X-means Clustering for Wireless Sensor Networks
Authors : Abdelrahman Radwan, Nazhatul Kamarudin, Mahmud Iwan Solihin, Hungyang Leong, Mohamed Rizon, Hazry Desa, Muhammad Azizi Bin Azizan
Abstract : K-means clustering algorithms of wireless sensor networks are potential solutions that prolong the network lifetime. However, limitations hamper these algorithms, where they depend on a deterministic K-value and random centroids to cluster their networks. But, a bad choice of the K-value and centroid locations leads to unbalanced clusters, thus unbalanced energy consumption. This paper proposes X-means algorithm as a new clustering technique that overcomes K-means limitations; clusters constructed using tentative centroids called parents in an initial phase. After that, parent centroids split into a range of positions called children, and children compete in a recursive process to construct clusters. Results show that X-means outperformed the traditional K-means algorithm and optimized the energy consumption.
Keywords : K-means, X-means, Clustering, Wireless, Sensors, Networks
Visit
Slitting K-means clusters to X-means clusters for prolonging wireless sensor networks lifetime
Authors : Abdelrahman Radwan, Nazhatul Hafizah Kamarudin, Mahmud Iwan Solihin, Chun Kit Ang, Hungyang Leong, Mohamed Rizon
Abstract : The network lifetime is a vital research area of wireless sensor networks (WSNs), K-means are potential solutions that prolongs the network lifetime. However, there are limitations hampering these algorithms, such as the number of clusters is a fixed value, and the initial positions of the cluster centroids are predetermined locations. A bad choice of initial centroids leads to a bad clustering and a high energy consumption; thus, this paper proposes two phases optimization of initial centroids called X-means; first K-means cluster formed then they are slit into multiple clusters. In general, clusters constructed using tentative CHs selected by K-means algorithm as an initial phase, after that, each cluster gives birth to children, then these children compete to form clusters and the surviving children are the final clusters. The simulation results showed that X-means outperforms the traditional K-means algorithm, and it has reduced energy consumption by optimizing clusters initial positions.
Keywords : K-means, X-means, Clustering, Wireless, Sensors, Networks
Visit
A Survey on Energy Efficient Clustering Algorithms for Wireless Sensor Networks
Authors : Abdelrahman Radwan, Nazhatul Hafizah Kamarudin, Mahmud Iwan Solihin, Hungyang Leong, Jimmy Mok Vee Hoong.
Abstract : This article provides a taxonomy on the cluster characteristics that have not been addressed in previous literature, and it points out the cluster-head properties that minimize members energy consumption. Herein clustering algorithms classified into two main types; traditional algorithms and intelligent algorithms. A thorough analysis and comparison of both types carried out, advantages and drawbacks of each algorithm discussed. Results reveal that both types of algorithms optimize nodes energy consumptions by rotating the role of cluster-head among nodes. The traditional algorithms select nodes as cluster-heads using randomized mechanisms to keep a low network overhead and a short converge-time. These algorithms do not have solid techniques to force an optimal cluster-heads count, which causes a variation in the number of clusters formed. On the contrary, the intelligent algorithms follow systematic mechanisms to select cluster-heads based on feedbacks to fitness functions, then select fittest nodes as cluster-heads and determine an optimal number of clusters-heads; therefore, the intelligent algorithms suffer of a high network overhead and need a long converge-time to establish a network. Hence it concluded that there is a need for a clustering algorithm that enforces an optimal cluster-heads count and forms optimal size clusters while maintaining a low network overhead and a short converge-time.
Keywords : Energy Efficient, Wireless Sensor, routing algorithms
Visit
Implementation of the X-means Clustering Algorithm for Wireless Sensor Networks
Authors : Abdelrahman Radwan, Nazhatul Hafizah Kamarudin, Mahmud Iwan Solihin, Hungyang Leong, Chun Kit Ang.
Abstract : K-means clustering algorithms in wireless sensor networks (WSNs) are potential solutions that prolong the network lifetime. However, there are limitations hampering these algorithms, such as size of clusters and the number of clusters. This paper proposes implementing X-means algorithm as a new clustering technique that overcomes Kmeans limitations; clusters constructed using tentative CHs and tentative area of centroids in an initial phase, After that, if a cluster meets splitting criteria, new centroids selected and new clusters constructed.
Keywords : K-means, X-means, Clustering, Wireless, Sensors, Networks
Visit