., infrastructure and spectrum, to participating network operators of the RAN for resource-sharing purposes, which violates the principles of network abstraction and makes network management even more complicated. This paper presents AppRAN, an application-oriented framework for RAN sharing in mobile networks, which decouples network operators from radio resource by providing application-level services with Quality of Service (QoS) guarantee. AppRAN defines a serial of abstract applications with distinct QoS requirements and periodically computes application-level resource allocation for each radio element at a central controller w.r.t. traffic demands and average channel condition. The radio elements are allowed to independently determine flow-level resource allocation within each application afterwards. We formulate the application-level resource allocation as an optimization problem and develop a fast algorithm to solve it with a provably approximate guarantee. The efficacy of AppRAN is validated through theoretical analysis and computer simulations. We show that AppRAN is in line with the design of software-defined RAN.

Software Defined Networking (SDN) is an emerging networking paradigm which intends to merge networks into the age of the cloud, providing fine-grained control, simplified configurations, unprecedented flexibility and seamless scalability. However, due to the large set of unresolved challenges as well as the deployment cost, network evolution to fully SDN systems will take a long time. In fact, SDN elements are incrementally deployed in enterprise networks, producing a transitional network form of hybrid SDN (H-SDN). An H-SDN system consists of traditional networking elements and SDN elements, accommodating both conventional traffic and SDN traffic. In this paper, we investigate traffic engineering (TE) in H-SDN, where the SDN controller strategically routes SDN traffic so as to optimize the TE performance over all network links shared with uncontrollable conventional traffic. Two hybrid modes are studied: (1) the barrier mode, where the two forms of traffic are routed in separated capacity spaces; and (2) the hybrid mode, where each link can be fully occupied by either form of traffic. We propose fast algorithms for the TE problems in both scenarios with provable approximation guarantees. Theoretical analysis and computer simulations validate the efficacy of our algorithms.

Mobile networks are undergoing fast evolution from the fourth-generation (4G)/Long Term Evolution (LTE) to the fifth generation (5G) so as to keep pace with the ever-increasing data traffic, mainly fueled by large-object delivery, such as video streams. To cope with the traffic growth, next evolution will integrate functionalities of content distribution networks (CDNs) in various manners, from in-network caching and mobile CDNs to virtualized source-service points in a software-defined mobile core. In accordance with these developments, we consider the emerging request routing problem of joint source redirection and flow routing in mobile networks with built-in content sources. We develop a fast request routing protocol, which intelligently distributes traffic demands among sourcing nodes and strategically routes flows through intermediate nodes. Theoretical analysis and computer simulations show that our protocol achieves (1 +ω)-optimal of traffic engineering for any ω > 0. Advanced features of source virtualization and data aggregation are also supported in the protocol.

In this paper, we analyze a cooperative medium access scheme in a wireless relaying network using Bayesian games, where the participating nodes are peers subject to the half-duplex constraint and they choose to cooperate or not cooperate based on its expected utility. We first set up a one-stage game and derive the ex-post utility. A two-stage game with incomplete information is further formulated to incorporate an incentive mechanism, which charges the cooperation requester and rewards the helper via adapting their channel access probabilities. We prove that the not-cooperating strategy can always achieve a Nash equilibrium (NE) in one-stage and two-stage games as long as the access cost is constrained. More importantly, we derive the sufficient conditions so that cooperating is an NE strategy and supports higher utility than the not-cooperating strategy. Numerical results are presented to validate our analysis and demonstrate that optimal tuning factors can be determined to ensure NE and maximize system utility.

