An Asynchronous Clustering and Mobile Data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many applications.They provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of networks.The clustering process takes sensors’location and residual energy into consideration to elect suitable cluster heads.Furthermore,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to itself.Related experimental results display that the presented method can avoid long distance communicate between sensor nodes.Furthermore,this algorithm reduces energy consumption effectively and improves package delivery rate.

Energy-Efficient Mobile Data Collection Adopting Node Cooperation in an Underwater Acoustic Sensor Network

This paper considers an underwater acoustic sensor network with one mobile surface node to collect data from multiple underwater nodes,where the mobile destination requests retransmission from each underwater node individually employing traditional automatic-repeat-request(ARQ) protocol.We propose a practical node cooperation(NC) protocol to enhance the collection efficiency,utilizing the fact that underwater nodes can overhear the transmission of others.To reduce the source level of underwater nodes,the underwater data collection area is divided into several sub-zones,and in each sub-zone,the mobile surface node adopting the NC protocol could switch adaptively between selective relay cooperation(SRC) and dynamic network coded cooperation(DNC) .The difference of SRC and DNC lies in whether or not the selected relay node combines the local data and the data overheard from undecoded node(s) to form network coded packets in the retransmission phase.The NC protocol could also be applied across the sub-zones due to the wiretap property.In addition,we investigate the effects of different mobile collection paths,collection area division and cooperative zone design for energy saving.The numerical results showthat the proposed NC protocol can effectively save energy compared with the traditional ARQ scheme.

Advances in Mobile Data Communications

This special issue of ZTE Communications focuses on recent advances in mobile data communications for the ICT and telecommunications industries. The ever-increasing amount of mobile data traffic has beenthe subject of many studies. This research area is widely applicable to contemporary technology and network optimization techniques.

Broadband Mobile Data Service Era is Coming

For China’s telecom industry,2009 is destined to be an extraordinary year due to the approach of long-thirsted-for mobile 3G era,which will have significant impact on current work and lifestyles.2009 will also be a year full of opportunities and challenges because the coming 3G era will bring limitless business opportunities and impose more challenges on Chinese telecom operators.The reshuffling of Chinese telecom markets has been brought to an end.The new China Unicom,China Mobile and China Telecom all focus their strategies on broadband mobile data services in order to achieve the objective of a smooth transforming from voice services to data services.Technologically,various 3G technologies and their evolutions become great concerns of telecom operators;while in terms of services,the key for 3G systems is their data services.As a result,high speed broadband data services see an era of rapid development.

Empowering smart city situational awareness via big mobile data

Smart city situational awareness has recently emerged as a hot topic in research societies,industries,and governments because of its potential to integrate cutting-edge information technology and solve urgent challenges that modern cities face.For example,in the latest five-year plan,the Chinese government has highlighted the demand to empower smart city management with new technologies such as big data and Internet of Things,for which situational awareness is normally the crucial first step.While traditional static surveillance data on cities have been available for decades,this review reports a type of relatively new yet highly important urban data source,i.e.,the big mobile data collected by devices with various levels of mobility representing the movement and distribution of public and private agents in the city.We especially focus on smart city situational awareness enabled by synthesizing the localization of hundreds of thousands of mobile software Apps using the Global Positioning System(GPS).This technique enjoys advantages such as a large penetration rate(∼50%urban population covered),uniform spatiotemporal coverage,and high localization precision.We first discuss the pragmatic requirements for smart city situational awareness and the challenges faced.Then we introduce two suites of empowering technologies that help fulfill the requirements of(1)cybersecurity insurance for smart cities and(2)spatiotemporal modeling and visualization for situational awareness,both via big mobile data.The main contributions of this review lie in the description of a comprehensive technological framework for smart city situational awareness and the demonstration of its feasibility via real-world applications.

SDN-Based Data Offloading for 5G Mobile Networks

The rapid growth of 3G/4G enabled devices such as smartphones and tablets in large numbers has created increased demand for mobile data services. Wi-Fi offloading helps satisfy the requirements of data-rich applications and terminals with improved multi- media. Wi-Fi is an essential approach to alleviating mobile data traffic load on a cellular network because it provides extra capacity and improves overall performance. In this paper, we propose an integrated LTE/Wi-Fi architecture with software-defined networking （SDN） abstraction in mobile baekhaul and enhanced components that facilitate the move towards next-generation 5G mo- bile networks. Our proposed architecture enables programmable offloading policies that take into account real-time network conditions as well as the status of devices and applications. This mechanism improves overall network performance by deriving real- time policies and steering traffic between cellular and Wi-Fi networks more efficiently.

