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.
Indoor Localization Using Local Node Density In Ad Hoc WSNsjoaquin_gonzalez
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 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.
Indoor Localization Using Local Node Density In Ad Hoc WSNsjoaquin_gonzalez
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 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.
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.
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.
Performance Analysis of Fault Detection in Round Trip Delay and Path Wireless...Editor IJMTER
In recent years, wsns detect to the fault sensor node based on round trip delay using path
in wireless sensor networks. Portable sensor node is low cost in Wsns . Measured in the round trip
delay time and number of sensor node. Existing method is used to large value of sensor node,
identification of sensor node time and distance . it is used to linear selection path, disadvantages are
data loss, more number of path, complexity. in this proposed method using distributed autonomous
sensor software implementation in NS2.it is detected fault sensor node and malfunction ,in this
analysis time and path using discrete Rtp. real time applicability in received signal strength ,separate
wavelength for end of the node avoid the data loss and complexity. Hardware implementation using
ZigBee and Microcontroller .Equal to the hardware and software implementation. It is overcomes to
the data loss. comparing the threshold and Rtd time. Finally, the algorithm is tested under different
number of faulty sensors in the same area. Our Simulation results demonstrate that the time
consumed to find out the faulty nodes in our proposed algorithm is relatively less with a large
number of faulty sensors existing in the network.
Proactive Data Reporting of Wireless sensor Network using Wake Up Scheduling ...ijsrd.com
In Wireless Sensor Network (WSNs), gather the data by using mobile sinks has become popular. Reduce the number of messages which is used for sink location broadcasting, efficient energy data forwarding, become accustomed to unknown earthly changes are achieved by a protocol which is projected by a SinkTrail. The forecast of mobile sinks’ location are done by using logical coordinate system. When sensor nodes don’t have any data to send, at that time they switch to sleep mode to save the energy and to increase the network lifetime. And due to this reason there is a chance of the involvement of nodes that are in sleeping state between the path sources to the mobile sink which is selected by the SinkTrail protocol. Before become the fully functional and process the information, these sleeping nodes can drop the some information. Due to this reason, it is vital to wake-up the sleeping nodes on the path earlier than the sender can start transferring of sensed data. In this paper, on-demand wake-up scheduling algorithm is projected which is used to activates sleeping node on the path before data delivery. Here, in this work the multi-hop communication in WSN also considers. By incorporating wake-up scheduling algorithm to perk up the dependability and improve the performance of on-demand data forwarding extends the SinkTrail solution in our work. This projected algorithm improves the quality of service of the network by dishonesty of data or reducing the loss due to sleeping nodes. The efficiency and the effectiveness projected solution are proved by the evaluation results.
DATA TRANSMISSION IN WIRELESS SENSOR NETWORKS FOR EFFECTIVE AND SECURE COMMUN...IJEEE
Data transmission occurs from transmitting node to sink node, which communicate each other via large number of intermediate nodes or directly to an external base station. A network consists of numbers of nodes with one as a source and one or more as a destination node.
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
Node Deployment in Homogeneous and Heterogeneous Wireless Sensor NetworkIJMTST Journal
Optimal sensor deployment is necessary condition in homogeneous and heterogeneous wireless sensor
network. Effective deployment of sensor nodes is a major point of concern as performance and lifetime of any
WSN. Proposed sensor deployment in WSN explore every sensor node sends its data to the nearest sink node
of the WSN. In addition to that system proposes a hexagonal cell based sensor deployment which leads to
optimal sensor deployment for both homogeneous and heterogeneous sensor deployment. Wireless sensor
networks are receiving significant concentration due to their potential applications ranging from surveillance
to tracking domains. In limited communication range, a WSN is divided into several disconnected sub-graphs
under certain conditions. We deploy sensor nodes at random locations so that it improves performance of the
network.This paper aims to study, discuss and analyze various node deployment strategies and coverage
problems for Homogeneous and Heterogeneous WSN.
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
A Novel Three-Dimensional Adaptive Localization (T-Dial) Algorithm for Wirele...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Sensor Deployment Algorithm for Hole Detection and Healing By Using Local Hea...paperpublications3
Abstract: The main services provided by a WSN (wireless sensor network) is monitoring a particular region ie, RoI or specified region. The emergence of hole in that particular region is unavoidable. Due to some environmental factors, Random deployment and external attacks etc. In this work hole detection and hole healing process are discussed .Here show that heal deals with some virtual forces. According to the size of hole and also to improve the coverage area and need more energy efficient process.
Enhanced Zigbee Tree Routing In Wireless Sensor Networkpaperpublications3
Abstract: Multipath routing is an efficient technique to route data in wireless sensor networks (WSNs) because it can provide reliability, security and load balance, which are particularly critical in the resource constrained system such as WSNs. The existing protocols are not fully satisfied. In this paper propose a new routing protocol that is enhanced zigbee tree routing (EZTR), to satisfy the QoS parameters. The new protocol provides less delay as compared with other protocol.
Adaptive and Energy Efficient Compressive Sensing Clustering for Wireless Sen...paperpublications3
Abstract: Adaptive and energy efficient compressive sensing clustering for wireless sensor networks. In this paper mainly used the term CS that is compressive sensing. Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. In this paper used compressive sensing used for clustering method. The enhanced hybrid compressive sensing method of using CS was proposed to reduce the number of transmissions in sensor networks.
Voltage Stability Calculations in Power Transmission Lines: Indications and A...paperpublications3
Abstract: Power system is facing new challenges as the present system is subjected to severely stressed conditions. Voltage instability is a quite frequent phenomenon under such a situation rendering degradation of power system performance. In order to avoid system blackouts, power system is to be analyzed in view of voltage stability for a wide range of system conditions. In voltage stability analysis, the main objective is to identify the system maximum loadability limit and causes of voltage instability. Static voltage stability analysis with some approximations gives this information. Voltage stability problem is related to load dynamics and therefore di erent load characteristics are to be considered in the voltage stability analysis. This paper presents an efficient method for conducting line voltage stability analysis in power systems. This newly developed method is accurate, fast, simple, and theoretically proven for finding precise voltage collapse points and for determining voltage stability at each transmission line. Voltage stability margins can be easily calculated, providing an indication of how far the transmission line is from its severe load condition and allowing separate analysis if one transmission line is highly stressed. The proposed method was demonstrated on the IEEE 30-bus system and compared with existing methods to show its effectiveness and efficiency.
