Aidan Boran

Runtime Semantic Interoperability for Gathering Ontology-based Network Context

The trends for pushing more operational intelligence towards network elements to achieve more context-aware and self-managing behavior often requires elements to gather network knowledge without necessarily binding explicitly to all of the potential sources of that knowledge. Though event-based publish-subscribe models allow efficient distribution of knowledge where the event types are known globally, dynamic service chains, ad hoc networks and pervasive computing application all introduce a more fluid and heterogeneous range of context knowledge. This requires some runtime translation of knowledge between sources and sinks of network context. This paper builds on existing mapping techniques that use ontological forms of existing management information models to examine the extent to which these can be employed for runtime semantic interoperability for network knowledge. It presents results in developing a management knowledge delivery framework based on existing models and platforms, but which offers a more decentralized knowledge exchange mechanism

Transforming XML Schema to OWL Using Patterns

One of the promises of the Semantic Web is to support applications that easily and seamlessly deal with heterogeneous data. Most data on the Web, however, is in the Extensible Markup Language (XML) format, but using XML requires applications to understand the format of each data source that they access. To achieve the benefits of the Semantic Web involves transforming XML into the Semantic Web language, OWL (Ontology Web Language), a process that generally has manual or only semi-automatic components. In this paper we present a set of patterns that enable the direct, automatic transformation from XML Schema into OWL allowing the integration of much XML data in the Semantic Web. We focus on an advanced logical representation of XML Schema components and present an implementation, including a comparison with related work.

BodyScan : Enabling Radio-based Sensing on Wearable Devices for Contactless Activity and Vital Sign Monitoring

Wearable devices are increasingly becoming mainstream consumer products carried by millions of consumers. However, the potential impact of these devices is currently constrained by fundamental limitations of their built-in sensors. In this paper, we introduce radio as a new powerful sensing modality for wearable devices and propose to transform radio into a mobile sensor of human activities and vital signs. We present BodyScan, a wearable system that enables radio to act as a single modality capable of providing whole-body continuous sensing of the user. BodyScan overcomes key limitations of existing wearable devices by providing a contactless and privacy-preserving approach to capturing a rich variety of human activities and vital sign information. Our prototype design of BodyScan is comprised of two components: one worn on the hip and the other worn on the wrist, and is inspired by the increasingly prevalent scenario where a user carries a smartphone while also wearing a wristband/smartwatch. This prototype can support daily usage with one single charge per day. Experimental results show that in controlled settings, BodyScan can recognize a diverse set of human activities while also estimating the users breathing rate with high accuracy. Even in very challenging real-world settings, BodyScan can still infer activities with an average accuracy above 60% and monitor breathing rate information a reasonable amount of time during each day.

Enabling decentralised management through federation

Cross-domain management is an increasingly important concern in network management and such management capability is a key-enabler of many emerging computing environments. This paper analyses the requirements for management systems that aim to support flexible and general capability sharing between autonomously managed domains. It introduces a novel Layered Federation Model (LFM) to structure this requirements analysis and describes the Federal Relationship Manager (FRM) which instantiates several layers of this model. The FRM combines semantic mapping management and authority management technologies to help solve several of the general management problems that are encountered whenever organisations enter into capability sharing agreements. An overview of related work on federation and the technical underpinnings of our approach are discussed and our works particular relevance to real world problems is explained through two service-centric use cases which involve the end-to-end delivery of a multimedia stream to a users home across several independent operators. Finally, experimental results are presented to highlight the practical advantages of our approach.

Semantic web services enabled b2b integration

The use of Semantic Web Service (SWS) technologies have been suggested to enable more dynamic B2B integration of heterogeneous systems and partners. We present our approach to accomplish dynamic B2B integrations based on the WSMX SWS environment. We particularly show how WSMX can be made to support the RosettaNet e-business framework and how it can add dynamics to B2B interactions by automating mediation of heterogeneous messages. This is illustrated through a purchasing scenario. The benefits of applying SWS technologies include more flexibility in accepting heterogeneity in B2B integrations and easing back-end integrations. This allows for example to introduce more competition into the purchasing process within e-business frameworks.

DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware

Wearable devices with built-in cameras present interesting opportunities for users to capture various aspects of their daily life and are potentially also useful in supporting users with low vision in their everyday tasks. However, state-of-the-art image wearables available in the market are limited to capturing images periodically and do not provide any real-time analysis of the data that might be useful for the wearers. In this paper, we present DeepEye - a match-box sized wearable camera that is capable of running multiple cloud-scale deep learn- ing models locally on the device, thereby enabling rich analysis of the captured images in near real-time without offloading them to the cloud. DeepEye is powered by a commodity wearable processor (Snapdragon 410) which ensures its wearable form factor. The software architecture for DeepEye addresses a key limitation with executing multiple deep learning models on constrained hardware, that is their limited runtime memory. We propose a novel inference software pipeline that targets the local execution of multiple deep vision models (specifically, CNNs) by interleaving the execution of computation-heavy convolutional layers with the loading of memory-heavy fully-connected layers. Beyond this core idea, the execution framework incorporates: a memory caching scheme and a selective use of model compression techniques that further minimizes memory bottlenecks. Through a series of experiments, we show that our execution framework outperforms the baseline approaches significantly in terms of inference latency, memory requirements and energy consumption.

A smart campus prototype for demonstrating the semantic integration of heterogeneous data

This paper describes the implementation of a Smart Campus application prototype that integrates heterogeneous data using semantic technologies. The prototype is based on a layered semantic architecture that facilitates semantic data access and integration using OWL, SWRL and SPARQL. The focus of the paper is on the prototype implementation and the lessons learned from its development.

Exploring space syntax on entrepreneurial opportunities with Wi-Fi analytics

Industrial events and exhibitions play a powerful role in creating social relations amongst individuals and firms, enabling them to expand their social network so to acquire resources. However, often these events impose a spatial structure which impacts encounter opportunities. In this paper, we study the impact that the spatial configuration has on the formation of network relations. We designed, developed and deployed a Wi-Fi analytics solution comprising of wearable Wi-Fi badges and gateways in a large scale industrial exhibition event to study the spatio-temporal trajectories of the 2.5K+ attendees including two special groups: 34 investors and 27 entrepreneurs. Our results suggest that certain zones with designated functionalities play a key role in forming social ties across attendees and the different behavioural properties of investors and entrepreneurs can be explained through a spatial lens. Based on our findings we offer three concrete recommendations for future organisers of networking events.

Sensing WiFi network for personal IoT analytics

We present the design, implementation and evaluation of an enabling platform for locating and querying physical objects using existing WiFi network. We propose the use of WiFi management probes as a data transport mechanism for physical objects that are tagged with WiFi-enabled accelerometers and are capable of determining their state-of-use based on motion signatures. A local WiFi gateway captures these probes emitted from the connected objects and stores them locally after annotating them with a coarse grained location estimate using a proximity ranging algorithm. External applications can query the aggregated views of state-of-use and location traces of connected objects through a cloud-based query server. We present the technical architecture and algorithms of the proposed platform together with a prototype personal object analytics application and assess the feasibility of our different design decisions. This work makes important contributions by demonstrating that it is possible to build a pure network-based IoT analytics platform with only location and motion signatures of connected objects, and that the WiFi network is the key enabler for the future IoT applications.

Capturing Personal and Crowd Behavior with Wi-Fi Analytics

We present a solution for analysing crowds at events such as conferences where people have networking opportunities. Often, potential social relations go unexploited because no business cards were exchanged or we forget about interesting people we met earlier. We created a solution built on top of ubiquitous Wi-Fi signals that is able to create a memory of human trajectories and touch points. In this paper we elaborate on the technological assets we designed to perform crowd anlaytics. We present small wearable Wi-Fi badges that last for the duration of an event (up to 3 days) with a single charge, as well as network equipment that senses the signals radiating from these badges and contemporary mobile devices.