Presentation for Master Thesis "Indoor Localization Using Local Node Density In Ad Hoc WSNs", research supported by Free University Berlin. Coordinators: Freddy Lopez Villafuerte, Gianluca Cornetta.
Localization with mobile anchor points in wireless sensor networksHabibur Rahman
The document describes a range-free localization scheme that uses mobile anchor points equipped with GPS to periodically broadcast their positions. Sensor nodes calculate their own positions based on the localization information from at least three mobile anchors without needing additional interactions. Simulation results showed the approach achieved fine-grained accuracy and was distributed, scalable, effective and power efficient. It performed better than other range-free localization mechanisms.
This document discusses localization techniques in wireless sensor networks. It begins with introducing wireless sensor networks and their components. It then discusses the need for localization to track objects within sensor networks. There are two main types of localization schemes - range-based which uses distance or angle measurements, and range-free which uses approximate distance estimates. Examples of range-based techniques include time of arrival, time difference of arrival, received signal strength indicator, and angle of arrival. Range-free techniques include proximity and distance-based localization using hop counts. The document compares the advantages and disadvantages of different localization methods.
The document is a presentation on implementing node localization for wireless sensor networks. It discusses wireless sensor networks and their applications. It then covers issues related to wireless sensor networks, including localization. Localization aims to estimate the position of sensor nodes that are randomly deployed. The presentation reviews several papers on localization systems and algorithms. It discusses approaches like using beacon nodes, distance estimation techniques, and localization algorithms that involve trilateration, triangulation or Voronoi diagrams. The conclusion covers challenges in localization and potential areas of future work.
A Survey on Localization of Wireless SensorsKarthik Mohan
The document summarizes localization techniques for wireless sensor nodes. It discusses several common localization methods including known location-based using GPS, proximity-based using signal strength, angle-based using angle of arrival, and range-based using time of arrival or time difference of arrival. It also covers some challenges with each approach like accuracy limitations and environmental factors. Finally, it provides a brief comparison of the localization techniques and their typical accuracy ranges from 1-15 meters depending on the method.
Wireless sensor networks localization algorithms a comprehensive surveyIJCNCJournal
Wireless sensor networks (WSNs) have recently gained a lot of attention by scientific community. Small
and inexpensive devices with low energy consumption and limited computing resources are increasingly
being adopted in different application scenarios including environmental monitoring, target tracking and
biomedical health monitoring. In many such applications, node localization is inherently one of the system
parameters. Localization process is necessary to report the origin of events, routing and to answer
questions on the network coverage ,assist group querying of sensors. In general, localization schemes are
classified into two broad categories: range-based and range-free. However, it is difficult to classify hybrid
solutions as range-based or range-free. In this paper we make this classification easy, where range-based
schemes and range-free schemes are divided into two types: fully schemes and hybrid schemes. Moreover,
we compare the most relevant localization algorithms and discuss the future research directions for
wireless sensor networks localization schemes.
The document discusses localization techniques in wireless sensor networks (WSNs). It begins with an introduction to WSNs and why GPS is not suitable for localization in these networks. It then covers taxonomy of localization methods, including target/source localization, node self-localization techniques like range-based and range-free methods. Specific techniques discussed include DV-Hop, pattern matching localization, and classifications like centralized vs distributed localization. The summary restates key points about distance estimation methods, single/multiple localization, and classifications of localization approaches.
Localization with mobile anchor points in wireless sensor networksHabibur Rahman
The document describes a range-free localization scheme that uses mobile anchor points equipped with GPS to periodically broadcast their positions. Sensor nodes calculate their own positions based on the localization information from at least three mobile anchors without needing additional interactions. Simulation results showed the approach achieved fine-grained accuracy and was distributed, scalable, effective and power efficient. It performed better than other range-free localization mechanisms.
This document discusses localization techniques in wireless sensor networks. It begins with introducing wireless sensor networks and their components. It then discusses the need for localization to track objects within sensor networks. There are two main types of localization schemes - range-based which uses distance or angle measurements, and range-free which uses approximate distance estimates. Examples of range-based techniques include time of arrival, time difference of arrival, received signal strength indicator, and angle of arrival. Range-free techniques include proximity and distance-based localization using hop counts. The document compares the advantages and disadvantages of different localization methods.
The document is a presentation on implementing node localization for wireless sensor networks. It discusses wireless sensor networks and their applications. It then covers issues related to wireless sensor networks, including localization. Localization aims to estimate the position of sensor nodes that are randomly deployed. The presentation reviews several papers on localization systems and algorithms. It discusses approaches like using beacon nodes, distance estimation techniques, and localization algorithms that involve trilateration, triangulation or Voronoi diagrams. The conclusion covers challenges in localization and potential areas of future work.
A Survey on Localization of Wireless SensorsKarthik Mohan
The document summarizes localization techniques for wireless sensor nodes. It discusses several common localization methods including known location-based using GPS, proximity-based using signal strength, angle-based using angle of arrival, and range-based using time of arrival or time difference of arrival. It also covers some challenges with each approach like accuracy limitations and environmental factors. Finally, it provides a brief comparison of the localization techniques and their typical accuracy ranges from 1-15 meters depending on the method.
Wireless sensor networks localization algorithms a comprehensive surveyIJCNCJournal
Wireless sensor networks (WSNs) have recently gained a lot of attention by scientific community. Small
and inexpensive devices with low energy consumption and limited computing resources are increasingly
being adopted in different application scenarios including environmental monitoring, target tracking and
biomedical health monitoring. In many such applications, node localization is inherently one of the system
parameters. Localization process is necessary to report the origin of events, routing and to answer
questions on the network coverage ,assist group querying of sensors. In general, localization schemes are
classified into two broad categories: range-based and range-free. However, it is difficult to classify hybrid
solutions as range-based or range-free. In this paper we make this classification easy, where range-based
schemes and range-free schemes are divided into two types: fully schemes and hybrid schemes. Moreover,
we compare the most relevant localization algorithms and discuss the future research directions for
wireless sensor networks localization schemes.
The document discusses localization techniques in wireless sensor networks (WSNs). It begins with an introduction to WSNs and why GPS is not suitable for localization in these networks. It then covers taxonomy of localization methods, including target/source localization, node self-localization techniques like range-based and range-free methods. Specific techniques discussed include DV-Hop, pattern matching localization, and classifications like centralized vs distributed localization. The summary restates key points about distance estimation methods, single/multiple localization, and classifications of localization approaches.
3D Localization Algorithms for Wireless Sensor NetworksIOSR Journals
This document discusses localization algorithms for 3D wireless sensor networks. It begins by explaining that localization in 3D spaces poses unique challenges compared to 2D, as strategies used in 2D do not directly extend to 3D. It then reviews common range-based localization methods like received signal strength and time-based methods, as well as range-free methods like centroid and DV-HOP algorithms. The document aims to address the problem of localization for sensor networks deployed in 3D surfaces.
Optimum Sensor Node Localization in Wireless Sensor Networkspaperpublications3
Abstract: Scientists, engineers, and researchers use wireless sensor networks (WSN) for a wide array of applications. Many of these applications rely on knowledge of the precise position of each node. An optimum localization algorithm can be used for determining the position of nodes in a wireless sensor network. This paper provides an overview of different approach of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Experiments were performed in a testbed area containing anchor and blind nodes deployed in it to characterize the pathloss exponent and to determine the localization error of the algorithm. Details regarding the implementation of new algorithm are also discussed in this paper.
LOCALIZATION ALGORITHM USING VARYING SPEED MOBILE SINK FOR WIRELESS SENSOR NE...ijasuc
Localization of sensor nodes is important in many aspects in wireless sensor networks. The known
location of sensor node helps in determining the event of interest. A mobile sink is introduced to track the
event driven sensor nodes in the path of the event, thus conserving energy and time. We present a novel
range based localization algorithm which helps the mobile sink to compute the location of the sensor
nodes efficiently. The data transfer from the mobile sink and the sensor nodes is used to estimate the
sensor location. The sensor nodes do not need to spend energy on neighbouring interaction for
localization. The localization mechanism has been implemented in TOSSIM. The simulation results show
that our scheme performed better than other range-based schemes.
AN IMPROVED DECENTRALIZED APPROACH FOR TRACKING MULTIPLE MOBILE TARGETS THROU...ijwmn
Target localization and tracking problems in WSNs have received considerable attention recently, driven
by the requirement to achieve high localization accuracy, with the minimum cost possible. In WSN based
tracking applications, it is critical to know the current location of any sensor node with the minimum
energy consumed. This paper focuses on the energy consumption issue in terms of communication
between nodes whenever the localization information is transmitted to a sink node. Tracking through
WSNs can be categorized into centralized and decentralized systems. Decentralized systems offer low
power consumption when deployed to track a small number of mobile targets compared to the centralized
tracking systems. However, in several applications, it is essential to position a large number of mobile
targets. In such applications, decentralized systems offer high power consumption, since the location of
each mobile target is required to be transmitted to a sink node, and this increases the power consumption
for the whole WSN. In this paper, we propose a power efficient decentralized approach for tracking a
large number of mobile targets while offering reasonable localization accuracy through ZigBee network
The document proposes a new localization method called A2L (Angle to Landmark) for wireless sensor networks. A2L uses angle of arrival measurements between sensor nodes and a subset of nodes equipped with GPS (landmarks) to determine the positions of non-landmark nodes. Compared to previous methods like APS and AHLoS that also use angle and distance measurements, simulations show that A2L can locate a greater number of nodes with higher accuracy while requiring fewer connections between nodes. The method is also low-cost since it does not require each node to have GPS or other expensive equipment.
Iaetsd improving the location of nodes in wireless adIaetsd Iaetsd
The document proposes an Improvised LAL approach to localize nodes in wireless ad hoc and sensor networks. It aims to identify and convert non-localizable nodes to localizable nodes in a single round. The approach has three main modules: 1) Identifying localizable and non-localizable nodes, 2) Analyzing the network structure using a distance graph, and 3) Making adjustments to distinctively convert non-localizable nodes. Additional techniques like add-heuristic and geographical routing are used. The approach decomposes the distance graph and manages components as a tree to efficiently make adjustments along paths from roots to leaves. This allows all nodes to be localized using standard localization algorithms.
