SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment

The increasing maturity of big data applications has led to a proliferation of models targeting the same objectives within the same scenarios and datasets. However, selecting the most suitable model that considers model's features while taking specific requirements and constraints into account still poses a significant challenge. Existing methods have focused on worker-task assignments based on crowdsourcing, they neglect the scenario-dataset-model assignment problem. To address this challenge, a new problem named the Scenario-based Optimal Model Assignment (SOMA) problem is introduced and a novel framework entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a heterogeneous information framework that can integrate various types of information to intelligently select a suitable dataset and allocate the optimal model for a specific scenario. To comprehensively evaluate models, a new score function that utilizes multi-head attention mechanisms is proposed. Moreover, a novel memory mechanism named the mnemonic center is developed to store the matched heterogeneous information and prevent duplicate matching. Six popular traffic scenarios are selected as study cases and extensive experiments are conducted on a dataset to verify the effectiveness and efficiency of SMAP and the score function

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

Peer-to-Peer Energy Trading in Smart Residential Environment with User Behavioral Modeling

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid. Trading energy among users in a decentralized fashion has been referred to as Peer- to-Peer (P2P) Energy Trading, which has attracted significant attention from the research and industry communities in recent times. However, previous research has mostly focused on engineering aspects of P2P energy trading systems, often neglecting the central role of users in such systems. P2P trading mechanisms require active participation from users to decide factors such as selling prices, storing versus trading energy, and selection of energy sources among others. The complexity of these tasks, paired with the limited cognitive and time capabilities of human users, can result sub-optimal decisions or even abandonment of such systems if performance is not satisfactory. Therefore, it is of paramount importance for P2P energy trading systems to incorporate user behavioral modeling that captures users’ individual trading behaviors, preferences, and perceived utility in a realistic and accurate manner. Often, such user behavioral models are not known a priori in real-world settings, and therefore need to be learned online as the P2P system is operating. In this thesis, we design novel algorithms for P2P energy trading. By exploiting a variety of statistical, algorithmic, machine learning, and behavioral economics tools, we propose solutions that are able to jointly optimize the system performance while taking into account and learning realistic model of user behavior. The results in this dissertation has been published in IEEE Transactions on Green Communications and Networking 2021, Proceedings of IEEE Global Communication Conference 2022, Proceedings of IEEE Conference on Pervasive Computing and Communications 2023 and ACM Transactions on Evolutionary Learning and Optimization 2023

When in doubt ask the crowd : leveraging collective intelligence for improving event detection and machine learning

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Augmented Human Machine Intelligence for Distributed Inference

With the advent of the internet of things (IoT) era and the extensive deployment of smart devices and wireless sensor networks (WSNs), interactions of humans and machine data are everywhere. In numerous applications, humans are essential parts in the decision making process, where they may either serve as information sources or act as the final decision makers. For various tasks including detection and classification of targets, detection of outliers, generation of surveillance patterns and interactions between entities, seamless integration of the human and the machine expertise is required where they simultaneously work within the same modeling environment to understand and solve problems. Efficient fusion of information from both human and sensor sources is expected to improve system performance and enhance situational awareness. Such human-machine inference networks seek to build an interactive human-machine symbiosis by merging the best of the human with the best of the machine and to achieve higher performance than either humans or machines by themselves. In this dissertation, we consider that people often have a number of biases and rely on heuristics when exposed to different kinds of uncertainties, e.g., limited information versus unreliable information. We develop novel theoretical frameworks for collaborative decision making in complex environments when the observers may include both humans and physics-based sensors. We address fundamental concerns such as uncertainties, cognitive biases in human decision making and derive human decision rules in binary decision making. We model the decision-making by generic humans working in complex networked environments that feature uncertainties, and develop new approaches and frameworks facilitating collaborative human decision making and cognitive multi-modal fusion. The first part of this dissertation exploits the behavioral economics concept Prospect Theory to study the behavior of human binary decision making under cognitive biases. Several decision making systems involving humans\u27 participation are discussed, and we show the impact of human cognitive biases on the decision making performance. We analyze how heterogeneity could affect the performance of collaborative human decision making in the presence of complex correlation relationships among the behavior of humans and design the human selection strategy at the population level. Next, we employ Prospect Theory to model the rationality of humans and accurately characterize their behaviors in answering binary questions. We design a weighted majority voting rule to solve classification problems via crowdsourcing while considering that the crowd may include some spammers. We also propose a novel sequential task ordering algorithm to improve system performance for classification in crowdsourcing composed of unreliable human workers. In the second part of the dissertation, we study the behavior of cognitive memory limited humans in binary decision making and develop efficient approaches to help memory constrained humans make better decisions. We show that the order in which information is presented to the humans impacts their decision making performance. Next, we consider the selfish behavior of humans and construct a unified incentive mechanism for IoT based inference systems while addressing the selfish concerns of the participants. We derive the optimal amount of energy that a selfish sensor involved in the signal detection task must spend in order to maximize a certain utility function, in the presence of buyers who value the result of signal detection carried out by the sensor. Finally, we design a human-machine collaboration framework that blends both machine observations and human expertise to solve binary hypothesis testing problems semi-autonomously. In networks featuring human-machine teaming/collaboration, it is critical to coordinate and synthesize the operations of the humans and machines (e.g., robots and physical sensors). Machine measurements affect human behaviors, actions, and decisions. Human behavior defines the optimal decision-making algorithm for human-machine networks. In today\u27s era of artificial intelligence, we not only aim to exploit augmented human-machine intelligence to ensure accurate decision making; but also expand intelligent systems so as to assist and improve such intelligence

