Energy-Efficient Embedded IoT Framework for Scalable Sensor Network Monitoring Using Edge Computing

Abstract

Blistering growth of Internet of Things (IoT) sensor networks has presented important challenges of resource constraints, latency, and scale especially in highly constrained embedded systems. Traditional architectures based on clouds will experience a lot of communication overhead and have large response time hence they cannot be considered in real time monitoring applications. In summary of these shortcomings, this paper suggests an energy efficient embedded IoT platform, which uses edge computing to streamline the process of data processing and transmission. The suggested structure has reduced communication energy because it conducts the local data filtering, aggregation and decision making at the edge layer thus minimising the cloud infrastructure dependence. A more serious energy consumption model is created to study the cost of sensing, processing and transmission costs with particular emphasis on how cost reduction of transmission affects the efficiency of the entire system. The framework is offered based on the low-power embedded nodes combined with the edge gateways and the performance is measured in terms of the network scale variations. The experimental evidences prove that energy consumption and latency are significantly minimised in relation to the conventional solutions, whereas the scale and stability of the system are kept. The given system is also suitable to use in real-time applications like smart agriculture, environmental monitoring and industrial IoT systems where performance is important and fast reaction is vital.