“Time–Location–Frequency”–aware Internet of things service selection based on historical records

Qi, Lianyong; Dai, Peiqiang; Yu, Jiguo; Zhou, Zhili; Xu, Yanwei

doi:10.1177/1550147716688696

Cited by 35 publications

(14 citation statements)

References 46 publications

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“…3. For simplicity, crowdsourcing task execution context is assumed to be fixed in this paper; in the future, we will improve our exception handling method to be more comprehensive and reasonable by considering more context factors, e.g., time factor that is crucial in many domains [64,65]. 4.…”

Section: Discussionmentioning

confidence: 99%

A privacy-preserving exception handling approach for dynamic mobile crowdsourcing applications

Yan

2019

J Wireless Com Network

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The ever-increasing popularity of mobile devices (e.g., mobile phones and smart watches) has created a variety of crowdsourcing applications by employing the massive and distributed mobile computing resources. Typically, a task requester sends his/her task request and constraint conditions to a crowdsourcing platform, and then the crowdsourcing platform is responsible for finding a set of appropriate workers (e.g., mobile users) from massive candidates to satisfy the task request. However, for a mobile crowdsourcing task being executed by a set of workers, a pre-selected worker may become unavailable due to various exceptions. In this situation, it is significant for the crowdsourcing platform to quickly find another similar worker to replace the unavailable worker so as to smooth the crowdsourcing process. However, the above exception handling process is often challenging as candidate workers are often not willing to release their sensitive information to the platform due to privacy concerns. In view of this challenge, in this paper, a novel privacy-preserving exception handling approach, named ExH Simhash , is put forward based on Simhash technique. Finally, through a set of simulated experiments, we validate the feasibility of ExH Simhash in terms of substitution equivalence and computational time.

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Section: Discussionmentioning

confidence: 99%

A privacy-preserving exception handling approach for dynamic mobile crowdsourcing applications

Yan

2019

J Wireless Com Network

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“…Next, we elaborate on the concrete condition relaxation process. Suppose h (u i ) = (R i,1 , …, R i,p ) and h(u*) = (R *,1 , …, R *,p ), then h (u i ) ⊕ h(u*) can be defined as in (4). Thus the original neighbor search condition that is too tight, i.e., h (u i ) = h(u*) can be converted into another condition h (u i ) ⊕ h(u*) = 0.…”

Section: Case 1: Search Condition Relaxationmentioning

confidence: 99%

“…With the advent of the big data age, the volume and variety of available data both increase quickly, which make it hard for a user to select valuable information that matches his/her preferences [1][2][3][4]. Therefore, to decrease the heavy burden on users' service selection decisions, diverse service recommendation techniques are brought forth accordingly [5,6].…”

Section: Introductionmentioning

confidence: 99%

Privacy-aware cross-cloud service recommendations based on Boolean historical invocation records

Wei

Wang²,

Zhang

et al. 2019

J Wireless Com Network

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In the age of big data, service recommendation has provided an effective manner to filter valuable information from massive data. Generally, by observing the past service invocation records (Boolean values) distributed across different cloud platforms, a recommender system can infer personalized preferences of a user and recommend him/her new services to gain more profits. However, the historical service invocation records are a kind of private information for users. Therefore, how to protect sensitive user data distributed across multiple cloud platforms is becoming a necessity for successful service recommendations. Additionally, the historical service invocation records often update with time, which call for an efficient and scalable service recommendation method. In view of these challenges, we introduce the multi-probe Simhash technique in information retrieval domain into the recommendation process and further put forward a privacy-preserving recommendation method based on historical service invocation records. At last, we design several experiments on the real-world service quality data in set WS-DREAM. Experimental results show the feasibility of the proposal in terms of producing accurate recommended results while protecting users' private information contained in historical service invocation records.

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“…The literature 29 proposed and implemented the ArrayTrack system. 30,31 It uses MIMO technology to track wireless customers in real time and combines FPGA and general calculation methods. The results show that the location of smaller errors can be obtained.…”

Section: Related Workmentioning

confidence: 99%

High-accuracy localization for indoor group users based on extended Kalman filter

Wang

Liang

Mei

et al. 2018

International Journal of Distributed Sensor Networks

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Indoor localization has attracted increasing research attentions in the recent years. However, many important issues still need to be further studied to keep pace with new requirements and technical progress, such as realtime operation, high accuracy, and energy efficiency. In order to meet the high localization accuracy requirement and the high localization dependable requirement in some scenarios, we take the users as a group to utilize the mutual distance information among them to get better localization performance. Moreover, we design a mobile group localization method based on extended kalman filter and believable factor of non-localized nodes, which can alleviate the influence caused by environmental noisy and unstable wireless signals to improve the localization accuracy. Besides, we implement a real system based on ZigBee technique and perform experiments on the campus of Huaqiao University. Experimental results and theoretical analysis validate the effectiveness of the proposed method.

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“Time–Location–Frequency”–aware Internet of things service selection based on historical records

Cited by 35 publications

References 46 publications

A privacy-preserving exception handling approach for dynamic mobile crowdsourcing applications

A privacy-preserving exception handling approach for dynamic mobile crowdsourcing applications

Privacy-aware cross-cloud service recommendations based on Boolean historical invocation records

High-accuracy localization for indoor group users based on extended Kalman filter

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