POSTER: Privacy-preserving Indoor Localization

Upcoming WiFi-based localization systems for indoor environments face a conflict of privacy interests: Server-side localization violates location privacy of the users, while localization on the user's device forces the localization provider to disclose the details of the system, e.g., sophisticated classification models. We show how Secure Two-Party Computation can be used to reconcile privacy interests in a state-of-the-art localization system. Our approach provides strong privacy guarantees for all involved parties, while achieving room-level localization accuracy at reasonable overheads.Comment: Poster Session of the 7th ACM Conference on Security & Privacy in Wireless and Mobile Networks (WiSec'14

A Survey of Positioning Techniques and Location Based Services in Wireless Networks

International audiencePositioning techniques are known in a wide variety of wireless radio access technologies. Traditionally, Global Positioning System (GPS) is the most popular outdoor positioning system. Localization also exists in mobile networks such as Global System for Mobile communications (GSM). Recently, Wireless Local Area Networks (WLAN) become widely deployed, and they are also used for localizing wireless-enabled clients. Many techniques are used to estimate client position in a wireless network. They are based on the characteristics of the received wireless signals: power, time or angle of arrival. In addition, hybrid positioning techniques make use of the collaboration between different wireless radio access technologies existing in the same geographical area. Client positioning allows the introduction of numerous services like real-time tracking, security alerts, informational services and entertainment applications. Such services are known as Location Based Services (LBS), and they are useful in both commerce and security sectors. In this paper, we explain the principles behind positioning techniques used in satellite networks, mobile networks and Wireless Local Area Networks. We also describe hybrid localization methods that exploit the coexistence of several radio access technologies in the same region, and we classify the location based services into several categories. When localization accuracy is improved, position-dependant services become more robust and efficient, and user satisfaction increases

Distributed Recognition of Reference Nodes for Wireless Sensor Network Localization

All known localization techniques for wireless sensor and ad-hoc networks require certain set of reference nodes being used for position estimation. The anchor-free techniques in contrast to anchor-based do not require reference nodes called anchors to be placed in the network area before localization operation itself, but they can establish own reference coordinate system to be used for the relative position estimation. We observed that contemporary anchor-free localization algorithms achieve a low localization error, but dissipate significant energy reserves during the recognition of reference nodes used for the position estimation. Therefore, we have proposed the optimized anchor-free localization algorithm referred to as BRL (Boundary Recognition aided Localization), which achieves a low localization error and mainly reduces the communication cost of the reference nodes recognition phase. The proposed BRL algorithm was investigated throughout the extensive simulations on the database of networks with the different number of nodes and densities and was compared in terms of communication cost and localization error with the known related algorithms such as AFL and CRP. Through the extensive simulations we have observed network conditions where novel BRL algorithm excels in comparison with the state of art

Outdoor node localization using random neural networks for large-scale urban IoT LoRa networks

Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m

Comparative evaluation of various GPS-free localization algorithm for wireless sensor networks

Wireless Sensor Networks (WSN) are tremendously being used in different environments to perform various monitoring tasks such as search, rescue, disaster relief, target tracking and a number of tasks in smart environments. For example wireless sensors nodes can be designed to detect the ground vibrations generated by silent footsteps of a burglar and trigger an alarm. In many difficult and complex tasks, node localization is very important and critical step to fulfill the purpose of WSN. This project was conducted on the basis of localization of sensor nodes in the scope of GPS-free localizations schemes. We firstly investigated the current localization techniques in wireless scenario for the aim of designing a GPS-free localization scheme based on the local coordinate system formation. A multidimensional scaling method based on dynamic curvilinear belt structure and cooperative localization method was used in this project. Then a simulation result and comparison were carried in MATLAB. The vast majority of current materials on spot discovery in WSNs reflect some beacon nodes with known place. Their spots are then used to look for the positions connected with other normal sensor nodes. Manual rating and configuration means of obtaining spot don't scale and are also error-prone, and equipping sensors with GPS is normally expensive and rule isn't followed in indoor and urban environment. As such, sensor sites can therefore gain from a selfsetting up method where nodes cooperate with each other, estimate nearby distances on their neighbors, and converge to some consistent organize system containing only translation freedom. Dis