Prepared by:- Rishika Bhattacharjee CSE 4 th year Roll no- 130010023 CS-704D.

Sensor Networks Home Assignment

Sensor Networks Home Assignment prepared by:-Rishika BhattacharjeeCSE 4th yearRoll no- 130010023CS-704D Abstract Node localization is commonly employed in wireless networks. For example, it is used to improve routingand enhance security. Localization algorithms can be classified as range-free or range-based. Range-basedalgorithms use location metrics such as ToA, TDoA, RSS, and AoA to estimate the distance between two nodes. Proximity sensing between nodes is typically the basis for range-free algorithms. A tradeoff exists since range-based algorithms are more accurate but also more complex. However, in applications such as target tracking, localization accuracy is very important. In this paper, we propose a new range-based algorithm which is based on the density-based outlier detection algorithm (DBOD) from data mining. It requires selection of the K-nearest neighbours (KNN).Range-Based Localisation Using Wireless Networks Introduction

The process of finding the spatial location of nodes in ageolocation, and self-organizing in the literature.The term localization is the most popular and so is usedhere. In a wireless network, we can classify the nodesinto three categories, anchor, unlocalized, and localizeThe first group of nodes know their position or coordinates,and are called anchors. Nodes in the second groupdo not know their position and are called unlocalized.The third group contains those nodes which were in thesecond group but subsequently had their positions estimated,and thus are called localized.

