In-network wireless sensor network query processors: State of the art, challenges and future directions

Jabeen, Farhana; Nawaz, Syed Junaid

doi:10.1016/j.inffus.2015.01.007

Cited by 31 publications

(17 citation statements)

References 55 publications

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“…Technology advanced the internet of objects in this sector can help to reduce the consumption of resources related to building (electricity, water) as well as to improve the level of satisfaction of humans inhabiting, or workers in office buildings or tenants of private houses [35]. Solutions such as smart meters are becoming increasingly popular for measuring energy consumption and transmission, by phone or by current holder in line to the manager of metering data.…”

Section: The Intelligent Building and Instrumentation Of The Buildingsmentioning

confidence: 99%

Internet of Things: Visions, Technologies, and Areas of Application

Rghioui¹

2017

ACIS

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Abstract:The internet of things (IoT), also called internet of all, is a new paradigm that combines several technologies such as computers, the internet, sensors network, radio frequency identification (RFID), communication technology and embedded systems to form a system that links the real worlds with digital worlds. IoT is recognized as one of the most important areas of the technology of the future and wins attention from a wide range of industries. Currently, a large number of smart objects and different types of devices are interconnected and communicate using the internet protocol. With an increase in the deployment of smart objects, the internet of things should have a significant impact on human life in the near future. To understand the development of the IoT, this paper reviews the current research of the IoT, key technologies, the main applications of the IoT in various field, and identifies research challenges. In this article, we present the main visions and the scope of the internet of things and the futuristic research and its fields of application. the main contribution of this review article is that it summarizes the current state of the IoT technology in several areas, and also the applications of IoT that cause side effects on our environment for monitoring and evaluation of the impact of human activity on the environment around us, and also provided an overview of some of the main challenges of IoT, and also shows that application of the IoT. This article presents not only the problems and challenges of IoT, but also solutions that help overcome some of the problems and challenges.

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Section: The Intelligent Building and Instrumentation Of The Buildingsmentioning

confidence: 99%

Internet of Things: Visions, Technologies, and Areas of Application

Rghioui¹

2017

ACIS

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“…An efficient implementation of a practical WSN, however, is not a trivial task. Even on small scale WSNs, the amount of data that can be sampled by the sensor nodes is considerably more than what can be sustainably transmitted using current WSN radios with current battery technology [2,3,4]. …”

Section: Introductionmentioning

confidence: 99%

“…This significantly reduces the amount of data that needs to be transmitted. From now on, we will call this online processing [2]. …”

Section: Introductionmentioning

confidence: 99%

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QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms

Zwartjes

Havinga

Smit

et al. 2016

Sensors

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In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

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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%