Exploring Temporal Activity Patterns of Urban Areas Using Aggregated Network-driven Mobile Phone Data:A Case Study of Wuhu,China

The increasing availability of data in the urban context(e.g.,mobile phone,smart card and social media data)allows us to study urban dynamics at much finer temporal resolutions(e.g.,diurnal urban dynamics).Mobile phone data,for instance,are found to be a useful data source for extracting diurnal human mobility patterns and for understanding urban dynamics.While previous studies often use call detail record(CDR)data,this study deploys aggregated network-driven mobile phone data that may reveal human mobility patterns more comprehensively and can mitigate some of the privacy concerns raised by mobile phone data usage.We first propose an analytical framework for characterizing and classifying urban areas based on their temporal activity patterns extracted from mobile phone data.Specifically,urban areas’diurnal spatiotemporal signatures of human mobility patterns are obtained through longitudinal mobile phone data.Urban areas are then classified based on the obtained signatures.The classification provides insights into city planning and development.Using the proposed framework,a case study was implemented in the city of Wuhu,China to understand its urban dynamics.The empirical study suggests that human activities in the city of Wuhu are highly concentrated at the Traffic Analysis Zone(TAZ)level.This large portion of local activities suggests that development and planning strategies that are different from those used by metropolitan Chinese cities should be applied in the city of Wuhu.This article concludes with discussions on several common challenges associated with using network-driven mobile phone data,which should be addressed in future studies.

CPFinder: Finding an unknown caller's profession from anonymized mobile phone data

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones might exhibit their application preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation of an anonymized dataset of 1,282 users over a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.

Exploring relations between city regions based on mobile phone data

City regions often have great diversity in form and function. To better understand the role of each region, the relations between city regions need to be carefully studied. In this work, the human mobility relations between regions of Shanghai based on mobile phone data is explored. By formulating the regions as nodes in a network and the commuting between each pair of regions as link weights, the distribution of nodes degree, and spatial structures of communities in this relation network are studied. Statistics show that regions locate in urban centers and traffic hubs have significantly larger degrees. Moreover, two kinds of spatial structures of communities are found. In most communities, nodes are spatially neighboring. However, in the communities that cover traffic hubs, nodes often locate along corridors.

Toward Data-Driven Digital Therapeutics Analytics:Literature Review and Research Directions

With the advent of digital therapeutics(DTx),the development of software as a medical device(SaMD)for mobile and wearable devices has gained significant attention in recent years.Existing DTx evaluations,such as randomized clinical trials,mostly focus on verifying the effectiveness of DTx products.To acquire a deeper understanding of DTx engagement and behavioral adherence,beyond efficacy,a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis.In this work,the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets,to investigate contextual patterns associated with DTx usage,and to establish the(causal)relationship between DTx engagement and behavioral adherence.This review of the key components of datadriven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets,which helps to iteratively improve the receptivity of existing DTx.

Understanding the spatiotemporal patterns of nighttime urban vibrancy in central Shanghai inferred from mobile phone data

In recent years,major cities around the world such as New York in USA,Melbourne in Australia,and Shanghai in China,have planned to boost their nighttime urban vibrancy levels to spur the economy and achieve cultural diversity.The study of nighttime urban vibrancy from the perspective of spatiotemporal characteristics is increasingly being recognized as part of the essential work in the field of urban planning and geography.This research used mobile phone signaling records to measure urban vibrancy in central Shanghai and revealed its spatiotemporal patterns during nighttime.Specifically,this research explored the changes of urban vibrancy within a day,studied the distribution of urban vibrancy during the nighttime,and visually presented the spatiotemporal changes of nighttime urban vibrancy in central Shanghai.Moreover,on the basis of the behavior pattern of each mobile user,we classified nighttime urban vibrancy into three different types:nighttime working vibrancy,nighttime leisure vibrancy,and nighttime floating vibrancy.We then tried to determine how land use affected nighttime leisure vibrancy.The results showed that urban vibrancy in central Shanghai exhibits a periodic pattern over one-day period.A high-level nighttime urban vibrancy belt is present within central Shanghai.Business offices,hotels,entertainment and recreational districts,wholesale markets,and express services contribute most to the vibrancy at nighttime.In addition,the correlation analysis shows that public and commercial facilities generate high levels of nighttime leisure vibrancy than residential facilities.The mixed land use of public and commercial facilities and residential facilities within 500 m is more critical than the mixed use of a single land lot.The research can be a basis for supporting land use planning and providing evidence for policy-making to improve the level of nighttime urban vibrancy in cities.