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.
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.
Performance Analysis of Fault Detection in Round Trip Delay and Path Wireless...Editor IJMTER
In recent years, wsns detect to the fault sensor node based on round trip delay using path
in wireless sensor networks. Portable sensor node is low cost in Wsns . Measured in the round trip
delay time and number of sensor node. Existing method is used to large value of sensor node,
identification of sensor node time and distance . it is used to linear selection path, disadvantages are
data loss, more number of path, complexity. in this proposed method using distributed autonomous
sensor software implementation in NS2.it is detected fault sensor node and malfunction ,in this
analysis time and path using discrete Rtp. real time applicability in received signal strength ,separate
wavelength for end of the node avoid the data loss and complexity. Hardware implementation using
ZigBee and Microcontroller .Equal to the hardware and software implementation. It is overcomes to
the data loss. comparing the threshold and Rtd time. Finally, the algorithm is tested under different
number of faulty sensors in the same area. Our Simulation results demonstrate that the time
consumed to find out the faulty nodes in our proposed algorithm is relatively less with a large
number of faulty sensors existing in the network.
Proactive Data Reporting of Wireless sensor Network using Wake Up Scheduling ...ijsrd.com
In Wireless Sensor Network (WSNs), gather the data by using mobile sinks has become popular. Reduce the number of messages which is used for sink location broadcasting, efficient energy data forwarding, become accustomed to unknown earthly changes are achieved by a protocol which is projected by a SinkTrail. The forecast of mobile sinks’ location are done by using logical coordinate system. When sensor nodes don’t have any data to send, at that time they switch to sleep mode to save the energy and to increase the network lifetime. And due to this reason there is a chance of the involvement of nodes that are in sleeping state between the path sources to the mobile sink which is selected by the SinkTrail protocol. Before become the fully functional and process the information, these sleeping nodes can drop the some information. Due to this reason, it is vital to wake-up the sleeping nodes on the path earlier than the sender can start transferring of sensed data. In this paper, on-demand wake-up scheduling algorithm is projected which is used to activates sleeping node on the path before data delivery. Here, in this work the multi-hop communication in WSN also considers. By incorporating wake-up scheduling algorithm to perk up the dependability and improve the performance of on-demand data forwarding extends the SinkTrail solution in our work. This projected algorithm improves the quality of service of the network by dishonesty of data or reducing the loss due to sleeping nodes. The efficiency and the effectiveness projected solution are proved by the evaluation results.
DATA TRANSMISSION IN WIRELESS SENSOR NETWORKS FOR EFFECTIVE AND SECURE COMMUN...IJEEE
Data transmission occurs from transmitting node to sink node, which communicate each other via large number of intermediate nodes or directly to an external base station. A network consists of numbers of nodes with one as a source and one or more as a destination node.
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
Node Deployment in Homogeneous and Heterogeneous Wireless Sensor NetworkIJMTST Journal
Optimal sensor deployment is necessary condition in homogeneous and heterogeneous wireless sensor
network. Effective deployment of sensor nodes is a major point of concern as performance and lifetime of any
WSN. Proposed sensor deployment in WSN explore every sensor node sends its data to the nearest sink node
of the WSN. In addition to that system proposes a hexagonal cell based sensor deployment which leads to
optimal sensor deployment for both homogeneous and heterogeneous sensor deployment. Wireless sensor
networks are receiving significant concentration due to their potential applications ranging from surveillance
to tracking domains. In limited communication range, a WSN is divided into several disconnected sub-graphs
under certain conditions. We deploy sensor nodes at random locations so that it improves performance of the
network.This paper aims to study, discuss and analyze various node deployment strategies and coverage
problems for Homogeneous and Heterogeneous WSN.
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
A Novel Three-Dimensional Adaptive Localization (T-Dial) Algorithm for Wirele...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Sensor Deployment Algorithm for Hole Detection and Healing By Using Local Hea...paperpublications3
Abstract: The main services provided by a WSN (wireless sensor network) is monitoring a particular region ie, RoI or specified region. The emergence of hole in that particular region is unavoidable. Due to some environmental factors, Random deployment and external attacks etc. In this work hole detection and hole healing process are discussed .Here show that heal deals with some virtual forces. According to the size of hole and also to improve the coverage area and need more energy efficient process.
Enhanced Zigbee Tree Routing In Wireless Sensor Networkpaperpublications3
Abstract: Multipath routing is an efficient technique to route data in wireless sensor networks (WSNs) because it can provide reliability, security and load balance, which are particularly critical in the resource constrained system such as WSNs. The existing protocols are not fully satisfied. In this paper propose a new routing protocol that is enhanced zigbee tree routing (EZTR), to satisfy the QoS parameters. The new protocol provides less delay as compared with other protocol.
Adaptive and Energy Efficient Compressive Sensing Clustering for Wireless Sen...paperpublications3
Abstract: Adaptive and energy efficient compressive sensing clustering for wireless sensor networks. In this paper mainly used the term CS that is compressive sensing. Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. In this paper used compressive sensing used for clustering method. The enhanced hybrid compressive sensing method of using CS was proposed to reduce the number of transmissions in sensor networks.
Voltage Stability Calculations in Power Transmission Lines: Indications and A...paperpublications3
Abstract: Power system is facing new challenges as the present system is subjected to severely stressed conditions. Voltage instability is a quite frequent phenomenon under such a situation rendering degradation of power system performance. In order to avoid system blackouts, power system is to be analyzed in view of voltage stability for a wide range of system conditions. In voltage stability analysis, the main objective is to identify the system maximum loadability limit and causes of voltage instability. Static voltage stability analysis with some approximations gives this information. Voltage stability problem is related to load dynamics and therefore di erent load characteristics are to be considered in the voltage stability analysis. This paper presents an efficient method for conducting line voltage stability analysis in power systems. This newly developed method is accurate, fast, simple, and theoretically proven for finding precise voltage collapse points and for determining voltage stability at each transmission line. Voltage stability margins can be easily calculated, providing an indication of how far the transmission line is from its severe load condition and allowing separate analysis if one transmission line is highly stressed. The proposed method was demonstrated on the IEEE 30-bus system and compared with existing methods to show its effectiveness and efficiency.
