Mobile Crowd-sensing Applications: Data Redundancies, Challenges, and Solutions

Mobile Crowdsensing (MCS) systems are under rapid development due to the popularization of smart mobile devices with various sensing abilities. MCS systems are useful in applications that require large scale spatial data collecting. To provide satisfactory service, the MCS system is expected to contain effective and efficient task management strategies, considering worker qualification, and possible worker moving. Most existing works design moving trajectory for each worker, in order to improve task accomplishment ratio (TAR) for QoS consideration. Other than great real time operation cost, it is also not practical to control workers’ moving due to privacy issue. In this work, we propose a MCS model with comprehensive integrated parameters to support the design of effective QoS aware task assignment strategies. We design strategies to perform task assignment based on worker qualification, using gradient to represent worker moving probability. We develop the accumulated gradient based reward allocation (AGRA) that improves QoS by motivating worker moving. Our experiments show that the proposed QoS MCS management strategies improve task accomplishment ratio significantly.

The mobile crowd sensing (MCS) network is a new reliable and robust paradigm It consists of the Internet of Things (IoTs), wireless sensor networks (WSNs), and mobile personal devices. MCS is commonly used for social, infrastructural, and environmental data collection. Therefore, MCS architecture is utilized for a real-time traffic flow measurement and for predicting the quickest path. Nowadays, traffic congestion is becoming a severe concern in urban areas. The main reasons for traffic congestion and traffic jam on the roads of metropolitan cities are ever-increasing population and vehicle production. Therefore, in this paper, we propose an MCS system, which provides the user congestion-free path to reach the destination in minimum travel time. The MCS architecture exploits collected smartphone data (e.g., speed, direction, and location) for a real-time traffic prediction. Subsequently, K-means Clustering is used to divide the traffic into small clusters. Then the convex hull algorithm is used to calculate the weights of each cluster. In this manner, the proposed system can competently determine the quickest path. The MCS system updates the user about the real-time traffic flow and suggests the quickest path after a specific interval of time until the user reaches the destination. We evaluate the proposed system by comparison with the traditional systems. The obtained results demonstrate that the proposed system provides less distance and reduces the travel time for different traffic scenarios as compared to traditional systems.

With the enrichment of types and numbers of sensing terminals, more and more devices such as mobile phones, smart wearables, mobile robots, drones have appeared in our life, enabling the development of Mobile Crowd Sensing (MCS) technology. MCS systems has gradually changed from isomorphic sensing to heterogeneous collaborative sensing, and finally evolved into a heterogeneous multi-source sensing mode of the fusion of humans, machines and objects (things). However, state-of-the-art systems/frameworks do not well support efficient interactions and Heterogeneous Crowd Agents (HCA) management in Heterogeneous MCS (H-MCS) systems. With this in mind, this article aims at two major gaps: Efficient interaction and collaboration of HCA, automated modeling and flexible management of HCA. To deal with the challenges, we design an ontology-based interaction and management middleware (CrowdManager). Three core modules that constitute the middleware: HCA Information Extraction and Representation (HER), Ontology-based HCA Construction & Management (OCM), and Communication and Interaction Module (CIM) are well demonstrated. Extensive comparative evalution suggests that our approach not only brings rich and efficient HCA management and interactive functions to H-MCS systems, but also reduces communication time and various resource occupancy rates by more than 50%.

With the rapid development of mobile technology and subsequent mass adoption of mobile devices, mobile crowdsensing (MCS) has gained a lot of research attention. In MCS systems, trust is a key focus in the overall improvement in the participant uptake of the sensing tasks. The trust-based scheme of MCS is studied to predict the damage level, the scores of quality-of-service (QoS), and the levels of quality-of-data (QoD) of MCS systems. Users can participate in MCS sensing based on trustworthy indicators that are related to user experience and system reputation, as well as the knowledge obtained about the MCS systems. This paper illustrates the establishment of user confidence during recruitment in MCS as it is very critical for the success of MCS systems and proposes a simulation trust-based mechanism (SiTBaM) approach. The level of MCS security is enhanced to protect the privacy of participants, so that participants can be assured that the MCS system they are working with during sensing moment is trustworthy. The application of SiTBaM in MCS is verified to yield better results as the simulations show that it offers higher QoS levels, QoD scores, as well as low damage levels in the presence of any task or many malicious users. These results were validated through comparisons with other schemes.

