3,288 research outputs found

    Adaptive decentralized re-clustering protocol for wireless sensor networks

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    AbstractWireless sensor networks are composed of a large number of sensor nodes with limited energy resources. One critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way since the energy is limited. The clustering algorithm is a technique used to reduce energy consumption. It can improve the scalability and lifetime of wireless sensor network. In this paper, we introduce an adaptive clustering protocol for wireless sensor networks, which is called Adaptive Decentralized Re-Clustering Protocol (ADRP) for Wireless Sensor Networks. In ADRP, the cluster heads and next heads are elected based on residual energy of each node and the average energy of each cluster. The simulation results show that ADRP achieves longer lifetime and more data messages transmissions than current important clustering protocol in wireless sensor networks

    Proactive Highly Ambulatory Sensor Routing (PHASeR) protocol for mobile wireless sensor networks

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    This paper presents a novel multihop routing protocol for mobile wireless sensor networks called PHASeR (Proactive Highly Ambulatory Sensor Routing). The proposed protocol uses a simple hop-count metric to enable the dynamic and robust routing of data towards the sink in mobile environments. It is motivated by the application of radiation mapping by unmanned vehicles, which requires the reliable and timely delivery of regular measurements to the sink. PHASeR maintains a gradient metric in mobile environments by using a global TDMA MAC layer. It also uses the technique of blind forwarding to pass messages through the network in a multipath manner. PHASeR is analysed mathematically based on packet delivery ratio, average packet delay, throughput and overhead. It is then simulated with varying mobility, scalability and traffic loads. The protocol gives good results over all measures, which suggests that it may also be suitable for a wider array of emerging applications

    Improved fuzzy c-means algorithm based on a novel mechanism for the formation of balanced clusters in WSNs

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    The clustering approach is considered as a vital method for many fields suchas machine learning, pattern recognition, image processing, information retrieval, data compression, computer graphics, and others.Similarly, it hasgreat significance in wireless sensor networks (WSNs) by organizing thesensor nodes into specific clusters. Consequently, saving energy and prolonging network lifetime, which is totally dependent on the sensor’s battery, that is considered asa major challenge in the WSNs. Fuzzyc-means (FCM) is one of classification algorithm, which is widely used in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces this algorithm to produce unbalanced clusters, which adversely affects the lifetime of the network.To overcome this problem, a new clustering method called FCM-CMhas been proposed by improving the FCM algorithm to form balanced clustersfor random nodes deployment. The improvement is conductedby integrating the FCM with a centralized mechanism(CM).The proposed method will be evaluated based on four new parameters. Simulation result shows that our proposed algorithm is more superior to FCM by producing balanced clustersin addition to increasing the balancing of the intra-distances of the clusters, which leads to energy conservation and prolonging network lifespan
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