Cloud Enabled Emergency Navigation Using Faster-than-real-time Simulation

State-of-the-art emergency navigation approaches are designed to evacuate civilians during a disaster based on real-time decisions using a pre-defined algorithm and live sensory data. Hence, casualties caused by the poor decisions and guidance are only apparent at the end of the evacuation process and cannot then be remedied. Previous research shows that the performance of routing algorithms for evacuation purposes are sensitive to the initial distribution of evacuees, the occupancy levels, the type of disaster and its as well its locations. Thus an algorithm that performs well in one scenario may achieve bad results in another scenario. This problem is especially serious in heuristic-based routing algorithms for evacuees where results are affected by the choice of certain parameters. Therefore, this paper proposes a simulation-based evacuee routing algorithm that optimises evacuation by making use of the high computational power of cloud servers. Rather than guiding evacuees with a predetermined routing algorithm, a robust Cognitive Packet Network based algorithm is first evaluated via a cloud-based simulator in a faster-than-real-time manner, and any "simulated casualties" are then re-routed using a variant of Dijkstra's algorithm to obtain new safe paths for them to exits. This approach can be iterated as long as corrective action is still possible.Comment: Submitted to PerNEM'15 for revie

ConXsense - Automated Context Classification for Context-Aware Access Control

We present ConXsense, the first framework for context-aware access control on mobile devices based on context classification. Previous context-aware access control systems often require users to laboriously specify detailed policies or they rely on pre-defined policies not adequately reflecting the true preferences of users. We present the design and implementation of a context-aware framework that uses a probabilistic approach to overcome these deficiencies. The framework utilizes context sensing and machine learning to automatically classify contexts according to their security and privacy-related properties. We apply the framework to two important smartphone-related use cases: protection against device misuse using a dynamic device lock and protection against sensory malware. We ground our analysis on a sociological survey examining the perceptions and concerns of users related to contextual smartphone security and analyze the effectiveness of our approach with real-world context data. We also demonstrate the integration of our framework with the FlaskDroid architecture for fine-grained access control enforcement on the Android platform.Comment: Recipient of the Best Paper Awar

Snapshots of the EYES project

The EYES project (IST-2001-34734) is a three years European research project on self-organizing and collaborative energy-efficient sensor networks. It addresses the convergence of distributed information processing, wireless communications, and mobile computing. The goal of the project is to develop the architecture and the technology which enables the creation of a new generation of sensors that can effectively network together so as to provide a flexible platform for the support of a large variety of mobile sensor network applications. This paper provides a broad overview of the EYES project and highlights some approaches and results of the architecture

Early forest fire detection by vision-enabled wireless sensor networks

Wireless sensor networks constitute a powerful technology particularly suitable for environmental monitoring. With regard to wildfires, they enable low-cost fine-grained surveillance of hazardous locations like wildland-urban interfaces. This paper presents work developed during the last 4 years targeting a vision-enabled wireless sensor network node for the reliable, early on-site detection of forest fires. The tasks carried out ranged from devising a robust vision algorithm for smoke detection to the design and physical implementation of a power-efficient smart imager tailored to the characteristics of such an algorithm. By integrating this smart imager with a commercial wireless platform, we endowed the resulting system with vision capabilities and radio communication. Numerous tests were arranged in different natural scenarios in order to progressively tune all the parameters involved in the autonomous operation of this prototype node. The last test carried out, involving the prescribed burning of a 95 x 20-m shrub plot, confirmed the high degree of reliability of our approach in terms of both successful early detection and a very low false-alarm rate. Journal compilationMinisterio de Ciencia e Innovación TEC2009-11812, IPT-2011-1625-430000Office of Naval Research (USA) N000141110312Centro para el Desarrollo Tecnológico e Industrial IPC-2011100

EZ-AG: Structure-free data aggregation in MANETs using push-assisted self-repelling random walks

This paper describes EZ-AG, a structure-free protocol for duplicate insensitive data aggregation in MANETs. The key idea in EZ-AG is to introduce a token that performs a self-repelling random walk in the network and aggregates information from nodes when they are visited for the first time. A self-repelling random walk of a token on a graph is one in which at each step, the token moves to a neighbor that has been visited least often. While self-repelling random walks visit all nodes in the network much faster than plain random walks, they tend to slow down when most of the nodes are already visited. In this paper, we show that a single step push phase at each node can significantly speed up the aggregation and eliminate this slow down. By doing so, EZ-AG achieves aggregation in only O(N) time and messages. In terms of overhead, EZ-AG outperforms existing structure-free data aggregation by a factor of at least log(N) and achieves the lower bound for aggregation message overhead. We demonstrate the scalability and robustness of EZ-AG using ns-3 simulations in networks ranging from 100 to 4000 nodes under different mobility models and node speeds. We also describe a hierarchical extension for EZ-AG that can produce multi-resolution aggregates at each node using only O(NlogN) messages, which is a poly-logarithmic factor improvement over existing techniques

On the Analysis and Detection of Flames Withan Asynchronous Spiking Image Sensor

We have investigated the capabilities of a customasynchronous spiking image sensor operating in the NearInfrared band to study flame radiation emissions, monitortheir transient activity, and detect their presence. Asynchronoussensors have inherent capabilities, i.e., good temporal resolution,high dynamic range, and low data redundancy. This makesthem competitive against infrared (IR) cameras and CMOSframe-based NIR imagers. In this paper, we analyze, discuss,and compare the experimental data measured with our sensoragainst results obtained with conventional devices. A set ofmeasurements have been taken to study the flame emissionlevels and their transient variations. Moreover, a flame detectionalgorithm, adapted to our sensor asynchronous outputs, has beendeveloped. Results show that asynchronous spiking sensors havean excellent potential for flame analysis and monitoring.Universidad de Cádiz PR2016-07Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2012-2338Office of Naval Research (USA) N00014141035