Progress in Wireless Communications over the Last Decade - Essay Example

Introduction: There has been great progress in wireless communications over the last decade, causing the available mobile tools and the emerging mobile applications to become more sophisticated. At the same time, wireless networking is becoming a critical component of networking infrastructure. Wireless technology enables mobility, which, in turn, creates a need for location-aware applications. The recent interest in location sensing for network applications and the growing need for large-scale commercial deployment of such systems has brought network researchers up against a fundamental and well-studied problem in the field of robotics: determination of physical position using uncertain sensors (localization). If accurate movement prediction per mobile was possible, the task of locating mobiles given their last location would become substantially efficient in terms of both speed and system resources used. Being able to determine the mobile's future locations and access points as it moves inside the network while being connected can result in significant improvement in system efficiency and connection quality. PDA based Location: The wireless terminal (laptop or PDA) is equipped with an IEEE 802.11b -compatible network card and a web browser. The web server handles the presentation logic. The application and location servers' constitute the application logic. One possible solution for improving the quality of the service is to use a vector image format. The use of vector data is essential, in order to improve the response time and the power of the client application. Using a vector format, there is no need to request a new image file every time the user zooms or pans the image or something changes in that particular image. At present, the most promising vector format for Internet-use is SVG, which is a W3C recommendation for describing dynamic and interactive two-dimensional graphics in XML. These graphics can contain vector graphic shapes, raster images and text. SVG is ideal for visualising geographical information on the Web, since it has many features that are used in traditional cartography such as polygons, lines, points and text. More sophisticated features suitable for mapping in SVG include layering, opacity, gradient fills, stroke options, clipping, masking, scripting, animations and filter effects. SVG can also handle coordinate transformations and enables high quality paper prints. In addition, SVG maps are freely pannable and zoomable. Cellular Location Methods Cellular location methods use the signals of the cellular system to find the location of a mobile station. Since cellular systems were not originally designed for positioning, the implementation of different location methods may require new equipment to make the necessary measurements for location determination and new signalling to transfer the measurement results to the location determination unit. Before presenting the cellular location methods and their implementation aspects, some concepts that will be used to classify different methods based on the role of the mobile station (MS) and the network or on the location measurement principle are defined. Based on the functions of the MS and the network, implementation of a location method belongs to one of the following categories: Network-based Mobile-based Mobile-assisted In network-based implementation one or several base stations (BSs) make the necessary measurements and send the measurement results to a location center where the position is calculated. Network-based implementation does not require any changes to existing handsets, which is a significant advantage compared to mobile-based or most mobile-assisted solutions. However, the MS must be in active mode to enable location measurements and thus positioning in idle mode is impossible. In mobile-based implementation the MS makes measurements and position determination. This allows positioning in idle mode by measuring control channels, which are continuously transmitted. Some assisting information, e.g. BS coordinates, might be needed from the network to enable location determination in the MS. Mobile-based implementation does not support legacy handsets The third category, mobile-assisted implementation, includes solutions where the MS makes measurements and sends the results to a location centre in the network for further processing. Thus, the computational burden is transferred to a location centre where powerful processors are available. However, signalling delay and signalling load increase compared to a mobile-based solution, especially if the location result is needed at MS. Although mobile-assisted solutions typically do not support legacy handsets, it is possible to use the measurement reports that are continuously sent by GSM handsets to the network in active mode. Techniques that use these measurement reports, e.g. signal strength measurements, are often classified as network-based since they do not require any changes to existing handsets. Nevertheless, it is the MS that makes the