With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)

Imran, Muhammad Ali

Ni, Qiang

Qadir, Junaid

Vasilakos, Athanasios V.

Yau, Kok-Lim Alvin

English

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IEEE Access special section editorial: Artificial intelligence enabled networking

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SPECIAL SECTION ON ARTIFICIAL INTELLIGENCE ENABLED NETWORKINGDate of current version January 14, 2016.Digital Object Identifier 10.1109/ACCESS.2015.2507798EDITORIALIEEE ACCESS SPECIAL SECTION EDITORIAL:ARTIFICIAL INTELLIGENCE ENABLEDNETWORKINGWith today’s computer networks becoming increasinglydynamic, heterogeneous, and complex, there is great inter-est in deploying artificial intelligence (AI) based techniquesfor optimization and management of computer networks.AI techniques—that subsume multidisciplinary techniquesfrom machine learning, optimization theory, game theory,control theory, and meta-heuristics—have long been appliedto optimize computer networks inmany diverse settings. Suchan approach is gaining increased traction with the emergenceof novel networking paradigms that promise to simplifynetwork management (e.g., cloud computing, network func-tions virtualization, and software-defined networking) andprovide intelligent services (e.g., future 5Gmobile networks).Looking ahead, greater integration of AI into networkingarchitectures can help develop a future vision of cognitivenetworks that will show network-wide intelligent behavior tosolve problems of network heterogeneity, performance, andquality of service (QoS).IEEE ACCESS is the new multidisciplinary, flagshipopen-access journal of IEEE that is committed to present-ing the results of high-quality research across all of theIEEE’s fields of interest. The objective of this SpecialSection on Artificial Intelligence Enabled Networking inIEEE ACCESS is to document the state of the art in thisfast-developing exciting area of networking research. Thetopic of this Special Section lies at the intersection of anumber of complementary specializations such as machinelearning/AI, cognitive sciences, big data, etc. This is reflectedin the diversity of techniques and networking configurationsthat we observe in the papers accepted in this Special Section.Overall seven high-quality papers have been accepted fromleading groups around the world after a rigorous peer-reviewprocess. The accepted papers include comprehensive survey/tutorial and position papers from leading experts as well asoriginal research on new and emerging topics. The acceptedpapers focus on a number of distinct network configura-tions such as Internet of Things (IoT); 4G and 5G networkssuch as long-term evolution (LTE) and heterogeneousnetworks (HetNets); cognitive networks; and vehicularad-hoc networks. These papers propose a wide spectrum oftechniques such as reinforcement learning (RL), deep neuralnetworks (DNN), and tensor voting.We start by discussing the two survey papers, and theposition paper, that have been accepted in this SpecialSection.In the article ‘‘Artificial intelligence based techniques foremerging heterogeneous networks: State of the arts, opportu-nities and challenges’’, Xiaofei Wang et al. provide a detailedsurvey of how AI-based techniques apply to emergingheterogeneous networks. The use of AI-based techniques formobile cellular networks is becoming increasingly impor-tant as mobile networks are becoming more complex. Theauthors describe how AI can be used to surmount the myriadchallenges related to management, optimization, andmaintenance of mobile networks that crop up due to thefast developing mobile communication industry. The use ofAI techniques can be use to enable HetNets in becomingintelligent, self-organizing, and autonomously-evolvingnetworks. The authors provide a detailed taxonomy ofAI-related techniques from a wide variety of fields (includ-ing machine-learning, bio-inspired algorithms, fuzzy neuralnetworks).In the article ‘‘Neighbour discovery for opportunisticnetworking in Internet of Things scenarios: A survey’’,Pozza et al. have provided a survey of neighbor discov-ery techniques for opportunistic networking in Internet ofThings (IoT) scenarios. The problem of neighbor discoveryis becoming increasingly important with the emergence ofopportunistic networking in IoT scenarios (such as smartcities)—in such scenarios, acquiring and predicting pat-terns of node encounters becomes fundamentally importantsince the success of communication relies on exploitingthe availability of fleetingly-available dynamic end-to-endpaths. AI techniques can be leveraged in such settings tolearn and thereafter exploit knowledge of the mobility pat-terns of users and devices. In this survey paper, the authorspresented a detailed taxonomy through which mobility-aware and mobility-agnostic neighbor discover approacheswere introduced. It was shown that mobility-aware neighbordiscovery approaches can can profit significantly by optimiz-ing from their knowledge of mobility.Michele Zorzi et al. presents a detailed position paper titled‘‘Cognition-based networks: A new perspective on networkoptimization using learning and distributed intelligence’’VOLUME 3, 20152169-3536  2016 IEEE. Translations and content mining are permitted for academic research only.Personal use is also permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 3079IEEE Access Special Section Editorialthat provides a deeply interdisciplinary look into the sys-tematic application of advanced machine learning techniquesto system-wide learning, modeling, optimization, and datarepresentation. In particular, a blueprint of a new class ofcognition-based networks (dubbed COBANETs) that willincorporate advanced machine learning—particularly,unsupervised