Open AccessEnergy optimization of sustainable Internet of Things (IoT) systems using an energy harvesting medium access protocol
Author(s) -
Shaik Shabana Anjum,
Rafidah Md Noor,
Ismail Ahmedy,
Mohammad Hossein Anisi
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/268/1/012094
Subject(s) - computer science , 6lowpan , osi model , wireless sensor network , computer network , energy consumption , ipv6 , routing protocol , energy harvesting , efficient energy use , distributed computing , routing (electronic design automation) , energy (signal processing) , the internet , interconnection , engineering , statistics , mathematics , world wide web , electrical engineering
The process of modelling the energy expenditure for IoT systems is distinct when compared to Wireless Sensor Networks (WSN), due to a number of factors and metrics. Few of such factors to mention are the IoT layers being different from the Open System Interconnection (OSI) with communication protocols like IPv6 Low power Wireless Personal Area Networks (6LoWPAN), Routing for Low Power and Lossy networks (RPL) and Constrained Application Protocol (CoAP). This leads to the demand for designing efficient Medium Access Control (MAC) protocols to serve the purpose of balance between the performance of the system and minimum energy consumption. The challenge of compatibility of MAC protocols for IoT deployment needs to be addressed. The proposed work is aimed at developing energy efficient framework for optimal balance between energy consumed by connected devices (sensor networks) in a complex and time-critical IoT system through performance monitoring of underlying communication technologies. It also focuses to address the trade-off between energy expenditure and performance of the network for the communicating nodes. An Energy Harvesting MAC protocol is designed and developed after modelling of the nodes using Reinforcement Learning (RL) for time critical IoT systems. The results have shown that the energy expenditure of the IoT devices is considerably minimized and the performance is increased by nearly 80% when compared to the state-of-art energy harvesting solutions for sustainable IoT systems. This research also plays a significant role in matching the energy predictions and the experimentations that validate the IoT systems in a real-world scenario.