## 2 Application of Networked Mobile Sensors
### Surveillance  
In the past decade, terrorist attacks have raised some new research issues like that of surveillance using networked autonomous vehicles. Surveillance is the mission of observing the movements of targets navigating in a risky urban environment, such that robots can be considered to assume the role of security guards. Static sensor network approaches have been commonly used for such missions, but they were found to fail in complex applications such as surveying dynamic and open areas. The reasons for failure are generally about the limited sensing range of the sensing devices, the very large work space to be covered during observations and the non-existence of prior knowledge about how to deploy the sensors [43, 44]. Nowadays, networked mobile sensor systems are widely used for surveillance missions because of the decreasing cost of the autonomous platform development and the increasing abilities in navigation, sensing and computation [45]. Surveillance missions generally include target detection and tracking, under the coordination of mobile robots of the network. Especially in video based surveillance methods, target detection is a considerably problematic issue because of natural, uncontrolled environmental conditions such as scene variety and illumination. [PERSON] _et al._[46] used a hybrid Gaussian mixture model with temporal frame difference for detecting moving objects in environments where illumination can change suddenly. Their approaches also handle wrong labeling, which occurs when targets come at very close proximity to each other using a \"record\" strategy. Another research area dealing with surveillance is the physical tracking of targets, which involves continuously adjusting velocities of robots in order to keep track of the target within the viewing distance of the tracker robot. [PERSON] _et al._[47] handled the tracking of a target using multi robot systems, using a reference list that a robot maintains, so that it does not track an encountered target if that target is being tracked by another team member. [PERSON] [48] developed a distributed coordination control strategy that observes the movements of targets in the task area for being processed by networked robot systems. This work is the first research regarding multi-robot surveillance in complex and geometrical environments. Mobility and cooperation abilities provide successful task accomplishment despite a robot's limited sensing range. Robots can follow targets using local force vector based methodology and these vectors also handle the consistency problem of observing a target by multiple robots using a back force. The given algorithm minimizes the total time in which targets can escape from the pursuit by some robot team member in the area of interest, which is an important real-time need in unknown and dynamic environments. The limitation of the method relates to the work environment: the algorithm works only in areas without obstacles or where only a few simple convex obstacles exist, since the algorithm is very sensitive to detection errors. A distributed coordination control algorithm for surveillance missions by multiple robots was presented by [PERSON] and [PERSON], whose method is an extension of the work by [PERSON] [49]. Their algorithm, named Behavioral Cooperative Multi-robot Observation of Multiple Moving Targets (B-CMOMMT), is based on Explore and Help behaviors that keep the targets under observation by utilizing information supplied by sensors. However, this method also suffers from constraints of the operation environment. [PERSON] and [PERSON] [50] later presented a graph theoretic solution to the environmental problem encountered in their previously referred work, by modeling the complex work area as a graph. In their model, the environment is partitioned into nodes and their Graph-Clear module determines the minimum number of robots needed to mark all targets. Consequently, robots coordinate their actions to achieve this goal of marking targets in the graph modeled complex environment.