A Distributed Publisher-Driven Secure Data Sharing Scheme for Information-Centric IoT

Li, Ruidong; Asaeda, Hitoshi; Li, Jie

doi:10.1109/jiot.2017.2666799

Cited by 46 publications

(13 citation statements)

References 27 publications

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“…For example, in [15], Ahlgren et al propose a method for finding the newest element in an IoT data stream. Li et al [16] focus on the data authorization problem of IoT data streams, and propose a distributed secure data sharing structure, which contains a self-update mechanism for this problem.…”

Section: Streaming Data Processing In Ic-iot Systemsmentioning

confidence: 99%

Near-Optimal Data Structure for Approximate Range Emptiness Problem in Information-Centric Internet of Things

et al. 2019

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The approximate range emptiness problem requires a memory-efficient data structure D to approximately represent a set S of n distinct elements chosen from a large universe U = {0, 1, • • • , N − 1} and answer an emptiness query of the form ''S ∩ [a; b] = ∅?'' for an interval [a; b] of length L (a, b ∈ U ), with a false positive rate ε. The designed D for this problem can be kept in high-speed memory and quickly determine approximately whether a query interval is empty or not. Thus, it is crucial for facilitating online query processing in the information-centric Internet of Things applications, where the IoT data are continuously generated from a large number of resource-constrained sensors or readers and then are processed in networks. However, the existing works on the approximate range emptiness problem only consider the simple case when the set S is static, rendering them unsuitable for the continuously generated IoT data. In this paper, we study the approximate range emptiness problem over sliding windows in the IoT Data streams, denoted by ε-ARESD-problem, where both insertion and deletion are allowed. We first prove that, given a sliding window size n and an interval length L, the lower bound of memory bits needed in any data structure for ε-ARESD-problem is n log 2 (nL/ε) + (n). Then, a data structure is proposed and proved to be within a factor of 1.33 of the lower bound. The extensive simulation results demonstrate the advantage of the efficiency of our data structure over the baseline approach.INDEX TERMS Approximate range emptiness, data structure, information-centric network, Internet of Things, space lower bound.

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Section: Streaming Data Processing In Ic-iot Systemsmentioning

confidence: 99%

Near-Optimal Data Structure for Approximate Range Emptiness Problem in Information-Centric Internet of Things

et al. 2019

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“…Information-centric networking (ICN) [9] is a similar approach for enhancing the Internet infrastructure to explicitly support content-based routing and forwarding. However, the CDN and ICN paradigms are limited to non-interactive content [10]; for instance, IoT data can be cached at the edge of the network [11,12]. On the other hand, edge computing servers also provide computational capabilities and can host interactive applications that support user mobility.…”

Section: Introductionmentioning

confidence: 99%

Edge Computing for the Internet of Things: A Case Study

Premsankar

Francesco

Taleb

2018

IEEE Internet Things J.

535

238

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The amount of data generated by sensors, actuators and other devices in the Internet of Things (IoT) has substantially increased in the last few years. IoT data are currently processed in the cloud, mostly through computing resources located in distant data centers. As a consequence, network bandwidth and communication latency become serious bottlenecks. This article advocates edge computing for emerging IoT applications that leverage sensor streams to augment interactive applications. First, we classify and survey current edge computing architectures and platforms, then describe key IoT application scenarios that benefit from edge computing. Second, we carry out an experimental evaluation of edge computing and its enabling technologies in a selected use case represented by mobile gaming. To this end, we consider a resource-intensive 3D application as a paradigmatic example and evaluate the response delay in different deployment scenarios. Our experimental results show that edge computing is necessary to meet the latency requirements of applications involving virtual and augmented reality. We conclude by discussing what can be achieved with current edge computing platforms and how emerging technologies will impact on the deployment of future IoT applications.

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“…D Ue to the rapidly growing amount of information and explosive appearance of new services available in the web, the overloaded information prevents users from obtaining useful information conveniently [1], [2], [3], [4], [5], [6]. How to help the overwhelmed users to select the interested part of online information is becoming an unprecedentedly important task.…”

Section: Introductionmentioning

confidence: 99%

Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks

Wan

Xia

Kong

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied. The trust information in social networks among users is an important factor for improving recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. However, the prediction performance of matrix factorization based methods largely depends on the matrixes initialization of users and items. To address this challenge, we develop a novel trust-aware approach based on deep learning to alleviate the initialization dependence. First, we propose two deep matrix factorization (DMF) techniques, i.e., linear DMF and non-linear DMF to extract features from the user-item rating matrix for improving the initialization accuracy. The trust relationship is integrated into the DMF model according to the preference similarity and the derivations of users on items. Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix factorized from the user-item rating matrix. The community regularization is integrated in the joint optimization function to take neighbours' effects into consideration. The results of DMF are applied to initialize the updating variables of Deep-MDAE in order to further improve the recommendation performance. Finally, we validate that the proposed approach outperforms state-of-the-art baselines for recommendation, especially for the cold-start users.

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A Distributed Publisher-Driven Secure Data Sharing Scheme for Information-Centric IoT

Cited by 46 publications

References 27 publications

Near-Optimal Data Structure for Approximate Range Emptiness Problem in Information-Centric Internet of Things

Near-Optimal Data Structure for Approximate Range Emptiness Problem in Information-Centric Internet of Things

Edge Computing for the Internet of Things: A Case Study

Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks

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