Team, Visitors, External Collaborators
Overall Objectives
Research Program
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

On the use of controlled mobility

Participant : Nathalie Mitton.

Relying on controlled mobility as enabled by drones or robots could be a great asset for task management, data collection or quality of network deployment.


Robots and controlled mobility can help in the dynamic coverage of an area. In [22], we address the problem of defining a wireless sensor network by deploying sensors with the aim of guaranteeing the coverage of the area and the connectivity among the sensors. The wireless sensor networks are widely studied since they provide several services, e.g., environmental monitoring and target tracking. We consider several typologies of sensors characterized by different sensing and connectivity ranges. A cost is associated with each typology of sensors. In particular, the higher the sensing and connectivity ranges, the higher the cost. We formulate the problem of deploying sensors at minimum cost such that each sensor is connected to a base station with either a one-or a multi-hop and the area is full covered. We present preliminary computational results by solving the proposed mathematical model, on several instances. We provide a simulation-based analysis of the performances of such a deployment from the routing perspective.

Robots could be helpful when called upon an alert sent by sensors. But to intervene quickly, they need to locate or follow back the alert source as fast as possible. Two new algorithms (GFGF1 and GFGF2) for event finding in wireless sensor and robot networks based on the Greedy-Face-Greedy (GFG) routing are proposed in [27]. The purpose of finding the event (reported by sensors) is to allocate the task to the closest robot to act upon the event. Using two scenarios (event in or out of the network) and two topologies (random and random with hole) it is shown that GFGF1 always find the closest robot to the event but with more than twice higher communication cost compared to GFG, especially for the outside of the network scenario. GFGF2 features more than 4 times communication cost reduction compared to GFG but with percentage of finding the closest robot up to 90%.


Disaster scenarios are particularly devastating in urban environments, which are generally very densely populated. Disasters not only endanger the life of people, but also affect the existing communication infrastructure. In fact, such an infrastructure could be completely destroyed or damaged; even when it continues working, it suffers from high access demand to its resources within a short period of time, thereby compromising the efficiency of rescue operations. [31], [25] leverage the ubiquitous presence of wireless devices (e.g., smartphones) in urban scenarios to assist search and rescue activities following a disaster. This work considers multi-interface wireless devices and drones to collect emergency messages in areas affected by natural disasters. Specifically, it proposes a collaborative data collection protocol that organizes wireless devices in multiple tiers by targeting a fair energy consumption in the whole network, thereby extending the network lifetime. Moreover, it introduces a scheme to control the path of drones so as to collect data in a short time. Simulation results in realistic settings show that the proposed solution balances the energy consumption in the network by means of efficient drone routes, thereby effectively assisting search and rescue operations.