Multi-agent Based Distributed Semi-automatic Sensors Surveillance System Architecture

Proceedings of: Forth International Workshop on User-Centric Technologies and applications (CONTEXTS 2010). Valencia, 07-10 September , 2010.In the present paper, we describes a semi-automated and decision support sensor surveillance architecture used to develop an intelligent sensor surveillance system. The proposed architecture is grouped in three agents layers: the sensors agents layer, sensor processing agents layer and finally, the support assistant agents layers. The sensor agents layer is formed by sensor managing agents and sensor data flow agents that they control the sensor devices and retransmit data streams to upper layer respectively. In sensor processing agents layer is an agents collection that process data flows produced by sensors, allowing elements tracking. The last layer is formed by special agents for helping and supporting the user monitoring and user choice. This architecture proposes a fully decentralized multi-agent system using FIPA Agent Communication Language.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/ TIC-1485) and DPS2008-07029-C02-0

A sparsity-driven approach to multi-camera tracking in visual sensor networks

In this paper, a sparsity-driven approach is presented for multi-camera tracking in visual sensor networks (VSNs). VSNs consist of image sensors, embedded processors and wireless transceivers which are powered by batteries. Since the energy and bandwidth resources are limited, setting up a tracking system in VSNs is a challenging problem. Motivated by the goal of tracking in a bandwidth-constrained environment, we present a sparsity-driven method to compress the features extracted by the camera nodes, which are then transmitted across the network for distributed inference. We have designed special overcomplete dictionaries that match the structure of the features, leading to very parsimonious yet accurate representations. We have tested our method in indoor and outdoor people tracking scenarios. Our experimental results demonstrate how our approach leads to communication savings without significant loss in tracking performance

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs