Using Markov Chains for Time Series Analysis of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have enabled new and revolutionary technological packages by using imparting self-reliant sensing, control, and verbal exchange capabilities. Time collection analysis is an important approach used to become aware of significant styles and correlations within WSNs. especially, Markov chains were used to investigate time series responses of WSNs. A Markov chain is a stochastic version which captures deterministic and stochastic behavior by way of representing the possibility of a destiny kingdom primarily based upon the contemporary state. Inside the context of WSNs, a Markov chain is used to version the reputation of a sensor node given the statuses of its neighboring nodes. Via the usage of Markov chain analysis, important information may be determined which may be used to optimize the performance of a WSN, together with knowledge energy intake quotes and figuring out correlations among nodes. As a result, making use of Markov chains in WSNs can provide higher predictive and analytical capabilities for improving a WSN's operations.