A joint time synchronization and localization method without known clock parameters

Wu, Xiaoping; Gu, Z. H.

doi:10.1016/j.pmcj.2016.09.006

Cited by 13 publications

(7 citation statements)

References 43 publications

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“…Last but not least, another important observation is that the differences of capacities among three relay selection schemes are not significant for short range of wireless communication. Although the nodes' locations can be obtained by GPS, it is not practical to use GPS due to its high power consumption in wireless sensor networks, especially in EH scenarios [30], [31]. In other words, the random relay selection will be a more feasible and reasonable scheme for EH-CRSNs scenarios due to its low complexity implementation.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Transmission Capacity Analysis for Underlay Relay-Assisted Energy Harvesting Cognitive Sensor Networks

et al. 2019

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Energy harvesting cognitive radio sensor networks (EH-CRSNs) are an emerging technology for low-cost, green monitoring of a wide range of environments. How to analyze the transmission capacity is a fundamental and challenging problem in EH-CRSNs due to the dynamics of spectrum and energy arrivals. In this paper, transmission capacity analysis of underlay relay-assisted EH-CRSNs is considered, where some nodes serve as decode-and-forward relays to assist the communication between one secondary source and one destination node. To characterize the end-to-end performance of underlay relay-assisted EH-CRSNs, we first assume that EH devices use harvest-store-use (HSU) mode and formulate the battery states with M/M/1/c model for the arbitrary integer value of transmission energy level threshold. Then, the closed-form expressions of transmission capacity are derived for the random, the nearest, and the farthest relay selection, respectively, based on stochastic geometry. In addition, the transmission capacities with the variable source-destination distance are also analyzed for the three kinds of relay selections. Finally, numerical simulations show that the transmission capacity of underlay relay-assisted EH-CRSNs can be influenced by large amounts of factors, including secondary access probability, source-destination distance, signal-to-interference ratio, and battery transmission energy level threshold, and the different relay selection schemes. The results also confirm that the random relay selection, compared with the nearest and farthest relay selection, is a more feasible and reasonable scheme for short range of underlay EH-CRSNs due to its low complexity implementation.INDEX TERMS Energy harvesting, cognitive radio sensor networks, relay selection, transmission capacity, access probability.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Transmission Capacity Analysis for Underlay Relay-Assisted Energy Harvesting Cognitive Sensor Networks

et al. 2019

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“…where NV indicates the total votes, i = 1, 2, 3, 4. However, votes 5 assigned to the feature attraction is always the same and expressed by…”

Section: Vote Assignmentmentioning

confidence: 99%

“…Recently, wearable devices have been widely used in human activity monitoring, such as the monitoring of falls for the elderly, real-time patient monitoring, and the recording of motion data. [1][2][3][4][5][6] To detect human activities, all kinds of sensors like accelerometers, gyroscopes or magnetometers, barometer are equipped to wearable devices. Then the devices are placed on human body at different positions so that human activities can be comprehensively judged based on data collected by these sensors.…”

Section: Introductionmentioning

confidence: 99%

Human activity recognition method based on molecular attributes

Fang

et al. 2019

International Journal of Distributed Sensor Networks

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Acceleration sensor is extensively used in the field of human activity recognition, since it provides better recognition rate of human activity. Based on the principle of molecular attribute, a simple and adaptive activity recognition method is proposed using the acceleration data flow, which constitutes a serial activity, when the acceleration data are treated as the material flow with certain molecular structure. Then five molecular attributes including relative molecular mass, density, internal forces in a molecule, molecule stability, and attraction between molecules are introduced to recognize six human activities, since the closer molecular attribute means the more similar activity. Based on the calculated molecular attributes, a reliability-based voting method for human activity recognition is developed. Since each activity has respective motion cycle, a sliding window with variable sizes is put forward to enhance the recognition rate. Furthermore, adaptive incremental learning is designed to adapt to the different users. The long-time experimental results show that the proposed method is rather accurate and robust for different crowds. The average recognition rate achieves 97.2% for six human activities including walking, jogging, running, going upstairs, going downstairs, and sitting down.

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“…Extending the localization to handle multiple sources is nontrivial, as many existing methods for single source localization would fail to work. One should be noted that many previous works in the literature, such as [19], [25], [24], [27], [33], and [34],…”

Section: Introductionmentioning

confidence: 99%

Multiple Sources Localization by the WSN Using the Direction-of-Arrivals Classified by the Genetic Algorithm

Zhang

Yu-hua

2019

IEEE Access

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Simultaneously locating multiple sources passively in the wireless sensor networks (WSN) is challenging in the internet of things (IoT) applications, where reducing the computation and communication load is of great importance due to the requirement on real-time processing and the energy constraint. This is especially true when the number of sources or the number of sensor nodes is large. In this paper, a localization algorithm to estimate multiple sources' positions in the three-dimensional space is proposed. With the direction-of-arrival (DOA) estimates for multiple sources obtained at each sensor node, it is crucial to discriminate which estimate corresponds to which source. To save the computation resources, a classification method based on the genetic algorithm is proposed to handle the multiple sources. A fitness function is designed to assess the clustering of the DOA estimates. Extensive simulations are carried out to analyze the algorithm performance under the various settings. Numerical examples show that the proposed method could lower the computational burden by orders of magnitude compared to the conventional method, without significantly sacrificing the estimation accuracy.

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A joint time synchronization and localization method without known clock parameters

Cited by 13 publications

References 43 publications

Transmission Capacity Analysis for Underlay Relay-Assisted Energy Harvesting Cognitive Sensor Networks

Transmission Capacity Analysis for Underlay Relay-Assisted Energy Harvesting Cognitive Sensor Networks

Human activity recognition method based on molecular attributes

Multiple Sources Localization by the WSN Using the Direction-of-Arrivals Classified by the Genetic Algorithm

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