On-demand Customizable Wireless Sensor Network

Miyazaki, Toshiaki; Li, Peng; Guo, Song; Kitamichi, Junji; Hayashi, Takafumi; Tsukahara, Takehiko

doi:10.1016/j.procs.2015.05.089

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…In this phase, conditions of the IoT edge sensor board such as battery level, board temperature and confidence level measured by the sensors is a very important issue, since bad conditions will reduce the accuracy of measured data. For example, in order to read the temperature sensor, the study in [51] recommended to use the sensor board with battery level that should be greater than or equal to a specified threshold.…”

Section: ) Rdcm -Sensing Phasementioning

confidence: 99%

RDCM: An Efficient Real-Time Data Collection Model for IoT/WSN Edge With Multivariate Sensors

2019

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In the application of the Internet of Things (IoT), a sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery lifetime , since there are additional data transmissions that must be supported by the board causing it to drain the battery much faster than the sensor board with one sensor. The main aim of this paper is to increase the battery life of the IoT sensor node. To do so, this paper proposes an efficient real-time data collection model for multivariate sensors in IoT/WSN applications named RDCM. The general structure of RDCM is composed of two main levels: the IoT sensor board level and the fusion center level. The IoT sensor board level is implemented in real time by all the IoT sensor boards simultaneously in each cycle and fusion center level is executed by the fusion center. The IoT sensor board level includes various stages as follows: check the physical conditions of the IoT edge device (board) stage and update data strategy stage, data validation stage, and sensed data reduction stage. The average of the total percentage of energy saved by the application of RDCM to real-time data sets injected with various percentages of errors for all nodes is 98%. In summary, the RDCM has a very high performance in terms of energy consumption compared with other algorithms. This paper concludes with the limitation of the current study and some further research opportunities.

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Section: ) Rdcm -Sensing Phasementioning

confidence: 99%

RDCM: An Efficient Real-Time Data Collection Model for IoT/WSN Edge With Multivariate Sensors

2019

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“… Support the "separation of concern" : Regarding the research efforts to study new challenging technologies with potential benefits for WSAN systems, there is a trend for decoupling the WSAN from the application [ 91 ]. Also, several other studies, e.g., for WSAN nodes’ scenario reconfiguration [ 92 , 93 ], for strategies on WSAN power management [ 94 ], for data acquisition development, or for implementing technologies like Wakeup-Radio (WuR) [ 95 ], and many others, are indicatory cases where the decoupling from the wireless networking is required. This decoupling is practically achieved either by the addition of more than one MCU on-board, or by the usage of FPGAs, or by the addition of extra RF communication circuits or modules.…”

Section: The Sensotube Architecturementioning

confidence: 99%

SensoTube: A Scalable Hardware Design Architecture for Wireless Sensors and Actuators Networks Nodes in the Agricultural Domain

Piromalis

Arvanitis

2016

Sensors

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Wireless Sensor and Actuators Networks (WSANs) constitute one of the most challenging technologies with tremendous socio-economic impact for the next decade. Functionally and energy optimized hardware systems and development tools maybe is the most critical facet of this technology for the achievement of such prospects. Especially, in the area of agriculture, where the hostile operating environment comes to add to the general technological and technical issues, reliable and robust WSAN systems are mandatory. This paper focuses on the hardware design architectures of the WSANs for real-world agricultural applications. It presents the available alternatives in hardware design and identifies their difficulties and problems for real-life implementations. The paper introduces SensoTube, a new WSAN hardware architecture, which is proposed as a solution to the various existing design constraints of WSANs. The establishment of the proposed architecture is based, firstly on an abstraction approach in the functional requirements context, and secondly, on the standardization of the subsystems connectivity, in order to allow for an open, expandable, flexible, reconfigurable, energy optimized, reliable and robust hardware system. The SensoTube implementation reference model together with its encapsulation design and installation are analyzed and presented in details. Furthermore, as a proof of concept, certain use cases have been studied in order to demonstrate the benefits of migrating existing designs based on the available open-source hardware platforms to SensoTube architecture.

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“…It is made by the convergence of sensor, nested computation, networks and wireless communication technologies [3], and it is a new research area of computer science and technology and has a wide application future [4]. A WSN differs from traditional network in four principal aspects.…”

Section: Introductionmentioning

confidence: 99%