The document presents a localization technique for wireless sensor networks that uses trilateration based on received signal strength. It begins with an introduction to wireless sensor networks and localization. It then discusses related work on trilateration localization techniques. The document proposes a methodology that uses signal propagation models to estimate distances between nodes from received signal strength, and then applies trilateration to compute node positions in 3D space using four or more anchor nodes. It presents simulation results showing the mean square error decreases and localization accuracy increases as the number of anchor nodes increases. The technique provides more accurate localization than existing fuzzy logic approaches, with average localization errors less than 0.5 units when using 100 anchor nodes.
This document discusses localization techniques in wireless sensor networks (WSNs). It begins with an introduction to WSNs and their applications that require location information. While GPS could provide location data, it is not practical for WSNs due to cost and physical constraints. The document then categorizes localization methods as range-based, which use distance or angle measurements, and range-free, which do not directly measure distance. Specific techniques like time of arrival, received signal strength, and DV-Hop localization are described. The document concludes with classifications of localization methods and topics for future work.
A New Approach for Error Reduction in Localization for Wireless Sensor Networksidescitation
This paper proposes an improved RSSI-based localization method for wireless sensor networks to reduce localization error. The key points are:
1) Experimental RSSI measurements are taken between sensor nodes at various transmission power levels in an indoor environment.
2) A path loss model is fitted to the RSSI data to estimate distances, but this results in significant errors.
3) The model is improved by incorporating the mean error observed for each power level, which reduces localization error by 31-53% across power levels.
4) The improved method provides more accurate localization especially at higher transmission powers, important for applications requiring precise location information.
Effective range free localization scheme for wireless sensor networkijmnct
Location aware sensors can be used in many areas such as military and civilian applications. Wireless
Sensor Networks help to identify the accurate location of the event. In this paper a cost effective schema for
localization has been proposed. It uses two beacon nodes to identify the location of unknown nodes. It
also uses flooding and estimating method to accurately identify the location of other nodes. Available area
is divided into zones and beacons are provided for each zone. Beacon nodes are placed in appropriate
locations normally two in a zone to provide location information. Using the two nodes location of unknown
nodes can be calculated accurately.
Location Fingerprinting is a very familiar Wi-Fi positioning method, which determines a device by retrieving the information recorded containing the location fingerprint. These methods deploy the signal strength (RSS) to predict the coordinate. There are feedbacks for using the absolute RSS either the absolute RSS in a time interval may not be representable of the IEEE 802.11 signal, as the signal may fluctuate or a manual error prone calibration is needed across different mobile platform. The main target is to propose the use of Fourier descriptors in LF. We convert the IEEE 802.11b Wi-Fi signal into a Fourier domain. Then, the Fourier descriptors are used to predict the location by applying the K-Nearest Neighbor algorithm. The results show that the effectiveness of LF methods based on Fourier descriptors lead to substantially more accurate and robust localization.
This document analyzes node localization in wireless sensor networks. It compares three range-based localization algorithms (TOA, AOA, RSSI) based on their standard deviation of localization error under varying network parameters. Through simulations, it finds that the TOA algorithm generally provides the lowest error compared to the other two algorithms. Specifically, it finds that standard deviation decreases with increasing network density and anchor node density, but first decreases and then increases with network size. It concludes that the TOA algorithm provides the best accuracy for localization based on its analysis of parameter effects.
A self localization scheme for mobile wireless sensor networksambitlick
This document describes a self-localization scheme for mobile wireless sensor networks. The scheme selects optimal relay nodes to transmit location information from anchor nodes to sensor nodes beyond one-hop range. The relay nodes are selected based on maintaining proximity to anchor nodes as they move. This allows accurate tracking of anchor node positions while reducing energy consumption by activating only selected relay nodes. Sensor nodes then use the location data from three relay nodes to triangulate their own position. The scheme enables energy-efficient self-localization of mobile sensor nodes in wireless networks.
HORIZONTAL AND VERTICAL ZONE BASED LOCATION TECHNIQUES FOR WIRELESS SENSOR NE...ijwmn
Localization is an important feature in Wireless sensor networks (WSNs). Accuracy in node localization with proper synchronization and required localization of sensor nodes, save node energy and enhance the performance of communication network protocols. In this paper we propose distributed localization algorithms and assume position known Cluster Head (CH) and position unknown three beacon nodes for each cluster. Using trilateration technique beacon nodes are located. Additional beacon node is added to confirm the location of beacon nodes and maintain location accuracy. These position localized beacon nodes help to locate other sensor nodes. The proposed two distributed zone based localization algorithms
are (i) Horizontal Location Position System (H-LPS), where cluster is divided into Horizontal Zones (HZs) and beacon nodes locate in horizontal direction and (ii) Vertical Location Position System (V-LPS), where cluster is divided into Vertical Zones (VZs) and beacon nodes locate in vertical direction. The main advantage of zone based localization is nodes belonging to a bounded zone (horizontal or vertical) are localized and participate in WSN computing. If a bounded zone is eliminated during localization, then nodes do not participate in localization and thus save WSN computing. We provide zone based simulations for H-LPS and V-LPS in comparison with existing localization algorithms like Ad hoc Positioning System (APS), Recursive Positioning Estimation (RPE) and Directed Positioning Estimation (DPE). Performance evaluation of H-LPS and V-LPS illustrate that for zone based localization, H-LPS
and V-LPS perform better that existing localization techniques. Bounded z
This document summarizes localization algorithms in wireless sensor networks. It discusses how node localization is an important challenge for wireless sensor networks. It reviews different approaches to node localization, including centralized and distributed algorithms. Centralized algorithms migrate data to a central station, while distributed algorithms perform computations locally. Specific algorithms discussed include MDS-MAP, simulated annealing approaches, beacon-based, relaxation-based, and coordinate system stitching approaches. The document also discusses hybrid localization techniques and future challenges in improving localization.
CTAS is a collaborative two-level task scheduling algorithm for wireless sensor nodes with multiple sensing units:
1) It performs coarse-grain scheduling at the group level, scheduling event types and data transmissions for neighboring sensor nodes based on their overlapping sensing areas.
2) It performs fine-grain scheduling at the individual node level, scheduling the tasks of the assigned event types for each sensor node.
3) Simulation results show CTAS significantly improves energy consumption by up to 67% and reduces event misses by 75% compared to existing techniques.
Range Free Localization using Expected Hop Progress in Wireless Sensor NetworkAM Publications
Wireless sensor network (WSN) combines the concept of wireless network with sensors. Wireless Sensor Networks
have been proposed for a multitude of location-dependent applications. Localization (location estimation) capability is
essential in most wireless sensor network applications. In environmental monitoring applications such as animal habitat
monitoring, bush fire surveillance, water quality monitoring and precision agriculture, the measurement data are
meaningless without an accurate knowledge of the location from where the data are obtained. Finding position without the
aid of GPS in each node of an ad hoc network is important in cases where GPS is either not accessible, or not practical to use
due to power, form factor or line of sight conditions. So here we are going to used DV-Hop algorithm, i.e. distance vector
routing algorithm for finding the position of sensor. Here we summarizes the performance evaluation criteria of the
wireless sensor network and algorithms, classification methods, and highlights the principles and characteristics of the
algorithm and system representative of the field in recent years, and several algorithms simulation and analysis.
AN EFFICIENT SLEEP SCHEDULING STRATEGY FOR WIRELESS SENSOR NETWORKijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Spatial Correlation Based Medium Access Control Protocol Using DSR & AODV Rou...IOSR Journals
The document summarizes a proposed spatial correlation-based medium access control protocol for wireless sensor networks that aims to improve energy efficiency. It discusses how sensor nodes are spatially distributed and correlated in detecting events. An iterative node selection algorithm is used to select a minimum set of representative sensor nodes based on a distortion constraint, in order to reduce redundant transmissions. The protocol uses vector quantization to calculate distances between nodes and a mobile element. It then evaluates the performance of using the DSR and AODV routing protocols with this spatial correlation-based MAC protocol in terms of energy consumption and packet drop ratio through simulations. The simulation results show that the protocol with AODV routing performs better than with DSR routing.
TARGET LOCALIZATION IN WIRELESS SENSOR NETWORKS BASED ON RECEIVED SIGNAL STRE...sipij
We consider the problem of localizing a target taking the help of a set of anchor beacon nodes. A small
number of beacon nodes are deployed at known locations in the area. The target can detect a beacon
provided it happens to lie within the beacon’s transmission range. Thus, the target obtains a measurement
vector containing the readings of the beacons: ‘1’ corresponding to a beacon if it is able to detect the
target, and ‘0’ if the beacon is not able to detect the target. The goal is twofold: to determine the location
of the target based on the binary measurement vector at the target; and to study the behaviour of the
localization uncertainty as a function of the beacon transmission range (sensing radius) and the number of
beacons deployed. Beacon transmission range means signal strength of the beacon to transmit and receive
the signals which is called as Received Signal Strength (RSS). To localize the target, we propose a gridmapping
based approach, where the readings corresponding to locations on a grid overlaid on the region
of interest are used to localize the target. To study the behaviour of the localization uncertainty as a
function of the sensing radius and number of beacons, extensive simulations and numerical experiments
are carried out. The results provide insights into the importance of optimally setting the sensing radius and
the improvement obtainable with increasing number of beacons.
A Fuzzy Based Priority Approach in Mobile Sensor Network CoverageIDES Editor
In this paper a new fuzzy based approach for
improving network coverage in wireless mobile sensor
networks is proposed. In the proposed approach firstly
each mobile sensor node determines its neighbors and its
distance from borders and obstacles. According to these
values, fuzzy inference engine calculates the priority of
node for movement. Then according to the priority, in
turn, nodes move away from each other to increase
coverage area in the target field. Simulation results show
that our fuzzy approach can reach higher degree of
coverage against other common approaches like FOA,
VEC and TRI algorithms.
Hybrid Target Tracking Scheme in Wireless Sensor NetworksIRJET Journal
1) The document discusses a hybrid target tracking scheme for wireless sensor networks that combines range-free and range-based localization techniques to more accurately determine the location of unknown nodes.
2) It describes some of the challenges with localization in wireless sensor networks, including environmental factors that can impact variables used for localization like transmission range and signal strength.
3) The hybrid approach determines the exact location of an unknown node using a combination of range-free and range-based methods to improve accuracy compared to using either method alone.
