  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.

Distributed, Parallel, and Cluster Computing;Systems and Control;Systems and Control