Crowdsensing Platform Research Articles

Secured fog-based vehicular crowd-sensing protocol by Modified Attribute based Encryption Model

The contemporary fog computing paradigm evolved from traditional cloud computing to give storage and computation resources at the network’s edge. Fog enabled vehicular processing is anticipated to be a fundamental component that may expedite a variety of applications, notably crowd-sensing, when applied to vehicular networks. As a result, the confidentiality and safety of vehicles participating in the crowd-sensing platform are now recognized as critical challenges for smart police and cyber defence. Furthermore, sophisticated access control is essential to meet the requirements of crowd-sensing users of information. This work presents a novel secured fog-based protocol for vehicular crowd sensing utilizing an attribute-based encryption model. The proposed framework incorporates a two-layered fog architecture termed fog layer A and fog layer B. Fog layer A includes data owners (vehicles) and fog nodes, while fog layer B consists of fog nodes integrated with Road Side Units (RSUs). The Transport Triggered Architecture (TTA) governs the retrieval of regular or specialized data as per the request of data users. The data collection procedure varies based on the type of data being processed. Regular data is gathered from data owners, aggregated using the Multiple Kernel Induced in Kernel Least Mean Square (MKI-KLMS) technique, which employs kernel least mean square and hierarchical fractional bidirectional least mean square methods to handle redundancy in data aggregation. Subsequently, the aggregated data is encrypted using the Proposed Fusion Key for Encryption (PFKE) technique, which employs a fusion key for message encryption. Additionally, an enhanced Blowfish algorithm is applied for an extra layer of encryption on the already encrypted data. The encrypted data is then transmitted to the TTA. The decryption process utilizes the same key to retrieve the original message. The Modified Attribute based Encryption Model (MAEM) scheme achieves the highest efficiency of 90.9476.

A trust and privacy-preserving intelligent big data collection scheme in mobile edge-cloud crowdsourcing

As one of important Edge-Cloud solutions, mobile crowd sensing (MCS) platform resides in the cloud, and recruits massive workers in the edge network to sense data, so big data can be collected to build various applications or services to consumers, which is a promising computing paradigm. There are two critical properties that are not well addressed. One is current privacy preserving (PP) scheme pays little attention to quality-ware based data collection, so malicious or low-trust workers can cause false or low-quality data attacks, which will seriously affect the foundation of data-based services. The second is the lack of an intelligent worker selection scheme so to reduce costs and improve data quality under PP condition. To address the above issues, in this paper, we first propose a big data collection framework that not only has the function of PP but also can effectively identify worker trust, which is not achieved by previous studies. In particular, the worker trust identification approach in this paper abandons the past unreasonable assumption that data quality can be identified by the obtained data of data requester (DR). Then, we propose a reinforcement-based worker selection scheme that comprehensively considers workers’ trust, workers’ quotation and workers’ locations, so as to overcome the problem of worker recruitment that cannot be quality-ware in the past location privacy protection. Theoretical analysis and massive experimental results demonstrate that the proposed strategy is better than the state-of-the-art strategies in data quality, system cost and other performance indicator

Heterogeneous multi-project multi-task allocation in mobile crowdsensing using an ensemble fireworks algorithm

With the development of Internet of Things (IoT), Mobile CrowdSensing (MCS) platform will release projects consisting of heterogeneous tasks, requiring participants with different skills to collaborate to develop such systems. In this paper, a heterogeneous multi-project multi-task allocation model is proposed based on the group collaboration mode to cater for this problem state. Our method would distinguish the roles of members within the group, and incorporate the inherent attributes of participants like skill level and social competence. With the constraints of skill matching and completion time, one needs to simultaneously maximize the sensing quality and to minimize the platform cost by finding an optimal task-participant allocation schedule. To solve the established model, a multi-objective fireworks algorithm with dual-feedback ensemble learning framework is proposed. The weight of the weak optimizer would be adjusted automatically by the evolutionary significance, for which the individual generation method more suitable for the current state would be chosen. The individual evaluation mechanism is updated by the objective exploration degree, so that the evolutionary direction can be adaptively adjusted. To experimentally evaluate the proposed approach, it would be compared with five representative algorithms on 12 real-world instances. Experimental results show that our algorithm can assist platform managers in making better decisions.

