Spectrum Sensing with VSS-NLMS Process in Femto/Macro-cell Environments

Handover is a process that allows a mobile node to change its attachment point. A mobile node connected to a network can, in order to improve the quality of service, have the need to leave it to connect to a cell either of the same network or of a new network. The present paper introduce three techniques using adaptive Variable Step-Size Least Mean Square (VSSLMS) filter combined with spectrum sensing probability method to detect the triggering of handover in heterogeneous LTE networks. These techniques are Normalized LMS (NLMS), Kwong-NLMS and Li-NLMS. The simulation environment is composed of two femtocells belonging to a macrocell. Five User Equipements (UEs) are positioned in one femtocell and are assumed closest to its circumference. Simulation results show that sensing probability with Li-NLMS algorithm has a better performance compared with classical NLMS and Kwong-NLMS

Clustered chain founded on ant colony optimization energy efficient routing scheme for under-water wireless sensor networks

One challenge in under-water wireless sensor networks (UWSN) is to find ways to improve the life duration of networks, since it is difficult to replace or recharge batteries in sensors by the solar energy. Thus, designing an energy-efficient protocol remains as a critical task. Many cluster-based routing protocols have been suggested with the goal of reducing overall energy consumption through data aggregation and balancing energy through cluster-head rotation. However, the majority of current protocols are concerned with load balancing within each cluster. In this paper we propose a clustered chain-based energy efficient routing algorithm called CCRA that can combine fuzzy c-means (FCM) and ant colony optimization (ACO) create and manage the data transmission in the network. Our analysis and results of simulations show a better energy management in the network

Node Localization based on Anchor Placement using Fuzzy C-Means in a Wireless Sensor Network

Localization is one of the oldest mathematical and technical problems that have been at the forefront of research and development for decades. In a wireless sensor network (WSN), nodes are not able to recognize their position. To solve this problem, studies have been done on algorithms to achieve accurate estimation of nodes in WSNs. In this paper, we present an improvement of a localization algorithm namely Gaussian mixture semi-definite programming (GM-SDP-2). GMSDP is based on the received signal strength (RSS) to achieve a maximum likelihood location estimator. The improvement lies in the placement of anchors through the Fuzzy C-Means clustering method where the cluster centers represent the anchors' positions. The simulation of the algorithm is done in Matlab and is based on two evaluation metrics, namely normalized root-mean-squared error (RMSE) and cumulative distribution function (CDF). Simulation results show that our improved algorithm achieves better performance compared to those usinga predetermined placement of anchors