Hybrid intelligent decision support system for distributed detection based on ad hoc integrated WSN & RFID

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe real time monitoring of environment context aware activities, based on distributed detection, is becoming a standard in public safety and 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 immediate reaction to potential hazards identified in real time, at an early stage to engage appropriate control actions. Effective emergency response can be supported only by available and acquired expertise or elaborate collaborative knowledge in the domain of distributed detection that include indoor sensing, tracking and localizing. This research proposes a hybrid conceptual multi-agent framework for the acquisition of collaborative knowledge in dynamic complex context aware environments for distributed detection. This framework has been applied for the design and development of a hybrid intelligent multi-agent decision system (HIDSS) that supports a decentralized active sensing, tracking and localizing strategy, and the deployment and configuration of smart detection devices associated to active sensor nodes wirelessly connected in a network topology to configure, deploy and control ad hoc wireless sensor networks (WSNs). This system, which is based on the interactive use of data, models and knowledge base, has been implemented to support fire detection and control access fusion functions aimed at elaborating: An integrated data model, grouping the building information data and WSN-RFID database, composed of the network configuration and captured data, A virtual layout configuration of the controlled premises, based on using a building information model, A knowledge-based support for the design of generic detection devices, A multi-criteria decision making model for generic detection devices distribution, ad hoc WSNs configuration, clustering and deployment, and Predictive data models for evacuation planning, and fire and evacuation simulation. An evaluation of the system prototype has been carried out to enrich information and knowledge fusion requirements and show the scope of the concepts used in data and process modelling. It has shown the practicability of hybrid solutions grouping generic homogeneous smart detection devices enhanced by heterogeneous support devices in their deployment, forming ad hoc networks that integrate WSNs and radio frequency identification (RFID) technology. The novelty in this work is the web-based support system architecture proposed in this framework that is based on the use of intelligent agent modelling and multi-agent systems, and the decoupling of the processes supporting the multi-sensor data fusion from those supporting different context applications. Although this decoupling is essential to appropriately distribute the different fusion functions, the integration of several dimensions of policy settings for the modelling of knowledge processes, and intelligent and pro-active decision making activities, requires the organisation of interactive fusion functions deployed upstream to a safety and emergency response.Saudi government, represented by the Ministry of Interior and General Directorate of Civil Defenc

3D GEOSPATIAL INDOOR NAVIGATION FOR DISASTER RISK REDUCTION AND RESPONSE IN URBAN ENVIRONMENT

Disaster management for urban environments with complex structures requires 3D extensions of indoor applications to support better risk reduction and response strategies. The paper highlights the need for assessment and explores the role of 3D geospatial information and modeling regarding the indoor structure and navigational routes which can be utilized as disaster risk reduction and response strategy. The reviewed models or methods are analysed testing parameters in the context of indoor risk and disaster management. These parameters are level of detail, connection to outdoor, spatial model and network, handling constraints. 3D reconstruction of indoors requires the structural data to be collected in a feasible manner with sufficient details. Defining the indoor space along with obstacles is important for navigation. Readily available technologies embedded in smartphones allow development of mobile applications for data collection, visualization and navigation enabling access by masses at low cost. The paper concludes with recommendations for 3D modeling, navigation and visualization of data using readily available smartphone technologies, drones as well as advanced robotics for Disaster Management

Analyzing Enterprise WiFi Session Data for Modeling Building Occupancy, Evacuation, and Energy Consumption

Buildings are the prime components of office complexes, university campuses, and city centers. They are expensive to build and expensive to operate. Building managers are under constant pressure to keep them efficient and safe. However, they are often stymied by lack of fine-grained data that can help them optimize occupancy levels so as to make most efficient use of space, evacuation patterns that can ensure safety in the event of emergencies, and energy usage behavior that can help reduce operating costs. While several modern buildings are increasingly being equipped with sensors for detecting people presence, movement patterns, and thermal conditions, such instrumentation can often be expensive and limited in scale. This thesis investigates the potential to use data generated by the pervasive WiFi infrastructure that is present in all buildings. Specifically, we evaluate the use of WiFi data to model room usage, anatomize emergency evacuations, and reduce energy excursion costs associated with evacuation events. We begin this thesis by surveying data-driven approaches for efficient building operation and management, while reviewing existing technologies for measuring occupancy using both existing and purpose-built sensing infrastructure. Central to this thesis is the data we have collected and analyzed on WiFi session logs from a dense wireless network consisting of nearly 5000 access points across 50 buildings in a large university campus over a period of 2 years. For our first contribution, we use this data to develop a machine learning-based method to estimate classroom occupancy in near real-time. The output of our method is compared to that from specialized people-counting sensors, and the symmetric Mean Absolute Percentage Error is no more than 13%. Our second contribution develops a systematic method to evaluate emergency evacuation events using building WiFi session data. Our systematic analysis of 43 planned and unplanned evacuation events across 14 buildings quantifies important measures such as evacuation speed, number of evacuees, and typicality of occupancy levels, demonstrating that WiFi data enables accurate and scalable evaluation of building evacuations, corroborating current manual records and revealing new insights. For our third and final contribution, we show that evacuations (particularly during summer) can result in HVAC power excursions of up to 150% above the agreed threshold, imposing heavy power tariffs. We develop a cooling strategy that allows the power cost to be traded off against thermal comfort of occupants post evacuation in a tunable manner. Application of our algorithm to typical building evacuation scenarios shows that the power excursion costs can be largely mitigated for as little as 5 minutes of delay in achieving ideal indoor temperatures. Taken together, our contributions equip building operators with tools and techniques to improve efficiency and safety by leveraging existing WiFi data with no additional infrastructure costs

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

A Decentralized Architecture for Active Sensor Networks

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms

End user oriented BIM enabled multi-functional virtual environment supporting building emergency planning and evacuation

Relevant research has identified that high level of building emergency casualty (e.g. due to fire) has direct link with the delayed evacuation especially in residential and high-rising buildings. The traditional fire drill can only passively identify some bottleneck for evacuation after the building has been constructed and under its operation stage; and end-users normally lack of means to be effectively involved in the decision making process in the first place (e.g. building emergency planning and design) and lack of cost-effective and convenient means to be well trained about emergency evacuation at later operation stage. Modern building emergency management research has highlighted the need for the effective utilization of dynamically updated building emergency information. Building Information Modelling (BIM) has become the information backbone which can enable integration and collaboration throughout the entire building life cycle. BIM can play a significant role in building emergency management due to its comprehensive and standardized data format and integrated life cycle process. This PhD research aims at developing an end user oriented BIM enabled virtual environment to address several key issues for building emergency evacuation and planning. The focus lies on how to utilize BIM as a comprehensive building information provider to work with virtual reality technology to build an adaptable immersive serious game for complex buildings to provide general end users emergency evacuation training/guides. The contribution lies on the seamless integration between BIM and a serious game based Virtual Reality (VR) environment, which enables effective engagement of end-uses. By doing so potential bottlenecks for existing and new buildings for emergency evacuation can be identified and rectified in a timely and cost-effective manner. The system has been tested for its robustness and functionality against the research hypothesis and research questions, and the results show promising potential to support more effective fire emergency evacuation and planning solutions