System Abstractions for Scalable Application Development at the Edge

Recent years have witnessed an explosive growth of Internet of Things (IoT) devices, which collect or generate huge amounts of data. Given diverse device capabilities and application requirements, data processing takes place across a range of settings, from on-device to a nearby edge server/cloud and remote cloud. Consequently, edge-cloud coordination has been studied extensively from the perspectives of job placement, scheduling and joint optimization. Typical approaches focus on performance optimization for individual applications. This often requires domain knowledge of the applications, but also leads to application-specific solutions. Application development and deployment over diverse scenarios thus incur repetitive manual efforts. There are two overarching challenges to provide system-level support for application development at the edge. First, there is inherent heterogeneity at the device hardware level. The execution settings may range from a small cluster as an edge cloud to on-device inference on embedded devices, differing in hardware capability and programming environments. Further, application performance requirements vary significantly, making it even more difficult to map different applications to already heterogeneous hardware. Second, there are trends towards incorporating edge and cloud and multi-modal data. Together, these add further dimensions to the design space and increase the complexity significantly. In this thesis, we propose a novel framework to simplify application development and deployment over a continuum of edge to cloud. Our framework provides key connections between different dimensions of design considerations, corresponding to the application abstraction, data abstraction and resource management abstraction respectively. First, our framework masks hardware heterogeneity with abstract resource types through containerization, and abstracts away the application processing pipelines into generic flow graphs. Further, our framework further supports a notion of degradable computing for application scenarios at the edge that are driven by multimodal sensory input. Next, as video analytics is the killer app of edge computing, we include a generic data management service between video query systems and a video store to organize video data at the edge. We propose a video data unit abstraction based on a notion of distance between objects in the video, quantifying the semantic similarity among video data. Last, considering concurrent application execution, our framework supports multi-application offloading with device-centric control, with a userspace scheduler service that wraps over the operating system scheduler

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

ITR/SY: a distributed programming infrastructure for integrating smart sensors

Issued as final reportNational Science Foundation (U.S.

Distributed deep learning inference in fog networks

Today's smart devices are equipped with powerful integrated chips and built-in heterogeneous sensors that can leverage their potential to execute heavy computation and produce a large amount of sensor data. For instance, modern smart cameras integrate artificial intelligence to capture images that detect any objects in the scene and change parameters, such as contrast and color based on environmental conditions. The accuracy of the object recognition and classification achieved by intelligent applications has improved due to recent advancements in artificial intelligence (AI) and machine learning (ML), particularly, deep neural networks (DNNs). Despite the capability to carry out some AI/ML computation, smart devices have limited battery power and computing resources. Therefore, DNN computation is generally offloaded to powerful computing nodes such as cloud servers. However, it is challenging to satisfy latency, reliability, and bandwidth constraints in cloud-based AI. Thus, in recent years, AI services and tasks have been pushed closer to the end-users by taking advantage of the fog computing paradigm to meet these requirements. Generally, the trained DNN models are offloaded to the fog devices for DNN inference. This is accomplished by partitioning the DNN and distributing the computation in fog networks. This thesis addresses offloading DNN inference by dividing and distributing a pre-trained network onto heterogeneous embedded devices. Specifically, it implements the adaptive partitioning and offloading algorithm based on matching theory proposed in an article, titled "Distributed inference acceleration with adaptive dnn partitioning and offloading". The implementation was evaluated in a fog testbed, including Nvidia Jetson nano devices. The obtained results show that the adaptive solution outperforms other schemes (Random and Greedy) with respect to computation time and communication latency

Dynamic Reconfiguration in Camera Networks: A Short Survey

There is a clear trend in camera networks towards enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally we discuss the main open research challenges in camera network reconfiguration

Distributed Architectures for Intensive Urban Computing: A Case Study on Smart Lighting for Sustainable Cities

New information and communication technologies have contributed to the development of the smart city concept. On a physical level, this paradigm is characterized by deploying a substantial number of different devices that can sense their surroundings and generate a large amount of data. The most typical case is image and video acquisition sensors. Recently, these types of sensors are found in abundance in urban spaces and are responsible for producing a large volume of multimedia data. The advanced computer vision methods for this type of multimedia information means that many aspects can be dynamically monitored, which can help implement value-added applications in the city. However, obtaining more elaborate semantic information from these data poses significant challenges related to a large amount of data generated and the processing capabilities required. This paper aims to address these issues by using a combination of cloud computing technologies and mobile computing techniques to design a three-layer distributed architecture for intensive urban computing. The approach consists of distributing the processing tasks among a city’s multimedia acquisition devices, a middle computing layer, known as a cloudlet, and a cloud-computing infrastructure. As a result, each part of the architecture can now focus on a small number of tasks for which they are specially designed, and data transmission communication needs are significantly reduced. To this end, the cloud server can hold and centralize the multimedia analysis of the processed results from the lower layers. Finally, a case study on smart lighting is described to illustrate the benefits of using the proposed model in smart city environments.This work was supported in part by the Spanish Research Agency (AEI) and the European Regional Development Fund (FEDER) through the project CloudDriver4Industry under Grant TIN2017-89266-R, in part by the Spanish Ministry of Science, Innovation and Universities through the Project ECLIPSE-UA under Grant RTI2018-094283-B-C32, and in part by the Conselleria de Educación, Investigación, Cultura y Deporte of the Community of Valencia, Spain, within the Program of Support for Research under Project AICO/2017/134 and Project PROMETEO/2018/089