Sensor-Mission Assignment In Constrained Environments

Proceeding of the 8th IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, IEEE SECON 2011, 2011

Sensor mission assignment concerns matching the sensing resources of a wireless sensor network (WSN) to appropriate tasks (missions), which may come to the network dynamically. Although solutions for WSNs with battery-operated nodes have been proposed for this problem, no attention has been given to networks whose nodes have energy harvesting capabilities, which impose quite a different energy model. In this paper we address this problem by providing both an analytical model and a distributed heuristic, called EN-MASSE, for energy harvesting WSNs. The objective of both model and EN-MASSE is to maximize the profit of the network, fully exploiting the harvesting technologies, while ensuring the execution of the most critical missions within a given target WSN lifetime. The performance of EN-MASSE is evaluated by simulations based on real solar energy traces. Our experiments show that EN-MASSE behaves very closely to the optimum provided by our model and significantly outperforms previously proposed solutions.

2011 - MILCOM 2011 Military Communications Conference, 2011

This paper proposes a combinatorial auction based approach to solving a multiple dynamic mission assignment (MDMA) problem. The problem presents itself in scenarios in which mobile nodes perform multiple missions, and the missions are supported by multiple nodes in a mobile ad hoc network. In this scenario, missions may dynamically arrive, may have to be completed at different times, and different numbers of members may be required for mission execution. The proposed MDMA protocol aims to minimize communication overhead caused by the mission assignment while maximizing each node's utilization (i.e., minimizing its idle time) and maximizing the fraction of mission completion over the initial missions given. We compare the proposed MDMA protocol with baseline mechanisms such as a single-item auction or a single-minded auction based on the above performance metrics.

2009

ABSTRACT A sensor network may be required to support multiple mission to be accomplished simultaneously. Furthermore, the environment may change at any time; ie a new mission may arrive at any time. In solving this many-mission, manysensor problem in dynamic environments, conflicts between missions may occur for the use of sensor resources. A mechanism to match sensor resources to mission demands thus becomes necessary.

TELKOMNIKA Indonesian Journal of Electrical Engineering

Placement of nodes has been the major challenge in wireless sensor networks. The data reported from a sensor is only useful when the position of that sensor is found. In this context, several techniques have been proposed to conserve power consumption and to prolong the lifetime of the wireless sensor networks. In this paper, we propose a new algorithm that accurately finds the best locations of sensors while minimizing the average energy consumed in the network. More precisely, we consider a critical network in each sensor satisfying its own missions and depending on its locations. In addition to fulfill their mission, the sensor tries to maintain a good neighboring nodes quality. we determine the location of node by using two criteria: the cost and the quality of communication. The aim of this work is to develop a new algorithm so as to solve the complicated optimization problem posed in this case while minimize the total energy consumption. Our simulation results demonstrate that...

2008

Abstract We consider the problem of sensor-mission assignment as that of allocating a collection of intelligence, surveillance and reconnaissance (ISR) assets (including sensors and sensor platforms) to a set of mission tasks in an attempt to satisfy the ISR requirements of those tasks. This problem is exacerbated in a coalition context because the full range of possible ISR solutions is not easy to obtain at-a-glance.

2009

We consider a scenario in which there are resources at or near nodes of a network, which are either static (e.g. fire stations, parking spots) or mobile (e.g. police cars). Over time, events (fires, crime reports, cars looking for parking) arise one-by-one at arbitrary nodes, and need to be quickly matched to and serviced by an appropriate nearby resource, without knowledge of future requests, and without the ability to alter any decision once it has been made.

Le Centre pour la Communication Scientifique Directe - HAL - Diderot, 2022

2009

In recent years, improved wireless technologies have enabled the low-cost deployment of large numbers of sensors for a variety of applications across different engineering disciplines. Because of the computational resources (processing capability, storage capacity, etc.) distributed throughout these sensing networks, it is often possible to perform advanced data analysis tasks autonomously and in-network, eliminating the need for the post-processing of sensor data. With new parallel algorithms being developed for in-network computation, it has become necessary to create a framework in which the computational resources available throughout a wireless sensing network can be best utilized in the midst of competing computational requirements. In this study, a Pareto-optimal market-based method is developed in order to autonomously distribute various computational tasks with competing objectives and/or resource demands across available network resources. This method is experimentally validated on a network of wireless sensing prototypes.