Gregory M. P. O'Hare

Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor

Sensing, monitoring and actuating systems are expected to play a key role in reducing buildings overall energy consumption. Leveraging sensor systems to support energy efficiency in buildings poses novel research challenges in monitoring space usage, controlling devices, interfacing with smart energy meters and communicating with the energy grid. In the attempt of reducing electricity consumption in buildings, identifying individual sources of energy consumption is key to generate energy awareness and improve efficiency of available energy resources usage. Previous work studied several non-intrusive load monitoring techniques to classify appliances; however, the literature lacks of an comprehensive system that can be easily installed in existing buildings to empower users profiling, benchmarking and recognizing loads in real-time. This has been a major reason holding back the practice adoption of load monitoring techniques. In this paper we present RECAP: RECognition of electrical Appliances and Profiling in real-time. RECAP uses a single wireless energy monitoring sensor easily clipped to the main electrical unit. The energy monitoring unit transmits energy data wirelessly to a local machine for data processing and storage. The RECAP system consists of three parts: (1) Guiding the user for profiling electrical appliances within premises and generating a database of unique appliance signatures; (2) Using those signatures to train an artificial neural network that is then employed to recognize appliance activities (3) Providing a Load descriptor to allow peer appliance benchmarking. RECAP addresses the need of an integrated and intuitive tool to empower building owners with energy awareness. Enabling real-time appliance recognition is a stepping-stone towards reducing energy consumption and allowing a number of major applications including load-shifting techniques, energy expenditure breakdown per appliance, detection of power hungry and faulty appliances, and recognition of occupant activity. This paper describes the system design and performance evaluation in domestic environment.

Radio Sleep Mode Optimization in Wireless Sensor Networks

Energy efficiency is a central challenge in sensor networks, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low-power radio mode for putting the radio to sleep. Fixed low-power modes involve an inherent trade-off: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low-power sleep modes based on current traffic conditions in the network. It first introduces a comprehensive node energy model, which includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded microcontroller energy component for determining the optimal sleep mode and MAC protocol to use for given traffic scenarios. The model is then used for evaluating the energy-related performance of our recently proposed RFID impulse protocol enhanced with adaptive low-power modes, and comparing it against BMAC and IEEE 802.15.4, for both MicaZ and TelosB platforms under varying data rates. The comparative analysis confirms that RFID impulse with adaptive low-power modes provides up to 20 times lower energy consumption than IEEE 802.15.4 in low traffic scenario. The evaluation also yields the optimal settings of low-power modes on the basis of data rates for each node platform, and provides guidelines and a simple algorithm for the selection of appropriate MAC protocol, low-power mode, and node platform for a given set of traffic requirements of a sensor network application.

Presence equation: an investigation into cognitive factors underlying presence

The relationship between presence and cognitive factors such as absorption, creative imagination, empathy, and will ingness to experience presence was investigated. Presence was defined, operationalized, and measured using a ques tionnaire that we devised. Absorption and creative imagina tion were measured using questionnaires developed in the area of hypnosis, and empathy was assessed through an interpersonal reactivity index. Results indicated significant correlations between presence and each cognitive factor. They showed that persons who are highly fantasy prone, more empathic, more absorbed, more creative, or more willing to be transported to the virtual world experienced a greater sense of presence. Regression analysis led to a pres ence equation, which could be used to predict presence based on the investigated cognitive factors. Findings are congruent with user characteristics presented by the pres ence literature and support the position that individual dif ferences are important for the study of presence.

Autonomic wireless sensor networks

This paper seeks to demonstrate that autonomic behaviour is not restricted to resource-rich system, as typified by large servers, but can be incorporated into distributed and computationally challenged devices. Methods regarding how wireless sensor networks can benefit from the use of autonomic techniques without being overburdened with additional computing costs will be discussed. This will be achieved through the use of multi-agent systems (MAS). The discussion is grounded in the development of an autonomic wireless sensor network aimed at environmental sensing, an environmental nervous system. Finally, we provide empirical evidence of self-management via the use of distributed agents.

Gulliver's Genie: a multi-agent system for ubiquitous and intelligent content delivery

This paper introduces Gullivers Genie a context-aware tourist guide that assists roaming tourists. The approach adopted within this system is the deployment of intelligent agents, which collectively determine the user context and retrieve and assemble multi-media presentations that are wirelessly transmitted and displayed on a Personal Digital Assistant (PDA). As a backdrop, we first consider the state of the art in terms of context sensitive tourist guides, telecommunications, positioning technology and agent technologies. Gullivers Genie considers user context in terms of position, orientation and user profile. System agents are strong intentional agents that base deductions on a mental state comprising of Beliefs, Desires and Intentions (BDI). This paper presents the design of the system together with a glimpse of the user experience.