A Cyber Physical System Crowdsourcing Inference Method Based on Tempering: An Advancement in Artificial Intelligence Algorithms

Activity selection is critical for the smart environment and Cyber-Physical Systems (CPSs) that can provide timely and intelligent services, especially as the number of connected devices is increasing at an unprecedented speed. As it is important to collect labels by various agents in the CPSs, crowdsourcing inference algorithms are designed to help acquire accurate labels that involve high-level knowledge. However, there are some limitations in the algorithm in the existing literature such as incurring extra budget for the existing algorithms, inability to scale appropriately, requiring the knowledge of prior distribution, difficulties to implement these algorithms, or generating local optima. In this paper, we provide a crowdsourcing inference method with variational tempering that obtains ground truth as well as considers both the reliability of workers and the difficulty level of the tasks and ensure a local optimum. The numerical experiments of the real-world data indicate that our novel variational tempering inference algorithm performs better than the existing advancing algorithms. Therefore, this paper provides a new efficient algorithm in CPSs and machine learning, and thus, it makes a new contribution to the literature

RL-OPRA: Reinforcement Learning for Online and Proactive Resource Allocation of crowdsourced live videos

© 2020 Elsevier B.V. With the advancement of rich media generating devices, the proliferation of live Content Providers (CP), and the availability of convenient internet access, crowdsourced live streaming services have witnessed unexpected growth. To ensure a better Quality of Experience (QoE), higher availability, and lower costs, large live streaming CPs are migrating their services to geo-distributed cloud infrastructure. However, because of the dynamics of live broadcasting and the wide geo-distribution of viewers and broadcasters, it is still challenging to satisfy all requests with reasonable resources. To overcome this challenge, we introduce in this paper a prediction driven approach that estimates the potential number of viewers near different cloud sites at the instant of broadcasting. This online and instant prediction of distributed popularity distinguishes our work from previous efforts that provision constant resources or alter their allocation as the popularity of the content changes. Based on the derived predictions, we formulate an Integer-Linear Program (ILP) to proactively and dynamically choose the right data center to allocate exact resources and serve potential viewers, while minimizing the perceived delays. As the optimization is not adequate for online serving, we propose a real-time approach based on Reinforcement Learning (RL), namely RL-OPRA, which adaptively learns to optimize the allocation and serving decisions by interacting with the network environment. Extensive simulation and comparison with the ILP have shown that our RL-based approach is able to present optimal results compared to heuristic-based approaches.This work was supported by the Qatar Foundation

Learning To Scale Up Search-Driven Data Integration

A recent movement to tackle the long-standing data integration problem is a compositional and iterative approach, termed “pay-as-you-go” data integration. Under this model, the objective is to immediately support queries over “partly integrated” data, and to enable the user community to drive integration of the data that relate to their actual information needs. Over time, data will be gradually integrated. While the pay-as-you-go vision has been well-articulated for some time, only recently have we begun to understand how it can be manifested into a system implementation. One branch of this effort has focused on enabling queries through keyword search-driven data integration, in which users pose queries over partly integrated data encoded as a graph, receive ranked answers generated from data and metadata that is linked at query-time, and provide feedback on those answers. From this user feedback, the system learns to repair bad schema matches or record links. Many real world issues of uncertainty and diversity in search-driven integration remain open. Such tasks in search-driven integration require a combination of human guidance and machine learning. The challenge is how to make maximal use of limited human input. This thesis develops three methods to scale up search-driven integration, through learning from expert feedback: (1) active learning techniques to repair links from small amounts of user feedback; (2) collaborative learning techniques to combine users’ conflicting feedback; and (3) debugging techniques to identify where data experts could best improve integration quality. We implement these methods within the Q System, a prototype of search-driven integration, and validate their effectiveness over real-world datasets

PLATFORM-DRIVEN CROWDSOURCED MANUFACTURING FOR MANUFACTURING AS A SERVICE

Platform-driven crowdsourced manufacturing is an emerging manufacturing paradigm to instantiate the adoption of the open business model in the context of achieving Manufacturing-as-a-Service (MaaS). It has attracted attention from both industries and academia as a powerful way of searching for manufacturing solutions extensively in a smart manufacturing era. In this regard, this work examines the origination and evolution of the open business model and highlights the trends towards platform-driven crowdsourced manufacturing as a solution for MaaS. Platform-driven crowdsourced manufacturing has a full function of value capturing, creation, and delivery approach, which is fulfilled by the cooperation among manufacturers, open innovators, and platforms. The platform-driven crowdsourced manufacturing workflow is proposed to organize these three decision agents by specifying the domains and interactions, following a functional, behavioral, and structural mapping model. A MaaS reference model is proposed to outline the critical functions and inter-relationships. A series of quantitative, qualitative, and computational solutions are developed for fulfilling the outlined functions. The case studies demonstrate the proposed methodologies and can pace the way towards a service-oriented product fulfillment process. This dissertation initially proposes a manufacturing theory and decision models by integrating manufacturer crowds through a cyber platform. This dissertation reveals the elementary conceptual framework based on stakeholder analysis, including dichotomy analysis of industrial applicability, decision agent identification, workflow, and holistic framework of platform-driven crowdsourced manufacturing. Three stakeholders require three essential service fields, and their cooperation requires an information service system as a kernel. These essential functions include contracting evaluation services for open innovators, manufacturers' task execution services, and platforms' management services. This research tackles these research challenges to provide a technology implementation roadmap and transition guidebook for industries towards crowdsourcing.Ph.D