Mobile Computing Improving QOS In Mobile Networks Computer Science Essay

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Location dependent queries of the mobile users increases as the mobile applications which support location dependent services comes into existences. Wireless data broadcast is the promising technique and various localization mechanisms for the mobile nodes are proposed. In this project the proposed mechanism uses fixed number of beacon transceiver which will be equipped with the GPS functionality and able to transmit a gradient of power levels.The several power levels serve to identify the distance by means of power without using Received signal strength indicator. The location estimation is then completed using linear algebra. This new localization method improves the quality of service in mobile networks with respect to scalability, power management and capable to deployed both n networks with movable and stationary nodes.

1.1 Introduction

Advances in mobile technology have made it achievable to build ad hoc sensor networks using cost effective nodes of a low power processor, a wireless network transceiver and a sensor board with a modest amount of memory. That type of node is in size of 2 AA batteries. The applications like habitat monitoring, smart detection and reporting of building failure and target tracking are emerging based on the location based services. For these applications the result data which we get must be geographically meaningful. It can be done by accurately orienting the nodes with respect to a global coordinate system. Furthermore, basic middle ware services such as routing often rely on location information (e.g., geographic routing).

Ad hoc sensor networks present novel tradeoffs in system design. On the one hand, the low cost of the nodes facilitates massive scale and highly parallel computation. On the other hand, each node is likely to have limited power, limited reliability, and only local communication with a modest number of neighbors. These application contexts and potential massive scale make it unrealistic to rely on careful placement or uniform arrangement of sensors.

Rather than use globally accessible beacons or expensive GPS to localize each sensor, we would like the sensors to self-organize a coordinate system. In this chapter, we review localization hardware, discuss issues in localization algorithm design, present the most important localization techniques, and finally suggest future directions in localization.

LOCALIZATION is to outline the technical foundations of today's localization techniques and present the tradeoffs inherent in algorithm design. No specific algorithm is a clear favorite across the spectrum. For example, some algorithms rely on prepositioned nodes while others are able to do without. Other algorithms require expensive hardware capabilities. Some algorithms need a way of performing off-line computation, while other algorithms are able to do all their calculations on the sensor nodes themselves. Localization is still a new and exciting field, with new algorithms, hardware, and applications being developed at a feverish pace; it is hard to say what techniques and hardware will be prevalent in the end.

1.2 Overview 

Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, this paper discuss the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization.

A wireless sensor network is a distributed collection of nodes which are resource constrained and capable of operating with minimal user attendance. Some of the potential applications of wireless sensors include environmental monitoring, military surveillance, search-and-rescue operations, tracking patients and doctors in a hospital and other commercial applications.

Wireless sensor nodes operate in a cooperative and distributed manner. Such nodes are usually embedded in the physical environment and report sensed data to a central base station; however, for a sensor network to achieve its purpose, it is essential to know where the information is sensed.

We define the problem of localization as estimating the position or spatial coordinates of wireless sensor nodes. Localization is an inevitable challenge when dealing with wireless sensor nodes, and a problem which has been studied for many years. Nodes can be equipped with a Global Positioning System (GPS) [1], but this is a costly solution in terms of volume, money and power consumption. While much research has focused on developing different algorithms for localization, less attention has been paid to the problem of range measurement inaccuracy.

The wireless sensor networks are an exciting concept of a wireless network having a wide variety of promising applications in real life. One of the important technical issues that characterize the functionality of this type of networks is the self-localization of nodes. It is often necessary that the data transmitted to the sink of the network are accompanied by the location information. The localization system should be cheap, power-aware and variably accurate depending on the application.

1.2.1 Existing system

There are several approaches to enable localization functionality for a wireless sensor network; Sivalingham 2004 and Savvides 2001 have provided overviews on this matter. The reference point is either a centroid point, as in (He 2003) and (Bulusu 2000), or more frequently a GPS receiver mounted on a sensor node or used separately. The transceiver that carries the GPS reference position information is called wireless beacon. The beacon broadcasts the reference position information and the random sensor node calculates its position by measuring a metric such as the signal strength (Mondinelli 2004 and McGuire 2003), the time of arrival and direction of arrival (Moses 2002), the time difference of arrival (Rappaport 1996), the angle of arrival (Klukas 1998) or even the antenna directivity (Nasipuri 2002). Moreover, the beacon nodes can be stationary or mobile (Sichitiu 2004), one time used or constantly deployed.

