Ines Khoufi

Three dimensional mobile wireless sensor networks redeployment based on virtual forces

In many applications such as precision agriculture (fruit tree plantation, olive groves) or environmental monitoring, wireless sensors are, very often, randomly scattered in the 3D area of interest. Such applications require full three-dimensional coverage. Undoubtedly, an initial random deployment does not achieve neither full coverage of the 3D area of interest, nor network connectivity. Thus, a redeployment algorithm has to be introduced in order to ensure these two goals. Our contribution is the design of 3D-DVFA, a distributed deployment algorithm based on virtual forces in three dimensional wireless sensor networks where sensor nodes are assumed to be mobile and autonomous. We simulate its behavior with NS3. The extensive simulations results demonstrate the effectiveness of the 3D algorithm proposed that provides full 3D area coverage and network connectivity.

A simple method for the deployment of wireless sensors to ensure full coverage of an irregular area with obstacles

In this paper, we focus on the deployment of wireless sensor nodes in an arbitrary realistic area with an irregular shape, and with the presence of obstacles that may be opaque. Moreover, we propose a simple projection-based method that tends to minimize the number of sensor nodes needed to fully cover such an area. This method starts with the optimal uniform deployment based on the triangular tessellation encompassing the whole area. Then, it projects some external sensor nodes on the border to ensure full coverage and connectivity. We show that this method outperforms the contour-based one using various types of irregular areas.

Relocation of mobile wireless sensors in the presence of obstacles

In many applications (e.g military, environment monitoring), wireless sensors are randomly deployed in a given area. Unfortunately, this deployment is not efficient enough to ensure area coverage and network connectivity. Algorithms based on Virtual Forces are used to improve the random initial deployment. In this paper, we want to ensure coverage and network connectivity in a given area containing obstacles. We enhance the Distributed Virtual Forces Algorithm (DVFA) to cope with obstacles. Obstacles are characterized by prohibiting both the physical presence of sensors and the wireless communication. Performance evaluation shows that DVFA provides an efficient deployment even if obstacles exist in the considered area.

GDVFA: A distributed algorithm based on grid and virtual forces for the redeployment of WSNs

The distributed virtual forces deployment algorithm provides a very good area coverage and guarantees network connectivity for a sufficient number of sensor nodes. It relies on local information between neighboring sensor nodes. However, its main drawback is node oscillations and hence a high amount of sensor node energy wasted. The grid based strategy divides the monitored area into virtual cells. Each cell center determines the position of a sensor node. In this paper we propose GDVFA that combines the advantages of both strategies: on the one hand coverage and connectivity for virtual forces strategy and on the other hand avoidance of node oscillations for grid strategy. Simulation results reported in this paper show that GDVFA considerably reduces the energy consumed by sensor nodes. This comes from: the detection of redundant nodes that are put in sleep mode, and the avoidance of node oscillations by stopping nodes.

A game theory-based approach for robots deploying wireless sensor nodes

Wireless Sensor Networks (WSNs) are deployed in many fields of application. Depending on the application requirements, sensor nodes can either be mobile and autonomous or static. In both cases, they are able to cooperate together in order to monitor a given area or some given Points of Interest (PoIs). Static sensor nodes need one or several agent(s) (humans or robots) to deploy them. In this paper, we focus on the deployment of static sensor nodes in an area containing obstacles, using two mobile robots. We want to minimize the time needed by the two robots to deploy all the sensor nodes and to return to their starting position. We require that each sensor node is placed at a PoI position, no PoI position is empty and no PoI position is occupied by more than one sensor node. The problem consists in determining the best strategy for each robot in order to meet these constraints. We adopt a game theory approach to solve this problem.

