Mobile crowdsensing(MCS)has become an effective paradigm to facilitate urban sensing.However,mobile users participating in sensing tasks will face the risk of location privacy leakage when uploading their actual sensi...Mobile crowdsensing(MCS)has become an effective paradigm to facilitate urban sensing.However,mobile users participating in sensing tasks will face the risk of location privacy leakage when uploading their actual sensing location data.In the application of mobile crowdsensing,most location privacy protection studies do not consider the temporal correlations between locations,so they are vulnerable to various inference attacks,and there is the problem of low data availability.In order to solve the above problems,this paper proposes a dynamic differential location privacy data publishing framework(DDLP)that protects privacy while publishing locations continuously.Firstly,the corresponding Markov transition matrices are established according to different times of historical trajectories,and then the protection location set is generated based on the current location at each timestamp.Moreover,using the exponential mechanism in differential privacy perturbs the true location by designing the utility function.Finally,experiments on the real-world trajectory dataset show that our method not only provides strong privacy guarantees,but also outperforms existing methods in terms of data availability and computational efficiency.展开更多
During the process of urbanization,community environments encounter challenges such as data disconnection and the underutilization of small and micro spaces.The establishment of“complete communities”necessitates the...During the process of urbanization,community environments encounter challenges such as data disconnection and the underutilization of small and micro spaces.The establishment of“complete communities”necessitates the implementation of refined governance strategies.This research develops a path for the precise establishment of community micro-gardens driven by mobile measurement.It involves the collection of environmental data via mobile devices equipped with various types of sensors,the generation of visualization maps that are adjusted for spatio-temporal synchronization,and the identification of environmental paint points,including areas of excessive temperature exposure and zones with elevated noise levels.Based on the aforementioned considerations,various plant allocation strategies have been proposed for distinct areas.For instance,the implementation of a composite shade and cooling vegetation system is recommended for regions experiencing high temperatures,while a triple protection structure is suggested for areas affected by odor contamination.The efficacy of these strategies is demonstrated through a case study of the micro-garden transformation in the Dongjie Community of Wulituo Street,Shijingshan,Beijing.The study presents operational technical pathways and plant response solutions aimed at facilitating data-driven governance of community micro-environments.展开更多
Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.Howeve...Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.However,in practice,opportunistic MCS has several challenges from both the perspectives of MCS participants and the data platform.On the one hand,participants face uncertainties in conducting MCS tasks,including their mobility and implicit interactions among participants,and participants’economic returns given by the MCS data platform are determined by not only their own actions but also other participants’strategic actions.On the other hand,the platform can only observe the participants’uploaded sensing data that depends on the unknown effort/action exerted by participants to the platform,while,for optimizing its overall objective,the platform needs to properly reward certain participants for incentivizing them to provide high-quality data.To address the challenge of balancing individual incentives and platform objectives in MCS,this paper proposes MARCS,an online sensing policy based on multi-agent deep reinforcement learning(MADRL)with centralized training and decentralized execution(CTDE).Specifically,the interactions between MCS participants and the data platform are modeled as a partially observable Markov game,where participants,acting as agents,use DRL-based policies to make decisions based on local observations,such as task trajectories and platform payments.To align individual and platform goals effectively,the platform leverages Shapley value to estimate the contribution of each participant’s sensed data,using these estimates as immediate rewards to guide agent training.The experimental results on real mobility trajectory datasets indicate that the revenue of MARCS reaches almost 35%,53%,and 100%higher than DDPG,Actor-Critic,and model predictive control(MPC)respectively on the participant side and similar results on the platform side,which show superior performance compared to baselines.展开更多
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 phon...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.展开更多
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...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.展开更多
Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless ...Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
In order to design a new kind of mobile database management system (DBMS)more suitable for mobile computing than the existent DBMS, the essence of database systems in mobilecomputing is analyzed. An opinion is introdu...In order to design a new kind of mobile database management system (DBMS)more suitable for mobile computing than the existent DBMS, the essence of database systems in mobilecomputing is analyzed. An opinion is introduced that the mobile database is a kind of dynamicdistributed database, and the concept of virtual servers to translate the clients' mobility to theservers' mobility is proposed. Based on these opinions, a kind of architecture of mobile DBMS, whichis of versatility, is presented. The architecture is composed of a virtual server and a local DBMS,the virtual server is the kernel of the architecture and its functions are described. Eventually,the server kernel of a mobile DBMS prototype is illustrated.展开更多
Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications.The ability to accurately and extensively monitor and analyze these data is necessary.Much concern in cel...Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications.The ability to accurately and extensively monitor and analyze these data is necessary.Much concern in cellular data analysis is related to human beings and their behaviours.Due to the potential value that lies behind these massive data,there have been different proposed approaches for understanding corresponding patterns.To that end,analyzing people's activities,e.g.,counting them at fixed locations and tracking them by generating origindestination matrices is crucial.The former can be used to determine the utilization of assets like roads and city attractions.The latter is valuable when planning transport infrastructure.Such insights allow a government to predict the adoption of new roads,new public transport routes,modification of existing infrastructure,and detection of congestion zones,resulting in more efficient designs and improvement.Smartphone data exploration can help research in various fields,e.g.,urban planning,transportation,health care,and business marketing.It can also help organizations in decision making,policy implementation,monitoring,and evaluation at all levels.This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.展开更多
Mobile sink is the challenging task for wireless sensor networks(WSNs).In this paper we propose to design an efficient routing protocol for single mobile sink and multiple mobile sink for data gathering in WSN.In this...Mobile sink is the challenging task for wireless sensor networks(WSNs).In this paper we propose to design an efficient routing protocol for single mobile sink and multiple mobile sink for data gathering in WSN.In this process,a biased random walk method is used to determine the next position of the sink.Then,a rendezvous point selection with splitting tree technique is used to find the optimal data transmission path.If the sink moves within the range of the rendezvous point,it receives the gathered data and if moved out,it selects a relay node from its neighbours to relay packets from rendezvous point to the sink.Proposed algorithm reduces the signal overhead and improves the triangular routing problem.Here the sink acts as a vehicle and collect the data from the sensor.The results show that the proposed model effectively supports sink mobility with low overhead and delay when compared with Intelligent Agent-based Routing protocol(IAR) and also increases the reliability and delivery ratio when the number of sources increases.展开更多
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 ...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.展开更多
The ability of accurate and scalable mobile device recognition is critically important for mobile network operators and ISPs to understand their customers' behaviours and enhance their user experience.In this pape...The ability of accurate and scalable mobile device recognition is critically important for mobile network operators and ISPs to understand their customers' behaviours and enhance their user experience.In this paper,we propose a novel method for mobile device model recognition by using statistical information derived from large amounts of mobile network traffic data.Specifically,we create a Jaccardbased coefficient measure method to identify a proper keyword representing each mobile device model from massive unstructured textual HTTP access logs.To handle the large amount of traffic data generated from large mobile networks,this method is designed as a set of parallel algorithms,and is implemented through the MapReduce framework which is a distributed parallel programming model with proven low-cost and high-efficiency features.Evaluations using real data sets show that our method can accurately recognise mobile client models while meeting the scalability and producer-independency requirements of large mobile network operators.Results show that a 91.5% accuracy rate is achieved for recognising mobile client models from 2 billion records,which is dramatically higher than existing solutions.展开更多
Recently, use of mobile communicational devices in field data collection is increasing such as smart phones and cellular phones due to emergence of embedded Global Position System GPS and Wi-Fi Internet access. Accura...Recently, use of mobile communicational devices in field data collection is increasing such as smart phones and cellular phones due to emergence of embedded Global Position System GPS and Wi-Fi Internet access. Accurate timely and handy field data collection is required for disaster management and emergency quick responses. In this article, we introduce web-based GIS system to collect the field data by personal mobile phone through Post Office Protocol POP3 mail server. The main objective of this work is to demonstrate real-time field data collection method to the students using their mobile phone to collect field data by timely and handy manners, either individual or group survey in local or global scale research.展开更多
With the increasing popularity of wireless sensor network and GPS （ global positioning system）, uncertain data as a new type of data brings a new challenge for the traditional data processing methods. Data broadcast...With the increasing popularity of wireless sensor network and GPS （ global positioning system）, uncertain data as a new type of data brings a new challenge for the traditional data processing methods. Data broadcast is an effective means for data dissemination in mobile networks. In this paper, the def＇mition of the mean uncertainty ratio of data is presented and a broadcasting scheme is proposed for uncertain data dissemination. Simulation results show that the scheme can reduce the uncertainty of the broadcasted uncertain data effectively at the cost of a minor increase in data access time, in the case of no transmission error and presence of transmission errors. As a result, lower uncertainty of data benefits the qualifies of the query results based on the data.展开更多
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 applicatio...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.展开更多
