Adaptive neighbor discovery for mobile and low power wireless sensor networks
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
A fuzzy-based data replication system for QoS improvement in MANETs
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
Gossip-based density estimation in dynamic heterogeneous wireless sensor networks
International Journal of Autonomous and Adaptive Communications Systems
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It is generally foreseen that the number of wirelessly connected networking devices will increase in the next decades, leading to a rise in the number of applications involving large-scale networks. A major building block for enabling self-* system properties in ad-hoc scenarios is the run-time discovery of neighboring devices and somewhat equivalently, the estimation of the local node density. This problem has been studied extensively before, mainly in the context of fully-connected, synchronized networks. In this paper, we propose a novel adaptive and decentralized solution, the NetDetect algorithm, to the problem of discovering neighbors in a dynamic wireless network. The main difference with existing state of the art is that we target dynamic scenarios, i.e., multihop mesh networks involving mobile devices. The algorithm exploits the beaconing communication mechanism, dynamically adapting the beacon rate of the devices in the network based on local estimates of neighbor densities. We evaluate NetDetect on a variety of networks with increasing levels of dynamics: fully-connected networks, static and mobile multi-hop mesh networks. Results show that NetDetect performs well in all considered scenarios, maintaining a high rate of neighbor discoveries and good estimate of the neighborhood density even in very dynamic situations. More importantly, the proposed solution is adaptive, tracking changes in the local environment of the nodes without any additional algorithmic reconfiguration. Comparison with existing approaches shows that the proposed scheme is efficient from both convergence time and energy perspectives.