3D Localization Algorithms for Wireless Sensor NetworksIOSR Journals
This document discusses localization algorithms for 3D wireless sensor networks. It begins by explaining that localization in 3D spaces poses unique challenges compared to 2D, as strategies used in 2D do not directly extend to 3D. It then reviews common range-based localization methods like received signal strength and time-based methods, as well as range-free methods like centroid and DV-HOP algorithms. The document aims to address the problem of localization for sensor networks deployed in 3D surfaces.
Optimum Sensor Node Localization in Wireless Sensor Networkspaperpublications3
Abstract: Scientists, engineers, and researchers use wireless sensor networks (WSN) for a wide array of applications. Many of these applications rely on knowledge of the precise position of each node. An optimum localization algorithm can be used for determining the position of nodes in a wireless sensor network. This paper provides an overview of different approach of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Experiments were performed in a testbed area containing anchor and blind nodes deployed in it to characterize the pathloss exponent and to determine the localization error of the algorithm. Details regarding the implementation of new algorithm are also discussed in this paper.
LOCALIZATION ALGORITHM USING VARYING SPEED MOBILE SINK FOR WIRELESS SENSOR NE...ijasuc
Localization of sensor nodes is important in many aspects in wireless sensor networks. The known
location of sensor node helps in determining the event of interest. A mobile sink is introduced to track the
event driven sensor nodes in the path of the event, thus conserving energy and time. We present a novel
range based localization algorithm which helps the mobile sink to compute the location of the sensor
nodes efficiently. The data transfer from the mobile sink and the sensor nodes is used to estimate the
sensor location. The sensor nodes do not need to spend energy on neighbouring interaction for
localization. The localization mechanism has been implemented in TOSSIM. The simulation results show
that our scheme performed better than other range-based schemes.
AN IMPROVED DECENTRALIZED APPROACH FOR TRACKING MULTIPLE MOBILE TARGETS THROU...ijwmn
Target localization and tracking problems in WSNs have received considerable attention recently, driven
by the requirement to achieve high localization accuracy, with the minimum cost possible. In WSN based
tracking applications, it is critical to know the current location of any sensor node with the minimum
energy consumed. This paper focuses on the energy consumption issue in terms of communication
between nodes whenever the localization information is transmitted to a sink node. Tracking through
WSNs can be categorized into centralized and decentralized systems. Decentralized systems offer low
power consumption when deployed to track a small number of mobile targets compared to the centralized
tracking systems. However, in several applications, it is essential to position a large number of mobile
targets. In such applications, decentralized systems offer high power consumption, since the location of
each mobile target is required to be transmitted to a sink node, and this increases the power consumption
for the whole WSN. In this paper, we propose a power efficient decentralized approach for tracking a
large number of mobile targets while offering reasonable localization accuracy through ZigBee network
The document proposes a new localization method called A2L (Angle to Landmark) for wireless sensor networks. A2L uses angle of arrival measurements between sensor nodes and a subset of nodes equipped with GPS (landmarks) to determine the positions of non-landmark nodes. Compared to previous methods like APS and AHLoS that also use angle and distance measurements, simulations show that A2L can locate a greater number of nodes with higher accuracy while requiring fewer connections between nodes. The method is also low-cost since it does not require each node to have GPS or other expensive equipment.
Iaetsd improving the location of nodes in wireless adIaetsd Iaetsd
The document proposes an Improvised LAL approach to localize nodes in wireless ad hoc and sensor networks. It aims to identify and convert non-localizable nodes to localizable nodes in a single round. The approach has three main modules: 1) Identifying localizable and non-localizable nodes, 2) Analyzing the network structure using a distance graph, and 3) Making adjustments to distinctively convert non-localizable nodes. Additional techniques like add-heuristic and geographical routing are used. The approach decomposes the distance graph and manages components as a tree to efficiently make adjustments along paths from roots to leaves. This allows all nodes to be localized using standard localization algorithms.
The document presents a localization technique for wireless sensor networks that uses trilateration based on received signal strength. It begins with an introduction to wireless sensor networks and localization. It then discusses related work on trilateration localization techniques. The document proposes a methodology that uses signal propagation models to estimate distances between nodes from received signal strength, and then applies trilateration to compute node positions in 3D space using four or more anchor nodes. It presents simulation results showing the mean square error decreases and localization accuracy increases as the number of anchor nodes increases. The technique provides more accurate localization than existing fuzzy logic approaches, with average localization errors less than 0.5 units when using 100 anchor nodes.
This document discusses localization techniques in wireless sensor networks (WSNs). It begins with an introduction to WSNs and their applications that require location information. While GPS could provide location data, it is not practical for WSNs due to cost and physical constraints. The document then categorizes localization methods as range-based, which use distance or angle measurements, and range-free, which do not directly measure distance. Specific techniques like time of arrival, received signal strength, and DV-Hop localization are described. The document concludes with classifications of localization methods and topics for future work.
A New Approach for Error Reduction in Localization for Wireless Sensor Networksidescitation
This paper proposes an improved RSSI-based localization method for wireless sensor networks to reduce localization error. The key points are:
1) Experimental RSSI measurements are taken between sensor nodes at various transmission power levels in an indoor environment.
2) A path loss model is fitted to the RSSI data to estimate distances, but this results in significant errors.
3) The model is improved by incorporating the mean error observed for each power level, which reduces localization error by 31-53% across power levels.
4) The improved method provides more accurate localization especially at higher transmission powers, important for applications requiring precise location information.
Effective range free localization scheme for wireless sensor networkijmnct
Location aware sensors can be used in many areas such as military and civilian applications. Wireless
Sensor Networks help to identify the accurate location of the event. In this paper a cost effective schema for
localization has been proposed. It uses two beacon nodes to identify the location of unknown nodes. It
also uses flooding and estimating method to accurately identify the location of other nodes. Available area
is divided into zones and beacons are provided for each zone. Beacon nodes are placed in appropriate
locations normally two in a zone to provide location information. Using the two nodes location of unknown
nodes can be calculated accurately.
Location Fingerprinting is a very familiar Wi-Fi positioning method, which determines a device by retrieving the information recorded containing the location fingerprint. These methods deploy the signal strength (RSS) to predict the coordinate. There are feedbacks for using the absolute RSS either the absolute RSS in a time interval may not be representable of the IEEE 802.11 signal, as the signal may fluctuate or a manual error prone calibration is needed across different mobile platform. The main target is to propose the use of Fourier descriptors in LF. We convert the IEEE 802.11b Wi-Fi signal into a Fourier domain. Then, the Fourier descriptors are used to predict the location by applying the K-Nearest Neighbor algorithm. The results show that the effectiveness of LF methods based on Fourier descriptors lead to substantially more accurate and robust localization.
This document analyzes node localization in wireless sensor networks. It compares three range-based localization algorithms (TOA, AOA, RSSI) based on their standard deviation of localization error under varying network parameters. Through simulations, it finds that the TOA algorithm generally provides the lowest error compared to the other two algorithms. Specifically, it finds that standard deviation decreases with increasing network density and anchor node density, but first decreases and then increases with network size. It concludes that the TOA algorithm provides the best accuracy for localization based on its analysis of parameter effects.
A self localization scheme for mobile wireless sensor networksambitlick
This document describes a self-localization scheme for mobile wireless sensor networks. The scheme selects optimal relay nodes to transmit location information from anchor nodes to sensor nodes beyond one-hop range. The relay nodes are selected based on maintaining proximity to anchor nodes as they move. This allows accurate tracking of anchor node positions while reducing energy consumption by activating only selected relay nodes. Sensor nodes then use the location data from three relay nodes to triangulate their own position. The scheme enables energy-efficient self-localization of mobile sensor nodes in wireless networks.
HORIZONTAL AND VERTICAL ZONE BASED LOCATION TECHNIQUES FOR WIRELESS SENSOR NE...ijwmn
Localization is an important feature in Wireless sensor networks (WSNs). Accuracy in node localization with proper synchronization and required localization of sensor nodes, save node energy and enhance the performance of communication network protocols. In this paper we propose distributed localization algorithms and assume position known Cluster Head (CH) and position unknown three beacon nodes for each cluster. Using trilateration technique beacon nodes are located. Additional beacon node is added to confirm the location of beacon nodes and maintain location accuracy. These position localized beacon nodes help to locate other sensor nodes. The proposed two distributed zone based localization algorithms
are (i) Horizontal Location Position System (H-LPS), where cluster is divided into Horizontal Zones (HZs) and beacon nodes locate in horizontal direction and (ii) Vertical Location Position System (V-LPS), where cluster is divided into Vertical Zones (VZs) and beacon nodes locate in vertical direction. The main advantage of zone based localization is nodes belonging to a bounded zone (horizontal or vertical) are localized and participate in WSN computing. If a bounded zone is eliminated during localization, then nodes do not participate in localization and thus save WSN computing. We provide zone based simulations for H-LPS and V-LPS in comparison with existing localization algorithms like Ad hoc Positioning System (APS), Recursive Positioning Estimation (RPE) and Directed Positioning Estimation (DPE). Performance evaluation of H-LPS and V-LPS illustrate that for zone based localization, H-LPS
and V-LPS perform better that existing localization techniques. Bounded z
This document summarizes localization algorithms in wireless sensor networks. It discusses how node localization is an important challenge for wireless sensor networks. It reviews different approaches to node localization, including centralized and distributed algorithms. Centralized algorithms migrate data to a central station, while distributed algorithms perform computations locally. Specific algorithms discussed include MDS-MAP, simulated annealing approaches, beacon-based, relaxation-based, and coordinate system stitching approaches. The document also discusses hybrid localization techniques and future challenges in improving localization.
CTAS is a collaborative two-level task scheduling algorithm for wireless sensor nodes with multiple sensing units:
1) It performs coarse-grain scheduling at the group level, scheduling event types and data transmissions for neighboring sensor nodes based on their overlapping sensing areas.
2) It performs fine-grain scheduling at the individual node level, scheduling the tasks of the assigned event types for each sensor node.
3) Simulation results show CTAS significantly improves energy consumption by up to 67% and reduces event misses by 75% compared to existing techniques.