deep learning and probabilistic generativemodels using techniques such as generative deep neuralnetworks (GDNN)—in a setup that exploits recent network-architectural advances such as network virtualization andsoftware-defined networking. Such a networking visionis compelling and can break new ground in developingbetter ways to manage and optimize telecommunicationnetworks. Preliminary investigation has been conductedto train the GDNNs using the size of the encoded dataframes in order to improve the estimation of the quality-rate characteristics of video flows, an important function inquality-of-experience (QoE)-aware resource managementschemes. Various research challenges that need to beaddressed in GDNNs are presented.Other than the two survey papers and the positionpaper discussed above, we have accepted four researchpapers that have proposed original AI-based solutions fornetworking problems. We briefly introduce these papersnext.In the article ‘‘Resource management and inter-cell-interference coordination in LTE uplink system using ran-dom neural network and optimization’’, Adeel Ahsan et al.present an AI-based framework based on random neuralnetworks (RNN) and genetic algorithms (GA) for resourcemanagement and inter-cell interference (ICI) coordinationin LTE uplink system. The presence of inter-cell interfer-ence, caused by collisions between resource blocks, can limitthe coverage and capacity of the uplink system in LTE.The author propose their power-control-based ICI solutionbased on RNN and GA which significantly improves thestate-of-the-art solutions based on traditional artificial neuralnetwork (ANN) models.Reinforcement learning (RL) is an AI-based approachthat enables a decision maker to observe, learn, and takeactions in its operating environment in order to increaseits accumulated reward. RL promises to play a major rolein AI-enabled cognitive networks of the future. In thisspecial section, we have two papers that have proposedRL-based solutions. In the work by Morozs Nil et al.(‘‘Heuristically accelerated reinforcement learning fordynamic secondary spectrum sharing’’), an enhancedRL approach called heuristically accelerated reinforcementlearning (HARL) is applied to the problem of dynamicspectrum sharing in LTE cellular networks. HARL utilizesexternal information, specifically radio environment map(REM), to guide, and hence speed up, the learning process.Compared to the traditional RL approach and a heuristicLTE solution, HARL has been shown to reduce the secondarysystems’ interference to the primary systems, as well as theprobability of retransmission, while achieving higher systemthroughput compared to the state-of-the-art reinforcementlearning or purely heuristic LTE solutions. In the work byCelimuge Wu et al. (‘‘Packet size-aware broadcasting inVANETs with fuzzy logic and RL-based parameter adap-tation’’), a fuzzy logic-based algorithm consists of fuzzymembership functions is applied to a broadcast protocol toselect the best relay node by taking into account multiplemetrics including link quality, inter-vehicle distance andvehicle mobility. RL is applied to tune the fuzzy membershipfunctions according to the network environment so that thefuzzy logic-based algorithm can adapt to various networkscenarios. Both real-world experiments and computer sim-ulations have been shown to increase packet forwarding ratioand reduce number of required transmissions in multi-hopbroadcasting.In the article ‘‘Tensor voting techniques and applications inmobile trace inference’’, Erte Pan et al. have proposed the useof tensor voting techniques to address the tracking problemof inferring human mobility traces. While the use of tensormathematics is common in maths and physics, very littlework has focused on the use of tensor techniques—that havesignificant applications for the AI task of automatic infer-ence and perceptual grouping—in wireless networks. Thehuman-tracking mobile trace inference problem is becom-ing increasingly important with the emergence of location-based networking services in modern life where smartphonesare becoming the hub of interpersonal communication. Theauthors consider the mobile trace inference problem whenthe recorded location information is noisy and missing dataand propose a tensor voting based approach that uses a sparsetensor voting algorithm. The authors have evaluated theirproposed solution on real human mobility traces with theresults demonstrating the ability of the proposed solution toeffectively recover human mobility trace from incompleteand noisy data.While the topic of AI-enabled networking is vast, we arehappy with the technical depth and the range of topics cov-ered in this special section. In the future, we anticipate thatmachine learning and artificial intelligence will be appliedto more and more facets of communications and networking.We hope that the networking community benefits from theinsights presented in this paper and that it provides a balancedsnapshot of the range and breadth of AI-enabled networking.We sincerely thank all the authors and reviewers for their helpand efforts in helping us curate a high-quality special sectionon an important topic. We will also like to thank the staffmembers and the Editor-in-Chief of IEEE ACCESS for theirguidance and help.JUNAID QADIRKOK-LIM ALVIN YAUMUHAMMAD ALI IMRANQIANG NIATHANASIOS V. VASILAKOS3080 VOLUME 3, 2015IEEE Access Special Section EditorialJUNAID QADIR (SM’14) received the B.S. degree in electrical engineering from UET,Lahore, Pakistan, and the Ph.D. degree from the University of New South Wales,Australia, in 2008. He served as an Assistant Professor at the School of Electrical Engineeringand Computer Sciences, National University of Sciences and