One of the most challenging issues in cooperative wireless networks at the medium access control (MAC) layer is to select a good helper (relay node) to start cooperation. The helpers' information is often stored in a CoopTable and updated via overhearing of the helpers' traffic. However, when the helpers are transmitting data infrequently or having random mobility, the cooperation may suffer from the out-of-date information in the CoopTable, since the source cannot gather enough timely information to make a good cooperation decision. In this paper, we propose to use link availability prediction to address such out-of-date information problem at the MAC layer. Making use of the possibly out-of-date information, our proposed solution does not introduce any additional signalling overhead but enables the source node to estimate the probability that the helper may appear in each cooperation zone. As such, the source node is able to make an intelligent decision even with the out-of-date information and benefit from a successful cooperation. The simulation results well demonstrate the source throughput improvement even when the helpers are less active in transmission and experiencing random walk mobility.

We study how to achieve optimal network capacity in the most energy-efficient manner over a general large-scale wireless network, say, a multi-hop multi-radio multi-channel (MR-MC) network. We develop a multi-objective optimization framework for computing the resource allocation that leads to optimal network capacity with minimal energy consumption. Our framework is based on a linear programming multi-commodity flow (MCF) formulation augmented with scheduling constraints over multi-dimensional conflict graph (MDCG). The optimization problem however involves finding all independent sets (ISs), which is NP-hard in general. Novel delayed column generation (DCG) based algorithms are developed to effectively solve the optimization problem. The DCG-based algorithms have significant advantages of low computation overhead and achieving high energy efficiency, compared to the common heuristic algorithm that randomly searches a large number of ISs to use. Extensive numerical results demonstrate the energy efficiency improvement by the proposed energy-efficient optimization techniques, over a wide range of networking scenarios.

Many simulation tools (e.g., NS-2) can examine the quality of service (QoS) of networks but cannot demonstrate the visual and auditory effects of wireless transmission on multimedia quality. This paper presents a wireless multimedia simulator (WMS), which uses a compact graphical user interface to present the real-time packet delay with the playback of streaming media over a wireless channel based on classic radio channel models and IEEE 802.11 medium access control. By using captured packets and reproduced traces, WMS can demonstrate the visual and auditory effects of fading errors, packet delay and loss. Leveraging the real-time playback function, WMS enables quality of experience (QoE) evaluation of multimedia transmission in a controlled wireless environment. We carried out QoE evaluation with 30 participants for 104 test cases comprising 2 videos and 2 audio clips produced by WMS. The valuable test results enable us to quantify the relationship of QoE in terms of mean opinion score (MOS) with network traffic load and QoS metrics such as bit error probability (BEP). We find the subjective QoE is sensitive to media content although consistent with objective QoS metrics. Statistical difference-of-means tests show the video with slower motion and fewer colours is likely to offer better delay tolerance, and audio is less sensitive to bit errors while video is more resistant to network congestion.

Drive-thru Internet is a promising technique that exploits the inter-connected roadside access points (APs) to offer Internet access. However, there will be very high costs to deploy and maintain a large number of APs to provide ubiquitous coverage. There are many studies on vehicle-to-vehicle (V2V) relay to further improve the performance of the drive-thru Internet system. In this paper, we investigate an opportunistic V2V relay protocol with adaptive modulation and propose an analytical approach to evaluate the access performance in terms of average packet delay and overall success probability. Based on the analysis, the modulation modes can be adapted to guarantee a required transmission success probability while minimizing the average packet delay. The numerical and simulation results demonstrate the accuracy of the analysis and the effectiveness of the proposed V2V relay protocol with adaptive modulation.

Multipath transport control protocol (MPTCP) standardised by Internet Engineering Task Force offers a promising solution to support simultaneous delivery of transport control protocol packets over multiple interfaces of multi‐radio mobile devices. Even when there is no multiple access coverage, it is still possible to enable multipath transmission by involving mobile devices in vicinity as cooperative relays. However, the available bandwidth provided by relays can be highly varying because of wireless channel fading or dynamic local traffic load. Although MPTCP can pool the available bandwidths of multiple paths, it is challenging to address the dynamics of the cooperative wireless network and ensure a stable performance. In this study, the authors extend MPTCP for the long‐term evolution network with cooperative relays by an integrated solution in three aspects, namely, subset ‘sum‐based relay selection’, ‘adaptive congestion control’ and ‘differentiated packet forwarding’. Extensive simulations in NS‐3 show that the proposed modules can achieve a stable aggregate throughput by engaging a small number of relays and significantly improve the goodput in various scenarios.

