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Front cover

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Presents the front cover for this issue of the publication.
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Cover 2

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Table of Contents

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

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The President's Page

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Presents the President’s message for this issue of the publication.
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Emerging Technologies Committee: Letter from the Chair

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Presents the introductory editorial for this issue of the publication.
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On the Evolution of ComSoc's Technical Activities

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Reports on the development and evolution of hte ComSoc Society's Technical Activities group.
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Conference Calendar

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Presents the conference calendar for the ComSoc Society.
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Global Communications Newsletter

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Presents key events and topics in the global communications industry.
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Comsoc Membership

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Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities

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The articles in this special section focus on Big Data as it impacts future smart cities. The world is experiencing a period of extreme urbanization. Moreover, this process will continue, and the global urban population is expected to double by 2050. Smart city has been proposed to improve the efficiency of services and meet residents’ needs for better quality of life. Essentially, smart city integrates the Internet of Things and emerging communication technologies such as fifth generation (5G) solutions to manage the citys’ assets, including transportation systems, hospitals, water supply networks, waste management, and so on. Therefore, smart city is driving innovation and new technologies, especially big data technologies for the big data era. In the future smart city, there is an urgent need to address the following issues: how to design algorithms to process mass data and how to utilize big data to improve the quality of service (QoS) for future smart cities.
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A Data-Driven Robustness Algorithm for the Internet of Things in Smart Cities

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The Internet of Things has been applied in many fields, especially in smart cities. The failure of nodes brings a significant challenge to the robustness of topologies. The IoT of smart cities is increasingly producing a vast amount different types of data, which includes the node's geographic information, neighbor list, sensing data, and so on. Thus, how to improve the robustness of topology against malicious attacks based on big data of smart cities becomes a critical issue. To tackle this problem, this article proposes an approach to improve the robustness of network topology based on a multi-population genetic algorithm (MPGA). First, the geographic information and neighbor list of nodes are extracted from a big data server. Then a novel MPGA with a crossover operator and a mutation operator is proposed to optimize the robustness of topology. Our algorithm keeps the initial degree of each node unchanged such that the optimized topology will not increase the energy cost of adding edges. The extensive experiment results show that our algorithm can significantly improve the robustness of topologies against malicious attacks.
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Secure Multimedia Big Data in Trust-Assisted Sensor-Cloud for Smart City

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Lately, with the prevalence of digital devices and social network applications, the explosive growth of multimedia big data poses many challenges for users to obtain them securely in various application scenarios. In this article, investigating secure multimedia big data application in trust-assisted sensor cloud (TASC), which is one kind of SC for smart city, the recent work about multimedia big data and SC is reviewed first. Further, the critical issues that affect the success of secure multimedia big data in TASC are identified. With that, motivated by addressing the identified critical issues, this article proposes two types of TASC: TASC-S (TASC with a single trust value threshold), and TASC-M (TASC with multiple trust value thresholds). Finally, with extensive simulation results about TASC-S and TASC-M as well as SC without trust assistance (SCWTA), the following insights into secure multimedia big data in TASC are achieved: the throughput of TASC-S and TASC-M can both be generally higher than that of SCWTA; the throughput of TASC-S can trend with tuned trust value threshold; and the throughput of TASC-M can fluctuate with the same trust value thresholds.
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Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach

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Recent advances in networking, caching, and computing have significant impacts on the developments of smart cities. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on smart cities. In this article, we propose an integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of applications for smart cities. Then we present a novel big data deep reinforcement learning approach. Simulation results with different system parameters are presented to show the effectiveness of the proposed scheme.
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A Socially-Aware In-Network Caching Framework for the Next Generation of Wireless Networks