Range Free Localization using Expected Hop Progress in Wireless Sensor NetworkAM Publications
Wireless sensor network (WSN) combines the concept of wireless network with sensors. Wireless Sensor Networks
have been proposed for a multitude of location-dependent applications. Localization (location estimation) capability is
essential in most wireless sensor network applications. In environmental monitoring applications such as animal habitat
monitoring, bush fire surveillance, water quality monitoring and precision agriculture, the measurement data are
meaningless without an accurate knowledge of the location from where the data are obtained. Finding position without the
aid of GPS in each node of an ad hoc network is important in cases where GPS is either not accessible, or not practical to use
due to power, form factor or line of sight conditions. So here we are going to used DV-Hop algorithm, i.e. distance vector
routing algorithm for finding the position of sensor. Here we summarizes the performance evaluation criteria of the
wireless sensor network and algorithms, classification methods, and highlights the principles and characteristics of the
algorithm and system representative of the field in recent years, and several algorithms simulation and analysis.
AN EFFICIENT SLEEP SCHEDULING STRATEGY FOR WIRELESS SENSOR NETWORKijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Spatial Correlation Based Medium Access Control Protocol Using DSR & AODV Rou...IOSR Journals
The document summarizes a proposed spatial correlation-based medium access control protocol for wireless sensor networks that aims to improve energy efficiency. It discusses how sensor nodes are spatially distributed and correlated in detecting events. An iterative node selection algorithm is used to select a minimum set of representative sensor nodes based on a distortion constraint, in order to reduce redundant transmissions. The protocol uses vector quantization to calculate distances between nodes and a mobile element. It then evaluates the performance of using the DSR and AODV routing protocols with this spatial correlation-based MAC protocol in terms of energy consumption and packet drop ratio through simulations. The simulation results show that the protocol with AODV routing performs better than with DSR routing.
TARGET LOCALIZATION IN WIRELESS SENSOR NETWORKS BASED ON RECEIVED SIGNAL STRE...sipij
We consider the problem of localizing a target taking the help of a set of anchor beacon nodes. A small
number of beacon nodes are deployed at known locations in the area. The target can detect a beacon
provided it happens to lie within the beacon’s transmission range. Thus, the target obtains a measurement
vector containing the readings of the beacons: ‘1’ corresponding to a beacon if it is able to detect the
target, and ‘0’ if the beacon is not able to detect the target. The goal is twofold: to determine the location
of the target based on the binary measurement vector at the target; and to study the behaviour of the
localization uncertainty as a function of the beacon transmission range (sensing radius) and the number of
beacons deployed. Beacon transmission range means signal strength of the beacon to transmit and receive
the signals which is called as Received Signal Strength (RSS). To localize the target, we propose a gridmapping
based approach, where the readings corresponding to locations on a grid overlaid on the region
of interest are used to localize the target. To study the behaviour of the localization uncertainty as a
function of the sensing radius and number of beacons, extensive simulations and numerical experiments
are carried out. The results provide insights into the importance of optimally setting the sensing radius and
the improvement obtainable with increasing number of beacons.
A Fuzzy Based Priority Approach in Mobile Sensor Network CoverageIDES Editor
In this paper a new fuzzy based approach for
improving network coverage in wireless mobile sensor
networks is proposed. In the proposed approach firstly
each mobile sensor node determines its neighbors and its
distance from borders and obstacles. According to these
values, fuzzy inference engine calculates the priority of
node for movement. Then according to the priority, in
turn, nodes move away from each other to increase
coverage area in the target field. Simulation results show
that our fuzzy approach can reach higher degree of
coverage against other common approaches like FOA,
VEC and TRI algorithms.
Hybrid Target Tracking Scheme in Wireless Sensor NetworksIRJET Journal
1) The document discusses a hybrid target tracking scheme for wireless sensor networks that combines range-free and range-based localization techniques to more accurately determine the location of unknown nodes.
2) It describes some of the challenges with localization in wireless sensor networks, including environmental factors that can impact variables used for localization like transmission range and signal strength.
3) The hybrid approach determines the exact location of an unknown node using a combination of range-free and range-based methods to improve accuracy compared to using either method alone.
The document discusses energy-efficient intrusion detection techniques for wireless sensor networks. It summarizes routing protocols used in WSNs and proposes using hybrid anomaly and misuse detection at cluster heads in hierarchical routing to increase detection rates while reducing energy consumption. For flat-based routing, it suggests using statistical anomaly detection at each node. For location-based routing, it proposes detecting intrusions based on location and trust information to limit communication between distant nodes and the base station. Simulation results on real sensor network data show the approaches can effectively detect intrusions while preserving energy.
Estimating Parameters of Multiple Heterogeneous Target Objects Using Composit...ambitlick
This article proposes a method for estimating parameters of multiple heterogeneous target objects (objects with different sizes and shapes) using networked binary sensors. The sensors are simple and only report detections, but no individual sensor location is known. The method introduces "composite sensor nodes" containing multiple sensors in a fixed arrangement. This provides relative location information to help distinguish individual target objects. As an example, the article considers a composite node with two sensors on a line segment. Measures from these nodes can identify target shapes and estimate object parameters like radius and side lengths. Numerical tests demonstrate networked composite sensors can estimate parameters of multiple target objects.
Analysis of GPSR and its Relevant Attacks in Wireless Sensor NetworksIDES Editor
Most of the routing protocols proposed for ad-hoc
networks and sensor networks are not designed with security
as a goal. Hence, many routing protocols are vulnerable to an
attack by an adversary who can disrupt the network or harness
valuable information from the network. Routing Protocols
for wireless sensor networks are classified into three types
depending on their network structure as Flat routing protocols,
Hierarchical routing protocol and Geographic routing
protocols. We mainly concentrate on location-based or
geographic routing protocol like Greedy Perimeter Stateless
Routing Protocol (GPSR). Sybil attack and Selective
forwarding attack are the two attacks feasible in GPSR. These
attacks are implemented in GPSR and their losses caused to
the network are analysed
A Novel Three-Dimensional Adaptive Localization (T-Dial) Algorithm for Wirele...iosrjce
The document presents a novel three-dimensional adaptive localization (T-Dial) algorithm for wireless sensor networks. The proposed algorithm works in four primary phases: 1) Neighbor formation where nodes broadcast information to nodes within transmission range to form neighbor tables; 2) Group formation where anchor nodes connect to non-anchor nodes to divide the network into smaller manageable groups; 3) Edge node marking where edge nodes on the network boundary are detected and marked; and 4) Localization error correction where missing nodes from initial setup are rediscovered and corrected. Simulation results show the proposed algorithm improves localization rate, reduces localization error, and increases positioning rate compared to existing algorithms.
The document presents a novel three-dimensional adaptive localization (T-Dial) algorithm for wireless sensor networks. The proposed algorithm works in four primary phases: 1) Neighbor formation where nodes broadcast information to nodes within transmission range to form neighbor tables; 2) Group formation where anchor nodes connect to nearby nodes to divide the network into manageable groups; 3) Edge node marking where edge nodes on the network boundary are detected and marked; and 4) Localization error correction where missing nodes from initial setup are rediscovered and corrected. Simulation results show the proposed algorithm improves localization rate, reduces localization error, and increases positioning rate compared to existing algorithms.
VEBEK is an energy-efficient framework for secure communication in wireless sensor networks. It uses dynamic encryption keys based on the residual virtual energy of sensor nodes, eliminating the need for rekeying messages. Each packet is encrypted with a different one-time key, improving security. VEBEK provides authentication, integrity, and non-repudiation without enlarging packets through modular design. It can efficiently detect and filter malicious data through two operational modes: VEBEK-1 watches all neighbors, VEBEK-2 watches some nodes statistically. Evaluation shows VEBEK eliminates malicious data without transmission overhead.
Accurate and Energy-Efficient Range-Free Localization for Mobile Sensor Networksambitlick
The document summarizes an algorithm called WMCL that improves the sampling efficiency and localization accuracy of existing SMC-based localization algorithms for mobile sensor networks. WMCL achieves higher sampling efficiency by further reducing the size of sensor nodes' bounding boxes, which restrict the scope from which candidate samples are selected, by up to 87%. This improves the sampling efficiency by up to 95%. WMCL also improves localization accuracy by using estimated position information from sensor neighbors, achieving similar accuracy with less communication and computation compared to other algorithms using similar methods.
Preparation gade and idol model for preventing multiple spoofing attackers in...prjpublications
This document proposes the GADE and IDOL models for detecting and localizing multiple spoofing attackers in wireless networks. GADE uses spatial correlation of received signal strength readings and cluster analysis to detect spoofing attacks and determine the number of attackers. IDOL builds on GADE and uses additional localization algorithms to pinpoint the locations of multiple adversaries. The models were evaluated using both 802.11 and 802.15.4 networks in real office environments, achieving over 90% accuracy in detecting attacks and localizing adversaries. Support vector machines were also used to improve determination of the number of attackers when training data is available.
Performance Evaluation of ad-hoc Network Routing Protocols using ns2 SimulationIDES Editor
Ad-hoc networks are basically peer to peer multihop
mobile wireless networks in which the information packets
are transmitted in a ‘store and forward’ manner from a source
to an arbitrary destination via intermediate nodes. The main
objective of this paper is to evaluate the performance of various
ad-hoc networks routing protocols viz. DSDV (Destination
Sequence Distance Vector), DSR (Dynamic Source Routing)
and AODV (Ad-hoc On Demand Distance Vector). The
comparison of these protocols is based on different
performance metrics, which are throughput, packet delivery
ratio, routing overheads, packet drop and average end to end
delay. The performance evaluation has been done by using
simulation tool NS2 (Network Simulator) which is the main
simulator.
High uncertainty aware localization and error optimization of mobile nodes fo...IAESIJAI
The localization of mobile sensor nodes in a wireless sensor network (WSN) is a key research area for the speedy development of wireless communication and microelectronics. The localization of mobile sensor nodes massively depends upon the received signal strength (RSS). Recently, the least squared relative error (LSRE) measurements are optimized using traditional semidefinite programming (SDP) and the location of the mobile sensor nodes was determined using the previous localization methods like least squared relative error and semidefinite programming (LSRE-SDP), and approximate nonlinear least squares and semidefinite programming (ANLS-SDP). Therefore, in this work, a novel high uncertainty aware-localization error correction and optimization (HUA-LECO) model is employed to minimize the aforementioned problems regarding the localization of mobile sensor nodes and enhance the performance efficiency of root mean square error (RMSE) results. Here, the position of target mobile sensor nodes is evaluated based on the gathered measurements while discarding faulty data. Here, an iterative weight updation approach is utilized to perform localization based on Monte Carlo simulations. Simulation results show significant improvement in terms of RMSE results in comparison with traditional LSRE-SDP and ANLS-SDP methods under high uncertainty.
