## 4 Hybrid Sensor Network  
Hybrid sensor networks consist of static and mobile units working together to achieve a given common goal. Static nodes are used to collect data about the physical environmental conditions or to detect events, while mobile nodes are used to build upon acquired data for a more detailed analysis of the environment or to improve coverage by using their mobility capability. Collaboration and coordination between network units increase the efficiency of the task execution in networked hybrid sensor based systems. Coverage of very large areas has become an important issue for static sensor networks because of their limited number of static nodes, sensor failures due to limited energy or environmental conditions, and heterogeneous sensor distribution caused by random sensor distribution over the area. To reliably accomplish missions, especially in large and irregular regions, a huge number of sensor nodes are necessary, which is not always feasible with the current technology. Mobile nodes have to be used to improve area coverage and enhance sensing and communication abilities of the overall sensor network in the area of interest. [PERSON] and [PERSON] [101] presented area monitoring methodology by using hybrid wireless sensor networks with stationary and mobile nodes. Mobile robots collaborate with the stationary nodes so that they navigate towards areas that are the least covered by the stationary sensor nodes. Static sensors work as pathfinders for mobile ones and undetected targets can be detected via improved coverage. However, in this approach, mobile robots cannot control their actions according to the other mobile units' navigation paths, so multiple area coverage occurs and energy efficiency is considerably decreased. [PERSON] _et al._[102] proposed a distributed hybrid sensor network control algorithm to increase the efficiency of event detection in a given sensing area, based on the minimization of total energy consumption. In their work, static sensors completely cover the area of interest and can detect all extraordinary actions. After the detection of events by static sensor network, mobile sensor units are sent to event-related locations to identify the details of the event. Static sensor localization is very important in hybrid networks: they are randomly deployed in the environment by using airplanes, therefore, mobile robots are used to localize static sensors via signal strength of received radio messages [103]. The localization problem is solved by the Robust Extended Kalman Filter (REKF) based state estimator, which is more efficient then the classic Extended Kalman Filter (EKF) in terms of computation cost and robustness. The accuracy of the proposed methodology has proven to be approximately one meter, which is very good when compared to other methodologies that use received signal strength. However, combining static sensor networks with mobile sensor networks shifts the load of correcting the heterogeneity of the coverage inherent to static subnetworks, to the nodes of the mobile subnetwork. This compensation that is required from mobile sensor networks increases the energy need, thus tremendously decreasing energy efficiency. Wearable or sticking wireless sensor networks (WSN) on different carrying agents are now appearing in the literature [104, 105] in order to handle energy inefficiency. All these wearable WSN approaches aim to get human centered data from the human himself or herself and his/her environment under his/her knowledge. In such wearable WSNs, it is assumed that the sinks, which are the final destinations for the data collected, are within the wireless range, _i.e.,_ that they are connected [105, 106]. In systems such as transporter deployment architectures, the static network has an infinite energy source and the WSN is able to connect to distant nodes like satellites [107]. [PERSON] _et al._[108] aimed to generate network structures that have the ability to control coverage while still being static sensor networks.