The session initiation protocol (SIP) is widely used for controlling multimedia communication sessions over the Internet Protocol (IP). Effectively detecting a flooding attack to the SIP proxy server is critical to ensure robust multimedia communications over the Internet. The existing flooding detection schemes are inefficient in detecting low-rate flooding from dynamic background traffic, or may even totally fail when flooding is launched in a multi-attribute manner by simultaneously manipulating different types of SIP messages. In this paper, we develop an online detection scheme for SIP flooding attacks, by integrating a novel three-dimensional sketch design with the Hellinger distance (HD) detection technique. In our sketch design, each SIP attribute is associated with a two-dimensional sketch hash table, which summarizes the incoming SIP messages into a probability distribution over the sketch table. The evolution of the probability distribution can then be monitored through HD analysis for flooding attack detection. Our three-dimensional design offers the benefit of high detection accuracy even for low-rate flooding, robust performance under multi-attribute flooding, and the capability of selectively discarding the offending SIP messages to prevent the attacks from bringing damages to the network. Furthermore, we design a scheme to control the distribution of the normal traffic over the sketch. Such a design ensures our detection scheme's effectiveness even under the severe distributed denial of service (DDoS) scenario, where attackers can flood over all the sketch table entries. In this paper, we not only theoretically analyze the performance of the proposed detection techniques, but also resort to extensive computer simulations to thoroughly examine the performance.

Multipath Transport Control Protocol (MPTCP) has been standardized by Internet Engineering Task Force (IETF) to support simultaneous delivery of transport control protocol (TCP) packets over multiple interfaces of multi-radio mobile devices. Although MPTCP provides an efficient solution to aggregate the available bandwidth of multiple paths, the goodput of MPTCP is usually far lower than the aggregate throughput due to out-of-order received packets. One key reason for the out-of-order issue is the large variation of end-to-end delay for multiple paths over wireless channels. In this paper, we propose a congestion window adaption algorithm for the MPTCP source (referred to as CWA-MPTCP), which dynamically adjusts the congestion window for each TCP subflow so as to mitigate the variation of end-to-end path delay. We consider typical multipath transmission scenarios over wireless links, as well as a cooperative multi-hop wireless network with multiple relays. For wired paths with stable end-to-end delay, we further develop a proactive scheduling algorithm to determine the packet sending sequence to each path. This algorithm effectively reduces the out-of-order packets by predicting the receiving sequence. Experiments are conducted to evaluate the goodput performance of the two enhancements to MPTCP. Significant performance gain is achieved in terms of goodput, while the receive buffer requirement is minimized.

Packet scheduler at the medium access control (MAC) layer is essential to improve radio resource utilization in the Long Term Evolution (LTE) network. The MAC scheduler allocates resource blocks to user terminals (UEs) according to the priority metric, which varies in different scheduling algorithms. Although there have been many studies on the performance of LTE schedulers at the MAC layer, it is interesting to evaluate the impact of different LTE MAC schedulers on the transport layer, particularly on the transmission control protocol (TCP). In this study, we implement three mainstream LTE MAC schedulers in Network Simulator-3 (NS-3), namely, maximum throughput (MT), blind equal throughput (BET) and proportional fair (PF). Extensive simulations are conducted to examine the different TCP throughput achieved with the frequency domain version and the time domain version of these schedulers in a vehicular environment. The performance difference is attributed to important factors such as the resource allocation granularity, channel-awareness in scheduling, and the number of UEs.

