Editorial Location-Based Mobile Marketing Innovations

EditorialLocation-Based Mobile Marketing Innovations

Jaegeol Yim,1 Subramaniam Ganesan,2 and Byeong Ho Kang3

1Department of Computer Engineering, Dongguk University, Gyeongju, Republic of Korea2Electrical and Computer Engineering, Oakland University, Rochester, MI, USA3School of Engineering and ICT, University of Tasmania, Hobart, TAS, Australia

Correspondence should be addressed to Jaegeol Yim; [email protected]

Received 10 July 2017; Accepted 10 July 2017; Published 12 October 2017

Copyright © 2017 Jaegeol Yim et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The increasing complexity of the industry means that mar-keters must now be experts not only in marketing but alsoin people, data, delivery platforms, and mobile location-based marketing. The objective of location-based marketingvia mobile devices is to encourage those activities as wellas drive foot traffic, share discounts, and build customerloyalty. Mobile devices have been used to gather informationabout nearby businesses including reviews, directions, callingthe business, and using the businesses’ mobile app. Withlocation-based mobile marketing, the business is easy to findand have skillfully combined location-based marketing withan overall targeted marketing approach that includes socialmedia, push notifications, email newsletters, and also offlinemarketing.

The objective of this special issue is to bring togetherresearch contributions of unpublished research on the recentdevelopment and innovations about location-based mobilemarketing (LBMM). The fundamental infrastructure ofLBMM is obviously location-based service (LBS) systems.The most distinguished parts of LBS systems include posi-tioning components and map provisioning components thatare running on the general computing systems. On top ofthe LBS system, LBMM requires providing more intelligentmarketing oriented services to customers. More and moremachine learning methods, especially convolutional neuralnetworks, have been used to analyze user behavior andclassify usage patterns in development of intelligent services.With increasing model training parameters and data scales,the traditional single machine training method cannot meetthe requirements of time complexity in practical applicationscenarios. Therefore, the data parallel model or parallelmethod has been employed in order to speed up the training

process. The current training framework often uses simpledata parallel or model parallel methods that do not fully uti-lize heterogeneous computing resources. The paper entitled“A Parallel Strategy for Convolutional Neural Network BasedonHeterogeneous Cluster forMobile Information System” byJ. Zhang et al. proposes a delayed synchronization convolu-tional neural network using the heterogeneous system. Thisis a parallel strategy based on both synchronous parallel andasynchronous parallel approaches, and the model trainingprocess can reduce the dependence on the heterogeneousarchitecture in the premise of ensuring the model conver-gence.Therefore, the convolution neural network frameworkis better suited for different heterogeneous system environ-ments.

Intelligent service is to guess desired services that the cus-tomermight want and recommend them to the customer.TheSlopeOne model has been widely used in guessing desiredservices.Thepaper entitled “CollaborativeQoSPrediction forMobile Service with Data Filtering and SlopeOne Model” byY. Yin et al. proposes a data filtering-extended SlopeOnemodel (based on collaborative filtering).The idea behind thismodel is based on the characteristics of a mobile service andthe relation with location. With the model, the quality ofservice (QoS) values can be predicted.

As an important source of mobile location-based data,taxi mobility information can be referred to when makingmarketing decisions. Studying the behavioral patterns of taxisin a city during the holidays using the global positioning sys-tem (GPS) can yield remarkable insights into people’s holidaytravel patterns, as well as the odd-even day vehicle prohibi-tion system. Using GPS data, the paper entitled “ExploringIntracity Taxi Mobility During Holidays for Location-Based

HindawiMobile Information SystemsVolume 2017, Article ID 1303919, 3 pageshttps://doi.org/10.1155/2017/1303919

https://doi.org/10.1155/2017/1303919

2 Mobile Information Systems

Marketing” by W.Wang et al. studied the behavioral patternsof taxis during specific holidays in terms of pick-up anddrop-off locations, travel distance, mobile step length, traveldirection, and radius of gyration.

LBMM gathers and uses information about users’ loca-tions. Therefore, ensuring information security and privacyis one of the most important concerns of users. The paperentitled “AHybrid LocationPrivacy Solution forMobile LBS”by R. Gupta andU. P. Rao proposes a hybrid solution,HYB, toachieve location privacy for themobile userswhouse locationservices frequently. The proposed HYB scheme is based onthe collaborative preprocessing of location data and utilizesthe benefits of homomorphic encryption technique. Locationprivacy is achieved at two levels, namely, at proximity leveland at distant level. The proposed HYB solution preservesuser’s location privacy effectively under specific, pull-based,sporadic query scenario.

