Abstract
Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of sensors in networks increases, low energy requirements and changing network connections complicate event detection and measurement. We present a data fusion scheme for use in mobile wireless sensor networks with high energy efficiency and low network delays, that still produces reliable results. In the first phase, we used a network simulation where mobile agents dynamically select the next hop migration node based on the stability parameter of the link, and perform the data fusion at the migration node. Agents use the fusion results to decide if it should return the fusion results to the processing center or continue to collect more data. In the second phase. The feasibility of data fusion at the node level is confirmed by an experimental design where fused data from color sensors show near-identical results to actual physical temperatures. These results are potentially important for new large-scale sensor network applications.
Highlights
In the past, sensors were manually installed in fixed positions
Based on these and other similarities, we developed an equivalent model for mobile wireless sensor networks
This is the first use of the Agent Based Network Model [14], coming from the
Summary
Sensors were manually installed in fixed positions. Over the years, the use of sensors has expanded with the use of mobile sensors with wireless connections in Wireless Sensor Networks (WSNs). A Mobile Agent is a computer program with autonomous mobility function [1], which can gather data while moving in Wireless Sensor Networks [2,3,4,5,6,7,8] This special software saves its own state, transfers its state to a new host, and resumes its task at the new host. Sending data by mobile agents in fused format produces reliable data while lowering energy consumption and network delays, especially in larger networks. These results are potentially important in emerging large-scale mobile wireless networks. This is a network simulation comparing energy requirements and network delays between fused and unfused data, and with our improved routing decision making.
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