An important challenge for supporting multimedia applications in the Internet of Things is the security heterogeneity of wired and wireless sensor and actuator networks. In this work, we design a new ...and efficient media-aware security framework for facilitating various multimedia applications in the Internet of Things. First, we present a novel multimedia traffic classification and analysis method for handling the heterogeneity of diverse applications. Then a media-aware traffic security architecture is proposed based on the given traffic classification to enable various multimedia services being available anywhere and anytime. Furthermore, we provide a design rule and strategy to achieve a good trade-off between a system¿s flexibility and efficiency. To the best of our knowledge, this study is the first to provide general media-aware security architecture by jointly considering the characteristics of multimedia traffic, security service, and the Internet of Things.
Nowadays, smartphones have become indispensable to everyone, with more and more built-in location-based applications to enrich our daily life. In the last decade, fingerprinting based on RSS has ...become a research focus in indoor localization, due to its minimum hardware requirement and satisfiable positioning accuracy. However, its time-consuming and labor-intensive site survey is a big hurdle for practical deployments. Fingerprint crowdsourcing has recently been promoted to relieve the burden of site survey by allowing common users to contribute to fingerprint collection in a participatory sensing manner. For its promising commitment, new challenges arise to practice fingerprint crowdsourcing. This article first identifies two main challenging issues, fingerprint annotation and device diversity, and then reviews the state of the art of fingerprint crowdsourcing-based indoor localization systems, comparing their approaches to cope with the two challenges. We then propose a new indoor subarea localization scheme via fingerprint crowdsourcing, clustering, and matching, which first constructs subarea fingerprints from crowdsourced RSS measurements and relates them to indoor layouts. We also propose a new online localization algorithm to deal with the device diversity issue. Our experiment results show that in a typical indoor scenario, the proposed scheme can achieve a 95 percent hit rate to correctly locate a smartphone in its subarea.
Data mining in distributed environment: a survey Gan, Wensheng; Lin, Jerry Chun‐Wei; Chao, Han‐Chieh ...
Wiley interdisciplinary reviews. Data mining and knowledge discovery,
November/December 2017, Letnik:
7, Številka:
6
Journal Article
Recenzirano
Due to the rapid growth of resource sharing, distributed systems are developed, which can be used to utilize the computations. Data mining (DM) provides powerful techniques for finding meaningful and ...useful information from a very large amount of data, and has a wide range of real‐world applications. However, traditional DM algorithms assume that the data is centrally collected, memory‐resident, and static. It is challenging to manage the large‐scale data and process them with very limited resources. For example, large amounts of data are quickly produced and stored at multiple locations. It becomes increasingly expensive to centralize them in a single place. Moreover, traditional DM algorithms generally have some problems and challenges, such as memory limits, low processing ability, and inadequate hard disk, and so on. To solve the above problems, DM on distributed computing environment also called distributed data mining (DDM) has been emerging as a valuable alternative in many applications. In this study, a survey of state‐of‐the‐art DDM techniques is provided, including distributed frequent itemset mining, distributed frequent sequence mining, distributed frequent graph mining, distributed clustering, and privacy preserving of distributed data mining. We finally summarize the opportunities of data mining tasks in distributed environment. WIREs Data Mining Knowl Discov 2017, 7:e1216. doi: 10.1002/widm.1216
This article is categorized under:
Application Areas > Business and Industry
Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Technologies > Computer Architectures for Data Mining
An overview of distributed data mining.
