Privacy preserving, real-time and location secured biometrics for mCommerce authentication

Secure wireless connectivity between mobile devices and financial/commercial establishments is mature, and so is the security of remote authentication for mCommerce. However, the current techniques are open for hacking, false misrepresentation, replay and other attacks. This is because of the lack of real-time and current-precise-location in the authentication process. This paper proposes a new technique that includes freshly-generated real-time personal biometric data of the client and present-position of the mobile device used by the client to perform the mCommerce so to form a real-time biometric representation to authenticate any remote transaction. A fresh GPS fix generates the "time and location" to stamp the biometric data freshly captured to produce a single, real-time biometric representation on the mobile device. A trusted Certification Authority (CA) acts as an independent authenticator of such client's claimed real time location and his/her provided fresh biometric data. Thus eliminates the necessity of user enrolment with many mCommerce services and application providers. This CA can also "independently from the client" and "at that instant of time" collect the client's mobile device "time and location" from the cellular network operator so to compare with the received information, together with the client's stored biometric information. Finally, to preserve the client's location privacy and to eliminate the possibility of cross-application client tracking, this paper proposes shielding the real location of the mobile device used prior to submission to the CA or authenticators

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

Forests

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms |, |, |, |, |, |, and |. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.U01 OH010841/OH/NIOSH CDC HHSUnited States/U54 OH007544/OH/NIOSH CDC HHSUnited States

Analyses of location-based services in Africa and investigating methods of improving its accuracy

The subject area of this thesis analyses the provision of location-based services (LBS) in Africa and seeks methods of improving their positional accuracy. The motivation behind this work is based on the fact that mobile technology is the only modern form of information and communication technology available to most people in Africa. Therefore all services that can be offered on the mobile network should be harnessed and LBS are one of these services. This research work is novel and is the first critical analysis carried out on LBS in Africa; therefore it had to be carried out in phases. A study was first carried out to analyse the provision of LBS in Africa. It was discovered that Africa definitely lags much of the World in the provision of LBS to its mobile subscribers; only a few LBS are available and these are not adapted to the needs of the African people. A field data empirical investigation was carried out in South Africa to evaluate the performance of LBS provided. Data collected indicated that the LBS provided is not dependable due to the inaccuracy introduced by two major factors - the positioning method and the data content provided. Analyzing methods to improve the positional accuracy proved quite challenging because Africa being one of the poorest continents has most mobile subscribers using basic mobile phones. Consequently, LBS often cannot be provided in Africa based on the capability of the mobile phones but rather on the capability of the mobile operator’s infrastructure. However, provision of LBS using the network-based positioning technologies poses the challenge of dynamically varying error sources which affects its accuracy. The effect of some error sources on network-based positioning technologies were analysed and a model developed to investigate the feasibility of making the RSS-based geometric positioning technologies error aware. Major consideration is given to the geometry of the BSs whose measurements are used for position estimation. Results indicated that it is feasible to improve location information in Africa not just by improving the positioning algorithms but also by using improved prediction algorithms, incorporating up-to-date geographical information and hybrid technologies. It was also confirmed that although errors are introduced due to location estimation methods, it is impossible to model the error and make it applicable for all algorithms and all location estimations. This is because the errors are dynamically varying and unpredictable for every measurement

Avionics-Based GNSS Integrity Augmentation for UAS mission planning and real-time trajectory optimisation

This paper explores the potential of integrating Global Navigation Satellite System (GNSS) Avionics Based Integrity Augmentation (ABIA) functionalities in Unmanned Aerial Systems (UAS) to perform mission planning and real-time trajectory optimisation tasks. In case of mission planning, a pseudo-spectral optimization technique is adopted. For real-time trajectory optimisation a Direct Constrained Optimisation (DCO) method is employed. In this method the aircraft dynamics model is used to generate a number of feasible flight trajectories that also satisfy the GNSS integrity constraints. The feasible trajectories are calculated by initialising the aircraft dynamics model with a manoeuvre identification algorithm. The performance of the proposed GNSS integrity augmentation and trajectory optimisation algorithms was evaluated in representative simulation case studies. Additionally, the ABIA performance was compared with Space-Based and Ground-Based Augmentation Systems (SBAS/GBAS). Simulation results show that the proposed integration scheme is capable of performing safety-critical UAS tasks (CAT III precision approach, UAS Detect-and-Avoid, etc.) when GNSS is used as the primary source of navigation data. There is a synergy with SBAS/GBAS in providing suitable (predictive and reactive) integrity flags in all flight phases. Therefore, the integration of ABIA with SBAS/GBAS is a clear opportunity for future research towards the development of a Space-Ground-Avionics Augmentation Network (SGAAN) for UAS SAA and other safety-critical aviation applications

Multi-Dimensional-Personalization in mobile contexts

During the dot com era the word "personalisation” was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user’s privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wall“ will be described

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Data from mobile phone operators: A tool for smarter cities?

Abstract The use of mobile phone data provides new spatio-temporal tools for improving urban planning, and for reducing inefficiencies in present-day urban systems. Data from mobile phones, originally intended as a communication tool, are increasingly used as innovative tools in geography and social sciences research. Empirical studies on complex city systems from human-centred and urban dynamics perspectives provide new insights to develop promising applications for supporting smart city initiatives. This paper provides a comprehensive review and a typology of spatial studies on mobile phone data, and highlights the applicability of such digital data to develop innovative applications for enhanced urban management