[论文解读] Finite Time Exact Quantized Average Consensus with Limited Resources and Transmission Stopping for Energy-Aware Networks

Apostolos I. Rikos, Christoforos N. Hadjicostis+1 more|arXiv (Cornell University)|Oct 1, 2021

Energy Efficient Wireless Sensor Networks被引用 4

一句话总结

该论文提出了一种新颖的事件触发分布式算法，用于在节能型无线网络中实现有限时间内的精确量化平均一致性。该算法使节点能够在有限时间内计算出初始量化值的精确平均值，在达成一致后立即停止传输，从而通过在任意强连通有向图上进行高效且自适应的更新，显著降低能量和通信开销。

ABSTRACT

Composed of spatially distributed sensors and actuators that communicate through wireless networks, networked control systems are emerging as a fundamental infrastructure technology in 5G and IoT technologies, including diverse applications, such as autonomous vehicles, UAVs, and various sensing devices. In order to increase flexibility and reduce deployment and maintenance costs, many such applications consider battery-powered or energy-harvesting networks, which bring additional limitations on the energy consumption of the wireless network. Specifically, the operation of battery-powered or energy-harvesting wireless communication networks needs to guarantee (i) efficient communication between nodes and (ii) preservation of available energy. Motivated by these novel requirements, in this paper, we present and analyze a novel distributed average consensus algorithm, which (i) operates exclusively on quantized values (in order to guarantee efficient communication and data storage), and (ii) relies on event-driven updates (in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We characterize the properties of the proposed algorithm and show that its execution, on any time-invariant and strongly connected digraph, will allow all nodes to reach, in finite time, a common consensus value that is equal to the exact average (represented as the ratio of two quantized values). Furthermore, we show that our algorithm allows each node to cease transmissions once the exact average of the initial quantized values has been reached (in order to preserve its battery energy). Then, we present upper bounds on (i) the number of transmissions and computations each node has to perform during the execution of the algorithm, and (ii) the memory and energy requirements of each node in order for the algorithm to be executed.

研究动机与目标

• 解决在物联网和5G应用中用于电池供电或能量采集的无线网络中的能量约束问题。
• 仅使用量化值实现精确平均一致性，避免量化引起的误差。
• 通过在达成一致后允许节点停止传输，降低能量消耗。
• 为实际部署提供每节点传输次数、计算量、内存和能量的上界。
• 设计一种确定性算法，适用于任意时不变、强连通有向图，且无需拓扑限制。

提出的方法

• 该算法仅在量化值上运行，通过两个整数的比值表示平均值，以确保精确性。
• 基于三个条件触发事件驱动更新，以确定节点何时应发送信息。
• 每个节点维护本地状态变量，并使用分布式规则更新这些变量，确保收敛到精确平均值。
• 当所有节点均满足事件触发条件并达到相同值时，强制执行传输停止。
• 该算法在任意时不变、强连通有向图上进行分析，证明了其在有限时间内收敛。
• 基于图的大小和初始值范围，推导出传输次数、时间步数、内存和能量的上界。

实验结果

研究问题

• RQ1 能否设计一种分布式算法，在有限时间内仅使用量化值实现精确平均一致性？
• RQ2 如何通过在达成一致后允许停止传输，最小化无线网络中的能量消耗？
• RQ3 此类算法每节点所需的传输次数、计算量、内存和能量的上界是什么？
• RQ4 该算法是否无需依赖特定网络拓扑（如无向图或平衡图）即可运行？
• RQ5 该算法在资源有限的随机有向图上实际表现如何？

主要发现

• 该算法在任意时不变、强连通有向图上实现了有限时间内的精确量化平均一致性，收敛保证在有界步数内完成。
• 在1,000个随机20个节点的有向图上的仿真中，收敛的平均时间步数为103.875，最大值为209，最小值为5。
• 所有节点的总传输次数平均为240.547，最小值为103，最大值为368。
• 在约210个时间步后，活跃发送节点的平均数量降至接近1，到第210步时所有传输均停止，表明已达成一致。
• 一致值精确等于初始量化值的平均值（例如示例中的10.7），证实了其精确性。
• 理论推导出的传输次数和时间步数的上界远高于实际观测值，表明该算法具有极强的实际效率。