Distributed Spectrum Sensing for Cognitive Radio Sensor Networks using Diffusion Adaptation

Amirhosein Hajihoseini, Seyed Ali Ghorashi

Abstract:

Cognitive radio is a practical solution for spectrum scarcity. In cognitive networks, unlicensed (secondary) users should sense the spectrum before any usage, to make sure that the licensed (primary) users do not use the spectrum at that time. Due to the importance of spectrum sensing in cognitive networks, this should be fast and reliable, particularly in networks with communication link failure, which leads to network topology change. Decentralized decision making algorithms are known as a promising technique to provide reliability, scalability and adaptation, especially in sensor networks. In this paper, we propose a distributed diffusion based method in which, secondary users (sensors) cooperate to improve the performance of spectrum sensing. The proposed method provides a significant improvement in convergence rate and reliability. Simulation results indicate that the proposed algorithm shows an acceptable performance and converges twice faster than recently proposed consensus based spectrum sensing algorithms in the literature, and is almost insensitive to communication link failure.

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Decentralized Consensus Based Target Localization in Wireless Sensor Networks

Amirhosein Hajihoseini Gazestani, Reza Shahbazian, Seyed Ali Ghorashi

Abstract:

Target localization is an attractive subject for modern systems that utilize different types of distributed sensors for location based services such as navigation, public transport, retail services and so on. Target localization could be performed in both centralized and decentralized manner. Due to drawbacks of centralized systems such as security and reliability issues, decentralized systems are become more desirable. In this paper, we introduce a new decentralized and cooperative target localization algorithm for wireless sensor networks. In cooperative consensus based localization, each sensor knows its own location and estimates the targets position using the ranging techniques such as received signal strength. Then, all nodes cooperate with their neighbours and share their information to reach a consensus on targets location. In our proposed algorithm, we weight the received information of neighbour nodes according to their estimated distance toward the target node. Simulation results confirm that our proposed algorithm is faster, less sensitive to targets location and improves the localization accuracy by 85% in comparison with distributed Gauss–Newton algorithm.

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