Optimized trajectory of a robot deploying wireless sensor nodes

Mobile robots can be used to deploy static wireless sensor nodes to achieve the coverage and connectivity requirements of the applications considered. Many solutions have been provided in the literature to compute the set of locations where the sensor nodes should be placed. In this paper, we show how this set of locations can be used by a mobile robot to optimize its tour to deploy the sensor nodes to their right locations. In order to reduce both the energy consumed by the robot, its exposure time to a hostile environment, as well as the time at which the wireless network becomes operational, the optimal tour of the robot is this minimizing the delay. This delay must take into account not only the time needed by the robot to travel the tour distance but also the time spent in the rotations performed by the robot each time it changes its direction. This problem is called the Robot Deploying Sensor nodes problem, in short RDS. We first show how this problem differs from the well-known traveling salesman problem. We then propose an integer linear program formulation of the RDS problem. We propose various algorithms relevant to iterative improvement by exchanging tour edges, genetic approach and hybridization. The solutions provided by these algorithms are compared and their closeness to the optimal is evaluated in various configurations.

Optimized trajectories of multi-robot deploying wireless sensor nodes

A main reason to the growth of wireless sensor networks deployed worldwide is their easy and fast deployment. In this paper we consider deployments assisted by mobile robots where static sensor nodes are deployed by mobile robots in a given area. Each robot must make a tour to place its sensor nodes. All sensor nodes must be placed at their precomputed positions. The Multi-Robot Deploying wireless Sensor nodes problem, called the MRDS problem, consists in minimizing the longest tour duration (i.e. the total deployment duration), the number of robots used and the standard deviation between duration of robots tours. After a formal definition of the MRDS problem, we show how to use a multi-objective version of genetic algorithms, more precisely the NSGA-II algorithm, to solve this multi-objective optimization problem. The solutions belonging to the best Pareto front are given to the designer in charge of selecting the best trade-off taking into account various criteria. We then show how to extend this method to take obstacles into account, which is more representative of real situations.

Increasing reliability of a TSCH network for the industry 4.0

Time Slotted Channel Hopping (TSCH) networks are emerging as a promising technology for the Internet of Things and the Industry 4.0 where ease of deployment, reliability, short latency, flexibility and adaptivity are required. Our goal is to improve reliability of data gathering in such wireless sensor networks. We present three redundancy patterns to build a reliable path from a source to a destination. The first one is the well-known two node-Disjoint paths. The second one is based on a Triangular pattern, and the third one on a Braided pattern. A comparative evaluation is carried out to analyze the reliability achieved, the number of failures tolerated, the number of message copies generated and the energy consumed by each node to ensure that at least one copy of the message is delivered to the destination. These results are validated by simulations.

Path planning of mobile sinks in charge of data gathering: A coalitional game theory approach

Game theory is often used to find equilibria where no player can unilaterally increase its own payoff by changing its strategy without changing the strategies of other players. In this paper, we propose to use coalition formation to compute the optimized tours of mobile sinks in charge of collecting data from static wireless sensor nodes. Mobile sinks constitute a very attractive solution for wireless sensor networks, WSNs, where the application requirements in terms of node autonomy are very strong unlike the requirement in terms of latency. Mobile sinks allow wireless sensor nodes to save energy The associated coalition formation problem has a stable solution given by the final partition obtained. However, the order in which the players play has a major impact on the final result. We determine the best order to minimize the number of mobile sinks needed. We evaluate the complexity of this coalitional game as well as the impact of the number of collect points per surface unit on the number of mobile sinks needed and on the maximum tour duration of these mobile sinks. In addition, we show how to extend the coalitional game to support different latencies for different types of data. Finally, we formalize our problem as an optimization problem and we perform a comparative evaluation.

OA-DVFA: A Distributed Virtual Forces-based Algorithm to monitor an area with unknown obstacles

Deployment of sensor nodes to fully cover an area has caught the interest of many researchers. However, some simplifying assumptions are adopted such as knowledge of obstacles, centralized algorithm... To cope with these drawbacks, we propose OA-DVFA (Obstacles Avoidance Distributed Virtual Forces Algorithm) a self-deployment algorithm to ensure full area coverage and network connectivity. This fully distributed algorithm is based on virtual forces to move sensor nodes. In this paper, we show how to avoid the problem of node oscillations and to detect the end of the deployment in a distributed way. We evaluate the impact of the number, shape and position of obstacles on the coverage rate, the distance traveled by all nodes and the number of active nodes. Simulation results show the very good behavior of OA-DVFA.