The rapid growth in technology and wide use of internet has increased smart applications such as intelligent transportation control system, and Internet of Things, which heavily rely on an efficient and reliable connectivity network. To overcome high bandwidth work load on the network, as well as minimize latency for real-time applications, the computation can be moved from the central cloud to a distributed edge cloud. The edge computing benefits various smart applications that uses distributed network for data analytics and services. Different from the existing cloud management solutions, edge computing needs to move cloud management services towards distributed heterogeneous edge nodes for multi-tenant user applications. However, existing cloud management services do not offer remote deployment of multi-tenant user applications on the cloud of edge nodes. In this paper, we propose a practical edge cloud software framework for deploying multi-tenant distributed smart applications. Having multiple distributed end nodes, auto discovery of all active end nodes is required for deploying multi-tenant user applications. However, existing cloud solutions require either private network or fixed IP address, which is not achievable for the distributed edge nodes. Most of the edge nodes connected through the public internet without fixed IP, and some of them even connect through IEEE 802.15 based sensor networks. We propose to build a software platform to manage the distributed edge nodes as well as support services to deploy and launch isolated, multi-tenant user applications through a lightweight container. We propose an architectural solution to remotely access edge cloud management services through intermittent internet connections. We open sourced our whole set of software solutions, and analyzed the major performance metrics of the edge cloud platform.
more »
« less
Service Discovery for The Connected Car with Semantic Accessors
Connected cars have the potential to transform
a vehicle from a transportation platform to a platform for
integrating humans with a city. To that end we introduce
semantic accessors (actor based local proxies for remote services)
as a novel, and powerful discovery mechanism for
connected vehicles that bridges the domains of Internet of
Things (IoT) composition frameworks and the semantic web
of things. The primary components of this approach include
a local semantic repository used for maintaining the vehicle’s
perspective of its real-world context, accessors for querying
and dynamically updating the repository to match evolving
vehicular context information, accessors for services (such as
parking) linked to a service ontology, and a swarmlet controller
responsible for managing the above in accordance with user
input. We demonstrate this semantic accessor architecture with
a prototype Dashboard display that downloads accessors for
new services as they become available and dynamically renders
their self-described user interface components.
more »
« less
- Award ID(s):
- 1836601
- PAR ID:
- 10112212
- Date Published:
- Journal Name:
- IEEE Inteligent Vehicles Symposium (IV)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Contextual information has been widely used in many computer vision tasks. However, existing approaches design specific contextual information mechanisms for different tasks. In this work, we propose a general context learning and reasoning framework for object detection tasks with three components: local contextual labeling, contextual graph generation and spatial contextual reasoning. With simple user defined parameters, local contextual labeling automatically enlarge the small object labels to include more local contextual information. A Graph Convolutional Network learns over the generated contextual graph to build a semantic space. A general spatial relation is used in spatial contextual reasoning to optimize the detection results. All three components can be easily added and removed from a standard object detector. In addition, our approach also automates the training process to find the optimal combinations of user defined parameters. The general framework can be easily adapted to different tasks. In this paper we compare our framework with a previous multistage context learning framework specifically designed for storefront accessibility detection and a state of the art detector for pedestrian detection. Experimental results on two urban scene datasets demonstrate that our proposed general framework can achieve same performance as the specifically designed multistage framework on storefront accessibility detection, and with improved performance on pedestrian detection over the state of art detector.more » « less
-
With sensors becoming increasingly ubiquitous, there is tremendous potential for Internet of Things (IoT) services that can take advantage of the data collected by these sensors. Although there are a growing number of technologies focused on IoT services, there is relatively limited foundational work on them. This is partly because of the lack of precise understanding, specification, and analysis of such services, and, consequently, there is limited platform support for programming them. In this paper, we present a formal model for understanding and enabling reasoning about distributed IoT services. The paper first studies the key properties of the IoT services profoundly, and then develops an approach for fine-grained resource coordination and control for such services. The resource model identifies the core mechanisms underlying IoT services, informing design and implementation decisions about them if implemented over a middleware or a platform. We took a multi-agent systems approach to represent IoT services, broadly founded in the actors model of concurrency. Actor-based services can be built by composing simpler services. Furthermore, we created a proximity model to represent an appropriate notion of IoT proximity. This model represents the dynamically evolving relationship between the service’s sensing and acting capabilities and the environments in which these capabilities are exercised. The paper also presents the design of a runtime environment to support the implementation of IoT services. Key mechanisms required by such services will be implemented in a distributed middleware.more » « less
-
null (Ed.)This paper describes the development of services and tools for scaling data curation services at the Qualitative Data Repository (QDR). Through a set of open-source tools, semi-automated workflows, and extensions to the Dataverse platform, our team has built services for curators to efficiently and effectively publish collections of qualitatively derived data. The contributions we seek to make in this paper are as follows: 1. We describe ‘human-in-the-loop’ curation and the tools that facilitate this model at QDR; 2. We provide an in-depth discussion of the design and implementation of these tools, including applications specific to the Dataverse software repository, as well as standalone archiving tools written in R; and 3. We highlight the role of providing a service layer for data discovery and accessibility of qualitative data.more » « less
-
This paper investigates the challenges posed by delays in Closed-Loop Sense-Act Systems in the context of Adversarial Internet of Things (IoT) applications. Prior work focused on studying the impact of delays on a single resource-constrained platform. To capitalize on the capabilities of different computing platforms, this work investigates the adaptation of control placement to optimize application performance in distributed settings. An Adaptive Control Placement (ACP) strategy is introduced, which dynamically switches between a local controller with lower accuracy and a cloud controller with higher accuracy based on network dynamics, optimizing overall application performance. The effectiveness of the ACP strategy is evaluated using a simulated Vehicle Following application in the PyBullet simulator. The results demonstrate that in terms of a time-to-complete (TTC) metric, the ACP strategy consistently outperforms strategies that use a fixed combination of controller type and location (e.g., PID at Local and MPC at Cloud) across various deadline scenarios.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
