## 5 Emerging Challenges and Opportunities
Cloud-assisted collaborative sensing can then control the sampling rates of sensors to achieve a required level of accuracy. The tradeoff between data quality and quantity collected by mobile devices also raises open research issues of energy management for mobile clients. QoS-guaranteed networks and systems also raise challenges in emergencies especially for time-constrained and resource-constrained responses. A cloud-related service's service-level agreements (e.g., response time), the scheduling of concurrent tasks, parallel programming aspects and big data processing and management all create challenging performance problems. Another important aspect in emergency management is the appropriate balance between simulation, which is extensively used, and modelling using probability models [61, 62], which was first suggested many years ago [63], but which has been very seldom used. Simulation will necessarily be time consuming, both in programming and development to incorporate new features and details and in run time. Mathematical models can potentially be much faster, but require significant computation to obtain \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Technical scope** & **Advantage** & **Disadvantage** & **Open research problems** \\ \hline Network integration & \(\bullet\) interoperability with wireless sensor networks (WSNs) & \(\bullet\)integration via gateway nodes & \(\bullet\) seamless OppNets \\  & \(\bullet\) compliance with networking & & \(\bullet\) networking virtualisation \\  & standards & & \(\bullet\) knowledge-centric networks \\ \hline Security and privacy & \(\bullet\) proximity-based authentication & \(\bullet\) device-level sharing & \(\bullet\) security of opportunistic communications \\  & \(\bullet\) pull-based service content & \(\bullet\) only system-defined privacy & \(\bullet\) privacy-preservation knowledge inferences \\  & \(\bullet\) separate private data sets & \(\bullet\) divulgence of privacy by inference & \(\bullet\) trust, security,and privacy in mobile systems \\ \hline Mobile sensing systems and Cloud-enabled decision systems & \(\bullet\) knowledge from the wisdom of crowds & \(\bullet\) simple activity inferences & \(\bullet\) participatory and collaborative sensing models \\  & \(\bullet\) scalable sensing/decision-making model & \(\bullet\) one-way knowledge flow & \(\bullet\) energy management of mobile devices \\  & \(\bullet\) optimization of task migration & & \(\bullet\) QoS–guaranteed cloud and big data in cloud \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The technical progress and open research problems toward cyber-physical evacuation and rescue systems. the normalising constants that are needed to compute all the event probabilities. Some recent promising work [64, 65] indicates that graph theoretic models, as well as queueing theory, can help identify the most vulnerable or busy areas of an evacuation scene. Open queueing models can also provide lower and upper bounds to evacuation times and have the potential for providing insight in a computationally fast manner into the major bottlenecks and the time bounds related to an evacuation, and this analysis can also lead to further research on task and resource assignment algorithms [66] for emergencies. We feel that there is an opportunity for more work in these directions.