## 1 Introduction  
The large amount of multi-domain sensory information generated by increasingly advanced sensor networks can aggravate the burden of emergency navigation systems, which demand a time-critical response with respect to data collection, interpretation and transmission. Traditional WSN-based emergency management systems, which consist of functionality-identical sensors, have difficulty in offering optimal evacuations in a timely manner in the presence of time-varying hazards, due to the limitation in processing capability, battery power and communication speed [1]. Although the evolution from homogeneous architectures to heterogeneous architectures with functionally-separated nodes makes WSN-based systems more energy-efficient and fault-tolerant, this structure still suffers from resource restriction problems due to cost and unattended operation during lifetime. Owing to the high processing power, large storage, low risk level and high interoperability, cloud computing has become a dominating technology and has the potential to revolutionise the emergency management landscape. Accompanying this tendency, a new interest has been aroused to consider smart phones as simple clients for the back-end cloud due to their large-scale adoption and sensing abilities [2,3]. Compared with WSN-based counterparts, smart phone-aided, cloud-based emergency response systems are more flexible due to the elimination of both the extensive time to setup a permanent WSN and the routine maintenance, such as battery changes. Furthermore, owing to the enormous processing power, cloud computing can be beneficial in providing better quality of information (QoI) [4] and making more sophisticated decisions. Additionally, cloud-based emergency navigation systems can decrease the likelihood of being affected by the ongoing hazard and reduce energy consumption of thin clients by offloading computations to the remote cloud. However, state-of-the-art cloud-based emergency navigation systems, which are based on the client-server model, still face many challenges. For instance, most systems adopt optimal path-finding algorithms that demand time-consuming full graph searches and information synchronization processes, especially when a large-scale environment is involved. Meanwhile, the gathering and disseminating of a large volume and a wide variety of sensory information, such as multi-media geographical data, can deplete client devices quickly. Moreover, clients in existing systems are presumed to be trusted and cooperative. Hence, misbehaving clients or incorrect reading of sensors can have a significant impact on the system performance. Thus, in this paper, we propose a novel cloud-based emergency navigation system that borrows the concept of a cognitive packet network (CPN) [5] and social potential fields [6] to evacuate civilians in built environments. As a software-defined network, CPN can be completely implemented in software and, therefore, is easy to deploy on cloud servers as virtual \"CPN nodes\" in a distributed manner. In addition, CPN can achieve fast adjustments, because each CPN node operates as a sub-system to detect the environment, and no synchronization procedure is required. In this framework, smart phones are usedas clients to connect to the access point(s) of cloud servers that carry out the intensive computations. Evacuees are directed to exits with a cooperative strategy to improve the elasticity in communication and localization, as well as to increase the possibility for a civilian to obtain assistance when at risk. A power-aware QoS metric [7], which is based on the _ad hoc_ cognitive packet network (AHCPN) protocol [8] that uses an energy-aware sensible routing technique [9], is also presented to prolong the lifetime of smart handsets. One advantage of the AHCPN protocol is that it can locate malicious users readily, as detailed path information is contained in the route-search packets.