Section: New Results

Controlled mobility

Participants : Milan Erdelj, Valeria Loscri, Kalypso Magklara, Karen Miranda, Enrico Natalizio, Jean Razafimandimby Anjalalaina, Tahiry Razafindralambo, David Simplot-Ryl, Dimitrios Zormpas.

Controlled mobility [5] is a new paradigm that leads to a set of great new challenges.

Target coverage

One of the main operations in wireless sensor networks is the surveillance of a set of events (targets) that occur in the field. In practice, a node monitors an event accurately when it is located closer to it, while the opposite happens when the node is moving away from the target. This detection accuracy can be represented by a probabilistic distribution. Since the network nodes are usually randomly deployed, some of the events are monitored by a few nodes and others by many nodes. In applications where there is a need of a full coverage and of a minimum allowed detection accuracy, a single node may not be able to sufficiently cover an event by itself. In this case, two or more nodes are needed to collaborate and to cover a single target. Moreover, all the nodes must be connected with a base station that collects the monitoring data.

In [15] , we describe the problem of the minimum sampling quality, where an event must be sufficiently detected by the maximum possible amount of time. Since the probability of detecting a single target using randomly deployed static nodes is quite low, we present a localized algorithm based on mobile nodes. Our algorithm sacrifices a part of the energy of the nodes by moving them to a new location in order to satisfy the desired detection accuracy. It divides the monitoring process in rounds to extend the network lifetime, while it ensures connectivity with the base station. Furthermore, since the network lifetime is strongly related to the number of rounds, we propose two redeployment schemes that enhance the performance of our approach by balancing the number of sensors between densely covered areas and areas that are poorly covered. Finally, our evaluation results show an over 10 times improvement on the network lifetime compared to the case where the sensors are static. Our approaches, also, outperform a virtual forces algorithm when connectivity with the base station is required. The redeployment schemes present a good balance between network lifetime and convergence time.

[47] , [28] assume that these targets to cover are dynamic. We assume that no knowledge about either event position or duration is given a priori. Nonetheless, the events need to be monitored and covered thanks to mobile wireless sensors. Thus, mobile sensors have to discover the events and move towards a new Zone of Interest (ZoI) when the previous monitored event is over. An efficient, distributed and localized solution of this problem would be immediately exploitable by several applications domains, such as environmental, civil, etc. We propose two novel approaches to deal with dynamic event coverage. The first one is a modified version of the PSO, where particles (mobile sensors, nodes or devices in the following) update their velocity by using only local information coming from their neighbors. In practice, the velocity update is performed by considering neighbors' sensed events. Our distributed version of PSO is integrated with a distributed version of the Virtual Force Algorithm (VFA). Virtual Force approach has the ability to "position" nodes with no overlap, by using attractive and repulsive forces based on the distance between nodes. The other proposed algorithm is a distributed implementation of the VFA by itself. Both techniques are able to reach high levels of coverage and show a satisfying reactivity when the ZoI changes. This output parameter is measured as the capability for the sensors to "follow" a sequence of events happening in different ZoIs. The effectiveness of our techniques is shown through a series of simulations and comparisons with the classical centralized VFA.

On another approach consists in using flying drone to cover this set of targets. [39] focuses on the energy efficiency problem where camera equipped flying drones are able to detect and follow mobile events that happen on the ground. We give a mathematical formulation of the problem of minimizing the total energy consumption of a fleet of drones when coverage of all events is required. Due to the extremely high complexity of the binary optimization problem, the optimum solution cannot be obtained even for small instances. On the contrary, we present LAS, a localized solution for the aforementioned problem which takes into account the ability of the drones to fly at lower altitudes in order to conserve energy. We simulate LAS and we compare its performance to a centralized algorithm and to an approach that uses static drones to cover all the terrain. Our findings show that LAS performs similar to the centralized algorithm, while it outperforms the static approach by up to 150% in terms of consumed energy. Finally, the simulation results show that LAS is very sustainable in presence of communication errors.

Multiple Point of Interest coverage

The coverage of Points of Interest (PoI) is a classical requirement in mobile wireless sensor applications. Optimizing the sensors self-deployment over a PoI while maintaining the connectivity between the sensors and the base station is thus a fundamental issue.

The problems of multiple PoI discovery, coverage and data report are still solved separately and there are no works that combine the aforementioned problems into a single deployment scheme. In [9] , [2] , we present a novel approach for mobile sensor deployment, where we combine multiple PoI discovery and coverage with network connectivity preservation in order to capture the dynamics of the monitored area. Furthermore, we derive analytical expressions for circular movement parameters and examine the performance of our approach through extensive simulation campaigns.

[10] addresses the problem of autonomous deployment of mobile sensors that need to cover a predefined PoI with a connectivity constraint. In our algorithm, each sensor moves toward a PoI but has also to maintain the connectivity with a subset of its neighboring sensors that are part of the Relative Neighborhood Graph (RNG). The Relative Neighborhood Graph reduction is chosen so that global connectivity can be provided locally. Our deployment scheme minimizes the number of sensors used for connectivity thus increasing the number of monitoring sensors. Analytical results, simulation results and practical implementation are provided to show the efficiency of our algorithm.

Robot cooperation

The concept of autonomous mobile agents gets a lot of attention in the domain of WSN or wireless sensor and actuator networks (WSAN). Multiple robots that coordinate or cooperate with other sensors, robots or human operator, allow the WSN/WSAN to perform tasks that are far beyond the scope of single robot unit. In[23] , we describe the robot middleware architecture that allows networked multi-robot control and data acquisition in the context of wireless sensor networks. Furthermore, we present three examples of robot network deployment and illustrate the proposed architecture usability: the robotic network deployment with the goal of covering the Point of Interest, adaptable multi-hop video transmission scenario, and the case of obtaining the energy consumption during the deployment.

Substitution networks

A substitution network [4] is a temporary network that will be deployed to support a base network in trouble and help it to provide the best service.

WSN are widely deployed nowadays on a large variety of applications. The major goal of a WSN is to collect information about a set of phenomena. Such process is non trivial since batteries' life is limited and thus wireless transmissions as well as computing operations must be minimized. A common task in WSNs is to estimate the sensed data and to spread the estimated samples over the network. Thus, time series estimation mechanisms are vital on this type of processes so as to reduce data transmission. In [30] , we assume a single-hop clustering mechanism in which sensor nodes are grouped into clusters and communicate with a sink through a single hop. We propose a couple of autoregressive mechanisms to predict local sensed samples in order to reduce wireless data communication. We compare our proposal with a model called EEE that has been previously proposed in the literature. We prove the efficiency of our algorithms with real samples publicly available and show that they outperform the EEE mechanism.

In [32] , we propose an algorithm to efficiently (re)-deploy the wireless mobile routers of a substitution network by considering the energy consumption, a fast deployment scheme and a mix of the network metric. We consider a scenario where we have two routers in a fixed network and where connectivity must be restored between those two routers with a wireless mobile router. The main objective of the wireless mobile router is to increase the communication performance such as the throughput by acting as relay node between the two routers of the fixed network. We present a fast, adaptive and localized approach which takes into account different network metrics such as Received Signal Strength (RSS), Round-Trip Time (RTT) and the Transmission Rate, between the wireless mobile router and the two routers of the fixed network. Our method ameliorates the performance of our previous approach from the literature by shortening the deployment time, increasing the throughput, and consuming less energy in some specific cases.