Section: New Results

Controlled Mobility for additional services

Participants : Nathalie Mitton, Valeria Loscri, Jean Cristanel Razafimandimby Anjalalaina.

Wireless sensor networks (WSNs) have been of very high interest for the research community since years, but most of the time, the mobility of nodes have been considered as an obstacle to overcome. In the contrary, in have tried to adopt another perspective and see it as an asset to exploit to provide additional services.

In [19], we leverage on the ability of mobile nodes to replace or recharge static sensors. Two main approaches can be identified that target this objective: either “recharging” or “replacing” the sensor nodes that are running out of energy. Of particular interest are solutions where mobile robots are used to execute the above mentioned tasks to automatically and autonomously maintain the WSN, thus reducing human intervention. Recently, the progress in wireless power transfer techniques has boosted research activities in the direction of battery recharging, with high expectations for its application to WSNs. Similarly, also sensor replacement techniques have been widely studied as a means to provide service continuity in the network. Objective of [19] is to investigate the limitations and the advantages of these two research directions. Key decision points must be identified for effectively supporting WSN self-maintenance: (i) which sensor nodes have to be recharged/replaced; (ii) in which order the mobile robot is serving (i.e., recharging/replacing) the nodes and by following which path; (iii) how much energy is delivered to a sensor when recharged. The influence that a set of parameters, relative to both the sensors and the mobile robot, on the decisions will be considered. Centralized and distributed solutions are compared in terms of effectiveness in prolonging the network lifetime and in allowing network self-sustainability. The performance evaluation in a variety of scenarios and network settings offers the opportunity to draw conclusions and to discuss the boundaries for one technique being preferable to the other.

Mobility can also help for collecting data in wireless sensor networks [29]. The sensor data collection problem using data mules have been studied fairly extensively in the literature. However, in most of these studies, while the mule is mobile, all sensors are stationary. The objective of most of these studies is to minimize the time needed by the mule to collect data from all the sensors and return to the data collection point, from where it embarked on its data collection journey. The problem studied in this paper has two major differences with the earlier studies. First, in this study we assume that both the mule as well as the sensors are mobile. Second, we do not attempt to minimize the data collection time. Instead we minimize the number of mules that will be needed to collect data from all the sensors, subject to the constraint that the data collection process has to be completed within some pre-specified time. We show that the mule minimization problem is NP-Complete and provide a solution by first transforming it to a generalized version of the minimum flow problem in a network and then solving it optimally using Integer Linear Programming. Finally, we evaluate our algorithms through extensive simulation and present the results.

Internet of Robotic Things (IoRT) is a new concept introduced for the first time by ABI Research. Unlike the Internet of Things (IoT), IoRT provides an active sensorization and is considered as the new evolution of IoT.

This new concept will bring new opportunities and challenges, while providing new business ideas for IoT and robotics' entrepreneurs.

In [46], we focus particularly on two issues: (i) connectivity maintenance among multiple IoRT robots, and (ii) their collective coverage.

We propose (i) IoRT-based, and (ii) a neural network control scheme to efficiently maintain the global connectivity among multiple mobile robots to a desired quality-of-service (QoS) level. The proposed approaches will try to find a trade-off between collective coverage and communication quality.

The IoT-based approach is based on the computation of the algebraic connectivity and the use of virtual force algorithm.

The neural network controller, in turn, is completely distributed and mimics perfectly the IoT-based approach. Results show that our approaches are efficient, in terms of convergence, connectivity, and energy consumption.