Ranging TechniquesTime of Arrival (ToA, time of flight)" distance between sender and receiver of a signal can be determined using the measured signal propagation time and known signal velocity" sound waves: 343m/s, i.e., approx. 30ms to travel 10m" radio signals: 300km/s, i.e., approx. 30ns to travel 10m" One-way ToA" one-way propagation of signal " requires highly accurate synchronization of sender and receiver clocks" Two-way ToA" round-trip time of signal is measured at sender device" third message if receiver wants to know the distance"Angle of Arrival (AoA)" direction of signal propagation" typically achieved using an array of antennas or microphones" angle between signal and some reference is orientation" spatial separation of antennas or microphones leads to differences in arrival times, amplitudes, and phases" accuracy can be high (within a few degrees)" adds significant hardware cost" Received Signal Strength (RSS)" signal decays with distance" many devices measure signal strength with received signal strength indicator (RSSI)" vendor-specific interpretation and representation" typical RSSI values are in range of 0..RSSI_Max" common values for RSSI_Max: 100, 128, 256" in free space, RSS degrades with square of distance" expressed by Friis transmission equation" in practice, the actual attenuation depends on multipath propagation effects, reflections, noise, etc." realistic models r eplace R2 with Rn (n=3..5)"TriangulationExample of range-based localization" Uses the geometric properties of triangles to estimate location" Relies on angle (bearing) measurements" Minimum of two bearing lines (and the locations of anchor nodes or the distance between them) are needed for two-dimensional space"TrilaterationLocalization based on measured distances between a node and a number of anchorpoints with known locations" Basic concept: given the distance to an anchor, it is known that the node must bealong the circumference of a circle centered at anchor and a radius equal to thenode-anchor distance" In two-dimensional space, at least three non-collinear anchors are needed and inthree-dimensional space, at least four non-coplanar anchors are needed "Iterative/Collaborative MultilaterationProblem: what if node does not have at least three neighboring anchors?!Solution: once a node has determined its position, it becomes an anchor"Iterative multilateration:" repeats until all nodes have been localized" error accumulates with each iteration" Collaborative multilateration:"goal: construct a graph of participating nodes, i.e., nodes that are anchors orhave at least three participating neighbors"node then tries to estimate its position by solving the corresponding system ofoverconstrained quadratic equations relating the distances among the node andits neighborsGPS-Based LocalisationGlobal Positioning System" most widely publicized location-sensing system"provides lateration framework for determining geographic positions" originally established as NAVSTAR (Navigation Satellite Timing and Ranging)" only fully operational global navigation satellite system (GNSS)" consists of at least 24 satellites orbiting at approx. 11,000 miles"started in 1973, fully operational in 1995" Two levels of service:" Standard Positioning Service (SPS) " available to all users, no restrictions or direct charge" high-quality receivers have accuracies of 3m and better horizontally" Precise Positioning Service (PPS)" used by US and Allied military users"uses two signals to reduce transmission errors"Satellites are uniformly distributed in six orbits (4 satellites per orbit)" Satellites circle earth twice a day at approx. 7000 miles/hour" At least 8 satellites can be seen simultaneously from almost anywhere" Each satellite broadcasts coded radio waves (pseudorandom code),containing" identity of satellite"location of satellite" the satellites status" data and time when signal was sent" Six monitor stations constantly receive satellite data and forward data to amaster control station (MCS)" MCS is located near Colorado Springs, Colorado" MCS uses the data from monitor stations to compute corrections to theSatellites orbital and clock information which are sent back to the satellitesAd Hoc Positioning System Example of a range-free localization approach"based on connectivity information instead of distance/anglemeasurements" no additional hardware required (cost-effective)" APS is a distributed connectivity-based localization algorithm" estimates node locations with the support of at least three anchor nodes"localization errors can be reduced by increasing the number ofanchors" uses concept of DV (distance vector), where nodes exchange routingtables with their one-hop neighbors"Most basic scheme of APS: DV-hop" each node maintains a table {Xi, Yi, hi} (location of node i and distancein hops between this node and node i)"when an anchor obtains distances to other anchors, it determines theaverage hop length (correction factor ci), which is then propagatedthroughout the network" given the correction factor and the anchor locations, a node can performtrilateration" Example with three anchors"A1 knows its distance to A2 (50m) and A3 (110m)" A1 knows its hop distance to A2 (2) and A3 (6)"correction factor: (50+110)/(2+6) = 20 (estimated distance of a hop)" corrections are propagated using controlled flooding, i.e., a node onlyuses one correction factor and ignores subsequently received one Variation of this approach: DV-distance method" distances are determined using radio signal strength measurements" distances are propagated to other nodes"provides finer granularity (not all hops are estimated to be the samesize)" more sensitive to measurement errors" Another variation: Euclidean method"true Euclidian distances to anchors are used" node must have at least two neighbors that have distancemeasurements to anchors and the distance between the two neighborsis known"simple trigonometric relationships are used to determine the distancebetween node and anchor" Dilution of Precision Dilution of precision is a metric which describes how good an anchor node geometry is for localization.Thedistance measurements used to compute the node coordinates always contain some error. These measurement errors result in errors in the computed node coordinates.The magnitude of the final error depends on both themeasurement errors and the geometry of the structureinduced by the nodes. The contribution due to geometryis called the geometric dilution of precision (GDOP).GDOP is used extensively in the GPS community as ameasure of localization performance .

