Mobile Crowdsensing Platform Research Articles

Continuous Probabilistic Skyline Query for Secure Worker Selection in Mobile Crowdsensing

Worker selection is always one of the most fundamental problems in mobile crowdsensing (MCS), since the reliability of workers' sensing data is hugely significant to the service quality. In the worker selection process, it is inevitable for the workers to share some of their sensitive information. Consequently, numerous studies are conducted on the problem of privacy-preserving worker selection in MCS platforms. However, most of the existing methods focus on static and short-term situations. As a result, they are inapplicable to the highly dynamic environments where the MCS tasks are long term and the workers can continuously arrive at/leave the system. To solve these problems, in this article, we propose a privacy-preserving worker selection scheme based on the probabilistic skyline over sliding windows. Specifically, the proposed scheme can select reliable workers for each current sliding window in terms of working experience, expiry time, and trustability. Besides, we design an ElGamal encryption-based scheme for securely outsourcing and comparing workers' personal information without revealing their privacy. Detailed security analysis shows that the workers' sensitive information, e.g., working experience and trustability, are not revealed to any authorized parties during the process of MCS under our security model. Furthermore, extensive experiments on both real-world and simulated data sets demonstrate that our proposed scheme outperforms the baseline method in two application scenarios, i.e., 1) continuous worker arrival and 2) continuous worker departure.

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.

On blockchain integration into mobile crowdsensing via smart embedded devices: A comprehensive survey

As an integral part of Internet of Things (IoT), mobile crowdsensing (MCS) via smart embedded devices has recently gained significant attention for being effective in a wide range of applications and services, particularly in smart environments. In order to collect data, MCS systems seek ubiquitous sensing services from smart mobile devices such as smartphones, tablets and wearables, which are equipped with a variety of sensors, and provide analytics, visualization and recommendation services. Despite its benefits, the distributed nature of MCS results in confronting a number of challenges in terms of incentives, privacy, security and dependability. In this survey, blockchains, as a state of the art technology that builds on decentralization, immutability and consensus characteristics, are comprehensively reviewed with their potential to address main challenges in MCS. To this end, this article reviews the MCS platforms by investigating the existing works along with their challenges. Furthermore, the article reviews blockchain-integrated MCS solutions to address the incentive mechanisms, data quality, and privacy and security issues. This survey’s objective is not only to analyze existing works but also to present potential directions for future research to leverage MCS and blockchains in smart environments.

Locally reconfigurable Self Organizing Feature Map for high impact malicious tasks submission in Mobile Crowdsensing

Location-based clogging attacks in a Mobile Crowdsensing (MCS) system occur following upon the submission of fake tasks, and aim to consume the batteries and hardware resources of smart mobile devices such as sensors, memory and processors. Intelligent modeling of fake task submissions is required to enable the development of effective defense mechanisms against location-based clogging attacks with fake task submissions. An intelligent strategy for fake task submission would aim to maximize the impact on the participants of an MCS system. With this in mind, this paper introduces new algorithms exploiting the Self-Organizing Feature Map (SOFM) to identify attack locations where fake sensing tasks submitted to an MCS platform are centered around. The proposed SOFM-based model addresses issues in the previously proposed SOFM-based attack models by proposing two ways of refinement. When compared to the former models, which also use SOFM architectures, simulation results show that up to 139.9% of impact improvement can be modeled under the reconfigurable SOFM architectures.

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.

