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In tactical environments, mobility has a significant impact on the performance such as handover delay. In this paper, we design and implement an integrated tactical mobility testbed that consists of (1) host identify protocol module for secure initial registration, (2) Proxy Mobile IPv6-based distributed mobility management module for infrastructure-based mobility cases, and (3) delay/disruption tolerant networking module for opportunistic mobility cases. The implementation and experimental results demonstrate that the integrated testbed can be used to consider diverse tactical mobility scenarios and to evaluate the handover performance.

Programmable data plane (PDP) is an emerging technology for programming packet processing tasks by means of a domain-specific high-level language (e.g., programming protocol-independent packet processor (P4)) and programmable switch chips. Recently, several PDP virtualization schemes have been introduced to enable more flexible and elastic network management. In this article, we first give an overview PDP and P4. After that, existing PDP virtualization schemes are classified into hypervisor- and compiler-based approaches and their pros and cons are analyzed in detail. Finally, open challenges for PDP virtualization are identified and future research directions are presented.

In low earth orbit (LEO) satellite-based applications (e.g., remote sensing and surveillance), it is important to efficiently transmit collected data to ground stations (GS). However, LEO satellites’ high mobility and resultant insufficient time for downloading make this challenging. In this paper, we propose a deep-reinforcement-learning (DRL)-based cooperative downloading scheme, which utilizes inter-satellite communication links (ISLs) to fully utilize satellites’ downloading capabilities. To this end, we formulate a Markov decision problem (MDP) with the objective to maximize the amount of downloaded data. To learn the optimal approach to the formulated problem, we adopt a soft-actor-critic (SAC)-based DRL algorithm in discretized action spaces. Moreover, we design a novel neural network consisting of a graph attention network (GAT) layer to extract latent features from the satellite network and parallel fully connected (FC) layers to control individual satellites of the network. Evaluation results demonstrate that the proposed DRL-based cooperative downloading scheme can enhance the average utilization of contact time by up to 17.8% compared with independent downloading and randomly offloading schemes.