ISSUES ON LOCALIZATION IN SENSOR NETWORKS in .NET framework

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ISSUES ON LOCALIZATION IN SENSOR NETWORKS
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sources. Alternatively, localization schemes can allow a remote station, such as a base station, to determine the locations of all sensor nodes in the network from signals received from them. Such remote localization schemes suffer from scalability and communication cost problems. It should be noted that the resulting difference between self- and remote positioning is where the computations take place and the resultant need for communication between the sensor nodes and the remote station. 7. Cost. This is an important issue, since the requirements for designing largescale sensor networks are to (a) keep the cost (and complexity) of each node low and (b) bene t from the collective sensing and computation capabilities of a large number of nodes in the network. Hence, it is highly desirable that the localization system does not require expensive hardware at the sensor nodes. The cost of building external infrastructure for enabling localization also plays a role. However, that is usually considered less critical because it does not increase with the size of the network. 8. Form Factor. Wireless sensor nodes must have a small form factor, which eliminates localization schemes that require large components such as antenna arrays. The size of the sensor node thus plays a critical role in determining the mechanism that can be effectively used for localization in sensor networks. 9. Passive versus Active Localization. Some location estimation schemes require the unknown node to play an active role for position estimation, while others can work even without any action from the target node requiring localization. In sensor networks, the sensor nodes can allow active localization within the limitations of its hardware and cost constraints. For instance, a sensor node may act as a transponder for a transmitted RF signal to estimate round-trip transmission delay between a remote transmitter and the node. However, it may be dif cult to implement a system that requires the sensor node to estimate the direction of arrival of the received signal and send that information back because that usually requires complex antenna arrays. 12.3.2 Challenges From the above discussion, it can be concluded that the main challenges for designing a localization scheme for wireless sensor nodes arise from the need to deal with the low hardware complexity and cost of implementation, small form factor of the nodes, and their arbitrary locations (indoor, outdoor, and in uncharacterized regions). Typically, the natural choice for location estimation is to use triangulation, which requires estimation of distances or angles from xed reference points. In this section, we discuss the challenges involved with obtaining these estimates from the perspective of sensor networks. Ranging Issues. Of the two primary techniques for ranging, measuring time of ight using RF signals is dif cult for applications in sensor networks. This is because RF signals travel at the speed of light and measuring the extremely short travel times
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LOCATION DISCOVERY IN SENSOR NETWORKS
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within the spacial domain of a sensor network (that can be room sized or of equivalent size) is a technical hurdle. It would require extremely accurate clock synchronization between the sensor node and the transmitter that remains to be a technological challenge. Consequently, time-of- ight measurements have been explored using acoustic and ultrasonic signals that have a much slower propagation speed than RF. Acoustic ranging has been used in many different distance measuring devices due to their low cost and accuracy for indoor use. These devices can provide accuracies within 10 cm. The comparatively higher frequency utrasonic signals (frequencies typically within 24 40 kHz) can be used to provide accuracies within 2 5 cm. However, they have a smaller range as compared to acoustic signals. Usage of acoustic or ultrasonic signals for ranging in sensor networks also faces other challenges. Firstly, acoustic sources and detectors are generally larger in size as compared to RF sources, due to their larger wavelength. This poses a problem for small sensor nodes. Secondly, again due to their larger wavelength, acoustic signals cannot propagate through physical obstructions. They also suffer from severe multipath effects, making it hard to design a reliable distance estimation system for arbitrarily placed sensor nodes in unknown environments. Systems such as Cricket [11] and BAT [12] use the time-difference-of-arrivals of ultrasound and RF signals from the same source to perform indoor localization. Assuming that the RF signal is received instantaneously, the corresponding delay of the much slower ultrasound signal provides the needed distance estimate. Usage of RSSI for ranging requires the knowledge of the corresponding signal propagation model. However, even with extensive channel estimation and modeling, ranging using RSSI faces inaccuracies caused by shadowing, multipath re ections, refractions, and scattering effects. Nevetheless, because of the ease of implementation, localization schemes using RSSI have been researched extensively [6]. It has been applied in a number of localization schemes such as RADAR [13], AHLoS [14], and APS [15]. The RADAR indoor location system applies a wall attenuation factor (WAF) and signal strength maps that are obtained from extensive of ine measurements of indoor signal propagation characteristics. AHLoS assumes that a limited number of nodes know their locations, either from using GPS or from manual con gurations. Other nodes use a combination of RSSI and ToA ranging techniques to determine their positions with respect to the beacons. AHLoS utilizes collaborative multilateration, a mechanism that is explained in the next section. A decentralized approach to RF-based localization in indoor environments was presented in reference 16.
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Angle of Arrival (AOA) Estimation. Estimating the direction of arrival of a wireless signal requires an antenna with extremely small beamwidth. This can be achieved with an antenna array, which can be prohibitvely large for use in wireless sensor nodes. Hence alternative techniques need to be used for AOA estimation for applications in sensor networks. A possible approach to reduce the size of the antenna is to use ultrasound signals, which is used in the Cricket Compass project [17] to determine angles from phase difference and time difference of arrivals of an ultrasound pulse on multiple detectors that are placed in a speci c pattern within a space of few centimeters. Such a device can determine the angle of arrival with accuracy of 5 within
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