Editorial Internet of Everythingdownloads.hindawi.com/journals/misy/2017/8035421.pdfInternet of Everything LaurenceT.Yang,1 BeniaminoDiMartino,2 andQingchenZhang1 1St.FrancisXavierUniversity,Antigonish,NS,Canada

EditorialInternet of Everything

Laurence T. Yang,1 Beniamino Di Martino,2 and Qingchen Zhang1

1St. Francis Xavier University, Antigonish, NS, Canada2University of Campania “Luigi Vanvitelli”, Caserta, Italy

Correspondence should be addressed to Laurence T. Yang; [email protected]

Received 16 May 2017; Accepted 16 May 2017; Published 3 July 2017

Copyright © 2017 Laurence T. Yang et al. This 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 is properlycited.

Due to recent advancements in big data, connection tech-nologies, and smart devices, our environment is transforminginto an “Internet of Everything” (IoE). The Internet ofEverything has become a catch-all phrase to describe addingconnectivity and intelligence to just about every device inorder to give them special functions. However, this can bequite reductive, as IoE provides links among not only things,but also data, people, and (business) processes. Evolution ofcurrent sensor and device networks, with strong interactionwith people and social environments, will have a dramaticimpact on everything from city planning, first responders,military, and health. Several Internet and connection-basedparadigms fall under the IoE umbrella, such as Internet ofThings (IoT), Internet of People (IoP), and Industrial Internet(II). While such areas cover many aspects of today’s life, thereis still the strong requirement to contextualize and integratedata and information coming from different networks andframeworks. Indeed, there is a need to provide a commonground for integrating information coming from heteroge-neous sources. Such a shared ecosystem would allow for theinteraction among data, sensor inputs, and heterogeneoussystems.

Therefore, it is high time to call for contributions tofurther stimulate the continuing efforts to enable such anintegrated framework; some fundamental issues are requiredto be addressed to provide the necessary bridge betweendifferent data representations and to solve terminologyincongruence. This special issue attracted numerous submis-sions, out of which twenty papers have been accepted to bepublished. These accepted papers are expected to highlight

some of the important aspects outlined above. It is thepleasure for the guest editorial team to introduce these papersas follows.

The paper entitled “Implementation and Optimization ofGPU-Based Static State Security Analysis in Power Systems”by Y. Chen et al. contributes to static state security in powersystems. A sensitivity analysis-based method with graph-ics processing unit (GPU) is proposed for power systems,which involves load flow analysis and sensitivity analysis. Amultifrontal method for sparse LU factorization is exploredon GPU in the load flow analysis while the varying matrixoperations during sensitivity analysis on GPU are highlyoptimized.

The paper entitled “RAID-6Plus: A Comprised Method-ology for Extending RAID-6 Codes” by M.-Z. Deng etal. focuses on RAID-6 code. In particular, a new RAID-6 code extending methodology with shorter reconstructionwindow is developed in this study, which provides a balancedtradeoff of flexible reliability and better system performance.Furthermore, an example extension code called RDP+ ispresented based on RDP in terms of encoding and singlefailure reconstruction improvement. In order to validate andevaluate the presented extending methodology, a new metriccalled Q-metric is proposed.

In the paper entitled “Recommending Locations Basedon Users’ Periodic Behaviors” by B. Xu et al., the challeng-ing topic of location recommendation based on user’s lifebehavior is addressed. In view of multiple periodic behaviorsexisting in time series, an algorithm which can mine allperiods in time series is proposed in this paper. Particularly,

HindawiMobile Information SystemsVolume 2017, Article ID 8035421, 3 pageshttps://doi.org/10.1155/2017/8035421

https://doi.org/10.1155/2017/8035421

2 Mobile Information Systems

locations using item-based collaborative filtering algorithmare recommended based on the periodic behaviors.

The paper entitled “Recovering Individual’s CommuteRoutes Based on Mobile Phone Data” by X. Song et al., aimsto propose a commute routes recovering model to recoverindividuals’ commute routes based on passively generatedmobile phone data, which applies two modules. The first isdata preprocessing module, which extracts commute trajec-tories from raw dataset and formats the road network into abetter modality. The second module combines two kinds ofinformation together and generates the commute route withthe highest possibility.