To enhance the security of user data in the clouds,we present an adaptive and dynamic data encryption method to encrypt user data in the mobile phone before it is uploaded.Firstly,the adopted data encryption algorithm...To enhance the security of user data in the clouds,we present an adaptive and dynamic data encryption method to encrypt user data in the mobile phone before it is uploaded.Firstly,the adopted data encryption algorithm is not static and uniform.For each encryption,this algorithm is adaptively and dynamically selected from the algorithm set in the mobile phone encryption system.From the mobile phone's character,the detail encryption algorithm selection strategy is confirmed based on the user's mobile phone hardware information,personalization information and a pseudo-random number.Secondly,the data is rearranged with a randomly selected start position in the data before being encrypted.The start position's randomness makes the mobile phone data encryption safer.Thirdly,the rearranged data is encrypted by the selected algorithm and generated key.Finally,the analysis shows this method possesses the higher security because the more dynamics and randomness are adaptively added into the encryption process.展开更多
With the popularity of sensor-rich mobile devices,mobile crowdsensing(MCS)has emerged as an effective method for data collection and processing.However,MCS platform usually need workers’precise locations for optimal ...With the popularity of sensor-rich mobile devices,mobile crowdsensing(MCS)has emerged as an effective method for data collection and processing.However,MCS platform usually need workers’precise locations for optimal task execution and collect sensing data from workers,which raises severe concerns of privacy leakage.Trying to preserve workers’location and sensing data from the untrusted MCS platform,a differentially private data aggregation method based on worker partition and location obfuscation(DP-DAWL method)is proposed in the paper.DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group according to group size(the number of workers).Then each worker’s location is obfuscated and his/her sensing data is perturbed by adding Laplace noise before uploading to the platform.In the stage of data aggregation,DP-DAWL method adopts an improved Kalman filter algorithm to filter out the added noise(including both added noise of sensing data and the system noise in the sensing process).Through using optimal estimation of noisy aggregated sensing data,the platform can finally gain better utility of aggregated data while preserving workers’privacy.Extensive experiments on the synthetic datasets demonstrate the effectiveness of the proposed method.展开更多
Recently, the exponential rise in communication system demands has motivated global academia-industry to develop efficient communication technologies to fulfill energy efficiency and Quality of Service (QoS) demands. ...Recently, the exponential rise in communication system demands has motivated global academia-industry to develop efficient communication technologies to fulfill energy efficiency and Quality of Service (QoS) demands. Wireless Sensor Network (WSN) being one of the most efficient technologies possesses immense potential to serve major communication purposes including civil, defense and industrial purposes etc. The inclusion of sensor-mobility with WSN has broadened application horizon. The effectiveness of WSNs can be characterized by its ability to perform efficient data gathering and transmission to the base station for decision process. Clustering based routing scheme has been one of the dominating techniques for WSN systems;however key issues like, cluster formation, selection of the number of clusters and cluster heads, and data transmission decision from sensors to the mobile sink have always been an open research area. In this paper, a robust and energy efficient single mobile sink based WSN data gathering protocol is proposed. Unlike existing approaches, an enhanced centralized clustering model is developed on the basis of expectation-maximization (EEM) concept. Further, it is strengthened by using an optimal cluster count estimation technique that ensures that the number of clusters in the network region doesn’t introduce unwanted energy exhaustion. Meanwhile, the relative distance between sensor node and cluster head as well as mobile sink is used to make transmission (path) decision. Results exhibit that the proposed EEM based clustering with optimal cluster selection and optimal dynamic transmission decision enables higher throughput, fast data gathering, minima delay and energy consumption, and higher展开更多
During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place i...During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.展开更多
Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent years.The mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interc...Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent years.The mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all things.The variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication environments.Ensuring data secure transmission is critical for mobile IIoT networks.This paper investigates the data secure transmission performance prediction of mobile IIoT networks.To cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first derived.Then,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction algorithm.For mobile signals,the important features may be removed by the pooling layers.This will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is designed.Out of the input and output layers,it removes the pooling layer and contains six convolution layers.Elman,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed algorithm.Through simulation analysis,good prediction accuracy is achieved by the CNN algorithm.The prediction accuracy obtains a 59%increase.展开更多
基金supported by the Inner Mongolia Natural Science Foundation(Grant No.2023MS06022)the University Youth Science and Technology Talent Development Project(Innovation Group Development Plan)of Inner Mongolia A.R.of China(Grant No.NMGIRT2318)+1 种基金the“Inner Mongolia Science and Technology Achievement Transfer and Transformation Demonstration Zone,University Collaborative Innovation Base,and University Entrepreneurship Training Base”Construction Project(Supercomputing Power Project)(Grant No.21300-231510)the Engineering Research Center of Ecological Big Data,Ministry of Education.
