A task-efficient sink node based on embedded multi-core SoC for Internet of Things

Qiu, Tie; Zhao, Aoyang; Ma, Rong; Chang, Victor; Liu, Fangbing; Fu, Zhangjie

doi:10.1016/j.future.2016.12.024

Cited by 24 publications

(8 citation statements)

References 36 publications

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“…cially in networking applications, resource optimization, and performance modeling of computer systems [15][16][17][18][19][20]. In their taxonomy of scheduling in general-purpose distributed computing systems [21], Casavant and Kuhl include queueing theoretic as a basic category of task allocation algorithms which can be used to arrive at an assignment of processes to processors.…”

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confidence: 99%

General queuing model for optimal seamless delivery of payload processing in multi-core processors

et al. 2017

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Recent developments in unmanned aerial systems (UAS) provide new opportunities in remote sensing application. In contrast to satellite and conventional (manned) aerial tasks, UAS flights can be operated in a very short period of time. UAS can also be more specifically focused toward a given task such as crop reconnaissance or electric line tower inspection. For some applications, the delivery time of the remote sensing results is crucial. The current three-phase procedure of data acquisition, data downloading and data processing, performed sequentially in time, represents a drawback that reduces the benefits of using unmanned aerial systems. In this paper, we present a parallel processing strategy, based on queuing theory, in which the data processing phase is performed on board in parallel with data acquisition. The unmanned aerial system payload has been enlarged with low-cost, lightweight, multicore boards to facilitate remote sensing data processing during flight. The storage of the raw sensing data is also done for possible further analysis; however, the ultimate decision support information can be seamless delivered to the customer upon landing. Furthermore, text alarms and limited imagery can also be provided during flight.

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confidence: 99%

General queuing model for optimal seamless delivery of payload processing in multi-core processors

et al. 2017

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“…Such a strategy seems to provide satisfiable location privacy. However, many works have pointed out that, without accessing GPS data, Apps can still infer private information, such as input on touch-screen [16] and motion status [17], through the data collected by general embedded sensors in mobile devices [18][19][20][21][22][23][24][25][26][27]. Unfortunately, few works try to utilize built-in sensors like accelerometer, magnetometer, gyroscope, and so on to do localization.…”

Section: Introductionmentioning

confidence: 99%

Location Privacy Leakage through Sensory Data

Liang

Cai

Han

et al. 2017

Security and Communication Networks

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Mobile devices bring benefits as well as the risk of exposing users' location information, as some embedded sensors can be accessed without users' permission and awareness. In this paper, we show that, only by using the data collected from the embedded sensors in mobile devices instead of GPS data, we can infer a user's location information with high accuracy. Three issues are addressed which are route identification, user localization in a specific route, and user localization in a bounded area. The Dynamic Time Warping based technique is designed and we develop a Hidden Markov Model to solve the localization problem. Real experiments are performed to evaluate our proposed methods.

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“…The authors employed condition monitoring in the design improvement process by evaluating the system performance, detecting system failure and estimating system heath status. Qiu et al designed a task-efficient sink node (TESN) based on embedded multi-core system-on-chip (SoC) for IoT [6]. The authors also proposed master-slave architecture and a weighted-least connection (WLC) task schedule strategy to allocate the tasks for slave cores in order to achieve lower congestion and better load balance for the parallel computing.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of a Real-Time Monitoring System for Multiple Lead–Acid Batteries Based on Internet of Things

et al. 2017

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Abstract:In this paper, real-time monitoring of multiple lead-acid batteries based on Internet of things is proposed and evaluated. Our proposed system monitors and stores parameters that provide an indication of the lead acid battery's acid level, state of charge, voltage, current, and the remaining charge capacity in a real-time scenario. To monitor these lead-acid battery parameters, we have developed a data acquisition system by building an embedded system, i.e., dedicated hardware and software. The wireless local area network is used as the backbone network. The information collected from all the connected battery clients in the system is analyzed in an asynchronous transmission control protocol/user datagram protocol-based C server program running on a personal computer (server) to determine important parameters like the state of charge of the individual battery, and if required, appropriate action can be taken in advance to prevent excessive impairment to the battery. Further, data are also displayed on an Android mobile device and are stored in an SQL server database. We have developed a real prototype to devise an end product for our proposed system.

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A task-efficient sink node based on embedded multi-core SoC for Internet of Things

Cited by 24 publications

References 36 publications

General queuing model for optimal seamless delivery of payload processing in multi-core processors

General queuing model for optimal seamless delivery of payload processing in multi-core processors

Location Privacy Leakage through Sensory Data

Design and Development of a Real-Time Monitoring System for Multiple Lead–Acid Batteries Based on Internet of Things

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