measurements and therefore these techniques will be called mobile-assisted in the following. The requirements set by different applications may favour different kinds of implementations. For example, emergency call location requires high reliability and it is highly desirable to locate these calls from legacy phones as well as new phones. Applications that use continuous tracking, e.g. route directions, require high accuracy and fast location with a fixed update rate. Since the location result is needed at MS in this case; these requirements are best met with a mobile-based solution. Some applications, e.g. traffic monitoring and location-aided network planning (LAP), require mass location capability at network. These requirements can only be met by network-based or mobile-assisted implementations. Cell Identification Positioning The simplest method for locating a mobile phone is based on cell identification. Since this is an inherent feature of all cellular systems, minimal changes to existing systems are needed. The cell ID only has to be associated with location, i.e. the coordinates of the BSs must be known. This is a bilateral location principle that can be implemented as a network-based or mobile-based technique. In mobile-based implementation, the network would have to continuously transmit the coordinates on a control channel. Angle of Arrival Positioning [ Signal angle of arrival (AOA) information, measured at the BS using an antenna array, can be used for positioning. Assuming two-dimensional geometry, angle of arrival measurement at two BSs is sufficient for unique location, where the user location is determined as the point of intersection of two lines drawn from the BSs. It is seen that AOA technique requires line of sight between the MS and the BSs for accurate results. Also, the uncertainty in AOA measurement causes a position uncertainty that increases with MS-BS distance. Achieved accuracy depends on the number of available measurements, geometry of BSs around the MS and multipath propagation also. Since AOA method needs line-of-sight propagation conditions to obtain correct location estimates, it is clearly not the method of choice in dense urban areas where line of sight to two BSs is seldom present. In, an rms location error of approximately 300 m with two BSs and 200 m with three BSs in an urban environment was observed. However, the AOA technique could be used in rural and suburban areas where the attainable accuracy is better and it is an advantage to be able to locate a MS, which can only be measured by two BSs. A major barrier to implement AOA method in existing 2G networks is the need for an antenna array at each BS. It would be very expensive to build an overlay of AOA sensors to existing cellular network. However, since it is a network-based method and supports legacy handsets, several companies develop it as an E911 solution. Uplink time (difference) of arrival Signal time of arrival (TOA) measurements, performed either at the BSs or at the MS, can be used for positioning. If the BSs and the MS are fully synchronized, TOA measurements are directly related to the BS-MS distances and three measurements are needed for unique 2D location. However, if the network is not synchronized, such as GSM and UMTS FDD networks, TOA measurements can only be used in differential manner. Even in this case, a common time reference for the BSs is needed. Two TOA measurements then define a hyperbola, and four measurements are needed for unambiguous 2D location. If the measurements are performed at BSs, it is a network-based multilateral technique. This technique has two drawbacks compared to downlink method: it is only possible to perform the measurements in dedicated mode and there may be capacity problems due to the multilateral measurement principle. The advantage is that due to the network-based implementation, uplink TOA supports legacy phones. It was taken into GSM standardization as a candidate E911 solution. In GSM implementation of uplink TOA technique, a common time reference, e.g. GPS receiver, is needed at each BS site. The location of an MS with call on is accomplished by forcing the MS to request a handover to several neighbouring BSs. The MS then sends access bursts at full power, and TOA measurements are made from these bursts. Downlink observed time differences In the downlink time difference techniques, the MS observes time differences of signals from several BSs. These signals are typically control channel signals and therefore the MS can perform the measurements in idle mode as well as in dedicated mode. Having a reference receiver at known location continuously measuring the observed time differences can solve the clock differences of the BSs. This is much simpler and more economical than synchronizing the BS transmissions. The accuracy of all time difference based techniques (uplink as well as downlink) depend on several factors. The accuracy of an individual time difference measurement depends on signal bandwidth and multipath channel. In an urban area the error margin is typically larger, since heavy multipath