Abstract: Android based E-home is an application of embedded system which integrates Android operating system, Arduino controller and GSM for the implementation of Smart Home. Any Android device can act as a transmitting device. The user can control any appliance through an user friendly mobile application built in Android platform. A unique code for controlling each appliance installed with the system is assigned in the program and it is transmitted from the Android phone to the CPU installed in receiver, through the application, as Text messages. The GSM receiver in the CPU receives the text messages from the transmitting device and transfers it to the controller. The Controller decodes the message based on the decoding algorithm stated in the program and based on the command from the user; the controller sends signals to the switching circuitry. The Switching circuitry is the device which is in direct contact with the home appliances through wires and it controls the devices connected to it. Thus, by controlling the Switching circuitry, it is possible to control the various devices. Backward communication is also enabled when an event occurs in the mechanical switch. In this case, the GSM shield acts as a transmitter and sends a message to the user. Thus, ‘Android based E-Home’ provides a cost efficient, long range solution for the implementation of a smart home using less complex circuits and also provides an user friendly environment through the android app. There is no need for an extra hardware as the Android smart phone itself acts as a transmitter which in turn contributes to the cost efficiency, ease of access and efficient operation of the system.
Shared Steering Control between a Driver and an Automation: Stability in the ...paperpublications3
Abstract: Now-a-days the Automatic control has been increasingly implemented for vehicle control system. Especially the steering control is essential for preventing accidents. In the existing systems there is no fully automatic steering control and it has serious problems. When it is made automatic, the system complexity is more. So, the shared steering concept is used in the proposed system to avoid accidents. In this, the position of the road is found using the web camera installed in front of the vehicle which is connected to the PC installed with MATLAB. Using MATLAB the image is processed to check the road characteristics. This paper presents an advanced driver assistance system (ADAS) for lane keeping, together with an analysis of its performance and stability with respect to variations in driver behavior. The automotive ADAS proposed is designed to share control of the steering wheel with the driver in the best possible way. Its development was derived from an H2-Preview optimization control problem, which is based on a global driver–vehicle–road (DVR) system. The DVR model makes use of a cybernetic driver model to take into account any driver–vehicle interactions. Such a formulation allows 1) Considering driver assistance cooperation criteria in the control synthesis, 2) improving the performance of the assistance as a cooperative copilot, and 3) analyzing the stability of the whole system in the presence of driver model uncertainty. The developed assistance system improved lane-keeping performance and reduced the risk of a lane departure accident. Good results were obtained using several criteria for human–machine cooperation. Poor stability situations were successfully avoided due to the robustness of the whole system, in spite of a large range of driver model uncertainty.
A Distinctive Analysis between Distributed and Centralized Power Generationpaperpublications3
Abstract: The role of Distributed Generation (DG) is ever increasingly being recognized as a supplement and an alternative to large conventional Centralized Generation (CG). Besides, there is also a debate regarding the genuine prospects of DG; always prevailed between industry stakeholders and other interest groups. Within the scope of this review, a comparative study of CG and DG has been presented. In this report, a broad spectrum of issues is being considered to depict the paradigm, drives, shortcomings and future challenges for CG and DG.
Throughput maximization in multiuser wireless powered communication network b...paperpublications3
Abstract: WPCN (wireless powered communication network) is the newly emerging network in which an access point called hybrid access point (HP) broadcast energy in the downlink and in the upper link wireless information transfer take place from sensors to hybrid access point. A protocol called “harvest-thentransmit” protocol is proposed where all users first harvest the wireless energy from the H-AP, in the downlink (DL) and then send their independent information to the HAP in the uplink (UL) by time-division-multiple-access (TDMA). A set of relay nodes used in the network enhances the over all throughput than sum throughput and common through hput. More energy efficient network can be got by using relay nodes. Simulation result showing the priority of the network that using relay nodes than other ordinary wireless sensor networks.
Implementation of Energy Management System to PV-Diesel Hybrid Power Generati...paperpublications3
Abstract: The main objective of this proposed system is to provide Uninterruptible Power Supply to the load. This proposed system mainly deals with the Energy Management of the DC microgrid System. The Power sources employed in the proposed system composed of PV, and Diesel power generation system. The storage system consists of the batteries, and has the EB system acts as a standby system, which delivers power only during the power failure conditions. The RS 485 and ZigBee communication protocol, employed in the communication purposes. The Energy Management System (EMS) incorporates the fuzzy control manages the State of Charge (SoC) of the battery.
Rate Adaptation for Time Varying Channels Using Distributed Relay Selectionpaperpublications3
Abstract: Fixed systems used in cooperative communication suffer from multiplexing loss and low spectral efficiency due to the half duplex constraint of relays. To improve the multiplexing gain, successive relaying is proposed. This allows concurrent transmission of the source and relays. However, the severe inter-relay interference becomes a key challenge. Here Rate Adaptation for Time Varying Channels Using Distributed Relay Selection is proposed, which is capable of adapting the relay’s rate using distributed relay selection.
Abstract: Wireless location finding is one of the key technologies for wireless sensor networks. GPS is the technology used but it can be used for the outdoor location. When we deal with the indoor locations GPS does not work. Indoor locations include buildings like supermarkets, big malls, parking, universities, and locations under the same roof. In these areas the accuracy of the GPS location is greatly reduced. Location showed on the map in not correct when the GPS is used under the indoor environments. But for the indoor localization it requires the higher accuracy sp GPS is not feasible for the current view. And also when the GPS is used in the mobile device it consumes a lot of the mobile battery to run the application which causes the drainage of the mobile battery within some hours. So to find out the accurate location for indoor environment we use the RSSI based trilateral localization algorithm. The algorithm has the low cost and the algorithm does not require any additional hardware support and moreover the algorithm is easy to understand. The algorithm consumes very less battery as compared to the battery consumption of the GPS. Because of these this algorithm has become the mainstream localization algorithm in the wireless sensor networks. With the development of the wireless sensor networks and the smart devices the WIFI access points are also increasing. The mobile smart devices detect three or more known WIFI hotspots positions. And using the values from the WIFI routers it calculates the current location of the mobile device. In this paper we have proposed a system so that we can find out the exact location of the mobile device under the indoor environment and can navigate to the destination using the navigation function and also can enable the low consumption of the smart mobile battery for the tracking purpose.