Privacy-Preserving and Syscall-Based Intrusion Detection System for IoT Spectrum Sensors Affected by Data Falsification Attacks

Crowdsensing platforms collect, process, transmit, and analyze spectrum data worldwide to optimize radio frequency spectrum usage. However, Internet-of-Things (IoT) spectrum sensors, performing some of the previous tasks, are exposed to software manipulation aiming to execute spectrum sensing data falsification (SSDF) attacks to compromise data integrity and spectrum optimization. Novel intrusion detection systems (IDSs) combining device fingerprinting with Machine and Deep Learning (ML/DL) improve the limitation of traditional solutions and remove the necessity of redundant sensors and reputation mechanisms. However, they fail when detecting SSDF attacks accurately while protecting sensors privacy. This work proposes a novel host-based and federated learning-oriented IDS for IoT spectrum sensors that considers unsupervised ML/DL and fingerprints based on system calls. The framework detection performance and consumption of resources are analyzed in local and federated scenarios with six spectrum sensors deployed on Raspberry Pis. The obtained results significantly improve related work when detecting SSDF attacks while protecting sensors privacy, and consuming CPU, memory, and storage of sensors in a reduced manner.

Beach and Weather: A Mobile Crowdsensing platform to report beach conditions

Beach and Weather: A Mobile Crowdsensing platform to report beach conditions

A reliability and truth-aware based online digital data auction mechanism for cybersecurity in MCS

Mobile Crowd Sensing (MCS) platform recruits participants to sense and report data, thus to build and provide services to consumers, which is a promising computing paradigm. However, malicious attacks based on false data may damage network security, and some low-trust participants may not complete tasks or submit false data since it costs resources to sense data. Both these behaviors do great harm to cybersecurity and reduce the benefit of the platform. Even if many incentive mechanisms have been proposed, there are three critical properties that are not well considered, i.e., truth-reduction, reliability-zero and time-discounting properties, which correspond to the effects of malicious attacks and the uncertain behaviors of participants. In this paper, we take these properties into account and propose a Time, Reliability and Truth-aware Online Auction (TRT-OA) mechanism to ensure cybersecurity and maximize the benefit of the platform, introducing a function T̃ and a RT coefficient to select more secure and profitable participants. We prove that TRT-OA mechanism achieves computational efficiency, budget feasibility, truthfulness, individual rationality and strategy-proofness. By comparing it with OMG and TDMC, we show that the benefit of TRT-OA mechanism increases by 44.06%.

Budget Allocation for Incentivizing Mobile Users for Crowdsensing Platform

Budget Allocation for Incentivizing Mobile Users for Crowdsensing Platform

#Safe Mapping Platform: A GIS Mobile Crowd Sensing Platform for COVID-19 Self-Tracking and Self-Risk Managing

Increases in the social sector of open data and online mapping technologies are starting new chances for interactive mapping in many research applications. Mobile crowd sensing is an application that gathers data from a network of conscientious volunteers and implements it for a public benefit which is very helpful for collecting related information during the COVID-19 situation. The paper aims to demonstrate the concept of #Safe Mapping Platform which followed a framework of opensource technology and implementation aspects. The #Safe Mapping Platform was established for self-tracking and self-risk managing by integrating GIS opensource technologies, location-based services, and LINE application. The developed platform can be adapted to the public for self-tracking and self-risk managing in any health issues in the future.

A crowdsensing platform for real-time monitoring and analysis of noise pollution in smart cities

This paper presents a crowdsensing platform for real-time monitoring and analysis of noise pollution in smart cities. The aim is to develop a comprehensive methodology and scalable infrastructure for measuring noise using mobile crowdsensing, storing and analysis of gathered data. The developed system consists of: 1) a mobile application for participatory and opportunistic crowdsensing of noise data from different microlocations, pre-processing of collected data, and sending data to the cloud, 2) a big data infrastructure for storing data and real-time big data analysis, and 3) a web application for decision support. Further, we have created a methodology that lets us select priority microlocations not typically covered by stationary measuring. The system has been evaluated experimentally; more than 4000 measurements were collected at five microlocations in the city of Belgrade, Serbia. Data was analysed in order to find the patterns that could serve for decision support to different stakeholders.

