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IEEE Computational Intelligence Magazine - new TOC



TOC Alert for Publication# 10207



 



Front Cover

Feb. 2018

Presents the front cover for this issue of the publication.



2018 IEEE Smart World Congress

Feb. 2018

Presents information on the 2018 IEEE Smart World Congress.



Table of Contents

Feb. 2018

Presents the table of contents for this issue of the publication.



CIM Editorial Board

Feb. 2018

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.



Father of Fuzzy Logic [Editor's Remarks]

Feb. 2018

Presents the introductory editorial for this issue of the publication.



IEEE World Congress on Computational Intelligence

Feb. 2018

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.



CIS Society Officers

Feb. 2018

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.



Some Haphazard Thoughts [President's Message]

Feb. 2018

Presents the President’s message for this issue of the publication.



Call for Papers for Journal Special Issues

Feb. 2018

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.



Introduction to the IEEE CIS TC on Smart World (SWTC) [Society Briefs]

Feb. 2018

Presents information on the CIS Smart World.



2018 IEEE CIS Awards [Society Briefs]

Feb. 2018

Presents the recipients of 2018 CIS society awards.



Obituary for Lotfi A. Zadeh [In Memoriam]

Feb. 2018

Recounts the career and contributions of Lotfi A. Zadeh.






CIS Publication Spotlight [Publication Spotlight]

Feb. 2018

Presents reviews of recent articles from select IEEE publications.



Computational Intelligence Techniques for Mobile Network Optimization [Guest Editorial]

Feb. 2018

Modern society has become increasingly reliant on mobile networks for their communication needs. Such networks are characterized by their dynamic, heterogeneous, complex, and data intensive nature, which makes them more amenable to automated mobile network optimization performed using “computational intelligence’’ (CI) techniques rather than traditional optimization approaches. CI techniques—which subsume multidisciplinary techniques from machine learning (ML), optimization theory, game theory, control theory, and meta-heuristics— have a rich history in terms of being deployed in networking. CI techniques are highly suited to the mobile networking architectures and the dynamic environments they characterize. Looking ahead, it looks likely that CI will play a leading role in upcoming 5th generation (5G) wireless mobile networks for developing optimized solutions for vexing problems—such as traffic scheduling and routing, capacity, coverage, and power optimization—in the face of stringent requirements and highly dynamic conditions. The importance of our proposed theme of mobile network optimization (MNO) motivated us to propose this special issue in the IEEE Computational Intelligence Magazine (CIM)—the premier IEEE magazine for professionals interested in CI techniques and their applications.



FUZZ-IEEE 2019

Feb. 2018

Presents information on the FUZZ-IEEE 2019 conference.



A Fast, Adaptive, and Energy-Efficient Data Collection Protocol in Multi-Channel-Multi-Path Wireless Sensor Networks

Feb. 2018

A wireless sensor network (WSN) consists of sensor nodes which can self-organize to relay information such as measurements to a base station. To reduce latency and increase data transmission throughput, multi-channel data collection protocols have been proposed to enable simultaneous parallel transmissions between pairs of nodes within the network. However, the existing protocols require long scheduling phase, are less dynamic to network traffic changes, and/or compromise on efficiency by relying on the back-off mechanism such as carrier sense multiple access with collision avoidance (CSMA/CA). This paper proposes a fast, adaptive, and energyefficient data collection protocol in multi-channel-multi-path WSN. The protocol consists of two major phases. The first phase is the node-channel assignment that uses the graph coloring technique to resolve the issue of node overhearing and interference. The second phase is the scheduling and packet forwarding, in which a novel three-dimensional parallel iterative matching (3DPIM) algorithm is proposed to pair up sensor nodes in different time slots so as to enable collision-free multiple simultaneous data transmissions in every time slot. Simulation results show that our proposed protocol can achieve fast and energy-efficient data collection while being adaptive to the change of network traffic in WSN.



Cluster-Based Content Distribution Integrating LTE and IEEE 802.11p with Fuzzy Logic and Q-Learning

Feb. 2018

There is an increasing demand for distributing a large amount of content to vehicles on the road. However, the cellular network is not sufficient due to its limited bandwidth in a dense vehicle environment. In recent years, vehicular ad hoc networks (VANETs) have been attracting great interests for improving communications between vehicles using infrastructure-less wireless technologies. In this paper, we discuss integrating LTE (Long Term Evolution) with IEEE 802.11p for the content distribution in VANETs. We propose a two-level clustering approach where cluster head nodes in the first level try to reduce the MAC layer contentions for vehicle-to-vehicle (V2V) communications, and cluster head nodes in the second level are responsible for providing a gateway functionality between V2V and LTE. A fuzzy logic-based algorithm is employed in the first-level clustering, and a Q-learning algorithm is used in the second-level clustering to tune the number of gateway nodes. We conduct extensive simulations to evaluate the performance of the proposed protocol under various network conditions. Simulation results show that the proposed protocol can achieve 23% throughput improvement in high-density scenarios compared to the existing approaches.



Machine Learning for Performance Prediction in Mobile Cellular Networks

Feb. 2018

In this paper, we discuss the application of machine learning techniques for performance prediction problems in wireless networks. These problems often involve using existing measurement data to predict network performance where direct measurements are not available. We explore the performance of existing machine learning algorithms for these problems and propose a simple taxonomy of main problem categories. As an example, we use an extensive real-world drive test data set to show that classical machine learning methods such as Gaussian process regression, exponential smoothing of time series, and random forests can yield excellent prediction results. Applying these methods to the management of wireless mobile networks has the potential to significantly reduce operational costs while simultaneously improving user experience. We also discuss key challenges for future work, especially with the focus on practical deployment of machine learning techniques for performance prediction in mobile wireless networks.



Conference Calendar [Conference Calendar]

Feb. 2018

Presents the CIS committee calendar of upcoming events and meetings.



CIG 2018

Feb. 2018

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IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

Feb. 2018

Presents information on the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology.



2018 IEEE SSCI

Feb. 2018

Presents information on the 2018 IEEE SSCI conference.