Applying an extended theory of planned behaviour to predict breakfast consumption in adolescents

BACKGROUND/OBJECTIVES: Breakfast skipping increases during adolescence and is associated with lower levels of physical activity and weight gain. Theory-based interventions promoting breakfast consumption in adolescents report mixed findings, potentially because of limited research identifying which determinants to target. This study aimed to: (i) utilise the Theory of Planned Behaviour (TPB) to identify the relative contribution of attitudes (affective, cognitive and behavioural) to predict intention to eat breakfast and breakfast consumption in adolescents and (ii) determine whether demographic factors moderate the relationship between TPB variables, intention and behaviour. SUBJECTS/METHODS: Questionnaires were completed by 434 students (mean 14+/-0.9 years) measuring breakfast consumption (0-2, 3-6 or 7 days), physical activity levels and TPB measures. Data were analysed by breakfast frequency and demographics using hierarchical and multinomial regression analyses. RESULTS: Breakfast was consumed everyday by 57% of students, with boys more likely to eat a regular breakfast, report higher activity levels and report more positive attitudes towards breakfast than girls (P<0.001). The TPB predicted 58% of the variation in intentions. Overall, the model was predictive of breakfast behaviours (P<0.001), but the relative contribution of TPB constructs varied depending on breakfast frequency. Interactions between gender and intentions were significant when comparing 0-2- and 3-6-day breakfast eaters only highlighting a stronger intention-behaviour relationship for girls. CONCLUSIONS: Findings confirm that the TPB is a successful model for predicting breakfast intentions and behaviours in adolescents. The potential for a direct effect of attitudes on behaviours should be considered in the implementation and design of breakfast interventions

Leveraging Multi-radio Communication for Mobile Wireless Sensor Networks

An important challenge in mobile sensor networks is to enable energy-efficient communication over a diversity of distances while being robust to wireless effects caused by node mobility. In this thesis, we argue that the pairing of two complementary radios with heterogeneous range characteristics enables greater range and interference diversity at lower energy cost than a single radio. We make three contributions towards the design of such multi-radio mobile sensor systems. First, we present the design of a novel reinforcement learning-based link layer algorithm that continually learns channel characteristics and dynamically decides when to switch between radios. Second, we describe a simple protocol that translates the benefits of the adaptive link layer into practice in an energy-efficient manner. Third, we present the design of Arthropod, a mote-class sensor platform that combines two such heterogeneous radios (XE1205 and CC2420) and our implementation of the Q-learning based switching protocol in TinyOS 2.0. Using experiments conducted in a variety of urban and forested environments, we show that our system achieves up to 52% energy gains over a single radio system while handling node mobility. Our results also show that our system can handle short, medium and long-term wireless interference in such environments

SRCP: Simple Remote Control for Perpetual High-Power Sensor Networks

Abstract. Remote management is essential for wireless sensor networks (WSNs) designed to run perpetually using harvested energy. A natural division of function for managing WSNs is to employ both an in-band data plane to sense, store, process, and forward data, and an out-of-band management plane to remotely control each node and its sensors. This paper presents SRCP, a Simple Remote Control Protocol that forms the core of an out-of-band management plane for WSNs. SRCP is motivated by our target environment: a perpetual deployment of high-power, aggressively duty-cycled nodes capable of handling high-bandwidth sensor data from multiple sensors. The protocol runs on low-power always-on control processors using harvested energy, distills an essential set of primitives, and uses them to control a suite of existing management functions on more powerful main nodes. We demonstrate SRCP’s utility by presenting a case study that (i) uses it to control a broad spectrum of management functions and (ii) quantifies its efficacy and performance.

The Visionary Minimalist

Abstract—An important challenge in mobile sensor networks is to enable energy-efficient communication over a diversity of distances while being robust to wireless effects caused by node mobility. In this paper, we argue that the pairing of two complementary radios with heterogeneous range characteristics enables greater range diversity at lower energy cost than a single radio. We make three contributions towards the design of such multi-radio mobile sensor systems. First, we present the design of a novel reinforcement learning-based link layer algorithm that continually learns channel characteristics and dynamically decides when to switch between radios. Second, we describe a simple protocol that translates the benefits of the adaptive link layer into practice in an energy-efficient manner. Third, we present the design of Arthropod, a mote-class sensor platform that combines two such heterogneous radios (XE1205 and CC2420) and our implementation of the Q-learning based switching protocol in TinyOS 2.0. Using experiments conducted in a variety of urban and forested environments, we show that our system achieves up to 52 % energy gains over a single radio system. I

Programmable Radio Environments for Smart Spaces

Smart spaces, such as smart homes and smart offices, are common Internet of Things (IoT) scenarios for building automation with networked sensors. In this paper, we suggest a different notion of smart spaces, where the radio environment is programmable to achieve desirable link quality within the space. We envision deploying low-cost devices embedded in the walls of a building to passively reflect or actively transmit radio signals. This is a significant departure from typical approaches to optimizing endpoint radios and individual links to improve performance. In contrast to previous work combating or leveraging per-link multipath fading, we actively reconfigure the multipath propagation. We sketch design and implementation directions for such a programmable radio environment, highlighting the computational and operational challenges our architecture faces. Preliminary experiments demonstrate the efficacy of using passive elements to change the wireless channel, shifting frequency “nulls” by nineWi-Fi subcarriers, changing the 2×2 MIMO channel condition number by 1.5 dB, and attenuating or enhancing signal strength by up to 26 dB