We investigate the problem of co-designing computation and communication in a multi-agent system (e.g. a sensor network or a multi-robot team). We consider the realistic setting where each agent acquires sensor data and is capable of local processing before sending updates to a base station, which is in charge of making decisions or monitoring phenomena of interest in real time. Longer processing at an agent leads to more informative updates but also larger delays, giving rise to a delay-accuracy-tradeoff in choosing the right amount of local processing at each agent. We assume that the available communication resources are limited due to interference, bandwidth, and power constraints. Thus, a scheduling policy needs to be designed to suitably share the communication channel among the agents. To that end, we develop a general formulation to jointly optimize the local processing at the agents and the scheduling of transmissions. Our novel formulation leverages the notion of Age of Information to quantify the freshness of data and capture the delays caused by computation and communication. We develop efficient resource allocation algorithms using the Whittle index approach and demonstrate our proposed algorithms in two practical applications: multi-agent occupancy grid mapping in time-varying environments, and ride sharing in autonomous vehicle networks. Our experiments show that the proposed co-design approach leads to a substantial performance improvement (18-82% in our tests).
翻译:我们研究多试剂系统(例如传感器网络或多机器人团队)共同设计计算和通信的问题。我们考虑现实的环境,即每个代理商获得传感器数据,能够在向基地站发送更新之前进行本地处理,该基地站负责实时决策或监测感兴趣的现象。一个代理商的更长时间处理导致信息更新,但也有更大的延误,导致在选择每个代理商正确的本地处理量方面出现延迟和不准确的权衡。我们假定,现有的通信资源因干扰、带宽和电力限制而受到限制。因此,需要设计一个时间安排政策,以便在各代理商之间适当共享通信渠道。为此,我们制定一个总体的提法,共同优化代理商的地方处理和传输时间安排。我们的新提法利用信息时代的概念来量化数据的新颖性,并捕捉计算和通信造成的延误。我们利用Whittle指数方法开发高效的资源分配算法,并在两个实用应用中展示我们拟议的算法:多代理人占用率式网络共享通信渠道。为此,我们设计了一个自动定位系统测试188的运行环境。