A slicing-based collaborative task offloading framework for space-air-ground integrated vehicular networks is proposed in this study, which can provide differentiated quality-of-service (QoS) guarantees for task offloading for high-speed vehicles while maximizing the number of completed tasks. A service-oriented radio access network (RAN) slicing framework is presented that supports slicing window adaptation, spectrum and computing resource orchestration, and collaboration among heterogeneous base stations. Based on the queuing model, the collaborative decision-making of RAN slicing and task offloading is modeled as a problem of maximizing the number of long-term task completions, which consists of three subproblems-slicing window division, resource slicing, and task scheduling-which are solved by a multi-access edge computing (MEC)-enabled controller, forming a closed loop with the slicing window as the period. When a new slicing window arrives, the controller determines its duration according to task traffic fluctuations and allocates resources to RAN slices through an optimization method. A double deep Q-learning network (DDQN)-based algorithm is developed for scheduling workflow on small time scales within a slicing window. Simulation results demonstrate that the proposed scheme performs better than existing approaches in terms of adaptability, task completion rate, and control overhead.

Barrier coverage is attractive for many practical applications of directional sensor networks. Power conservation is one of the important issues in directional sensor networks. In this article, we address energy-efficient barrier coverage for directional sensor networks with mobile sensors. First, we derive the critical condition for mobile deployment. We assume that a number of stationary directional sensors are placed independently and randomly following a Poisson point process in a two-dimensional rectangular area. Our analysis shows that the critical condition only depends on the deployment density ($\lambda$ ) and the sensing radius (r). When the initial deployment satisfies $\lambda <8\ln 2/r^2$ , barrier gaps may exist, so we need to redeploy mobile sensors to improve the barrier coverage. Then, we propose an energy-efficient barrier repair algorithm to construct an energy-efficient barrier to detect intruders moving along restricted crossing paths in the target area. Through extensive simulations, the results show that the energy-efficient barrier repair algorithm improves the barrier coverage and prolongs the network lifetime by minimizing the maximum sensor moving distance. And in comparison with the energy-efficient barrier coverage algorithm (previous works), the energy-efficient barrier repair algorithm increases by 18% of network lifetime on average.

In this paper, a live layered video multicast framework based on unmanned aerial vehicle (UAV) relays is presented for on-orthogonal multiple access (NOMA) networks, aiming to maximize the aggregated video reception quality for cell-edge users. A visualizable graph model is constructed to characterize the coupling of the deployment of UAVs and their association with multicast groups with interference cancellation. Based on this graph model, the problem of maximizing multicast group video reception quality is modeled as a clique-based nonlinear integer programming and decoupled into UAV placement and UAV-group association subproblem and subchannel allocation subproblem. For mathematical traceability, the former is transformed into a unique maximum weight clique problem under the available UAV number constraint, solved by an improved branch-and-bound algorithm. For the latter, a heuristic resource-matching strategy is designed to obtain a near-optimal subchannel allocation with low computational complexity. Simulation results demonstrate that the proposed scheme outperforms mainstream benchmarks in terms of aggregate peak signal-to-noise ratio (PSNR), spectrum utilization, network coverage, and adaptability.

Along with the rapid proliferation of mobile applications, Mobile Edge Computing (MEC) attracts growing interests in recent years. The number of service handovers due to users' movements between different MEC regions impacts heavily on QoE and service operation cost. In this poster, we propose a randomized algorithm to minimize the number of possible handovers between different MEC regions by carefully dividing a metropolitan area into disjoint clusters. Preliminary experiments exhibit that the proposed algorithm could find sub-optimal partitions with reduced total number of handovers.

Network coding is critical to wireless broadcast for real-time applications. Most of the existing approaches make strong assumptions either on application requirements (e.g., single constraint type or the same constraint level) or on data transmission (e.g., ideal channel, fixed transmission rate, or packet length) in order to facilitate formalization and solution. Those assumptions limit the applicability of previous approaches. This work studies quality-of-service-guaranteed broadcast scheduling over wireless networks with network coding and rate selection capabilities, focusing on reducing broadcast completion delay while maximizing the number of packet receptions that satisfy heterogeneous deadline and reliability requirements. To begin with, a multirate graph model is constructed to formulate the optimal broadcast scheduling problem, which is proved to be NP-hard. Then, an adaptive graph compression policy is proposed to reduce the computational burden significantly without sacrificing performance. Furthermore, an approximation framework is presented for each propagation. In the framework, the coding strategy and rate selection can be formulated as a complexity-adjustable clique problem. Finally, a progressive clique search algorithm is designed to make decision on each broadcast. Simulation results demonstrate that compared with typical heuristic algorithms, the proposed algorithm achieves significant performance improvement with lower complexity.