Previous Research Projects

Energy Cost of Coordination Algorithms for Wireless Mobile Sensor Networks  pdf

Support: NSF grants IIS-0093581 and CCR-0330342

 

Abstract:

Wireless mobile sensor networks are becoming increasingly popular. They mostly consist of low-cost mobile platforms equipped with sensors and an on-board battery. Energy consumption is therefore typically one of the main factors that needs to be addressed during the deployment. Communication accounts for much of the energy expenditures in a sensor network. The goal of this project was to establish lower bounds on energy cost due to communication for several robot coordination schemes. In particular, we studied how the energy cost changes with the number of mobile nodes in the wireless network, and we compared a peer-to-peer communication model with a multicast system.

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Task Switching in Mobile Wireless Sensor Networks pdf

Support: NSF grants IIS-0093581 and CCR-0330342

 

Abstract:

Mobile wireless sensor networks have the ability to monitor wide geographical areas. A suitable distribution of sensors can provide an optimal coverage (according to a predefined criterion) of the area of interest. On the other hand, mobile sensors can also move to the point of interest if necessary, thereby possibly leaving large regions vulnerable. In this project we studied how to balance optimal coverage with the need to quickly respond to an event. In particular, we showed that by coordinating their actions, sensors can effectively estimate the location of a biochemical source while still providing a high level of coverage of the area of interest. We described two switching control laws that achieve such a coordination and analyze their performance.

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Balancing Sensing and Coverage in Mobile Sensor Networks: Aggregation Based Approach pdf

Support: NSF grants IIS-0093581 and CCR-0330342

 

Abstract:

Folowing the results obtained in our previous project, here we wanted to correct some of the drawbacks of our previous approach. As before, we addressed the case when a set of autonomous mobile sensing robots is deployed inside a convex region, and the robots need to distribute themselves over the region uniformly, while also quickly locating one or more biochemical sources that might appear inside their region. The main issue is then which robots should be assigned to the sensing task and move towards the chemical source, thereby diminishing the coverage of the region. The right balance between the sensing performance and coverage performance depends on the application in which the network is used. We thus proposed an algorithm that given the area of the region and the number of agents in the network, makes the task assignment so that the number of sensing robots equals an arbitrary given function of these two parameters. We also showed how these parameters can be estimated using aggregation algorithm as an alternative to less robust consensus protocols.

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Balancing Sensing and Coverage in Mobile Sensor Networks: A Min-Max Approach pdf 

Support: NSF grants IIS-0093581 and CCR-0330342

 

Abstract:

In this project we work on the drawbacks obtained in Balancing Sensing and Coverage in Mobile Sensor Networks: an Aggregation Based Approach.   In such work, we assumed that each sensor was capable of correctly detecting and estimating every source that could appear in the region.  Furthermore, the generalization of such work implicitly assumed a universal labelling for the sources.  Here, we remove those assumptions, and present a novel min-max algorithm for the assignment.  The algorithm minimizes the maximum penalty that can be imposed if one of the tasks is not fully attended.  The limited sensing capabilities of each robot imply that there might be the need for agents that do not currently sense the source to help locate it. The issue is then how to assign sufficient number of robots to the sensing task and move them towards the source (even though some of them might initially not sense it) thereby diminishing the coverage of the region. We show that our algorithm converges towards a stable equilibrium point. The algorithm is shown to be optimal, fully distributed and thus scalable.

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