Intermittent computing systems undergo frequent power failure, hindering necessary data sample capture or timely on-device computation. These missing samples and deadlines limit the potential usage of intermittent computing systems in many time-sensitive and fault-tolerant applications. However, a group/swarm of intermittent nodes may amalgamate to sense and process all the samples by taking turns in waking up and extending their collective on-time. However, coordinating a swarm of intermittent computing nodes requires frequent and power-hungry communication, often infeasible with limited energy. Though previous works have shown promises when all intermittent nodes have access to the same amount of energy to harvest, work has yet to be looked into scenarios when the available energy distribution is different for each node. The proposed AICS framework provides an amalgamated intermittent computing system where each node schedules its wake-up schedules based on the duty cycle without communication overhead. We propose one offline tailored duty cycle selection method (Prime-Co-Prime), which schedules wake-up and sleep cycles for each node based on the measured energy to harvest for each node and the prior knowledge or estimation regarding the relative energy distribution. However, when the energy is variable, the problem is formulated as a Decentralized-Partially Observable Markov Decision Process (Dec-POMDP). Each node uses a group of heuristics to solve the Dec-POMDP and schedule its wake-up cycle.

翻译：间歇计算系统经常遭遇电源故障，阻碍了必要数据样本捕获或设备上的及时计算。这些缺失的样本和截止时间限制了间歇计算系统在许多时间敏感和容错应用中的潜在用途。然而，一组/一群间歇节点可以通过轮流唤醒并延长它们的集体在线时间，合并起来感知和处理所有样本。但是，协调一群间歇计算节点需要频繁且高功耗的通信，这在能量受限的情况下通常不可行。尽管先前的研究在当所有间歇节点能够获取相同能量进行采集时展示出前景，但尚未有研究探讨每个节点可用能量分布不同时的场景。所提出的AICS框架提供了一种可合并的间歇计算系统，其中每个节点基于占空比自主调度其唤醒计划，无需通信开销。我们提出了一种离线定制占空比选择方法（Prime-Co-Prime），该方法根据每个节点测量到的可采集能量，以及关于相对能量分布的先前知识或估计，为每个节点调度唤醒和休眠周期。然而，当能量可变时，该问题被建模为分散式部分可观测马尔可夫决策过程（Dec-POMDP）。每个节点使用一组启发式算法来求解Dec-POMDP并调度其唤醒周期。