Localization Technique For Wireless Mesh Networks Computer Science Essay

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Wireless Mesh Networks are dynamically self-organized and self configured networks which resolve the limitations and combine advantages of both Wireless Local Area Networks (WLAN) and Mobile Ad-hoc Network (MANET). Existing wireless localization techniques are categorized as range-free and range-aware algorithms depending on whether they use distance information. The Received Signal Strength (RSS) values are available with the wireless systems but they are not used effectively in the existing localization techniques. Localization is important in wireless applications where exact position of nodes is not known beforehand or if there are mobile nodes in the network. Not much work is done in localization in WMN as compared to that of WLAN and MANET. This paper presents a study of localization techniques and issues in localization in wireless mesh networks. In this paper, we propose a new approach to Received Signal Strength based localization algorithm that is based on range-aware distance measurement method. Simulation model is built by Network Simulator (NS-2). Position accuracy given by the algorithm is analyzed.

1. INTRODUCTION:

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Wireless Mesh network consists of two types of nodes: mesh routers and mesh clients. Mesh clients can be stationary or mobile. They can form client mesh connectivity with other clients and with the mesh routers. Mesh routers are stationary and form the backbone of WMNs (referred to as backhaul tier) between the mesh clients and Internet Gateway. Wireless Mesh Router is equipped with multiple wireless interfaces and it covers same area, as that of covered by normal routers, with much lower transmission power. As a result, two transmissions of two nearby pairs can be simultaneously scheduled if non-overlapping channels are assigned. Mesh Clients usually have only one wireless interface. They have additional functions for mesh networking so that they can also take part in packet forwarding. PDAs, laptops, Wi-Fi phones are some of the examples of mesh clients.

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Fig 1- Wireless Mesh Networks and its components

Localization is important when there is an uncertainty of the exact location of some fixed or mobile devices. In order to develop an algorithm for localization of nodes in a wireless mesh network. The algorithm should be distributed and executed in individual nodes. Since the algorithm should be run in individual nodes, the solution has to be relatively simple, and demand limited resources (in terms of computation, memory and communication overhead). The goal is to be able to position nodes with a high accuracy, or minimum position error.

2. LITERATURE REVIEW:

2.1 Received Signal Strength Indication (RSSI) In telecommunications, Received Signal Strength Indicator (RSSI) is a measurement of the power present in a received radio signal. RSSI is a metric in radio technology. These values are usually invisible to the user of device containing the receiver, but are directly known to users of wireless networking of IEEE 802.11 protocol family. RSSI values are available with all the nodes because RSSI is supported by 802.11

NICs, so wireless nodes do not require extra hardware. The RSSI-based distance measure is a low-power, low-cost method [2] and has obvious advantages of passing through obstacles compared to an ultrasonic measurement method.

2.2 Range-aware localization

The approach in which the position of a node is computed relative to other nodes is called Range-aware localization. The distance between sender and receiver can be estimated by one of the several features of communication signal. Range-aware techniques usually require hardware support. The features of the communication signal, which are generally used in range-based localization, are called distance information.

2.2.1 Distance Information

The distance information, which is found out by distance measurement method, is the basic need of the wireless localization technique. It is very important because based on this distance information, the position of the mobile nodes would be found out. The accuracy of the distance information determines the accuracy of the localization technique. Distance measurement methods use available recourses at respective nodes to calculate the distance information. So, distance measurement method must be accurate and resources must not be over utilized by it.

There are mainly three types of distance measurement methods which are based on signal transmission time, signal transmission route and signal power (Received Signal Strength Indication, RSSI).

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1. Based on Transmission Time: This method relies on Time-of-Arrival (TOA) measurement. The most basic localization system to use TOA techniques is GPS [3]. If the speed of the signal and time between signal sent and received are known then distance can be calculated by formula distance = speed * time. In this method, for calculating the TOA requires time synchronization between each pair of nodes. And also measuring the speed of radio frequency signal is very difficult. So extra hardware is required at each node to determine the speed using ultrasonic waves which have less transmission speed than radio signal.

2. Based on Transmission Route: This technique is used in Ad-hoc positioning systems [4]. The nodes which know their position are known as anchor nodes. These nodes flood the network with their positions; other nodes get average hop distances from these nodes. By using triangulation, the node determines its own position. This method generally used in the systems where high accuracy is not needed because they can be used only in multi-hop networks. When we want to measure 1-hop (neighbor) node distance, this method fails to give correct results.

3. Based on Transmission signal power: The transmission signal power is known as Received Signal Strength Indication (RSSI) which is used to translate signal strength into distance estimates. RSSI technology such as RADAR [5] has been proposed for hardware constrained systems. RSSI values are available with all the nodes because RSSI is supported by 802.11 NICs, so wireless nodes do not require extra hardware. The RSSI-based distance measure is low-power, low-cost method [6] and has advantages over other methods.

3. METHODOLOGY:

Range-aware method [7] is carried out for getting the distance information. It makes use of available resources to find out the distance information. Range-aware method is the first phase of the localization technique. For the localization problem, the network is modeled as a connected, undirected graph G = (N, E), where N is the set of wireless nodes (mesh router/mesh client) and E is the set of edges connecting neighboring nodes. Each edge e (u, v) is associated with a value z ∈ Z. Let (x, y) be the unknown coordinates of u ∈ N. Let R ⊂ N is the set of mesh routers with known coordinates. The localization problem is to find coordinates (x, y) of each node u ∈ V \R.

