## 4 Optimized Hidden Node Detection Paradigm
### Hidden Node Detection
Each neighbor node '\(N_{n}\)' sends on NHI message that can be obtained by:

\[N_{n}=\int\limits_{j=1}^{1}\left(N_{id}\right)+C_{l} \tag{11}\]

In Equation (11), multimedia sensor node unicasts the neighbor handshake indication process that contains node identity '\(N_{id}\)' and the current location '\(C_{l}\)':

\[N_{n}\ \to R_{m}=\int\limits_{i=1}^{1}\left(N_{id}\right)+C_{l}\times \sum_{N_{n}\ \in\ \beta}^{T_{n}}\left(\beta\right)\cdot N_{n} \tag{12}\]

In Equation (12), each neighbor node returns the message '\(R_{m}\)' with a node overlapping report '\(\beta\)' including the current location of each neighbor node. ### Location Detection

The location detection of the sensor node is of paramount significance for continuous communication. Thus, there are several events that can be implicitly adjusted and responded to only if the correct location of the event is detected. The location detection plays a key role for understanding the multimedia- application- contents. There are three advantages of detecting the location of the multimedia sensor node. First, location detection is required to determine the event of the interest. For example, the location of a fire, location of an intruder, or the location of the opponent's tank in the arena are critical for deploying the relief troops and rescue squads. Second, location detection enables several application services, such as helping doctors to gain the information of medical gears and personnel in smart hospitals. Third, the location detection helps in several system functionalities, such as network coverage checking, geographical routing, and location-based information querying. This phase helps to detect a sensor's maximum view. The location detection involves the node boundary and viewpoint. In the node boundary, we detect the area in which the node is capable of broadcasting the message. The viewpoint covers the degree of the node from 0 to 360 in which the node can be located at any degree. First, the node attempts to determine the location of the neighbor node within the boundary by broadcasting the message. If a node fails to locate the node within the boundary, then it initiates the neighbor distance search process by checking the distance of the node viewpoint of the neighbor node. Last, if the previous process fails, then an obstacle-distance process is used [4]. If the first two processes and later process fail, then the sensor node uses an optimized occlusion free viewpoint to determine the largest viewpoint [28]. Let us assume that sensor node \(N_{s}\) is located in the node boundary area and has three obstacles inside the viewpoint that are relatively close to \(N_{s}\). As such, these three obstacles restrict the sensor node from detecting the location of another node. Thus, we can obtain the location error rate by applying Equation (13):

\[\theta\ell_{e}=\frac{\sqrt{\left(l_{e1}-l_{al}\right)^{2}+\left(l_{e2}-l_{a2} \right)^{2}\times\Psi^{2}}}{T_{sn}} \tag{13}\]

The average location detection time is an important factor that helps determine the detection capacity of the multimedia sensor nodes. As such, this feature has an impact on the performance and extensibility of the network. Thus, Equation (14) provides an average location detection time for each multimedia sensor node as follows:

\[\theta\ell_{t}=\sum_{i=0}^{l}{(\frac{t_{ex}}{T_{sn}})}i \tag{14}\]

In Figure 4, we show a complete node boundary process with the obstacles. The intersection of the curves on the given node boundary are displayed by including points A and B for the first obstacle, C and D for the second obstacle, and E and C for the third. Hence, the multimedia sensor node can determine whether there exists the obstacles \(\Psi\) that can be expressed as:

\[\Psi\cap<A\mathbb{R}\mathrm{B}=0\,\ \Psi\cap<D\mathbb{R}\mathrm{C}=0\ \&\ \Psi\cap<E\mathbb{R}\mathrm{C}=0\]

then \"\(\Psi\)\" is visible to the viewpoint that refers to an AB clockwise curve; CD, and EC are available on the blocked curve of the viewpoint within the multimedia sensor node.