A New Clustering-Based Approach for Target Tracking to Optimize Energy Consumption in Wireless Sensor Networks

Hosseini, Rahim; Mirvaziri, Hamid

doi:10.1007/s11277-020-07534-5

Cited by 7 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the figure it is inferred that increasing the sensor odes increases the objects or targets to be tracked and obviously results in the increase in the energy being consumed for target tracking. Simulations conducted with 50 sensors found to be 0.0015J of energy being consumed using RD-LCRR, 0.0020J of energy being consumed using [1] and 0.0025J consumed using [2]. From this simulation results it is inferred that the energy consumption using RD-LCRR is found to be comparatively lesser than the existing methods.…”

Section: Figure 4 Graphical Representation Of Energy Consumptionmentioning

confidence: 76%

“…As a matter of fact, this may lead to the group formation with some of the objects being sensed as the edges also. The other drawback is the target tracking algorithm used in [2] the accuracy with which target tracking obtained was less focused. In this article, we proposed two new algorithms for optimizing the edge-based clustering and improving target tracking accuracy via machine learning.…”

Section: Our Methodology and Contributionsmentioning

confidence: 99%

“…A strategy for target tracking using tube shaped layering and dynamic clustering was presented in [2] with minimum tracking errors. A special type of layering based on tube shaped was designed that helps routing with no optimization issue by integrating both static and dynamic clustering patterns.…”

Section: Figure 1 Scenario For Smart Healthcare Environment Based On Iotmentioning

confidence: 99%

“…Finally, target tracking time is measured based on the total number of sensors and time for tracking. The scope of the experiments incorporates a juxtaposition of the proposed Radial Distance and Lucas Cluster-based Ridge Regression (RD-LCRR) method against existing multiple target tracking in WSN [1] and tube shaped layering and dynamic clustering [2] that have been introduced in the literature for target tracking applications.…”

Section: Experiments Evaluationmentioning

confidence: 99%

See 3 more Smart Citations

Target Tracking Using Radial Distance and Lucas Cluster-Based Ridge Regression in Wireless Sensor Network

2021

Preprint

View full text Add to dashboard Cite

Target tracking is one of the most significant concerns in Wireless Sensor Networks (WSNs). In a densely deployed WSN, spatial trajectories in future mission monitoring can provide comprehensive communication but with dynamically changing network, targets were lost, compromising stability. Some approaches pursue minimum overload and energy consumption occurring during tracking at the cost of tracking accuracy, while some other mechanisms aim to achieve the best tracking accuracy without consideration of energy consumption. In this paper, we present a new method called, Radial Distance and Lucas Cluster-based Ridge Regression (RD-LCRR) to detect and track the target in WSN considering both energy efficiency and accuracy. Initially, we sense the object via Radial Distance Edge Propagation Object Sensing model based on the cluster formation and then identify moving objects through additive value. Next, the object to be tracked is obtained by applying Lucas Kanade Cluster-based Ridge Regression model. Here, target detection is first initialized via Lucas Kanade and finally, the tracking of the actual target is performed through Ridge Regression. The performance of the RD-LCRR method is considered for different scenarios and experiments are performed in a simulation environment against state-of-the-art methods. Results of the experiments show that the presented target detection and tracking method enables a significant reduction in the amount of energy consumption, target tracking time with higher accuracy rate.

show abstract

Section: Figure 4 Graphical Representation Of Energy Consumptionmentioning

confidence: 76%

Section: Our Methodology and Contributionsmentioning

confidence: 99%

Section: Figure 1 Scenario For Smart Healthcare Environment Based On Iotmentioning

confidence: 99%

Section: Experiments Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Target Tracking Using Radial Distance and Lucas Cluster-Based Ridge Regression in Wireless Sensor Network

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To address the energy consumption problem, Hosseini and Mirvaziri in [ 36 ] introduced a dynamic K-means clustering-based approach to minimize the target tracking error and energy consumption in wireless sensor networks (WSNs). The proposed technique uses a tube-shaped layering method for the sensor nodes to reduce energy dissipation during target tracking.…”

Section: Related Workmentioning

confidence: 99%

Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning

Sultan

Waleed

Pyun

et al. 2021

Sensors

View full text Add to dashboard Cite

The Internet of Things (IoT)-based target tracking system is required for applications such as smart farm, smart factory, and smart city where many sensor devices are jointly connected to collect the moving target positions. Each sensor device continuously runs on battery-operated power, consuming energy while perceiving target information in a particular environment. To reduce sensor device energy consumption in real-time IoT tracking applications, many traditional methods such as clustering, information-driven, and other approaches have previously been utilized to select the best sensor. However, applying machine learning methods, particularly deep reinforcement learning (Deep RL), to address the problem of sensor selection in tracking applications is quite demanding because of the limited sensor node battery lifetime. In this study, we proposed a long short-term memory deep Q-network (DQN)-based Deep RL target tracking model to overcome the problem of energy consumption in IoT target applications. The proposed method is utilized to select the energy-efficient best sensor while tracking the target. The best sensor is defined by the minimum distance function (i.e., derived as the state), which leads to lower energy consumption. The simulation results show favorable features in terms of the best sensor selection and energy consumption.

show abstract

Tracking Algorithm of Leisure Mountaineering Based on Hadoop

Duan

2022

Application of Intelligent Systems in Multi-Modal Information Analytics

View full text Add to dashboard Cite

A New Clustering-Based Approach for Target Tracking to Optimize Energy Consumption in Wireless Sensor Networks

Cited by 7 publications

References 21 publications

Target Tracking Using Radial Distance and Lucas Cluster-Based Ridge Regression in Wireless Sensor Network

Target Tracking Using Radial Distance and Lucas Cluster-Based Ridge Regression in Wireless Sensor Network

Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning

Tracking Algorithm of Leisure Mountaineering Based on Hadoop

Contact Info

Product

Resources

About