A Localization System for Optimizing the Deployment of Small Cells in 2-Tier Heterogeneous Wireless Networks

Due to the ever growing population of mobile device users and expansion on the number of devices and applications requiring data usage, there is an increasing demand for improved capacity in wireless cellular networks. Cell densification and 2-tier heterogeneous networks (HetNets) are two solutions which will assist 5G systems in meeting these growing capacity demands. Small-cell deployment over existing heterogeneous networks have been considered by researchers. Different strategies for deploying these small-cells within the existing network among which are random, cell-edge and high user concentration (HUC) have also been explored. Small cells deployed on locations of HUC offloads traffic from existing network infrastructure, ensure good Quality of Service (QoS) and balanced load in the network but there is a challenge of identifying HUC locations. There has been considerable research performed into techniques for determining user location and cell deployment. Currently localization can be achieved using time dependent methods such as Time of Arrival (ToA), Time Difference of Arrival (TDoA), or Global Positioning Systems (GPS). GPS based solutions provide high accuracy user positioning but suffer from concerns over user privacy, and other time dependent approaches require regular synchronization which can be difficult to achieve in practice. Alternatively, Received Signal Strength (RSS) based solutions can provide simple anonymous user data, requiring no extra hardware within the mobile handset but often rely on triangulation from adjacent Base Stations (BS). In mobile cellular networks such solutions are therefore often only applicable near the cell edge, as installing additional BS would increase the complexity and cost of a network deployment. The work presented in this thesis overcomes these limitations by providing an observer system for wireless networks that can be used to periodically monitor the cell coverage area and identify regions of high concentrations of users for possible small cell deployment in 2-tier heterogeneous networks. The observer system comprises of two collinear antennas separated by λ/2. The relative phase of each antenna was varied using a phase shifter so that the combined output of the two antennas were used to create sum and difference radiation patterns, and to steer the antenna radiation pattern creating different azimuth positions for AoA estimation. Statistical regression analysis was used to develop range estimation models based on four different environment empirical pathloss models for user range estimation. Users were located into clusters by classifying them into azimuth-range classes and counting the number of users in each class. Locations for small cell deployment were identified based on class population. BPEM, ADEM, BUEM, EARM and NLOS models were developed for more accurate range estimation. A prototype system was implemented and tested both outdoor and indoor using a network of WiFi nodes. Experimental results show close relationship with simulation and an average PER in range estimation error of 80% by applying developed error models. Based on both simulation and experiment, system showed good performance. By deploying micro-, pico-, or femto-cells in areas of higher user concentration, high data rates and good quality of service in the network can be maintained. The observer system provides the network manager with relative angle of arrival (AoA), distance estimation and relative location of user clusters within the cell. The observer system divides the cell into a series of azimuthal and range sectors, and determines which sector the users are located in. Simulation and a prototype design of the system is presented and results have shown system robustness and high accuracy for its purpose

Determining the Position and Orientation of In-body Medical Instruments Using Reactive Magnetic Field Mapping