Cooperation among wireless nodes at the medium access control (MAC) layer has attracted a lot of research attention in recent years. Most of existing cooperative MAC protocols focus on the scenarios with static helpers (relay nodes). However, when the helpers are moving around, the source node may choose a leaving helper with out-of-date information, which could cause performance deterioration. Hence, an optimal helper should not only support a high transmission rate but also have a low mobility. It can be a challenging problem to distinguish such an optimal helper when there are moving helpers of various mobility. In this paper, we extend the cooperative MAC protocol in [1] by means of perceptron training, referred to as PTCoopMAC. Making use of the handshaking messages in the original CoopMAC protocol, PTCoopMAC collects history data on the signal strength of overheard packets. Then, PTCoopMAC applies the perceptron training technique to obtain a weight vector to examine the stability of the helpers. Extending the CoopTable, PTCoopMAC selects the optimal helper depending on the achievable data rate as well as the prediction on whether a helper is reliable. The simulations results well demonstrate the throughput improvement of PTCoopMAC and its robustness to high mobility of helper nodes.

In the past decade, there has been ever‐increasing research attention to user cooperation in the wireless communication networks. The unique challenges of wireless networks such as channel fading and variation can be addressed well by taking advantage of relaying among cooperating mobile terminals. There are many studies on cooperative communications at the physical layer to exploit spatial diversity for improving channel capacity. In recent years, user cooperation from the perspective of the medium access control (MAC) layer becomes a promising new research area. In this study, the authors present a comprehensive survey on the mainstream cooperative MAC protocols in the literature. Focusing on the contention‐based solutions, the authors classify the well‐known proposals according to how they address two fundamental questions for user cooperation, that is, when to cooperation and whom to cooperate with. In addition to analysing the essential features of classic cooperative MAC protocols, the authors also discuss the major research challenges and project future research directions for MAC‐layer cooperation.

A mobile hotspot in a vehicular environment consists of a group of end users that move as a whole in a public transit. This work analyzes the performance of video service in mobile hotspots based on heterogeneous wireless technologies. We jointly consider the contention-based random access of the WLAN for the link layer, adaptive modulation and coding of the WWAN link at the physical layer, and batch packet arrivals of video traffic at the application layer. Taking into account the highly varying WWAN link due to vehicle mobility and multipath fading, we analyze the delay and loss performance of video service at the packet level. The numerical examples demonstrate the effects of link bandwidth, vehicle mobility, and channel fading.

Pooling mobile nodes in vicinity as a collaborative community offers an opportunity for multi-homed mobile nodes to enable multipath transmission even when there is no multiple access coverage. However, packet collisions exist within the collaborative community when a contention-based channel access such as IEEE 802.11 is applied. As a result, the overall multipath performance may degrade if a regular multipath transmission protocol is used in a collaborative community consisting of multiple relays. In this paper, we extend the multipath transport control protocol (MPTCP) at the receiver side for a collaborative community. Our proposed extensions, referred to as Co-MPTCP, take advantage of fast ACKs and receive buffer sharing at relays. A collaborative community consisting of a root node and multiple relays presents as a virtual multi-homed receiver to the sender. Extensive simulations are conducted to demonstrate the effectiveness of our extensions in terms of throughput, goodput, and receive buffer relief.

Popular smart wireless devices become equipped with multiple radio interfaces. Multihoming support can be enabled to allow for multiple simultaneous associations with heterogeneous networks. In this study, we focus on video streaming traffic and propose analytical approaches to evaluate the packet-level and call-level performance of a multipath transmission scheme, which sends video traffic bursts over multiple available channels in a probabilistic manner. A probability generation function (PGF) and z-transform method is applied to derive the PGF of packet delay and any arbitrary moment in general. Particularly, we can obtain the average delay, delay jitter, and delay outage probability. The essential characteristics of video traffic are taken into account, such as deterministic burst intervals, highly dynamic burst length, and batch arrivals of transmission packets. The video substream traffic resulting from the probabilistic flow splitting is characterized by means of zero-inflated models. Further, the call-level performance, in terms of flow blocking probability and system throughput, is evaluated with a three-dimensional Markov process and compared with that of an always-best access selection. The numerical and simulations results demonstrate the effectiveness of our analysis framework and the performance gain of multipath transmission.

