Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

Perera, Charith; Zaslavsky, Arkady; Liu, Chi Harold; Compton, Michael; Christen, Peter; Georgakopoulos, Dimitrios

doi:10.1109/jsen.2013.2282292

Cited by 175 publications

(88 citation statements)

References 26 publications

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“…Perera et al [11] proposed a method for sensor searching, selection, called CASSARAM, in order to select the sensors in an efficient way by considering their capabilities and functions. The sensor searching considered user priorities.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Clustering of Sensors using a Meta Heuristic Algorithm for IoT

Deshpande¹

2017

IJCA

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In recent years, with the increase in the devices like Smartphone's, Global Positioning System (GPS) monitoring devices, tablets, surveillance cameras etc...The numbers of devices that have been connected to the Internet have crossed the world population. The decrease in the cost of the devices, with increase in their capability was one of the major reasons for this change. With the increase in the devices connected to the network, there is enormous data that is produced by the devices on daily basis which has caused problem of selecting the devices (sensors) based on the data produced by them. The users have to face the problem of manually selecting the set of sensors required by them. To target this problem, the proposed work has implemented, the ant based clustering algorithm with some novel changes in order to provide the user with the optimal set of sensors. The algorithm intakes the whole sensor space and outputs the clusters from which the user can choose the one which is most optimal according to the user needs. The antclust algorithm strives to provide optimal solution and maintain its performance while fighting the dynamicity of the Internet. The proposed work shows better scalability and adaptability. Also has faster search of the sensors than the other works previously proposed.

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Section: Related Workmentioning

confidence: 99%

“…The index value was calculated using weighted Euclidean distance in multidimensional space technique. Perera et al [11] did not consider the changes that occur in IoT. In other words, they ignored the performance of their system in dealing with the changes (e.g.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Clustering of Sensors using a Meta Heuristic Algorithm for IoT

Deshpande¹

2017

IJCA

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“…The authors present a solution to infer situations from contexts acquired from various sources based on a cloud service defined as yNFerence-as-a-Service (yNFaaS). Perera et al present in [16] a contextaware sensor model designed for efficiently selecting relevant sensor information from enormous amounts of data acquired with embedded sensors of yoT. The paper highlights the importance of combining semantic and quantitative reasoning in searching and selecting information.…”

Section: Related Workmentioning

confidence: 99%

Context-aware Control Platform for Sensor Network Integration in IoT and Cloud

Merezeanu¹,

Vasilescu²,

Dobrescu³

2016

SIC

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“…Other approaches are distributed such as [5,6]. However, they are complex or do not support efficient updating for indexing structure once the indexes are constructed [7] which make them not applicable for wide-scale deployment of IoT.…”

Section: Introductionmentioning

confidence: 99%

Distributed spatial indexing for the Internet of Things data management

Fathy

Barnaghi

Tafazolli

2017

2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)

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Abstract-The Internet of Things (IoT) has become a new enabler for collecting real-world observation and measurement data from the physical world. The IoT allows objects with sensing and network capabilities (i.e. Things and devices) to communicate with one another and with other resources (e.g. services) on the digital world. The heterogeneity, dynamicity and ad-hoc nature of underlying data, and services published by most of IoT resources make accessing and processing the data and services a challenging task. The IoT demands distributed, scalable, and efficient indexing solutions for large-scale distributed IoT networks. We describe a novel distributed indexing approach for IoT resources and their published data. The index structure is constructed by encoding the locations of IoT resources into geohashes and then building a quadtree on the minimum bounding box of the geohash representations. This allows to aggregate resources with similar geohashes and reduce the size of the index. We have evaluated our proposed solution on a large-scale dataset and our results show that the proposed approach can efficiently index and enable discovery of the IoT resources with 65% better response time than a centralised approach and with a high success rate (around 90% in the first few attempts).

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Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

Cited by 175 publications

References 26 publications

An Efficient Clustering of Sensors using a Meta Heuristic Algorithm for IoT

An Efficient Clustering of Sensors using a Meta Heuristic Algorithm for IoT

Context-aware Control Platform for Sensor Network Integration in IoT and Cloud

Distributed spatial indexing for the Internet of Things data management

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