Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas.

/pdf/a-survey-on-mobile-crowdsensing-systems-challenges-solutions-224oot2vez.pdf

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Recently, due to the increasing popularity of enjoying various multimedia services on mobile devices (e.g., smartphones, ipads, and electronic tablets), the generated mobile data traffic has been explosively growing and has become a serve burden on mobile network operators. To address such a serious challenge in mobile networks, an effective approach is to manage data traffic by using complementary technologies (e.g., small cell network, WiFi network, and so on) to achieve mobile data offloading. In this paper, we discuss the recent advances in the techniques of mobile data offloading. Particularly, based on the initiator diversity of data offloading, we classify the existing mobile data offloading technologies into four categories, i.e., data offloading through small cell networks, data offloading through WiFi networks, data offloading through opportunistic mobile networks, and data offloading through heterogeneous networks. Besides, we show a detailed taxonomy of the related mobile data offloading technologies by discussing the pros and cons for various offloading technologies for different problems in mobile networks. Finally, we outline some opening research issues and challenges, which can provide guidelines for future research work.

A Survey on Mobile Data Offloading Technologies

The General Data Protection Regulation (GDPR) gives control of personal data back to the owners by appointing higher requirements and obligations on service providers who manage and process personal data. As the verification of GDPR-compliance, handled by a supervisory authority, is irregularly conducted; it is challenging to be certified that a service provider has been continuously adhering to the GDPR. Furthermore, it is beyond the data owner's capability to perceive whether a service provider complies with the GDPR and effectively protects her personal data. This motivates us to envision a design concept for developing a GDPR-compliant personal data management platform leveraging the emerging blockchain and smart contract technologies. The goals of the platform are to provide decentralised mechanisms to both service providers and data owners for processing personal data; meanwhile, empower data provenance and transparency by leveraging advanced features of the blockchain technology. The platform enables data owners to impose data usage consent, ensures only designated parties can process personal data, and logs all data activities in an immutable distributed ledger using smart contract and cryptography techniques. By honestly participating in the platform, a service provider can be endorsed by the blockchain network that it is fully GDPR-compliant; otherwise, any violation is immutably recorded and is easily figured out by associated parties. We then demonstrate the feasibility and efficiency of the proposed design concept by developing a profile management platform implemented on top of the Hyperledger Fabric permissioned blockchain framework, following by valuable analysis and discussion.

pdf/gdpr-compliant-personal-data-management-a-blockchain-based-4w26ph85c0.pdf

GDPR-Compliant Personal Data Management: A Blockchain-based Solution

The Internet of Things (IoT) infrastructure, systems, and applications demonstrate potential in serving smart city development. Crowdsensing approaches for road surface conditions monitoring can benefit smart city road information services. Deteriorated roads induce vehicle damage, traffic congestion, and driver discomfort which influence traffic management. In this paper, we propose a framework for monitoring road surface anomalies. We analyze the common road surface types and irregularities as well as their impact on vehicle motion. In addition to the traditional use of sensors available in smart devices, we utilize the vehicle motion sensors (accelerometers and gyroscopes) presently available in most land vehicles. Various land vehicles were used in this paper, spanning different sizes, and year model for extensive road experiments. These trajectories were used to collect and build multiple labeled data sets that were used in the system structure. In order to enhance the performance of the sensor measurements, wavelet packet de-noising is used in this paper to enable efficient classification of road surface anomalies. We adopt statistical, time domain, and frequency domain features to distinguish different road anomalies. The descriptive data sets collected in this paper are used to build, train, and test a system classifier through machine learning techniques to detect and categorize multiple road anomalies with different severity levels. Furthermore, we analyze and assess the capabilities of the smart devices and the other vehicle motion sensors to accurately geo-reference the road surface anomalies. Several road test experiments examine the benefits and assess the performance of the proposed architecture.

Towards a Practical Crowdsensing System for Road Surface Conditions Monitoring

The mobile crowdsensing (MCS) technology with a large number of Internet of Things (IoT) devices provides an economic and efficient solution to participation in coordinated large-scale sensing tasks. Edge computing powers MCS to form the mobile edge crowdsensing (MECS) framework. Privacy disclosure of sensing data in multiple stages is a significant challenge in the MECS. To tackle this issue, combining machine learning with game theory, in this article, we propose an artificial intelligence (AI)-enabled three-party game (ATG) framework for guaranteed data privacy in the MECS of IoT. Specifically, based on the random forest classifier and the $k$ -anonymity algorithm, we propose a classification-anonymity model that effectively guarantees the privacy of sensitive data. Moreover, we construct a three-party game model for analyzing the data privacy leakage in different phases in the MECS. Finally, we conduct numerical and theoretical analyses and ample simulations. The results indicate that the ATG framework is effective and efficient, and better suited to the MECS of IoT.

An AI-Enabled Three-Party Game Framework for Guaranteed Data Privacy in Mobile Edge Crowdsensing of IoT

Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their smart devices to cooperatively perform a large-scale sensing job. One of the users’ main concerns is the cost of data uploading, which affects their willingness to participate in a crowdsensing task. In this paper, we propose an efficient Prediction-based User Recruitment for mobile crowdsEnsing (PURE), which separates the users into two groups corresponding to different price plans: Pay as you go (PAYG) and Pay monthly (PAYM). By regarding the PAYM users as destinations, the minimizing cost problem goes to recruiting the users that have the largest contact probability with a destination. We first propose a semi-Markov model to determine the probability distribution of user arrival time at points of interest (PoIs) and then get the inter-user contact probability. Next, an efficient prediction-based user-recruitment strategy for mobile crowdsensing is proposed to minimize the data uploading cost. We then propose PURE-DF by extending PURE to a case in which we address the tradeoff between the delivery ratio of sensing data and the recruiter number according to Delegation Forwarding. We conduct extensive simulations based on three widely-used real-world traces: roma/taxi , epfl , and geolife . The results show that, compared with other recruitment strategies, PURE achieves a lower recruitment payment and PURE-DF achieves the highest delivery efficiency.

An Efficient Prediction-Based User Recruitment for Mobile Crowdsensing

Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users, who provide the sensing services. The tasks, in practice, often have various spatiotemporal requirements, which make it hard to select suitable user set to perform the tasks. In this paper, we use the mobility prediction model to deal with this challenge and then propose the user selection algorithm to solve the user selection problem. From the perspective of mobility, it is probabilistic that the timesensitive tasks will be done on time and we use the time-related Markov model to achieve the probabilities. Furthermore, we consider the different uploading ways and obtain the corresponding probabilities, which could be used to propose a greedy user selection algorithm to select the suitable user set under the budget constraint. Extensive simulations have been conducted over two real-life mobile traces and the results prove the efficiency of our proposed algorithm.

Prediction Based User Selection in Time-Sensitive Mobile Crowdsensing

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An Efficient Prediction-Based User Recruitment for Mobile Crowdsensing

Prediction Based User Selection in Time-Sensitive Mobile Crowdsensing