Nowadays, the pervasive wireless networks enable ubiquitous high-rate wireless access from everywhere. In addition to extensive interworking studies in an indoor environment, there are ever-increasing demands for systematic deployment of moving networks in a vehicular environment such as public transits or transitory personal area networks which are irregularly powered on. In this paper, the delay performance is investigated for video traffic over two-hop wireless moving networks. A Markov-modulated process is used to characterize the variation and auto-correlation of video frame size rather than the traffic rate. In view of the burst arrival process of video traffic, a discrete-time batch arrival queueing process is employed to analyze the statistics of packet transfer delay. On the other hand, since traffic flows from a moving network can be multiplexed at a local gateway, a fractional Brownian motion (FBM) process is considered to model the self-similar aggregate traffic. Based on the evaluation of overall packet transfer delay for two-hop moving networks, we can estimate bandwidth requirements for a backhaul connection and local links with individual video flows within the moving network. Numerical and simulation study are conducted to validate the accuracy of the analysis.

With the ever-growing demands of wireless communications, traffic load sharing is essentially important to provide consistent high quality of service (QoS). It is known that the QoS of mobile users is affected by the limited bandwidth in both wireless link and wired backhaul link beyond a base station (BS). Femtocell is a promising technology to bear part of the traffic load from the BS. Nonetheless, due to the small coverage and possible closed service policy, the number of users within a femtocell is so restricted that only a small part of the traffic load can be transferred via the femtocell. Hence, it has little help to relieve the regular BS traffic load. In this paper, we propose a femtocell relay method to increase the number of users using femtocell and further reduce the traffic load in macrocell. Specifically, femtocell users provide relay service for nearby users connected to the macrocell BS and get reward bonus through balancing the traffic load. The simulation results have shown that our method can effectively reduce the traffic load in the macrocell and improve the delivery performance for regular network traffic.

Nowadays, the healthcare market keeps growing with an increasing aging population. As a promising technology, a body area network (BAN) consisting of biomedical sensors across a human body can gather vital life signals such as electrocardiogram (ECG), pulse, and blood pressure to facilitate effective diagnosis. The BAN can be further integrated with existing wireless infrastructure to offer mHealth services. In this paper, we analyze the transmission performance of compressed ECG data over a heterogeneous multi-hop wireless channel. The ECG data from a BAN are compressed and sent through a Bluetooth-enabled ECG monitor to a smart phone and thereafter to a cellular base station. Due to potentially life-threatening situations, timely delivery of ECG data is an essential requirement. Exploiting the inherent heartbeat pattern in ECG traffic, we introduce a context-aware packetization for ECG transmission. Further, a non-preemptive priority rule is applied to mitigate the impact of background traffic and prioritize the transmission of critical ECG data. Then, we analytically evaluate the overall transmission delay of ECG packets.

Nowadays, the two most popular wireless networks, the third generation (3G) cellular networks and wireless local area networks (WLANs) are being deployed widely. Network selection becomes an essential issue when network operators integrate multiple access networks to enhance service provisioning. There have been many researches on various network selection schemes. In this paper, we propose a novel modelling approach based on Petri net to effectively analyze the performance of a randomized network selection scheme, which is actually a generalized extension of simple network selection algorithms such as the WLAN-first scheme. The Petri net-based modelling approach can characterize the complex user mobility and network selection procedure in a straightforward manner. To further address a large state space, we also develop a decomposition approach for the Petri net model to efficiently evaluate the system performance. Numerical results are provided to demonstrate the effectiveness and low complexity of our proposed approaches.

