Spatio-Temporal Data Correlation with Adaptive Strategies in Wireless Sensor Networks

TL;DR

This paper introduces adaptive models for wireless sensor networks that optimize data accuracy and reduce communication overhead by leveraging spatio-temporal correlations without prior signal statistics, also enabling malicious node detection.

Contribution

It presents novel adaptive accuracy and prediction models (ADA and STDP) that improve data transmission efficiency and security in sensor networks without requiring prior data statistics.

Findings

ADA model effectively selects sensor nodes for accurate data transmission.

STDP model reduces communication overhead by exploiting data correlations.

Mechanism for malicious node detection under extreme environments.

Abstract

One of the major task of sensor nodes in wireless sensor networks is to transmit a subset of sensor readings to the sink node estimating a desired data accuracy. Therefore in this paper, we propose an accuracy model using Steepest Decent method called Adaptive Data Accuracy (ADA) model which doesn't require any a priori information of input signal statistics to select an optimal set of sensor nodes in the network. Moreover we develop another model using LMS filter called Spatio-Temporal Data Prediction (STDP) model which captures the spatial and temporal correlation of sensing data to reduce the communication overhead under data reduction strategies. Finally using STDP model, we illustrate a mechanism to trace the malicious nodes in the network under extreme physical environment. Computer simulations illustrate the performance of ADA and STDP models respectively.