Low-Latency Dissemination Scheduling Scheme for Collaborative Transmission Within Heterogeneous Networks

Abstract

Many reconnaissance missions require a group of mobile terminals (such as soldiers, mobile robots, and unmanned boats) to jointly operate within a region which is far away from the command centre. When a critical event occurs and is detected by a terminal, it is often required for the terminal to upload some critical data to the command centre (or via the satellite). As the bandwidth of the upload link is usually low due to the long distance, uploading the critical data often has long latency. To reduce the latency, a feasible way is to utilize the nearby terminals’ idle uplinks to help with the upload process, which requires the terminal’s data to be disseminated to other terminals as soon as possible. This is a new dissemination problem because the data being disseminated is also partially being uploaded, which seems as a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-leaking</i> dissemination problem. To solve it, we propose LHDS (Low-latency Heterogeneous Dissemination Scheduling) scheme by transforming the problem into two special sub-problems, i.e., constructing a special degree-decreasing tree with maximum multichild nodes, and designing a leaking-sustained dissemination schedule for each subtree. Extensive simulation experiments have been conducted on LHDS as well as two heuristic algorithms (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DBO</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S-GA</i>) designed for baselines. The results show that LHDS scheme significantly outperforms the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DBO</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S-GA</i> algorithms in terms of total collaborative data uploading latency, with saving 41% and 42% latency on average, respectively.