Technology, Pakistan, from2008 to 2015, where he also directed the Cognet Laboratory. He is currently an AssociateProfessor with Information Technology University Punjab, Lahore. His research interestsinclude the application of algorithmic, machine learning, and optimization techniques innetworks, in particular, wireless networks, cognitive networking, software-defined networks,and cloud computing. He is a member of ACM. He has received the Highest National TeachingAward in Pakistan—the Higher Education Commission’s Best University Teacher Award—forthe year 2012–2013. He has been nominated for this award twice (2011, and 2012–2013).He is a regular reviewer for a number of journals and has served on the program committeeof a number of international conferences. He serves as an Associate Editor of IEEE ACCESS,the IEEE Communications Magazine, and Big Data Analytics.KOK-LIM ALVIN YAU received the B.Eng. (Hons.) degree in electrical and electronicsfrom the Universiti Teknologi Petronas, Malaysia, in 2005, the M.Sc. degree in electricalengineering from the National University of Singapore, in 2007, and the Ph.D. degree innetwork engineering from the Victoria University of Wellington, New Zealand, in 2010.He is currently an Associate Professor with the Department of Computer Science andNetworked Systems, Faculty of Science and Technology, Sunway University. He has authoredor co-authored over 40 publications in international journals, book chapters, and conferences.The focus of his research is in the areas of cognitive radio, wireless networking, appliedreinforcement learning, network routing, network security, and information and communica-tions technology in education. He received the 2014 Sunway University Award for Excellencein Teaching, and the 2007 Professional Engineer Board of Singapore Gold Medal for beingthe best graduate of the M.Sc. degree in 2006/07. He serves as the TPC and Reviewer forvarious major international conferences, including ICC, VTC, LCN, Globecom, and AINA.He also served as the General Co-Chair of IET ICFCNA’14 and the Co-Chair of the Organizing Committee of IET ICWCA’12.He is the Guest Editor of the Special Issue on Creating a Smarter Environment through the Advancement of CommunicationSystems, Networks and Applications of IET Networks. He is a regular reviewer for over 15 journals, including the IEEE journalsand magazines, Ad Hoc Networks, IET Communications, and others.MUHAMMAD ALI IMRAN (SM’12) received the M.Sc. (Hons.) and Ph.D. degrees fromImperial College London, U.K., in 2002 and 2007, respectively. He is currently a Reader withthe Centre for Communication Systems Research, University of Surrey, U.K. He has a globalcollaborative research network spanning both academia and key industrial players in the field ofwireless communications. He has played a lead role in a number of multimillion internationalresearch projects, including in the area of new physical layer work for the 5G Innovation Centreat Surrey. He has supervised 17 successful Ph.D. graduates and authored over 150 peer-reviewedresearch papers, including in over 20 IEEE journals. His research interests include the derivationof information theoretic performance limits, energy efficient design of cellular system, andlearning/self-organizing techniques for optimization of cellular system operation. He is a fellowof the Higher Education Academy, UK. He is an Associate Editor of IET Communications anda Guest Editor of special issues in the IEEE Communications Magazine and the IEEE WirelessCommunications Magazine.VOLUME 3, 2015 3081IEEE Access Special Section EditorialQIANG NI (SM’08) received the B.Sc., M.Sc., and Ph.D. degrees in engineering fromthe Huazhong University of Science and Technology, Wuhan, China. He led the IntelligentWireless Communication Networking Group, Brunel University, London, U.K. He was aSenior Researcher with the Hamilton Institute, National University of Ireland Maynooth. From2001 to 2004, he was with INRIA France as a Researcher. His main research interests arecommunications, networking, cognitive radio networks, and smart grid technologies. He iscurrently a Professor (Chair) and the Head of the Communications Group with the Schoolof Computing and Communications, Lancaster University, Lancaster, U.K. He has authoredover 100 papers that have attracted over 4200 citations. He is a fellow of IET and The HigherEducation Academy, and was an IEEE 802.11 Wireless Standard Working Group VotingMember and a Contributor to the IEEE wireless standards. He is the Editor of the Journal ofSecurity and Communication Networks (Wiley), KSII Transactions on Internet and InformationSystems, and IEEK Transactions on Smart Processing & Computing. He is the Guest Editorfor IET Networks’ Special Issue on Creating a Smarter Environment through the Advancement of Communication Systems,Networks and Applications, July 2015.ATHANASIOS V. VASILAKOS is currently a Professor with the Lulea University of Tech-nology, Sweden. He served/serves as an Editor for many technical journals, such as theIEEE TRANSACTIONS ON NETWORK AND SERVICEMANAGEMENT, the IEEE TRANSACTIONS ONCLOUD COMPUTING, the IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,the IEEE TRANSACTIONS ON CYBERNETICS, the IEEE TRANSACTIONS ON NANOBIOSCIENCE,the IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, ACM Transac-tions on Autonomous and Adaptive Systems, and the IEEE JOURNAL ON SELECTED AREASIN COMMUNICATIONS. He is the Founding Editor-in-Chief of the International Journal ofAdaptive and Autonomous Communications Systems and the International Journal of Arts andTechnology. He is also the General Chair of the European Alliances for Innovation.3082 VOLUME 3, 2015

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