Jurnal Kejuruteraan 31(1) 2019: 77-83 https://doi.org/10.17576/jkukm-2019-31(1)-09 Energy Models of Zigbee-Based Wireless Sensor Networks for Smart-Farm (Model Tenaga untuk Rangkaian Tanpa Wayar Berasaskan Zigbee di Ladang Pintar) Hilal Bello Said a,* , Rosdiadee Nordin b & Nor Fadzilah Abdullah b a Department of Information and Communication Technology, Faculty of Engineering and Environmental Design, Usmanu Danfodiyo University Sokoto, Nigeria. b Centre of Advanced Electronic & Communication Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Malaysia. *Corresponding author: hilalbello@yahoo.com Received 20 July 2018, Received in revised form 2 November 2018 Accepted 8 February 2019, Available online 30 April 2019 ABSTRACT In this paper, we evaluated several network routing energy models for smart farm application with consideration of several factors, such as mobility, traffc 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 specifcation 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 traffc 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 traffc. 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. Keywords: Energy models; Smart farm; Internet of Things; WSN; ZigBee; Evaluation INTRODUCTION The world’s population is expected to reach between 8.3 and 10.9 billion by 2050. The United Nations Food and Agricultural Organizations (FAO) estimate that 70% more food will need to be produced to feed the additional 2.3 billion people by 2050 (Alexandratos et al. 2012). This develops the increased need for the agricultural sector to devise smarter and more effcient ways of farming, as farmers seek to minimize costs and maximize yields. Smart farming deploys seamless monitoring and controlling system. Achieving high frequency and density monitoring depends on wireless sensor nodes. The smart farm concept is depicted in Figure 1. A smart farm environmental monitoring system based on ZigBee is seen as one of the most practical solutions to these various problems due to its reduced complication and lower cost (Watthanawisuth et al. 2009). Our major contributions on the subject of smart farming are: 1. Developing a WSN with focus on energy consumption based on three energy models (Generic, MICA and MICAz) for potential smart farm application using wireless ZigBee mesh topology. The energy consumption evaluation of the models is presented. 2. Investigating the effect of source traffc, nodes mobility and nodes size/density in terms of throughput, packets delivery ratio (PDR), average end-to-end delay, average jitter and energy consumption in the potential smart farm scenarios. The rest of the paper is structured as follows: Section 2 consists of the related works on smart farm networks evaluation. Section 3 describes the modeling and simulation process of the smart farm environment. In Section 4, experimental results and analysis are presented. Section 5 presents conclusions and suggests possible future works from the paper. JK 31(1) Bab 9 .indd 77 4/12/2019 10:45:26 AM