Hybrid Wireless Sensors Deployment Scheme with Connectivity and Coverage Maintaining in Wireless Sensor Networks

With the rapid growth of the internet of things (IoT), an impressive number of IoT’s application based on wireless sensor networks (WSNs) has been deployed in various domain. Due to its wide ranged applications, WSNs that have the capability to monitor a given sensing field, became the most used platform of IoT. Therefore, coverage becomes one of the most important challenge of WSNs. The search for better positions to assign to the sensors in order to control each point of an area of interest and the collection of data from sensors are major concerns in WSNs. This work addresses these problems by providing a hybrid approach that ensures sensors deployment on a grid for targets coverage while taking into account connectivity. The proposed sequential hybrid approach is based on three algorithms. The first places the sensors so as to all targets are covered. The second removes redundancies from the placement algorithm to reduce the number of sensors deployed. The third one, based on the genetic algorithm, aims to generate a connected graph which provide a minimal path that links deployed sensors and sink. Simulations and a comparative study were carried out to prove the relevance of the proposed method.

     

Using an evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networks

A mobile ad hoc network (MANET) is a collection of mobile nodes communicating through wireless connections without any prior network infrastructure. In such a network the broadcasting methods are widely used for sending safety messages and routing information. To transmit a broadcast message effectively in a wide and high mobility MANET (for instance in vehicular ad hoc network) is a hard task to achieve. An efficient communication algorithm must take into account several aspects like the neighborhood density, the size and shape of the network, the use of the channel. Probabilistic strategies are often used because they do not involve additional latency. Some solutions have been proposed to make their parameters vary dynamically. For instance, the retransmission probability increases when the number of neighbors decreases. But, the authors do not optimize parameters for various environments. This article aims at determining the best communication strategies for each node according to its neighborhood density. It describes a tool combining a network simulator (ns-2) and an evolutionary algorithm (EA). Five types of context are considered. For each of them, we tackle the best behavior for each node to determine the right input parameters. The proposed EA is first compared to three EAs found in the literature: two well-known EAs (NSGA-II and SPEA2) and a more recent one (DECMOSA-SQP). Then, it is applied to the MANET broadcasting problem.