In this paper, we propose a distributed key management framework based on group signature to provision privacy in vehicular ad hoc networks (VANETs). Distributed key management is expected to facilitate the revocation of malicious vehicles, maintenance of the system, and heterogeneous security policies, compared with the centralized key management assumed by the existing group signature schemes. In our framework, each road side unit (RSU) acts as the key distributor for the group, where a new issue incurred is that the semi-trust RSUs may be compromised. Thus, we develop security protocols for the scheme which are able to detect compromised RSUs and their colluding malicious vehicles. Moreover, we address the issue of large computation overhead due to the group signature implementation. A practical cooperative message authentication protocol is thus proposed to alleviate the verification burden, where each vehicle just needs to verify a small amount of messages. Details of possible attacks and the corresponding solutions are discussed. We further develop a medium access control (MAC) layer analytical model and carry out NS2 simulations to examine the key distribution delay and missed detection ratio of malicious messages, with the proposed key management framework being implemented over 802.11 based VANETs.

As two most popular wireless networks, the third generation (3G) cellular networks and wireless local area networks (WLANs) can be integrated to enhance service provisioning. However, most of the previous studies on cellular/WLAN interworking focus on WLANs in a static indoor environment such as offices, hotels, and cafes. Actually, the two heterogeneous technologies can be integrated to support mobile hotspots, which are usually in and around a moving vehicle, such as a bus, a railway train, and even a flight cabin. Different from traditional single-hop wireless networks, the mobile hotspot can adopt a two-hop relay for wireless access. In this paper, we analyze the delay performance for such a cellular/WLAN integrated mobile hotspot. In particular, we take into account the heavy-tailedness of data file size and self-similarity of aggregate traffic. Based on the analysis, we can determine the cellular channel bandwidth to be reserved for aggregate handoff traffic of the mobile hotspot.

As a very promising technique, multi-hop relay has been considered in many wireless networks. It can take advantage of the inherent broadcasting nature of wireless transmission and facilitate cooperative communications. In this paper, we develop an effective analytical framework to study the delay performance of cooperative retransmission strategies. All neighbor nodes overhearing the in-progress transmission cooperate in a distributed manner and contribute to retransmissions. In particular, we focus on the application of cooperative retransmission for delay-sensitive real-time services. Based on the proposed analytical framework, the cumulative distribution function of packet transfer delay can be numerically evaluated. Accordingly, we investigate the delay outage probability (i.e., the probability of violating the maximum delay bound), which is an essential statistical quality-of-service (QoS) metric for real-time services. Further, an enhancement approach is proposed to reduce unnecessary power consumption on retransmissions. It dynamically adapts the transmission probabilities of all participating nodes, depending on current retransmission count. As shown in the numerical results, the adaptive cooperative strategy can achieve a better trade-off between satisfying delay constraint and minimizing total power consumption.

As specified by the third-generation (3G) wireless networks such as the universal mobile telecommunication system (UMTS), interactive data services, such as Web browsing, voice messaging, and file transfer, represent a major service class in operation nowadays. In this paper, we develop an analytical approach to evaluate the performance of interactive data services under sharing and preemptive scheduling. Specifically, we take into account user interactions in data sessions and the heavy-tailed data file size. Both the mean and the standard deviation of data transfer delay are investigated for the two representative scheduling disciplines. Numerical results are given to show the validity of the evaluation approach and the impact of the on-off user behavior under the scheduling disciplines.

With the interworking between a cellular network and wireless local area networks (WLANs), an essential aspect of resource management is taking advantage of the overlay network structure to efficiently share the multi-service traffic load between the interworked systems. In this study, we propose a new load sharing scheme for voice and elastic data services in a cellular/WLAN integrated network. Admission control and dynamic vertical handoff are applied to pool the free bandwidths of the two systems to effectively serve elastic data traffic and improve the multiplexing gain. To further combat the cell bandwidth limitation, data calls in the cell are served under an efficient service discipline, referred to as shortest remaining processing time (SRPT). The SRPT can well exploit the heavy-tailedness of data call size to improve the resource utilization. An accurate analytical model is developed to determine an appropriate size threshold so that data calls are properly distributed to the integrated cell and WLAN, taking into account the load conditions and traffic characteristics. It is observed from extensive simulation and numerical analysis that the new scheme significantly improves the overall system performance.

Multi-service support is an important motivation for interworking between the cellular network and wireless local area networks (WLANs). The complementary strengths of the two networks can be effectively combined to enhance service provisioning. In this paper, we investigate how to properly allocate the overall resources in the integrated network to conversational, streaming, and interactive services. The proposed strategy exploits the essential traffic characteristics such as bursty video streams and elastic data traffic. The system performance is significantly improved over the WLAN-first scheme by applying admission control with service-differentiated session assignment and session migration via vertical handoff.

Resource management is an important aspect for the integrated system of cellular networks and wireless local area networks (WLANs). This study proposed a resource management scheme for the cellular/WLAN integrated network to support multiple service classes, namely, the conversational, streaming, and interactive classes. Resource sharing based on virtual partitioning is employed in the cellular network to overcome the limitation of contention-based WLANs in multi-class support. Further, a dynamic load balancing policy is proposed to distribute the traffic load to the integrated cell and WLAN based on factors such as service class and system utilization. The dedicated resource allocation of the cellular network is exploited to avoid the large overhead paid for WLANs to support realtime conversational and streaming classes. The elastic interactive traffic is manipulated flexibly to minimize the bursty overflow traffic and balance the utilization of both systems. The overall performance of the integrated network is significantly improved by the proposed resource management scheme.

This paper presents an analytical approach to determining the maximum number of on/off voice flows that can be supported over a wireless local area network (WLAN), under a quality of service (QoS) constraint the authors consider multiclass distributed coordination function (DCF) based medium access control (MAC) that can provision service differentiation via contention window (CW) differentiation. Each on/off voice flow specifies a stochastic delay bound at the network layer as the QoS requirement. The downlink voice flows are multiplexed at the access point (AP) to alleviate the MAC congestion, where the AP is assigned a smaller CW compared to that of the mobile nodes to guarantee the aggregate downlink throughput. There are six-fold contributions in this paper: 1) a nonsaturated multiclass DCF model is developed; 2) a cross-layer framework is proposed, which integrates the network-layer queueing analysis with the multiclass DCF MAC modeling; 3) the channel busyness ratio control is included in the framework to guarantee the analysis accuracy; 4) the framework is exploited for statistical multiplexing gain analysis, network capacity planning, contention window optimization, and voice traffic rate design; 5) a head-of-line outage dropping (HOD) scheme is integrated with the AP traffic multiplexing to further improve the MAC channel utilization; 6) performance of the proposed cross-layer analysis and the associated applications are validated by extensive computer simulations.

In this paper, we study voice and data service provisioning in an integrated system of cellular and wireless local area networks (WLANs). With the ubiquitous coverage of the cellular network and the disjoint deployment of WLANs in hot-spot areas, the integrated system has a two-tier overlaying structure. As an essential resource allocation aspect, admission control can be used to properly admit voice and data calls to the overlaying cells and WLANs. A simple admission scheme is proposed in this study to analyze the dependence of resource utilization and the impact of user mobility and traffic characteristics on admission parameters. Both admission control and rate control are considered to limit the input traffic to the WLAN, so that the WLAN operates in its most efficient states and effectively complements the cellular network. The call blocking/dropping probabilities and data call throughput are evaluated for effective and accurate derivation of the admission parameters. It is observed that the utilization varies with the configuration of admission parameters, which properly distributes the voice and data traffic load to the cells and WLANs. Mobility and traffic variability have a significant impact on the selection of the admission parameters.

