EditorialAdvances in Mobile Networking for IoT Leading the 4thIndustrial Revolution

Jeongyeup Paek ,1 Andrea Gaglione ,2 Omprakash Gnawali,3 Marcos A. M. Vieira ,4

and Shuai Hao 5

1Chung-Ang University, Seoul, Republic of Korea2Digital Catapult, London, UK3University of Houston, Houston, TX, USA4Universidade Federal de Minas Gerais, Belo Horizonte, Brazil5AT&T Labs Research, Bedminster, NJ, USA

Correspondence should be addressed to Jeongyeup Paek; jpaek@cau.ac.kr

Received 15 July 2018; Accepted 19 July 2018; Published 18 December 2018

Copyright © 2018 Jeongyeup Paek et al. $is is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

$e explosion of connected lightweight devices is startingthe era of the Internet of things (IoT) where physical worlddevices have a digital presence on the Internet. Today,connected embedded devices are being placed everywhere inour everyday life, and tens of billions of these devices areexpected to be connected to the Internet in the near future.Technologies for thing-to-thing communication betweenthese devices, along with machine-learning and big datatechnologies, will be one of the key enablers for the trulyautonomous and distributed IoT leading the 4th industrialrevolution. However, there are still several technical chal-lenges remaining before the next industrial revolution. Forexample, how to efficiently distribute and manage net-working resources for more scalable services, as well as howto improve the energy efficiency and bandwidth utilizationof the whole system, is a significant challenge impeding thedevelopment and implementation of truly autonomous anddistributed applications and services for IoT.

$is special issue has focused on technical challengesthat can enable IoT. We called for manuscripts presentingand discussing the most recent advances in networkedsystems for IoT. Until the deadline of the special issue, 20manuscripts have been received worldwide. After the reviewprocess, 14 manuscripts have been accepted by this specialissue. $e accepted research manuscripts have focused onlarge-scale industrial IoT services, SDN and network vir-tualization for IoT, efficient communication methods for

IoT, and various emerging IoT applications. Acceptedmanuscripts present the important research findings, andthese advances will contribute to the development of IoTleading the 4th industrial revolution.We provide a summaryof the accepted papers below.

Large-scale industrial IoTservices are emerging in smartfactory domains such as factory clouds which integratedistributed small factories into a large virtual factory withdynamic combinations based on orders of consumers. Sincea smart factory has many industrial elements includingvarious sensors/actuators, gateways, controllers, applicationservers, and IoT clouds and their connections and relationsare complex, it is hard to handle them in a point-to-pointmanner. To address this challenge, a novel software-definednetwork multicast based on the group-shared tree is pro-posed which includes the near-receiver rendezvous pointselection algorithm and group-shared tree-switchingmechanism. $e proposed multicast mechanism can re-duce packet loss and delay compared to the legacy methodsunder severe congestion condition.

In smart manufacturing, production machinery andauxiliary devices, referred to as industrial Internet of things(IIoT), are connected to a unified networking infrastructurefor management and command deliveries in a preciseproduction process. However, providing autonomous, re-liable, and real-time services for such a production is anopen challenge since these IIoT devices are assumed

HindawiMobile Information SystemsVolume 2018, Article ID 8176158, 3 pageshttps://doi.org/10.1155/2018/8176158

lightweight embedded platforms with limited computingperformance. To overcome this challenge, a pattern-identifiedonline task scheduling (PIOTS) mechanism for the net-working infrastructure is proposed where multitier edgecomputing is provided to handle the offloaded tasks in realtime. Historical IIoT task patterns in every timeslot are used totrain a self-organizing map (SOM), which represents thefeatures of the task patterns within defined dimensions.Consequently, offline task scheduling among edge computing-enabled entities is performed on the set of all SOM neuronsusing the Hungarian method to determine the expected op-timal task assignments. Whenever a task arrives at the in-frastructure, the expected optimal assignment for the task isscheduled to the appropriate edge computing-enabled entity.

Making SDN data plane flexible enough to satisfy thevarious requirements of heterogeneous IoT applications isdesirable in terms of software-defined IoT (SD-IoT). For thispurpose, a new in-network data-processing scheme for theSD-IoT data plane is proposed that defines an event-drivendata-processing model which can express a variety of in-network data-processing (e.g., the sensing-data aggregationfrom thousands of sensor nodes) cases in the SD-IoT en-vironment. Also, the proposed model comprises a languagefor the programming of the data-processing procedures,while a flexible data-plane structure that can install andexecute the programs at runtime is additionally introduced.

Container-based virtualization can offer advantages suchas high performance, resource efficiency, agile environment,and ease of management for IoTdevices. However, differentnetwork modes of containers and their performance issueshave not been investigated so far. $us, an analysis of thecontainer network performance on an IoT device is pre-sented. $e results show that the network performance ofcontainers is lower than that of the native Linux, with anaverage performance difference of 6% and 18% for TCP andUDP, respectively. In addition, the network performance ofcontainers varies depending on the network mode. When asingle container runs, the bridge mode achieves higherperformance than the host mode by 25%, while the hostmode shows better performance than the bridge mode by45% in the multicontainer environment.

