Abstract
The radio operation in wireless sensor networks (WSN) in Internet of Things (IoT) applications is the most common source for power consumption. Consequently, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. Among essential factors affecting radio operation, the time spent for checking the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in radio duty cycles and ContikiMAC. ContikiMAC is a low-power radio duty-cycle protocol in Contiki OS used in WakeUp mode, as a clear channel assessment (CCA) for checking radio status periodically. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Furthermore, we propose a lightweight CCA (LW-CCA) as an extension to ContikiMAC to reduce the Radio Duty-Cycles in false WakeUps and idle listening though using dynamic received signal strength indicator (RSSI) status check time. The simulation results in the Cooja simulator show that LW-CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR).
Highlights
The advancement of hardware systems for Internet of Things (IoT) is an essential research domain of large-scale wireless sensor networks (WSN)
We carefully investigate the time factors associated with a radio WakeUp on the ContikiMAC and to reduce the radio power consumption by dynamic radio check time in WakeUps on the Rx mode
We focus on Contiki available features such as platforms, network protocols, and radio duty cycles to provide an optimized way to reduce energy consumption in IoT nodes [30]
Summary
The advancement of hardware systems for IoT is an essential research domain of large-scale wireless sensor networks (WSN) In this realm, the development of low-power wireless communication is of utmost importance. The development of low-power wireless communication is of utmost importance Since these types of devices are usually powered by low capacity batteries to provide both sensing and actuation capabilities, managing power consumption is one of the major challenges in designing their hardware and software. The nodes are in hard-to-reach areas, so their power supply is usually battery or harvest energy from the environment In both models of power supply the management of power consumption in the nodes is mandatory, in the former case to avoid battery replacement and in latter case to reduce the cost of the energy harvesting system [1,2,3]
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