makes it more difficult to detect the time of arrival of the first echo. If there is no line of sight between the MS and the BSs involved, the location estimates will be biased away from the BSs with no line of sight to the MS. This is a problem especially in urban areas. In open areas the geometry of the BS configuration around the MS may introduce an additional error, which is described by geometrical dilution of precision (GDOP). A favorable geometry is a uniform distribution of BSs around the MS. Also the number of available measurements has an effect on accuracy: generally it is better to have as many measurements as possible. Hierarchical Location Prediction The proposition of hierarchical location-prediction (HLP) algorithm is due to the fact that it substantially increases the system's probability of providing uninterrupted service to the mobile user while consuming minimal resources from the network. Derived from some classical and well-established stochastic signal processing techniques, HLP is a two-tier scheme that combines location updating with location prediction to offer enhanced connection management functions. HLP raises the level of intelligence within the wireless ATM system so that the system aggressively and effectively maintains connectivity (essential for providing QoS features) with the mobile. HLP is composed of an approximate pattern-matching algorithm that extracts any existing regular movement pattern to estimate the global intercell direction, and uses an extended self-learning Kalman filter that deals with "unclassifiable" random movements by tracking intracell trajectory and predicting the next-cell crossing. The performance of predicting algorithms in the presence of path loss, shadow fading, and random user movements. Simulation results and performance analysis show that this algorithm is robust in the presence of noisy input, being able to predict the speed and direction of travel of the mobile with a high degree of accuracy. With good next-cell prediction, algorithms that improve handoffs, relieve congestion, provide advance resource reservations and advance optimal route establishment, and which improve the overall QoS in wireless ATM networks can be built easily. User Mobility Model The mobility model we advocate in this paper attempts to mimic human (operator) movement behavior. Our model is built as a two-level hierarchy in which the top level is the global mobility model or GMM whose resolution is in terms of cells crossed by the mobile during the lifetime of the connection, and the bottom level is the local mobility model (LMM), whose resolution is in terms of a 3-tuple sample The growing need for location support systems underscores the importance of addressing location-awareness problem. For example, government initiatives require that cellular phone providers should develop a way to locate any phone that makes an emergency. In outdoor settings, GPS [29] has been used in many commercial applications, as in the case of locating automobiles. Despite the extraordinary advances in GPS technology, though, many indoor spaces cannot reliably receive GPS signals. An indoor system must use different sensors, such as infrared (IR), sonar, vision, or radio (RF), to infer position of a mobile device. Location-aware applications based on these sensors could enable users to discover resources in their physical proximity, such as active maps of their surroundings and adaptive interfaces to the user's location. Specific applications of such a system vary from tracking a guard's position in a penitentiary institution to hospitals where equipment and people must be efficiently located. These applications can also be useful in large office environments, where the loss of valuable equipment such as laptop computers has become a serious problem and locating resources such as printers takes time and disrupts other activities. Mobile Robotics. Many mobile robot platforms make extensive use of wireless networking to communicate with off-line computing resources, other robots, and various user-interface devices. Since the advent of inexpensive wireless networking, many mobile robots have been equipped with 802.11b wireless Ethernet. In many applications, a sensor from which position can be inferred directly without the computational overhead of image processing or the material expense of laser range finders is of great use. Many robotics applications would benefit from being able to use wireless Ethernet for both sensing and communication. For example, exploration, map building and navigation with low-cost wheeled robots could be readily achieved using wireless Ethernet and sonar. A broad definition of sensor fusion is the combination of multiple independent observations to obtain a more robust and precise estimate of the measured variables. It is an important notion in robotic localization. This can be implemented in terms of integrating sensor readings over time or in the synthesis of measurements from multiple sensors. Most of the recent work in robot localization has been in improving and implementing sensor fusion for