Triangulation positioning system - Network and topology issues

This paper presents ongoing work on localization and positioning through triangulation procedure. Issues of scalability and topology are also examined and areas that are problematic and need further analysis and implementation in the network are analyzed. In a Fixed Stations Network, as it was presented in [1] a triangulation problem is becoming high complicated when we have a large number of sensors and transmitters.Sensors bearings and data readings have to be checked on a case by case basis.The combination and processing of a vast number of data needs filtering and implementation of the various cases, whilst synchronously data processing in various stages can provide accurate results

Fault-tolerant Coverage in Dense Wireless Sensor Networks

In this paper, we present methods to detect and recover from sensor failure in dense wireless sensor networks. In order to extend the lifetime of a sensor network while maintaining coverage, a minimal subset of the deployed sensors are kept active while the other sensors can enter a low power sleep state. Several distributed algorithms for coverage have been proposed in the literature. Faults are of particular concern in coverage algorithms since sensors go into a sleep state in order to conserve battery until woken up by active sensors. If these active sensors were to fail, this could lead to lapses in coverage that are unacceptable in critical applications. Also, most algorithms in the literature rely on an active sensor that is about to run out of battery waking up its neighbors to trigger a reshuffle in the network. However, this would not work in the case of unexpected failures since a sensor cannot predict the occurrence of such an event. We present detection and recovery from sensor failure in dense networks. Our algorithms exploit the density in the recovery scheme to improve coverage by 4-12% in the event of random failures. This fault tolerance comes at a small cost to the network lifetime with observed lifetime being reduced by 6-10% in our simulation studies

Set It and Forget It: Approximating the Set Once Strip Cover Problem

We consider the Set Once Strip Cover problem, in which n wireless sensors are deployed over a one-dimensional region. Each sensor has a fixed battery that drains in inverse proportion to a radius that can be set just once, but activated at any time. The problem is to find an assignment of radii and activation times that maximizes the length of time during which the entire region is covered. We show that this problem is NP-hard. Second, we show that RoundRobin, the algorithm in which the sensors simply take turns covering the entire region, has a tight approximation guarantee of 3/2 in both Set Once Strip Cover and the more general Strip Cover problem, in which each radius may be set finitely-many times. Moreover, we show that the more general class of duty cycle algorithms, in which groups of sensors take turns covering the entire region, can do no better. Finally, we give an optimal O(n^2 log n)-time algorithm for the related Set Radius Strip Cover problem, in which all sensors must be activated immediately.Comment: briefly announced at SPAA 201

Energy Efficient Dynamic Cluster Algorithm for Ad-Hoc Sensor Networks

An important issue in ad hoc sensor networks is the limited energy supply within network nodes. Therefore, power consumption is crucial in the routing design. Cluster schemes are efficient in energy saving. This paper proposes a new algorithm called dynamic cluster in which energy in the entire network is distributed and unique route from the source to the destination is designed. In this algorithm, energy efficiency is distributed and improved by (1) optimizing the selection of clusterheads in which both residual energy of the nodes and total power consumption of the cluster are considered; (2) optimizing the number of nodes in the clusters according to the size of the networks and the total power consumption of the cluster; (3) rotating the roles of clusterheads to average the power consumption among clusterheads and normal nodes; and (4) breaking the clusters and reforming them to compensate the difference of the power consumption in different area. Energy efficiency is also improved by defining a unique route to reduce flooding in route discovery and to avoid duplicate data transmission by multiple routes

Restricted Strip Covering and the Sensor Cover Problem

Given a set of objects with durations (jobs) that cover a base region, can we schedule the jobs to maximize the duration the original region remains covered? We call this problem the sensor cover problem. This problem arises in the context of covering a region with sensors. For example, suppose you wish to monitor activity along a fence by sensors placed at various fixed locations. Each sensor has a range and limited battery life. The problem is to schedule when to turn on the sensors so that the fence is fully monitored for as long as possible. This one dimensional problem involves intervals on the real line. Associating a duration to each yields a set of rectangles in space and time, each specified by a pair of fixed horizontal endpoints and a height. The objective is to assign a position to each rectangle to maximize the height at which the spanning interval is fully covered. We call this one dimensional problem restricted strip covering. If we replace the covering constraint by a packing constraint, the problem is identical to dynamic storage allocation, a scheduling problem that is a restricted case of the strip packing problem. We show that the restricted strip covering problem is NP-hard and present an O(log log n)-approximation algorithm. We present better approximations or exact algorithms for some special cases. For the uniform-duration case of restricted strip covering we give a polynomial-time, exact algorithm but prove that the uniform-duration case for higher-dimensional regions is NP-hard. Finally, we consider regions that are arbitrary sets, and we present an O(log n)-approximation algorithm.Comment: 14 pages, 6 figure

Heterogeneity-aware and energy-aware scheduling and routing in wireless sensor networks

A Wireless Sensor Network (WSN) is a group of specialized transducers, called sensor nodes, with a communication infrastructure intended to monitor and record conditions at diverse locations. Since WSN applications are usually deployed in an open environment, the network is exposed to rough weather conditions, such as rain and snow. Another problem that WSN applications need to deal with is the energy constraints of sensor nodes. Both problems adversely affect the lifetime of WSN applications. A lot of research has been conducted to prolong the lifetime of WSN applications considering energy constraints of sensor nodes, but not much research has gone into tackling both the environmental effects and energy constraints. The goal of this research is to efficiently deal with these two problems and provide a solution for scheduling and routing in a heterogeneous sensor network. The research has been divided into two phases - Scheduling and Routing. In the scheduling phase, only some sensor nodes are scheduled to run for a particular timeslot and during that timeslot other sensor nodes are kept in sleep mode. A set of sensor nodes for a timeslot is chosen based on their positional information. In the routing phase, a least cost route from a sensor to the sink is dynamically determined to prolong the lifetime of the sensor network