There has been a huge demand for localizing in-body medical instruments (IBMI), such as wireless capsule endoscope (WCE) and nasogastric tube (NGT). Some stud ies have been conducted to solve this issue over the last three decades. In these studies, they either used a permanent magnet (PM), a static current source (SCS), radio frequency (RF) fields or integration of two of these. The PM is a stable and reliable magnetic field source. However, due to the size restriction of the NGT and the WCE, only a small PM can be used. Subsequently, the small size issue causes low power delivery at the larger tracking distance. Also, the PM field is very susceptible to ambient noise, and the PM-based localization is not possible in ap plications requiring robotic actuation. Even though an SCS can be used to replace the permanent magnet, and thus the current level can be varied in relation to the dis tance for optimized power delivery, it requires a relatively high power to generate a higher strength magnetic field. Consequently, a more powerful and larger battery is needed to feed the circuit.Radio frequency field sources require high frequencies to achieve sufficient precision, but these frequencies undergo high attenuation in the body. Therefore, the low-frequency RF field is preferred 1 . In the near-field 2 , plane wave assumption of the far-field fails for localization methods since the waves in this region are spherical. Hence, the wave-front has to be formulated by both the range and the direction of arrival (DOA). The DOA requires the phase difference of neighbouring sensors to be calculated. However, if the operating wavelength is much larger than the distance between the source and the receiver, it is not feasible to compute the phase difference between the neigh bouring sensors. Thus, there are numerous algorithms in the literature to overcome these issues, such as MUSIC or ESPRIT which are either complicated or computa tionally expensive. In RF-based localization, generally the time of arrival (TA), the time differ ence of arrival (TDA), the angle of arrival (AOA) and the received signal strength (RSS) are widely used for localization. However, the TA and TDA require accu rate knowledge of field speed and good time synchronization. It is not possible to accurately know while travelling through the body tissues due to complexity of the tissues. The AOA is also impractical for intra-body applications owing to multiple reflections signal from the tissues, commonly known as the multipath effect. The RSS precision is dependent on good knowledge of power loss in complex body tis sues. Also, the RSS method requires accurate knowledge of the transmitted signal strength. However, the power of transmitted frequencies may vary due to the ca pacitive effect of human tissue on Resonant frequency of source, hence RSS-based techniques prove difficult in practice. Therefore, a novel method of mapping the magnetic field vector in the near field region is proposed. This magnetic field mapping (MFM) uses single-axis coils placed orthogonally with respect to a sensor plane (SP). These single-axis sensors pick up only the orthogonal component of the magnetic field, which varies as a function of the orientation of the source and distance to the source. Thus, using this information, the field strength captured by each sensor is mapped to its correspond ing position on the SP as pixels. Next, these field strengths with known positions are used to detect the location and orientation of the field source relative to the SP. MATLAB and CST Microwave simulation were conducted, and many laboratory experiments were performed, and we show that the novel technique not only over comes the issues faced in the methods mentioned above but also accomplishes an accurate source positioning with a precision of better than ± 0.5 cm in 3-D and orientation with a maximum error of ±5◦

Anchor-Free Localization in Mixed Wireless Sensor Network Systems

Recent technological advances have fostered the emergence of Wireless Sensor Networks (WSNs), which consist of tiny, wireless, battery-powered nodes that are expected to revolutionize the ways in which we understand and construct complex physical systems. A fundamental property needed to use and maintain these WSNs is ``localization\u27\u27, which allows the establishment of spatial relationships among nodes over time. This dissertation presents a series of Geographic Distributed Localization (GDL) algorithms for mixed WSNs, in which both static and mobile nodes can coexist. The GDL algorithms provide a series of useful methods for localization in mixed WSNs. First, GDL provides an approximation called ``hop-coordinates\u27\u27, which improves the accuracy of both hop-counting and connectivity-based measurement techniques. Second, GDL utilizes a distributed algorithm to compute the locations of all nodes in static networks with the help of the hop-coordinates approximation. Third, GDL integrates a sensor component into this localization paradigm for possible mobility and as a result allows for a more complex deployment of WSNs as well as lower costs. In addition, the development of GDL incorporated the possibility of manipulated communications, such as wormhole attacks. Simulations show that such a localization system can provide fundamental support for security by detecting and localizing wormhole attacks. Although several localization techniques have been proposed in the past few years, none currently satisfies our requirements to provide an accurate, efficient and reliable localization for mixed WSNs. The contributions of this dissertation are: (1) our measurement technique achieves better accuracy both in measurement and localization than other methods; (2) our method significantly improves the efficiency of localization in updating location in mixed WSNs by incorporating sensors into the method; (3) our method can detect and locate the communication that has been manipulated by a wormhole in a network without relying on a central server

Comparison of 802.11 Wireless Transmitter Localization Techniques

The presence of an unsecured 802.11 access point is a major information security threat to government and business organizations. Thus it is necessary to accurately identify and isolate these devices. The purpose of this study is to evaluate various collection techniques used to geographically locate these unsecured access points. Using low cost hardware to passively capture data, a comparison is made between the use of omnidirectional and directional antennas. Stationary as well as mobile collections are performed for each of these antenna types. Various propagation models and estimation algorithms are compared to determine which method provides the most accurate results over varying and uncertain wireless environments. Testing methods utilizing directional antennas exhibited very accurate results for a range of collection environments. Even in the presence of severe multipath, the analysis techniques were able to identify these reflections and compensate. The methods employing omnidirectional antennas, however, produced inconsistent overall results. While the propagation model estimates were accurate for some sites, they were highly inaccurate for others. Due to the varying environment types that are encountered during wireless data collection, the use of generalized propagation models to provide source location is unreliable.School of Electrical & Computer Engineerin