DETECTION OF SYBIL ATTACK IN MOBILE ADHOCK NETWORKINGPrakash Kumar
The document discusses a proposed system to detect Sybil attackers in a wireless network using a centralized base station without additional hardware like GPS. It aims to identify Sybil identities with accuracy even when nodes are mobile. A Sybil attacker can create multiple identities on a single device to launch coordinated attacks. The proposed system uses a Neighbor Discovery Distance algorithm and centralized authentication server to reduce packet delay and detect attackers, enabling efficient secure data transmission. It evaluates topology design, neighbor discovery, and how Sybil attacks work to spoof identities and impact networks. Simulation results showed the scheme can effectively detect static and mobile Sybil attackers.
CONTRADICTION COMMUNICATION RANGE OF SENSOR NETWORKSpharmaindexing
This document discusses location verification in wireless sensor networks. It describes two categories of location verification: on-spot verification and in-region verification. On-spot verification checks if a sensor's estimated location matches its true location, while in-region verification checks if a sensor is located within an application-specific region. The document proposes two lightweight algorithms, GFM and GFT, for on-spot verification using neighborhood observations. It also describes a probabilistic algorithm to compute the confidence that a sensor is inside the verification region for in-region verification. The proposed verification system can effectively verify sensor locations without relying on specialized hardware or deployment knowledge, making it suitable for low-cost wireless sensor networks.
Wi-Fi fingerprinting-based floor detection using adaptive scaling and weighte...CSITiaesprime
In practical applications, accurate floor determination in multi-building/floor environments is particularly useful and plays an increasingly crucial role in the performance of location-based services. An accurate and robust building and floor detection can reduce the location search space and ameliorate the positioning and wayfinding accuracy. As an efficient solution, this paper proposes a floor identification method that exploits statistical properties of wireless access point propagated signals to exponent received signal strength (RSS) in the radio map. Then, using single-layer extreme learning machine-weighted autoencoder (ELM-WAE) main feature extraction and dimensional reduction is implemented. Finally, ELM based classifier is trained over a new feature space to determine floor level. For the efficiency evaluation of our proposed model, we utilized three different datasets captured in the real scenarios. The evaluation result shows that the proposed model can achieve state-of-art performance and improve the accuracy of floor detection compared with multiple recent techniques. In this way, the floor level can be identified with 97.30%, 95.32%, and 96.39% on UJIIndoorLoc, Tampere, and UTSIndoorLoc datasets, respectively.
paper presentation _ survey of wireless sensor netwrokejbyun77
The document discusses recent trends in wireless sensor network research, including an overview of different wireless sensor network technologies and applications. It also examines the role of middleware in supporting wireless sensor networks by providing common communication mechanisms and processing sensed data to abstract high-level information. Several existing middleware platforms and programming models are described that aim to achieve scalability, low power consumption, and efficient data aggregation and querying in wireless sensor networks.
This document summarizes an evaluation of ad-hoc routing protocols for wireless sensor networks. It analyzes the performance of three protocols - DSDV, AODV, and DSR - through simulation. The results show that AODV has the best performance with less degradation and packet loss compared to DSDV and DSR as node mobility increases. AODV is therefore identified as the most suitable routing protocol for use in mobile wireless sensor networks based on its ability to handle topology changes from node movement.
The document summarizes a study on using Wi-Fi signals for indoor location fingerprinting. It discusses how fingerprinting involves two phases: a calibration phase where signal strength is recorded at calibration points, and a location estimation phase where current signal strength is compared to the fingerprint map. It evaluates the k-nearest neighbor algorithm using Euclidean, Manhattan, and Chebychev distances to estimate location. Tests of this approach involved collecting Wi-Fi signal data at calibration points in four rooms and a hall to generate a fingerprint map for location estimation. The accuracy of Euclidean and Manhattan distances was found to be better than Chebychev distance for this location fingerprinting method.
The document summarizes a study on using Wi-Fi signals for indoor location fingerprinting. It discusses how fingerprinting involves two phases: a calibration phase where signal strength is recorded at calibration points, and a location estimation phase where current signal strength is compared to the fingerprint map. It evaluates the k-nearest neighbor algorithm using Euclidean, Manhattan, and Chebychev distances to estimate location. Tests of this approach involved collecting Wi-Fi signal data at calibration points in four rooms and a hall to generate a fingerprint map for location estimation. The accuracy of Euclidean and Manhattan distances was found to be better than Chebychev distance for this location fingerprinting method.
EFFECTIVE AND SECURE DATA COMMUNICATION IN WSNs CONSIDERING TRANSFER MODULE O...IJEEE
A Bio-inspired clustering algorithm based on BFO has been proposed and investigation on energy efficient clustering algorithms related to WSNs has been done in this paper. The contribution of this paper related to use of Bacteria foraging algorithm firstly for WSNs for enhancing network lifetime of sensor nodes.
Similar to Indoor Localization Using Local Node Density In Ad Hoc WSNs (20)
EFFECTIVE AND SECURE DATA COMMUNICATION IN WSNs CONSIDERING TRANSFER MODULE O...
Indoor Localization Using Local Node Density In Ad Hoc WSNs
1. Indoor Localization using Local Node Density
in Ad-Hoc Wireless Sensor Networks
Proyecto Final de Carrera
Ingeniería de Telecomunicación
Ingeniería Técnica en Informática de Sistemas
Joaquín González Guerrero
2. Octubre. 2009 Escuela Politécnica Superior
Universidad San Pablo CEU
2. Table of Contents
1. Objective and thesis contribution
2. Wireless Sensor Networks (WSNs)
3. Problem statement
4. State of the Art: Location Systems for WSNs
5. Localization algorithms overview
6. Simulation
7. Experimental evaluation
8. Conclusions
9. Future Work
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 2
3. Thesis contribution
Objective:
Deployment and performance characterization of indoor distributed location
algorithms for ad-hoc wireless sensor networks.
Contributions:
Detailed study of indoor positioning system based on Radio Signal Strength
(RSSI) range estimation.
First implementation and performance evaluation of novel Local Node
Density-based (LND) algorithm using simulation and real hardware.
Exhaustive comparison of LND against two distributed positioning algorithms
(DV-Hop, DV-Dist) over single self-developed simulation platform.
Quantitative performance analysis of five distributed positioning alternatives
in real indoor testbed environment.
Computational, communication and power cost associated to LND algorithm.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 3
4. Table of Contents
1. Objectives and thesis contribution
2. Wireless Sensor Networks (WSNs)
3. Problem statement
4. State of the Art: Location Systems for WSNs
5. Localization algorithms overview
6. Simulation
7. Experimental evaluation
8. Conclusions
9. Future Work
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 4
5. Wireless Sensor Networks (WSNs)
Collection of autonomous, spatially distributed devices.
Nodes have sensing capabilities.
Can communicate with each other to establish a network.
Resources limitations: size, cost, energy, computation, memory.
Applications:
Monitor physical conditions
Agriculture control, species monitoring
Forest fire surveillance
Detect structural damage
Early detection of leakages
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 5
6. Table of Contents
1. Objectives and thesis contribution
2. Wireless Sensor Networks (WSNs)
3. Problem statement
4. State of the Art: Location Systems for WSNs
5. Localization algorithms overview
6. Simulation
7. Experimental evaluation
8. Conclusions
9. Future Work
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 6
7. Problem statement
Goal:
Determine the location of individual sensor
nodes without relying on external infrastructure.
GPS unsuitable: unrealistically high costs,
coverage problems indoors.
WSNS: optimal alternative non-obstrusive,
infrastructure-free and low-cost
implementation.
Figure 1. Structural damage detection.
Motivation:
A myriad of applications rely on location data to
perform their tasks.
Physical measurements meaningless without
associated origin position.
Geographic and context-based routing protocols.
Figure 2. Forest fire surveillance
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 7
8. Table of Contents
1. Objectives and thesis contribution
2. Wireless Sensor Networks (WSNs)
3. Problem statement
4. State of the Art: Location Systems for WSNs
5. Localization algorithms overview
6. Simulation
7. Experimental evaluation
8. Conclusions
9. Future Work
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 8
9. Localization in WSNs: Overview
Area of intense research activity in the past years.
Broad spectrum of location techniques proposed.
Most proposals utilize a fraction of anchors with known positions.
Unknowns perform physical measurements to infer location.
Anchor
Unknown
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 9
10. Measurement Techniques
1. Distance related
Received Signal Strength Indicator (RSSI)
Time of Arrival (ToA)
Time Difference of Arrival (TDoA)
2. Angle of Arrival (AoA) Figure 3. Angulation based on two anchors [24].
Beamforming
Phase interferometry
Subspace-based
3. Scene analysis
RSSI-profiling (RADAR[6])
4. Connectivity-based (hop-count)
Figure 4. Hop-count measurement in anisotropic network.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 10
11. Location Systems for WSNs
One-hop Multihop
Range-free Range-based
Centralized Distributed
Active Badge [1] DV-Distance [8]
Active Office [2] N-hop multilateration [15]
DV-Hop [8]
Cricket [3] MDS range-based [12] Robust positioning [16]
Amorphous [9]
GPS-less [4] SDP range-based [13] Coordinate stitching [17,18]
SDP [10]
APIT [5] Simulated Annealing [14] Particle filters
MDS [11]
RSSI-profiling Kalman [19]
RADAR [6] Bayesian [20,21]
LANDMARK [7] Montecarlo [22,23]
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 11
12. Localization in WSNs:
General trends
Complexity/cost and accuracy tradeoff
Selection highly dependent on specific application requirements.
Accuracy Complexity Specialized HW Cost
Range-based ✓ ✗ Yes ✗
Range-free ✗ ✓ No ✓
Centralized vs Distributed localization algorithms
Implementation Accuracy Energy Cost
complexity consumption*
Centralized ✓ ✓ ✓ ↔it > hops ✗
Distributed ✗ ✗ ✓ ↔it < hops ✓
* It = Nº of iterations in distributed algorithm; hops = Avg. Nº of hops to central processing unit [25].
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 12
13. Table of Contents
1. Objectives and thesis contribution
2. Wireless Sensor Networks (WSNs)
3. Problem statement
4. State of the Art: Location Systems for WSNs
5. Localization algorithms overview
6. Simulation
7. Experimental evaluation
8. Conclusions
9. Future Work
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 13
14. Evaluated algorithms
Common features:
Truly distributed no external infrastructure or centralized processing unit.