Protecting Data Mobility in Cloud Networks Using Metadata Security

At present,health care applications,government services,and banking applications use big data with cloud storage to process and implement data.Data mobility in cloud environments uses protection protocols and algorithms to secure sensitive user data.Sometimes,data may have highly sensitive information,lead-ing users to consider using big data and cloud processing regardless of whether they are secured are not.Threats to sensitive data in cloud systems produce high risks,and existing security methods do not provide enough security to sensitive user data in cloud and big data environments.At present,several security solu-tions support cloud systems.Some of them include Hadoop Distributed File Sys-tem(HDFS)baseline Kerberos security,socket layer-based HDFS security,and hybrid security systems,which have time complexity in providing security inter-actions.Thus,mobile data security algorithms are necessary in cloud environ-ments to avoid time risks in providing security.In our study,we propose a data mobility and security(DMoS)algorithm to provide security of data mobility in cloud environments.By analyzing metadata,data are classified as secured and open data based on their importance.Secured data are sensitive user data,whereas open data are open to the public.On the basis of data classification,secured data are applied to the DMoS algorithm to achieve high security in HDFS.The pro-posed approach is compared with the time complexity of three existing algo-rithms,and results are evaluated.

Deriving Operational Origin-Destination Matrices From Large Scale Mobile Phone Data

A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns revealed from their mobile phones,researchers and practitioners are now equipped to derive the largest trip samples for a region.Because of its ubiquitous use,extensive coverage of telecommunication services and high penetration rates,travel demand can be studied continuously in fine spatial and temporal resolutions.The derived sample or seed trip matrices are coupled with surveyed commute flow data and prevalent travel demand modeling techniques to provide estimates of the total regional travel demand in the form of origindestination(OD)matrices.The methodology is evaluated in a series of real world transportation planning studies and proved its potentials in application areas such as dynamic traffic assignment modeling,integrated corridor management and online traffic simulations.

Utilization of online systems to promote youth participation in research:A methodological study

BACKGROUND Online surveys can align with youth’s increased use of the internet and can be a mechanism for expanding youth participation in research.This is particularly important during the coronavirus disease 2019(COVID-19)pandemic,when inperson interactions are limited.However,the advantages and drawbacks of online systems used for research need to be carefully considered before utilizing such methodologies.AIM To describe and discuss the strengths and limitations of an online system developed to recruit adolescent girls for a sexual health research study and conduct a three-month follow up survey.METHODS This methodology paper examines the use of an online system to recruit and follow participants three months after their medical visit to evaluate a mobile sexual and reproductive health application,Health-E You/Salud iTuTM,for adolescent girls attending school-based health centers(SBHCs)across the United States.SBHC staff gave adolescent girls a web link to an online eligibility and consent survey.Participants were then asked to complete two online surveys(baseline and 3-month follow-up).Surveys,reminders,and incentives to complete them were distributed through short message service(SMS)text messages.Upon completing each survey,participants were also sent an email with a link to an electronic gift card as a thank-you for their participation.Barriers to implementing this system were discussed with clinicians and staff at each participating SBHC.RESULTS This online recruitment and retention system enabled participant recruitment at 26 different SBHCs in seven states across the United States.Between September 2021 and June 2022,415 adolescent girls were screened using the Qualtrics online survey platform,and 182 were eligible to participate.Of those eligible,78.0%(n=142)completed the baseline survey.Participants were racially,geographically,and linguistically diverse.Most of the participants(89.4%)were non-White,and 40.8%spoke Spanish.A total of 62.0%(n=88)completed the 3-month follow-up survey.Limitations of this system included reliance on internet access(via Wi-Fi or cell service),which was not universally available or reliable.In addition,an individual unrelated to the study obtained the survey link,filled out multiple surveys,and received multiple gift cards before the research team discovered and stopped this activity.As a result,additional security protocols were instituted.CONCLUSION Online systems for health research can increase the reach and diversity of study participants,reduce costs for research personnel time and travel,allow for continued study operation when in-person visits are limited(such as during the COVID-19 pandemic),and connect youth with research using technology.However,there are challenges and limitations to online systems,which include limited internet access,intermittent internet connection,data security concerns,and the potential for fraudulent users.These challenges should be considered prior to using online systems for research.

Dynamic Assessment of Spatiotemporal Population Distribution Based on Mobile Phone Data: A Case Study in Xining City,China

High-resolution,dynamic assessments of the spatiotemporal distributions of populations are critical for urban planning and disaster management.Mobile phone big data have real-time collection,wide coverage,and high resolution advantages and can thus be used to characterize human activities and population distributions at fne spatiotemporal scales.Based on six days of mobile phone user-location signal(MPLS)data,we assessed the dynamic spatiotemporal distribution of the population of Xining City,Qinghai Province,China.The results show that strong temporal regularity exists in the daily activities of local residents.The spatiotemporal distribution of the local population showed a signifcant downtown-suburban attenuation pattern.Factors such as land use types,holidays,and seasons signifcantly afect the spatiotemporal patterns of the local population.By combining other spatiotemporal trajectory data,high-resolution and dynamic real-time population distribution evaluations based on mobile phone location signals could be better developed and improved for use in urban management and disaster assessment research.

