Autonomous Mobility and Energy Service Management in Future Smart Cities: An Overview

With the rise of transportation electrification, autonomous driving and shared mobility in urban mobility systems, and increasing penetrations of distributed energy resources and autonomous demand-side management techniques in energy systems, tremendous opportunities, as well as challenges, are emerging in the forging of a sustainable and converged urban mobility and energy future. This paper is motivated by these disruptive transformations and gives an overview of managing autonomous mobility and energy services in future smart cities. First, we propose a three-layer architecture for the convergence of future mobility and energy systems. For each layer, we give a brief overview of the disruptive transformations that directly contribute to the rise of autonomous mobility-on-demand (AMoD) systems. Second, we propose the concept of autonomous flexibility-on-demand (AFoD), as an energy service platform built directly on existing infrastructures of AMoD systems. In the vision of AFoD, autonomous electric vehicles provide charging flexibilities as a service on demand in energy systems. Third, we analyze and compare AMoD and AFoD, and we identify four key decisions that, if appropriately coordinated, will create a synergy between AMoD and AFoD. Finally, we discuss key challenges towards the success of AMoD and AFoD in future smart cities and present some key research directions regarding the system-wide coordination between AMoD and AFoD.Comment: 19 pages, 4 figure

Yodel: A Layer 3.5 Name-Based Multicast Network Architecture For The Future Internet

Multicasting refers to the ability of transmitting data to multiple recipients without data sources needing to provide more than one copy of the data to the network. The network takes responsibility to route and deliver a copy of each data to every intended recipient. Multicasting has the potential to improve the network efficiency and performance (e.g., throughput and latency) through transferring fewer bits in communicating the same data to multiple recipients compared with unicast transmissions, reduce the amount of networking resources needed for communication, lower the network energy footprint, and alleviate the occurrence of congestion in the network. Over the past few decades, providing multicast services has been a real challenge for ISPs, especially to support home users and multi-domain network applications, leading to the emergence of complex application-level solutions. These solutions like Content Delivery and Peer-to-Peer networks take advantage of complex caching, routing, transport, and topology management systems which put heavy strains on the underlying Internet infrastructures to offer multicasting services. In reality, the main motivation behind the design of these systems is rather sharing content than offering efficient multicast services. In this paper, we propound Yodel, a name-based multicast network architecture that can provide multi-domain multicast services for current and future Internet applications. Compared to the wider array of other name-based network architectures with clean-slate infrastructure requirements, Yodel is designed to provide multicast services over the current Internet infrastructure. Hence, Yodel puts forward several design goals that distinguish it from other name-based network architectures with inherent multicast capabilities. This paper is prepared to discuss the Yodel architecture, its design goals, and architectural functions.Comment: Contains animated figure

Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service

End-to-end delay is a critical attribute of quality of service (QoS) in application domains such as cloud computing and computer networks. This metric is particularly important in tandem service systems, where the end-to-end service is provided through a chain of services. Service-rate control is a common mechanism for providing QoS guarantees in service systems. In this paper, we introduce a reinforcement learning-based (RL-based) service-rate controller that provides probabilistic upper-bounds on the end-to-end delay of the system, while preventing the overuse of service resources. In order to have a general framework, we use queueing theory to model the service systems. However, we adopt an RL-based approach to avoid the limitations of queueing-theoretic methods. In particular, we use Deep Deterministic Policy Gradient (DDPG) to learn the service rates (action) as a function of the queue lengths (state) in tandem service systems. In contrast to existing RL-based methods that quantify their performance by the achieved overall reward, which could be hard to interpret or even misleading, our proposed controller provides explicit probabilistic guarantees on the end-to-end delay of the system. The evaluations are presented for a tandem queueing system with non-exponential inter-arrival and service times, the results of which validate our controller's capability in meeting QoS constraints.Comment: 8 pages, Accepted to AAAI 202

Intent Assurance using LLMs guided by Intent Drift

Intent-Based Networking (IBN) presents a paradigm shift for network management, by promising to align intents and business objectives with network operations--in an automated manner. However, its practical realization is challenging: 1) processing intents, i.e., translate, decompose and identify the logic to fulfill the intent, and 2) intent conformance, that is, considering dynamic networks, the logic should be adequately adapted to assure intents. To address the latter, intent assurance is tasked with continuous verification and validation, including taking the necessary actions to align the operational and target states. In this paper, we define an assurance framework that allows us to detect and act when intent drift occurs. To do so, we leverage AI-driven policies, generated by Large Language Models (LLMs) which can quickly learn the necessary in-context requirements, and assist with the fulfillment and assurance of intents

Estimation of Missing Data in Intelligent Transportation System

Missing data is a challenge in many applications, including intelligent transportation systems (ITS). In this paper, we study traffic speed and travel time estimations in ITS, where portions of the collected data are missing due to sensor instability and communication errors at collection points. These practical issues can be remediated by missing data analysis, which are mainly categorized as either statistical or machine learning(ML)-based approaches. Statistical methods require the prior probability distribution of the data which is unknown in our application. Therefore, we focus on an ML-based approach, Multi-Directional Recurrent Neural Network (M-RNN). M-RNN utilizes both temporal and spatial characteristics of the data. We evaluate the effectiveness of this approach on a TomTom dataset containing spatio-temporal measurements of average vehicle speed and travel time in the Greater Toronto Area (GTA). We evaluate the method under various conditions, where the results demonstrate that M-RNN outperforms existing solutions,e.g., spline interpolation and matrix completion, by up to 58% decreases in Root Mean Square Error (RMSE).Comment: presented at the 2020 92nd IEEE conference on vehicular technology, 18 Nov.-16 Dec 2020 6 pages, 5 figures, 2 table