Paper
2 June 2005 Robustness of predictive sensor network routing in fading channels
Author Affiliations +
Abstract
Sensors have varied constraints, which make the network challenging for communicating with peers. In this paper, an extension, to the physical layer of the previous predictive sensor network model using the ant system is proposed. The tiny and low-cost sensor nodes are made of RF wireless links, where the states of the nodes vary with respect to time and environment. The ant system is a learning algorithm, that can be used to solve any NP hard communication problem and possesses characteristics such as robustness and versatility. The ant system possesses unique features that keep the network functional by detecting weak links and re-routing the agents. The swarm agents are distributed along the network, where the agent communicates with its neighbors (agents) by means of pheromone deposition and tabu list. The transition probability in the ant system includes an objective function, which is influenced by the poset weights. The poset weights on each of the orthogonal communication parameters greatly affects the decisions made by ant system. The agents carry updated information of its previous nodes, which helps in monitoring the strength of the communication links. Through simulation, comparison between DSSS-BPSK and Bluetooth-GFSK signals are shown. This paper demonstrates the robustness of the model under slow/fast fading, and energy loss at node during transmission. Implementation of this algorithm should be able to handle hostile environmental conditions and human tampering of data. The performance of the network is evaluated based on accuracy and response time of the agents within the network.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rajani Muraleedharan and Lisa Ann Osadciw "Robustness of predictive sensor network routing in fading channels", Proc. SPIE 5819, Digital Wireless Communications VII and Space Communication Technologies, (2 June 2005); https://doi.org/10.1117/12.604122
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CITATIONS
Cited by 9 scholarly publications and 1 patent.
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KEYWORDS
Sensor networks

Sensors

Evolutionary algorithms

Fusion energy

Failure analysis

Artificial intelligence

Telecommunications

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