Communication protocol based on local broadcast transmissions.
Scalable to large WSNs (100+).
No specialized hardware requirements.
Execution divided into three stages:
Phase 1: Node-to-anchor distance estimation.
Phase 2: Initial node positions computation.
Phase 3: Iterative refinement (optional).
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 14
15. Algorithms overview
Range-based:
Local Node Density-based (LND)
DV-Dist
RSSI-based techniques (RSSI1 and RSSI2)
Range-free:
DV-Hop
Phase LND Algorithm A Algorithm B Algorithm C Algorithm D
1a. Range DIN DIN - RSSI-Approx1 RSSI-Approx2
1b. Distance Sum-dist DV-Dist DV-Hop RSSI-Approx1 RSSI-Approx2
1c. Distance FCH - - - -
correction
2. Initial Multilateration Multilateration Multilateration Multilateration Multilateration
position
3. Refinement PIV PIV PIV - -
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 15
16. LND Algorithm
Phase 1a. DIN internodal range
Local node density information to estimate distances
Execution procedure (pair of nodes nA, nB):
1. Exchange neighbour tables
2. Determine number of nodes in union (Ku) and intersection (Ku) areas
3. Calculate area relationship H(dn) = Ai/Au
4. Yield distance estimate (normalized distance ∙ R) dAB = dn ∙ R = f(H(dn)) ∙ R
nB
nA
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 16
17. LND Algorithm
Phase 1a. DIN internodal range
Local node density information to estimate distances
Execution procedure (pair of nodes nA, nB):
1. Exchange neighbour tables
2. Determine number of nodes in union (Ku) and intersection (Ku) areas
3. Calculate area relationship H(dn) = Ai/Au
4. Yield distance estimate (normalized distance ∙ R) dAB = dn ∙ R = f(H(dn)) ∙ R
R
R
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 17
18. LND Algorithm
Phase 1a. DIN internodal range
Local node density information to estimate distances
Execution procedure (pair of nodes nA, nB):
1. Exchange neighbour tables
2. Determine number of nodes in union (Ku) and intersection (Ku) areas
3. Calculate area relationship H(dn) = Ai/Au ≈ Ki/Ku
4. Yield distance estimate (normalized distance ∙ R) dAB = dn ∙ R = f(H(dn)) ∙ R
Ki = 4
Ku = 13
Intersection nodes
+ Union nodes
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 18
19. LND Algorithm
Phase 1a. DIN internodal range
Local node density information to estimate distances
Execution procedure (pair of nodes nA, nB):
1. Exchange neighbour tables
2. Determine number of nodes in union (Ku) and intersection (Ku) areas
3. Calculate area relationship H(dn) = Ai/Au ≈ Ki/Ku
4. Yield distance estimate (normalized distance ∙ R) dAB = dn ∙ R = f(H(dn)) ∙ R
Ki = 4
Ku = 13
H(dn) = 4/13
Intersection nodes
+ Union nodes
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 19
20. LND Algorithm
Phase 1a. DIN internodal range
Local node density information to estimate distances
Execution procedure (pair of nodes nA, nB):
1. Exchange neighbour tables
2. Determine number of nodes in union (Ku) and intersection (Ku) areas
3. Calculate area relationship H(dn) = Ai/Au ≈ Ki/Ku
4. Yield distance estimate (normalized distance ∙ R) dAB = dn ∙ R = f(H(dn)) ∙ R
Ki = 4
Ku = 13
dAB H(dn) = 4/13
dAB = dn ∙ R
Intersection nodes
28.4 H n 92.6 H n 118.4 H n 76.5H n 27.8H n 7.5H n 1.9, ki ku
6 5 4 3 2
+ Union nodes dn 1
, ki ku
ki 1
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 20
21. LND Algorithm
Phase 1b. Initial node-to-anchor distance estimation (Sum-dist)
Flood connectivity and distance data (distance-vector approach).
Process initiated at anchors.
Propagation control: forward packets with non-stale information.
[x1,y1,0] nC
nA
nB
nG nH
nD
nF
nE Anchor
Unknown
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 21
22. LND Algorithm
Phase 1b. Initial node-to-anchor distance estimation (Sum-dist)
Flooding procedure case scenario (1 hop)
[x1,y1,1,dCA]
[x1,y1,0] nC
nA
nB
nG nH
[x1,y1,1,dBA] [x1,y1,1,dDA]
nD
nF
nE Anchor
Unknown
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 22
23. LND Algorithm
Phase 1b. Initial node-to-anchor distance estimation (Sum-dist)
Flooding procedure case scenario (2 hops)
[x1,y1,1,dCA]
[x1,y1,0] nC
nA
nB [x1,y1,2,dCA+dGC]
nG nH
[x1,y1,1,dBA] [x1,y1,1,dDA]
nD
nF
[x1,y1,2,dDA+dED]
nE Anchor
Unknown
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 23
24. LND Algorithm
Phase 1b. Initial node-to-anchor distance estimation (Sum-dist)
Flooding procedure case scenario (Complete)
[x1,y1,1,dCA]
[x1,y1,0] nC
nA
nB [x1,y1,2,dCA+dGC]
nG nH
[x1,y1,1,dBA] [x1,y1,1,dDA]
[x1,y1,3,dCA+dGC+dHG]
nD
[x1,y1,3,dDA+dED+dFE]
nF
[x1,y1,2,dDA+dED]
nE Anchor
Unknown
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 24
25. LND Algorithm
Phase 1c. Factor Correction Hop (FCH)
1. Anchors capture network propagation error in correction factors (ci).
n n
d
j 1
r ,ij d e,ij
j 1
ij
hij hij
ci avg. error per hop, j i
n 1 n 1
2. Flood distance correction data throughout WSN.
3. Unknown corrects initial node-to-anchor distance to aj (de,ij) using cj and nº hops (hij).
de' ,ij de,ij (hij c j )
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 25
26. LND Algorithm
Phase 2. Initial node positions via multilateration
Computational method to solve system of linearized equations (Ax=b).
Linear equations from anchor coordinates (xi,yi) and distance estimates (di).
Minimum nº of equations: n > Dim (e.g., bidimensional space n > 2).
Overdetermined system counter range error with redundancy (least squares).
Simultaneous execution with Sum-dist and FCH stages (Phases 1b & 1c).
Figure 5. Trilateration visualization example.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 26
27. LND Algorithm
Phase 3. Positioning Iterative Vector (PIV) refinement
Increase accuracy of node position estimates in iterative manner.
Local information used to recompute initial estimate: neighbour coordinates ( xit , yit )
and DIN internodal ranges ( d it ).
At each iteration t+1, node updates its estimated coordinates ( xet , yet ):
1 k dit ei t
x t 1
e x
t
e ( xi xe )
t
k i 0 2dit
1 k dit ei t
y t 1
e y
t
e ( yi ye )
t
k i 0 2dit
Correction principle: minimize mismatch between real ( ei ) and virtual ranges
(estimated distance d it ).
Stop condition:
Fixed number of iterations.
Update magnitude lower threshold δ. ( xe1 xe ) ( ye1 ye )
t t t t
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 27
28. LND Algorithm – PIV Example
Execution
1. Exchange neighbour data
2. Update position
8 R
5
9
3
PIV refinement procedure case scenario
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 28
29. LND Algorithm – PIV Example
5 (x0,y0) = (5,9)
Execution
1. Exchange neighbour data
2. Update position
8
5 (xr,yr) = (3,6)
8
(2,7) 9
9 (6,5)
3
Real position
(1,2) 3
Estimated position
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 29
30. LND Algorithm – PIV Example
5 (x0,y0) = (5,9)
Execution
1. Exchange neighbour data
5
2. Update position
(x1,y1) = (4.47,7.54)
8 1 3 dit ei t
x1 x0 t
( xi xe ) ... 5 0.53 4.47
t
3 i 0 2d i
1 3 dit ei t
5 (xr,yr) = (3,6) y1 y0 t
( yi ye ) ... 9 1.45 7.55
t
8 3 i 0 2d i
(2,7) 9
9 (6,5)
3
Position error iter. 0 (ξ0 = 3.6)
Position error iter. 1 ( ξ1 = 2.13)
Relative improvement (%)
3 0 1
(1,2) r (%) 100 40.87%
0
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 30
31. Alternative hop-by-hop algorithms
DV-Dist:
Simplified version of LND (Sum-dist ≈ DV-Dist, FCH suppressed).
DV-Hop:
Connectivity-based distance estimation.
1. Anchors compute calibration factors (single-hop length estimation)
ci
( xi x j )2 ( yi y j ) 2
,i j
hj
2. Unknowns derive extended ranges using nº hops (hj) de,ij hij c j
Note:
Main difference: node-to-anchor distance estimation technique.
Phases 2 and 3 identical to LND (Multilateration + PIV).
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 31
32. RSSI-based algorithms
Range estimation: Relate RSSI and distance to sender.
dist f ( RSSI )
Preliminary study: transmission pattern analysis of ScatterWeb Modular Sensor Board (MSB)*.
40
45
40-45
50
45-50
55
60 50-55
-dBm
65 55-60
70 60-65
75 65-70
80 70-75
85 75-80
5
3,75
4,5
80-85
3,75
3
2,5
m
2,25
1,5
1,25
m
0,75
0
Figure 6. Signal strength measurements from the Spectrum Analyzer. Figure 7. Spectrum Analyzer RSSI measurements.
Tx power 0x01, node on lower-right corner. TX power 0x01, node on central position.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 32
33. RSSI-based algorithms
Use RSSI empirical data to yield 2 approximation range functions (MatLab):
f ( x) RSSI1 0.0127 x 2 0.3697 x 2.2688
f ( x) RSSI2 0.2996 x 2 1.407 x 33.7234
*Remarks empirical RSSI analysis
High spatial & temporal variability (no uniform
circular model!).
Chipcon CC1020 transceiver limited sensitivity
(5-15dBm difference vs Spectrum analyzer).
Figure 8. RSSI approximations for transmission power 0x01 indoors Tx power 0x01: higher spatial resolution.
using partial mapping.
Note:
Phase 2 identical to LND (Multilateration).
Lack Phase 3 (PIV refinement stage).
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 33
34. Table of Contents
1. Objectives and thesis contribution
2. Wireless Sensor Networks (WSNs)
3. Problem statement
4. State of the Art: Location Systems for WSNs
5. Localization algorithms overview
6. Simulation
7. Experimental evaluation
8. Conclusions
9. Future Work
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 34
35. Simulation environment
Self-implemented C-based simulator.
