An Algorithm of Mobile Sensors Data Fusion Tracking for Wireless Sensor Networks

Chen, Joy Iong‐Zong

doi:10.1007/s11277-009-9888-8

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…IoT system integration with its peripheral devices is a critical issue. It is well known that TM usage is one important security algorithm for protecting an IoT system [27]. Therefore, understanding the TM scheme operation is becoming very necessary.…”

Section: Simply Describe To the Tm Schemementioning

confidence: 99%

Algorithms Based on Block-Chain Applied to Develop the IoT Applications

Chen

Lee

2023

Preprint

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The current article presents a BC (block-chain) algorithm proposed for developing IoT (internet of things) applications. The TM (trust management) scheme is also embedded for improving secure protection. In the BC stage, specified parameters are extracted first according to the estimated trust value before the control information comes from the TM. A merit referred to as QoI (quality of information) judges the IoT technology parameter dependent on the trust value. The simulation results from experiments show that the security has more reliability after the proposed fusion schemes. The BC embedding scheme has always provided a higher QoI level. Furthermore, the outcomes are such that the jointed algorithm can enhance secure protection efficiency and solve the application tier security problem. Alternatively, reducing the QoI degree, such as the packet capacity and data rate, is the penalty factors. Eventually, the results from the proposed methods have significant delivery ratio outperform a relevant work, and there has a real implemented project is demonstrated by providing the mentioned assessment for a toxic chemical trade.

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Section: Simply Describe To the Tm Schemementioning

confidence: 99%

Algorithms Based on Block-Chain Applied to Develop the IoT Applications

Chen

Lee

2023

Preprint

Self Cite

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“…Tolstikov [34], Amoretti et al [35], Sebak et al [38], Tunca et al [10], Qui et al [ [36,37]. Furthermore, Qiu et al [4] proposed an extended Kalman filtering approach to integrate motion sensors for pedestrian navigation application and noted that such method provides a robust algorithm for human activity detection.…”

Section: Strength Weaknessmentioning

confidence: 99%

Multi-sensor fusion based on multiple classifier systems for human activity identification

Nweke

Wah

Mujtaba

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multiview ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multisensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system.

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“…Chen [ 126 ] developed an algorithm for data fusion to track both non-manoeuvring and manoeuvring targets with mobile sensors deployed in a wireless sensor network (WSN). It applies the GATING technique to solve the problem of mobile-sensor data fusion tracking (MSDF) for targets.…”

Section: Sensor Data Fusion In Mobile Devicesmentioning

confidence: 99%

“…Distributed systems for sensor data fusion with off-the-shelf mobile devices have been used for tracking [ 120 , 121 , 122 , 126 , 147 ], location detection [ 83 , 84 , 123 , 124 , 148 , 149 ], health monitoring [ 99 , 103 , 119 ], user monitoring [ 113 , 150 ], automotive systems [ 151 ] and other purposes.…”

Section: Application In Ambient Assisted Livingmentioning

confidence: 99%

From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Pires

García

Flórez-Revuelta

2016

Sensors

136

105

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This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

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An Algorithm of Mobile Sensors Data Fusion Tracking for Wireless Sensor Networks

Cited by 11 publications

References 10 publications

Algorithms Based on Block-Chain Applied to Develop the IoT Applications

Algorithms Based on Block-Chain Applied to Develop the IoT Applications

Multi-sensor fusion based on multiple classifier systems for human activity identification

From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

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