Game-Theoretic Recruitment of Sensing Service Providers for Trustworthy Cloud-Centric Internet-of-Things (IoT) Applications

Widespread use of connected smart devices that are equipped with various built-in sensors has introduced the mobile crowdsensing concept to the IoT-driven information and communication applications. Mobile crowdsensing requires implicit collaboration between the crowdsourcer/recruiter platforms and users. Additionally, users need to be incentivized by the crowdsensing platform because each party aims to maximize their utility. Due to the participatory nature of data collection, trustworthiness and truthfulness pose a grand challenge in crowdsensing systems in the presence of malicious users, who either aim to manipulate sensed data or collaborate unfaithfully with the motivation of maximizing their income. In this paper, we propose a game-theoretic approach for trustworthiness-driven user recruitment in mobile crowdsensing systems that consists of three phases: i) user recruitment, ii) collaborative decision making on trust scores, and iii) badge rewarding. Our proposed framework incentivizes the users through a sub-game perfect equilibrium (SPE) and gamification techniques. Through simulations, we show that the platform utility can be improved by up to the order of 50\% while the average user utility can be increased by at least 15\% when compared to fully-distributed and user-centric trustworthy crowdsensing

Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing

Ubiquity of mobile devices with rich sensory capabilities has given rise to the mobile crowd-sensing (MCS) concept, in which a central authority (the platform) and its participants (mobile users) work collaboratively to acquire sensory data over a wide geographic area. Recent research in MCS highlights the following facts: 1) a utility metric can be defined for both the platform and the users, quantifying the value received by either side; 2) incentivizing the users to participate is a non-trivial challenge; 3) correctness and truthfulness of the acquired data must be verified, because the users might provide incorrect or inaccurate data, whether due to malicious intent or malfunctioning devices; and 4) an intricate relationship exists among platform utility, user utility, user reputation, and data trustworthiness, suggesting a co-quantification of these inter-related metrics. In this paper, we study two existing approaches that quantify crowd-sensed data trustworthiness, based on statistical and vote-based user reputation scores. We introduce a new metric - collaborative reputation scores - to expand this definition. Our simulation results show that collaborative reputation scores can provide an effective alternative to the previously proposed metrics and are able to extend crowd sensing to applications that are driven by a centralized as well as decentralized control

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. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users for various MCS systems and, importantly, the proposed mechanism is practical to deploy in the real world

Privacy and trust in the internet of vehicles

The Internet of Vehicles aims to fundamentally improve transportation by connecting vehicles, drivers, passengers, and service providers together. Several new services such as parking space identification, platooning and intersection control--to name just a few--are expected to improve traffic congestion, reduce pollution, and improve the efficiency, safety and logistics of transportation. Proposed end-user services, however, make extensive use of private information with little consideration for the impact on users and third parties (those individuals whose information is indirectly involved). This article provides the first comprehensive overview of privacy and trust issues in the Internet of Vehicles at the service level. Various concerns over privacy are formalised into four basic categories: personal information privacy, multi-party privacy, trust, and consent to share information. To help analyse services and to facilitate future research, the main relevant end-user services are taxonomised according to voluntary and involuntary information they require and produce. Finally, this work identifies several open research problems and highlights general approaches to address them. These especially relate to measuring the trade-off between privacy and service functionality, automated consent negotiation, trust towards the IoV and its individual services, and identifying and resolving multi-party privacy conflicts

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

CrowdSenSim: a Simulation Platform for Mobile Crowdsensing in Realistic Urban Environments

Smart cities take advantage of recent ICT developments to provide added value to existing public services and improve quality of life for the citizens. The Internet of Things (IoT) paradigm makes the Internet more pervasive where objects equipped with computing, storage and sensing capabilities are interconnected with communication technologies. Because of the widespread diffusion of IoT devices, applying the IoT paradigm to smart cities is an excellent solution to build sustainable Information and Communication Technology (ICT) platforms. Having citizens involved in the process through mobile crowdsensing (MCS) techniques augments capabilities of these ICT platforms without additional costs. For proper operation, MCS systems require the contribution from a large number of participants. Simulations are therefore a candidate tool to assess the performance of MCS systems. In this paper, we illustrate the design of CrowdSenSim, a simulator for mobile crowdsensing. CrowdSenSim is designed specifically for realistic urban environments and smart cities services. We demonstrate the effectiveness of CrowdSenSim for the most popular MCS sensing paradigms (participatory and opportunistic) and we present its applicability using a smart public street lighting scenario