Remote and personalized healthcare is one of the keyapplications for IoT. Many of these applications are built onmobile devices connected to the cloud. Although appealing,however, prototyping and validating the feasibility of anapplication-level idea is yet challenging without a solidunderstanding of the cloud, mobile, and interconnectivityinfrastructure. A solution to this is provided by proposing aframework called HealthNode, which is a general-purposeframework for developing healthcare applications on cloudplatforms with a focus on ease of implementation.HealthNode presents an explicit guideline while supportingnecessary features to achieve quick and expandable cloud-based healthcare applications. A case study applyingHealthNode to various real-world health applications sug-gests that HealthNode can express the architectural structureeffectively within an implementation and that the proposedplatform can support system understanding and softwareevolution.

$e cognitive radio network is a key technique for theIoT and can effectively resolve the spectrum issues for IoTapplications. In this context, a novel method for IoT sensornetworks is proposed to obtain the optimal positions ofsecondary information-gathering stations (SIGSs) and toselect the optimal operating channel. $e objective is tomaximize secondary system capacity while protecting theprimary system. In addition, an appearance probabilitymatrix for secondary IoT devices (SIDs) is proposed tomaximize the supportable number of SIDs that can be in-stalled in a car, in wearable devices, or for other monitoringdevices, based on optimal deployment and probability.Fitness function is proposed based on the above objectivesand also considers signal-to-interference-plus-noise ratio(SINR) and position constraints. $e particle swarm opti-mization (PSO) technique is used to find the best positionand operating channel for the SIGSs.

Demand for autonomous vehicles is increasing rapidlyowing to their enormous potential benefits. However, severalchallenges such as vehicle localization are involved in thedevelopment of autonomous vehicles. A simple and securealgorithm for vehicle positioning is proposed withoutmassively modifying the existing transportation in-frastructure. For localization, vehicles on the road areclassified into two categories: host vehicles (HVs), which arethe ones used to estimate other vehicles’ positions, andforwarding vehicles (FVs), which are the ones that move infront of the HVs.$e FV transmits modulated data from thetail (or back) light, and the camera of the HV receives thatsignal using optical camera communication (OCC). Inaddition, the streetlight (SL) data are considered to ensurethe position accuracy of the HV. Using photogrammetry, thedistance between FV or SL and the camera of the HV iscalculated by measuring the occupied image area on theimage sensor. Comparing the change in distance betweenHV and SL with the change in distance between HV and FV,the positions of FVs can be determined.

$e vehicular communication network allows vehicleson the road to communicate with other vehicles or nodes inthe Internet. However, in the highway environment withsparsely placed roadside units (RSUs), communicationsbetween RSUs and vehicles are frequently disconnected dueto high vehicular speeds. To resolve this problem, an en-hanced routing mechanism based on AODV is proposedwhich utilizes both V2I and V2V communications so thatRSUs can provide continuous services to vehicles which maybe intermittently located outside the coverage areas of RSUs.To reduce the route recovery time and the number of routefailures in the sparsely placed RSU environment, backuproutes are established through the vehicles with longer directcommunication duration with the RSU. For efficienthandover to the next RSU, the route-shortening mechanismis also proposed.

Eye-blinking detection or eye-tracking algorithms havevarious applications in mobile environments such as acountermeasure against spoofing in face recognition sys-tems. In resource-limited smartphone environments, how-ever, one of the key issues is their computational efficiency.To tackle the problem, a hybrid approach combining the two

2 Mobile Information Systems

machine learning techniques SVM and CNN is proposedsuch that the eye-blinking detection can be performed ef-ficiently and reliably on resource-limited smartphones.Experimental results on commodity smartphones haveshown that the proposed approach achieves a precision of94.4% and a processing rate of 22 frames per second.

Finally, head-mounted displays (HMDs) are currentlyattracting great attention since they can provide animmersive virtual reality (VR) experience at an affordablecost. At the same time, 3D maps such as Google Earth andApple Maps 3D mode in which users can navigate in 3Dmodels of the real world are widely available in currentmobile and desktop environments. However, traditionalinterface methods such as keyboard and mouse are in-appropriate for the navigation through 3D maps in VRenvironments because the manipulation method does notresemble actual actions in reality. Motivated by this, animmersive gesture interfaces for the navigation through 3Dmaps are proposed which are suitable for HMD-basedvirtual environments. An algorithm to capture and recog-nize the gestures in real time using a Kinect depth camera isalso proposed.

Conflicts of Interest

$e editors declare that they have no conflicts of interest.

Acknowledgments

We would like to thank all the reviewers that have con-tributed their time and insight to this special issue.

Jeongyeup PaekAndrea Gaglione

Omprakash GnawaliMarcos A. M. Vieira

Shuai Hao

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