Goals:
1. Useful at the places where GPS cannot work
2. Reduces the battery consumption
3. Routers are used.
4. Provides the path as well as the information of the location as per the requirement of user.
A Wearable Textile Bed Sheet Cotton Substrate Material Micro Strip Antenna in...paperpublications3
Abstract: In this paper the design fabrication and measurement of an ISM band (5.8GHz) wearable cotton textile patch antenna is presented. The substrate of the designed antenna was made by bed sheet cotton textile material while the radiating element and ground plane was made by thin film copper foil. The copper foil was pasted by using synthetic resin adhesive on the textile (cotton) material. Cotton textile antenna impedance characteristics and radiation pattern characteristics were studied.
Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides
fundamental support for many location-aware protocols and applications. Constraints on cost and power
consumption make it infeasible to equip each sensor node in the network with a global position system
(GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use
anchor nodes, which are equipped with GPS units among unknown nodes and broadcast their current
locations to help nearby unknown nodes with localization. In this paper we can proposed a novel algorithm
of cuboid localization with the help of central point precision method. Simulation shows that the results are
far better then existing cuboid methods and gain accuracy of up to 83% with a localization error of 1.6m
and standard deviation of 2.7.
UWB LOCALIZATION OF NODES FOR SECURING A MANETijistjournal
Localization of nodes in an infrastructure less network serves many purposes. Several issues relating to security, routing, etc it can be solved if only the actual location of nodes were known. Existing approaches estimate the location of a node in a network by using received signal strength indicator (RSSI), Time of Arrival, Time difference of Arrival and, if directional antennas are available, Direction of Arrival. In these methods the localization accuracy is less (in the order of 20cm). The aim of this paper is to localize nodes in adhoc networks with improved accuracy using ultra wide band.The proposed method uses a train of low amplitude pulses of high bandwidth, which reduces the energy consumption, effects due to small scale fading, and dispersion in time and frequency. The network was simulated in NS-2 with UWB extension and the localization accuracy was found to be improved (upto 1cm).
Localization of nodes in an infrastructure less network serves many purposes. Several issues relating to
security, routing, etc it can be solved if only the actual location of nodes were known. Existing approaches
estimate the location of a node in a network by using received signal strength indicator (RSSI), Time of
Arrival, Time difference of Arrival and, if directional antennas are available, Direction of Arrival. In these
methods the localization accuracy is less (in the order of 20cm). The aim of this paper is to localize nodes
in adhoc networks with improved accuracy using ultra wide band.The proposed method uses a train of low
amplitude pulses of high bandwidth, which reduces the energy consumption, effects due to small scale
fading, and dispersion in time and frequency. The network was simulated in NS-2 with UWB extension and
the localization accuracy was found to be improved (upto 1cm).
A Novel Range-Free Localization Scheme for Wireless Sensor NetworksGiselleginaGloria
This paper present a low-cost yet effective localization scheme for the wireless sensor networks. There are many studies in the literature of locating the sensors in the wireless sensor networks. Most of them require either installing extra hardware or having a certain amount of sensor nodes with known positions. The localization scheme we propose in this paper is range-free, i.e., not requiring extra hardware devices, and meanwhile it only needs two anchor nodes with known position. Firstly, we install the first anchor node at the lower left corner (Sink X) and the other anchor node at the lower right corner (Sink Y). Then we calculate the minimum hop counts for each unknown node to both Sink X and Sink Y. According to the minimum hop count pair to Sink X and Sink Y of each node, we can virtually divide the monitored region into zones. We then estimate the coordinate of each sensor depending on its located zone. Finally, we adjust the location estimation of each sensor according to its relative position in the zone. We simulate our proposed scheme and the well-known DV-Hop method. The simulation results show that our proposed scheme is superior to the DV-Hop method under both low density and high density sensor deployments.
WIRELESS SENSOR NETWORK LOCALIZATION IN 3D USING STEERABLE ANCHORS’ ANTENNASijassn
Wireless sensor network localization plays an important role in mobile computing. Moreover, Sensor
nodes are often deployed non-uniformly in anisotropic WSNs with holes in various applications such as
monitoring area terrain. The existence of holes will invariably affect the Euclidean distances between
nodes and result in low accuracy of node localization. The proposed algorithm is suitable for four different
topologies, including the semi-C-shape topology, the O-shape topology, the multiple O-shape topology and
the concave-shape topology and is exceedingly accurate and efficient comparing with state-of-the-art
methods in anisotropic WSNs with holes. Our results show that the error in horizontal plane is less than
0.25 m while in the Z-axis is less than 0.5 m.
The Expansion of 3D wireless sensor network Bumps localizationIJERA Editor
Bump localization of wireless sensor network is a hot topic, but present algorithms of 3D wireless sensor node localization arenot accurate enough. In this paper, the DR-MDS algorithm is proposed, DR-MDS algorithm mainly calibrates the coordinatesof nodes and the ranging of nodes based on multidimensional scaling, it calculates the distance between any nodes exactlyaccording to the hexahedral measurement, introducing a modification factor to calibrate the measuring distance by ReceivedSignal Strength Indicator (RSSI). Results of simulation show that DR-MDS algorithm has significant improvement inlocalization accuracy compare with MDS-MAP algorithm.
A New Approach for Error Reduction in Localization for Wireless Sensor Networksidescitation
Localization is one of the most challenging and
important issues in wireless sensor networks (WSNs),
especially if cost effective approaches are demanded. Distance
measurement based on RSSI (Received Signal Strength
Indication) is a low cost and low complexity of the distance
measurement technique, and it is widely applied in the range-
based localization of the WSN. The RSS (Received Signal
Strength) used to estimate the distance between an unknown
node and a number of reference nodes with known co-ordinates.
Location of the target node is then determined by trilateration.
Log-normal shadowing model, can better describe the
relationship between the RSSI value and distance. Non-line
of sight and multipath transmission effects as the indoor
environment, the distance error or ranging error is large. In
this paper, experimental results that are carried out to analyze
the sensitivity of RSSI measurements in an indoor
environment for various power levels are presented. Location
error influenced by distance measure error and network
connectivity is analyzed.
Index Terms—
Multiagent based multipath routing in wireless sensor networksijwmn
This paper proposes a Multiagent Based Multipath Routing (MBMR) using a set of static and mobile agents
by employing localization technique. The operation of proposed routing technique can be briefly explained
as follows. (1) Anchor nodes are deployed evenly over the network environment. (2) Unknown sensor nodes
are deployed randomly over network environment and these nodes perform localization. (3) Source node
computes the shortest route to destination node through arbitrary midpoint node and intermediate nodes.
