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Grouping of sensor nodes into clusters is the most popular approach to support scalability in Wireless Sensor Networks (WSN). The performance of WSNs can be improved suitably by selecting the qualified nodes to form a stable backbone structure with guaranteed network coverage. In this paper, we propose a centralized and weighted algorithm for dynamic sensor networks which require reliable data gathering. The solution is based on a (k, r) -Connected Dominating Set, which is suitable for cluster-based hierarchical routing. This improves reliability, provides variable degree of c1us terhead redundancy and reduces route searching space in WSN. To create a stable and efficient backbone structure, backbone sensor nodes are selected based on quality, which is a function of the residual battery power, node degree, transmission range and mobility of the sensor nodes. Performance of the algorithm is evaluated through simulation. It is observed that the proposed algorithm performs well in terms of number of elements in the backbone structure, load balancing and the number of re-affiliations A sensor network consists of hundreds or thousands of sensor nodes with capabilities of sensing, processing and com municating. They are characterized by the dense deployment of energy-constrained nodes in uncontrolled hostile environ ment and for unattended operations. Failures are inevitable in WSNs, the battery-drained nodes create holes in the network topology, causing connectivity and information loss. There fore, use of energy-aware algorithms with failure detection and maintenance mechanism becomes an important factor in prolonging the lifetime of sensor nodes.Some of the most important characteristics of a WSN include dense deployment of sensor nodes, data-centric nature of the network, physical resource constraints, environment driven nature and correlated data problem (data collected by the nearby sensor nodes could be similar). A WSN is a distributed system of sensor nodes that collects data about the environment and sends the data to one or more coordinating centers called Sink. In most of the sensor applications, direct transfer of collected data to the sink is not viable in terms of energy consumption and bandwidth utilization. The data aggregation algorithms can effectively run at the sensor nodes to combine the data and send it to the Sink. In cluster-based approach, a clusterhead can perform this job more effectively.Sensor networks are used in many academic, industrial (au tomating manufacturing, monitoring product quality and manNational Institute of Technology, Cali cut, Kerala, India. email :anithavs@nitc.ac.in; Information Technology and System Group, Indian Institute of Management Kozhikode, Cali cut Kerala, India. email : sebasmp@iimk.ac.in 978-1-4244-5849-3/10/$26.00 ©20 1 0 IEEE aging inventory), military (air traffic control, traffic surveil lance, reconnaissance and targeting systems), security (intru sion detection and criminal hunting), environmental (early fire detection in forests, detection and monitoring...

Clustering is a fundamental mechanism used in the design of Wireless Sensor Network (WSN) protocols. The performance of WSNs can be improved by selecting the most suitable nodes to form a stable backbone structure with guaranteed network coverage. This paper proposes a base station-controlled centralized algorithm for static sensor networks and a distributed, weighted algorithm for dynamic sensor networks. The solutions are based on a (k, r)-Connected Dominating Set, which is suitable for cluster-based hierarchical routing. The clusterhead redundancy parameter k improves reliability, the multi-hop parameter r addresses the scalability issue and the combined weight metric improves the network lifespan and reduces the number of re-affiliations. To create a stable and efficient backbone structure, the backbone sensor nodes are selected based on quality, which is a function of the residual battery power, node degree, transmission range, and mobility of the sensor nodes. Simulation experiments are conducted to evaluate the performance of both the algorithms in terms of the number of elements in the backbone structure, re-affiliation frequency, load balancing, network lifespan, and the power dissipation. The results establish the potential of these algorithms for use in WSNs.