scholarly journals Development of a Wireless Sensor Network (WSN) Based Energy Efficient Cattle Monitoring System

2020 ◽  
pp. 1-9
Author(s):  
A. A. Ijah ◽  
O. W. Bolaji ◽  
O. O. Adedire ◽  
J. Z. Emmanuel ◽  
N. E. Onwuegbunam ◽  
...  
Keyword(s):  
Energy Source ◽  
Renewable Energy Source ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Average Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Consumption Pattern ◽  
Wireless Sensor Nodes ◽  
Cattle Rustling

This study shows how to monitor the movement of cattle using wireless sensor nodes powered by a renewable energy source capable of detecting location. Performance analysis was carried out on the energy consumption pattern of the nodes which indicated that throughout the monitoring period, the average energy consumed by the nodes was thus; master node 6450 joules, node one 1680 joules, node two 1656 joules, node three 1676 joules, node four 1656 joules. The rate of energy consumption was sustained by the renewable energy source. It was equally observed that energy consumption increased depending on how often query was sent and how often the conditions of monitoring was violated. This is to guarantee that information about cattle location gets to the base without delay due to battery failure which has been a major challenge faced with the current existing systems in tackling cattle rustling.

Short-Hops vs. Long-Hops

2014 ◽  
pp. 74-83
Author(s):  
Mekkaoui Kheireddine ◽  
Rahmoun Abdellatif
Keyword(s):  
Energy Source ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Large Area ◽  
Wireless Sensor Nodes ◽  
Energy Consuming

Sensor networks are composed of miniaturized wireless sensor nodes with limited capacity and energy source. Generally, these sensor networks are used, in many applications, to monitor inaccessible environments (battlefields, volcano monitoring, animal tracking…), hence the impossibility to replace or to recharge the batteries. As sensors may be deployed in a large area, radio transceivers are the most energy consuming of sensor nodes, which means that their usage needs to be very efficient in order to maximize node life, which leads us to maximize the network's life. In wireless sensor networks and in order to transmit its data, a node can route its messages towards destination, generally the base station, either by using small or large hops, so optimizing the hop length can extend significantly the lifetime of the network. This chapter provides a simple way to verify, which makes the energy consumption minimal by choosing proper hop length.

scholarly journals Calculating the Number of Cluster Heads Based on the Rate-Distortion Function in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Mingxin Yang ◽  
Jingsha He ◽  
Yuqiang Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Rate Distortion ◽  
Distortion Function ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Cluster Heads

Due to limited resources in wireless sensor nodes, energy efficiency is considered as one of the primary constraints in the design of the topology of wireless sensor networks (WSNs). Since data that are collected by wireless sensor nodes exhibit the characteristics of temporal association, data fusion has also become a very important means of reducing network traffic as well as eliminating data redundancy as far as data transmission is concerned. Another reason for data fusion is that, in many applications, only some of the data that are collected can meet the requirements of the sink node. In this paper, we propose a method to calculate the number of cluster heads or data aggregators during data fusion based on the rate-distortion function. In our discussion, we will first establish an energy consumption model and then describe a method for calculating the number of cluster heads from the point of view of reducing energy consumption. We will also show through theoretical analysis and experimentation that the network topology design based on the rate-distortion function is indeed more energy-efficient.

An Energy Aware Routing Based on Swarm Intelligence for Wireless Sensor Networks

2013 ◽  
Vol 706-708 ◽  
pp. 635-638
Author(s):  
Yong Lv
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Network Lifetime ◽  
Average Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Energy Efficient Routing

Wireless Sensor Networks consisting of nodes with limited power are deployed to collect and distribute useful information from the field to the other sensor nodes. Energy consumption is a key issue in the sensor’s communications since many use battery power, which is limited. In this paper, we describe a novel energy efficient routing approach which combines swarm intelligence, especially the ant colony based meta-heuristic, with a novel variation of reinforcement learning for sensor networks (ARNet). The main goal of our study was to maintain network lifetime at a maximum, while discovering the shortest paths from the source nodes to the sink node using an improved swarm intelligence. ARNet balances the energy consumption of nodes in the network and extends the network lifetime. Simulation results show that compared with the traditional EEABR algorithm can obviously improve adaptability and reduce the average energy consumption effectively.

scholarly journals Increasing network lifetime using data compression in wireless sensor networks with energy harvesting

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Chiwoo Cho ◽  
Kyung-Joon Park ◽  
Hyuk Lim
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Data Compression ◽  
Network Lifetime ◽  
Average Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Entire Network

In wireless sensor networks powered by battery-limited energy harvesting, sensor nodes that have relatively more energy can help other sensor nodes reduce their energy consumption by compressing the sensing data packets in order to consequently extend the network lifetime. In this article, we consider a data compression technique that can shorten the data packet itself to reduce the energies consumed for packet transmission and reception and to eventually increase the entire network lifetime. First, we present an energy consumption model, in which the energy consumption at each sensor node is derived. We then propose a data compression algorithm that determines the compression level at each sensor node to decrease the total energy consumption depending on the average energy level of neighboring sensor nodes while maximizing the lifetime of multihop wireless sensor networks with energy harvesting. Numerical simulations show that the proposed algorithm achieves a reduced average energy consumption while extending the entire network lifetime.

scholarly journals A Novel Approach to Reduce Energy Consumption By Clustering With Genetic Algorithm and Sleeping Time in The IoT

2021 ◽  
Author(s):  
Negin Babaei ◽  
Alireza Hedayati
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Average Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Novel Approach ◽  
Timing Mechanisms ◽  
Reduce Energy Consumption ◽  
Average Energy Consumption

Abstract Internet of things is one of the most important technologies in the last century which covers various domains such as wireless sensor networks. Wireless sensor networks consist of a large number of sensor nodes that are scattered in an environment and collect information from the surrounding environment and send it to a central station. One of the most important problems in these networks is saving energy consumption of nodes and consequently increasing lifetime of networks. Work has been done in various fields to achieve this goal, one of which is clustering and the use of sleep timing mechanisms in wireless sensor networks. Therefore, in this article, we have examined the existing protocols in this field, especially LEACH-based clustering protocols. The proposed method tries to optimize the energy consumption of nodes by using genetic-based clustering as well as a sleep scheduling mechanism based on the colonial competition algorithm. The results of this simulation show that our proposed method has improved network life (by 18%) and average energy consumption (by 11%) and reduced latency in these networks (by 17%).

Sign in / Sign up

Export Citation Format

Share Document