Energy-Efficient Constant Gain Kalman Filter Based Tracking in Wireless Sensor Network

Hirpara, Kirti; Rana, Keyur

doi:10.1155/2017/1390847

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…Mobile computing requires wireless network access [9], and it is much more difficult than wired communication because mobile computing should consider the signal, path, noise, delays, errors, power, and so on. One of the basic issues of mobile computing and wireless network is the energy constraint [10]. Thus, the high efficiency service is needed for mobile computing.…”

Section: Convex Hull Methods For Mobile Computingmentioning

confidence: 99%

An Indexing Method to Construct Unbalanced Layers for High-Dimensional Data in Mobile Environments

Ihm

Hur

2017

Wireless Communications and Mobile Computing

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A top-query processing is widely used in many applications and mobile environments. An index is used for efficient query processing and layer-based indexing methods are representative to perform the top-query processing efficiently. However, the existing methods have a problem of high index building time for multidimensional and large data; thus, it is difficult to use them. In this paper, we proposed a new concept of constructing layer-based index, which is called unbalanced layer (UB-Layer). The existing methods construct a layer as a balanced layer with outermost data and wrap the rest of the input data. However, UBLayer constructs a layer as an unbalanced layer that does not wrap the rest of the data. To construct UB-Layer, we fist divide the dimension of the input data into divided-dimensional data and compute the convex hull in each divided-dimensional data. And then, we combine divided-convex hull to build UB-Layer. We also propose UB-SelectAttribute algorithm for dividing the dimension with major attributes. We demonstrate the superiority of the proposed methods by the performance experiments.

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Section: Convex Hull Methods For Mobile Computingmentioning

confidence: 99%

An Indexing Method to Construct Unbalanced Layers for High-Dimensional Data in Mobile Environments

Ihm

Hur

2017

Wireless Communications and Mobile Computing

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“…Hirpara, K. and Rana, K. [ 12 ] proposed an energy-efficient target tracking model in which a trade-off between tracking accuracy and power consumption in tracking is attempted. The model combines the feature of node clustering and prediction-based collaborative tracking for effective tracking.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network

Dev

Mishra

2023

Sensors

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Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

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“…It involves a large amount of communication among various sensor nodes [39][51]. There is a tradeoff between sensing range and energy consumption [61]. Large sensing range can reduce packet drop ratios and reducing communication overhead but it requires more energy for each message transmission.…”

Section: A Sensing Range and Energy Consumptionmentioning

confidence: 99%

A Survey on Continuous Object Tracking and Boundary Detection Schemes in IoT Assisted Wireless Sensor Networks

et al. 2021

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With the new age of data innovation, the Internet of Things (IoT) proliferation has drawn enormous thought and has applied to help applications in different fields i.e. natural assurance, military observing, and industrial applications. WSNs are the essential segment of IoT for monitoring as well as tracking. The most preeminent applications provide confinement and identification of continuous objects i.e. wildfire, toxic gas, bio synthetics concoctions, and so forth. In the case of continuous objects such as fire and toxic gases are detected to identify the boundary of damage and alert teams for rescue efforts. It is also helpful in identifying safe paths for rescue. We have investigated various existing surveys that carried out different concepts associated with continuous object tracking and find out the deficit of boundary detection of object. In order to replete the present cleft of analysis, we have inspected various current state-of-the-art works on boundary detection of a continuous object that has yet not been added to the current writing. This paper presents an extensive overview of different continuous object tracking schemes which involve energy efficiency, boundary detection, communication, data aggregation, and network structural design in literature with the aid of featuring taxonomy. We summarized, compared, and classified these schemes along with their analysis and performance. Moreover, for further evaluation mechanism, strengths and weaknesses of these schemes are presented. Finally, various state-of-the-art open research challenges are identified. Moreover, there is a need to overcome these challenges through novel and reliable arrangements by the researchers.

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Energy-Efficient Constant Gain Kalman Filter Based Tracking in Wireless Sensor Network

Cited by 11 publications

References 23 publications

An Indexing Method to Construct Unbalanced Layers for High-Dimensional Data in Mobile Environments

An Indexing Method to Construct Unbalanced Layers for High-Dimensional Data in Mobile Environments

Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network

A Survey on Continuous Object Tracking and Boundary Detection Schemes in IoT Assisted Wireless Sensor Networks

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