文摘Mobile crowdsensing(MCS)has become an effective paradigm to facilitate urban sensing.However,mobile users participating in sensing tasks will face the risk of location privacy leakage when uploading their actual sensing location data.In the application of mobile crowdsensing,most location privacy protection studies do not consider the temporal correlations between locations,so they are vulnerable to various inference attacks,and there is the problem of low data availability.In order to solve the above problems,this paper proposes a dynamic differential location privacy data publishing framework(DDLP)that protects privacy while publishing locations continuously.Firstly,the corresponding Markov transition matrices are established according to different times of historical trajectories,and then the protection location set is generated based on the current location at each timestamp.Moreover,using the exponential mechanism in differential privacy perturbs the true location by designing the utility function.Finally,experiments on the real-world trajectory dataset show that our method not only provides strong privacy guarantees,but also outperforms existing methods in terms of data availability and computational efficiency.
文摘During the process of urbanization,community environments encounter challenges such as data disconnection and the underutilization of small and micro spaces.The establishment of“complete communities”necessitates the implementation of refined governance strategies.This research develops a path for the precise establishment of community micro-gardens driven by mobile measurement.It involves the collection of environmental data via mobile devices equipped with various types of sensors,the generation of visualization maps that are adjusted for spatio-temporal synchronization,and the identification of environmental paint points,including areas of excessive temperature exposure and zones with elevated noise levels.Based on the aforementioned considerations,various plant allocation strategies have been proposed for distinct areas.For instance,the implementation of a composite shade and cooling vegetation system is recommended for regions experiencing high temperatures,while a triple protection structure is suggested for areas affected by odor contamination.The efficacy of these strategies is demonstrated through a case study of the micro-garden transformation in the Dongjie Community of Wulituo Street,Shijingshan,Beijing.The study presents operational technical pathways and plant response solutions aimed at facilitating data-driven governance of community micro-environments.
基金sponsored by Qinglan Project of Jiangsu Province,and Jiangsu Provincial Key Research and Development Program(No.BE2020084-1).
文摘Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.However,in practice,opportunistic MCS has several challenges from both the perspectives of MCS participants and the data platform.On the one hand,participants face uncertainties in conducting MCS tasks,including their mobility and implicit interactions among participants,and participants’economic returns given by the MCS data platform are determined by not only their own actions but also other participants’strategic actions.On the other hand,the platform can only observe the participants’uploaded sensing data that depends on the unknown effort/action exerted by participants to the platform,while,for optimizing its overall objective,the platform needs to properly reward certain participants for incentivizing them to provide high-quality data.To address the challenge of balancing individual incentives and platform objectives in MCS,this paper proposes MARCS,an online sensing policy based on multi-agent deep reinforcement learning(MADRL)with centralized training and decentralized execution(CTDE).Specifically,the interactions between MCS participants and the data platform are modeled as a partially observable Markov game,where participants,acting as agents,use DRL-based policies to make decisions based on local observations,such as task trajectories and platform payments.To align individual and platform goals effectively,the platform leverages Shapley value to estimate the contribution of each participant’s sensed data,using these estimates as immediate rewards to guide agent training.The experimental results on real mobility trajectory datasets indicate that the revenue of MARCS reaches almost 35%,53%,and 100%higher than DDPG,Actor-Critic,and model predictive control(MPC)respectively on the participant side and similar results on the platform side,which show superior performance compared to baselines.
基金Under the auspices of the National Natural Science Foundation of China(No.41571146)China Postdoctoral Science Foundation(No.2019M651784)。
文摘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.
基金This work is supported by the National Natural Science Foundation of China(61772454,61811530332,61811540410,U1836208).
文摘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.
文摘Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
文摘In order to design a new kind of mobile database management system (DBMS)more suitable for mobile computing than the existent DBMS, the essence of database systems in mobilecomputing is analyzed. An opinion is introduced that the mobile database is a kind of dynamicdistributed database, and the concept of virtual servers to translate the clients' mobility to theservers' mobility is proposed. Based on these opinions, a kind of architecture of mobile DBMS, whichis of versatility, is presented. The architecture is composed of a virtual server and a local DBMS,the virtual server is the kernel of the architecture and its functions are described. Eventually,the server kernel of a mobile DBMS prototype is illustrated.