many systems. Much progress has been made in developing localization techniques since the problem first appeared in the literature. Dead reckoning can be used for pose maintenance, but requires some initial knowledge of location. Some of the simplest methods for global localization include landmark-based localization and triangulation. Probabilistic techniques, such as Kalman Altering, and later, Bayesian analysis, were developed to address laws in these systems. Finally, for when a grid-based map is inappropriate to the application or environment, topological approaches have been developed. Dead Reckoning. This is perhaps the simplest approach to the pose maintenance task, it keeps track of how far the robot moves in each direction and then to sum these motions to produce a net displacement that can be added to an initial position estimate. Keeping track of how much one moves by observing internal parameters without reference to the external world is known as dead reckoning and is usually implemented with an odometer. If only dead reckoning is used for position estimation, these errors are added to the absolute pose estimate and errors are accumulated. However, long-term localization must make reference to the external world for position correction. This involves the use of sensory data for recalibrating a robots sense of its own location with the environment. In some circumstances, such as the case of a wireless device that a person is moving around in space, we have no analogue of odometry. Triangulation. This is a technique used to determine distance to known landmarks and pose which can be computed with cameras, laser range finders, IR transmitters, sonar and other commonly used sensors. A nave approach is to take three distance measurements and triangulate position. This works when the sensors are reliable and relatively noise-free but leaves several problems un addressed. When the sensors are noisy, the calculations for triangulation become unstable for many positions and landmark arrangements and lead to significant loss of precision. Typically, multiple measurements are merged over time to try to compensate for this, however some care must be taken in choosing the method of merging or poor results will be obtained. In some cases where the sensors are fairly reliable and have simple noise distributions, direct triangulation or triangulation with differential windowing can produce excellent results. Noisy sensors, however, complicate triangulation-adding uncertainty to the results. GPS is perhaps the most- used sensor based on triangulation. Reference: Bagrodia. R, Gerla. M, Tang. K, Wang. L. & Zhang. L (1999) TCP over wireless multihop protocols: Simulation and experiments. Proceedings of IEEEICC. Bensaou. B, Ko. C.C & Wang.Y (2000) Fair medium access in 802.11 based wireless ad-hoc networks. Proceedings of ACM MobiHoc. Bertsekas. D & R. Gallager (1996) Data Networks. Prentice Hall, second edition. Bharghavan.V, Demers. A, Shenker.S, & Zhang.L (1994) MACAW: A media access Protocol for wireless LANs. Boppana.R.V & Dyer. T.D (2001) A comparison of TCP performance over three routing protocols for mobile ad hoc networks. Proceedings of ACM MobiHoc. Chandran, S. Prakash.R., Raghunathan, S & Venkatesan (2001), A feedback-based scheme for improving TCP performance in ad hoc wireless networks. IEEE Personal Communications Magazine. Chiu.D, Hawe.W & Jain R.A. (1984) quantitative measure of fairness and discrimination for resource allocation in shared systems". DEC Technical Report Floyd. J. & Jacobson. (1993) Random early detection gateways for congestion avoidance. IEEE/ACM Transactions on Networking. Fu. Z, Gerla M, Lu.S., Luo.H., Zerfos. P & Zhang. L. (2003). The impact of multihop wireless channel on TCP throughput and loss. IEEE Gerla.M & Tang.K (1999). Fair sharing of MAC under TCP in wireless ad hoc networks. Proceedings of IEEEMMT' Holland.G & Vaidya.N.H (1999) Analysis of TCP performance over mobile ad hoc networks. Proceedings of ACM MobiCom Jon W, Jun Y, M& Xuemin.S.S. (2005) IEEE Transactions on Mobile Computing. Kamerman. A & Monteban.L (1997) "WaveLANh-II: A High-Performance Wireless LAN for the Unlicensed Band," Bell Labs Technical Journal. Kim Y.Y, San-qi.L (2000) Modeling multipath Wireless Networks, v.6 n.6, p.481-492. Myllymki.P, Roos. T & Tirri.H (2002). A Statistical Modeling Approach to Location Estimation, IEEE Transactions on Mobile Computing. Qiao D, Sunghyun C, Shin. K.G, (2002) Goodput Analysis and Link Adaptation for IEEE 802.11a Wireless LANs, IEEE Transactions on Mobile Computing. QualNet. Network simulator. Accessed on 21st Aug 2006 http://www.qualnet.com Rappaport.T (1996) Wireless Communications: Principles and Practice. Prentice Hall, New Jersey Radio Communications Report (1996) (E-911) system providing a 911 agent with caller number and cell site Vol. 15, No. 51 Sung-Ju L, William S & Mario G (2001) Wireless ad hoc multicast routing with mobility prediction, Mobile Networks and Applications. Wu. H, Qiao. C, (2003) modeling iCAR via multi-dimensional Markov Chains, Mobile Networks and Applications. Read More