## 2 Technically Challenging Areas  
Since all of these requirements raise many critical challenges, two types of approaches have motivated considerable research to address issues in communications, information acquirement and dissemination, knowledge discovery, resource allocation and management, heterogeneous system integration and asynchronous control, and we discuss some of these challenges below. * Communication issues: _many-to-many information flow_[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23] and _opportunistic connection_[24] are inevitable in emergencies. Considering a fire emergency, to find safe paths, sensing information may need to be conveyed from many sensors to many mobile evacuees. This will obviously be more difficult, since communications may break down and evacuees will move to escape. In addition, query-and-reply communications may also proceed between different groups of people, e.g., first responders, evacuees, members of the press and robots. In these cases, typical communication protocols, such as broadcast, convergecast, unicast, and multicast, may be not able to deal with these diverse communication requirements. * Information acquirement and dissemination: _cross-domain sensing_[25, 26, 27] and _heterogeneous information flow_[28, 29, 30] are inherent features in an emergency response system. To guarantee the safety of people, information in different domains must be acquired (e.g., ultrasonic sensors for localizing people, temperature and gas sensors for identifying hazards, camera sensors for counting civilians and life detectors for searching civilians). Moreover, sensors are no longer the only information contributors, and _in situ_ interactions between sensors, actuators, people, objects and events will also be involved to disseminate and contribute high-level information. These features will raise a challenge to acquire and disseminate information in an efficient way. * Knowledge discovery: _partial information_ and _dynamic changes_ are inherent in an emergency. In such a rough environment, feasible and quick response must rely on data analysis technologiesto extract knowledge from sensing data (e.g., counting, discovery, localization and tracking of civilians) [30; 31; 32; 33; 34; 35; 36; 37; 38]. Moreover, dynamic prediction and forecast of environmental changes should be conducted to avoid unnecessary casualties [20; 21; 39; 40; 41; 42; 43; 44]. * Resource allocation and management: _limited resources_ make timely response more difficult. Unlike other sensor-aided applications, the needs of intelligent actuation, scheduling and efficient resource allocation will increase in emergency response systems. Intelligent scheduling is needed to select the best action, while scarce resources must be allocated efficiently to perform actions [45; 46; 47; 48; 49; 50]. * Heterogeneous system integration and asynchronous control: _multi-domain technologies_ will be needed to enhance the capability and diversity of emergency response [26; 27; 30; 38]. Furthermore, _functionality separated_ tasks, such as sensing, storage, computation and decision-making, need to be conducted by independent functional units, so as to facilitate the integrated asynchronous control of multiple technologies. To achieve this goal, parallel and virtualisation technologies (e.g., grid or cloud computing) may steer the emergency response systems toward functionality-separated approaches for resource scaling, fault-tolerance and computation speed-up [25]. For instance, in an outdoor city-wide environment, large numbers of civilians may be tracked, and massive sensing information can only be stored and computed rapidly by a powerful resource pool. * Comparison with military systems: the objective of search and rescue systems is emergency response, but military systems focus on extensive simulations for tactical planning and decision.