Multi-sensor IoT devices can gather different types of data by executing different sensing activities or tasks. Therefore, IoT applications are also becoming more complex in order to process multiple data types and provide a targeted response
to the monitored phenomena. However, IoT devices which are usually resource-constrained still face energy challenges since using each of these sensors has an energy cost. Therefore, energy-efficient solutions are needed to extend
the device lifetime while balancing the sensing data requirements of the IoT application. Cooperative monitoring is one approach for managing energy and involves reducing the duplication of sensing tasks between neighboring IoT devices.
Setting up cooperative monitoring is a scheduling problem and is challenging in a distributed environment with resource-constrained IoT devices. In this work, we present our Distributed Token and Tier-based task Scheduler (DTTS) for
a multi-sensor IoT network. Our algorithm divides the monitoring period (5 min epochs) into a set of non-overlapping intervals called tiers and determines the start deadlines for the task at each IoT device. Then to minimize temporal sensing overlap, DTTS distributes task executions throughout the epoch and uses tokens to share minimal information between IoT devices. Tasks with earlier start deadlines are scheduled in earlier tiers while tasks with later start deadlines are scheduled in later tiers. Evaluating our algorithm against a simple round-robin scheduler shows that the DTTS
algorithm always schedules tasks before their start deadline expires.
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This content will become publicly available on September 23, 2025
Sensing Together: Cooperative Task Adaptation and Scheduling for IoT-Nets Using Renewable Energy
IoT devices used in various applications, such as
monitoring agricultural soil moisture, or urban air quality assessment, 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 Distributed 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.
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- Award ID(s):
- 1818971
- PAR ID:
- 10548468
- Publisher / Repository:
- IEEE
- Date Published:
- Subject(s) / Keyword(s):
- multi-sensor IoT distributed scheduler energy efficiency task adaptation cooperative sensing comb placement problem
- Format(s):
- Medium: X
- Location:
- 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS)
- Sponsoring Org:
- National Science Foundation
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