WSN and RFID integration to support intelligent monitoring in smart buildings using hybrid intelligent decision support systems

The real time monitoring of environment context aware activities is becoming a standard in the service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt reaction to potential hazards identified at an early stage to engage appropriate control actions. This requires capturing real time data to process locally at the device level or communicate to backend systems for real time decision making. This research examines the wireless sensor network and radio frequency identification technology integration in smart homes to support advanced safety systems deployed upstream to safety and emergency response. These systems are based on the use of hybrid intelligent decision support systems configured in a multi-distributed architecture enabled by the wireless communication of detection and tracking data to support intelligent real-time monitoring in smart buildings. This paper introduces first the concept of wireless sensor network and radio frequency identification technology integration showing the various options for the task distribution between radio frequency identification and hybrid intelligent decision support systems. This integration is then illustrated in a multi-distributed system architecture to identify motion and control access in a smart building using a room capacity model for occupancy and evacuation, access rights and a navigation map automatically generated by the system. The solution shown in the case study is based on a virtual layout of the smart building which is implemented using the capabilities of the building information model and hybrid intelligent decision support system.The Saudi High Education Ministry and Brunel University (UK

Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding

A Cooperative Emergency Navigation Framework using Mobile Cloud Computing

The use of wireless sensor networks (WSNs) for emergency navigation systems suffer disadvantages such as limited computing capacity, restricted battery power and high likelihood of malfunction due to the harsh physical environment. By making use of the powerful sensing ability of smart phones, this paper presents a cloud-enabled emergency navigation framework to guide evacuees in a coordinated manner and improve the reliability and resilience in both communication and localization. By using social potential fields (SPF), evacuees form clusters during an evacuation process and are directed to egresses with the aid of a Cognitive Packet Networks (CPN) based algorithm. Rather than just rely on the conventional telecommunications infrastructures, we suggest an Ad hoc Cognitive Packet Network (AHCPN) based protocol to prolong the life time of smart phones, that adaptively searches optimal communication routes between portable devices and the egress node that provides access to a cloud server with respect to the remaining battery power of smart phones and the time latency.Comment: This document contains 8 pages and 3 figures and has been accepted by ISCIS 2014 (29th International Symposium on Computer and Information Sciences

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

Intelligent Agents for Disaster Management

ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains

NEMESYS: Enhanced Network Security for Seamless Service Provisioning in the Smart Mobile Ecosystem

As a consequence of the growing popularity of smart mobile devices, mobile malware is clearly on the rise, with attackers targeting valuable user information and exploiting vulnerabilities of the mobile ecosystems. With the emergence of large-scale mobile botnets, smartphones can also be used to launch attacks on mobile networks. The NEMESYS project will develop novel security technologies for seamless service provisioning in the smart mobile ecosystem, and improve mobile network security through better understanding of the threat landscape. NEMESYS will gather and analyze information about the nature of cyber-attacks targeting mobile users and the mobile network so that appropriate counter-measures can be taken. We will develop a data collection infrastructure that incorporates virtualized mobile honeypots and a honeyclient, to gather, detect and provide early warning of mobile attacks and better understand the modus operandi of cyber-criminals that target mobile devices. By correlating the extracted information with the known patterns of attacks from wireline networks, we will reveal and identify trends in the way that cyber-criminals launch attacks against mobile devices.Comment: Accepted for publication in Proceedings of the 28th International Symposium on Computer and Information Sciences (ISCIS'13); 9 pages; 1 figur

Key challenges in agent-based modelling for geo-spatial simulation

Agent-based modelling (ABM) is fast becoming the dominant paradigm in social simulation due primarily to a worldview that suggests that complex systems emerge from the bottom-up, are highly decentralised, and are composed of a multitude of heterogeneous objects called agents. These agents act with some purpose and their interaction, usually through time and space, generates emergent order, often at higher levels than those at which such agents operate. ABM however raises as many challenges as it seeks to resolve. It is the purpose of this paper to catalogue these challenges and to illustrate them using three somewhat different agent-based models applied to city systems. The seven challenges we pose involve: the purpose for which the model is built, the extent to which the model is rooted in independent theory, the extent to which the model can be replicated, the ways the model might be verified, calibrated and validated, the way model dynamics are represented in terms of agent interactions, the extent to which the model is operational, and the way the model can be communicated and shared with others. Once catalogued, we then illustrate these challenges with a pedestrian model for emergency evacuation in central London, a hypothetical model of residential segregation tuned to London data which elaborates the standard Schelling (1971) model, and an agent-based residential location built according to spatial interactions principles, calibrated to trip data for Greater London. The ambiguities posed by this new style of modelling are drawn out as conclusions