Mobile crowdsensing (MCS) is vulnerable to faked sensing attacks, as selfish smartphone users sometimes provide faked sensing results to the MCS server to save their sensing costs and avoid privacy leakage. In this paper, the interactions between an MCS server and a number of smartphone users are formulated as a Stackelberg game, in which the server as the leader first determines and broadcasts its payment policy for each sensing accuracy. Each user as a follower chooses the sensing effort and thus the sensing accuracy afterward to receive the payment based on the payment policy and the sensing accuracy estimated by the server. The Stackelberg equilibria of the secure MCS game are presented, disclosing conditions to motivate accurate sensing. Without knowing the smartphone sensing models in a dynamic version of the MCS game, an MCS system can apply deep Q-network (DQN), which is a deep reinforcement learning technique combining reinforcement learning and deep learning techniques, to derive the optimal MCS policy against faked sensing attacks. The DQN-based MCS system uses a deep convolutional neural network to accelerate the learning process with a high-dimensional state space and action set, and thus improve the MCS performance against selfish users. Simulation results show that the proposed MCS system stimulates high-quality sensing services and suppresses faked sensing attacks, compared with a Q-learning-based MCS system.

With the wide application of mobile electronic devices such as smartphones, the emerging mobile crowd sensing (MCS) has gradually become an effective way of real-time sensing and information collection. In the MCS system, worker recruitment is a core and common research issue. The cold start problem limits the application of traditional MCS worker recruitment methods. Introducing social relationships can solve the cold start problem to some extent. Therefore, this paper borrows the idea of influence propagation on social networks and proposes a worker recruitment algorithm based on hybrid network mixing social network and communication network. The core idea is that first select seed workers according to the recruitment probability by using the communication network, then initiate the task spread on social networks and communication networks at the same time in a greedy way. The goal of worker recruitment is to maximize task spatial coverage. When calculating the probability of recruitment, this paper considers various factors such as worker's ability, sojourn time and worker's movement to improve the accuracy of recruitment probability. The experimental results on real datasets show that compared with the existing algorithms, the algorithm in this paper can guarantee the time constraint of the task and have better performance in terms of spatial coverage and running time.

With the fast increasing popularity of mobile services, ubiquitous mobile devices with enhanced sensing capabilities collect and share local information towards a common goal. The recent Mobile Crowd Sensing (MCS) paradigm enables a broad range of mobile applications and undoubtedly revolutionizes many sectors of our life. A critical challenge for the MCS paradigm is to induce mobile devices to be workers providing sensing services. In this study, we examine the problem of sensing task assignment to maximize the overall performance in MCS system while ensuring reciprocal advantages among mobile devices. Based on the overlapping coalition game model, we propose a novel workload determination scheme for each individual device. The proposed scheme can effectively decompose the complex optimization problem and obtains an effective solution using the interactive learning process. Finally, we have conducted extensive simulations, and the results demonstrate that the proposed scheme achieves a fair tradeoff solution between the MCS performance and the profit of individual devices.

