Proactive Neighbor Localization Based on Distributed Geographic Table

Real-time tracking of massive numbers of mobile devices, either carried by humans or embedded into vehicles, is a challenging problem whose solution may pave the way for a large set of valuable applications, ranging from social net-working to ambient intelligence. A centralized approach, i.e. a server collects position data and provides it to inter-ested consumers, is highly questionable, as performance can hardly scale up to the needs several million concurrent users. On other hand, a decentralized peer-to-peer approach, for which positioning data would flow directly among mobile devices may be very appealing, provided that messages to be routed are not too frequent and too expensive in terms of bandwidth usage. In this context we propose a peer-to-peer overlay scheme called Distributed Geographic Ta-ble (DGT), where each participant can efficiently retrieve node or resource information (data or services) located near any chosen geographic position. In particular, we describe a DGT-based localization protocol, that allows each peer for proactively discovering and tracking all the peers that are geographically near to itself. We provide a performance analysis of our protocol, referring to a simulated (although realistic) scenario where several hundred vehicles move on a real map. Our results show that the solution is efficient, scalable and highly adaptable to different application sce-narios

Distributed Algorithms for Location Based Services

Real-time localization services are some of the most challenging and interesting mobile broadband applications in the Location Based Services (LBS) world. They are gaining more and more importance for a broad range of applications, such as road/highway monitoring, emergency management, social networking, and advertising. This Ph.D. thesis focuses on the problem of defining a new category of decentralized peer-to-peer (P2P) algorithms for LBS. We aim at defining a P2P overlay where each participant can efficiently retrieve node and resource information (data or services) located near any chosen geographic position. The idea is that the responsibility and the required resources for maintaining information about position of active users are properly distributed among nodes, for which a change in the set of participants causes only a minimal amount of disruption without reducing the quality of provided services. In this thesis we will assess the validity of the proposed model through a formal analysis of the routing protocol and a detailed simulative investigation of the designed overlay. We will depict a complete picture of involved parameters, how they affect the performance and how they can be configured to adapt the protocol to the requirements of several location based applications. Furthermore we will present two application scenarios (a smartphone based Traffic Information System and a large information management system for a SmartCity) where the designed protocol has been simulated and evaluated, as well as the first prototype of a real implementation of the overlay using both traditional PC nodes and Android mobile devices

Video big data: an agile architecture for systematic exploration and analytics

Video is currently at the forefront of most business and natural environments. In surveillance, it is the most important technology as surveillance systems reveal information and patterns for solving many security problems including crime prevention. This research investigates technologies that currently drive video surveillance systems with a view to optimization and automated decision support. The investigation reveals some features and properties that can be optimised to improve performance and derive further benefits from surveillance systems. These aspects include system-wide architecture, meta-data generation, meta-data persistence, object identification, object tagging, object tracking, search and querying sub-systems. The current less-than-optimum performance is attributable to many factors, which include massive volume, variety, and velocity (the speed at which streaming video transmit to storage) of video data in surveillance systems. Research contributions are 2-fold. First, we propose a system-wide architecture for designing and implementing surveillance systems, based on the authors’ system architecture for generating meta-data. Secondly, we design a simulation model of a multi-view surveillance system from which the researchers generate simulated video streams in large volumes. From each video sequence in the model, the authors extract meta-data and apply a novel algorithm for predicting the location of identifiable objects across a well-connected camera cluster. This research provide evidence that independent surveillance systems (for example, security cameras) can be unified across a geographical location such as a smart city, where each network is administratively owned and managed independently. Our investigation involved 2 experiments - first, the implementation of a web-based solution where we developed a directory service for managing, cataloguing, and persisting metadata generated by the surveillance networks. The second experiment focused on the set up, configuration and the architecture of the surveillance system. These experiments involved the investigation and demonstration of 3 loosely coupled service-oriented APIs – these services provided the capability to generate the query-able metadata. The results of our investigations provided answers to our research questions - the main question being “to what degree of accuracy can we predict the location of an object in a connected surveillance network”. Our experiment also provided evidence in support of our hypothesis – “it is feasible to ‘explore’ unified surveillance data generated from independent surveillance networks”