Fuzzy C-Means Clustering and Energy Efficient Cluster Head Selection for Cooperative Sensor Network

Abstract

We propose a novel cluster based cooperative spectrum sensing algorithm to save the wastage of energy, in which clusters are formed using fuzzy c-means (FCM) clustering and a cluster head (CH) is selected based on a sensor’s location within each cluster, its location with respect to fusion center (FC), its signal-to-noise ratio (SNR) and its residual energy. The sensing information of a single sensor is not reliable enough due to shadowing and fading. To overcome these issues, cooperative spectrum sensing schemes were proposed to take advantage of spatial diversity. For cooperative spectrum sensing, all sensors sense the spectrum and report the sensed energy to FC for the final decision. However, it increases the energy consumption of the network when a large number of sensors need to cooperate; in addition to that, the efficiency of the network is also reduced. The proposed algorithm makes the cluster and selects the CHs such that very little amount of network energy is consumed and the highest efficiency of the network is achieved. Using the proposed algorithm maximum probability of detection under an imperfect channel is accomplished with minimum energy consumption as compared to conventional clustering schemes.

Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

“…Otherwise, developed systems might be used in replace of K-means algorithm, and then the learning task is performed by centralized and resource capable computational units. [34,35,36,37], little of them have considered the use of the data science clustering techniques in a direct way. Instead, those data clustering techniques are used for the purpose of finding the similarities or correlations in data between neighboring nodes, and partition sensor nodes into clusters accordingly.…”

“…The following is the application of K-means in wireless networks. In [34,35,36,37], the sensory data is clustered via the distributed k-means clustering algorithms, and then is aggregated and transimitted towards a sink node. The purpose of such summary of data is to ensure the reduction of communication transmission and processing time, as well as the reduction of energy cost of the sensor nodes.…”

A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm

“…Then, the membership degree u cj and the cluster center c are respectively derived, and the constraint condition is substituted [33,34], thereby obtaining the calculation formulas of u cj and c , as shown in the Eqs. 29 and 30.…”

A Robust and Energy-Efficient Weighted Clustering Algorithm on Mobile Ad Hoc Sensor Networks †

“…Although both of them take the mobility, the limited energy and the degree of nodes into consideration, it is also not involved in how to enhance the robustness and anti-attacks ability. Based on energy-efficiency of network, HEED forms clusters through a distributed scheme to render the energy consumption of network communication minimized [22]. The algorithm elects the cluster heads based on the residual energy of nodes, which means that the nodes with high residual energy are more possible to be elected as cluster head than other nodes.…”