Multilevel Object Tracking in Wireless Multimedia Sensor Networks for Surveillance Applications Using Graph-Based Big Data

Küçükkeçeci, Cihan; Yazıcı, Adnan

doi:10.1109/access.2019.2918765

Cited by 18 publications

(6 citation statements)

References 56 publications

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“…It can be seen that the estimation bias of the original CSI ranging model is obviously larger than 0, which validates our analysis in Section 3. As with 0, as revealed in (9). Moreover, the estimation bias of original CSI ranging model in Non-LOS scenario is bigger than that in LOS scenario.…”

Section: Comparison On the Bias Of Csi Ranging Modelsmentioning

confidence: 63%

“…Wireless indoor localization has gained increasing research attention in past decades, as it plays an important role in many wireless applications such as personal surveillance, object tracking and vehicle navigation [9,18,26]. Various wireless indoor localization techniques have been proposed, including WiFi [35] [24], radio-frequency identification (RFID) [16] [4], Bluetooth [3] [12], Ultra Wide Bandwidth (UWB) [28] [10], etc.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of indoor localization based on channel state information ranging model

Gui

Xiao

Zhou

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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Due to robustness against multi-path effect, channel state information (CSI) of Orthogonal Frequency Division Multiplexing (OFDM) systems is supposed to provide accurate distance measurement for indoor localization. However, we find that the original CSI ranging model is biased, so the model cannot be used to directly derive Cramer-Rao lower bound (CRLB) of positioning error for CSI-ranging based localization scheme. In this paper we first analyze the estimation bias of the original CSI ranging model according to indoor wireless channel model. Then we propose a negative power summation ranging model which can be used as an unbiased ranging model for both Line-Of-Sight (LOS) and Non-LOS scenarios. Subsequently, based on the proposed model, we derive both the CRLB of ranging error and the CRLB of positioning error for CSI-ranging localization scheme. Through simulation we validate the bias of the original ranging model and the approximately zero bias of our proposed ranging model. Through comprehensive experiments in different indoor scenarios, localization errors by different ranging models are compared to the CRLB, meanwhile our proposed ranging model is demonstrated to have better ranging and localization accuracy than the original ranging model.

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Section: Comparison On the Bias Of Csi Ranging Modelsmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Performance analysis of indoor localization based on channel state information ranging model

Gui

Xiao

Zhou

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

View full text Add to dashboard Cite

show abstract

“…The article [12] highlights the importance of big data in surveillance applications and introduces a novel object-tracking approach using graph-based modeling and multilevel fusion. The performance evaluation results indicate the effectiveness of the proposed method [12].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Deep Learning and Softmax Routing for Energy-Efficient Wireless Sensor Networks in Public Space Design

Chang

2023

SCPE

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The increasing usage of several nodes to transfer the massive volume of data to the remotes in wireless sensor networks is a challenging task to reduce the loss. The high volumes of data transmission in wireless sensor networks (WSN) can surpass their capacity, resulting in congestion, latency issues, and packet loss. However, computational intelligence (CI) models can aid in managing and creating intelligent networks in WSN. The WSN congestion issues result in information loss and increased energy usage. CI-based models have been used to resolve this issue, reducing the latency. This paper proposes SoftMax Routing with Deep Neural Network (SRDNN) for efficient routing in WSN. This will route the data packets by choosing the high energy and lower load. It consists of two parts, such as the construction of the routing path, which determines the residual energy of the node. It is analyzed using SoftMax routing to decide whether the node is efficient in energy. The route request and reply established various paths between the source and destination. The path with minimum buffer space and maximum bandwidth is chosen in the optimal routing. The simulation results under the metrics such as energy consumption, data loss rate, throughput, and delay show the proposed model performance.

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“…The various application domains that use WMSNs are military, industry, healthcare, and defense [ 3 ]. Over the last year, several investigations on object tracking, environment detection, target detection, border surveillance, and enemy intrusion have been carried out [ 4 ]. Multimedia applications create significant network traffic volume, overheads, and minimizing energy consumption is a major challenge in WMSNs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Technique to Mitigate the Data Redundancy and to Improvise Network Lifetime Using Fuzzy Criminal Search Ebola Optimization for WMSN

Matheen

Sundar

2023

Sensors

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Wireless Multimedia Sensor Network (WMSN) is a powerful technology that is widely used to gather data and monitor the actual environment for analysis. Furthermore, multimedia applications’ needs and the features, such as constrained latency and high bandwidth consumption, complicate the design of WMSN routing protocols. Despite several methods, the trouble of designing WMSNs routing protocol remains a hurdle. The miniaturization and enhancement of hardware facilitate an extensive range of applications in the military and public sectors. On the contrary, the streaming of multimedia content is captured and generated due to some event-triggered surveillance for a long duration of time. Hence, it is necessary for wireless multimedia sensor network (WMSN) to provide a strong hardware foundation, thereby satisfying Quality of Service (QoS) requirements. Initially, the network is clustered into several clusters and the nodes with rich resources are chosen as cluster heads. The significant intention of this paper is to eliminate data redundancy and to select optimal cluster heads, thereby minimizing the energy consumption. Therefore, this paper proposes a novel Fuzzy Criminal Search Ebola Optimization (FCSEO) algorithm for optimal selection of cluster heads. In addition to this, the data redundancy present in the proposed algorithm is mitigated and thus the network lifetime is enhanced. Finally, extensive experimentation is carried out for various performance measures to determine the efficiency of the proposed approach.

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Multilevel Object Tracking in Wireless Multimedia Sensor Networks for Surveillance Applications Using Graph-Based Big Data

Cited by 18 publications

References 56 publications

Performance analysis of indoor localization based on channel state information ranging model

Performance analysis of indoor localization based on channel state information ranging model

Intelligent Deep Learning and Softmax Routing for Energy-Efficient Wireless Sensor Networks in Public Space Design

A Novel Technique to Mitigate the Data Redundancy and to Improvise Network Lifetime Using Fuzzy Criminal Search Ebola Optimization for WMSN

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