Measuring Semantic-Based Structural Similarity in Multi-Relational Networks

International Journal of Distributed Sensor Networks

et al. 2016

As the technology of the Internet of Things (IoT) becomes more widely used in large-scale monitoring networks, this paper proposes an optimized obtaining strategy (OFS) for large-scale sensor monitoring networks. First, because of the large-scale features of sensor node network, this paper proposes a large-scale monitoring network area clustering optimization strategy. Second, based on the characteristics of regular changes in the sensed data in large-scale monitoring networks, this paper proposes a strategy for acquiring sensor data based on an adaptive frequency conversion. The OFS optimization strategy can prolong network lifetime, reduce the transmission bandwidth resources, and reduce average energy consumption of the cluster head and network energy consumption.

Section: Network Energy Consumption Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimized Data Obtaining Strategy for Large-Scale Sensor Monitoring Networks

International Journal of Distributed Sensor Networks

et al. 2016

“…Nowadays applications using the sensor network monitoring strategy are being more and more widely used, such as in forest fire monitoring systems, CitySee, real time CO 2 monitoring systems, real time shortest path for drivers [ 1 ], a complex embedded system-CPS [ 2 ], graph similarity issue [ 3 ], digital library services [ 4 ], data security [ 5 ], the development of the Internet of Things [ 6 , 7 ], the development of the Web of Things [ 8 ], Semantic Link Network (SLN) [ 9 , 10 ], different bird species’ behavior research, air quality inspection [ 11 ] and so on. Big data analytics for these applications has become more and more essential [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Geometry-Based Distributed Spatial Skyline Queries in Wireless Sensor Networks

Song

et al. 2016

Sensors

Algorithms for skyline querying based on wireless sensor networks (WSNs) have been widely used in the field of environmental monitoring. Because of the multi-dimensional nature of the problem of monitoring spatial position, traditional skyline query strategies cause enormous computational costs and energy consumption. To ensure the efficient use of sensor energy, a geometry-based distributed spatial query strategy (GDSSky) is proposed in this paper. Firstly, the paper presents a geometry-based region partition strategy. It uses the skyline area reduction method based on the convex hull vertices, to quickly query the spatial skyline data related to a specific query area, and proposes a regional partition strategy based on the triangulation method, to implement distributed queries in each sub-region and reduce the comparison times between nodes. Secondly, a sub-region clustering strategy is designed to group the data inside into clusters for parallel queries that can save time. Finally, the paper presents a distributed query strategy based on the data node tree to traverse all adjacent sensors’ monitoring locations. It conducts spatial skyline queries for spatial skyline data that have been obtained and not found respectively, so as to realize the parallel queries. A large number of simulation results shows that GDSSky can quickly return the places which are nearer to query locations and have larger pollution capacity, and significantly reduce the WSN energy consumption.

“…However, such an approach treats each word individually, ignoring the relations between the text and the semantics that are hidden in the context keywords. Hence, we utilize the latent semantic analysis approach instead [28,29]. Semantic analysis shows its superiority on such a task as it organizes the text into a space semantic structure that keeps the relationships between the text and the words.…”

Section: Feature Weight Calculationmentioning

confidence: 99%

Automatic transfer learning for short text mining

Yang

Zhang

2017

J Wireless Com Network

As a new emerging technique, transfer learning enjoys the advantage of integrating the well-learnt knowledge from another related work to facilitate an improved learning result of one task. Most of the existing transfer learning methods are designed for long texts and short texts. However, the latter one distinguishes from the former one in terms of its sparse nature, noise words, syntactical structure, and colloquial terminologies used. A transfer learning algorithm called automatic transfer learning (AutoTL) is proposed for short text mining. By transferring knowledge automatically learnt from the online information, the proposed method enables training data to be selected automatically. Furthermore, it does not make any a priori assumption about probability distribution. Our experimental results on 20Newsgroups, Simulated Real Auto Aviation, and Reuter-21578 validate the higher performance of the proposed AutoTL over several state-of-of-the-art methods.