基金supported by Fundo para o Desenvolvimento das Ciencias e da Tecnologia(FDCT)(119/2014/A3)。
文摘Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications.The ability to accurately and extensively monitor and analyze these data is necessary.Much concern in cellular data analysis is related to human beings and their behaviours.Due to the potential value that lies behind these massive data,there have been different proposed approaches for understanding corresponding patterns.To that end,analyzing people's activities,e.g.,counting them at fixed locations and tracking them by generating origindestination matrices is crucial.The former can be used to determine the utilization of assets like roads and city attractions.The latter is valuable when planning transport infrastructure.Such insights allow a government to predict the adoption of new roads,new public transport routes,modification of existing infrastructure,and detection of congestion zones,resulting in more efficient designs and improvement.Smartphone data exploration can help research in various fields,e.g.,urban planning,transportation,health care,and business marketing.It can also help organizations in decision making,policy implementation,monitoring,and evaluation at all levels.This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.
文摘Mobile sink is the challenging task for wireless sensor networks(WSNs).In this paper we propose to design an efficient routing protocol for single mobile sink and multiple mobile sink for data gathering in WSN.In this process,a biased random walk method is used to determine the next position of the sink.Then,a rendezvous point selection with splitting tree technique is used to find the optimal data transmission path.If the sink moves within the range of the rendezvous point,it receives the gathered data and if moved out,it selects a relay node from its neighbours to relay packets from rendezvous point to the sink.Proposed algorithm reduces the signal overhead and improves the triangular routing problem.Here the sink acts as a vehicle and collect the data from the sensor.The results show that the proposed model effectively supports sink mobility with low overhead and delay when compared with Intelligent Agent-based Routing protocol(IAR) and also increases the reliability and delivery ratio when the number of sources increases.
基金the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No.824019 and China Scholarship Council(CSC)the Fundamental Research Funds for Central Universities(No.2020JJ014,YY19SSK05).
文摘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.
基金supported in part by the National Natural Science Foundation of China under Grant No.61072061the National Science and Technology Major Projects under Grant No.2012ZX03002008the Fundamental Research Funds for the Central Universities under Grant No.2012RC0121
文摘The ability of accurate and scalable mobile device recognition is critically important for mobile network operators and ISPs to understand their customers' behaviours and enhance their user experience.In this paper,we propose a novel method for mobile device model recognition by using statistical information derived from large amounts of mobile network traffic data.Specifically,we create a Jaccardbased coefficient measure method to identify a proper keyword representing each mobile device model from massive unstructured textual HTTP access logs.To handle the large amount of traffic data generated from large mobile networks,this method is designed as a set of parallel algorithms,and is implemented through the MapReduce framework which is a distributed parallel programming model with proven low-cost and high-efficiency features.Evaluations using real data sets show that our method can accurately recognise mobile client models while meeting the scalability and producer-independency requirements of large mobile network operators.Results show that a 91.5% accuracy rate is achieved for recognising mobile client models from 2 billion records,which is dramatically higher than existing solutions.
文摘Recently, use of mobile communicational devices in field data collection is increasing such as smart phones and cellular phones due to emergence of embedded Global Position System GPS and Wi-Fi Internet access. Accurate timely and handy field data collection is required for disaster management and emergency quick responses. In this article, we introduce web-based GIS system to collect the field data by personal mobile phone through Post Office Protocol POP3 mail server. The main objective of this work is to demonstrate real-time field data collection method to the students using their mobile phone to collect field data by timely and handy manners, either individual or group survey in local or global scale research.
基金Initial Research Foundation of Shanghai Second Polytechnic University ( No.001943)National High Technology Research and Development Program of China(863 Program) (No.2007AA01Z309)
文摘With the increasing popularity of wireless sensor network and GPS （ global positioning system）, uncertain data as a new type of data brings a new challenge for the traditional data processing methods. Data broadcast is an effective means for data dissemination in mobile networks. In this paper, the def＇mition of the mean uncertainty ratio of data is presented and a broadcasting scheme is proposed for uncertain data dissemination. Simulation results show that the scheme can reduce the uncertainty of the broadcasted uncertain data effectively at the cost of a minor increase in data access time, in the case of no transmission error and presence of transmission errors. As a result, lower uncertainty of data benefits the qualifies of the query results based on the data.