The paper entitled “A Process Mining Based ServiceComposition Approach for Mobile Information Systems”by C. Huang et al. investigates the connection betweenlarge scale of data and the associated business processes inthe Internet of Everything (IoE) environment. Particularly,a process mining based service composition approach isproposed in this paper in order to improve the adaptivenessand efficiency of compositions. Firstly, a preprocessing is con-ducted to extract existing service execution information fromserver-side logs. Then process mining algorithms are appliedto discover the overall event sequencewith preprocessed data.

The paper entitled “Energy-Efficient BroadcastingScheme for Smart Industrial Wireless Sensor Networks” byZ. Chen et al. proposes a novel energy-efficient broadcastscheme with adjustable broadcasting radius to improve theperformance of network upgrade in smart industrial wirelesssensor networks. In their scheme, the nonhotspots sensornodes take full advantage of their residual energy causedin data collection period to improve the packet receptionprobability and reduce the broadcasting delay of code packettransmission by enlarging the broadcasting radius, that is,the transmitting power.

The paper entitled “Exploiting Wireless Received SignalStrength Indicators to Detect Evil-Twin Attacks in SmartHomes” by Z. Tang et al. focuses on Evil-Twin attack in smarthome environments. In particular, this paper presents a novelEvil-Twin attack detection method based on the receivedsignal strength indicator (RSSI). This approach considers theRSSI as a fingerprint of APs and uses the fingerprint of thegenuine AP to identify fake ones. Furthermore, two schemesare presented to detect a fake AP in two different scenarioswhere the genuine AP can be located at either single ormultiple locations in the property, by exploiting themultipatheffect of the Wi-Fi signal.

In the paper entitled “An Extended Technology Accep-tance Model for Mobile Social Gaming Service PopularityAnalysis” by H. Chen et al., an empirical study on WeChat,China’s most popular mobile social network, is presented.Particularly, a technology acceptancemodel (TAM) is appliedto study the reasons beneath the popularity of games inmobile social networks. Furthermore, factors from social andmobile perspective are incorporated into the conventionalTAM and their influence and relationships are studied.

The paper entitled “Power-Aware Resource Reconfigu-ration Using Genetic Algorithm in Cloud Computing” byL. Deng et al. aims to address the power-aware resourcereconfiguration in cloud computing. In this paper, several

algorithms for VM placement (multiobjective genetic algo-rithm (MOGA), power-aware multiobjective genetic algo-rithm (pMOGA), and enhanced power-aware multiobjectivegenetic algorithm (EpMOGA)) are presented to improve sta-bility of VM placement pattern with less migration overhead.Furthermore, nondominated sorting genetic algorithm II(NSGAII) is used to select new generations during evolutionprocess.

The paper entitled “Delay-Aware Program Codes Dis-semination Scheme in Internet of Everything” by Y. Xuan etal. investigates a delay-aware programdissemination (DAPD)scheme to disseminate program codes with fast, reliable, andenergy-efficient style. DAPD scheme improves the perfor-mance of bulk codes dissemination through the following twoaspects. (1) Due to the fact that a high transmitting power cansignificantly improve the quality of wireless links, transmit-ting power of sensors with more residual energy is enhancedto improve link quality. (2) Due to the fact that performanceof correlated dissemination tends to degrade in a highlydynamic environment, link correlation is autonomouslyupdated in DAPD during codes dissemination to maintainimprovements brought by correlated dissemination.

The paper entitled “Channel Selection Policy inMulti-SUand Multi-PU Cognitive Radio Networks with Energy Har-vesting for Internet of Everything” by F. Hu et al. contributesto channel selection in a multi-SU and multi-PU cognitiveradio network. In this paper, the authors adopt cooperativesensingmethod to avoid the packet collision between SUs andPUs and focus on how to collect the spectrum sensing dataof SUs for cooperative sensing. Furthermore, they proposea competitive set based channel selection policy for multi-SU where all SUs competing for data transmission or energyharvesting in the same channel will form a competitive set.

The paper entitled “Making Image More Energy Effi-cient for OLED Smart Devices” by D. Li et al. focuses onenergy consumption of OLED displaying in smart devices.In particular, this paper proposes an approach to improveimage energy efficiency on OLED displays by perceivingimage content. The key idea of this approach is to eliminateundesired details while preserving the region of interest of theimage by leveraging the color and spatial information. First,we use edge detection algorithm to extract region of interest(ROI) of an image.