(4) Source node generates mobile agents for partial route discovery, which traverses to destination node
through the midpoint and intermediate nodes by carrying information. (5) Mobile agents update the
destination node with carried information. (6) Destination node computes route weight factor for all the
routes discovered by mobile agents. (7) Destination node computes the node disjoint routes and it selects
routes depending on the criticalness of event for communication. The performance of the proposed scheme
is evaluated in terms of performance parameters such as localization error, network lifetime, energy
consumption, cost factor, packet delivery ratio, and latency.
Recent advances in radio and embedded systems for completing the procedure of location estimation most
of the time sensor networks are fully dependent on the distance measurements that is present between the
sensor neighbourhood node. Techniques used for the localization can be categorized differently.
Techniques used for the measurement of the distance between the wireless sensor nodes, dependent upon
the physical means are divided into three broader categories namely Received signal strength (RSS), Angle
of Arrival (AOA) and propagation base on time measurements. This paper discusses the most of the
approached of WSN and IoT based positioning system.
Collaborative Re-Localization Method in Mobile Wireless Sensor Network Based ...CSCJournals
Localization in Mobile Wireless Sensor Networks (WSNs), particularly in areas like surveillance applications, necessitates triggering re-localization in different time periods in order to maintain accurate positioning. Further, the re-localization process should be designed for time and energy efficiency in these resource constrained networks. In this paper, an energy and time efficient algorithm is proposed to determine the optimum number of localized nodes that collaborate in the re-localization process. Four different movement methods (Random Waypoint Pattern, Modified Random Waypoint pattern, Brownian motion and Levy walk) are applied to model node movement. In order to perform re-localization, a server/head/anchor node activates the optimal number of localized nodes in each island/cluster. A Markov Decision Process (MDP) based algorithm is proposed to find the optimal policy to select those nodes in better condition to cooperate in the re-localization process. The simulation shows that the proposed MDP algorithm decreases the energy consumption in the WSN between 0.6% and 32%.
Wireless Sensor Networks are highly distributed self-organized systems. WSN have been deployed in various fields. Now a day, Topology issues have received more and more attentions in Wireless Sensor Networks (WSN). While WSN applications are normally optimized by the given underlying network topology, another trend is to optimize WSN by means of topology control. In this area, a number of approaches have been invested, like network connectivity based topology control, cooperating schemes, topology directed routing, sensor coverage based topology control. Most of the schemes have proven to be able to provide a better network monitoring and communication performance with prolonged system lifetime. In this survey paper, I provide a full view of the studies in this area.
Minimization of Localization Error using Connectivity based Geometrical Metho...Dr. Amarjeet Singh
Many localization schemes are designed for finding
the geographical coordinates of the unlocalized node in the
network. Still, it is a difficult problem to find accurate and
efficient localization schemes in the Wireless Sensor Networks
(WSNs). We proposed a new method, connectivity based WSN
node localization using one of the geometrical method namely
centroid of a triangle. By developing the centroid of a triangle
from the WSN network model in terms of localization
requirements. The simulation outcomes have shown that the
modified centroid (centroid_T) performs marginally better
than the existing centroid method with a marginally increase
in the computation process. We also observe the variation of
localization error with various anchor nodes, radio range, and
network size.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Development and performance evaluation of localization algorithm for variety ...eSAT Journals
Abstract Wireless sensor networks have emerged from military needs and found its way into civil applications. Today wireless sensor networks have become a key technology for different kinds of smart environments. Sensor node localization which is determining where a given sensor node is physically or relatively located is extremely crucial for most of the applications in wireless sensor networks. The procedure through which the sensor nodes obtain their positions is called localization. Many localization algorithms have been proposed for wireless sensor networks. In this article, we describe our newly developed localization algorithm and performance evaluation of this localization algorithm with square, ‘C’ and ‘L’ shape network topology. Keywords- sensor network, localization algorithms
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Optimum Sensor Node Localization in Wireless Sensor Networks
1. ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Vol. 2, Issue 4, pp: (30-39), Month: October - December 2015, Available at: www.paperpublications.org
Page | 30
Paper Publications
Optimum Sensor Node Localization in Wireless
Sensor Networks
1
Neha Verma, 2
Rakhi Rani, 3
Manju Kumari
1
M.Tech Student, 2
M.Tech Student, 3
Assistant Professor
1,2,3
Department of Electronics and Communication, YMCA University of Science and Technology Faridabad- 121006,
Haryana, India
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.
Keywords: Optimum; Node localization; Sensor Networks; Anchor node; precise positioning.
I. INTRODUCTION
Wireless sensor networks (WSN) consist of hundreds to thousands of low-power multifunctioning sensor nodes,
operating in an unattended environment, with limited computational and sensing capabilities. Awareness of location of
these nodes is one of the important and critical issue and challenge. It is an crucial requirements in designing of solutions
for various issues related to WSN. Being used in environmental applications to perform the number of task such as
environment monitoring, disaster relief, target tracking, defences and many more, node localization is inherently one of
the system parameters.
In order to take advantage of these of wireless sensor nodes, we need to account for certain constraints associated with
them. Since the sensor nodes are equipped with small, often irreplaceable, batteries with limited power capacity, it is
essential that the network be energy efficient in order to maximize the life span of the network [7, 8]. Conventional
techniques such as direct transmissions from any specified node to a distant base station have to be avoided. Although
Clustering and generating a sink node can be benificial but if the exact location of destination node is known, the
transmitted energy can be estimated well in advance and thus lifetime of sensor can be predicted. Thus we can say that
sensor node localization plays a very important role in any energy efficient algorithm at both inter-cluster and intra-
cluster levels.
II. RELATED WORK
The introduction of mobile sinks in WSN s has gained more and more attention to balance energy consumption and
prolong network lifetime. In 2005 and 2006, some predefined sink movement traj ectories were studied. In [5],the authors
investigated the benefits of a heterogeneous architecture for WSN s composed of a few resource-rich mobile nodes and a
large number of simple static nodes. They studied WSN s with one mobile sink and one mobile relay individually. In [6],
the authors studied a base station which moves along a predetermined movement path to collect data from cluster heads
2. ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Vol. 2, Issue 4, pp: (30-39), Month: October - December 2015, Available at: www.paperpublications.org
Page | 31
Paper Publications
(CHs). Sensor nodes which were closest to mobile sink trajectory will be chosen as CHs, and CHs sent data to mobile
node as it passed by.