A Throughput-Aware Joint Vehicle Route and Access Network Selection Approach Based on SMDP

In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading(MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process(SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio.Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.

uSensing：a Universal Platform to Support Smartphones Accessing Wireless Sensor Networks

This paper presents a universal platform "uSensing" to support smartphones to communicate with sensor nodes in Wireless Sensor Networks (WSNs).Since phones have different CPU processers and operating systems,it is a challenge to merge these heterogeneities and develop such a universal platform.In this paper,we design both hardware and software to support the "universal" feature of uSensing:1) "uSD" card:an IEEE 802.15.4 physical communication card with SD interface;2) "uSinkWare":a WSNs middleware running on smartphones.Integrated with uSD card and uSinkWare,phones become mobile data sinks to access into WSNs and parse messages from sensor nodes.We demonstrate the proposed uSensing platform in a commercial smartphone to connect with our WSNs testbed,and validate that the smartphone has the same WSNs functions as commercial fixed sink.Additionally,we evaluate the performance of uSensing platform through measuring phone's CPU load and power consumption,and analyze the performance of these metrics theoretically.The results suggest that the phone-based mobile sink has enough capability to serve as a mobile sink of WSNs and can work up to twenty hours due to low power consumption.

Population distribution modelling at fine spatio-temporal scale based on mobile phone data

Population distribution modelling can benefit many different domains,for example,transportation,urban planning,ecology or emergency management.Information about the location and number of people in an affected area is crucial for decision-makers during emergencies and crises.Mobile phone data represents relatively reliable and time accurate information on real-time population distribution,movement and behaviour.In this study,we evaluate the spatio-temporal distribution of population derived from phone data of the selected pilot area(City of Brno,Czech Republic).Analysis is based on the dataset describing the estimated human presence(EHP)with two values-visitors and transiting persons.The temporal change of data is first analysed and further processed using two methodological approaches.First,the dasymetric method is used where the building geometry and technical attributes served as a target layer.Second,the results of building level analysis are transformed into a regular grid zone of both visitors and the general EHP.Resulting spatio-temporal patterns are compared to the census data.Results demonstrate how the proposed building level dasymetric approach can improve the spatial granularity of EHP.Potential use of proposed methodology within selected emergency situations is further discussed.

Monitoring travel patterns in German city regions with the help of mobile phone network data

This paper discusses the possibility to use mobile phone network data to monitor spatial policies in land use and transport planning.Monitoring requires robust time series and reproducible concepts linking spatial policies to monitoring outcomes,a requirement differing from current literature where mobile phone data analysis is exemplified in selected areas with privileged data access.Concepts need to serve the evaluation of policy objectives,for example in regional or local area plans.In this study,we,therefore,extend the application of mobile phone network data to monitoring applications comparing urban settlement types and their characteristic mobility patterns.To accomplish this,we link mobile phone records with urban classifications and transport network data,using both visual and computational approaches to mine the data.The article presents comparisons of travel patterns for selected monocentric and polycentric city regions in Germany,testing hypotheses of transit-oriented regional development,as well as testing for congestion risks in the transport network.The results help us to gain a more detailed understanding of spatial and temporal patterns in mobility for different urban types and assess future potentials for monitoring spatial policies with mobile phone network data.

Location Privacy in Mobile Big Data:User Identifiability via Habitat Region Representation

Mobile big data collected by mobile network operators is of interest to many research communities and industries for its remarkable values.However,such spatiotemporal information may lead to a harsh threat to subscribers’privacy.This work focuses on subscriber privacy vulnerability assessment in terms of user identifiability across two datasets with significant detail reduced mobility representation.In this paper,we propose an innovative semantic spatiotemporal representation for each subscriber based on the geographic information,termed as daily habitat region,to approximate the subscriber’s daily mobility coverage with far lesser information compared with original mobility traces.The daily habitat region is realized via convex hull extraction on the user’s daily spatiotemporal traces.As a result,user identification can be formulated to match two records with the maximum similarity score between two convex hull sets,obtained by our proposed similarity measures based on cosine distance and permutation hypothesis test.Experiments are conducted to evaluate our proposed innovative mobility representation and user identification algorithms,which also demonstrate that the subscriber’s mobile privacy is under a severe threat even with significantly reduced spatiotemporal information.