A Hybrid Cluster and Chain-based Routing Protocol for Lifetime Improvement

International audienceThe main challenge in the field of Wireless Sensor Networks (WSNs) is the energy conservation as long as possible. Clustering paradigm has proven its ability to prolong the network lifetime. The present paper proposes two algorithms using an approach that combines fuzzy c-means and ant colony optimization to form the clusters and manage the transmission of data in the network. First, fuzzy c-means is used to construct a predefined number of clusters. Second, we apply Ant Colony Optimization (ACO) algorithm to form a local shortest chain in each cluster. A leader node is randomly chosen at the beginning since all cluster nodes have the same amount of energy. In the next transmission, a remaining energy parameter is employed to select leader node. In the first algorithm, leader nodes transmit data in single hop to the distant base station (BS) while in the second the ACO algorithm is applied again to form a global chain between leader nodes and the BS. Simulation results show that the second proposed algorithm consumes less energy and effectively prolongs the network lifetime compared respectively with the first proposed and the LEACH algorithms

An energy aware scheme for layered chain in underwater wireless sensor networks using genetic algorithm

Extending the network lifetime is a very challenging problem that needs to be taken into account during routing data in wireless sensor networks in general and particularly in underwater wireless sensor networks (UWSN). For this purpose, the present paper proposes a multilayer chain based on genetic algorithm routing (MCGA) for routing data from nodes to the sink. This algorithm consists to create a limited number of local chains constructed by using genetic algorithm in order to obtain the shortest path between nodes; furthermore, a leader node (LN) is elected in each chain followed by constructing a global chain containing LNs. The selection of the LN in the closest chain to the sink is as follows: Initially, the closest node to sink is elected LN in this latter because all nodes have initially the same energy value; then the future selection of the LN is based on the residual energy of the nodes. LNs in the other chains are selected based on the proximity to the previous LNs. Data transmission is performed in two steps: intra-chain transmission and inter-chain transmission. Furthermore, MCGA is simulated for different scenarios of mobility and density of nodes in the networks. The performance evaluation of the proposed technique shows a considerable reduction in terms of energy consumption and network lifespan

Detection of Drug Interactions via Android Smartphone: Design and Implementation

Despite the morbidity and cases of widespread drug poisoning, clinical guidelines are largely written by taking into account only one treatment at a time. The cumulative impact of multiple treatments is rarely considered. Drug treatment for people with several diseases produces a complex regimen called “polypharmacy” with a potential combination of harmful and even lethal drugs that can be prevented. This polypharmacy causes in many cases the death of some people due to drug interactions. The vast majority of these deaths can be prevented by detecting interactions before taking these medications. But the problem is that such information exists in a state that is difficult to access for the general public, much less for people with little knowledge in the field. Although the pharmacist is unmistakable and most viable source to avoid such a problem, he cannot know what the patient does not mention because he is not aware of what may affect his treatment. To remedy this, we aim in this paper to develop an ergonomic Android application that will inform the patient about the potential risks of such drug interactions. The application is optimized to handle various databases and operate automation of QR code

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

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

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

Abstracts of 1st International Conference on Computational & Applied Physics

This book contains the abstracts of the papers presented at the International Conference on Computational & Applied Physics (ICCAP’2021) Organized by the Surfaces, Interfaces and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria, held on 26–28 September 2021. The Conference had a variety of Plenary Lectures, Oral sessions, and E-Poster Presentations. Conference Title: 1st International Conference on Computational & Applied PhysicsConference Acronym: ICCAP’2021Conference Date: 26–28 September 2021Conference Location: Online (Virtual Conference)Conference Organizer: Surfaces, Interfaces, and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria