Journal Information
IEEE Communications Magazine
http://www.comsoc.org/commag
Impact Factor:
5.125
Publisher:
IEEE
ISSN:
0163-6804
Viewed:
5413
Tracked:
10

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IEEE Communications Magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications systems (PCS), ISDN, and more. It includes special feature technical articles and monthly departments: book reviews, conferences, short courses, standards, governmental regulations and legislation, new products, and Society news such as administration and elections.
Last updated by Dou Sun in 2016-10-17
Special Issues
Special Issue on Internet of Things and Information Processing in Smart Energy Applications
Submission Date: 2017-03-01

There are four major challenges for the current electricity grid - increasing electricity demand, ageing grid infrastructure, ever-increasing penetration of renewables, and significant uptake of electric vehicles and energy storage with behind-the-meter applications for residential and commercial buildings. To address these challenges, there must be strong and low-cost communications infrastructures that can support rapid and secure information exchange as well as consistent and efficient design of communication protocols and architectures to enable automation and effective use of smart energy resources. Internet of Things (IoT) could accelerate establishment of such infrastructures. With IoT technologies, a lot more devices could be controlled and managed through the Internet, and data pertaining to the grid, commercial buildings, and residential premises can be readily collected and utilized. To derive valuable information from the data, further information and data processing becomes essential. There are, however, a number of challenges to be addressed. This Feature Topic (FT) aims to disseminate general ideas extracted from cutting-edge research results spanning multiple disciplines. Potential authors will be able to share various viewpoints and the latest findings from research and ongoing projects relevant to smart energy applications from the perspectives of IoT and advanced information processing and communications technologies.
Last updated by Dou Sun in 2017-02-12
Special Issue on Emerging Trends, Issues and Challenges in Big Data and Its Implementation towards Future Smart Cities
Submission Date: 2017-04-01

The world is experiencing a period of extreme urbanization. Cities in the 21st century will account for nearly 90% of the global population growth, 80% of wealth-creation and 60% of total energy consumption. The world urbanization continues to grow, and the global population is expected to double by 2050. Smart Cities are emerging as a priority for research and development across the world. In general, Smart cities integrate multiple Internet of Things (IoT) and emerging communication technologies such as fifth generation (5G) solutions in a secure fashion to manage a city's assets, such as transportation systems, hospitals, water supply networks, waste management. The goal of building a smart city is to improve the quality of life by using technology to improve the efficiency of services and meet residents' needs. Smart cities' economic growth and large-scale urbanization drive innovation and new technologies. Technology is driving the way city officials interact with the community and the city infrastructure. The rapid progress in smart cities research is posing enormous challenge in terms of large amounts and various types of data at an unprecedented granularity, speed, and complexity are increasingly produced by the sensors of IoT via emerging communication technologies. Meanwhile, the accumulation of huge amounts of data can be used to support smart city components to reach the required level of sustainability and improve living standards. Smart cities have become data-driven, thus effective computing and utilization of big data such as distributed and parallel computing, artificial intelligence and cloud/fog computing are key factors for success in future smart cities. The use of big data can certainly help create cities where infrastructure and resources are used in a more efficient manner. Any smart city project willing to use big data will need to capture, store, process and analyze a large amount of data generated by several sources to transform the data into useful knowledge that is applicable to a decision-making process. For example, with the help of big data and its Implementation, citizens could rapidly find available parking slots in large urban areas; big data can contribute in the city's efforts to reduce pollution through the deployment of street sensors. These sensors can measure traffic flows at different times as well as total emissions. The government can implement actions to divert traffic to less congested areas in a move to reduce carbon emissions in a particular area. In this FT, we would like to try to answer some (or all) of the following questions: How to analyze the mass data that IOT devices produce by future smart cities? How to design the algorithm to process the mass data? How to utilize the machine learning and artificial intelligence techniques to improve the quality of life for future smart cities? How to utilize the "big data" to improve the QoS for future smart cities? How to guarantee the security and the privacy when mass data generated by IOT devices of future smart cities? How to diagnose the fault among the mass IOT devices of future smart cities? How to design the hardware to be suitable to process the mass data, among others?
Last updated by Dou Sun in 2017-02-12
Special Issue on Human-Driven Edge Computing and Communication
Submission Date: 2017-04-01

