The Internet of Vehicles (IoV) can offer safe and comfortable driving experience, by the enhanced advantages of space–air–ground-integrated networks (SAGINs), i.e., global seamless access, wide-area coverage, and flexible traffic scheduling. However, due to the huge popular traffic volume, limited cache/power resources, and the heterogeneous network infrastructures, the burden of backhaul link will be seriously enlarged, degrading the energy efficiency of IoV in SAGIN. In this article, to implement the popular content severing multiple vehicle users (VUs), we consider a cache-enabled satellite-UAV-vehicle-integrated network (CSUVIN), where the geosynchronous Earth orbit (GEO) satellite is regard as a cloud server, and unmanned aerial vehicles are deployed as edge caching servers. Then, we propose an energy-aware coded caching strategy employed in our system model to provide more multicast opportunities, and to reduce the backhaul transmission volume, considering the effects of file popularity, cache size, request frequency, and mobility in different road sections (RSs). Furthermore, we derive the closed-form expressions of total energy consumption both in single-RS and multi-RSs scenarios with asynchronous and synchronous services schemes, respectively. An optimization problem is formulated to minimize the total energy consumption, and the optimal content placement matrix, power allocation vector, and coverage deployment vector are obtained by well-designed algorithms. We finally show, numerically, our coded caching strategy can greatly improve energy efficient performance in CSUVINs, compared with other benchmarked caching schemes under the heterogeneous network conditions.

In this paper, we consider the problem of routing in DTN-based Earth-observing satellite networks, which are characterized by a frequently changing topology and potentially sparse and intermittent connectivity. To handle the routing challenges posed by these properties, we propose a joined spacetemporal routing algorithmic framework for those networks, where the time-varying topology is modeled as a space-time graph leveraging the predictability of satellites' relative motions. Based on this graph model, we devise a multipath routing algorithm (MCMP) to find a feasible set of available routing paths, through which a certain amount of mission data can be transferred back to ground stations within a tolerable delay with a minimum cost. To comparatively evaluate the performance of MCMP, moreover, we design an EAMP algorithm based on the typical contact graph routing algorithm. The performance comparisons among MCMP, EAMP and direct transfer strategy are simulated and analyzed.

In these years, Unmanned Aerial Vehicle (UAV) is widely employed as a flying server into the Mobile Edge Computing (MEC) network to carry stochastic computation task offloaded from mobile users. However, currently, much efforts are made on the individual computation capability of each UAV during the offloading task, while neglecting the coordination advantage of multiple UAVs in a fleeted way, leading to low efficiency on computing massive bursty data. In this work, therefore, we propose a twofold computation offloading mechanism in a collaborative way for a multi-UAV assisted MEC network using the Connected Dominating Set (CDS). In particular, we solve a system energy efficiency-maximizing optimization problem by using a well-tailored Alternating Direction Method of Multipliers (ADMM) algorithm and Lyapunov optimization. In the proposed mechanism, we develop a two-stage computation task partitioning strategy and a user scheduling scheme with optimized UAV trajectory via designing a CDS virtual backbone network. The numerical results indicate that the superiority of our proposed mechanism compared with the benchmark algorithm.

In a flying ad hoc network (FANET), reliable maintenance of topology confronts a huge challenge due to the highly time-varying connectivity between flying nodes. Aiming at mitigating this issue, this work proposes a connected dominating set (CDS) based topology control mechanism iteratively for the FANETs by solving a jointed optimization problem on adjusting the node’s transmission power and position during the construction of CDS. In particular, a well-tailored group of the topology constructing and maintaining algorithms are developed with a time-discretized approach. Simulation results show that the proposed mechanism has a better performance with respects to overheads and network stability than a general particle swarm optimization (PSO) algorithm, especially in a typical swarm pattern for unmanned aerial vehicle (UAV) team.

Integrated Satellite-Terrestrial (IST) networks consisting of low earth orbit (LEO) satellite constellation and terrestrial users are widely developed for potentially attractive requirements of content distribution, benefiting from low deployment cost and broader coverage capability. However, orbital paradigm of LEO constellation, as well as an accordingly time-changing topology, makes a huge challenge for distributing objective file due to heavy lack of end-to-end paths. To address this issue, in this paper, a Time-evolving Covering Set instructed in-network Caching (TCSC) mechanism is proposed to achieve file distribution in the IST networks with high efficiency. In the proposed mechanism, a novel Event Updating Graph (EUG) is put forward for capturing fine-grained topological information in time domain. With it, a Minimum Time-evolving Covering Set (MTCS) is constructed for designing the caching-in-network mechanism with appropriate intermediate nodes. Furthermore, a Query-based Minimum Delay Distribution Path (QMDP) algorithm is proposed for rapidly fetching objective file from cache nodes in MTCS by fully exploiting EUG-based topological information. The simulation results verified the proposed method can obviously degrade the overall overheads and access delay of all users, compared respectively with Network Central Location (NCL) metric, Random Cache metric and Maximum Node Degree Cache (MDC) Metric.