An Energy-Efficient Object Tracking Algorithm in Sensor Networks

Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks

“…These algorithms are validated also against the Naïve method as it is frequently used in the literature as a baseline of comparison. Furthermore, the effectiveness of the obtained simulation results is verified by comparing them with those obtained from previously relevant object tracking algorithms (Ren et al, 2008;Tynan et al, 2009;Jeong et al, 2007). However, Table 1 summarizes the main differences between our proposed algorithms and relevant previous work concerning tracking approaches, number of monitoring sensors, energy consumption, scalability, as well as tracking accuracy.…”

“…The main idea of these approaches is that the sensor nodes use past estimated locations collected during the target movement to activate a specific set of nodes in a range where the target may move toward (Xu et al, 2004). Such algorithms are usually complicated to be implemented on sensor nodes with limited capabilities (Ren et al, 2008) (i.e., power, computation, and memory storage for target history). Additionally, such algorithms need recovery mechanisms to deal with unavoidable target missing situations that occur due to unpredictable direction and speed changes of the mobile target (Bhatti and Xu, 2009;Raza et al, 2009).…”

“…In Ren et al (2008), energy efficient tracking (EET) algorithm has been proposed to reduce energy consumption for tracking a mobile target in WSN using static clustering architecture. The concept of clustering architecture is briefed by grouping the sensor nodes into clusters and accordingly electing a cluster head for each cluster (Jung et al, 2011).…”

Object Tracking in Wireless Sensor Networks: Challenges and Solutions

“…Typically, the cost of local computation is much lower than communication cost (Ren et al, 2008), which makes reducing the communication overhead a priority for any WSN algorithm.…”

“…Prediction relies on heuristics and attempts to anticipate the upcoming position of the moving object based on its historical positions observed over time and the spatial and temporal knowledge of sensors (Zhenga et al, 2014). Based on this prediction, sensor nodes get scheduled to be either active or asleep (Ren et al, 2008) during each defined time step (Mirsadeghi and Mahani, 2014). Due to the inevitable prediction mistakes, these algorithms have recovery mechanisms in order to make up for the inaccuracy of object localization.…”