The vision of Edge Computing considers that tasks are not exclusively allocated on centralized Cloud platforms, but are distributed towards the edge of the network (as in the Internet-of-Things and Fog Computing paradigms), and transferred closer to the business operations via the Content Delivery Networks. The traditional gateway becomes a set-top-box machine, with additional computation and storage capabilities, where micro tasks can be offloaded first, instead of directly to the Cloud. Mobile Edge Computing can also be a more suitable approach to extract knowledge also from privacy sensitive data, which are not to be transferred to third party entities (global cloud operators) for processing. The proliferation of the networking connectivity and the progressive miniaturization of the computing devices have paved the way to the sensor networks and their success in the automation of the several monitoring & control applications. Such networks are built in an ad hoc manner and deployed in an unsupervised manner, without an a-priori design. The consequent availability of long-range communication means at certain nodes of those networks has enabled the possibility of the Internet connection of the sensor network, to make use of cloud-based services. The new challenge addressed by this Feature Topic (FT) is how to put users in the loop so that they can retake control of their information. The massive proliferation of personal computing devices is opening new human-centered designs that blur the boundaries between man and machine. In addition, Edge services are also used to exchange the data collected and processed within the context of the IoT towards external services and/or to visualize them through traditional browser by the users. Now, the frontier for the research on the data management is related to the so-called Edge Computation and Communication, consisting of an architecture of one or more collaborative multitude of computing nodes that are placed between the sensor networks and the cloud-based services. Such a mediating level is responsible for carrying out a substantial amount of data storage and processing to reduce the retrieval time and have more control over the data with respect to the Cloud-based services and to consume less resources and energy to reduce the workload. The interdependencies among those three different levels of storage and computing within an IoT solution are complex and determining at which data should be collocated and elaborated is demanding but not simple to handle. Such a complex situation is further exacerbated if we consider to achieve Quality-of-Service goals such as reliability, availability, security, mobility and energy efficiency, without compromising the correct behavior of the system and the service duration of the devices batteries. Moreover, the interconnection between the sensor networks and the upper level is not simple to be supported, in fact, falls within those situations where traditional Internet architectures fail to provide it effectively. This is because the sensor networks are deployed on hostile and challenging environments implying intermittent connectivity, a heterogeneous mix of nodes, frequent nodal churn, and widely varying network conditions. The aim of this FT is to solicit novel contributions to the current debate on realizing the Edge Computing perspective to the Cloud platforms and Internet of Things by focusing on the human-driven resource management, challenging networking aspects and communication issues, by also seeking practical experiences in using these intelligent solutions in concrete use cases.
Last updated by Dou Sun in 2017-02-12
Special Issue on Amateur Drone Surveillance: Applications, Architectures, Enabling Technologies, and Public Safety
Submission Date: 2017-05-01