Detecting Fake Mobile Crowdsensing Tasks: Ensemble Methods Under Limited Data

The nondedicated sensing capabilities of smart mobile devices contribute to Internet of Things (IoT) ecosystems with integral building blocks called mobile crowdsensing (MCS) systems. The distributed and nontrusted nature of MCS systems leads to various threats for devices and MCS platforms as well as for end users. Out of the many threats, fake tasks may lead to drained resources at the participating devices and clogged resources at the MCS platforms. Furthermore, when limited data are available, it becomes a further challenge to identify maliciously submitted fake tasks. In this article, we introduce possible solutions that leverage ensemble learning against fake tasks submitted to MCS platforms. More specifically, boosting-based solutions, namely adaptive boosting for binary classification (AdaBoost), gentle adaptive boosting (GentleBoost), and random under-sampling boosting (RUSBoost), form the basis for learning the legitimacy of tasks submitted to MCS platforms. Over a six-day observation window, one day was used for training while the remaining five days were used for testing to evaluate the performance under limited data in terms of training the machine learning (ML) models. Through extensive simulations, we have shown that GentleBoostbased ensemble learning can achieve promising performance in detecting fake/illegitimate tasks submitted to an MCS platform.

Secure and Efficient Probabilistic Skyline Computation for Worker Selection in MCS

The rapid advance of the Internet of Things (IoT) has enabled a new paradigm of the sensing network, i.e., mobile crowdsensing (MCS). Primarily, in MCS systems, a crowd of participating mobile users, namely, workers, are allocated by the MCS platforms to outsource their sensory data for specific tasks. Obviously, the reliability of workers and the trustability of their sensing data play significant roles in the service quality, thus the worker selection becomes crucial for the success of MCS applications. However, due to either a large number of candidates or their dynamic natures, selecting reliable workers poses big challenges to the MCS platform. Evidently, workers' reputation-based characteristics, such as trustability and credibility, are also pivotal for the worker selection in MCS, but they were often neglected in previous literature. In this article, aiming at addressing the above challenges, we propose a new privacy-preserving worker selection scheme based on the probabilistic skyline computation technique. Specifically, our proposed scheme is characterized by: 1) assigning a trustability score to each worker based on his/her past performance without revealing his/her sensitive information and 2) efficiently selecting a subset of reliable workers for a particular task. Detailed security analysis shows that our proposed scheme can preserve workers' privacy. In addition, performance evaluations via extensive simulations are conducted, and the results also demonstrate its effectiveness and efficiency for reliable worker selection in MCS applications.

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.

Profit-Oriented Task Allocation for Mobile Crowdsensing With Worker Dynamics: Cooperative Offline Solution and Predictive Online Solution

Mobile crowdsensing (MCS) is a new paradigm of data collection with large-scale sensing. A group of mobile users are recruited as workers to move around in a specific region and carry out sensing tasks. A challenging problem of MCS is task allocation, especially when the MCS platform needs to assign tasks to selected workers among a large user pool and consider mixed spatial and temporal features, including locations and time windows of tasks, and trajectories and arrival time of workers. In this paper, we take into account these features and study the task allocation problem that assigns tasks to workers over time and guarantees the tasks are accomplished before their deadlines. We consider an offline scenario where the MCS platform is informed of all the information of tasks and workers in advance, and an online scenario where the platform does not know the information of workers before they enter the system. For the offline scenario, we provide a cooperative ant colony algorithm with swarm intelligence to approximate the optimal solution in large-scale cases. For the online scenario with incomplete information, we propose several online algorithms, among which the predictive online algorithm exploits historical records of workers and performs the best. Finally, we conduct simulations and evaluate the differences among the online solutions and offline solutions. The results show that the proposed online solutions can approach the offline optimal solution in small-scale cases, and its approximation obtained by the cooperative offline solution in large-scale cases.

