Location Centric Energy Harvesting Aware Routing Protocol for IoT in Smart Cities

Abstract

The advent of battery technology revolutionized power storage for various purposes, including uninterrupted operations of IoT-based Wireless Sensor Networks (WSNs). However, limited energy sources pose a challenge for sensor nodes. To overcome this, researchers introduced Energy Harvesting (EH) in IoT-based WSNs, enabling them to overcome energy limitations and recharge IoT devices. The shift towards energy harvesting-aware routing is necessary to achieve a near-perpetual network environment. In light of the energy consumption of IoT-based WSN nodes, a simple energy-efficient routing technique is proposed to consume less energy. The protocol incorporates efficient link selection based on the closest angle to the destination node and is divided into two main parts: distributed neighbor discovery and routing processes. By incorporating EH techniques, the proposed model aims to improve network longevity. Experimental results demonstrate successful routing and promising outcomes in terms of energy and utilization techniques. INDEX TERMS Internet of things, smart cities, energy harvesting, routing protocol, energy efficiency I. INTRODUCTION

Energy efficient reliable data transmission for optimizing IoT data transmission in smart city

“…The simulation environment employed the Sensoria simulator [28] to simulate both existing and proposed methodologies. The energy-aware iotrouting (EAIR) [25] and location-centric energy-harvesting aware-routing (LCEHAR) [26] algorithms, pivotal to the experimental framework, were implemented in the C# programming language. The Visual Studio.…”

“…Moreover, these methods have been shown to successfully fulfill the predetermined reliability standards. Zeb et al [26] introduced a novel routing approach that aimed to optimize energy efficiency by minimizing energy use. The protocol has been designed to include a highly effective link selection mechanism that relies on determining the nearest position relative to the location nodes.…”

Opportunistic Throughput Optimization in Energy Harvesting Dynamic Spectrum Sharing Wireless Networks

“…The same as previous constraints, these figures prove that over the intervals mentioned as a convex set the inequalities Ψ 1 (η) < 0 and Ψ 2 (η) < 0, and their convexity are satisfied for different numbers of SARs. To sum up, figures 2-7 verify the accuracy of the analyses and convexity of the optimization problem (29).…”

“…In the energy-deficit mode, we study the behavior of the objective function and constraints of the optimization problem (29) with respect to the decision threshold (η) for different numbers of SARs. Figures 2 to 7 present the results.…”