Simplified radio propagation: circular transmission model.
Absence of propagation effects best-case scenario.
Standard scenario:
L x L = 50 x 50 units square area.
Grid configuration.
Anchors at the edges (throughout perimeter).
PIV iterations = 200.
Variable network conditions:
L L
Transceiver communication radio (R) R L, R =
10 10
Number of references (A) A=4,8,16
Nº unknowns (N) 15 N 100, N =5
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 35
36. LND simulation results
Phase 1a: DIN
Best performance: low transmission radios.
Underestimation tendency: ↑ R ↓erel
R=L/3 |Er| < 1.84m, Stdv < ± 1.3m.
Figure 9. DIN ranging estimation error using 16 anchors
under varying number of deployed nodes.
Phase 1b: Sum-dist
2 opposite trends:
Indirect paths overshooting.
Distance-vector shortest path undershooting.
↑ R or ↑ N ↓erel ↑ |Er|
Best results: R < L/5 |Er| < 7.89m, Stdv < ± 4.56m.
Figure 10. DIN ranging estimation error using 16 anchors
under varying number of deployed nodes.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 36
37. LND simulation results
Phase 1c: FCH
Tackles undershooting.
Avg. improvement not ensured robust (?)
Distance mismatch reduction dependent on ability to capture
propagation error anchor placement critical.
Good performance: R=3L/10, 4L/10. Most cases: ∆=4.76-73.55%.
Phase 2: Multilateration
Sensitive to transmission range, insensitive to anchor fraction.
Error peaks insatisfactory FCH behaviour in given topology.
a) Multilateration
Best performance: low-medium communication radios.
L/10 < R < L/2 Er < 5m (<42.69%), Stdv < ± 2.7m
Why? R < L/2 most accurate DIN ranges best NTA distances!
Phase 3: PIV refinement
Performance highly dependent on DIN ranges accuracy.
Favourable conditions: low tx radios, high anchor fraction.
Most improvement: 30-40 first iterations (!).
Not robust: accuracy degradation in certain topologies.
b) PIV
Competitive final results Figure 11. Position error before and after PIV
R=3L/10, 4L/10 Er < 4.78m(22.88%) Stdv < ± 1.71m refinement phase (A=4).
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 37
38. Simulation performance comparison
Algorithms: LND, DV-Dist, DV-Hop.
Phase 1. Node-to-anchor distance estimation
Low-medium tx. radio (R ≤ L/2): comparable results 5 ≤|Et| ≤10m.
High tx radio (R > L/2):
DV-Hop: best performer. Stable and predictable behaviour, slight overshooting.
DV-Dist: performance degradation, dramatic undershooting (poorer DIN range estimates!).
Sum-dist/FCH: in most cases counters negative bias, excessive correction in certain scenarios.
a) Absolute distance error – 4A b) Relative distance error – 8A
Figure 12. Node-to-anchor distance estimation error for varying node transmission radio deploying 75 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 38
39. Simulation performance comparison
Phase 2. Initial position estimation (multilateration)
Low-medium tx radios (R ≤ L/2): similar accuracies.
DV-Hop usually worst performer.
Range-based? FCH generally outperforms DV-Dist
High tx radios (R > L/2):
DV-Dist usually poorest results |Et| ≤ 17m.
FCH accuracy enhancement not ensured.
DV-Hop most satisfactory estimates |Et| ≤ 11m.
a) 75 Nodes
Phases 1+2 conclusions
R ≤ L/2
accurate DIN ranges Range-based algorithms ✓
DV-Dist vs Sum-dist/FCH inconclusive results, captured
propagation error?
R ≤ L/2
DV-Hop best performer stable, predictable.
Range-based degradation due to poor DIN estimates.
b) 100 Nodes
Figure 13. Position error for varying node transmission radio using 4 anchors.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 39
40. Simulation performance comparison
Phase 3. PIV iterative refinement (it. 200)
Equalize performance convergence to almost identical final estimates.
DV-Dist cheapest method (communication, computation) most suitable for implementation!
Final accuracy most related with quality of internodal ranges (it → ∞).
Improvement ∆(%) dependent on:
a. Initial avg. accuracy. b. DIN neighbour distance estimates.
DV-Dist: moderate accuracy enhancements (10-40%) under most scenarios.
DV-Hop: benefit constrained to low tx radios (30-55%). High radios accuracy degradation!
Sum-dist/FCH: highly variable improvement.
Figure 14. PIV position error under varying node transmission radios Figure 15. PIV position improvement (%) under varying node
using 8 anchors and deploying 75 unknowns. transmission radios using 4 anchors and deploying 50 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 40
41. Table of Contents
1. Objectives and thesis contribution
2. Wireless Sensor Networks (WSNs)
3. Problem statement
4. State of the Art: Location Systems for WSNs
5. Localization algorithms overview
6. Simulation
7. Experimental evaluation
8. Conclusions
9. Future Work
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 41
42. Testbed setup
8 x 9m indoor area (seminar room).
Network configuration: uniform, horseshoe.
Nº unknowns (N): 50, 100.
Nº anchors (A): 4, 8.
Node model: ScatterWeb Modular Sensor Board (MSB).
Algorithms: LND, DV-Hop, DV-Dist, RSSI-based methods (RSSI1, RSSI2).
a) Horseshoe configuration b) Uniform configuration
Figure 16. Experimental testbed overview pictures.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 42
43. Overview horseshoe configuration
Anchor
Unknown
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 43
44. Implementation on real WSN hardware
Artificial circular transmission radio (R ≈ 3m):
RSSI threshold of 33 (-42.5dBm)
Chipcon CC1020 radio transceiver to tx. power 0x01 (-5dBm).
Collision avoidance (DIN, Sum-dist/DV-Hop/DV-Dist, FCH, PIV): round-robin oriented
communication protocol.
Central control unit functionality:
Experimental data retrieval.
Indication of algorithm phase execution initiation.
Monitoring and supervision.
Algorithms (DV-Hop, DV-Dist, RSSI) execution integrated in LND communication protocol.
Intermediate data & location results analysis: MatLab scripts.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 44
45. Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1)
DIN noticeably more accurate (>50%) and precise than RSSI-based methods.
Average range errors: DIN (|Et|=0.887-1.1338m ≈33%xR), RSSI-based (|Et|>2.14m).
Slightly better results of DIN in:
Isotropic configurations (2-15cm poorer in horseshoe).
High node densities (N=100).
Figure 17. Comparison of internodal range methods in horseshoe
configuration using 8 anchors and 100 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 45
46. Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1)
DIN: experimental vs simulation performance degradation (≈0.5m).
Causes undesireable propagation effects of wireless medium
Reflections, refractions, scattering
Selective fading
Link asymmetries
Figure 18. Detected link asymmetries during Neighbour Discovery.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 46
47. Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1)
Bias analysis
DIN: almost symmetric error distribution around 0, left slope extends to -5m (slight undershooting).
RSSI-based: clear negative bias (RSSI2 higher undershooting than RSSI1).
a) DIN b) RSSI1
c) RSSI2
Figure 19. Range error histogram in uniform configuration using 8 anchors and 100 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 47
48. Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1)
Error spatial distribution: greater at the edges of coverage area.
Why? Proximity to potentially distorting elements (furniture, metallic doors, blackboards)
a) DIN b) RSSI1
Figure 20. Absolute range error tridimensional representation in uniform configuration
using 8 anchors and 50 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 48
49. Phase 1b-c. Node-to-anchor ranges
(DV-Hop, DV-Dist, Sum-dist/FCH, RSSI1, RSSI1)
RSSI-based ✗
Usually poorest performers. RSSI1 (2.37-2.79m), RSSI2 (2.32-2.66m).
Undershooting tendency relative error < -0.3184 x dr.
Hop-by-hop alternatives >0.5m more accurate, ±20-30cm more precise.
DV-Hop
Worst non RSSI-based alternative. Inaccuracy 0.2-0.5m higher than DV-Dist or FCH.
Overshooting effect relative error ≥ 0.0184 x dr. Cause: short routes (diameter 4-5 hops).
DV-Dist ✓✓
Usually best performer despite lack of correction stage.
Accuracy: 1.46-2.05m.
Overestimation 0.35-0.5 x dr.
Sum-dist/FCH (LND algorithm) ✓
Second best behind simplest range-based alternative DV-Dist.
Accuracy: 1.59-2.66m.
Generally fails to reduce initial overshooting degradation.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 49
50. Phase 1b-c. NTA range error per node
a) DV-Hop b) DV-Dist
c) FCH d) RSSI1
Figure 21. Relative node-to-anchor distance error in uniform configuration using 4 anchors and 50 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 50
51. Phase 1b-c. Range error distribution
a) DV-Hop b) DV-Dist
c) FCH d) RSSI1
Figure 22. Spatial distribution of node-to-anchor distance error in uniform configuration using 8 anchors and 100 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 51
52. Phase 2. Initial node positions
Hop-by-hop algorithms
DV-Hop ✗
Poorest performer. Highest misplacement 2.41-3.52m and imprecision ±1.04-1.57m.
DV-Dist ✓✓
Usually best performer despite being cheapest/simplest alternative.
Accuracy: 1.87-2.63m.
Sum-dist/FCH (LND algorithm) ✓
Second best in most scenarios.
Benefit of running FCH stage questionable!
RSSI-based
Comparable accuracies to hop-by-hop techniques: RSSI1 (2.37-2.79m), RSSI2 (2.24-2.63m).
Better precision! ≤ ±0.98m (vs hop-by-hop ≤ ±1.55m).
General trends
Anisotropic topologies slight performance degradation.
Anchor fraction(A), node density(N) inconclusive results.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 52
53. Phase 2. Simulation vs Experimental
Uniform: pronounced performance gap (1-3m).
Horseshoe: nodes at edges benefit from transmission irregularities in real environments.
a) DV-Hop b) DV-Dist
Figure 23. Comparison of position errors per node in simulation and testbed environment in horseshoe
configuration using 4 anchors and 100 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 53
54. Phase 2. Correlation NTA inaccuracy – node
misplacement
a) DV-Hop b) Sum-dist/FCH
Figure 24. Comparison of NTA distance error vs node position errors in uniform configuration using 4
anchors and 50 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 54
55. Phase 2. Position error spatial distribution
a) DV-Hop b) DV-Dist
c) FCH d) RSSI1
Figure 25. Spatial distribution of position error in uniform configuration using 4 anchors and 100 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 55
56. Phase 3. PIV iterative position improvement
Algorithms: DV-Hop, DV-Dist, LND.
