Trajectroy prediction for target tracking using acoustic and image hybrid wireless multimedia sensors networks

Xiao, Shuo; Li, Wei; Jiang, Haifeng; Xu, Zhiou; Hu, Zihao

doi:10.1007/s11042-017-4846-z

Cited by 15 publications

(6 citation statements)

References 20 publications

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“…where, E elec =5nJ/byte is the send or receive each byte of data's energy consumption [7] , ε f x is the energy consumption of the amplifier that send each bytes. While the nodes are randomly distributed and the communication distance between cluster head and member exceeds d 0 , the average distance between cluster head sink is [16,17]…”

Section: Energy Consumption Analysismentioning

confidence: 99%

Coverage Adaptive Optimization Algorithm of Static‐Sensor Networks for Target Discovery

Xiao

Tang

et al. 2019

Chin. j. electron.

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Section: Energy Consumption Analysismentioning

confidence: 99%

Coverage Adaptive Optimization Algorithm of Static‐Sensor Networks for Target Discovery

Xiao

Tang

et al. 2019

Chin. j. electron.

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“…On the other hand. the power e ciency of the communications systems is considered in several research papers [12][13][14][15][16][17][18][19]. The power is main constraint for the application of the data protection techniques.…”

Section: Introductionmentioning

confidence: 99%

Optimizing of Communications Systems Power Efficiency Considerations: Efficient Implementing Approach of Hamming Codes Utilizing FS-GDI

El‐Bendary

El-Badry

2021

Preprint

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Due to the power efficiency importance of digital signal processing and data protection in different communications systems, this paper proposes an efficient design of different Hamming Codes utilizing Full Swing- Gate Diffusion Input (FS-GDI) approach. The proposed codes design aims to improve the power efficiency and the required area through reducing the required number of transistors. FS-GDI is a new low power VLSI design approach, it is a power effective approach for realizing the different logic gates. In this work, the Hamming codes (7, 4) and (11, 7) are designed by utilizing the original GDI, FS-GDI and the traditional CMOS approaches. The amount of consumed power, delay time, Power Delay Product (PDP) and hardware simplicity-Number of Transistors (No.Ts) are employed as a metrics for evaluating the efficiency of the proposed design compared to the traditional design. The design simulation experiments are executed utilizing Cadence Virtuoso simulator package under 65nm technology. The simulation experiments revealed these proposed codes achieve delay time reduction by 52.91% and 10% for Hamming codes (7, 4) and (11, 7), respectively On the other hand, the Hardware (H/W) of these codes became more simple where the H/W simplicity of the used Hamming codes is reduced 50 % CMOS approaches respectively. From the results analysis, the proposed design achieves efficient power and the delay optimizing of Hamming codes utilizing the FS-GDI approach. On the other hand, the power consumption and area in communications systems due to the encoding process can be reduces.

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“…In a similar approach, the authors of [4,19] introduced tracking methods based on capturing images of target objects and/or on recording their sound. Upon intensive image and acoustic data analysis, the system infers instant positions of a moving object that falls in the visible/audible range of sensors.…”

Section: Introductionmentioning

confidence: 99%

“…The solution is however expensive and highly object-selective. On the other hand, both acoustic and image data were made use of in [19] to locate target objects. The authors employed the Gauss-Markov mobility model to predict the target trajectory, which helps restrict the suspected region to locate and track.…”

Section: Introductionmentioning

confidence: 99%

Self-Navigating UAVs for Supervising Moving Objects over Large-Scale Wireless Sensor Networks

Van

Trung

2020

International Journal of Aerospace Engineering

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Increasingly inexpensive unmanned aerial vehicles (UAVs) are helpful for searching and tracking moving objects in ground events. Previous works either have assumed that data about the targets are sufficiently available, or they solely rely on on-board electronics (e.g., camera and radar) to chase them. In a searching mission, path planning is essentially preprogrammed before taking off. Meanwhile, a large-scale wireless sensor network (WSN) is a promising means for monitoring events continuously over immense areas. Due to disadvantageous networking conditions, it is nevertheless hard to maintain a centralized database with sufficient data to instantly estimate target positions. In this paper, we therefore propose an online self-navigation strategy for a UAV-WSN integrated system to supervise moving objects. A UAV on duty exploits data collected on the move from ground sensors together with its own sensing information. The UAV autonomously executes edge processing on the available data to find the best direction toward a target. The designed system eliminates the need of any centralized database (fed continuously by ground sensors) in making navigation decisions. We employ a local bivariate regression to formulate acquired sensor data, which lets the UAV optimally adjust its flying direction, synchronously to reported data and object motion. In addition, we also construct a comprehensive searching and tracking framework in which the UAV flexibly sets its operation mode. As a result, least communication and computation overhead is actually induced. Numerical results obtained from NS-3 and Matlab cosimulations have shown that the designed framework is clearly promising in terms of accuracy and overhead costs.

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Trajectroy prediction for target tracking using acoustic and image hybrid wireless multimedia sensors networks

Cited by 15 publications

References 20 publications

Coverage Adaptive Optimization Algorithm of Static‐Sensor Networks for Target Discovery

Coverage Adaptive Optimization Algorithm of Static‐Sensor Networks for Target Discovery

Optimizing of Communications Systems Power Efficiency Considerations: Efficient Implementing Approach of Hamming Codes Utilizing FS-GDI

Self-Navigating UAVs for Supervising Moving Objects over Large-Scale Wireless Sensor Networks

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