The advancement in communication, networking, computation, and sensing technologies has attracted researchers, hobbyists, and investors to deploy mini-drones, officially called unmanned aerial vehicles (UAVs), due to their enormous applications. Drones have boundless viable applications as well due to their small size and capability to fly without an on-board pilot such as in agriculture, photography, surveillance, and numerous public services. The use of drones for achieving high-speed wireless communication is one of the most significant applications for next-generation communication systems (5G). Indeed, drone-based communication network offers versatile solutions to provide wireless connectivity for devices without infrastructure coverage due to e.g., severe shadowing by urban or mountainous terrain, or damage to the communications infrastructure caused by natural disasters. But its deployment poses several public safety (PS) threats to national institutions and assets such as nuclear power plants, historical sites, and government leaders' houses because of drone's ability to carry the explosive and other destructive chemicals and agents. In order to cope with these security threats, surveillance drones (SDrs) deployment is required for surveillance, hunting, and jamming of the amateur drone (ADr). The main motivation of deploying SDrs is to keep an eye on the ADr which can lead to serious disasters in cases where no precautionary measures are taken in a timely manner. The SDr architecture should have the capability to self-configure in case of emergency situations without the help of the central ground control station (GCS). The increasing usage of SDr in surveillance of ADr presents some challenges such as robust detection, tracking, intruder localization, and jamming. The accuracy of detection is a basic requirement of the system. In general, the accurate detection is time-consuming. In fact, a precise moving object detection method makes tracking more reliable and faster, and supports correct classification, which is quite important for SDr to be successful. The existing motion detection algorithms have the problems of computational cost and lower robustness. However, because of rapidly changing extrinsic and intrinsic camera parameters such as pan, tilt, translation, rotation, and zooming, algorithms of highest accuracy are required. Moreover, the machine learning and pattern recognition algorithms are required to detect the ADr by using the characteristics of the electromagnetic waves, sound, images that can efficiently detect the ADr. The next major step after detecting the ADr is its tracking and the localization of the ADr intruder. To accurately estimate the position of the ADr and its intruder, the 3D position estimation algorithms are desired to accurately determine the position of the ADr. The FANET (Flying Ad Hoc Networks) architecture-based deployment and utilization of the commercial frequency bands for SDr also presents the challenges of interference management with the existing system. So, proper spectrum management schemes are desired which can take care of the dynamically changing environment while allocating the spectrum. The last important step after detection, localization, and tracking is the Jamming and hunting of the ADr. For example, the jamming technologies such as by using excess power and global positioning services (GPS) spoofing can generate the high interference to the SDr signal. So, Jamming signal power control algorithm needs to be designed to avoid surrounding SDr jamming. After jamming the ADr, hunting of the ADr should be done by taking care of security of the surrounding environment. That is, ADr in the air should be landed outside the highly sensitive areas. Thus, to get this goal context-aware path-design algorithms are required to safely land the hunted ADr. Hence, the technologies like frequency band recognition, power control, jamming and hunting are required to efficiently detect, control, jam, and hunt the ADr. The goal of the proposed Feature Topic (FT) is to publish comprehensive original research for all readers of the Magazine regardless of their specialty. The main objective of this FT is to bring most recent advances in amateur drone surveillance network architecture and technologies. Moreover, its goal is to address the challenges related to public safety issues posed by the flying of drone in the No-fly zone.
Last updated by Dou Sun in 2017-02-12
Special Issue on Education & Training: Scholarship of Teaching and Supervision
Submission Date: 2017-05-01

The Scholarship of Teaching and Learning (SoTL) encourages educators to examine their own classroom practice, record their successes and failures, and ultimately share their experiences in a formal and scholarly way so that others may reflect on their findings and build upon teaching and learning processes. SoTL acknowledges that concerns for privacy and other ethical issues associated with studies involving human subjects place limits on the types of research that can be conducted in classroom setting. Nevertheless, SoTL provides a mechanism for raising the standard of discussion concerning teaching and learning in the literature. The Scholarship of Research and Supervision (SoRL) is a related concept that invites the same reflective approach to improving the quality of training through research, especially that conducted at the postgraduate level. SoRL invites researchers to examine their own supervisory practice, record their successes and failures, and ultimately share their experiences in a formal and scholarly way so that others may reflect on their findings and improve research and supervision processes. This feature topic on the Scholarship of Teaching and Supervision is intended to hasten the incorporation of SoTL and SoRL into communications engineering curricula by providing educators and researchers with an opportunity to share their experience, best practices and case studies.
Last updated by Dou Sun in 2016-10-17
Special Issue on Heterogeneous Ultra Dense Networks
Submission Date: 2017-05-15

Driven by the development of mobile Internet and smart phones, data traffic grows exponentially in current mobile networks. Initial estimations indicate that, differently 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. The opportunities and challenges of the fifth-generation (5G) technologies rapidly gain great attention from academics, industries, and governments. Ultra dense network (UDN) is a promising technique to meet the requirements of explosive data traffic in 5G mobile communications. Moreover, when overlaid on top of the macrocells, low power small cells (such as femtocell and picocell) can improve the coverage and capacity of cellular networks by exploiting spatial reuse of limited spectrum. Dense small cells can also offload the wireless data traffic of user equipment (UE) from macrocells, especially for an indoor environment where more than 80% of data traffic takes place.
Last updated by Dou Sun in 2017-02-12
Special Issue on Heterogeneous Ultra Dense Networks
Submission Date: 2017-05-15