Most of the referenced methods proposed in the literature for localization use the range of communications between the node and the beacon in order to calculate the node position. These methods are called range-based methods. In general, the range-based methods are highly accurate methods for estimating location. The drawback, however, is mainly the node complexity, the power consumption and the node cost, all of which are very sensitive parameters for the implementation of a sensor node. On the other hand, the range-free solutions offer a low-cost, low-energy approach in estimating locations. He 2003, Bulusu 2000, Lazos 2004 and Ou 2005, all proposed methods where the node localization takes place without the need of specific range estimation equipment. However, accuracy in these methods is typically low and implementation in some application scenarios becomes slow and unrealistic. Energy awareness is studied by Zou 2003 and a general comparison of localization methods is given by Langendoen 2003.

1.2.2 Proposed System

The proposed scheme merges the two above approaches using a variable and small number of GPS beacons and a geometrical method to compute the node position. The localization is based on range estimation, but no range measurements take place in the sensor node. It is organized in a manner of time division transmits and promises very high speed location estimation as opposed to previous methods. This feature is very important in networks where real-time knowledge of mobile node location is needed. Moreover, Ou 2005, explained that high number of beacons increases cost and accuracy as well as restricts scalability.

2. Literature Review

The localization problem gives rise to two important hardware problems.

The problem of defining a coordinate system.

The second, which is the more technically challenging, is the problem of calculating the distance between sensors (the ranging problem).

To define the coordinate system beacon nodes are used and various techniques are discussed to solve the range problems. Out of those the below are discussed in this paper.

2.1 Anchor/Beacon nodes

2.2.1 Received Signal Strength Indication (RSSI)

2.2.2 Radio Hop Count

2.2.3 Time Difference of Arrival (TDoA)

2.2.4 Angle of Arrival (AoA), Digital Compasses

3. Design and Implementation

3.1 Wireless network environment creation

Wireless ad hoc networks are comprised of Mobile Nodes (MNs) that are self-organizing and cooperating to ensure routing of packets among themselves. They provide robust communication in a variety of hostile environments, such as communication for the military or in disaster recovery situations when all infrastructures are down. In this module, the Mobile Adhoc Network is constructed. Typical sensor networks consist of a large number of densely deployed sensor nodes that gather local data and communicate with each other. The sensed data are often meaningful only if we know where the data are from. Therefore, knowing the positions of sensor nodes is essential in wireless sensor networks. In addition, the position information is imperative in some routing protocols, information dissemination protocols, sensor query and processing systems.

3.2 Beacon node creation

Most of the referenced methods for localization use the range of communications between the node and the beacon in order to calculate the node position. The proposed scheme merges the two above approaches using a variable and small number of GPS beacons and a geometrical method to compute the node position. The localization is based on range estimation, but no range measurements take place in the sensor node. It is organized in a manner of time division transmits and promises very high speed location estimation as opposed to previous methods. This feature is very important in networks where real-time knowledge of mobile node location is needed.

So the beacon nodes are place such that all the nodes in the environment are covered. A signal is transmitted from beacon nodes to all sensors and receives the signal from sensor nodes.

The localization of the sensor nodes is based on the GPS information which is available to the beacon nodes. Instead of RSSI measurements, a different approach is used. Each beacon broadcasts its position N times with different power levels. At first, the power levels are low so as not to be detected by the sensor node receiver. Gradually, by increasing the power, and transmitting the information of the step sequence, every receiving node will detect the transmission and save the step information. The procedure is repeated for all beacon nodes in

a round robin manner.

In figure 3, an example of the proposed scheme is showcased. The sensor node S1, receives four broadcast packets from B1, B2, B3 and B4, each of which contain the relative beacon GPS coordinates and the step sequence. The level at which this will occur depends on the sensor node receiver threshold which is assumed unknown:

Where, PNthr is the node receiver threshold level, Gb and GN the gain for the beacon and sensor node antenna respectively, λ the wavelength, PBT the transmit power of the beacon for the i step and di the distance between the two transceivers. A is an arbitrary constant.

A calibration is used, in which the power gradient is received by another beacon. In this process, the power gradient is calibrated. Unfortunately, the calibration is valid only for a beacon receiver, which in general is not the same with the sensor node receiver. However, we get:

Where dGPS is the known distance between the two beacons and B is another constant. At this point, the volume of the power step ΔP can be defined so as to have a linear quantization of the space between the transceivers:

Where kB is a constant for the beacon receiver. As a result, the power should be calibrated as shown in figure 2. In the event that PNthr equals PBthr and A equals B, the sensor node can calculate its position with no quantization error using only three beacon broadcasts. The calculated d1, d2 and d3 are three circles that pass through the sensor position. The method that estimates the correct position is called trilateration (for information see Navidi 1998). In case the sensor node has a different transceiver from the beacon node, the distances d1, d2 and d3 are found with an analogy:

Let B1(x1,y1), B2(x2,y2), B3(x3,y3) be the beacon nodes and S(x0,y0) the sensor node. The following Cartesian transformation is used:

And the new coordinates will be B1(0,0), B2(dAB,0), B3(x3',y3') and S1(x,y); x, y and k can be calculated by the system of equations:

Topology of the sensor network. B1, B2, B3 and B4 are beacon nodes equipped with GPS receiver

Beacon Output Power gradient for N=10 steps. The power increment should be of the power of 2 for the space to be linear.