30 iterations.
2 evaluation scenarios:
High node density (N=50, 100).
Low node density (N=9).
Highly satisfactory performance. Most experiments:
∆DIN ≥ 10%. a) Uniform – 8A 50N
Absolute accuracy improvement 0.3-1.2m.
Improvement not ensured Horseshoe 4A-100N DV-Dist (-5.45%).
Variability in convergence ratio between methods (2-8%).
Anchor fraction positive impact in PIV performance:
↑A ↑↑ ∆DIN
b) Horseshoe – 4A 100N
Figure 26. PIV absolute accuracy improv./it.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 56
57. Phase 3. PIV iterative position improvement
Comparable improvements (%) in algorithms accross experiments.
Determinant factor: initial position error.
DV-Dist outperforms FCH (2-20cm better) correction benefit questionable!
DV-Dist: best final results. Accuracy 1.37-3.53m. ✓ ✓
DV-Hop: Worst performer. Lowest accuracy 1.58-3.78m and precision ±0.88-1.99m. ✗
a) Uniform – 4A 100N b) Horseshoe – 8A 50N
Figure 27. PIV average position error/it.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 57
58. Phase 3. PIV improvement per node
a) DV-Hop b) DV-Dist
c) FCH
Figure 28. Absolute position improvement per node in horseshoe configuration using 8 anchors and 100 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 58
59. Phase 3. PIV improvement spatial distribution
a) DV-Hop – Initial pos. error b) DV-Hop – PIV pos. improv.
c) FCH – Initial pos. error d) FCH – PIV pos. improv.
Figure 29. Spatial distribution of initial pos. error vs PIV pos. Improv. in uniform configuration using 8 anchors and 50 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 59
60. Table of Contents
1. Objectives and thesis contribution
2. Wireless Sensor Networks (WSNs)
3. Problem statement
4. State of the Art: Location Systems for WSNs
5. Localization algorithms overview
6. Simulation
7. Experimental evaluation
8. Conclusions
9. Future Work
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 60
61. Conclusions
Simulation:
No best performer in all scenarios: selection dependent on network conditions
(communication range, anchor fraction, topology, node density).
LND algorithm: positive results for low transmission radios R=0.3-0.4L. Absolute position
error ≤ 3.943m, standard deviation ≤ ±1.71m.
Experimental study:
First step to bridge gap between simulations and real-world positioning systems.
Internodal ranging: DIN >50% more accurate than RSSI-based methods (≤33%R).
Range-based hop-by-hop methods outperform range-free counterpart (DV-Hop).
RSSI-based alternatives comparable initial positions despite signal strength variability.
Benefit of running additional FCH correction stage questionable.
PIV highly satisfactory performance for low and medium-high node densities
(∆DIN ≥ 10%, Absolute improvement 0.3-1.2m).
LND algorithm: competitive final position errors for 8 anchors 1.37-2.07m.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 61
62. Future Work
Extensive simulation over ns-2 or OMNet++ discrete event platforms.
Determine optimal context factors for FCH corrective procedure.
Formal analysis of PIV robustness: study network constraints to guarantee
convergence to more accurate position estimates.
Enhancements to original PIV implementation:
Filter out adjacent nodes based on consistency indicator (e.g., nº hops to anchors).
Reformulation as weighted least-squares problem, associate confidence to nodes:
Check convex constraints
Anchor nodes are assigned maximum confidence.
More and larger testbeds over extended deployment areas (multiple rooms).
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 62
63. References
[1] R. Want, A. Hopper, V. Falcao, and J. Gibbons. The active badge location system. ACM Trans. Inf. Syst., 10(1):91–102, 1992.
[2] A. Ward and A. Jones. A new location technique for the active office. IEEE Personal Communications, 4:42–47, 1997.
[3] N. B. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket location-support system. In MobiCom ’00: Proceedings of the
6th annual international conference on Mobile computing and networking, pages 32–43. ACM, 2000.
[4] N. Bulusu, J. Heidemann, and D. Estrin. Gps-less low cost outdoor localization for very small devices. IEEE Personal
Communications Magazine, 7(5):28–34, October 2000.
[5] T. He, C. Huang, B. M. Blum, J. A. Stankovic, and T. Abdelzaher. Range-free localization schemes for large scale sensor networks.
In MobiCom ’03: Proceedings of the 9th annual international conference on Mobile computing and networking, pages 81–95,
2003.
[6] P. Bahl and V. N. Padmanabhan. Radar: an in-building rf-based user location and tracking system. In INFOCOM 2000.
Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, volume 2, pages
775–784, 2000.
[7] L. Ni, Y. Liu, Y. Lau, and A. Patil. Landmarc: indoor location sensing using active rfid. Wirel. Netw., 10(6):701–710, 2004.
[8] D. Niculescu and B. Nath. Ad Hoc Positioning System (APS). In IEEE GLOBECOM, volume 5, pages 2926–2931, 2001.
[9] R. Nagpal, H. Shrobe, and J. Bachrach. Organizing a global coordinate system from local information on an ad hoc sensor
network. 2nd International Workshop on Information Processing in Sensor Networks (IPSN), April 2003.
[10] L. Doherty, K. Pister, and L. Ghaoui. Convex position estimation in wireless sensor networks. In Proceedings of INFOCOM
2001, volume 3, pages 1655–1663, 2001.
[11] Y. Shang, W. Ruml, Y. Zhang, and M. Fromherz. Localization from connectivity in sensor networks. IEEE Transactions on Parallel
and Distributed Systems, 15(11):961–974, 2004.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 63
64. References
[12] X. Ji and H. Zha. Multidimensional scaling based sensor positioning algorithms in wireless sensor networks. In SenSys ’03:
Proceedings of the 1st international conference on Embedded networked sensor systems, pages 328–329, 2003.
[13] P. Biswas and Y. Ye. Semidefinite programming for ad hoc wireless sensor network localization. In IPSN ’04: Proceedings of the
3rd international symposium on Information processing in sensor networks, pages 46–54, 2004.
[14] A. A. Kannan, G. Mao, and B. Vucetic. Simulated annealing based localization in wireless sensor networks. The 30th IEEE
Conference on Local Computer Networks, pages 513–514, 2005.
[15] A. Savvides, H. Park, and M. B. Srivastava. The bits and flops of the n-hop multilateration primitive for node localization
problems. In WSNA ’02: Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications,
pages 112–121. ACM, 2002.
[16] C. Savarese, J. Rabaey, and K. Langendoen. Robust positioning algorithms for distributed ad-hoc wireless sensor networks. In
USENIX Technical Annual Conference, pages 317–327, 2002.
[17] S. Capkun, M. Hamdi, and J. Hubaux. Gps-free positioning in mobile ad-hoc networks. In HICSS ’01: Proceedings of the 34th
Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 9, page 9008, Washington, DC, USA, 2001.
IEEE Computer Society.
[18] D. Moore, J. Leonard, D. Rus, and S. Teller. Robust distributed network localization with noisy range measurements. In SenSys
’04: Proceedings of the 2nd international conference on Embedded networked sensor systems, pages 50–61, New York, NY,
USA, 2004. ACM.
[19] K. Sreenath, Frank L. Lewis, and Dan O. Popa. Simultaneous adaptive localization of a wireless sensor network. SIGMOBILE
Mob. Comput. Commun. Rev., 11(2):14–28, 2007.
[20] V. Fox, J. Hightower, L. Lin, D. Schulz, and G. Borriello. Bayesian filtering for location estimation. IEEE Pervasive Computing,
2(3):24–33, 2003.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 64
65. References
[21] X. Sheng, Yu-Hen Hu, and P. Ramanathan. Distributed particle filter with gmm approximation for multiple targets localization
and tracking in wireless sensor network. In IPSN ’05: Proceedings of the 4th international symposium on Information
processing in sensor networks, page 24, 2005.
[22] L. Hu and D. Evans. Localization for mobile sensor networks. In MobiCom ’04: Proceedings of the 10th annual international
conference on Mobile computing and networking, pages 45–57, 2004.
[23] M. Coates. Distributed particle filters for sensor networks. In IPSN ’04: Proceedings of the 3rd international symposium on
Information processing in sensor networks, pages 99–107, 2004.
[24] H. Karl and A. Willig. Protocols and Architectures for Wireless Sensor Networks. John Wiley & Sons, 2005.
[25] M. Rabbat and R. Nowak. Distributed optimization in sensor networks. In IPSN ’04: Proceedings of the 3rd international
symposium on Information processing in sensor networks, pages 20–27, 2004.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 65
66. Thank you for your attention.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 66
69. ScatterWeb Modular Sensor Board
Table 1. Key features of the ScatterWeb Modular Sensor Board (MSB-430).
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 69
70. Empirical analysis of FCH effectivity
Figure 30. Analysis of FCH correction procedure effectivity . Horseshoe configuration using 8 anchors and 50 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 70
71. Analysis of DV-Hop effectivity
Figure 31. Analysis of DV-Hop calibration effectivity . Uniform configuration using 8 anchors and 100 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 71
72. Extended ranges – Hop-by-hop methods
a) Absolute error b) Relative error
Figure 32. Comparison of NTA distance error per anchor in horseshoe configuration using 8 anchors and 100 unknowns.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 72
73. LND algorithm power cost
Estimates dependent on:
Network connectivity c (avg. Neighbours/node).
Nº deployed anchors a.
Nº iterations executed in PIV algorithm it.
Nº iterations executed for square root calculation n (Babylonian numerical method).
Power cost of single transmission(Ctx) or reception(Crx) of broadcast packet (transceiver-specific).
Power cost of single execution flop F (microcontroller specific).
Nº dimensions of coordinates systems Dim.
Table 2. Communication costs of the LND localization algorithm.
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 73
74. LND algorithm power cost
Table 3. Computational costs of the LND localization algorithm.
Table 4. Computational costs of the LND localization algorithm in bidimensional space (Dim = 2).
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 74
75. LND algorithm power cost
CC1020 current consumption (868MHz transmit/receive mode)
Single broadcast packet transmission P=0x01 (-5dBm) Ctx = 17.0mA
Single broadcast packet reception Crx = 19.9mA
Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 75