Frugal Sensor Assignment

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.

A sensor network may be required to support multiple mission to be accomplished simultaneously. Furthermore, the environment may change at any time; i.e. 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. In this paper, motivated by the conservation of resources, we consider the problem of sensor-mission assignment, in which sensors may be shared and reassigned between tasks. To achieve this, sensors are represented by agents, which coordinate to establish virtual organizations to meet mission requirements. The agent coordinating the achievement of a mission utilises a novel multi-round, Knapsack based algorithm, GAP-E, to allocate sensor agents to tasks based on bids received. Through simulations, we empirically demonstrate that this model provides a significant improvement in the number of completed missions as well as execution time.

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...

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.

2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE, 2010

Wireless Sensor Networks (WSNs) composed of inexpensive devices allow the possibility of large deployments aimed at monitoring remote, or possibly hostile, areas. However, due to the unique traffic patterns exhibited by monitoring sensor networks, the lifetime of a large WSN is constrained by the burden placed on nodes near the sink node to forward additional traffic as more nodes are deployed. We discuss approaches for deploying a WSN that will maximize the lifetime of an initial deployment. We then consider cases where the mission lifetime is of a duration such that overdeploying an initial network to meet this mission criteria would become prohibitively expensive. We then propose a replenishment control framework where additional nodes are added to an initial deployment in consecutive batches in order to meet mission lifetimes while reducing cost. The control framework consists of a failure process model used to forecast sensor failures due to energy depletion, and a two-stage limited lookahead controller used to determine the number of nodes to be added to the network and the approximate locations of their deployment.

A wireless sensor network (WSN) of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. Wireless sensor networks (WSNs) are known to be highly energy-constrained and each network lifetime has a strong dependence on the nodes battery capacity. As such, the network lifetime has been a critical concern in WSN research. The coverage of sensor nodes deployment becomes one key work that is how to make use of effective node deployment to achieve maximum coverage, it provide good connectivity and energy saving performance. In this project Sensor deployment initially has been done randomly, here Euclidean Minimum Spanning Tree deployment has been proposed. With this algorithm can able to deploy the sensor and compute the coverage matrix in order to get the maximum upper bound network lifetime. Coverage matrix can be achieved by determine the monitoring of sensors. Finally obtained sensor nodes are scheduled using a heuristic so as to achieve the theoretical upper bound of network lifetime. This method helps to prolong the network lifetime. A schedule for notifications on a sensor, rather than disable checking for the sensor all together. The performance can be measured by the calculation of number of sensors, the sensing radius of the deployed sensor and the standard deviation.

Proceedings of the eleventh ACM international symposium on Mobile ad hoc networking and computing, 2010

Recent years have witnessed the emergence of shared sensor networks as integrated infrastructure for multiple applications. It is important to allocate multiple applications in a shared sensor network, in order to maximize the overall Quality of Monitoring (QoM) subject to resource constraints (e.g., in terms of memory and network bandwidth). The resulting constrained optimization problem is a difficult and open problem since it is discrete, nonlinear, and not in closed-form. This paper makes several important contributions towards optimal multi-application allocation in shared sensor networks. (1) We formulate the optimal application allocation problem for a common class of distributed sensing applications whose QoM can be modeled as variance reduction functions. (2) We prove key theoretical properties of the optimization problem, including the monotonicity and submodularity of the variance reduction functions and the multiple knapsack structure of constraints; (3) By exploiting these properties, we propose a local search algorithm, which is efficient and has a good approximation bound, for application allocation in shared sensor networks. Simulations based on both real-world datasets and randomly generated networks demonstrate that our algorithm is competitive against simulated annealing in term of QoM, with up to three orders of magnitude reduction in execution times, making it a practical solution towards multi-application allocation in shared sensor networks.

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

IEEE Access

Finding an optimal node deployment strategy in Wireless Sensor networks (WSNs) that would reduce cost, be robust to node failures, reduce computation and communication overhead, and guarantee a high level of coverage along with network connectivity is a difficult problem. In fact, Sensing coverage and network connectivity are two of the most fundamental problems in WSNs as they can directly impact the network lifetime and operation. In this paper, we consider deriving optimal conditions for connectivity with coverage in WSNs. Most versions of this problem are (NP-complete) while approximation algorithms cannot be developed for some versions of polynomial time, unless P = NP. Hence, we also develop a heuristic for some versions of the problem and the efficacy of the heuristic will be evaluated through extensive simulations. We are also interested in determining the probability of finding a path between a given pair of nodes over a given topology of WSNs. This will serve as a measure of connectivity with coverage of the network. Hence, we derive necessary and sufficient conditions for connectivity with coverage over a clustered structure in WSNs. Then, employing queuing networks modeling techniques, we present a dynamic programming study of the connectivity with coverage of clustered structure and its effect on routing in generalized WSNs. The performance evaluation of the proposed schemes shows that availability of nodes, sensor node coverage, and the connectivity were sufficiently enhanced to maximize network lifetime.