The solution is two cases of k. As k increases, the common solutions of circle d1 and d2 are running towards opposite directions. The upper point (s1) becomes the first common solution with circle d3. The second case is subject to the velocity of the second point (s2) and the point c2 on the d3 circle, which for a proper large k will be identical with s2. Since both points are running towards the same direction, the velocity of c2 should be greater than that of s2. In order to decide upon the correct solution (between the two), topology information is required. This can be obtained either by pre defining the three beacons position and proper software, or by using a fourth beacon. If the cost of a beacon node is an issue, then the software solution should be chosen. If the randomness during installation process is more important, then the fourth beacon should be included in the scheme. In figure 4, the area in bold defines the boundary between the two cases. When, the node position is inside the area, the smaller root of the two should be chosen. When the node position is outside the area, the greater solution will be the correct one. The curve between the two areas is an ellipse and it is given by below equation.

3.3 Equipment Requirements

From the above it follows that the proposed scheme requires four functional beacon nodes each of which should have a GPS receiver, a location estimator and the ability to transmit a gradient of power levels (figure 1). The sensor nodes should only be equipped with the ability to make some simple calculations in order to estimate their position by means of trilateration.

Restoring position information at the sensor node (s1). The three bold circles are scaled in order to find two possible solutions.

When the node is known to reside inside the dark area the smaller solution of the two is correct, otherwise the greater should be chosen.

3.4 MAC modifications

Two new sink commands should be introduced for the proposed scheme to be functional, the calibration command and the measurement command. After a calibration command, the beacon nodes calibrate their transceivers and configure their dynamic range. The sensor nodes remain with their transceivers powered off. After a measurement command, the beacon nodes broadcast the calibrated power gradient in a round robin sequence. In the same time, the sensor nodes turn their receivers on until the first power level reaches them. Then, they shut the receiver down and schedule a restart for the next beacon broadcast. The calibration command takes place before a measurement command and only on the occasion that calibration information does not exist or it is considered unreliable. The measurement command is used whenever the location information needs to be updated.

3.5 Algorithm description


4.1 GloMoSim

Global Mobile Information System Simulator (GloMoSim) is a scalable simulation environment for large wireless and wire line communication networks .GloMoSim uses a parallel discrete-event simulation capability provided by Parsec. GloMoSim simulates networks with up to thousand nodes linked by a heterogeneous communications capability that includes multicast, asymmetric communications using direct satellite broadcasts, multi-hop wireless communications using ad-hoc networking, and traditional Internet protocols. The following table lists the GloMoSim models currently available at each of the major layers:

The node aggregation technique is introduced into GloMoSim to give significant benefits to the simulation performance. Initializing each node as a separate entity inherently limits the scalability because the memory requirements increase dramatically for a model with large number of nodes. With node aggregation, a single entity can simulate several network nodes in the system. Node aggregation technique implies that the number of nodes in the system can be increased while maintaining the same number of entities in the simulation. In GloMoSim, each entity represents a geographical area of the simulation. Hence the network nodes which a particular entity represents are determined by the physical position of the nodes.

4.2 Use of GloMoSim Simulator

After successfully installing GloMoSim, a simulation can be started by executing the following command in the BIN subdirectory.

./glomosim < inputfile >

The <input file> contains the configuration parameters for the simulation (an example of such file is CONFIG.IN). A file called GLOMO.STAT is produced at the end of the simulation and contains all the statistics generated.

4.3 The Visualization Tool

GloMoSim has a Visualization Tool that is platform independent because it is coded in Java. To initialize the Visualization Tool, we must execute from the java gui directory the following: java GlomoMain. This tool allows to debug and verify models and scenarios; stop, resume and step execution; show packet transmissions, show mobility groups in different colors and show statistics. The radio layer is displayed in the Visualization Tool as follows: When a node transmits a packet, a yellow link is drawn from this node to all nodes within it's power range. As each node receives the packet, the link is erased and a green line is drawn for successful reception and a red line is drawn for unsuccessful reception. No distinction is made between different packet types (ie: control packets vs. regular pacekts, etc

5. Simulation results

6. Results and conclusion

7. Future direction