Emergency navigation systems for buildings and other built environments, such as sport arenas or shopping centres, typically rely on simple sensor networks to detect emergencies and, then, provide automatic signs to direct the evacuees. The major drawbacks of such static wireless sensor network (WSN)-based emergency navigation systems are the very limited computing capacity, which makes adaptivity very difficult, and the restricted battery power, due to the low cost of sensor nodes for unattended operation. If static wireless sensor networks and cloud-computing can be integrated, then intensive computations that are needed to determine optimal evacuation routes in the presence of time-varying hazards can be offloaded to the cloud, but the disadvantages of limited battery life-time at the client side, as well as the high likelihood of system malfunction during an emergency still remain. By making use of the powerful sensing ability of smart phones, which are increasingly ubiquitous, this paper presents a cloud-enabled indoor emergency navigation framework to direct evacuees in a coordinated fashion and to improve the reliability and resilience for both communication and localization. By combining social potential fields (SPF) and a cognitive packet network (CPN)-based algorithm, evacuees are guided to exits in dynamic loose clusters. Rather than relying on a conventional telecommunications infrastructure, we suggest an ad hoc cognitive packet network (AHCPN)-based protocol to adaptively search optimal communication routes between portable devices and the network egress nodes that provide access to cloud servers, in a manner that spares the remaining battery power of smart phones and minimizes the time latency. Experimental results through detailed simulations indicate that smart human motion and smart network management can increase the survival rate of evacuees and reduce the number of drained smart phones in an evacuation process.
Urban road networks are typical complex systems, which are crucial to our society and economy. In this study, topological characteristics of a number of urban road networks purely based on physical roads rather than routes of vehicles or buses are investigated in order to discover underlying unique structural features, particularly compared to other types of transport networks. Based on these topological indices, correlations between topological indices and small-worldness of urban road networks are also explored. The finding shows that there is no significant small-worldness for urban road networks, which is apparently different from other transport networks. Following this, community detection of urban road networks is conducted. The results reveal that communities and hierarchy of urban road networks tend to follow a general nature rule.
The frequency, destruction and costs of natural and human-made disasters in modern highly-populated societies have resulted in research on emergency evacuation and wayfinding, which has drawn considerable attention. The subject is now a multidisciplinary area of research where information and communication technologies (ICT), and in particular the Internet of Things (IoT), have a significant impact on sensing and computing dynamic reactions that mitigate or prevent the worst outcomes of disasters. This paper offers state-of-the-art knowledge in this area so as to share ongoing research results, identify the research gaps and address the need for future research. We present a comprehensive review of research on emergency evacuation and wayfinding, focusing on the algorithmic and system design aspects. Starting from the history of emergency management research, we identify the emerging challenges concerning system optimisation, evacuee behaviour optimisation and data analysis, and the additional energy consumption by ICT equipment that operates the emergency management infrastructure.
Abstract-This paper explores the idea of smart building evacuation when evacuees can belong to different categories with respect to their ability to move and their health conditions. This leads to new algorithms that use the Cognitive Packet Network concept to tailor different quality of service needs to different evacuees. These ideas are implemented in a simulated environment and evaluated with regard to their effectiveness.
Emergency navigation algorithms for evacuees in confined spaces typically treat all evacuees in a homogeneous manner, using a common metric to select the best exit paths. In this paper, we present a quality of service (QoS) driven routing algorithm to cater to the needs of different types of evacuees based on age, mobility, and level of resistance to fatigue and hazard. Spatial information regarding the location and the spread of hazards is also integrated into the routing metrics to avoid situations where evacuees may be directed toward hazardous zones. Furthermore, rather than persisting with a single decision algorithm during an entire evacuation process, we suggest that evacuees may adapt their course of action with regard to their ongoing physical condition and environment. A widely tested routing protocol known as the cognitive packet network with random neural networks and reinforcement learning are employed to collect information and provide advice to evacuees, and is beneficial in emergency navigation owing to its low computational complexity and its ability to handle multiple QoS metrics in its search for safe exit paths. The simulation results indicate that the proposed algorithm, which is sensitive to the needs of evacuees, produces better results than the use of a single metric. Simulations also show that the use of dynamic grouping to adjust the evacuees' category, and routing algorithms that have regard for their on-going health conditions and mobility, can achieve higher survival rates.INDEX TERMS Emergency navigation, QoS driven protocol, dynamic grouping, cognitive packet network, discrete event simulation.
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