Efficient Data Reporting in a Multi-Object Tracking Using WSNs

Abstract

Object tracking is one of the most important applications in wireless sensor networks (WSNs). Many recent articles have been dedicated to localization of objects; however, few of these articles were concentrated on the reliability of network data reporting along with objects localization. In this work, the authors propose an efficient data reporting method for object tracking in WSNs. This paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. Furthermore, data reliability is considered in the authors' model where the sensed data can reach the sink node in a more reliable way. This work first formulates the problem as 0/1 Integer Linear Programming (ILP) problem, and then proposes a SWARM intelligence for solving the optimization problem. Through simulation, the performance of proposed method to report information about the detected objects to the sink is compared with the previous works related to the authors' topic, such as LR-based object tracking algorithm, SEB, EPWSN, and ACO.

Research and Statistics

“…As the WHO notes in its Situation Report 27, this is the result of a change in reporting methodology to include clinically-diagnosed cases in addition to laboratory-confirmed cases as previously reported. This change in methodology only affected figures in the Hubei province in China, but due to the large number of cases in this region it had a significant impact on global figures too (Elfouly et al, 2017). The large apparent increase in confirmed cases in China, and globally, on 17th February therefore reflects this change in methodology rather than an actual sudden increase in cases on that particular day (Figures 1-2).…”

An Approach in Big Data Analytics to Improve the Velocity of Unstructured Data Using MapReduce

“…This proposed system will help to increase the speed of data processing from huge data set clusters like social media, cloud framework for business applications (Auxilia et al, 2020), Sustainable development (Zelinka & Amadei, 2019), Climate change monitoring Naugle, A. B et al ( 2019)), the health care sector (Majhi, 2018), (Dutta, 2017), Multi object tracking in Transportations (Elfouly et al, 2017), banking sector, education sector, and industries. Moreover unwanted and duplicated data can be removed from the data cleaning process because the data storage method in HDFS will be effective for ETL.…”

“…The entire data set has accessed using data mining algorithms and associated according to the user inputs. (Elfouly et al, 2017) proposed a algorithm to find Multi object tracking in Transportations of metropolitan cities. The huge crowd of people and vehicles are tracked by multi object system and find the exact matches using pattern recognition and object classification.…”

Multi-Layer and Clustering-Based Security Implementation for an IoT Environment

“…For example, the city administrator home will take care of the communication between devices that fall under the smart home cluster. The grain level of the hierarchy is the different sensors in a separate home (Elfouly et al, 2017). The regime of communication between devices falls in different groups is shown in figure 2, the hierarchy of communication between devices falls in different clusters.…”