Data collection from WSNs to the cloud based on mobile Fog elements

Wang, Tian; Zeng, Jiandian; Lai, Yongxuan; Cai, Yiqiao; Tian, Hui; Chen, Yonghong; Wang, Baowei

doi:10.1016/j.future.2017.07.031

Cited by 127 publications

(78 citation statements)

References 20 publications

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“…Step (10) (21) End if (22) If S new < min (23) min = S new (24) = ; //to select the that makes QoISD optimal (25) = ∇ (26) End if (27) End for (28) = + (29) real = ∑ =1 //to update the sensed data (30) End while (31) Return the new reward Algorithm 1: The QoISD optimization algorithm. questionnaire survey, the probability parameter is 1.745.…”

Section: Methodology and Setupmentioning

confidence: 99%

“…In such applications, due to the need to collect a wide range and large amount of data even in a long-term continuous period, the cost of traditional method deploying sensing devices or specialized employee to collect the data is so high limiting the development of applications [20,21]. Hence, a new method of data collection named crowd sensing (or participatory sensing) is adopted to collect data [21][22][23][24][25][26]. First, there are a large number of users who can participate in data collection.…”

Section: Introductionmentioning

confidence: 99%

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A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Liu

et al. 2018

Wireless Communications and Mobile Computing

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To tackle the issue in deep crowd sensing, a Time and Location Correlation Incentive (TLCI) scheme is proposed for deep data gathering in crowdsourcing networks. In TLCI scheme, a metric named "Quality of Information Satisfaction Degree" (QoISD) is to quantify how much collected sensing data can satisfy the application's QoI requirements mainly in terms of data quantity and data coverage. Two incentive algorithms are proposed to satisfy QoISD with different view. The first algorithm is to ensure that the application gets the specified sensing data to maximize the QoISD. Thus, in the first incentive algorithm, the reward for data sensing is to maximize the QoISD. The second algorithm is to minimize the cost of the system while meeting the sensing data requirement and maximizing the QoISD. Thus, in the second incentive algorithm, the reward for data sensing is to maximize the QoISD per unit of reward. Finally, we compare our proposed scheme with existing schemes via extensive simulations. Extensive simulation results well justify the effectiveness of our scheme. The QoISD can be optimized by 81.92%, and the total cost can be reduced by 31.38%.

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Section: Methodology and Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Liu

et al. 2018

Wireless Communications and Mobile Computing

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“…24 Some records are shown in Table 1. The dataset contains anonymized user behavior logs and user attributes.…”

Section: Raw Datamentioning

confidence: 99%

Brand purchase prediction based on time‐evolving user behaviors in e‐commerce

Dong

Jiang

2018

Concurrency and Computation

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Purchase prediction is a key function in the e-commerce recommendation system. Existing works usually focus on item-level purchase prediction, which faces two issues of high cost and low accuracy. In this paper, we study brand purchase prediction by exploring behaviors, which may lead to brand purchases. We make three progresses. (1) We analyze a real world e-commerce data from multiple angles. Focusing on users' brand purchases, we find behaviors' evolution with time and behaviors' interaction.(2) For different behaviors, we extract different time-evolving features that can serve as indicators of users' brand purchase. (3) We use a logistic regression-based model by adjusting the parameters of time-evolving feature and others in two different scenarios (the promotion purchase prediction and the daily purchase prediction) to construct two experiments.The experiment results show that the model using three types features performs the best in both scenarios, and the time-evolving feature plays the most important role among them. (4) We distinguish the feature importance in different scenarios. Based on the importance, we find that users' purchases in the promotion scenario are likely to be impulsive, while purchases in the daily scenario are more likely to be influenced by users' activities. KEYWORDSconsumer behaviors, e-commerce, purchase prediction INTRODUCTIONPurchase prediction aims at predicting whether users would purchase something, which is a research topic that will benefit sellers' investment strategy. 1 However, in real e-commerce platforms, there are large numbers of items, which belong to a relatively small brands. In conducting traditional item-level prediction, we will face the issue of high cost and low accuracy due to the sparsity of the data. For example, the T-mall dataset we used contains more than 600 000 items, more than 30 000 users, and about 7000 brands. To overcome this issue, we study brand purchase prediction like the previous works of Jia et al 2 Zhao et al. 3 Our main idea is from exploring user behaviors on brands to exploit their time-evolving features and conduct the effective brand purchase prediction.There are various types of user behaviors, such as clicking, purchasing, adding to shopping cart, and collecting. More and more researches have been done from the analysis of user behaviors 4,5 to find users' purchase intentions. Hu et al 6 use behaviors as an implicit feedback, and users' credibilities of behaviors are used to measure their purchasing intentions. Sohail et al 7 use online reviews to obtain opinion features and uses these features to predict users' purchases of books. These works both use multi-dimensional features to construct purchase prediction models.Purchase prediction are mainly constructed from the item, while some work has studied from the perspectives of brands or categories. 8 Zhang and Pennacchiotti 9 and Sohail et al 10 proposed a brand recommendation model from the perspective of social networks and obtained good recommendation results. These works incite ...

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“…The tree-like multihop network routing topology is often used for large-scale WSNs. The CS based data gathering algorithm with multihop routing is studied in paper [14,15], where the unreliability of the wireless link was ignored while special attention was paid to the optimal matching between the measurement matrix in CS and the structure of the tree-like routing topology. However, the transmission of CS data packets under the multihop routing requires the weighted superposition of the data from multiple nodes.…”

Section: Related Workmentioning

confidence: 99%

“…When a node recognizes its packet loss in this process, the probability density functions f 1(z) and f 2(z) of the random variable z under two different packet loss types are determined according to the current bit error rate Pb of the link and the data packet length . According to (15), the decision threshold for the two types of packet loss is calculated. In the sliding window, the value of the random variable is calculated and we further compare the value of with the threshold .…”

Section: Data Gathering and Analysesmentioning

confidence: 99%

CS‐PLM: Compressive Sensing Data Gathering Algorithm Based on Packet Loss Matching in Sensor Networks

Sun

Tao

Xiong

2018

Wireless Communications and Mobile Computing

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The data transmission process in Wireless Sensor Networks (WSNs) often experiences errors and packet losses due to the environmental interference. In order to address this problem, we propose a Compressive Sensing data gathering algorithm based on Packet Loss Matching (CS-PLM). It is proven that, under tree routing, the packet loss on communication links would severely undermine the data reconstruction accuracy in Compressive Sensing (CS) based data gathering process. It is further pointed out that the packet loss in CS based data gathering exhibits the correlation effect. Meanwhile, we design a sparse observation matrix based on packet loss matching and verify that the designed matrix satisfies the Restricted Isometry Property (RIP) with a probability arbitrarily close to 1. Therefore, reliable transmission of the compressed data can be guaranteed by adopting the multipath backup routing among CS nodes. It is shown in the simulation results that, with a 60% packet loss ratio of the link, the CS-PLM algorithm can still ensure the effective reconstruction of the data gathered by the CS algorithm and the relative reconstruction error is lower than 5%. Therefore, it is verified that the proposed algorithm could effectively alleviate the sensitivity to packet losses for the CS based data gathering algorithm on unreliable links.

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Data collection from WSNs to the cloud based on mobile Fog elements

Cited by 127 publications

References 20 publications

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Brand purchase prediction based on time‐evolving user behaviors in e‐commerce

CS‐PLM: Compressive Sensing Data Gathering Algorithm Based on Packet Loss Matching in Sensor Networks

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