LBMM systems and as an example the sensor nodes oftenface various attacks where the attackers try to manipulatethe estimated location or try to provide false beacons.The paper entitled “A Secure Localization Approach UsingMutual Authentication and Insider Node Validation inWire-less Sensor Networks” by G. Kumar et al. has proposeda methodology that will address this problem of securityaspects in localization of the sensor nodes. Moreover, theyhave considered the network environmentwith randomnodedeployment and mobility as these two conditions are lessaddressed in previous research works. Further, they proposedan algorithm that requires low overhead due to the usageof less control messages in a limited transmission range. Inaddition, they have also proposed an algorithm to detectthe malicious anchor nodes inside the network. The paperentitled “Location Privacy Protection Based on Improved K-Value Method in Augmented Reality on Mobile Devices” byC. Yin et al. proposes a privacy protectionmethod combiningthe k-anonymity with pseudonym methods and shows thatthe method can effectively anonymize all service requests.The paper titled “An Enhancement of Optimized DetectionRule of Security Monitoring and Control for Detection ofCyber Threat in Location-Based Mobile” by W. Park andB. H. Kang analyzes SNORT detection rules and proposesa guideline of SNORT rule optimizations to improve theefficiency and accuracy of intrusion detection operations.This enables the intrusion detection system to bemore securein location-based mobile services.

In the mobile service environment, it is required thatmobile terminals should efficiently use wireless networkresources. In addition, because video streaming becomes amajor service among the data services of mobile terminalsin heterogeneous networks, the necessity of the efficientnetwork access control for heterogeneous wireless networksis raised as an important topic. The paper titled “NetworkAccess Control for Location-Based Mobile Services in Het-erogeneous Wireless Networks” by D.-Y. Kim et al. proposesa novel network access control in heterogeneous wirelessnetworks.The proposedmethod estimates the network statuswith Naıve Bayesian Classifier and performs network accesscontrol according to the estimated network status. Thus, it

improves data transmission efficiency to satisfy the quality ofservices.

Mobility management is an essential challenge for sup-porting reliable multimedia data streaming over wirelessand mobile networks in the Internet of Things (IoT) forLBMM applications. The paper titled “Design of OptimizedMultimedia Data Streaming Management Using OMDSMover Mobile Networks” by B. Park et al. introduces a newenhanced data streaming route optimization scheme thatuses an optimized Transmission Control Protocol (TCP)realignment algorithm in order to prevent the packet disor-dering problem whenever the nodes in the IoT environmentare communicating with each other. With the proposedscheme, data packets sequence realignment can be prevented,the packet traffic speed can be controlled, and the TCPperformance can be improved.

In large-scale location-based services, an ideal situationis that self-adapting routing strategies use future traffic dataas input to generate a topology which could adapt well tothe changing traffic. The paper entitled “A Traffic Predic-tion Model for Self-Adapting Routing Overlay Network inPublish/Subscribe System” by M. Chi et al. proposes a trafficprediction model for the broker in publish/subscribe system,which can predict the traffic of the link in future by usingneural networks.

As LBMM systems evolve and become more and moreintelligent, they require more and more complicated oper-ations and computing resources. As a result, executingthem on the mobile computing systems takes too muchtime or becomes impossible. To overcome this problem, acomputation offloading technique can be used to executecertain tasks of LBMM in cloud and fog environments.The paper entitled “Offloading Method for Efficient Use ofLocal Computational Resources in Mobile Location-BasedServices Using Clouds” by Y. Son and Y. Lee introduces acomputation offloading technique that utilizes fog computingto improve the performance of virtual machines running onmobile devices. LBMM systems make use of virtual machinetechnology in order to run on a variety of platforms.

LBMM systems, with most components of them run-ning on mobile terminals with limited computing resour-ces, should be optimized in terms of resource consumption—especially battery power. The paper entitled “AutomaticOptimizer Generation Method Based on Location and Con-text Information to Improve Mobile Services” by Y. Sonet al. introduces a technique to automatically generate acustomized service optimizer for each application, servicetype, and platform using location and situation information.By using the proposed technique, energy and computingresources can be more efficiently employed for each service.Thus, users should receive more effective LBSs on mobiledevices, such as smartphones.

If we can detect the difference between two businessprocess models, we can quickly build a new model byadjusting the existing one. The paper entitled “DetectingDifference between Process Models Based on the RefinedProcess Structure Tree” by J. Fan et al. presents a newapproach that detects the difference of the entire composition.Firstly, it parses the process models to their corresponding

Mobile Information Systems 3

refined process structure trees (PSTs). Then it converts thePSTs into their corresponding task based process structuretrees (TPSTs). As a consequence, the problem of detectingdifferences between two process models is transformed todetect difference between two TPSTs. Finally, it obtains thedifferences between two TPSTs based on the divide-and-conquer strategy, where the differences are described by anedit script and it makes the cost of the edit script close tominimum.

New information technologies including sensors, indoorpositioning, augmented reality, vision, and interactive sys-tems should be utilized by retailers in order to improveoperational efficiency and customer experience. The paperentitled “Use of the Smart Store for PersuasiveMarketing andImmersive Customer Experiences: A Case Study of KoreanApparel Enterprise” by H. Hwangbo et al. employs the term“smart store” to indicate retail stores equippedwith these newtechnologies and modern marketing concepts. They summa-rize discussions related to smart stores and their possibleapplications in a real business environment. Furthermore,they present a case study of a business that applies the smartstore concept to its fashion retail shops in Korea.

Acknowledgments

Yim’s work was supported by Small and Medium Busi-ness Association (C0443742), by Dongguk University Re-search Fund, and by the Ministry of Knowledge Economy(10037393).

Jaegeol YimSubramaniam Ganesan

Byeong Ho Kang

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Editorial Location-Based Mobile Marketing Innovations