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A significant amount of research has been conducted in the field of Localization in indoor environment. After the clear research here we come with most serious challenges. To overcome the loop holes in the localization of indoor environment we propose a variety of techniques, concepts and methodologies.
A wireless sensor network with beacon nodes in an indoor environment in which, beacon nodes are placed uniformly to optimize the number of beacons and decrease localization error. There may be change in the building infrastructure in the case of localization error. The beacon node placement is non-optimal and reorganization of beacon nodes for modifying localization error is time consuming. In the present work, a simple approach is proposed to move beacon nodes efficiently which would optimize the movement of beacon nodes and the number of beacons
Aim & Objective:
To move the beacon nodes efficiently which would, optimize the movement of beacon nodes and the number of beacons.
Minimize the localization service interruption as much as possible and thus reduce the downtime of the service.
Reducing the time complexity in localization service by dividing the entire building into grids of a particular size and compare the corresponding grids for closest beacon instead of comparing the beacons in the entire building.
Finding the malicious beacons and rectifying them.
There are several methods that we are going to discuss in the project. Among all we chose the best methods that give good results to our project.
We have come across different algorithms like predictive algorithm, heap algorithm, relaxation based algorithm, distribution algorithm and some other but most of this is not suitable for the project.
Here I am going to propose greedy algorithm and beacon placement algorithm with closest pair of points application.
Technologies like infrared, ultrasound, radio signal. Location metrics like TOA, TDOA, RSS, AOA and Proximity, beacon placement approaches.
To implement and test localization in indoor environment in MATLAB using the proposed algorithms that gives effective results which overcomes the localization error, failure of beacon nodes, beacon placement.
Research about the Project:
Wireless sensor networks are developed for monitoring host environmental characters like pressure, light, sound, vibrations, temperature and other. The duty of WSN is to monitor, track and control. In WSN we have two types of nodes such as beacon nodes and user nodes. Beacon nodes are static which is known and user nodes are dynamic that are unknown. There are different positioning systems to determine the location of the user nodes such as GPS, active badges, radio tags, cellular phone based systems and many more. 
GPS is the best locator of the object with the help of the satellite network in the outdoor environment but it has limitations in indoor environment as it requires LOS (Line of Sight) with the satellites which, is highly impossible in the indoor environment. Hence we need different localization system in indoor environment that gives accurate results such as Wi-Fi, Bluetooth, radio signals and many more. 
Factors affecting Localization system in indoor environment:
There are two important factors that affect the localization system in the indoor environment such as beacon placement and measurement errors.
Beacon nodes determining the position of user nodes. Therefore placement of beacons in indoor environment has strong impact on the quality of localization technique. Hence beacons are placed accurately to increase the quality of localization system.
Localization accuracy is more important in indoor environment when compared to outdoor environment because the walls present in the indoor environment obstruct the signal propagation, resulting errors in measuring localization of the user nodes and detecting malicious beacon nodes.
Studies on Specific Issues:
The study for the topic is to introduce algorithms for the localization in indoor environment, which overcome the localization error, beacon placement error, detecting malicious beacons. Some algorithms like beacon placement algorithm, beacon based distribution algorithm, relaxation based distributed algorithm, hybrid localization algorithm, heap algorithm, predictive algorithm and many more. Most of this is not successful in identifying the user nodes in indoor environment. So here i am proposing new algorithm that is greedy algorithm and beacon with closest pair of points approach. 
Technologies for Implementation:
Sensing Technologies: Various sensing technologies that are used for localization are infrared, ultrasound, radio signal and other. 
Infrared: Infrared signals give more perfect location of user node but requires installation of additional hardware to detect infrared signal, range of signal is small, no penetration ability, limited to line of sight, poor performance in the presence of direct sunlight. 
Ultrasound: Used nodes need to be equipped with sound transceivers, slow propagation speed, requires installation of additional hardware. 
Radio signal: This is popular and cost effective, penetrates into most building material, no need of installation of additional hardware as the radio signal transceiver is already available with the communication device.
Location Metrics: 
This captures the position of user nodes with the help of beacon nodes. Relative position represents distance and angle between the user node and the beacon node. Location metrics like TOA, TDOA, RSS (Relative distance),AOA (relative angle). After capturing distance or angle user nodes use different methods like triangulation, trilateration or maximum likelihood estimation to estimate positions.
Time Based Methods: TOA and TDOA methods estimate by propagation time between transmitter and receiver. This can be used by different signals such as infrared, ultrasound and RF. TDOA does not meet LOS in some environments, speed of sound varies with temperature and humidity.[3,4]
Angle of Arrival (AOA): Estimates the angle from the direction it obtained which captures the relative angle of the beacon node with user node. The limitation of this approach is that it requires directional antennas at the beacon and additional hardware at user node to detect the direction of the signal.[3,4]
Proximity: This is simplest of all metrics. It checks user node is near to beacon node or not. If user node is able to detect the signals transmitted by beacon, then the user node is said to be in proximity to the beacon node.
Received Signal Strength (RSS): Used with radio technology. Using the signal strength the relative position of user node with respect to beacon node can be determined. Advantage of this metric is that it requires neither synchronized clocks nor high precision clocks to measure RSS, but the disadvantage of this approach is due to obstructions in indoor environments the signal strength varies in which the accuracy of localization system reduces. 
From all the above location metrics, the most suitable metric that is compatible with radio signal is RSS.
Beacon Placement Approach: 
Effect of Beacon Density on Localization: Fig. 1 shows the impact of density on localization, shaded region represents the Locality of the user node. In Fig. 1(a) user hearing 3 beacons and in Fig. 1(b) user hears 6. Granularity of of user node decreases with increase of beacon. Hence beacon density has significant impact on localization accuracy.
Effect of Beacon Placement on Localization: Placement of beacon nodes is very important similar to the beacon density, from localization accuracy point of view. Fig. 2(a) and Fig. 2(b) depict the case where beacons are placed in a uniform and collinear manner respectively. The difference between the user nodes localities obtained in these cases can also be easily observed. 
Figure 1: BEACON DENSITY EFFECT ON LOCALIZATION 
Figure 2: BEACON PLACEMENT EFFECT ON LOCALIZATION 
TECHNOLOGIES USED FOR PROJECT IMPLEMENTATION:
Application Software : MATLAB
O.S Requirements : Windows XP SP-3 or Vista or Windows 7 (either 32 or 64 bit
Hardware Requirements : INTEL or AMD x86 Processor, 2 GB RAM
Disk Space Requirements : 3-4 GB HDD space for MATLAB installation
Analysis of Work Accomplished:
The project is discussed with the potential supervisor to get feedback about the task done and supervisor guidelines and suggestions will be included in the project.
This project addresses the localization of beacon nodes in indoor environment, highlights the issues in the project like error localization, beacon placement and methods proposed to overcome the problem.