An experimental study on geospatial indexing for sensor service discovery

Wang, Wei; De, Suparna; Cassar, Gilbert; Moessner, Klaus

doi:10.1016/j.eswa.2014.11.058

Cited by 38 publications

(44 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our approach can successfully answer all sets of different queries comparing to the proposed solution in [11] which can not find responses to all requested queries (this work is discussed in Section II).…”

Section: B Success Ratementioning

confidence: 99%

“…With regards to the other similar works in this area, recent experiments that have been conducted in [11] using R-tree with Figure 6. Ratio of success rate to answer queries geohash can answer successfully roughly 712 queries (12%) of the total number of requested queries on a dataset with a size of 10, 000 resources.…”

Section: B Success Ratementioning

confidence: 99%

“…IR) where the data values are stored given the location and type key attributes. The main advantages of our approach comparing to using R-tree with geohash in [11] is that we identify the MBR of each geohash in a simple way taking into account that the indexing is extendible to cover the entire globe (longitude has a range of [−180…”

Section: A Spatial Indexingmentioning

confidence: 99%

“…Answering queries utilises the constructed index to efficiently find the region and the location of the resources that should be accessed. Different types of indexing methods and solutions have been proposed such as [5,10,11,12]. Ratnasamy et al [13] propose Geographic Hash Table (GHT). GHT is a distributed indexing that is constructed by hashing types of events as keys and geographical coordinates as values.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Wang et al [11] introduce R-Tree-based spatial indexing approach to index Minimum Bounding Rectangles (MBRs). Each node in the tree is associated with MBR that represents the location range of its child nodes.…”

Section: Background and Related Workmentioning

confidence: 99%

See 4 more Smart Citations

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)

View full text Add to dashboard Cite

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).

show abstract

Section: B Success Ratementioning

confidence: 99%

Section: B Success Ratementioning

confidence: 99%

Section: A Spatial Indexingmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

See 3 more Smart Citations

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)

View full text Add to dashboard Cite

show abstract

A survey on indexing techniques for mobility in Internet of Things': Challenges, performances, and perspectives

2020

View full text Add to dashboard Cite

SummaryMobile nodes arouse new challenges that expand the performance in those environments. The nodes of Internet of Things (IoT) generate a large amount of data, which have to be stored and processed in a seamless and interpretable form by indexing. Therefore, indexing is a challenge in IoT, particularly when the nodes are mobile. The current indexing techniques dedicated to mobile environments are unsatisfying because the data are transmitted from different locations within different time periods and randomness of sensor movement. Although huge research efforts have been dedicated to this subject amid the last decades, little consideration has been paid for the research summarization and guidance. The objective of this survey is to identify the relationship between activities of mobile sensors in the context of IoT, that concerned on transferring and collecting data as well as the effectiveness of indexing techniques. The contribution of this review is to investigate the techniques of mobile IoT nodes, to find the source of challenges that adversely affect the index effectiveness. In‐depth investigation and analyses of approaches to apply IoT mobile nodes will enable the researcher to understand the behavior of mobile environments to extract the deformities that adversely affect the indexing effectiveness. The analyzed approaches are meta‐heuristic, spatial‐temporal indexing, and prediction model approach. Each approach is analyzed by discussing the features and limitations from the mobility perspectives. Furthermore, the indexing techniques are analyzed according to their features and limitations and mobile IoT indexing requirements. In conclusion, recognize and contemplate different open issues that got little focus or still unknown at this point.

show abstract