Energy-efficient Communications in Cloud, Mobile Cloud and Fog Computing

This thesis studies the problem of energy efficiency of communications in distributed computing paradigms, including cloud computing, mobile cloud computing and fog/edge computing. Distributed computing paradigms have significantly changed the way of doing business. With cloud computing, companies and end users can access the vast majority services online through a virtualized environment in a pay-as-you-go basis. %Three are the main services typically consumed by cloud users are Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Mobile cloud and fog/edge computing are the natural extension of the cloud computing paradigm for mobile and Internet of Things (IoT) devices. Based on offloading, the process of outsourcing computing tasks from mobile devices to the cloud, mobile cloud and fog/edge computing paradigms have become popular techniques to augment the capabilities of the mobile devices and to reduce their battery drain. Being equipped with a number of sensors, the proliferation of mobile and IoT devices has given rise to a new cloud-based paradigm for collecting data, which is called mobile crowdsensing as for proper operation it requires a large number of participants. A plethora of communication technologies is applicable to distributing computing paradigms. For example, cloud data centers typically implement wired technologies while mobile cloud and fog/edge environments exploit wireless technologies such as 3G/4G, WiFi and Bluetooth. Communication technologies directly impact the performance and the energy drain of the system. This Ph.D. thesis analyzes from a global perspective the efficiency in using energy of communications systems in distributed computing paradigms. In particular, the following contributions are proposed: - A new framework of performance metrics for communication systems of cloud computing data centers. The proposed framework allows a fine-grain analysis and comparison of communication systems, processes, and protocols, defining their influence on the performance of cloud applications. - A novel model for the problem of computation offloading, which describes the workflow of mobile applications through a new Directed Acyclic Graph (DAG) technique. This methodology is suitable for IoT devices working in fog computing environments and was used to design an Android application, called TreeGlass, which performs recognition of trees using Google Glass. TreeGlass is evaluated experimentally in different offloading scenarios by measuring battery drain and time of execution as key performance indicators. - In mobile crowdsensing systems, novel performance metrics and a new framework for data acquisition, which exploits a new policy for user recruitment. Performance of the framework are validated through CrowdSenSim, which is a new simulator designed for mobile crowdsensing activities in large scale urban scenarios

High-Precision Design of Pedestrian Mobility for Smart City Simulators

The unprecedented growth of the population living in urban environments calls for a rational and sustainable urban development. Smart cities can fill this gap by providing the citizens with high-quality services through efficient use of Information and Communication Technology (ICT). To this end, active citizen participation with mobile crowdsensing (MCS) techniques is a becoming common practice. As MCS systems require wide participation, the development of large scale real testbeds is often not feasible and simulations are the only alternative solution. Modeling the urban environment with high precision is a key ingredient to obtain effective results. However, currently existing tools like OpenStreetMap (OSM) fail to provide sufficient levels of details. In this paper, we apply a procedure to augment the precision (AOP) of the graph describing the street network provided by OSM. Additionally, we compare different mobility models that are synthetic and based on a realistic dataset originated from a well known MCS data collection campaign (ParticipAct). For the dataset, we propose two arrival models that determine the users’ arrivals and match the experimental contact distribution. Finally, we assess the scalability of AOP for different cities, verify popular metrics for human mobility and the precision of different arrival models

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks

Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Smart City Applications

Mobile crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. In MCS, citizens actively participate in the sensing process by contributing data with their smartphones, tablets, wearables and other mobile devices to a collector. As citizens sustain costs while contributing data, i.e., the energy spent from the batteries for sensing and reporting, devising energy efficient data collection frameworks (DCFs) is essential. In this work, we compare the energy efficiency of several DCFs through CrowdSenSim, which allows to perform large-scale simulation experiments in realistic urban environments. Specifically, the DCFs under analysis differ one with each other by the data reporting mechanism implemented and the signaling between users and the collector needed for sensing and reporting decisions. Results reveal that the key criterion differentiating DCFs' energy consumption is the data reporting mechanism. In principle, continuous reporting to the collector should be more energy consuming than probabilistic reporting. However, DCFs with continuous reporting that implement mechanisms to block sensing and data delivery after a certain amount of contribution are more effective in harvesting data from the crowd