Empowering Self-Organized Feature Maps for AI-Enabled Modeling of Fake Task Submissions to Mobile Crowdsensing Platforms

Mobile crowdsensing (MCS) has emerged as a ubiquitous solution for data collection from embedded sensors of smart devices to improve the sensing capacity and reduce sensing costs in large regions. Due to the ubiquitous nature of MCS services, smart devices require awareness of against misbehaving users that are becoming smarter to clog the resources in such a nondedicated sensing environment. In an MCS setting, the primary goal of a fake sensing task submission is to keep participant devices occupied, such as the battery, sensing, storage, and computing. Since the development of robust sensing campaigns highly depends on the existence of a realistic model of misbehaving users, this article leverages artificial intelligence and introduces a region-based self-organizing feature map (SOFM)-based model on user movement patterns so as to place the fake sensing tasks with the objective of maximum impacted participants and recruits. Uniformly and randomly initialized neurons are designed with fixed and adaptive quantities that are determined based upon the affected area on the covered terrain. Through numerical studies, we show that the impact of the SOFM structures can affect up to 46% of the participants and up to 37% of the recruits under various SOFM topologies. Furthermore, SOFM-based task submission models can increase the energy consumption in recruited devices by up to 39% due to the illegitimate task submission.

Optimizing Mobile Crowdsensing Platforms for Boundedly Rational Users

In participatory mobile crowdsensing (MCS) users repeatedly make <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">choices</i> among a finite set of alternatives, i.e., whether to contribute to a task or not and which task to contribute to. The platform coordinating the MCS campaigns often <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">engineers</i> these choices by selecting MCS tasks to recommend to users and offering monetary or in-kind rewards to motivate their contributions to them. In this paper, we revisit the well-investigated question of how to optimize the contributions of mobile end users to MCS tasks. However, we depart from the bulk of related literature by explicitly accounting for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bounded rationality</i> evidenced in human decision making. Bounded rationality is a consequence of cognitive and other kinds of constraints, e.g., time pressure, and has been studied extensively in behavioral science. We first draw on work in the field of cognitive psychology to model the way boundedly rational users respond to MCS task offers as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fast-and-Frugal-Trees (FFTs)</i> . With each MCS task modeled as a vector of feature values, the decision process in FFTs proceeds through sequentially parsing lexicographically ordered features, resulting in choices that are satisfying but not necessarily optimal. We then formulate, analyze and solve the novel optimization problems that emerge for both nonprofit and for-profit MCS platforms in this context. The evaluation of our optimization approach highlights significant gains in both platform revenue and quality of task contributions when compared to heuristic rules that do not account for the lexicographic structure in human decision making. We show how this modeling framework readily extends to platforms that present multiple task offers to the users. Finally, we discuss how these models can be trained, iterate on their assumptions, and point to their implications for applications beyond MCS, where end-users make choices through the mediation of mobile/online platforms.

The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection

The Multi-access Edge Computing (MEC) paradigm increases the computational capabilities of distributed sensing architectures, such as Mobile CrowdSensing platforms, which are designed to collect heterogeneous data from the crowd by exploiting mobile devices. In this context, our work focusses on the impact of three community detection algorithms to our edge selection strategy. In particular, we study TILES, Infomap, and iLCD which are specifically designed to identify evolving communities of users in dynamic networks. Our analysis is based on the ParticipAct data set that offers real human mobility data. We first measure the quality of the data set during an observation period of 1 year, during which the data set provides the 75% of the expected traces collected by approximately 170 users. We then compare some structural properties of the communities detected, namely Similarity, Forward Stability, Cohesion and Coverage. We conclude our study with a performance analysis of the selected Mobile MECs by varying the community detection algorithms adopted. In particular, we measure the latency and the number of satisfied requests and we show that the average latency obtained with Infomap is slightly lower than that of the other algorithms, while the average number of satisfied requests is higher when we adopt the TILES algorithm.

Towards Profit Optimization During Online Participant Selection in Compressive Mobile Crowdsensing

A mobile crowdsensing (MCS) platform motivates employing participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit of the platform, i.e., the charge of a sensing task minus the payments to participants that execute the task. In this article, we improve the profit via the data reconstruction method, which brings new challenges, because it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In particular, two Profit-driven Online Participant Selection (POPS) problems under different situations are studied in our work: (1) for S-POPS, the sensing cost of the different parts within the target area is the Same. Two mechanisms are designed to tackle this problem, including the ProSC and ProSC+. An exponential-based quality estimation method and a repetitive cross-validation algorithm are combined in the former mechanism, and the spatial distribution of selected participants are further discussed in the latter mechanism; (2) for V-POPS, the sensing cost of different parts within the target area is Various, which makes it the NP-hard problem. A heuristic mechanism called ProSCx is proposed to solve this problem, where the searching space is narrowed and both the participant quantity and distribution are optimized in each slot. Finally, we conduct comprehensive evaluations based on the real-world datasets. The experimental results demonstrate that our proposed mechanisms are more effective and efficient than baselines, selecting the participants with a larger profit for the platform.

Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform.

mHealth technologies are increasingly utilized in various medical contexts. Mobile crowdsensing is such a technology, which is often used for data collection scenarios related to questions on chronic disorders. One prominent reason for the latter setting is based on the fact that powerful Ecological Momentary Assessments (EMA) can be performed. Notably, when mobile crowdsensing solutions are used to integrate EMA measurements, many new challenges arise. For example, the measurements must be provided in the same way on different mobile operating systems. However, the newly given possibilities can surpass the challenges. For example, if different mobile operating systems must be technically provided, one direction could be to investigate whether users of different mobile operating systems pose a different behaviour when performing EMA measurements. In a previous work, we investigated differences between iOS and Android users from the TrackYourTinnitus mHealth crowdsensing platform, which has the goal to reveal insights on the daily fluctuations of tinnitus patients. In this work, we investigated differences between iOS and Android users from the TrackYourHearing mHealth crowdsensing platform, which aims at insights on the daily fluctuations of patients with hearing loss. We analyzed 3767 EMA measurements based on a daily applied questionnaire of 84 patients. Statistical analyses have been conducted to see whether these 84 patients differ with respect to the used mobile operating system and their given answers to the EMA measurements. We present the obtained results and compare them to the previous mentioned study. Our insights show the differences in the two studies and that the overall results are worth being investigated in a more in-depth manner. Particularly, it must be investigated whether the used mobile operating system constitutes a confounder when gathering EMA-based data through a crowdsensing platform.

A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective

Historically, cities follow reactive planning models where managers make decisions as problems occur. On the other hand, the exponential growth of Information and Communication Technologies (ICT) has allowed the connection of a diverse array of sensors, devices, systems, and objects. These objects can then generate data that can be transformed into information and used in a more efficient urban planning paradigm, one that allows decisions to be made before the occurrence of problems and emergencies. Therefore, this article aims to propose a platform capable of estimating the amount of carbon dioxide based on sensor readings in vehicles, indirectly contributing to a more proactive city planning based on the monitoring of vehicular pollution. Crowdsensing techniques and an On-Board Diagnostic (OBD-II) reader are used to extract data from vehicles in real time, which are then stored locally on the devices used to perform data collection. With the performed experiments, it was possible to extract information about the operation of the vehicles and their dynamics when moving in a city, providing valuable information that can support auxiliary tools for the management of urban centers.

