Routing Emergency Evacuees with Cognitive Packet Networks

Bi, Huibo; Desmet, Antoine; Gelenbe, Erol

doi:10.1007/978-3-319-01604-7_29

Cited by 18 publications

(22 citation statements)

References 26 publications

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“…The use of fast autonomic RNN-based routing of evacues using CPN (Bi, Desmet, and Gelenbe [13]) and directional navigation were also developed (Gelenbe and Bi [73], Kokuti and Gelenbe [166], Gelenbe, Akinwande, and Bi [172]). There have been several applications of this approach to emergency management (Gelenbe and Wu [146,148]), and routing algorithms specifically for this area are discussed in (Desmet and Gelenbe [23], Gelenbe, Gorbil, and Wu [88], Gelenbe and Wu [147]).…”

Section: Autonomic Systems and Cognitive Packet Network (Cpns)mentioning

confidence: 99%

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

Caglayan¹

2017

Prob. Eng. Inf. Sci.

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This paper introduces a special issue of this journal (Probability in the Engineering and Informational Sciences) that is devoted to G(elenbe)-Networks and their Applications. The special issue is based on revised versions of some of the papers that were presented at a workshop held in early January 2017 at the Séminaire Saint-Paul in Nice (France). It includes contributions in several research directions that followed from the introduction of the G-Network in the late 1980s. The papers present original theoretical developments, as well as applications of G-Networks to Machine Learning, to the performance optimization of energy systems via the novel Energy Packet Networks formalism for systems that operate with renewable and intermittent energy sources, and to packet network routing and Cloud management over the Internet. We introduce these contributions from the perspective of an overview of recent work based on G-Networks.

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Section: Autonomic Systems and Cognitive Packet Network (Cpns)mentioning

confidence: 99%

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

Caglayan¹

2017

Prob. Eng. Inf. Sci.

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“…CPN has been applied to many domains, including emergency navigation [44,45] and emergency resource allocation [35,[46][47][48]. As the world's first software-defined network (SDN), it can be installed on computers seamlessly [49].…”

Section: Cognitive Packet Networkmentioning

confidence: 99%

“…In our treatment, we replace the local control forces with the CPN-based algorithm to control the individual behaviour of evacuees and use the global forces to regulate the intra-group behaviours. Details of the CPN-based emergency evacuation algorithm can be seen in [44] and [45]. The force between two evacuees can be calculated from the following equation:…”

Section: : End Whilementioning

confidence: 99%

Emergency navigation without an infrastructure

Bi¹

2014

IEEE SENSORS 2014 Proceedings

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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.

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“…The authors in [2] present a distributed decision support system for building evacuation based on the distributed simulator. Regarding evacuating civilians quickly and safely, [14] illustrates that routing emergency evacuees with a cognitive packet network (CPN) [15][16][17] can alleviate slow convergence time for large and fast-changing networks, and [18] proposes the use of Opportunistic Communications (oppcomms), which can provide emergency support when a stable communication infrastructure is unavailable.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We assume that this is a fire-related emergency evacuation, and the area to evacuate is a building based on the three lower floors of Imperial College London's EEEbuilding [14]. A coarse graph representation of this building is shown in Figure 2.…”

Section: Description Of the Simulation Modelmentioning

confidence: 99%

Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm

Han

2013

Future Internet

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Emergency rescues require that first responders provide support to evacuate injured and other civilians who are obstructed by the hazards. In this case, the emergency personnel can take actions strategically in order to rescue people maximally, efficiently and quickly. The paper studies the effectiveness of a random neural network (RNN)-based task assignment algorithm involving optimally matching emergency personnel and injured civilians, so that the emergency personnel can aid trapped people to move towards evacuation exits in real-time. The evaluations are run on a decision support evacuation system using the Distributed Building Evacuation Simulator (DBES) multi-agent platform in various emergency scenarios. The simulation results indicate that the RNN-based task assignment algorithm provides a near-optimal solution to resource allocation problems, which avoids resource wastage and improves the efficiency of the emergency rescue process.

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Routing Emergency Evacuees with Cognitive Packet Networks

Cited by 18 publications

References 26 publications

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

Emergency navigation without an infrastructure

Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm

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