In the paper entitled “Speed-Density Model of Inter-rupted Traffic Flow Based on Coil Data” by C. Yu et al., anewmethod which can accurately describe the speed-densityrelation of interrupted traffic flow is proposed for speedfluctuation characteristics. The model of upper and lowerbounds of critical values obtained by fitting the data of thecoils on urban roads can accurately and intuitively describethe state of urban road traffic, and the physical meaning ofeach parameter plays an important role in the prediction andanalysis of such traffic.

The paper entitled “A Novel Exercise ThermophysiologyComfort Prediction Model with Fuzzy Logic” by N. Jia etal. aims to address the prediction of exercise accidents ina regular exercise program. Particularly, a human thermo-physiology regulatory model is designed to enhance thehuman thermophysiology simulation in the HCE system.

Mobile Information Systems 3

Some important thermal and physiological performances canbe simulated. According to the simulation results, a humanexercise thermophysiology comfort predictionmethod basedon fuzzy inference system is proposed.

The paper entitled “Time-Aware IoE Service Recom-mendation on Sparse Data” by L. Qi et al. investigates thechallenges of the recommendation technique on the serviceselection decision of target users. In view of the challenges, atime-aware service recommendation approach is proposed inthis paper. Concretely, the time-aware user similarity is firstcalculated; afterwards, indirect friends of the target user areinferred by Social Balance Theory (e.g., “enemy’s enemy is afriend” rule); finally, the services preferred by indirect friendsof the target user are recommended to the target user.

The paper entitled “Congestion Control Mechanism forIntermittently Connected Wireless Network” by R. Wang etal. proposes a congestion control mechanism that is based onthe network state dynamic perception. Specifically, throughestimating the congestion risk when a node receives packets,ICWN can reduce the probability of becoming congested.Moreover, due to ICWN’s network dynamics, the congestionrisk threshold is determined by jointly taking into accountthe average packet size, average forwarding risk, and availablebuffer resources. Further, the service ability of a node ina distributed manner is evaluated by integrating the rec-ommendation information from other intermediate nodes.Additionally, a node is selected as a relay node according toboth the congestion risk and service ability.

The paper entitled “STLIS: A Scalable Two-Level IndexScheme for Big Data in IoT” by Y. Leng et al. focuses on indexscheme for big data in IoT. In particular, this paper proposesa scalable two-level index scheme (STLIS) for RDF data. Inthe first level, a compressed path template tree (CPTT) indexis proposed based on S-tree to retrieve the candidate sets offull path. In the second level, a hierarchical edge index (HEI)and a node-predicate (NP) index are created to accelerate thematch.

In the paper entitled “An Indoor Ultrasonic PositioningSystem Based on TOA for Internet of Things” by J. Li etal., an ultrasonic indoor positioning system is presented,which can achieve centimeter-level precise positioning ofobjectsmoving indoors.The system is based on long-baselinepositioning technology that uses code division multiplexingaccess mechanism. Particularly, the system uses widebandpseudorandom noise signal called Gold sequences for mul-tiuser identification and slant range measurement.

The paper entitled “Phase Clustering Based ModulationClassification Algorithm for PSK Signal over Wireless Envi-ronment” by Q. An et al. proposes a new signal classifier forphase shift keying (PSK) signals. The periodicity of signal’sphase is utilized as the assorted character, with which afractional function is constituted for phase clustering. Partic-ularly, an advanced estimator is proposed for estimating thefrequency offset and balancing estimation accuracy and rangeunder low signal-to-noise ratio (SNR) conditions.

The paper entitled “A High-Order CFS Algorithm forClustering Big Data” by F. Bu et al. investigates the clusteringscheme for big data in Internet of Things. In particular, thispaper proposes a high-order CFS algorithm (HOCFS) to

cluster heterogeneous data by combining the CFS clusteringalgorithm and the dropout deep learning model, whosefunctionality rests on three pillars: (i) an adaptive dropoutdeep learning model to learn features from each type ofdata, (ii) a feature tensor model to capture the correlationsof heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data.

Acknowledgments

Theguest editorial teamwould like to thank all the authors forsubmitting their manuscripts to this special issue and all theinvited reviewers for their time and constructive feedback.

Laurence T. YangBeniamino Di Martino

Qingchen Zhang

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Editorial Internet of Everythingdownloads.hindawi.com/journals/misy/2017/8035421.pdfInternet of Everything LaurenceT.Yang,1 BeniaminoDiMartino,2 andQingchenZhang1 1St.FrancisXavierUniversity,Antigonish,NS,Canada