Artificial Intelligence, Transport and the Smart City:Definitions and Dimensions of a New Mobility Era

Artificial intelligence (AI) is a powerful concept still in its infancy that has the potential, if utilised responsibly, to provide a vehicle for positive change that could promote sustainable transitions to a more resource-efficient livability paradigm. AI with its deep learning functions and capabilities can be employed as a tool which empowers machines to solve problems that could reform urban landscapes as we have known them for decades now and help with establishing a new era; the era of the “smart city”. One of the key areas that AI can redefine is transport. Mobility provision and its impact on urban development can be significantly improved by the employment of intelligent transport systems in general and automated transport in particular. This new breed of AI-based mobility, despite its machine-orientation, has to be a user-centred technology that “understands” and “satisfies” the human user, the markets and the society as a whole. Trust should be built, and risks should be eliminated, for this transition to take off. This paper provides a novel conceptual contribution that thoroughly discusses the scarcely studied nexus of AI, transportation and the smart city and how this will affect urban futures. It specifically covers key smart mobility initiatives referring to Connected and Autonomous Vehicles (CAVs), autonomous Personal and Unmanned Aerial Vehicles (PAVs and UAVs) and Mobility-as-a-Service (MaaS), but also interventions that may work as enabling technologies for transport, such as the Internet of Things (IoT) and Physical Internet (PI) or reflect broader transformations like Industry 4.0. This work is ultimately a reference tool for researchers and city planners that provides clear and systematic definitions of the ambiguous smart mobility terms of tomorrow and describes their individual and collective roles underpinning the nexus in scope

Cooperative Task Execution for Object Detection in Edge Computing:An Internet of Things Application

The development of computer hardware and communications has brought with it many exciting applications in the Internet of Things. More and more Single Board Computers (SBC) with high performance and low power consumption are used to infer deep learning models at the edge of the network. In this article, we investigate a cooperative task execution system in an edge computing architecture. In our topology, the edge server offloads different workloads to end devices, which collaboratively execute object detection on the transmitted sets of images. Our proposed system attempts to provide optimization in terms of execution accuracy and execution time for inferencing deep learning models. Furthermore, we focus on implementing new policies to optimize the E2E execution time and the execution accuracy of the system by highlighting the key role of effective image compression and the batch sizes (splitting decisions) received by the end devices from a server at the network edge. In our testbed, we used the You Only Look Once (YOLO) version 5, which is one of the most popular object detectors. In our heterogeneous testbed, an edge server and three different end devices were used with different characteristics like CPU/TPU, different sizes of RAM, and different neural network input sizes to identify sharp trade-offs. Firstly, we implemented the YOLOv5 on our end devices to evaluate the performance of the model using metrics like Precision, Recall, and mAP on the COCO dataset. Finally, we explore optimal trade-offs for different task-splitting strategies and compression decisions to optimize total performance. We demonstrate that offloading workloads on multiple end devices based on different splitting decisions and compression values improves the system’s performance to respond in real-time conditions without needing a server or cloud resources.</p

Task Allocation Methods and Optimization Techniques in Edge Computing:A Systematic Review of the Literature

Task allocation in edge computing refers to the process of distributing tasks among the various nodes in an edge computing network. The main challenges in task allocation include determining the optimal location for each task based on the requirements such as processing power, storage, and network bandwidth, and adapting to the dynamic nature of the network. Different approaches for task allocation include centralized, decentralized, hybrid, and machine learning algorithms. Each approach has its strengths and weaknesses and the choice of approach will depend on the specific requirements of the application. In more detail, the selection of the most optimal task allocation methods depends on the edge computing architecture and configuration type, like mobile edge computing (MEC), cloud-edge, fog computing, peer-to-peer edge computing, etc. Thus, task allocation in edge computing is a complex, diverse, and challenging problem that requires a balance of trade-offs between multiple conflicting objectives such as energy efficiency, data privacy, security, latency, and quality of service (QoS). Recently, an increased number of research studies have emerged regarding the performance evaluation and optimization of task allocation on edge devices. While several survey articles have described the current state-of-the-art task allocation methods, this work focuses on comparing and contrasting different task allocation methods, optimization algorithms, as well as the network types that are most frequently used in edge computing systems

Tumor endothelial cell up-regulation of IDO1 is an immunosuppressive feed-back mechanism that reduces the response to CD40-stimulating immunotherapy

CD40-stimulating immunotherapy can elicit potent anti-tumor responses by activating dendritic cells and enhancing T-cell priming. Tumor vessels orchestrate T-cell recruitment during immune response, but the effect of CD40-stimulating immunotherapy on tumor endothelial cells has not been evaluated. Here, we have investigated how tumor endothelial cells transcriptionally respond to CD40-stimulating immunotherapy by isolating tumor endothelial cells from agonistic CD40 mAb- or isotype-treated mice bearing B16-F10 melanoma, and performing RNA-sequencing. Gene set enrichment analysis revealed that agonistic CD40 mAb therapy increased interferon (IFN)-related responses in tumor endothelial cells, including up-regulation of the immunosuppressive enzyme Indoleamine 2, 3-Dioxygenase 1 (IDO1). IDO1 was predominantly expressed in endothelial cells within the tumor microenvironment, and its expression in tumor endothelium was positively correlated to T-cell infiltration and to increased intratumoral expression of IFN gamma. In vitro, endothelial cells up-regulated IDO1 in response to T-cell-derived IFN gamma, but not in response to CD40-stimulation. Combining agonistic CD40 mAb therapy with the IDO1 inhibitor epacadostat delayed tumor growth in B16-F10 melanoma, associated with increased activation of tumor-infiltrating T-cells. Hereby, we show that the tumor endothelial cells up-regulate IDO1 upon CD40-stimulating immunotherapy in response to increased IFN gamma-secretion by T-cells, revealing a novel immunosuppressive feedback mechanism whereby tumor vessels limit T-cell activation