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The modernization of urban scenarios includes improvements in wireless communication and efficient content dissemination to citizens. In these scenarios, the demographic densification and the phenomenon of popularization of mobile devices may characterize massive demands for online content in crowded regions, able to temporarily deplete the resources of the network infrastructure. To address this challenge, the next generation of wireless networks envision local cooperation among users using the D2D paradigm to improve content retrieval. Much effort has been dedicated to proposing mechanisms for cooperation and resource sharing; however, the advantages of big data have been little explored to support D2D to cope with urban and social dynamics. In this article, we investigate human mobility using data from online social networks, observing the mobility of 362,000 users during one year in New York City. We analyze the spatiotemporal features of the city, and their effects on encounters between users and content dissemination through D2D. Thus, we propose a framework for distributed caching based on social and spatiotemporal factors. The results of the experiments demonstrate the feasibility of D2D to offload the network demand and performance gain in the provision of content opportunistically.
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Time-Constrained Big Data Transfer for SDN-Enabled Smart City

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With advanced ICT, the ever-rapid development of informatization has become an integral part of smart city services in healthcare, transportation, energy, education, business, community life, and so on. A huge amount of data, called big data, is generated from various sources, and effective analysis and utilization of big data has become a key factor in the success of smart city services. However, in order to achieve precise big data analytics and make real-time decisions, one of the challenging issues is how to efficiently deliver the huge amounts of collected data to the processing servers. In this article, we first propose a novel architecture to support smart city services based on SDN technology. Then we study the time-constrained big data transfer scheduling (TBTS) problem under the proposed architecture, and present an intelligent strategy to address the TBTS issue by utilizing the SDN controller to conduct dynamic flow control and multi-path transfer scheduling. Simulation results demonstrate that the proposed strategy can efficiently support big data transfer in terms of low transfer delay and high bandwidth utilization.
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IEEE 802.11ax: Highly Efficient WLANs for Intelligent Information Infrastructure

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Recently, IEEE 802.11ax, introducing the fundamental improvement of WLANs, was approved as the next generation WLAN technology. Satisfying tremendous user demands for user experience, IEEE 802.11ax will fuel the future intelligent information infrastructure to serve big data transportation and diverse smart application scenarios. In this article, we overview the key technology features of IEEE 802.11ax such as OFDMA PHY, UL MU-MIMO, spatial reuse, OFDMA random access, power saving with TWT, and STA-2-STA operation, and explain translating these features to enhance user experience, highlighting the design principles to facilitate smart environments and identifying new technological opportunities.
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Cooperative Fog Computing for Dealing with Big Data in the Internet of Vehicles: Architecture and Hierarchical Resource Management

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As vehicle applications, mobile devices and the Internet of Things are growing fast, and developing an efficient architecture to deal with the big data in the Internet of Vehicles (IoV) has been an important concern for the future smart city. To overcome the inherent defect of centralized data processing in cloud computing, fog computing has been proposed by offloading computation tasks to local fog servers (LFSs). By considering factors like latency, mobility, localization, and scalability, this article proposes a regional cooperative fog-computing-based intelligent vehicular network (CFC-IoV) architecture for dealing with big IoV data in the smart city. Possible services for IoV applications are discussed, including mobility control, multi-source data acquisition, distributed computation and storage, and multi-path data transmission. A hierarchical model with intra-fog and inter-fog resource management is presented, and energy efficiency and packet dropping rates of LFSs in CFC-IoV are optimized.
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Heterogeneous Ultra-Dense Networks: Part 1

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The articles in this special section focus on heterogeneous ultra-dense networks. In recent years, the rapid growth of various wireless communication services has led to an explosion of wireless data traffi c. Therefore, a major challenge in the fi fth generation (5G) mobile networks is to effectively serve the exponentially growing data fl ows in wireless networks. Initial estimations indicate that, diff erent from the evolutionary path of previous cellular generations that were based on spectral efficiency improvements, the most substantial amount of future system performance gains will be obtained by means of network infrastructure densification. In order to meet the requirements of explosive data traffic in 5G mobile communications, ultra-dense networking (UDN) has become a promising technology to significantly improve the network spectral effi ciency and system performance. Heterogeneous ultra-dense networking (HUDN) refers to the idea of densifying the cellular networks with very high network densifi - cation, including both the mobile device densification and base station (BS) densifi cation, where the density of BSs may exceed that of mobile devices. Therefore, UDNs can make the access nodes as close as possible to the users, resulting in efficient reuse of network resources while achieving the highest possible transmission rates.
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