Delay-Sensitive Mobile Crowdsensing: Algorithm Design and Economics

In a delay-sensitive mobile crowdsensing (MCS) platform, a service provider offers monetary incentives to mobile users for participating in the data collection and reporting their obtained data by a deadline . One aspect missing from most prior literature in the incentive mechanism design is the consideration of the detailed data reporting process through cellular or Wi-Fi networks. In this paper, we consider the interactions between the service provider and the users in two stages. First, the service provider chooses a reward to maximize its expected profit under the incomplete information of the users’ responses. Next, given the reward, each user makes his participation and reporting decisions, which are complicated due to his mobility and network heterogeneity. We propose an algorithm to compute the optimal user's decisions under the general setting using dynamic programming, and derive closed-form decision criteria for the special yet practical case of a non-discounted reward. We compute the optimal reward by characterizing the solution set and the discontinuity in the profit function. Simulation results show that our proposed algorithm achieves a significant gain in the user payoff over three benchmark heuristic schemes. In addition, a service provider's profit is sensitive to the estimation of the users’ Wi-Fi availabilities.

VIVO: A secure, privacy-preserving, and real-time crowd-sensing framework for the Internet of Things

Smartphones are a key enabling technology in the Internet of Things (IoT) for gathering crowd-sensed data. However, collecting crowd-sensed data for research is not simple. Issues related to device heterogeneity, security, and privacy have prevented the rise of crowd-sensing platforms for scientific data collection. For this reason, we implemented VIVO, an open framework for gathering crowd-sensed Big Data for IoT services, where security and privacy are managed within the framework. VIVO introduces the enrolled crowd-sensing model, which allows the deployment of multiple simultaneous experiments on the mobile phones of volunteers. The collected data can be accessed both at the end of the experiment, as in traditional testbeds, as well as in real-time, as required by many Big Data applications. We present here the VIVO architecture, highlighting its advantages over existing solutions, and four relevant real-world applications running on top of VIVO.

SmartBike: an IoT Crowd Sensing Platform for Monitoring City Air Pollution

&lt;p&gt;In recent years, the Smart City concept is emerging as a way to increase efficiency, reduce costs, and improve the overall quality of citizen life. The rise of Smart City solutions is encouraged by the increasing availability of Internet of Things (IoT) devices and crowd sensing technologies. This paper presents an IoT Crowd Sensing platform that offers a set of services to citizens by exploiting a network of bicycles as IoT probes. Based on a survey conducted to identify the most interesting bike-enabled services, the SmartBike platform provides: real time remote geo-location of users’ bikes, anti-theft service, information about traveled route, and air pollution monitoring. The proposed SmartBike platform is composed of three main components: the SmartBike mobile sensors for data collection installed on the bicycle; the end-user devices implementing the user interface for geo-location and anti-theft; and the SmartBike central servers for storing and processing detected data and providing a web interface for data visualization. The suitability of the platform was evaluated through the implementation of an initial prototype. Results demonstrate that the proposed SmartBike platform is able to provide the stated services, and, in addition, that the accuracy of the acquired air quality measurements is compatible with the one provided by the official environmental monitoring system of the city of Turin. The described platform will be adopted within a project promoted by the city of Turin, that aims at helping people making their mobility behavior more sustainable.&lt;/p&gt;

Fostering participaction in smart cities: a geo-social crowdsensing platform

This article investigates how and to what extent the power of collective although imprecise intelligence can be employed in smart cities. The main visionary goal is to automate the organization of spontaneous and impromptu collaborations of large groups of people participating in collective actions (i.e., participAct), such as in the notable case of urban crowdsensing. In a crowdsensing environment, people or their mobile devices act as both sensors that collect urban data and actuators that take actions in the city, possibly upon request. Managing the crowdsensing process is a challenging task spanning several socio-technical issues: from the characterization of the regions under control to the quantification of the sensing density needed to obtain a certain accuracy; from the evaluation of a good balance between sensing accuracy and resource usage (number of people involved, network bandwidth, battery usage, etc.) to the selection of good incentives for people to participAct (monetary, social, etc.). To tackle these problems, this article proposes a crowdsensing platform with three main original technical aspects: an innovative geo-social model to profile users along different variables, such as time, location, social interaction, service usage, and human activities; a matching algorithm to autonomously choose people to involve in participActions and to quantify the performance of their sensing; and a new Android-based platform to collect sensing data from smart phones, automatically or with user help, and to deliver sensing/actuation tasks to users.