Step 1: Get RSSI values from all neighbors

Received Signal Strength Indication (RSSI) is power present in the received signal. Range information is supported by standard 802.11 NICs [8] in the form of RSSI. Therefore it does not need extra hardware. For gathering the RSSI values from all the neighbors, each node requires one round of communication with all the neighbor nodes. edited 1.png

Fig-2 Transmission range (R) and neighbor nodes

Step 2: Ordering RSSI

Step 2 is a sorting process. All the r (u,g)s from g ∈ G are sorted and the nodes in G can be rearranged according to the sorted RSSIs.

Step 3: From ordered RSSI to range order

The maximum distance up to which a node can communicate to another node is called transmission range of that node.

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Fig-3 Ordering

The nodes in G are now arranged accordingly in such an order that the corresponding distances are in the increasing order. The result of this step is an ordered node list g ∈ G in the increasing range order.

Step 4: RSSI to Distance mapping

The mapping process involves dividing the transmission range into s smaller sets and assign appropriate sets to the node groups. We can build a mapping relationship between RSSI and distance. But signal goes through various changes because of the losses and gains occurred in it before it reaches to the receiver. So we cannot use one mapping relationship for various the environments. The PRA method [7] is based on the above concept which does not use RSSI as its measurement basis.

If n being total number of nodes, s is the total number of sets and nj number of nodes in jth set. For a node u, maximum (qjmax) and minimum (qjmin) range levels are assigned.

3.1 Simple Linear model

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In this model, the nj , qjmin and qjmax are formulated as,

nj= n/s

qjmin = (j-1)*R/S and qjmax = j*R/S

The neighboring nodes are divided into s sets and so the transmission ranges (R). This model divides distance set evenly according to the RSSI set division. As shown in the fig. 4, the 16 nodes are divided evenly into 4 sets. It does not consider the true range which belongs to corresponding RSSI value. The nodes in G are split into s clusters and transmission range R is divided into s smaller levels. The nodes in the jth cluster are set to the jth range level whose minimum and maximum range levels would be qjmin and qjmax[3]

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Fig-4 Mapping in Simple Linear Model

3.2 Area Proportional model

Unlike simple linear model, this model does not use any mapping function. This model does not divide the RSSI set evenly as done in simple linear model. It estimates the distribution of the nodes in the neighborhood according to the RSSI values gathered.

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Fig-5 Area Proportional Model

Area proportional model[3] considers its transmission range as a circle and divides this circle into s rings. The area of a ring can be given as,

Aring = π - (Router2 - Rinner2)

Where, Router and Rinner is the ring's outer and inner radius respectively. Now the nodes falling into a particular ring would be assigned to that distance set. So in our example, the mapping will look like as shown in the Fig-6

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Fig-6 Mapping in Area Proportional Model

3.3 Rectangle formation based localization algorithm

In this method, we consider the area which is overlapped by the circles to determine the position. So we don't need specific values of distances to the mesh routers but the series of upper bound values.

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Fig-7 Rectangle formation algorithm

It is not sufficient to get the resulting rectangle. We have to determine the position of the mesh client. As sown in fig. 8, the rectangle is classifies as RUpper, RLower, RLeft and RRight. These values are determined by the circle bounder.

RUpper = Min (R1Upper, R2Upper , R3Upper )

RLower = Max (R1Lower , R2Lower , R3Lower)

RLeft = Max (R1Left , R2Left , R3Left)

RRight = Min (R1Right , R2Right , R3Right

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Fig-8 Calculating position

This algorithm gives 15% to 25% average position error. Here the area considering is very large so it is giving so much position error but it can be improved.

3.4 A new approach algorithm

The proposed algorithm will reduce the position error by reducing the scope of area. We are considering only the area which is shaded, here we will get the six intersection points out of which we are considering only three points and forming the median.

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Fig-9 New approach

4. SIMULATION RESULTS

Network Simulator-2 (NS-2) is used for the implementation purpose. These are the steps in implementing the algorithm

1. Step 1: Get RSSI values from all neighbors

The RSSI values are extracted and the graph is plotted versus distance. As the distance increases the RSSI value is going to decrease with respect to first node [9].

Step2: Ordering RSSI

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Fig-10 17 Nodes within the range of 16 mts

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Fig-11 RSSI vs Distance

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Fig-12 17 Nodes within the range of 250 mts

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Fig-13 RSSI vs Distance

Step 3: From ordered RSSI to range order

The maximum distance up to which a node can communicate to another node is called transmission range of that node.

Step 4: RSSI to Distance mapping

The mapping process involves dividing the transmission range into s smaller sets and assign appropriate sets to the node groups. We can build a mapping relationship between RSSI and distance. But signal goes through various changes because of the losses and gains occurred in it before it reaches to the receiver. So we cannot use one mapping relationship for various the environments. After these steps the position of node is to be measured. Since we are reducing the scope of area, the position error is going to reduce when compare to the rectangular formation based algorithm.

5. CONCLUSION AND FUTURE WORK

A new approach to Received signal strength based Localization technique for Wireless Mesh Networks (WMN) is introduced. It makes use of range aware method. In the range aware method, which is based on the Received Signal Strength Indication (RSSI), the mapping from RSSI to distance varies greatly with the environment. The set division results for one transmission model cannot be used for different transmission models. Therefore, the set divisions are neither fixed nor uniformly decided. Here the position error can be reduced to some range.

Localization is important in wireless applications where exact position of nodes is not known beforehand or if there are mobile nodes in the network. Not much work is done in localization in WMN as compared to that of WLAN and MANET so it's still a changeling area to work.