Parallel and distributed neurocomputing with wireless sensor networks

Serpen, Gürsel; Liu, Linqian

doi:10.1016/j.neucom.2015.08.074

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…In-network processing involves the processing of data as it travels via the WSN to the sink. For example, Serpen and Liu [ 13 ] demonstrated through simulations, a case study that leverages existing WSNs as a parallel and distributed hardware platform to implement computations for artificial neural network algorithms. The results of their simulation suggest that the WSN-based neurocomputing architecture is a feasible alternative for realizing parallel and distributed computation of artificial neural network algorithms.…”

Section: Distributed Computing In Wireless Sensor Networkmentioning

confidence: 99%

“…Other drawbacks include low reliability, longer response time, high bandwidth cost, low level data security and privacy [ 7 , 9 , 10 , 11 , 12 ]. Some works in the literature have demonstrated the feasibility of distributed computing in WSN and its promises of gain in performance and lower power consumption [ 5 , 7 , 11 , 12 , 13 ]. By performing more local computation, limiting exchanges only between neighboring nodes and reducing the number of messages that need to be transmitted, distributed computing in WSN has the potential of providing a solution to the drawbacks of the centralized approach [ 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…In some recent studies, distributed computing in WSNs has been proven to be much more efficient in terms of energy and performance than the traditional centralized computation schemes used in most WSN monitoring applications. However, most of these studies are validated based on simulations [ 11 , 12 , 13 ]. Our work provides a complete systemic approach, involving simulations, physical system design, field deployment and experimental validation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

Nkemeni

Mieyeville

Tsafack

2020

Sensors

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Wireless Sensor Network (WSN) applications that favor more local computations and less communication can contribute to solving the problem of high power consumption and performance issues plaguing most centralized WSN applications. In this study, we present a fully distributed solution, where leaks are detected in a water distribution network via only local collaborations between a sensor node and its close neighbors, without the need for long-distance transmissions via several hops to a centralized fusion center. A complete approach that includes the design, simulation, and physical measurements, showing how distributed computing implemented via a distributed Kalman filter improves the accuracy of leak detection and the power consumption is presented. The results from the physical implementation show that distributed data fusion increases the accuracy of leak detection while preserving WSN lifetime.

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Section: Distributed Computing In Wireless Sensor Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

Nkemeni

Mieyeville

Tsafack

2020

Sensors

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“…In other words, research has not explained the current situations and positions of specific technological fields on a technology map or identified technology hotspots from a comprehensive perspective. Furthermore, development of neurocomputing technology involves numerous technological fields [2,14,15], and such technology has limitless development potential. A hotspot refers to a study repeatedly cited in other different studies [16].…”

Section: Introductionmentioning

confidence: 99%

Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends

Chang¹,

Fan²

2020

Sustainability

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In recent years, development in the fields of big data and artificial intelligence has given rise to interest among scholars in neurocomputing-related applications. Neurocomputing has relatively widespread applications because it is a critical technology in numerous fields. However, most studies on neurocomputing have focused on improving related algorithms or application fields; they have failed to highlight the main technology hotspots and development trends from a comprehensive viewpoint. To fill the research gap, this study adopts a new viewpoint and employs technological fields as its main subject. Neurocomputing patents are subjected to network analysis to construct a neurocomputing technology hotspot. The results reveal that the neurocomputing technology hotspots are algorithms, methods or devices for reading or recognizing printed or written characters or patterns, and digital storage characterized by the use of particular electric or magnetic storage elements. Furthermore, the technology hotspots are discovered to not be clustered around particular fields but, rather, are multidisciplinary. The applications that combine neurocomputing with digital storage are currently undergoing the most extensive development. Finally, patentee analysis reveal that neurocomputing technology is mainly being developed by information technology corporations, thereby indicating the market development potential of neurocomputing technology. This study constructs a technology hotspot network model to elucidate the trend in development of neurocomputing technology, and the findings may serve as a reference for industries planning to promote emerging technologies.

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“…Recently, distributed control problems for a group of dynamical systems have attracted much attentions due to its wide applications, including coordination for multi-agent systems [1]- [5], synchronization in complex networks [6,7], distributed computing in sensor networks [8]- [10], multi-fingered hand grasping and manipulation [11,12]. Formation control is a significant issue in the distributed control field.…”

Section: Introductionmentioning

confidence: 99%

Time-varying formation tracking of multiple manipulators via distributed finite-time control

Guan

Yang

et al. 2016

Neurocomputing

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Comparing with traditional fixed formation for a group of dynamical systems, time-varying formation can produce the following benefits: i) covering the greater part of complex environments; ii) collision avoidance. This paper studies the time-varying formation tracking for multiple manipulator systems (MMSs) under fixed and switching directed graphs with a dynamic leader, whose acceleration cannot change too fast. An explicit mathematical formulation of time-varying formation is developed based on the related practical applications. A class of extended inverse dynamics control algorithms combining with distributed sliding-mode estimators are developed to address the aforementioned problem. By invoking finite-time stability arguments, several novel criteria (including sufficient criteria, necessary and sufficient criteria) for global finite-time stability of MMSs are established. Finally, numerical experiments are presented to verify the effectiveness of the theoretical results. (Z.-H Guan).1 required without losing system stability, which products the following benefits: i) covering the greater part of complex environments; ii) collision avoidance. However, to the authors' knowledge, the mathematical formulations of time-varying formation tracking are still not clear, which impedes the development and applications of the relative technologies.On the other hand, networked robotic systems have been broadly studied due to their various advantages, including flexibility, adaptivity, fault tolerance, redundancy, and the possibility to invoke distributed sensing and actuation [21]. Many control algorithms for global asymptotic tracking of networked robotic systems described by Euler-Lagrange systems can be found in the literature. Adaptive control approaches are proposed to address the leader-follower and leaderless coordination problems for multi-manipulator systems based on graph theory [22,23]. Distributed containment control had been developed for global asymptotic stability of Lagrangian networks under directed topologies containing a spanning tree [24]. Some distributed average tracking algorithms had been developed invoking extended PI control and applied to networked Euler-Lagrange systems [25]. The task-space tracking control problems of networked robotic systems under strongly connected graphs without task-space velocity measurements had been investigated [26]. In presence of kinematic and dynamic uncertainties, task-space synchronization had been addressed for multiple manipulators under strong connected graphs by invoking passivity control [27] and adaptive control [28]. All of the aforementioned control algorithms produce global asymptotic tracking of robotic manipulators, which implies that the system trajectories converge to the equilibrium as time goes to infinity. Finite-time stabilization of dynamical systems may give rise to fast transient and high-precision performances besides finite-time convergence to the equilibrium, and a lot of work has been done in the last several years [29]-[31].Motivated b...

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Parallel and distributed neurocomputing with wireless sensor networks

Cited by 6 publications

References 36 publications

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends

Time-varying formation tracking of multiple manipulators via distributed finite-time control

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