The Proposed AlgorithmThe first step in localization is to obtain distance estimates for the unlocalized nodes from the anchor and localized nodes that are within range. These estimates provide the radii for circles around the nodes. The intersection of these circles for an unlocalized node forms a set of points to be used in the remainder of the algorithm.The key is to choose candidate intersection points which are closest to each other.AlgorithmIn the ideal case the circles intersect on the unlocalized node. For example, when wehave three anchors, three intersection points lie on thenode, while the other three do not. However, in practicalsituations where noise and other sources of error exist,this event is unlikely and the circles intersect as in Fig--ure 1. In Figure 3, the intersection points of the circlesaround anchor nodes p1 = (x1,y1) and p2 = (x2,y2) are denotedas p12 and p21, and their coordinates are given byAlgo Contd..Each unlocalized node estimates its distance from eachanchor or localized node that it can receive a signal from.This node can estimate its position only if it is in rangeof three or more of these nodes. The intersection of the circles formed from all estimates of the unlocalized node provide a set of points. If we have m anchor and/or localized nodes, then they form g groups where g=(m/2)=m!/(2!(m-2)!)Related WorkMany range-free approaches have been proposed to determine sensor locations in WSNs. For example, the Centroid method is probably the earliest and simplest range-free approach, in which each node estimates its location by calculating the center of all the anchors it hears. APIT lets each node estimate whether it resides inside or outside several triangular regions bounded by the anchors it hears, and refines the computed location by overlapping the regions a sensor could possibly reside in. In order to improve accuracy, APIT needs many anchors and assumes that the anchors have radio ranges that are 10 times larger than those of ordinary nodes. Another proposed space embedding approach rely on Multidimensional Scaling (MDS) or Singular Value Decomposition (SVD) based techniques to project the node proximities into geographic distances.DV-Hop employs a constant number of anchors and relies on the heuristic of proportionality betweenthe distance and hop count in isotropic networks. The system estimates the average distance per hop from anchor locations and the hop count among anchors. Each node measures the hop count to at least three anchors and translates these into distances. By triangulation, the location is then calculated. However,the DV-Hop method yields high localization errors in anisotropic networks, where the existence of holes breaks the proportionality between the distance and hop count and thus leads to inaccurate location estimates.Network ModelWhen sensor nodes are randomly deployed in WSNs, we cannot assume any regularity in spacing or pattern of the sensors. This is due to the fact that most of the cases sensors are deployed from the low flying airplanes or unmanned ground vehicles. However anchors can be placed randomly or in the form of regular tile across the network so as to help in estimating the sensors positions [17]. In this paper weplace anchor and sensor nodes randomly, which is more practical.Simulation Results And PerformanceA series of simulations are conducted to evaluate the performance of our proposed scheme in isotropicand anisotropic WSNs, where anchor nodes are deployed randomly. For anisotropic network, weconsider O-shape and C-shape network topology. For each of the different types of network, we runthe simulation for 100 rounds and take the average over 100 runs, during which the quantity of thedeployed sensor nodes is kept unchanged. However, the topology of the network varies because weestablish connectivity between pair of sensor nodes randomly. To measure the accuracy of localization,the average localization error is used.Definition about wireless sensorsWireless Sensor NetworksApplication of WSN Application of WSN in ManufacturingLocalization What? Why?Classification of Localization AlgorithmsExamples of Localization Techniques

Outline

TransceiverMemoryEmbeddedProcessorSensorsBattery128KB-1MBLimited Storage1Kbps - 1Mbps, 3-100 Meters,Lossy Transmissions66% of Total CostRequires Supervision8-bit, 10 MHzSlow ComputationsLimited LifetimeEnergy Harvesting SystemNode HardwareFields of application of wireless sensor networks

Applications of sensor networksMilitary applications:Monitoring friendly forces, equipment and ammunitionExploration of opposing forces and terrainBattlefield surveillanceBattle damage assessmentNuclear, biological and chemical attack detection

27Health applications:Tele-monitoring of human physiological dataTracking and monitoring patients and doctors inside a hospital Drug administration in hospitals

28 Applications of sensor networks

28Example of Products Applicable for Health care Pulse Oximeter Glucose Meter Electrocardiogram (ECG) Social Alarm Devices

30Some Interesting ApplicationsOak Ridge National LaboratoryNose-on-a-chip is a MEMS-based sensor It can detect 400 species of gases and transmit a signal indicating the level to a central control station

MIT d'Arbeloff Lab The ring sensorMonitors the physiological status of the wearer and transmits the information to the medical professional over the Internet Range-FreeLocal TechniquesHop-Counting Techniques

Range-BasedReceived Signal Strength Indicator (RSSI)AttenuationRF signalTime of Arrival (ToA) time of flightTime Difference of Arrival (TDoA)requires time synchronizationelectromagnetic (light, RF, microwave)sound (acoustic, ultrasound)Angle of Arrival (AoA)RF signal

Range-Free vs Range-Based31Range-free:Only use connectivity informationNot very accurate

Range-based:require extra hardware therefore have a higher cost, Much more accurate