Wireless sensor networks (WSNs) have several important applications, both in research and domestic use. Generally, their main role is to collect and transmit data from an ROI (region of interest) to a base station for processing and analysis. Therefore, it is vital to ensure maximum coverage of the chosen area and communication between the nodes forming the network. A major problem in network design is the deployment of sensors with the aim to ensure both maximum coverage and connectivity between sensor node. The maximum coverage problem addressed here focuses on calculating the area covered by the deployed sensor nodes. Thus, we seek to cover any type of area (regular or irregular shape) with a predefined number of homogeneous sensors using a genetic algorithm to find the best placement to ensure maximum network coverage under the constraint of connectivity between the sensors. Therefore, this paper tackles the dual problem of maximum coverage and connectivity between sensor nodes. We define the maximum coverage and connectivity problems and then propose a mathematical model and a complex objective function. The results show that the algorithm, called GAFACM (Genetic Algorithm For Area Coverage Maximization), covers all forms of the area for a given number of sensors and finds the best positions to maximize coverage within the area of interest while guaranteeing the connectivity between the sensors.

Wireless Sensor Networks (WSNs) have become a critical technology in the Internet of Things (IoT) era, enabling the seamless integration of sensing, communication, and computational capabilities. WSNs have emerged as a transformative technology with applications ranging from environmental monitoring to industrial automation. As WSNs become more prevalent in various applications, the deployment phase takes center stage in defining their efficiency, effectiveness, and scalability. This paper provides a comprehensive overview of the multifaceted landscape of WSN deployment strategies. Digging deeper into sensor nodes, deployment types, goals, sensing patterns, and deployment methodologies, it comprehensively explores the multifaceted domain of WSN deployment and provides a map for optimizing the deployment across a spectrum of applications.

This paper proposes a novel approach for optimizing the coverage and deployment of Wireless Sensor Networks (WSNs) using image processing techniques. The proposed method involves the placement of an initial grid network on the map image, followed by the gradual decrease or increase of the radius of the initial operation and the adjustment of the placement of the nodes while also maintaining full connectivity communication between the sensors deployed. Our experiments demonstrate that this approach is effective in achieving the desired coverage and deployment of the wireless sensor network. It ensures efficient coverage and fully connectivity and can be applied to various application scenarios.

Visual Sensor Networks (VSNs) have been the focus of numerous studies, emphasizing the quality of the information they generate. The primary goal of these networks is to capture data from an area, extract the pertinent information from the visual scene, and facilitate communication. However, the presence of objects in the study area can impacted the network's efficiency, linking the quality of acquired data directly to the positioning of the cameras. Strategically positioning multiple cameras in a potentially complex area, while ensuring effective communication between nodes, presents a challenging issue in VSNs applications. This paper introduces a specific method to enhance the deployment of visual sensors in indoor environments. The research addresses the theme of multi-objective optimization of VSN deployment, integrating various criteria: maximizing overall visual coverage, maximizing the observation of zones of interest, maximizing observation redundancy of zones of interest and ensuring communication connectivity for data transfers. To tackle this problem, we simulate the observed environment incorporating cameras, obstacles, and considering zones of interest. Camera positioning is performed while considering visual constraints and the scene's characteristics. The quality of positioning is evaluated based on the defined objective functions. Initially, a comparative study is conducted with a state-of-the-art approach, integrating the first three objective functions. Subsequently, we incorporate the fourth objective function to ensure data transfers. The proposed method demonstrates efficient camera network deployments in indoor areas, considering global coverage and observation redundancy. In contrast to the state-of-the-art, these results are achieved in areas with visual obstacles while ensuring communication connectivity between the different cameras.

Un réseau de capteurs sans fil est constitué d'un ensemble de petites unités autonomes qui interagissent via un réseau construit par leurs modules de communication. Ils observent leur environnement, captent des informations, puis gèrent ces informations en fonction de leur capacité de calcul et/ou de stockage. Pour efficacement accomplir leur(s) tâche(s), ils ont besoin de couvrir autant que possible la zone d'intérêt. Il est donc indispensable de quantifier la qualité de leur couverture. Dans cette thèse, nous cherchons donc à couvrir au mieux une zone d'intérêt, avec un nombre précis de capteurs. Tout en prenant en compte les recouvrement possibles entre capteurs, nous déployons dans un premier temps dans une zone de dimensions régulières et évaluons à l'aide d'un algorithme génétique la couverture exacte. La fonction d'évaluation proposée nous permet de calculer sans biais la couverture exacte des capteurs. Une fois que nous savons couvir et évaluer la couverture d'une zone regulière, la deuxième étape consiste à déployer les capteurs sur une zone de formes irrégulières. L'objectif ici sera d'arriver non seulement à couvrir efficacement la zone en tenant compte des recouvrements des capteurs, mais aussi à privilégier l'intérieur de la zone d'intérêt par rapport à l'extérieur de celle-ci. Nous créons ensuite à l'intérieur de la zone de déploiement, des zones que nous appelons "zones de non intérêt", où les capteurs ne doivent pas être déployés. Enfin, s'il est important d'assurer une bonne couverture d'une zone d'intérêt, il est tout aussi important d'assurer qu'une fois les données captées, celles-ci puissent être envoyées vers les autres les capteurs pour traitement et/ou relais vers le(s) noeud(s) de traitement. Aux objectifs précédement énnoncés, nous ajoutons donc dans le deuxième cas de figure, la contrainte de connectivité totale entre les capteurs.