@INPROCEEDINGS{Liri2409:Sensing,
AUTHOR="Elizabeth Liri and K. K. Ramakrishnan and Koushik Kar and Flavio Esposito",
TITLE="Sensing Together: Cooperative Task Adaptation and Scheduling for {IoT-Nets}
using Renewable Energy",
BOOKTITLE="2024 IEEE 21st International Conference on Mobile Ad Hoc and Smart Systems
(MASS) (IEEE MASS 2024)",
ADDRESS="Seoul, Korea (South)",
PAGES="8.93",
DAYS=22,
MONTH=sep,
YEAR=2024,
KEYWORDS="multi-sensor IoT; distributed scheduler; energy efficiency; task
adaptation; cooperative sensing",
ABSTRACT={IoT devices used in various applications, such as monitoring agricultural
soil moisture, or urban air quality as- sessment, are typically
battery-operated and energy-constrained. We develop a lightweight and
distributed cooperative sensing scheme that provides energy-efficient
sensing of an area by reducing spatio-temporal overlaps in the coverage
using a multi- sensor IoT network. Our {"}Sensing Together{"} solution
includes two algorithms: Distributed Task Adaptation (DTA) and Dis-
tributed Block Scheduler (DBS), which coordinate the sensing operations of
the IoT network through information shared using a distributed {"}token
passing{"} protocol. DTA adapts the sensing rates from their {"}raw{"}
values (optimized for each IoT device independently) to minimize spatial
redundancy in coverage, while ensuring that a desired coverage threshold is
met at all points in the covered area. DBS then schedules task execution
times across all IoT devices in a distributed manner to minimize temporal
overlap. On-device evaluation shows a small token size and execution times
of less than 0.6s on average while simulations show average energy savings
of 5\% per IoT device under various weather conditions. Moreover, when
devices had more significant coverage overlaps, energy savings exceeded
30\% thanks to cooperative sensing. In simulations of larger networks,
energy savings range on average between 3.34\% and 38.53\%, depending on
weather conditions. Our solutions consistently demonstrate near-optimal
performance under various scenarios, showcasing their capability to
efficiently reduce temporal overlap during sensing task scheduling.}
}