Time-critical analytics applications are increasingly making use of distributed service interfaces (e.g., micro-services) that support the rapid construction of new applications by dynamically linking the services into different workflow configurations. Traditional service-based applications, in fixed networks, are typically constructed and managed centrally and assume stable service endpoints and adequate network connectivity. Constructing and maintaining such applications in dynamic heterogeneous wireless networked environments, where limited bandwidth and transient connectivity are commonplace, presents significant challenges and makes centralized application construction and management impossible. In this paper we present an architecture which is capable of providing an adaptable and resilient method for on-demand decentralized construction and management of complex timecritical applications in such environments. The approach uses a Vector Symbolic Architecture (VSA) to compactly represent an application as a single semantic vector that encodes the service interfaces, workflow, and the timecritical constraints required. By extending existing services interfaces, with a simple cognitive layer that can interpret and exchange the vectors, we show how the required services can be dynamically discovered and interconnected in a completely decentralized manner. We demonstrate the viability of this approach by using a VSA to encode various time-critical data analytics workflows. We show that these vectors can be used to dynamically construct and run applications using services that are distributed across an emulated Mobile Ad-Hoc Wireless Network (MANET). Scalability is demonstrated via an empirical evaluation.
There are a large number of workflow systems designed to work in various scientific domains, including support for the Internet of Things (IoT). One such workflow system is Node-RED, which is designed to bring workflow-based programming to IoT. However, the majority of scientific workflow systems, and specifically systems like Node-RED, are designed to operate in a fixed networked environment, which rely on a central point of coordination in order to manage the workflow. The main focus of the work described in this paper is to investigate means whereby we can migrate Node-RED workflows into a decentralized execution environment, so that such workflows can run on Edge networks, where nodes are extremely transient in nature. In this work, we demonstrate the feasibility of such an approach by showing how we can migrate a Node-RED based traffic congestion workflow into a decentralized environment. The traffic congestion algorithm is implemented as a set of Web services within Node-RED and we have architected and implemented a system that proxies the centralized Node-RED services using cognitively-aware wrapper services, designed to operate in a decentralized environment. Our cognitive services use a Vector Symbolic Architecture to semantically represent service descriptions and workflows in a way that can be unraveled on the fly without any central point of control. The VSA-based system is capable of parsing Node-RED workflows and migrating them to a decentralized environment for execution; providing a way to use Node-RED as a front-end graphical composition tool for decentralized workflows.
Future Multi-Domain Operations (MDO) will require the coordination of hundreds-even thousands-of devices and component services. This will demand the capability to rapidly discover the distributed devices/services and combine them into different workflow configurations, thereby creating the applications necessary to support changing mission needs. To meet these objectives, we envision a distributed Cognitive Computing System (CCS) that consists of humans and software that work together as a 'Distributed Federated Brain'. Motivated by neuromorphic processing models, we present an approach that uses hyperdimensional symbolic semantic vector representations of the services/devices and workflows. We show how these can be used to perform decentralized service/device discovery and workflow composition in the context of a dynamic communications re-planning scenario. In this paper, we describe how emerging analogue AI 'In Memory' and 'Near Memory' computing can be used to efficiently perform some of the required hyperdimensional vector computation (HDC). We present an evaluation of the performance of an energy-efficient phase change memory device (PCM) that can perform the required vector operations and discuss how such devices could be used in energy-critical 'edge of network' tactical MDO operations.
A non-technical description is given of a new, powerful, low cost field system (Infra-red Active Determination of Insect Flight Trajectories or IRADIT) for detailed and automatic remote sensing studies of natural insect flight behaviour. The special requirements and difficulties of the detection problem are defined. A series of examples of field devices and techniques are presented to illustrate the key factors of the optical sensing and tracking of insects in flight. In the finally adopted IRADIT system, flying insects are differentially illuminated, under all natural light conditions, by an intense beam of pulsed near-infra-red radiation and detected using a shuttered image intensifier linked with a video camera operating at a rate of 50 frames per second. Immediate fully-automatic determination of the flight trajectories of several simultaneously detected insects was achieved, at this same high rate and in the presence of sky background photon noise, by processing the video signals with electronic circuits and a microcomputer. Flight trajectories are influenced by the local wind, whose vector must be subtracted to study insect flight behaviour. This was achieved by the use of a specially developed sensitive three-vector vane anemometer, providing digital data to the microcomputer at a minimum rate of 5 Hz. In tests of the prototype IRADIT-anemometer system in the field, insects with wing area of only 1-5 mm 2 and flying against the midday sky were readily tracked at ranges up to 15 m. A range of at least 100 m is expected for nocturnal moth tracking.
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