But how will a sensor node keep track of the neighboring nodes? The answer is localization. Localization in sensor
networks can be defined as identification of sensor node's position for any WSN, the accuracy of its localization
technique is highly desired but the main problem we face in attaining it is locating the geometrical position of the sensor
node in the network. Localization problem is an estimation of position of wireless sensor nodes and to coordinate with
one another. Localization is a challenge which deals with wireless sensor nodes and it has been studied from many years.
There are different solutions and they are evaluated according to cost, size and power consumption.
Localization is important when there is an uncertainty of the exact location of some fixed or mobile devices. One example
has been in the supervision of humidity and temperature in forests and/or fields, where thousands of sensors are deployed
by a plane, giving the operator little or no possibility to influence the precise location of each node.[9,10]. Therefore, the
network localization problem, namely, the problem of determining the positions of nodes in a network, has attraction of
many engineering field and have been researched for many years. The device whose location is to be estimated is called
localization node, and the network entity with known location is called localization base station.
III. LOCALIZATION TECHNIQUES
Wireless sensor network consists of a large set of inexpensive sensor nodes with limited processing and computing
resources. Thus, algorithms designed for wireless sensor networks need to be both memory and energy efficient. In most
of the algorithms for wireless sensor network, it is assumed that the sensor nodes are aware of their locations and also
about the locations of their nearby neighbors. Hence, localization is a major research area in wireless sensor networks.
Nodes can utilize a global positioning system, but this solution is typically verycostly. Many researchers are focusing on
designing different algorithm but paying less attention on range measurement inaccuracy Localization is usually carried
out by measuring certain distance dependent parameters of wireless radio link between the localization node and different
localization base stations.
Existing location discovery approaches basically consists of two basic phases:
(1) Distance (or angle) estimation
(2) Distance (or angle) combining
The most popular methods for estimating the distance between two nodes are described below:
Received Signal Strength Indicator (RSSI): RSSI measures the power of the signal at the receiver and based on the
known transmit power, the effective propagation loss can be calculated. Next by using theoretical and empirical models
we can translate this loss into a distance estimate. This method has been used mainly for RF signals. RSSI is a relatively
cheap solution without any extra devices, as all sensor nodes are likely to have radios. The performance, however, is not
as good as other ranging techniques due to the multipath propagation of radio signals.
Time based methods (ToA, TDoA): These methods record the time-of-arrival (ToA) or time-difference-of-arrival
(TDoA). The propagation time can be directly translated into distance, based on the known signal propagation speed.
These methods can be applied to many different signals, such as RF, acoustic, infrared and ultrasound. TDoA methods
are impressively accurate under line-of-sight conditions. But this line-of-sight condition is difficult to meet in some
environments. Furthermore, the speed of sound in air varies with air temperature and humidity, which introduce
inaccuracy into distance estimation. Acoustic signals also show multi-path propagation effects that may impact the
accuracy of signal detection.
Angle-of-Arrival (AoA): AoA estimates the angle at which signals are received and use simple geometric relationships
to calculate node positions. Generally, AoA techniques provide more accurate localization result than RSSI based
techniques but the cost of hardware of very high in AoA.
For the combining phase, the most popular alternatives are:
Hyperbolic trilateration: The most basic and intuitive method is called hyperbolic trilateration. It locates a node by
calculating the intersection of 3 circles as shown in Fig. 1(a).
3. ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Vol. 2, Issue 4, pp: (30-39), Month: October - December 2015, Available at: www.paperpublications.org
Page | 32
Paper Publications
Triangulation: This method is used when the direction of the node instead of the distance is estimated, as in AoA
systems. The node positions are calculated in this case by using the trigonometry laws of sines and cosines (shown in Fig.
1(b)).
Maximum Likelihood (ML) estimation: ML estimation estimates the position of a node by minimizing the differences
between the measured distances and estimated distances (shown in Fig. 1(c)).
Fig. 1: Localization techniques (a) hyperbolic trilateration (b) Triangulation (c) Maximum Likelihood Estimation
IV. RELATED WORK
Consider the case when we have deployed a sensor network consist of N sensors at locations S = {S1, S2,…….,SN}. Let
Sx
i
refer to the x-coordinate of the location of sensor i and let Sy
i
and Sz
i
refer to the y and z coordinates, respectively.
Constraining Sz
i
to be 0 suffices the 2D version of this problem. Determining these locations constitutes the localization
problem. Some sensor nodes are aware of their own positions; these nodes are known as anchors or beacons. All the other
nodes localize themselves with the help of location references received from the anchors. So, mathematically the
localization problem can be formulated as follows: given a multihop network, represented by a graph G = (V, E), and a
set of beacon nodes B, their positions {xb, yb} for all b B, we want to find the position {xu, yu} for all unknown nodes u
U.
In this section, we present a probabilistic position estima-tion algorithm that considers range measurement inaccuracies.
Nodes in a sensor network can belong to two different classes, namely beacons and unknowns. We assume that the
beacons have known positions (either by being placed at known posi-tions or by using GPS), while the unknown nodes
estimate their position with the help of beacons. The first step in RF-based localization is range measurement, i.e
estimating the distance between two nodes, given the signal strength received by one node from the other. RF-based
(a)
(b)
(c)
Beacons or Anchor Nodes
Unknown Node that needs to be localized
4. ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Vol. 2, Issue 4, pp: (30-39), Month: October - December 2015, Available at: www.paperpublications.org
Page | 33
Paper Publications
signal strength measurements are usually prone to inaccuracies and errors and, hence, calibration of such measurements is
inevitable before using them for localization. For this algorithm to work, extensive preliminary field measurements and
calibrations were carried out as discussed in the following subsections.
At a high level, the guidelines are a result of the fact that the probability ofextremely high location error results from
anchor nodes being roughly in a geograph-ically straight line. As the anchor nodes are spread out from a straight line, the
probability of high errors decreases, leaving network designers a relatively simple chore when choosing anchor nodes
locations.
a. Basic Framework:
As far as empirical evidence is concerned, authors will come across anchor placement by accident and discuss it basedon
their own empirical evidence. Shang, et al. [2] and Li, et al. [3] both choose anchors at random within the network.
Although, Shang, et al., do mention that a co-linear set of anchors chosen in one example ”represents a rather unlucky
selection”, without supporting evidence of why this is unlucky. Earlier work by Doherty, et al. [1] requires anchor nodes
to be placed at the edges, and ideally at the corners of the network. In this case, however, the algorithm is a simple
constraint problem. One constraint requires that all the unknown nodes be placed within the convex hull of the anchors,
and therefore, better results are obtained when anchors are at the corners.