Mobile Crowdsensing Systems (MCS) rely on the collaborative participation of a multitude of individuals equipped with mobile devices capable of sensing and computing. Together, they share data and extract information to observe, map, analyze, estimate, or predict various processes of common interest. This approach offers the advantage of low deployment cost and extensive geographical coverage, making it applicable in various domains such as transportation, environmental monitoring, smart cities, pervasive healthcare, and more. However, MCS systems often encounter challenges related to security, privacy, and trust. The presence of motion sensors like accelerometers and gyroscopes in smartphones is crucial for monitoring our real-world surroundings. Unfortunately, these sensors also make us vulnerable to privacy invasion attacks, where leaked private information can reveal details about human behaviors, physical characteristics, and location. Furthermore, MCS systems are susceptible to side-channel attacks, where the operation of basic sensors can inadvertently leak sensitive data in mobile crowdsensing applications. While traditional cryptography methods can address some security and privacy concerns, they are not feasible for resource-constrained smart mobile or Internet of Things devices, limiting their application in MCS. In light of these issues, this chapter proposes an innovative Proactive Defense Mechanism using Blockchain based Mobile Crowdsensing (BMCS) that aims to intercept, disrupt, or deter attacks or threats before they can occur, ensuring the security of the mobile crowd sensing process. The proposed approach has been thoroughly analyzed, and the security proofs demonstrate that it significantly enhances the level of security in MCS.

Performance evaluation of hybrid crowdsensing systems with stateful CrowdSenSim 2.0 simulator

The ubiquity of sensor-rich mobile devices is pushing forward the paradigm of mobile crowdsensing, which facilitates convenient and cost-effective collection of large amounts of sensory data (e.g., traffic monitoring, environment monitoring, and mobile personalized recommendation services [1, 2]). In practice, however, the sensory data collected from different mobile devices are not always reliable, due to various factors like heterogeneous sensor quality, noise, etc. Hence, a realistic problem faced by mobile crowdsensing is how to extract truthful information from the unreliable sensory data, which is also known as truth discovery [3]. Meanwhile, data privacy has long been an acute concern in mobile crowdsensing systems, as sensory data may reveal sensitive information like daily routines, location, personal health, social relations, and more [1]. In light of these observations, there has been an increasing interest to explore and develop sound privacy-preserving truth discovery techniques for mobile crowdsensing in recent years [4, 5]. In this keynote, I will present our research on privacy-preserving truth discovery in mobile crowdsensing. Compared with prior work, our goal is to promise privacy preservation, cost efficiency, and mobile-friendly deployment at the same time for truth discovery in mobile crowdsensing. I will first discuss the research challenges, which mainly lie in the complicated functionality requirements of truth discovery and practical deployment. I will then introduce our solutions on privacy-preserving truth discovery in mobile crowd- sensing [6-8], which build on advanced cryptographic techniques (like garbled circuits and homomorphic encryption) and present in- depth customization aimed for practical performance. Finally, I will discuss some future directions for researchers and practitioners to further investigate innovative solutions toward privacy-preserving truth discovery in mobile crowdsensing.

Participant selection or task allocation is a key issue in Mobile Crowdsensing (MCS) systems. Previous participant assignment approaches mainly focus on selecting a proper subset of users for MCS tasks, but however how to ensure that users devote effort on their tasks is a challenging problem that arises in these approaches. This paper studies the task quality control issue, and proposes a quality-validation task assignment mechanism (QTAM) in MCS systems. We theoretically model this mechanism as a Stackelberg Game, in which the users’ instinct of maximizing their payoff and the validation workforce limitation are taken into account. An efficient approximation algorithm is designed to find a Strong Stackelberg Equilibrium (SSE) in the QTAM game. Extensive simulations demonstrate the efficiency and effectiveness of QTAM, which shows that QTAM can prevent untrustworthy behaviors and achieve optimal quality validation for sensing tasks.

The Internet of Everything (IoE) paradigm makes the Internet more pervasive, interconnecting every devices of everyday life, and it is a promising solution for the development of 5G network services. Nowadays, Internet-connected devices are equipped with various built-in sensors. Therefore, the concept of mobile crowdsensing (MC) has been introduced to the IoE-driven situation where mobile devices gather data with the aim of performing a specific application. In this paper, we propose a new cooperative game model for the privacy-driven device collaboration in the MC system. The major goal of our approach is to incentivize the participating devices for effective data acquisitions while protecting each individual privacy based on each device’s preference. According to the Owen value mechanism, the proposed scheme provides an effective payment solution for each MC participating device under privacy considered IoE environments. The main merit possessed by our MC control approach is to guide the cooperation of mobile devices in providing MC services. Performance evaluation reveals the superiority of our proposed scheme in terms of task success ratio, MC participating ratio, and payoff fairness. Finally, we provide the guidance on the future research direction of the MC system including other issues.