文摘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.
文摘To enhance the security of user data in the clouds,we present an adaptive and dynamic data encryption method to encrypt user data in the mobile phone before it is uploaded.Firstly,the adopted data encryption algorithm is not static and uniform.For each encryption,this algorithm is adaptively and dynamically selected from the algorithm set in the mobile phone encryption system.From the mobile phone's character,the detail encryption algorithm selection strategy is confirmed based on the user's mobile phone hardware information,personalization information and a pseudo-random number.Secondly,the data is rearranged with a randomly selected start position in the data before being encrypted.The start position's randomness makes the mobile phone data encryption safer.Thirdly,the rearranged data is encrypted by the selected algorithm and generated key.Finally,the analysis shows this method possesses the higher security because the more dynamics and randomness are adaptively added into the encryption process.
基金This research was funded by Key Research and Development Program of Shaanxi Province(No.2017GY-064)the National Key R&D Program of China(No.2017YFB1402102).
文摘With the popularity of sensor-rich mobile devices,mobile crowdsensing(MCS)has emerged as an effective method for data collection and processing.However,MCS platform usually need workers’precise locations for optimal task execution and collect sensing data from workers,which raises severe concerns of privacy leakage.Trying to preserve workers’location and sensing data from the untrusted MCS platform,a differentially private data aggregation method based on worker partition and location obfuscation(DP-DAWL method)is proposed in the paper.DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group according to group size(the number of workers).Then each worker’s location is obfuscated and his/her sensing data is perturbed by adding Laplace noise before uploading to the platform.In the stage of data aggregation,DP-DAWL method adopts an improved Kalman filter algorithm to filter out the added noise(including both added noise of sensing data and the system noise in the sensing process).Through using optimal estimation of noisy aggregated sensing data,the platform can finally gain better utility of aggregated data while preserving workers’privacy.Extensive experiments on the synthetic datasets demonstrate the effectiveness of the proposed method.
文摘Recently, the exponential rise in communication system demands has motivated global academia-industry to develop efficient communication technologies to fulfill energy efficiency and Quality of Service (QoS) demands. Wireless Sensor Network (WSN) being one of the most efficient technologies possesses immense potential to serve major communication purposes including civil, defense and industrial purposes etc. The inclusion of sensor-mobility with WSN has broadened application horizon. The effectiveness of WSNs can be characterized by its ability to perform efficient data gathering and transmission to the base station for decision process. Clustering based routing scheme has been one of the dominating techniques for WSN systems;however key issues like, cluster formation, selection of the number of clusters and cluster heads, and data transmission decision from sensors to the mobile sink have always been an open research area. In this paper, a robust and energy efficient single mobile sink based WSN data gathering protocol is proposed. Unlike existing approaches, an enhanced centralized clustering model is developed on the basis of expectation-maximization (EEM) concept. Further, it is strengthened by using an optimal cluster count estimation technique that ensures that the number of clusters in the network region doesn’t introduce unwanted energy exhaustion. Meanwhile, the relative distance between sensor node and cluster head as well as mobile sink is used to make transmission (path) decision. Results exhibit that the proposed EEM based clustering with optimal cluster selection and optimal dynamic transmission decision enables higher throughput, fast data gathering, minima delay and energy consumption, and higher
基金partially supported by the National Natural Science Foundation of China(61751306,61801208,61671233)the Jiangsu Science Foundation(BK20170650)+2 种基金the Postdoctoral Science Foundation of China(BX201700118,2017M621712)the Jiangsu Postdoctoral Science Foundation(1701118B)the Fundamental Research Funds for the Central Universities(021014380094)
文摘During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.
基金supported by the National Natural Science Foundation of China(No.62201313)the Opening Foundation of Fujian Key Laboratory of Sensing and Computing for Smart Cities(Xiamen University)(No.SCSCKF202101)the Open Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University)(No.MJUKF-IPIC202206).
文摘Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent years.The mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all things.The variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication environments.Ensuring data secure transmission is critical for mobile IIoT networks.This paper investigates the data secure transmission performance prediction of mobile IIoT networks.To cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first derived.Then,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction algorithm.For mobile signals,the important features may be removed by the pooling layers.This will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is designed.Out of the input and output layers,it removes the pooling layer and contains six convolution layers.Elman,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed algorithm.Through simulation analysis,good prediction accuracy is achieved by the CNN algorithm.The prediction accuracy obtains a 59%increase.
