Announcements
 
Moshe meets with the Prime Minister of Guyana, Mr. Sam Hinds
Oct 21, 2008
Moshe Kam met with the Prime Minister of Guyana, Mr. Sam Hinds. They discussed the future of engineering education in the Caribbean (specifically in Guyana) and cooperation in the area of distance learning.

High resolution photo of the meeting is available here.
 
David presents at SASO-08
Oct 20, 2008
David Dorsey presented a paper entitled "Self-Adaptive Dissemination of Data in Dynamic Sensor Networks" at the Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Venice, Italy. Details are available on the publications page. The abstract of the paper is as follows:

The distribution of data in large dynamic wireless sensor networks presents a difficult problem due to node mobility, link failures, and traffic congestion. In this paper, we propose a framework for adaptive flooding protocols suitable for disseminating data in large-scale dynamic networks without a central controlling entity. The framework consists of cooperating mobile agents and a reinforcement learning component with function approximation and state generalization. A component for agent coordination is provided, as well as rules for agent replication, mutation, and annihilation. We examine the adaptability of this framework to a data dissemination problem in a simulation experiment.
 
Alex presents at ICDSC-08
Sep 10, 2008
Alex Fridman presented a paper titled "Cooperative Surveillance in Video Sensor Networks" at 2nd ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC-08). Details (including the PDF) available on the publication page. The abstract of the paper is as follows:

In the energy-constrained medium of video sensor networks, the objective of much research has been to statistically minimize the number of nodes that will achieve a sufficient degree of coverage. We consider increasing the number of nodes beyond the threshold of full coverage, and cooperatively filtering out the high level of redundant data in the video streams to minimize per-node capacity requirements. The scenario we study is that of a swarm of robots, all with wireless communication capabilities. Some of the robots are equipped with video cameras and are thus considered sensors. A few select robots have sufficient battery and computational power to perform machine vision processing of the video stream. The goal of this scenario is to get the video from the sensors to the video-processing robots, which can then extract high-level surveillance information about the observed environment. We present an optimization framework for minimizing redundant visual data transmissions, while maximizing the throughput from sensors to processing nodes. We also characterize through simulation the performance gain on the sensor network as the video coverage increases.
 
Alex presents at ACC-08
Jun 12, 2008
Alex Fridman presented a paper titled "Distributed Path Planning for Connectivity Under Uncertainty by Ant Colony Optimization" at the 27th American Control Conference (ACC) in Seattle, Washington. Details (including the PDF) available on the publication page. The abstract of the paper is as follows:

Movement and allocation of network resources for a system of communicating agents are usually optimized independently. Path planning under kinematic restrictions and obstacle avoidance provides a set of paths for the agents, and given the paths, it is then the job of network design algorithms to allocate communication resources to ensure a satisfactory rate of information exchange. In this paper, we consider the multiobjective problem of path planning for the sometimes conflicting goals of fast travel time and good network performance. In previous work we considered this problem under the assumption of full knowledge of network topologies and unlimited computational resources. In this paper, nothing is known a priori about topology, information is exchanged between nodes within a connected component of the network, and sources of environment-dependent communication failure can only be approximately estimated through learning. All the planning must be done online in a distributed fashion. We apply ant colony optimization to this problem of planning under uncertain information, and show that significant benefit in network performance can be achieved even under the difficult conditions of the scenario. Furthermore, we show the ability of nodes to quickly learn the communication patterns of the arena, and use this information for improved path planning.
 
Pramod presents at ACC 2008
Jun 11, 2008
Pramod Abichandani presented a paper titled "Multi Vehicle Path Coordination under Communication Constraints" at the 27th American Control Conference (ACC) in Seattle, Washington. Details (including the PDF) available on the publication page. The abstract of the paper is as follows:

We generate time-optimal velocity profiles for a group of path-constrained vehicles with fixed and known initial and goal locations. Each vehicle robot must follow a fixed path, arrive at its goal as quickly as possible (or at least not increase the time for the last robot to arrive at its goal) and stay in communication with other robots in the arena throughout its journey. We seek to solve this multi-objective optimization problem by generating optimal velocities along the paths. The problem is formulated as a nonlinear programming problem (NLP) with constraints on the kinematics, dynamics, collision avoidance and communication. Solutions demonstrate the trade off between the arrival time, the required transmission power and the communication connectivity requirements. Typically the optimization improved connectivity at no appreciable cost in journey time (as measured by the time of arrival of the last-arriving robot).
Design and Engine by Alex