Timely and reliable information sharing among autonomous vehicles (AVs) provides a promising approach for reducing traffic congestion and improving traffic efficiency in future intelligent ...transportation systems. In this paper, we consider millimeter-wave (mmWave) based multi-hop vehicle-to-vehicle (V2V) communications to facilitate ultra-reliable low-latency information sharing among AVs. We propose a novel framework for performance analysis and design of relay selection schemes in mmWave multi-hop V2V communications, while taking into account the mmWave signal propagation characteristics, road topology, and traffic conditions. In particular, considering the minimum tracking distance requirement of road traffic, the headway, i.e., the distance between adjacent AVs, is modeled as shifted-exponential distribution. Moreover, we model the communication path losses using the Manhattan distance metric in the taxicab geometry, which can more accurately capture the characteristics of mmWave signal propagation in urban grid roads than conventional Euclidean distance geometry. Based on the proposed model, we investigate the latency and reliability of mmWave multi-hop V2V communications for three widely adopted relay selection schemes, i.e., random with forward progress (RFP), most forward with fixed radius (MFR), and nearest with forward progress (NFP), respectively. Furthermore, we propose a novel relay selection scheme for joint optimization of the single-hop forward progress (FP) and single-hop latency according to the AVs' instantaneous locations and an estimate of the residual multi-hop latency. Simulation results show that, by balancing the current single-hop latency and the residual multi-hop latency for the multi-hop V2V network, the proposed relay selection scheme significantly outperforms the MFR, NFP and RFP in both multi-hop transmission latency and reliability of mmWave V2V communications.
Wireless Sensors Networks (WSNs) are susceptible to many security threats, and because of communication, computation and delay constraints of WSNs, traditional security mechanisms cannot be used. ...Trust management models have been recently suggested as an effective security mechanism for WSNs. Considerable research has been done on modeling and managing trust. In this paper, we present a detailed survey on various trust models that are geared towards WSNs. Then, we analyze various applications of trust models. They are malicious attack detection, secure routing, secure data aggregation, secure localization and secure node selection. In addition, we categorize various types of malicious attacks against trust models and analyze whether the existing trust models can resist these attacks or not. Finally, based on all the analysis and comparisons, we list several trust best practices that are essential for developing a robust trust model for WSNs.
•We present a detailed survey on trust models that are geared towards WSNs.•The applications of trust models are presented.•Various types of malicious attacks against trust models are categorized.•Robustness of trust models against malicious attacks is analyzed.•Several trust best practices for developing a robust trust model are listed.
A Survey of Utility-Oriented Pattern Mining Gan, Wensheng; Lin, Jerry Chun-Wei; Fournier-Viger, Philippe ...
IEEE transactions on knowledge and data engineering,
2021-April-1, 2021-4-1, Letnik:
33, Številka:
4
Journal Article
Recenzirano
Odprti dostop
The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and ...evaluating the usefulness of different kinds of patterns, many techniques and constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, satisfaction, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM, or called utility mining). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented in detail, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.
With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is ...unwise considering that the end devices' energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.
In wireless communication systems (WCSs), the network optimization problems (NOPs) play an important role in maximizing system performance by setting appropriate network configurations. When dealing ...with NOPs by using conventional optimization methodologies, there exist the following three problems: human intervention, model invalidity, and high computation complexity. As such, in this article we propose an auto-learning framework to achieve intelligent and automatic network optimization by using machine learning (ML) techniques. We review the basic concepts of ML, and propose their rudimentary employment models in WCSs, including automatic model construction, experience replay, efficient trial and error, RL-driven gaming, complexity reduction, and solution recommendation. We hope these proposals can provide new insights and motivation in future research for dealing with NOPs in WCSs by using ML techniques.
Big data analytics: a survey Tsai, Chun-Wei; Lai, Chin-Feng; Chao, Han-Chieh ...
Journal of big data,
1/10, Letnik:
2, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The age of big data is now coming. But the traditional data analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance
...platform
to efficiently analyze big data and how to design an appropriate
mining algorithm
to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. Some important open issues and further research directions will also be presented for the next step of big data analytics.
We propose a new efficient and effective task scheduling approach with stochastic time cost for computation offloading in mobile edge computing. We developed an optimization model that minimizes the ...maximum tolerable delay (MTD) by considering both the average delay and delay jitter. We also proposed an efficient conservative heterogeneous earliest-finish-time algorithm to solve the MTD-minimization problem. Numerical results obtained with our proposed approach demonstrate its effectiveness over previously proposed techniques.
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