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Abstract
Wireless sensor nodes are heavily resource-constrained due to their edge form factor, which has motivated increasing battery life through low-power techniques. This paper proposes a power management method that leads to less energy consumption in an idle state than conventional power management systems used in wireless sensor nodes. We analyze and benchmark the power consumption between Sleep, Idle, and Run modes. To reduce sensor node power consumption, we develop fine-grained power modes (FGPM) with five states which modulate energy consumption according to the sensor node’s communication status. We evaluate the proposed method on a test bench Mica2. As a result, the power consumed is 74.2% lower than that of conventional approaches. The proposed method targets the reduction of power consumption in IoT sensor modules with long sleep mode or short packet data in which most networks operate.
Highlights
In the Internet of Things (IoT), a wireless sensor network node (WSN) is a system that recognizes physical changes or signals targeting various users or environments
Handling huge datasets using multiple sensory modalities is within the domain of machine learning, and interpreting the information from numerous signals is becoming increasingly important in an IoT-driven world
We show the advantages of the proposed fine-gained partitioned power mode that are analyzed using energy consumption benchmarks in conventional communication
Summary
In the Internet of Things (IoT), a wireless sensor network node (WSN) is a system that recognizes physical changes or signals targeting various users or environments. Introduced an extension of the TSCH (Time Slotted Channel Hopping) protocol to low data rate applications using the sub-GHz frequency bands operating on TI’s System-on-Chip They employed a special schedule for the network’s root nodes and their direct neighbors, as well as the option to have multiple root nodes in a single network. This paper is structured as follows: Section 2 will present the communication protocol in WSNs, Section 3 will propose the fine-grained power state approach, Section 4 will present simulation results and energy consumption data of our approach, with a comparison against comparable methods, before concluding the work
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