We consider networks of small, autonomous devices that communicate with each other wirelessly. Minimizing energy usage is an important consideration in designing algorithms for such networks, as battery life is a crucial and limited resource. Working in a model where both sending and listening for messages deplete energy, we consider the problem of finding a maximal matching of the nodes in a radio network of arbitrary and unknown topology. We present a distributed randomized algorithm that produces, with high probability, a maximal matching. The maximum energy cost per node is $O(\log^2 n)$, where $n$ is the size of the network. The total latency of our algorithm is $O(n \log n)$ time steps. We observe that there exist families of network topologies for which both of these bounds are simultaneously optimal up to polylog factors, so any significant improvement will require additional assumptions about the network topology. We also consider the related problem of assigning, for each node in the network, a neighbor to back up its data in case of node failure. Here, a key goal is to minimize the maximum load, defined as the number of nodes assigned to a single node. We present a decentralized low-energy algorithm that finds a neighbor assignment whose maximum load is at most a polylog($n$) factor bigger that the optimum.
翻译:我们考虑的是无线通信的小型自主装置网络。 最大限度地减少能源使用是设计此类网络算法的一个重要考虑因素, 因为电池寿命是一个关键和有限的资源。 在发送和收听信息耗竭能源的模型中, 我们考虑在任意和未知的地形学的无线电网络中找到最大匹配节点的问题。 我们提出了一个分布式随机算法, 产生高概率的最大匹配。 每个节点的最大能量成本是O( log2 n) $( $) 美元, 也就是网络的规模。 我们的算法总长度是 $( n) 美元( log n) 时间步骤。 我们观察到存在网络结构结构的组合, 这两条界限都与多元因素同时最佳匹配, 因此任何重大改进都需要对网络地形学的更多假设。 我们还考虑相关的问题, 每节点中每个节点都指定一个邻居在节点中备份数据, 以防失败。 这里的一个关键目标是将最大负载量最小的负载量降到最小, 也就是将最大负载量定为最低的 。