Use of context-awareness in mobile peer-to-peer networks

Mobile ad-hoc network are an emerging research field due to the potential range of applications that they support and for the problems they present due to their dynamic nature. Peer-to-peer is an example of a class of applications that have recently been deployed on top of ad-hoc networks. In this paper we propose an approach based on context-awareness to allow peer-to-peer applications to exploit information on the underlying network context to achieve better performance and better group organization. Information such as availability of resources, battery power, services in reach and relative distances can be used to improve the routing structures of the peer-to-peer network, thus reducing the routing overhead

Cooperative Decentralized Multi-agent Control under Local LTL Tasks and Connectivity Constraints

We propose a framework for the decentralized control of a team of agents that are assigned local tasks expressed as Linear Temporal Logic (LTL) formulas. Each local LTL task specification captures both the requirements on the respective agent's behavior and the requests for the other agents' collaborations needed to accomplish the task. Furthermore, the agents are subject to communication constraints. The presented solution follows the automata-theoretic approach to LTL model checking, however, it avoids the computationally demanding construction of synchronized product system between the agents. We suggest a decentralized coordination among the agents through a dynamic leader-follower scheme, to guarantee the low-level connectivity maintenance at all times and a progress towards the satisfaction of the leader's task. By a systematic leader switching, we ensure that each agent's task will be accomplished.Comment: full version of CDC 2014 submissio

Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks

Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network