Game Theory in Mobile CrowdSensing: A Comprehensive Survey

Mobile CrowdSensing (MCS) is an emerging paradigm in the distributed acquisition of smart city and Internet of Things (IoT) data. MCS requires large number of users to enable access to the built-in sensors in their mobile devices and share sensed data to ensure high value and high veracity of big sensed data. Improving user participation in MCS campaigns requires to boost users effectively, which is a key concern for the success of MCS platforms. As MCS builds on non-dedicated sensors, data trustworthiness cannot be guaranteed as every user attains an individual strategy to benefit from participation. At the same time, MCS platforms endeavor to acquire highly dependable crowd-sensed data at lower cost. This phenomenon introduces a game between users that form the participant pool, as well as between the participant pool and the MCS platform. Research on various game theoretic approaches aims to provide a stable solution to this problem. This article presents a comprehensive review of different game theoretic solutions that address the following issues in MCS such as sensing cost, quality of data, optimal price determination between data requesters and providers, and incentives. We propose a taxonomy of game theory-based solutions for MCS platforms in which problems are mainly formulated based on Stackelberg, Bayesian and Evolutionary games. We present the methods used by each game to reach an equilibrium where the solution for the problem ensures that every participant of the game is satisfied with their utility with no requirement of change in their strategies. The initial criterion to categorize the game theoretic solutions for MCS is based on co-operation and information available among participants whereas a participant could be either a requester or provider. Following a thorough qualitative comparison of the surveyed approaches, we provide insights concerning open areas and possible directions in this active field of research.

Scalable Privacy-Preserving Participant Selection for Mobile Crowdsensing Systems: Participant Grouping and Secure Group Bidding

Mobile crowdsensing (MCS) has been emerging as a new sensing paradigm where vast numbers of mobile devices are used for sensing and collecting data in various applications. Auction based participant selection has been widely used for current MCS systems to achieve user incentive and task assignment optimization. However, participant selection problems solved with auction-based approaches usually involve participants’ privacy concerns because a participant's bids may contain her private information (such as location visiting patterns), and disclosure of participants’ bids may disclose their private information as well. In this paper, we study how to protect such bid privacy in a temporally and spatially dynamic MCS system. We assume that both sensing tasks and mobile participants have dynamic characteristics over spatial and temporal domains. Following the classical VCG auction, we carefully design a scalable grouping based privacy-preserving participant selection scheme, where participants are grouped into multiple participant groups and then auctions are organized within groups via secure group bidding. By leveraging Lagrange polynomial interpolation to perturb participants’ bids within groups, participants’ bid privacy is preserved. In addition, the proposed solution does not affect the operation of current MCS platform since the groups act as regular users to the platform. Both theoretical analysis and real-life tracing data simulations verify the efficiency and security of the proposed solution.

Online Rating Protocol Using Endogenous and Incremental Learning Design for Mobile Crowdsensing

Although mobile crowdsensing (MCS) has become a new paradigm of collecting, analyzing, and exploiting massive amounts of sensory data, how to effectively incentivize users’ active participation and high quality data contribution in MCS has become a critical issue. Existing offline incentive mechanisms are inefficient to be applied to the online scenario, where a large number of heterogeneous users arrive and leave dynamically in a random manner, meanwhile the MCS platform has to make an irrevocable immediate decision on whether to accept or reject users’ requests, and how much a user should be paid under a strict budget constraint without knowing future information. Although there are a few online incentive mechanisms, these efforts tend to focus on achieving some desirable properties without addressing the issues of online incentive as a whole, and are not effective to motivate users’ active participation and high-quality sensory data contribution simultaneously. In this paper, we integrate the quality of sensing, rating update, user selection, and payment determination to develop the first online rating protocol for practical MCS to deal with adverse selection and moral hazard simultaneously. First, we design an endogenous learning approach to quantify the users’ ratings based on their historical contributions. Second, we exploit an incremental learning approach to compute the density threshold based on users’ historical information. Third, we theoretically prove that our online rating protocol achieves the desirable properties of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">computationally efficiency</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">budgetary feasibility</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual rationality</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">truthfulness</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">constant competitiveness</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rating coverage</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incentive effectiveness</i> . Finally, extensive evaluation results support the performance of our protocol and validate the theoretical properties derived in this paper.

