Energy Models of Zigbee-Based Wireless Sensor Networks for Smart-Farm

Abstract

In this paper, we evaluated several network routing energy models for smart farm application with consideration of several factors, such as mobility, traffic size and node size using wireless ZigBee technology. The energy models considered are generic, MICA and Zigbee compliant MICAz models. Wireless sensor networks deployment under several scenarios are considered in this paper, taken into account commercial farm specification with varying complex network deployment circumstances to further understand the energy constraint and requirement of the smart farm application. Several performance indicators, such as packet delivery ratio, throughput, jitter and the energy consumption are evaluated and analysed. The simulation result shows that both throughput and packet delivery ratio increases as the nodes density is increased, indicating that, smart farm network with higher nodes density have a superior Quality of Service (QoS) than networks with sparsely deployed nodes. It is also revealed that traffic from the mobile nodes causes increase in the energy consumption, overall network throughput, average end-to-end delay and average jitter, compared to static nodes traffic. Based on the results obtained, the Generic radio energy models consumed the highest total energy, while MICAz energy consumption model offers the least consumption, having the lowest ‘Idle’ and ‘receive’ modes consumption. The MICAz model also has the lowest total consumed energy as compared with the other energy models, suggesting that it is the most suitable energy model that should be adopted for future smart farm deployment.

Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

“…Therefore, to address these challenges and optimize conflicting objectives, techniques such as meta-heuristic searching have been extensively used to solve optimization problem with a multiobjective nature [ 11 ]. This method starts with the initial setting of random solutions, and it determines the heuristic interactions between the solutions for reaching a set of nondominated solutions after a set of iterations [ 12 ]. Multiobjective optimization is evaluated from various perspectives, namely, the Pareto front, hypervolume, delta metric, and generation distance [ 13 ].…”