Exploiting correlations for efficient content-based sensor search

Mietz, Richard; Römer, Kay Uwe

doi:10.1109/icsens.2011.6127082

Cited by 8 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ostermaier et al [15] proposed a real-time search engine Dyser, which supported the use of existing Web infrastructure to publish sensors dataset and retrieve the specified sensor. Mietz et al [16] proposed Bayesian network model with using the highly correlated output characteristics of many sensors to learn relevant structures from the historical data of sensors, which could get the requesting probability of sensor without knowing the current sensor output then it recommended sensor with high probability to the user. Truong et al [17] proposed a lightweight prediction model based on fuzzy logic algorithm to estimate the probability of sensor option and realize content-based sensor search in the Internet of things with low communication overhead and computational efficiency.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

While it is well understood that the Internet of things (IoT) can facilitate numerous applications (e.g., environmental supervision, forest fire prevention and Intelligent farming), it also brings a significant challenge for efficiently selecting sensors that meet users' preference and specific requirement from millions of heterogeneous sensors. In this paper, we propose an improved fast nondominated sorting algorithm for efficiently preference-based sensor selection in IoT. Specifically, this proposed method mainly includes three parts: 1) Offline constructing R-tree to search sensor resources and narrowing the size of dataset according to user's preference; 2) Using an improved fast nondominated sorting approach to get nondominated front; 3) Employing TOPSIS to characterize every sensor option of the nondominated front. In order to illustrate the usability of the model, we conduct experiments on several simulation datasets. Experimental results show that this method outperforms several baselines in terms of both response time and accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Besides, the traversal search mode has brought huge communication cost to the search system. To achieve low overhead search for dynamic locations of large-scale sensors, Mietz and Römer [16], developed the ECSS search system which built the prediction model of sensor location based on Bayesian Network theory to estimate the matching probability of the current sensor location with the requested location state, and then performed the sensor search process according to the matching probabilities of sensors. Shen et al [17], proposed a user-center search system, SCPS, which predicted the locations of entities by exploring the social relationships between users with mobile sensors to search for entities that met the location requirements.…”

Section: Related Workmentioning

confidence: 99%

“…Step 3: Calculate fitness function f P k i of each particle under the condition of current network structure parameter in the kth iteration according to equation (14), and then determine the local optimal scheme of each particle in the kth iteration L k i and the global optimal scheme of particle group G k . Step 4: Update optimization scheme P k+1 i , search speed V k+1 i (k ← k + 1), of each particle based on equation (15) and (16).…”

Section: Model Optimizingmentioning

confidence: 99%

“…Recent relevant research, like MAX [13], Microsearch [14], and Snoogle [15], drew on the idea of traditional Internet search engine that pre-stored the static keyword index information about the observed entity in the sensor, which perform an ergodic search for match sensors with dynamically varying state at the cost of large communication overhead. Aiming at addressing this issue, several search mechanisms with sensor state prediction methods were proposed in literature [16]- [23], where candidate entities were preliminarily chosen by predicting the current positions or observation content state of sensors in advance, and then the search system verified the actual state of candidates by accessing their associated sensors to find match entities. Thus only a fraction of sensors need to be accessed, so as to greatly reduce the communication overhead caused by existing ergodic search methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SDU: State-Based Dual-Mode Sensor Search Mechanism Toward Internet of Things

Zhang

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The wide-scale deployments and interconnections of massive diverse sensors in Internet of Things (IoT) enable the mapping of multimedia entity state data from the physical world into the cyberspace. Sensor search technologies further facilitate the access to multimedia entity state data. However, existing sensor search mechanisms overlook the search requirements for historical state information on entities. Besides, previous current state based sensor search mechanisms realize the low-cost sensor search through predicting the sensor current state based on shallow learning theory, which leads to limited prediction accuracy and higher communication overhead. Aiming at these shortages, an efficient dual-mode sensor search mechanism is firstly designed, including the current state based and historical state based search methods, to fulfill the search needs for current and historical state information on entities. Then based on deep learning theory, a high-accuracy data prediction method towards sensor current state is presented to improve the insufficient accuracy of existing prediction methods. Moreover, a lightweight data representation method is devised to fit the sensor historical state, thereby accomplishing space-saving and low computational overhead historical state based sensor search. Simulation results show that the proposed sensor search mechanism combined with the designed prediction method and approximate representation method can effectively enhance the communication overhead, recall ratio and precision ratio performances. INDEX TERMS Internet of Things, multimedia data, deep learning, sensor search, dual-mode.

show abstract

“…The prediction model used in Dyser periodically computes the states of entities based on the measurement data from sensors. The work in Mietz et al [ 80 ] applies Bayesian Network to automatically infer the states of the entities. SPITFIRE [ 75 ] infers states of things from the embedded sensors based on their semantic descriptions.…”

Section: Classification Viewpoint—contents Being Searchedmentioning

confidence: 99%

Search Techniques for the Web of Things: A Taxonomy and Survey

Zhou

Wang

et al. 2016

Sensors

View full text Add to dashboard Cite

The Web of Things aims to make physical world objects and their data accessible through standard Web technologies to enable intelligent applications and sophisticated data analytics. Due to the amount and heterogeneity of the data, it is challenging to perform data analysis directly; especially when the data is captured from a large number of distributed sources. However, the size and scope of the data can be reduced and narrowed down with search techniques, so that only the most relevant and useful data items are selected according to the application requirements. Search is fundamental to the Web of Things while challenging by nature in this context, e.g., mobility of the objects, opportunistic presence and sensing, continuous data streams with changing spatial and temporal properties, efficient indexing for historical and real time data. The research community has developed numerous techniques and methods to tackle these problems as reported by a large body of literature in the last few years. A comprehensive investigation of the current and past studies is necessary to gain a clear view of the research landscape and to identify promising future directions. This survey reviews the state-of-the-art search methods for the Web of Things, which are classified according to three different viewpoints: basic principles, data/knowledge representation, and contents being searched. Experiences and lessons learned from the existing work and some EU research projects related to Web of Things are discussed, and an outlook to the future research is presented.

show abstract

Exploiting correlations for efficient content-based sensor search

Cited by 8 publications

References 7 publications

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

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

SDU: State-Based Dual-Mode Sensor Search Mechanism Toward Internet of Things

Search Techniques for the Web of Things: A Taxonomy and Survey

Contact Info

Product

Resources

About