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A prediction-based method is presented to track mobile object and its location in a sensor network area. In recent years, energy consumption and high accuracy target tracking have been a challenge in wireless sensor network. A number of applications have been used to reduce energy depletion by involving only a few number of sensor nodes to contribu...
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The increasing usage of wireless sensor networks in human life is an indication of the high importance of this technology. Wireless sensor networks have a vast majority of applications in monitoring and care which are known as target tracking. In this application, the moving targets are monitored and tracked in the environment. One of the most impo...
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... Such techniques are not practicable for resource-constrained, large-scale event-driven WSNs with real-time needs for providing sensed data [22][23][24]. Most of the works on face structured WSNs utilized Relative Neighborhood Graph (RNG), Delaunay triangulation, Voronoi diagram, Gabriel Graph (GG), and some cross edge removal techniques for constructing the planar face structure and do not have fault tolerance capabilities on their own [25][26][27]. Node or link faults and failures in the existing models can result in coverage holes, damage the face structure, partition the network and degrade the Quality of Service (QoS). ...
Although researchers have investigated multiple facets of fault tolerance, majority
of them have overlooked fault tolerance in face structured WSNs. Motivated by this,
we propose a Fault-Tolerant Coverage Preserving Strategy for Face Topology-based
WSNs (FtCFt). Unlike existing methods of recovering failures by merging the adjacent faces, we propose a coverage-aware node replacement method to replace the
failing node with a suitable alternate node. This is signifcant because a mobile target will go undetected, and no evidence of it can be acquired until it leaves the hole
region and is sensed by a node. FtCFt ofers fault tolerance by incorporating node
self-check and link-check strategies that works in conjunction with one of its mobile
target tracking applications. Unlike existing works, the proposed restoration algorithm efectively repairs and restores the face structure to ensure network coverage
and connectivity. Simulation results reveal that FtCFt improves coverage, quality of
service and WSN liferime.
... The Face-based Target Tracking Technique for track mobile objects and their location in a sensor network area is presented in [13]. In this algorithm, the sensor node in the border detects the object and elects Triangular sensor Nodes in the face structure which are nearest to the object. ...
The date palm is a high-value fruit crops but unfortunately, the date production are in danger because of the Red Palm Weevil or Rhynchophorus Ferrugineus. The Rhynchophorus Ferrugineus can excavate holes in the trunks of palm tree up to 3.3ft long, thereby weakening and eventually killing the host plant. The aim of the present paper is use of wireless sensor network for automatic detection and tracking Rhynchophorus Ferrugineus. Since the energy of sensor nodes is limited, considering their energy level for enhancing network lifetime is of high significance. Data transmission can consume a great deal of energy. Hence, it is essential that a new method be designed for data transmission so as to enhance network lifetime. The method proposed in this paper is aimed at improving energy consumption of target-detecting applications in WSNs via data mining and Q-learning. The experiments and simulations are carried out on Opnet simulator. The simulation results indicate that using data mining and Q-learning can reduce energy consumption, data transmission delay and maintain load balance notably better than IEEE 802.15.4.
... Multiple sensors serve to reduce load on a particular node and hence, energy depletion will be much lower as the nodes cooperate with each other in tracking the target. This collaboration among multiple sensors is mandatory to increase accuracy factor and to decrease energy usage [32]. ...
... p b (x,y) is uniform, always in sensing region. Considering this, P(x, y|N) is shown in Equation (32). ...
Target Tracking (TT) is an application of Wireless Sensor Networks (WSNs) which necessitates constant assessment of the location of a target. Any change in position of a target and the distance from each intermediate sensor node to the target is passed on to base station and these factors play a crucial role in further processing. The drawback of WSN is that it is prone to numerous constraints like low power, faulty sensors, environmental noises, etc. The target should be detected first and its path should be tracked continuously as it moves around the sensing region. This problem of detecting and tracking a target should be conducted with maximum accuracy and minimum energy consumption in each sensor node. In this paper, we propose a Target Detection and Target Tracking (TDTT) model for continuously tracking the target. This model uses prelocalization-based Kalman Filter (KF) for target detection and clique-based estimation for tracking the target trajectories. We evaluated our model by calculating the probability of detecting a target based on distance, then estimating the trajectory. We analyzed the maximum error in position estimation based on density and sensing radius of the sensors. The results were found to be encouraging. The proposed KF-based target detection and clique-based target tracking reduce overall expenditure of energy, thereby increasing network lifetime. This approach is also compared with Dynamic Object Tracking (DOT) and face-based tracking approach. The experimental results prove that employing TDTT improves energy efficiency and extends the lifetime of the network, without compromising the accuracy of tracking.
Target tracking is an important application of Internet of Things (IoT) assisted Wireless Sensor Networks (WSNs) in which sensor nodes monitor and report the positions of continuously moving objects. However, continuous tracking of multiple objects causes network congestion. In order to solve this challenge and provide congestion-aware continuous target tracking and boundary detection, we propose a novel Energy-Aware Radial Clustering Based Piecewise Regressive Multi-objective Golden Eagle Optimized Deep Convolutive Learning (EARC-ODCL) algorithm. In particular, EARC-ODCL incorporates a Multi-objective Stochastic Sampled Golden Eagle Optimization to select the optimal cluster heads (CHs) for sending the target information. Also, a Fully Connected Deep Convolutive Neural Learning is employed to identify the boundary of sensor nodes. Furthermore, a Piecewise Linear Regression approach is utilized for network congestion prediction and minimize data loss. Thereby, the proposed EARC-ODCL is able to perform continuous target tracking and boundary detection with higher accuracy in IoT-assisted WSN. Extensive simulations are conducted to assess the performance of EARC-ODCL in terms of various metrics, such as target tracking accuracy and time, boundary detection accuracy, and data loss.