A Real-World-Oriented Multi-Task Allocation Approach Based on Multi-Agent Reinforcement Learning in Mobile Crowd Sensing

Mobile crowd sensing is an innovative and promising paradigm in the construction and perception of smart cities. However, multi-task allocation in real-world scenarios is a huge challenge. There are many unexpected factors in the execution of mobile crowd sensing tasks, such as traffic jams or accidents, that make participants unable to reach the target area. In addition, participants may quit halfway due to equipment failure, network paralysis, dishonest behavior, etc. Previous task allocation approaches mainly ignored some of the heterogeneity of participants and tasks in the real-world scenarios. This paper proposes a real-world-oriented multi-task allocation approach based on multi-agent reinforcement learning. Firstly, under the premise of fully considering the heterogeneity of participants and tasks, the approach enables participants as agents to learn multiple solutions independently, based on modified soft Q-learning. Secondly, two cooperation mechanisms are proposed for obtaining the stable joint action, which can minimize the total sensing time while meeting the sensing quality constraint, which optimizes the sensing quality of mobile crowd sensing (MCS) tasks. Experiments verify that the approach can effectively reduce the impact of emergencies on the efficiency of large-scale MCS platform and outperform baselines based on a real-world dataset under different experiment settings.

Band Steering Technology Based on QoE-Oriented Optimization in Wireless Network

In mobile crowd-sensing (MCS) network, it is necessary to reduce the cost of sensing data transformation from mobile devices to the MCS platform in the core of the network. With the widespread deployment of access point (AP), explosive data traffic of the mobile devices is offloaded from cellular base stations to Wi-Fi networks. The congestion of the 2.4 GHz band in dual-band wireless area network makes the lower quality of experience (QoE) of users. To ease the load of 2.4 GHz band, the band-steering technology has been widely used by equipment manufacturers. However, the fixed threshold configuration strategy in band steering technology makes it easy to be exploited by setting up a great number of connection requirements in a short time, which may prevent a normal connection requirement of a user, making it difficult to satisfy the dynamic resource requirements of different services. Moreover, if the threshold is configured too high, it may result in QoE imbalance in dual bands. Besides, when the band (such as 2.4 GHz) resources cannot meet the terminal requirement, the terminal cannot autonomously switch to another band (such as 5 GHz), and this may result in a very poor QoE of users. In this paper, a secure QoE-oriented wireless network optimization framework is proposed to improve the QoE of users in dual-band network. Firstly, to ensure the overall QoE of network and balancing the QoE between 2.4 GHz and 5 GHz bands, the framework dynamically adjusts the load threshold of the frequency band based on QoE information during the association of a terminal. Secondly, to meet the basic QoE requirement of each user, the framework preferentially switches the terminals with poor QoE to the idle band after users join the network. The experimental results show that the proposed framework can improve the QoE both before and after the terminal accesses the network. Additionally, it enables the better QoE balance in dual bands.

Multi-Task Allocation Under Time Constraints in Mobile Crowdsensing

Mobile crowdsensing (MCS) is a popular paradigm to collect sensed data for numerous sensing applications. With the increment of tasks and workers in MCS, it has become indispensable to design efficient task allocation schemes to achieve high performance for MCS applications. Many existing works on task allocation focus on single-task allocation, which is inefficient in many MCS scenarios where workers are able to undertake multiple tasks. On the other hand, many tasks are time-limited, while the available time of workers is also limited. Therefore, time validity is essential for both tasks and workers. To accommodate these challenges, this paper proposes a multi-task allocation problem with time constraints, which investigates the impact of time constraints to multi-task allocation and aims to maximize the utility of the MCS platform. We first prove that this problem is NP-complete. Then two evolutionary algorithms are designed to solve this problem. Finally, we conduct the experiments based on synthetic and real-world datasets under different experiment settings. The results verify that the proposed algorithms achieve more competitive and stable performance compared with baseline algorithms.

A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing

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.

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However, the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data or intentionally spreading disinformation for malevolent purposes, consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting high-quality data and providing better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this article, a novel trust model called Experience-Reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in a MCS platform. To enable the E-R model, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called Experience and Reputation are calculated accordingly. By incorporating the Experience and Reputation trust indicators (TIs), trust relationships between the users are established, evaluated and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in a MCS testbed which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers better quality for MCS services while being able to detect malicious users.