Driven by the development of mobile Internet and smart phones, data traffic grows exponentially in current mobile networks. Initial estimations indicate that, differently 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. The opportunities and challenges of the fifth-generation (5G) technologies rapidly gain great attention from academics, industries, and governments. Ultra dense network (UDN) is a promising technique to meet the requirements of explosive data traffic in 5G mobile communications. Moreover, when overlaid on top of the macrocells, low power small cells (such as femtocell and picocell) can improve the coverage and capacity of cellular networks by exploiting spatial reuse of limited spectrum. Dense small cells can also offload the wireless data traffic of user equipment (UE) from macrocells, especially for an indoor environment where more than 80% of data traffic takes place.
Last updated by Dou Sun in 2017-02-12
Special Issue on Point-to-Multipoint Communications and Broadcasting in 5G
Submission Date: 2017-06-30

In the last few years since the development of the Internet, streaming video and mobile devices such as Tablets and Smartphone have played prominent roles in modern daily life, and the global communications industry has started working on new, more effective digital broadcast systems that can simultaneously deliver signals to both fixed and mobile devices. Digital broadcast systems are suitable for large-volume broadband information delivery, especially for multimedia communication. In fact, the global rapid growth of mobile data traffic is primarily driven by the massive deployment of mobile video services on modern large-screen devices. In the 3rd Generation Partnership Project (3GPP) Release 9 standards, multimedia broadcast/multicast service (MBMS) has evolved to achieve improved performance with higher speed and more flexible service configuration, named as evolved MBMS (eMBMS). 3GPP Release 11 has also improved service layer with video codec for higher resolution and frame rate and introduced Forward Error Correction (FEC) technique. New technologies such as time frequency slicing (TFS) can increase the network spectral efficiency (in terms of bps/Hz) by potentially tolerating a higher carrier-to-interference signal (C/I) ratio for the network. The TFS is part of an informative (not normative) annex of the digital video broadcasting terrestrial 2nd generation (DVB-T2) specification and is fully adopted in the mobile broadcasting standard digital video broadcasting next generation handheld (DVB-NGH) specification. It is also proposed to adopt layered division multiplexing (LDM) in the upcoming advanced television system committee (ATSC) 3.0 standards, where multiple physical layer data streams are superimposed with different power levels, channel coding and modulation schemes for different services and reception environments. However, in 5G, traffic data volume and terminal mobility model will change dramatically compared with the existing 4G and other communications systems. Related studies and implementation of new technologies are constantly challenged with the growth of requirements by large numbers of users, improved device capabilities and deployment of higher capacity networks. To create new hybrid services and augmented regular broadcasts with greater interactivity, current development projects address the following requirements that may apply in 5G: 1) Higher quality signals and better source coding, such as immersive audio, Ultra HDTV, and multi-screen/multi-view system, 2) Simultaneously broadcast everywhere, to both fixed users at home and mobile users on smart phones and tablets. 3) Higher data volume and density of transmission, such as super layered division multiplexing (SLDM). 4) Terminal mobility considering fast moving scenarios. To meet these requirements, proposed solutions include the following items: 1) More effective utilization of spectrum. 2) Providing broadcasters the option to offer multiple channels within the same bandwidth, plus the ability to simultaneously broadcast to TVs at home and to Smartphones / Tablets on the go. 3) Cooperation among cells to support broadcasting demands, 4) Broadcast transmission of content developed for and sent over the Internet based on the Internet protocol. To seek solutions to the current challenges, we have planned this Feature Topic (FT) to describe recent progress in academic and industrial research and help both the industrial and academic research communities better understand the progress and potential research areas on the converging paths of point to multi-point communications and broadcasting in 5G.
Last updated by Dou Sun in 2017-02-12
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