A flexible building management framework based on wireless sensor and actuator networks

Future buildings will be constantly monitored and managed through intelligent systems that allow having information about the building health, keeping a good comfort level for the building inhabitants and optimizing the energy spent. Despite many WSN programming frameworks have been to date developed and, in some cases, applied to support monitoring of buildings, none of them possesses all the specific features needed to develop WSN-based building applications. In this article a multi-platform domain specific framework based on Wireless Sensor and Actuator Networks (WSANs) for enabling efficient and effective management of buildings is presented. The proposed Building Management Framework (BMF) provides powerful abstractions that capture the morphology of buildings to allow for the rapid development and flexible management of pervasive building monitoring applications. The functionalities of the framework are shown in an emblematic case study concerning the SmartEnergyLab that is an effective operating scenario related to the monitoring of the usage of workstations in laboratories and offices. Finally, a performance evaluation of a WSAN running the BMF in terms of network usage and system lifetime is shown.

Evaluation of energy-efficiency in lighting systems using sensor networks

In modern energy aware buildings, lighting control systems are put in place so to maximise the energy-efficiency of the lighting system without effecting the comfort of the occupant. In many cases this involves utilising a set of presence sensors, with actuators, to determine when to turn on/off or dim lighting, when it is deemed necessary. Such systems are installed using standard tuning values statically fixed by the system installer. This can cause inefficiencies and energy wastage as the control system is never optimised to its surrounding environment. In this paper, we investigate a Wireless Sensor Network (WSN) as a viable tool that can help in analysing and evaluating the energy-efficiency of an existing lighting control system in a low-cost and portable solution. We introduce LightWiSe (LIGHTting evaluation through WIreless SEnsors), a wireless tool which aims to evaluate lighting control systems in existing office buildings. LightWiSe determines points in the control system that exhibit energy wastage and to highlight areas that can be optimised to gain a greater efficiency in the system. It will also evaluate the effective energy saving to be obtained by replacing the control system with a more judicious energy saving solution. During a test performed in an office space, with a number of different lighting control systems we could highlight a number of areas to reduce waste and save energy. Our findings show that each system tested can be optimised to achieve greater efficiency. LightWiSe can highlight savings in the region of 50% to 70% that are achievable through optimising the current control system or installing an alternative.

Agents for wireless sensor network power management

The primary function of wireless sensor networks is data acquisition or monitoring of some medium, such as temperature. In many instances these networks are deployed throughout inaccessible or hazardous regions meaning frequent maintenance such as battery replacement is undesirable and in some cases impossible. Intelligent power management for these devices is critical in maximizing the networks life span and ultimately will dictate the success of such deployments. This longevity must, however, be achieved while maintaining the integrity of the sensory data harvested by the network. Due to the inherent distributed nature of wireless sensor networks, intelligent software agents lend themselves to performing this power management in such a distributed domain. In this paper we examine some of the potential decisions an agent may face regarding intelligent power management and we look at how the stronger notion of agency could be employed to allow a richer deliberation regarding potential decisions. Allowing more adaptive control of WSNs in light of their computationally challenged nature.

MERLIN: Cross-layer integration of MAC and routing for low duty-cycle sensor networks

Sensor network MAC protocols typically sacrifice packet latency to achieve energy efficiency. Such delays may well increase due to routing protocol operation. For this reason it is imperative that we attempt to quantify the end-to-end delay and energy consumption when jointly using low duty cycle MAC and routing protocols. In this paper, we present a comprehensive evaluation of MERLIN (MAC and efficient routing integrated with support for localization), a cross-layer protocol that integrates both MAC and routing features. In contrast to many sensor network protocols, it employs a multicast upstream and multicast downstream approach to relaying packets to and from the gateway. Simultaneous reception and transmission errors are notified by asynchronous burst ACK and negative burst ACK messages. A division of the network into timezones, together with an appropriate scheduling policy, enables the routing of packets to the closest gateway. An evaluation of MERLIN has been conducted through simulation, against both the SMAC and the ESR routing protocols (an improved version of the DSR algorithm). The results illustrate that the joint usage of both SMAC and ESR, in low duty cycle scenarios, causes extremely high end-to-end delays and prevents acceptable data delivery rate. MERLIN, as an integrated approach, notably reduces latency, resulting in nodes that can deliver data in a very low duty cycle, yielding a significant extension to network lifetime.

Dynamic sensor event segmentation for real-time activity recognition in a smart home context

Activity recognition is fundamental to many of the services envisaged in pervasive computing and ambient intelligence scenarios. However, delivering sufficiently robust activity recognition systems that could be deployed with confidence in an arbitrary real-world setting remains an outstanding challenge. Developments in wireless, inexpensive and unobtrusive sensing devices have enabled the capture of large data volumes, upon which a variety of machine learning techniques have been applied in order to facilitate interpretation of and inference upon such data. Much of the documented research in this area has in the main focused on recognition across pre-segmented sensor data. Such approaches are insufficient for near real-time analysis as is required for many services, such as those envisaged by ambient assisted living. This paper presents a novel near real-time sensor segmentation approach that incorporates the notions of both sensor and time correlation.