# A Review on Internet of Things for Defense and Public Safety
## 3 Target Scenarios for Mission-Critical IoT
### Collaborative and Crowd Sensing  
Collaborative sensing involves sharing sensing data among mobile devices combined with robust short range communications. IoT nodes would be able to utilize placement or other sensors to supplement their own sensing methods. Once security issues (such as trust and authentication) are resolved, the information can be made available to the users. Long-term maintenance of IoT services yield multiple benefits, such as trend or fault detection. Individual sensor parameters must be considered to assign a particular relevance to a given reporting device and its feedback can be improved upon data fusion approaches.  
IoT can ease ad-hoc mission-focused Intelligence, Surveillance and Reconnaissance (ISR) via pairing sensors with mission assignments [66]. Thus, sensors and sensor platforms would not have to be burdened with excessive equipment to handle mission scenarios on their own. For example, multiple devices entering an area of interest, each with their own mission but relying on collaborative sensing to accommodate new or unanticipated requirements. In the case of a soldier, its situational awareness increases, allowing for improved survival and mission success.  
Resource-rich devices might collect data from several sources to form a COP. This would allow for storing much of the collection and for processing the data locally. Higher level functions would aid reducing response times, improving decision-making and reducing backhaul communications requirements.  
Crowdsensing promises to be an inexpensive tool for flexible real time monitoring of large areas and assessment for mission impact, hence complementing services potentially available in smart cities. Researchers have devised services that allow for decoupling applications from the hardware [67]. Crowdsensing services might offer both recruitment (devices that should be instructed to perform sensing) and data collections (the system returns data directly). These applications entail challenges. First, there is a large number of users that have to served while offering them tangible benefits or incentives [68]. Secondly, they are required to ensure a desired level of quality of the data collected.  
From the perspective of gathering data from a community, deception could be achieved by compromising individual devices. Consequently, the security level is proportionate to the number of present devices, each representing a possible attack vector. Moreover, the paradigm \"Bring your own Device\" (BYOD) [69] introduces potential security concerns, since the user may have full access and make use of multiple heterogeneous devices that are difficult to control.  
Data validation is another domain-dependent task in the context of crowd sending, and, likewise, it is further complicated by the high heterogeneity of the devices. Even individual devices may vary in capabilities and performance over time. For example, for a smart phone, low battery levels could lead to infrequent GPS position updates, thus resulting in geo-tagging errors. Approaches to ensure data quality often rely on oversampling and filtering of outlier values [70], reputation schemes that provide trustworthy sensing for public safety [71], or trust network-based human intervention approaches.  
Moreover, potential threats to privacy can compromise crowdsensing services. Many of these activities require geo-tagging and timestamps, which can lead to disclosing the locations visited by the users. Another privacy issue is the metadata collected about devices performing a sensing activity.