An undirected topology of the sensor networks is described by the tuple T = (N; E; W), where N, E and W represent the
set of sensor nodes, edges connecting pair of nodes and weights assigned to edges between each pair of nodes
respectively. Weights in this context refer to the received signal observations assuming one node is transmitting and other
is receiving. The weight is invariant in both direction i.e. {wij = wji} R. The location of sources are assumed to be
known. Location of the ith source is represented by ( , 2 S , wherei S is the set of source nodes. ( xi
t; yi
t; zi
t), i
N denotes the location of unknown ith mobile node at each instant t. The problem of localization is to find the location of
these mobile nodes at each instant. To estimate the location of mobile nodes, distances from each of the sources are
computed.
The distance between pair of nodes is estimated based on received signal strength [12, 13] and shown below.
( ) ) + X (1)
Where, Pt and Pr are the transmitted and received signal powers in dB respectively. d, ne and X denote the distance
between pair of nodes, path loss exponent and noise respectively. Gt, Gr and are the transmitting antenna gain,
receiving antenna gain and carrier wavelength respectively. In the following Section, relationship between received signal
and distance is established experimentally for different modalities of signal.
b. Major sources of Error and Calibration
AOA measurements are impaired by the same sources discussed in the TOA section: additive noise and multipath. The
resulting AOA measurements are typically modeled as Gaussian, with ensemble mean equal to the true angle to the
source and stan-dard deviation σα. Theoretical results for acoustic-based AOA estimation show standard deviation
bounds on the order of σα=2◦
to σα=6◦
, depending on range [49]. Estimation errors for RF AOA on the order of σα=3◦
have
been reported using the RSS ratio method [14].
It is not likely that sensors will be placed with known orienta-tion. When sensor nodes have directionality, the network
local-ization problem must be extended to consider each sensor‟s orientation as an unknown parameter to be estimated
along with position. In this case, the unknown vector θ is augmented to include the orientation of each sensor. The models
presented earlier are sufficient to find bounds on localization performance in cooperative localization. These lower
bounds are not a function of the particular localization algorithm employed. Thus we have presented some of these
performance limitions in this section before discussing algorithm of this research.
In particular, each sensor estimates its coordinates by finding coordinates that minimize the total squared error between
calculated distances and estimated distances.
Sensor j's calculated distance to seed i is:
dji = √ [ (xi - xj)2
+ (yi - yj)2
(2)
and sensor j's total error is:
5. ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Vol. 2, Issue 4, pp: (30-39), Month: October - December 2015, Available at: www.paperpublications.org
Page | 34
Paper Publications
∑ (3)
In equation (2) and equation (3), n is the number of seed sensors and dˆji is the estimated distance computed through
gradient propagation. The coordinates are then incrementally updated in proportion to the gradient of the total error with
respect to that coordinate.
V. PROPOSED APPROACH
The goal of proposed algorithm is to determine specific blind node‟s location within the distributed nodes along the
testbed area. If the positions of the blind nodes are not known in a network, the event these monitor and report can not be
located if need be. The primary obstacles to localization in wireless sensor network is the sparse anchor node problem,
hence, this algorithm is structured to solve the problem.
Features of required Algorithm:
Interferomatric ranging based localization that takes error propagation into account.
Attack the challenges of Information Asymmetry.
Secure and robust.
Accuracy.
Incresed flexibility by taking obstacles into account.
Fig. 2: Difference in hop count distance in the presence of an obstacle.
Consider Fig. 2 where the hop count distance between 1 and 5 node is four hops due to the obstacle, but the real distance
is far lesser than four values. Thus it is important for algorithm to readjust itself in the presence of an obstacles. It requires
substantial node density before its accuracy reaches an acceptable level as the shown hop count is not reliable in
measurement because environmental obstacles can prevent edges from appearing in the connectivity graph that otherwise
would be present as shown in Fig 2.
In this section, we present a probabilistic position estima-tion algorithm that considers range measurement inaccuracies.
Nodes in a sensor network can belong to two different classes, namely beacons and unknowns. We assume that the
beacons have known positions (either by being placed at known posi-tions or by using GPS), while the unknown nodes
estimate their position with the help of beacons. The first step in RF-based localization is range measurement, i.e
estimating the distance between two nodes, given the signal strength received by one node from the other. RF-based
signal strength measurements are usually prone to inaccuracies and errors and, hence, calibration of such measurements is
inevitable before using them for localization.
The proposed algorithm is made up of two phases: startup phase and the resultant phase.
a. Startup phase:
The first phase is a heuristic that produces a graph embedding which looks similar to the original embedding. The authors
assume that each node has a unique identifier and the identifier of node i is denoted by IDi and the hop-count between
nodes i ad j is the number of nodes hi,j along the shortest path between i and j. The algorithm first elects the five reference
nodes in which four nodes n1, n2, n3 and n4 are selected such that they are on the periphery of the graph and the pair (n1,
n2) is roughly perpendicular to the pair (n3, n4). The node n5 is elected such that it is in the middle of the graph. At first
the node with smallest ID is selected.
3
1
2
5
4
Obstacle
Nodes
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Next the reference node n1 is selected to maximize h1,2. After that n3 is selected to minimize |h1,3– h2,3| and the tie-
breaking rule is to pick the node that minimizes h1,3+ h2,3. In the next stage n4 is selected to minimize |h1,4– h2,4| and the
ties are broken by picking the node that maximizes h3,4. Now for all nodes ni the heuristics uses the hop-counts h1,i, h2,i,
h3,i, h4,i, and h5,I from the chosen reference nodes to approximate the polar coordinates (ρi, θi) where:
(4)
– – (5)
b. Resultant phase:
In the second phase, each node ni calculates the estimated distance dˆi,j to each neighbours nj and it also knows the
measured distance ri,j to neighbour nj. Now if vˆi,j represent the unit vector in the direction from pˆi to pˆj ( pˆi and pˆj at the
current estimates of i and j respectively) then the force Fi,j in the direction vˆi,j is given by:
Fi,j = vˆi,j ( dˆi,j– ri,j) (6)
And the resultant force on node i is given by
Fi = ∑i,,j Fi,j (7)
The energy Ei,j of nodes ni and nj due to the difference in measured and estimated distances is the sequence of the
magnitude of Fi,j and the total energy of node i is equal to:
∑ ∑ – (8)
And the total energy of the system E is given by:
E = ∑i Ei (9)
Now the energy Ei of each node ni reduces when it moves by an infinitesimal amount in the direction of force Fi. In the
optimization, the magnitude of Fi for each node ni is zero and the global energy of the system E is also zero and the
algorithm converges.