Thanos: Incentive Mechanism with Quality Awareness for Mobile Crowd Sensing

Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing worker participation. Different from existing work, we propose an incentive framework for MCS systems, named Thanos, that incorporates a crucial metric, called workers’ quality of information (QoI). Due to various factors (e.g., sensor quality and environment noise), the quality of the sensory data contributed by individual workers varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, our design of Thanos is based on reverse combinatorial auctions. We investigate both the single- and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational, and computationally efficient mechanism that ensures a close-to-optimal social welfare. For the latter, we design an iterative descending mechanism that satisfies individual rationality and computational efficiency, and approximately maximizes the social welfare with a guaranteed approximation ratio. Through extensive simulations, we validate our theoretical analysis on the various desirable properties guaranteed by Thanos.

Randomization-Embedded Greed: Pursuing More Platform Profits in Mobile Crowd Sensing

The recent proliferation of sensor-equipped mobile phones, together with the intrinsic mobility of their users, has enabled mobile crowdsensing (MCS) to spring up. In a typical MCS system, the MCS platform recruits mobile phone users (workers) to perform the sensing tasks published by requesters. The requesters are interested in some urban events and are willing to pay for the sensing data returned by the workers. A lot of incentive mechanisms have been presented in the literature, in order to stimulate workers to participate into MCS campaigns or to maximize the social welfare. However, the community has not yet paid much attention to optimizing the platform profit, which is highly valued by the profit-making MCS organizers. In this paper, we consider a realistic MCS scenario that depends on probabilistic collaboration among workers and has constrained capacity of platform; and we focus on the platform profit maximization (PPM) in this MCS scenario. The PPM problem is NP-hard, and the key challenge stems mainly from the non-monotonicity caused by the probabilistic collaboration and the quality-based payment of the requester. First, we propose two polynomial-time approximation algorithms for the PPM problem: MaxG and RandG . Our main effort is dedicated to design RandG , which involves a randomization policy of selecting workers in its greedy framework, aimed at pursuing chances of skipping over incompetent local optima. We also prove that RandG can achieve a constant approximation ratio in expectation. Second, we present algorithm RandCom for general PPM problems, which combines MaxG and RandG to struggle to make as high platform profit as possible. Finally, we conduct extensive simulation to evaluate our designs in terms of platform profit.

Proximity discovery and data dissemination for mobile crowd sensing using LTE direct

The pervasive and mass-market usage of smartphones and the huge amount of information that those devices with their sensors can detect or produce are enabling novel and unforeseen smart city sensing opportunities through the so-called Mobile Crowd Sensing (MCS) paradigm. MCS aims to involve volunteering citizens in data sensing activities as they roam across the city. Notwithstanding the intense recent research activity in the MCS field, current MCS platforms still exhibit some technological limitations and do not exploit at best socio-technical mechanisms (e.g., economic/social incentives) to facilitate and encourage users’ participation. In this perspective, the recent evolution of direct Device-to-Device (D2D) communications, in particular novel proximity discovery technologies, represents a relevant opportunity for MCS frameworks. This paper proposes a novel solution for improving the MCS impact and citizens’ participation rate by leveraging LTE Direct, i.e., one of the most recent and promising technologies for D2D proximity discovery and local data dissemination in next generation 5G networks. Our original MCS extension supports not only novel interactions with smartphones that do not necessarily host our whole MCS client (only an LTE Direct-enabled sensing module is required), but also enables new Internet of Things (IoT) scenarios where it is possible to extend smartphone sensing capabilities through D2D interactions with sensors that are dynamically determined via LTE Direct proximity discovery. To enable these new MCS features, we propose to expand the communication and data dissemination capabilities of LTE Direct via an original multi-frame transport protocol, capable of working properly also in ultra-dense scenarios with very high frame loss probability.