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.

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Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications. We achieve this by (i) first learning vector embeddings of single graph nodes and (ii) then composing them to compactly represent node sequences. Specifically, we propose SENSE-S (Semantically Enhanced Node Sequence Embedding -for Single nodes), a skip-gram based novel embedding mechanism, for single graph nodes that co-learns graph structure as well as their textual descriptions. We demonstrate that SENSE-S vectors increase the accuracy of multi-label classification tasks by up to 50% and link-prediction tasks by up to 78% under a variety of scenarios using real datasets. Based on SENSE-S, we next propose generic SENSE to compute composite vectors that represent a sequence of nodes, where preserving the node order is important. We prove that this approach is efficient in embedding node sequences, and our experiments on real data confirm its high accuracy in node order decoding.

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