We describe a distributed simulation tool which addresses the unique needs for the simulation of emergency response scenarios. The simulation tool adopts the multi-agent paradigm, so as to facilitate the modelling of diverse and autonomous agents, and it provides mechanisms for the interaction of the entities that are being simulated. It operates in a distributed fashion to reduce the simulation time required for such large-scale systems. The simulation tool represents the individuals that need to be evacuated, the resources that contribute to the evacuation including human rescuers, and other active resources and entities which may include robots and which can autonomously interact with the environment and with each other and take individual or collaborative decisions. We illustrate the tool with an application and compare the results for both centralized and distributed execution. Our results also show the significant reduction in execution time that is achieved for different degrees of distribution of the simulator on multiple servers.
In the past, home automation was a small market for technology enthusiasts. Interconnectivity between devices was down to the owner's technical skills and creativity, while security was non-existent or primitive, because cyber threats were also largely non-existent or primitive. This is not the case any more. The adoption of Internet of Things technologies, cloud computing, artificial intelligence and an increasingly wide range of sensing and actuation capabilities has led to smart homes that are more practical, but also genuinely attractive targets for cyber attacks. Here, we classify applicable cyber threats according to a novel taxonomy, focusing not only on the attack vectors that can be used, but also the potential impact on the systems and ultimately on the occupants and their domestic life. Utilising the taxonomy, we classify twenty five different smart home attacks, providing further examples of legitimate, yet vulnerable smart home configurations which can lead to second-order attack vectors. We then review existing smart home defence mechanisms and discuss open research problems. Reference Key security properties Vulnerabilities/challenges Security recommended Open problems identified Komninos et al. [1] Confidentiality Connected to Internet Auto-immunity to threats Resilience Physical tampering Reliability, availability Lin et al. [2] Confidentiality Phys./netw. accessibility Gateway architecture Auto-configuration Authentication Constrained resources Updates Access control Heterogeneity Nawir et al. [6] Smart meter integrity Remote connectivity Techn. countermeasures Standardisation Privacy Physical tampering Regulatory initiatives Impact evaluation, metrics Non-repudiation Malicious actuation Intrusion detection Authorisation Logging for audit/forensics Ziegeldorf et al.[5]
Abstract-A reliable estimation of an area's occupancy can be beneficial to a large variety of applications, and especially in relation to emergency management. For example, it can help detect areas of priority and assign emergency personnel in an efficient manner. However, occupancy detection can be a major challenge in indoor environments. A recent technology that can prove very useful in that respect is Bluetooth Low Energy (BLE), which is able to provide the location of a user using information from beacons installed in a building. Here, we evaluate BLE as the primary means of occupancy estimation in an indoor environment, using a prototype system composed of BLE beacons, a mobile application and a server. We employ three machine learning approaches (k-nearest neighbours, logistic regression and support vector machines) to determine the presence of occupants inside specific areas of an office space and we evaluate our approach in two independent experimental settings. Our experimental results indicate that combining BLE with machine learning is certainly promising as the basis for occupancy estimation.
During emergency response situations, decisions have to be made in a timely manner. Multiple entities have to be optimally coordinated and numerous resources must be allocated efficiently, creating a very interesting and challenging technical problem. In this paper we present a simulation system that models the evacuation of a multi-storey building. Autonomous intelligent agents are used to represent various types of actors that interact inside a virtual physical world. We also model virtual hazards, such as fire, that spread inside the building evacuation simulator. A real wireless sensor network is used to monitor the spread of the hazards while an external event generator provides input to the sensors. We study the effect of different disaster scenarios and agent behaviours, such as human behaviour during an emergency, on the result of the evacuation procedure. Our initial results indicate that the safety of the evacuees and the evacuation time depend on local interactions between the participants and are affected by the actors' decisions. The integration with the wireless sensor network gives us the opportunity to investigate the effect of sensed information on resource allocation and allows us to study the impact of network issues on the decision making process.
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