VI. SIMULATION RESULTS AND ANALYSIS
To study the robustness of the proposed localization algorithm, a MATLAB program was developed; this program
implemented the algorithm using the input statement and other matlab statements which is more interactive and better for
analysis.
a. Data Collection
The Table1 is based on the existing experiments which uses a different randomly-generated topology and a Zigbee
CC2420 Module.
Table 1: Total Average Receive Signal Strength w.r.t distance
Distance (m) RSSI (dBm)
1 -44.8
2 -47.7
3 -48.7
4 -53.1
5 -55.6
6 -61.8
7 -67.2
8 -66.5
9 -69.0
10 -67.3
From Table 1 the data collected was used to develop a graph and for computing the pathloss exponent n of the testbed
area using eq. (1). From the computation, as shown in fig. 3 was computed to be 2.2. Hence, n = 2.2 will be used as the
pathloss exponent in this research work.
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Fig. 3: The average values of RSSI obtained vs. distance
b. Processing collected Data
As per an experiment [15] they measured the signal strength and noise in intervals of 2.5m up to 50m. For each distance,
we measured the data at 16 different positions. It was noted that a significant change in signal strength as a function of
distance, while the noise remained almost the same; so, we considered only the signal strength information in our
analysis.
Fig. 4: Probability distribution of signal strength with distance
Fig. 4 shows a graph with a normal fit for data collected with a received signal strength obtained by Zigbee module used.
Themean and standard deviation for each signal strength was noted and tabulated as a function of distance.
Fig. 5: Distribution of distances for a received signals and obtained RSSI
-80
-75
-70
-65
-60
-55
-50
-45
-40
0 2 4 6 8 10
RSSIValues(dBm)
Distance (m)
0
0.05
0.1
0.15
0.2
0.25
12.5 15 17.5 20 22.5 25 27.5 30 32.5 35 37.5 40
ProbabilityDistributionofRSSI
values
Distance (m)
0
0.01
0.025
0.1
0.2
0.075
0.025
0
0
0.05
0.1
0.15
0.2
0.25
0 10 20 30 40 50
ProbabilityDistributionFunction
Distance (m)
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A graphical view of the table is shown in Fig. 5 for signal strengths ranging from 66 to 90. As per our proposed
algorithm, the method uses this table for ranging; i.e., any node receiving a beacon packet will estimate itself to be
located on a surface that has a probability distribution dictated by themean and standard deviation corresponding to the
signal strength received. Note that the bound on standard deviation of local-ization error is proportional to (1/γ )1/2
. It
makes sense that the localization error is proportional to σT for TOA and σα for AOA.
c. Probabilistic Localization
Fig. 6. Shows results when only the distance based localization of a single target is considered. There can N anchor nodes
in the system and one mobile node, we use the measured distances and we find the error in location of the mobile node
using eq. (3).
Fig. 6: Implementing localization using 4 anchor and 1 mobile user to find detection error.
Initial Parameters:
N = 4 (number of anchors)
M = 1 (number of mobile nodes)
NoisePow = 20;
The estimated error w.r.t to correct postion of anchor is found out to be 1.8668m when we have considered a
100mX100m plot. It means that the accuracy of distance measurement is 90 % for instance the inaccuracy of a 1m
measured distance is around 0.1 meter. Thus Impliementing c. Probabilistic localization concept for self optimization of
sensor node is acheived.
d. Implementing Localization on a randomly deployed wireless network
The first hypothesis was to choose nodes as anchors that we expect to have high location accuracy. Because we are trying
to provide an a priori design technique for network planners, the information chosen to reach this goal must be applied to
the randomly deployed network, as shown in Fig. 7. With this implementation we found that on an average, increased
network density results in higher localization accuracy [2, 16]. Since the range-free algorithms depend heavily on
network connectivity, the theory is that nodes with more neighbors will have lower location errors. The lower error in the
anchors themselves should then translate into a more accurate transformation of the entire network.
Fig. 7: The sample WSN used for showing localization of randomly selected mobile node.
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A simulator can be very useful in geographic routing application of sensor localization where the use of the coordinates of
sensors can reduce routing tables and simplify routing algorithms. Localization errors, however, can adversely impact
routing algorithms, leading to longer paths and delivery failures [18]. For the purposes of routing efficiency, actual
geographical coordinates may be less useful than “virtual” coordinates [19] (i.e., a representation of a sensor‟s „location‟
in the graph of network connectivity).
Researchers who are testing localization algorithms, like those presented in the later section on algorithms for location
estimation, can use WSN localization simulators as well. These kind of softwares provide public access to a multifeatured
MATLAB-based code and GUI for the calculation of the localization as shown in Fig. 8. It allows the inclusion of device
orientation and clock biases as unknown nuisance parameters. Sensors can be arranged visually using the GUI and the
bound can be calculated.
Fig. 8: Using WSN localization Simulator for deploying MATLAB program designed for single mobile node in multiple node
environment
VII. CONCLUSION
Some localization schemes have fewer merits and greater demerits and some of them have less demerits and greater
merits. These merits and demerits were the main source for proposing the idea of a unique approach which is the
enhanced composite approach. In this paper, we have described a novel, robust and dis-tributed algorithm for localizing
nodes in sensor networks. The approach is probability based and takes range measurement inaccuracies into account,
which, according to our knowledge, have not yet been pursued in literature. Our Extensive outdoor field measurements
and calibration indicate that a received signal strength is inaccurate and the proposed approach uses this information
directly for ranging.
In this paper, we have described a novel, robust and dis-tributed algorithm for localizing nodes in sensor networks. Also,
this algorithm accounts for the impact of wireless channel and variation of received signal observation with time and due
to environmental conditions with the help of the volume constraints. The approach is RSSI based and takes range
measurement inaccuracies into account, which, according to our knowledge, have not yet been pursued in literature. The
proposed algorithm performs consistently well irrespective of the variation in the localization duration and radio
communication range. Our focus of future work is on developing a three dimensional localization algorithm in mobile
sensor networks using mobile nodes and anchors.
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