In the interworking between a cellular network and wireless local area networks (WLANs), a two-tier overlaying structure exists in the WLAN-covered areas. Due to the heterogeneous underlying quality-of-service (QoS) support, the admission of traffic in these areas has a significant impact on QoS satisfaction and overall resource utilization, especially when multiple services are considered. In this paper, we analyze the performance of a simple admission strategy, referred to as WLAN-first scheme, in which incoming voice and data service requests always first try to get admission to the WLAN whenever it is available. It is observed that the overall resource utilization can be maximized when the admission regions for voice and data services in a cell and a WLAN are properly configured

This paper presents an analytical model for evaluating the capability of wireless LANs (WLANs) to provision quantitative quality of service (QoS) guarantees. We consider a distributed medium access control (MAC) with class differentiation, where mobile nodes belonging to different classes may have heterogeneous traffic arrival processes or different contention windows. With on/off inputs, our analysis shows that the WLAN admission region under the QoS constraint can be significantly improved, when the statistical multiplexing effect is taken into account. Moreover, the statistical multiplexing gain can be further improved by aggregating the downlink flows at the access point (AP). We also demonstrate that the proper selection of contention windows plays an important role in improving the WLAN QoS capability, while the optimal contention window for each class and the maximum admission region can be jointly solved in our analytical model.

In this paper, we study the voice and data service provisioning in an integrated system of cellular and wireless local area networks (WLANs). To maximize the overall resource utilization of the integrated system, complementary quality of service (QoS) support capabilities of the two networks are exploited to serve voice and data traffic. As an essential resource allocation aspect, admission control can be used to properly admit voice and data calls to the overlaying cellular cells and WLANs. In this study, a generalized admission scheme is analyzed to investigate the dependence of resource utilization on admission parameters, which vary with user mobility and traffic variability. By applying an effective QoS evaluation approach, the admission parameters can be determined using a search algorithm.

Call admission control plays an important role in quality of service (QoS) provisioning in the interworking between the cellular network and wireless local area network (WLAN). Within the WLAN coverage, a service request can be admitted into the cellular network or the WLAN. Due to the heterogeneous underlying QoS support of the cellular network and WLANs, the admission of traffic in the WLAN coverage has a significant impact on QoS satisfaction and overall resource utilization, especially when multiple services are considered. A popular admission strategy (referred to as WLAN-first scheme) is to admit the incoming service requests into the WLAN whenever it is available so as to take advantage of the low cost and large bandwidth of the WLAN. In this paper, we investigate the performance of the WLAN-first scheme. It is observed that the overall resource utilization can be maximized when the admission regions for voice and data services in a cell and a WLAN are properly configured.

This paper presents an analytical model for evaluating the statistical multiplexing effect, admission region, and contention window design in multiclass wireless LANs (WLANs). We consider a distributed medium access control (MAC) which provisions service differentiation via contention window differentiation, where mobile nodes belonging to different service classes have different quality of service (QoS) requirements. With bursty input traffic, we show that the WLAN admission region under the QoS constraint can be significantly improved by exploiting the statistical multiplexing gain. Moreover, the statistical multiplexing gain can be further improved by aggregating the downlink flows at the access point (AP). We also demonstrate that the selection of contention windows plays an important role in improving the WLAN's QoS capability, while the optimal contention window for each class and the maximum admission region can be jointly solved from our analytical model. The analysis accuracy and the resource utilization improvement are demonstrated by extensive numerical results.

It is widely recognized that the next-generation wireless networks will integrate heterogeneous wireless technologies. The complementary characteristics of cellular networks and wireless local area networks (WLANs) make the interworking a promising trend. Admission control is one of the essential mechanisms to enhance QoS provisioning in a cellular/WLAN integrated network and utilize the overall resources efficiently. In this paper, we propose an effective admission control scheme for both voice and data services. The admission region is derived, taking into account the two-tier overlay structure, non-uniform traffic distribution and user mobility in the integrated network. The impact of traffic and mobility parameters on the admission scheme is evaluated, which provides some insights for further extension.