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.

In-network caching is one of the key features of information-centric networks (ICN), where forwarding entities in a network are equipped with memory with which they can temporarily store contents and satisfy en route requests. Exploiting in-network caching, therefore, presents the challenge of efficiently coordinating the forwarding of requests with the volatile cache states at the routers. In this paper, we address information-centric networks and consider in-network caching specifically for Named Data Networking (NDN) architectures. Our proposal departs from the forwarding algorithms which primarily use links that have been selected by the routing protocol for probing and forwarding. We propose a novel adaptive forwarding strategy using reinforcement learning with the random neural network (NDNFS-RLRNN), which leverages the routing information and actively seeks new delivery paths in a controlled way. Our simulations show that NDNFS-RLRNN achieves better delivery performance than a strategy that uses fixed paths from the routing layer and a more efficient performance than a strategy that retrieves contents from the nearest caches by flooding requests.

The performance of Emergency Management Systems (EMS) in confined spaces is highly dependent on the decision algorithm employed for the safe navigation of the evacuees to the available exits. In the algorithm proposed in this paper, we have considered evacuees under two groups, based on their age and physical condition, and we tailor two routing metrics, one for each group, in finding suitable paths for the evacuees. A dynamic grouping mechanism that can adjust an evacuee's group, and therefore routing metric, according to its on-going health condition is employed during the evacuation. To implement the routing metrics, we have used the Cognitive Packet Network (CPN) with random neural networks (RNN) and reinforcement learning. The CPN is an adaptive routing protocol that is loop-free at all times and easily handles multiple quality of service (QoS) metrics. Simulation results show that allowing the navigation system to be sensitive to the on-going health conditions and mobility of the evacuees, using our proposed dynamic grouping, can achieve higher survival rates.

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