Abstract
Presently, sensor-cloud based environment becomes highly beneficial due to its applicability in several domains. Wireless multimedia sensor network (WMSN) is one among them, which involves a set of multimedia sensors to collect data about the deployed region. Compared to traditional object tracking models, animal tracking in WMSN is a tedious process owing to the harsh, dynamic, and energy limited sensors. This article introduces a new Reliable Multi-Object Tracking Model using Deep Learning (DL) and Energy Efficient WMSN. Initially, the fuzzy logic technique is employed to determine the cluster heads (CHs) to attain energy efficiency. Next, in the second stage, a novel tracking algorithm by the use of Recurrent Neural Network (RNN) with a tumbling effect called RNN-T is developed. The proposed RNN-T model gets executed by every sensor node and the CHs execute the tracking algorithm to track the animals. Finally, the tracking results are transmitted to the cloud server for investigation purposes. In order to assess the performance of the presented model, an extensive experimental analysis is carried out by the use of a real-time wildlife video. The obtained results ensured that the RNN-T model has achieved better performance over the compared methods in different aspects.
Highlights
Wireless Multimedia Sensor Network (WMSN) includes a set of compact-sized, autonomous, energy limited, and distributed multimedia sensors which can transmit the multimedia data using wireless links
The proposed method operates on two stages: fuzzy based clustering algorithm and effective tracking algorithm using Recurrent Neural Network (RNN) with a tumbling effect named as RNN-T, which shows the novelty of the work
The proposed method operates on two stages: fuzzy based clustering algorithm and RNN-T based tracking algorithm
Summary
Wireless Multimedia Sensor Network (WMSN) includes a set of compact-sized, autonomous, energy limited, and distributed multimedia sensors which can transmit the multimedia data using wireless links. Y. Alqaralleh et al.: Reliable Multi-Object Tracking Model Using DL and Energy Efficient WMSNs may disturb the nature of animals. Scientists have modeled the computation of signal obtained from the sensor to calculate the time-dependent measurement for identifying the position and nature of the tracking animals. A distributed target tracking technique in WSN has been introduced in [16] This algorithm clusters the sensor and triplet triangulation is employed for the prediction of the target location. The proposed method operates on two stages: fuzzy based clustering algorithm and effective tracking algorithm using Recurrent Neural Network (RNN) with a tumbling effect named as RNN-T, which shows the novelty of the work. The clustering technique achieves energy efficiently by effectively handling data transmission between sensor nodes and BS.
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