e-Uber\textit{e-Uber}: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing

The sharing-economy-based business model has recently seen success in the transportation and accommodation sectors with companies like Uber and Airbnb. There is growing interest in applying this model to energy systems, with modalities like peer-to-peer (P2P) Energy Trading, Electric Vehicles (EV)-based Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Battery Swapping Technology (BST). In this work, we exploit the increasing diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly enables ride-sharing and energy-sharing through V2G and BST. e-Uber exploits spatial crowdsourcing, reinforcement learning, and reverse auction theory. Specifically, the platform uses reinforcement learning to understand the drivers' preferences towards different ride-sharing and energy-sharing tasks. Based on these preferences, a personalized list is recommended to each driver through CMAB-based Algorithm for task Recommendation System (CARS). Drivers bid on their preferred tasks in their list in a reverse auction fashion. Then e-Uber solves the task assignment optimization problem that minimizes cost and guarantees V2G energy requirement. We prove that this problem is NP-hard and introduce a bipartite matching-inspired heuristic, Bipartite Matching-based Winner selection (BMW), that has polynomial time complexity. Results from experiments using real data from NYC taxi trips and energy consumption show that e-Uber performs close to the optimum and finds better solutions compared to a state-of-the-art approachComment: Preprint, under revie

Capturing the City’s Heritage On-the-Go: Design Requirements for Mobile Crowdsourced Cultural Heritage

Intangible Cultural Heritage is at a continuous risk of extinction. Where historical artefacts engine the machinery of intercontinental mass-tourism, socio-technical changes are reshaping the anthropomorphic landscapes everywhere on the globe, at an unprecedented rate. There is an increasing urge to tap into the hidden semantics and the anecdotes surrounding people, memories and places. The vast cultural knowledge made of testimony, oral history and traditions constitutes a rich cultural ontology tying together human beings, times, and situations. Altogether, these complex, multidimensional features make the task of data-mapping of intangible cultural heritage a problem of sustainability and preservation. This paper addresses a suggested route for conceiving, designing and appraising a digital framework intended to support the conservation of the intangible experience, from a user and a collective-centred perspective. The framework is designed to help capture the intangible cultural value of all places exhibiting cultural-historical significance, supported by an extensive analysis of the literature. We present a set of design recommendations for designing mobile apps that are intended to converge crowdsourcing to Intangible Cultural Heritage

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Software systems are the joint creative products of multiple stakeholders, including both designers and users, based on their perception, knowledge and personal preferences of the application context. The rapid rise in the use of Internet, mobile and social media applications make it even more possible to provide channels to link a large pool of highly diversified and physically distributed designers and end users, the crowd. Converging the knowledge of designers and end users in requirements engineering process is essential for the success of software systems. In this paper, we report the findings of a survey of the literature on crowd-based requirements engineering research. It helps us understand the current research achievements, the areas of concentration, and how requirements related activities can be enhanced by crowd intelligence. Based on the survey, we propose a general research map and suggest the possible future roles of crowd intelligence in requirements engineering

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Understanding Crowdsourcing Contest Fitness Strategic Decision Factors and Performance: An Expectation-Confirmation Theory Perspective

Contest-based intermediary crowdsourcing represents a powerful new business model for generating ideas or solutions by engaging the crowd through an online competition. Prior research has examined motivating factors such as increased monetary reward or demotivating factors such as project requirement ambiguity. However, problematic issues related to crowd contest fitness have received little attention, particularly with regard to crowd strategic decision-making and contest outcomes that are critical for success of crowdsourcing platforms as well as implementation of crowdsourcing models in organizations. Using Expectation-Confirmation Theory (ECT), we take a different approach that focuses on contest level outcomes by developing a model to explain contest duration and performance. We postulate these contest outcomes are a function of managing crowdsourcing participant contest-fitness expectations and disconfirmation, particularly during the bidding process. Our empirical results show that contest fitness expectations and disconfirmation have an overall positive effect on contest performance. This study contributes to theory by demonstrating the adaptability of ECT literature to the online crowdsourcing domain at the level of the project contest. For practice, important insights regarding strategic decision making and understanding how crowd contest-fitness are observed for enhancing outcomes related to platform viability and successful organizational implementation

A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd

Mobile CrowdSensing (MCS), through employing considerable workers to sense and collect data in a participatory manner, has been recognized as a promising paradigm for building many large-scale applications in a cost-effective way, such as combating COVID-19. The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies assume that the qualities of workers are known in advance, or the platform knows the qualities of workers once it receives their collected data. In reality, to reduce their costs and thus maximize revenue, many strategic workers do not perform their sensing tasks honestly and report fake data to the platform. So, it is very hard for the platform to evaluate the authenticity of the received data. In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS. First, we model the worker recruitment as a multi-armed bandit reverse auction problem, and design an UCB-based algorithm to separate the exploration and exploitation, considering the Sensing Rates (SRs) of recruited workers as the gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL) approach is proposed to quickly and accurately obtain the workers' SRs, which consists of two phases, supervision and self-supervision. Last, SCMABA is designed organically combining the SRs acquisition mechanism with multi-armed bandit reverse auction, where supervised SR learning is used in the exploration, and the self-supervised one is used in the exploitation. We prove that our SCMABA achieves truthfulness and individual rationality. Additionally, we exhibit outstanding performances of the SCMABA mechanism through in-depth simulations of real-world data traces.Comment: 18 pages, 14 figure

Towards understanding the process of tournament crowdsourcing:the value co-creation perspective

In the contemporary business environment, firms are increasingly moving from creating business value internally to co-creating business value with external stakeholders. Value cocreation refers to the process where a focal firm involves external stakeholders in its previously in-house performed business processes and interacts intensively with each other to create a stream of value. Tournament crowdsourcing, as an application of crowdsourcing, has become an emerging approach for firms to engage with external crowds in pursuit of business value. In the existing Information Systems literature, scholars’ understanding of valueco-creation and crowdsourcing is still at an explorative stage. The process of value co-creation and crowdsourcing have not been extensively studied. In this research, we adopt an interpretive approach and employ multiple-case designs to investigate the process of tournament crowdsourcing through the lens of value co-creation. The findings of this research contribute to the literature on crowdsourcing by 1) introducing the process framework which examines value-generating phases and value propositions from both the perspective of the focal entity and the crowd, 2) revealing the dynamic involvement of the crowd, the process from value creation to value co-creation, and the dynamic value stream, 3) identifying the combined usage of multiple systems and mechanisms for tournament crowdsourcing by contemporary platforms, and potential conflicts related to the governance of the platform,and 4) identifying phases and associated activities relevant to finding the right crowd members from the perspective of the focal entity during the process of tournament crowdsourcing. The findings of this research also contribute to the literature on value cocreation by 1) introducing a thorough definition of value co-creation, 2) conceptually and empirically enriching the most salient components in value co-creation, and 3) bringing in new insights into the value co-creation phenomenon by examining the context of tournament crowdsourcing. In practical terms, the findings of this research may inspire practitioners of generating better understanding about their roles in facilitating value co-creation, the strategic usage of systems and mechanisms, being aware of potential conflicts and finding the right crowd members when conducting tournament crowdsourcing initiatives

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

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