An Efficient Preference-Based Sensor Selection Method in Internet of Things

Zheng, Zengwei; Tao, Yanyun; Chen, Yuanyi; Zhu, Fengle; Chen, Dan

doi:10.1109/access.2019.2953045

Cited by 6 publications

(7 citation statements)

References 36 publications

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“…Assuming all possible inputs are equally likely, and then all partitioning cases are equally likely. We will take each value in the interval, 1 with equal probability, so the average time complexity of the quick sort algorithm is as follows…”

Section: The Sensor Recommendation Modelmentioning

confidence: 99%

“…In previous studies, many scholars used local optimal solutions of sensor data set to improve traditional multi-criteria decision analysis algorithms, such as the fast non-dominated sorting algorithm and dynamic Skyline algorithm, but the high time complexity of these algorithms greatly affects the response time of the sensor recommendation. In order to conquer this problem, we proposed the improved fast non-dominated sorting algorithm 1 last year. This algorithm combines the idea of quick sort algorithm to improve the fast non-dominated sorting algorithm, and it reduces the time complexity of the fast non-dominated sorting algorithm from O ( m n 2 ) down to O ( n log mn ) .…”

Section: Sensor Recommend Modelmentioning

confidence: 99%

“…An efficient preference-based sensor selection algorithm: this algorithm 1 narrows the sensor data set based on the user’s preferences of sensor’s attributes, and it sets the number of sensors input to the improved fast non-dominated sorting algorithm. Decreasing the number of processing sensors makes the algorithm’s response time lower than the original algorithm.…”

Section: Experiments Evaluationmentioning

confidence: 99%

“…Due to the development of communication technology and the emergence of various intelligent objects, a large number of physical objects are connected to the IoT. 1 Cisco Corp. predicts that the number of objects in the IoT will be increased to 50 billion in 2020. And a large amount of sensor data are expected to be generated continuously.…”

Section: Introductionmentioning

confidence: 99%

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A graph-based sensor recommendation model in semantic sensor network

Chen

Lin

et al. 2022

International Journal of Distributed Sensor Networks

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In the past few years, introducing ontology to describe the concepts and relationships between different entities in semantic sensor network enhances the interoperability between entities. Existing works mostly based on SPARQL retrieval ignore the user’s specific requirements of sensor attributes. Therefore, the recommendation results cannot satisfy the user’s needs. In this article, we propose a graph-based sensor recommendation model. The model mainly includes two parts: (1) Filtering nodes in data graph. In addition to using the traditional graph matching algorithm, we propose a threshold pruning algorithm to narrow the matching scope and improve the matching efficiency. (2) Recommending top- k sensors. We use the improved fast non-dominated sorting algorithm to obtain the local optimal solutions of sensor data set, and we apply the simple additive weight algorithm to characterize and sort local optional solutions. Finally, we recommend the top- k sensors to the user. By comparison, the graph-based sensor recommendation algorithm meets user’s needs more than other algorithms, and experiments show that our model outperforms several baselines in terms of both response time and precision.

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Section: The Sensor Recommendation Modelmentioning

confidence: 99%

Section: Sensor Recommend Modelmentioning

confidence: 99%

Section: Experiments Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A graph-based sensor recommendation model in semantic sensor network

Chen

Lin

et al. 2022

International Journal of Distributed Sensor Networks

Self Cite

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“…This requires use of distributed gateways connected to a server, where each gateway responds to the users’ local requests. There is lower time complexity of the dynamic skyline algorithm, but it does require users to input the ideal values of sensor properties ( Zheng et al, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

A bio-inspired adaptive model for search and selection in the Internet of Things environment

Bouarourou

Boulaalam

Nfaoui

2021

PeerJ Computer Science

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The Internet of Things (IoT) is a paradigm that can connect an enormous number of intelligent objects, share large amounts of data, and produce new services. However, it is a challenge to select the proper sensors for a given request due to the number of devices in use, the available resources, the restrictions on resource utilization, the nature of IoT networks, and the number of similar services. Previous studies have suggested how to best address this challenge, but suffer from low accuracy and high execution times. We propose a new distributed model to efficiently deal with heterogeneous sensors and select accurate ones in a dynamic IoT environment. The model’s server uses and manages multiple gateways to respond to the request requirements. First, sensors were grouped into three semantic categories and several semantic sensor network types in order to define the space of interest. Second, each type’s sensors were clustered using the Whale-based Sensor Clustering (WhaleCLUST) algorithm according to the context properties. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was improved to search and select the most adequate sensor matching users’ requirements. Experimental results from real data sets demonstrate that our proposal outperforms state-of-the-art approaches in terms of accuracy (96%), execution time, quality of clustering, and scalability of clustering.

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Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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As today's agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading the farming practices by shifting towards Smart Farming (SF) -utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify the suitable technologies and best practices for a particular application. This paper provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers' contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, the paper discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

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An Efficient Preference-Based Sensor Selection Method in Internet of Things

Cited by 6 publications

References 36 publications

A graph-based sensor recommendation model in semantic sensor network

A graph-based sensor recommendation model in semantic sensor network

A bio-inspired adaptive model for search and selection in the Internet of Things environment

Technologies Driving the Shift to Smart Farming: A Review

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