One of the key problems in mobile crowdsensing (MCS) systems is the assignment of tasks to users. Most of the existing work aim to maximize a predefined system utility (e.g., quality of service or sensing), however, users (i.e., task requesters and performers/workers) may value different parameters and hence find an assignment unsatisfying if it is produced disregarding these parameters that define their preferences. While several studies utilize incentive mechanisms to motivate user participation in different ways, they do not take individual user preferences into account either. To address this issue, we leverage <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stable Matching Theory</i> which can help obtain a satisfying matching between two groups of entities based on their preferences. However, the existing approaches to find stable matchings do not work in MCS systems due to the many-to-one nature of task assignments and the budget constraints of task requesters. Thus, we first define two different stability conditions for user happiness in MCS systems. Then, we propose three efficient stable task assignment algorithms and discuss their stability guarantees in four different MCS scenarios. Finally, we evaluate the performance of the proposed algorithms through extensive simulations using a real dataset, and show that they outperform the state-of-the-art solutions.

One of the fundamental challenges in mobile crowdsensing (MCS) systems is efficient task assignment. Existing solutions consider the problem from system's point of view and try to maximize the system utility or minimize the cost of sensing. However, such a task assignment process does not consider the user (i.e., workers and task requesters) preferences and can yield unhappy users with their assignments, impairing the participation of users in the future. To incentivize the user participation, stable matching based solutions can be utilized to result in satisfactory assignments that will make the users happy based on their preferences. However, this may adversely affect the system utility especially when the set of eligible number of workers for each task is limited. To address this issue, we study the task assignment problem in MCS systems that maximizes the main system utility (i.e., number of workers and tasks assigned) as a system oriented goal while generating as happy users as possible with their assignment. As the problem is NP-complete, we first solve the problem optimally using Integer Linear Programming (ILP) and then we propose a heuristic based polynomial solution that runs very fast. Through simulations, we show that the proposed approach achieves the maximum possible system utility while generating small and close to optimal user unhappiness as in ILP results.

The IoT era observes the increasing demand for data to support various applications and services. The Mobile Crowdsensing (MCS) system then emerged. By utilizing the hybrid intelligence of humans and sensors, it is significantly beneficial to keep collecting high‐quality sensing data for all kinds of IoT applications, such as environmental monitoring, intelligent healthcare services, and traffic management. However, the service quality of MCS systems relies on a dedicated designed task allocation framework, which needs to consider the participant resource bottleneck and system utility at the same time. Recent studies tend to use a different solution to solve the two challenges. The incentive mechanism is for resolving the participant shortage problem, and task assignment methods are studied to find the best match of participants and system utility goal of MCS. Thus, existing task allocation frameworks fail to consider the participant’s expectations deeply. We propose a semiopportunistic concept‐based solution to overcome this issue. Similar to the “shared mobility” concept, our proposed task allocation framework can offer the participants routing advice without disturbing their original travel plan. The participant can accomplish the sensing request on his route. We further consider the system constraints to determine a subgroup of participants that can obtain the utility optimization goal. Specifically, we use the Graph Attention Network (GAT) to produce the target sensing area’s virtual representation and provide the participant with a payoff‐maximized route. Such a method makes our solution adapt to most of MCS scenarios’ conditions instead of using fixed system settings. Then, a reinforcement learning‐ (RL‐) based task assignment is adopted, which can help the MCS system towards better performance improvements while support different utility functions. The simulation results on various conditions demonstrate the superior performance of the proposed solution.