Applications of Prediction Approaches in Wireless Sensor Networks

*Felicia Engmann, Kofi Sarpong Adu-Manu, Jamal-Deen Abdulai and Ferdinand Apietu Katsriku*

#### **Abstract**

Wireless Sensor Networks (WSNs) collect data and continuously monitor ambient data such as temperature, humidity and light. The continuous data transmission of energy constrained sensor nodes is a challenge to the lifetime and performance of WSNs. The type of deployment environment is also and the network topology also contributes to the depletion of nodes which threatens the lifetime and the also the performance of the network. To overcome these challenges, a number of approaches have been proposed and implemented. Of these approaches are routing, clustering, prediction, and duty cycling. Prediction approaches may be used to schedule the sleep periods of nodes to improve the lifetime. The chapter discusses WSN deployment environment, energy conservation techniques, mobility in WSN, prediction approaches and their applications in scheduling the sleep/wake-up periods of sensor nodes.

**Keywords:** prediction models, wireless sensor networks, time series models

## **1. Introduction**

Wireless Sensor Networks (WSNs) is made up of sensor nodes that are capable of sensing environmental phenomena and cooperatively transferring the sensed data to a base station without the use of wires. The sensor nodes are spatially distributed in their deployable environment to observe some phenomena within their immediate neighborhood. They can be deployed in the tens, hundreds or thousands depending on the application requirements. These sensor nodes are smart devices and may monitor environments such as homes, inventory, transportation, traffic situation, health of humans, structural health, track animals, air quality, water quality, military, and may even serve as surveillance systems [1]. Over the years, WSNs is gradually becoming the technology of choice for industrial applications and research, for environmental monitoring (EM) applications considering the number of advantages that comes with its use [2, 3]. For example, a WSN is resilient (i.e., adaptive to node failures), scalable (i.e., easy to add nodes to the network), robust (i.e., can withstand harsh environmental conditions), flexible to setup and deploy, cheap, and the network requires no infrastructure [4]. Despite the large number of advantages, WSNs are challenged with a number of issues. These include but are not limited to communication, memory size, energy, processing capacity, and security [5].
