Design of miniaturized wireless sensor mote and actuator for building monitoring and control

In this paper, a wireless sensor network mote hardware design and implementation are introduced for building deployment application. The core of the mote design is based on the 8 bit AVR microcontroller, Atmega1281 and 2.4 GHz wireless communication chip, CC2420. The module PCB fabrication is using the stackable technology providing powerful configuration capability. Three main layers of size 25 mm2 are structured to form the mote; these are RF, sensor and power layers. The sensors were selected carefully to meet both the building monitoring and design requirements. Beside the sensing capability, actuation and interfacing to external meters/sensors are provided to perform different management control and data recording tasks. Experiments show that the developed mote works effectively in giving stable data acquisition and owns good communication and power performance.

Development of miniaturized wireless sensor nodes suitable for building energy management and modelling

Buildings consume 40% of Ireland's total annual energy translating to 3.5 billion (2004). The EPBD directive (effective January 2003) places an onus on all member states to rate the energy performance of all buildings in excess of 50m2. Energy and environmental performance management systems for residential buildings do not exist and consist of an ad-hoc integration of wired building management systems and Monitoring & Targeting systems for non-residential buildings. These systems are unsophisticated and do not easily lend themselves to cost effective retrofit or integration with other enterprise management systems. It is commonly agreed that a 15-40% reduction of building energy consumption is achievable by efficiently operating buildings when compared with typical practice. Existing research has identified that the level of information available to Building Managers with existing Building Management Systems and Environmental Monitoring Systems (BMS/EMS) is insufficient to perform the required performance based building assessment. The cost of installing additional sensors and meters is extremely high, primarily due to the estimated cost of wiring and the needed labour. From this perspective wireless sensor technology provides the capability to provide reliable sensor data at the required temporal and spatial granularity associated with building energy management. In this paper, a wireless sensor network mote hardware design and implementation is presented for a building energy management application. Appropriate sensors were selected and interfaced with the developed system based on user requirements to meet both the building monitoring and metering requirements. Beside the sensing capability, actuation and interfacing to external meters/sensors are provided to perform different management control and data recording tasks associated with minimisation of energy consumption in the built environment and the development of appropriate Building information models(BIM)to enable the design and development of energy efficient spaces.

Initial losses, corporate governance and earnings management

This thesis examines the relationship between initial loss events and the corporate governance and earnings management behaviour of these firms. This is done using four years of corporate governance information spanning the report of an initial loss for companies listed on the UK Stock Exchange. An industry- and sizematched control sample is used in a difference-in-difference analysis to isolate the impact of the initial loss event during the period. It is reported that, in general, an initial loss motivates an improvement in corporate governance in those loss firms where a relative weakness existed prior to the loss and that these changes mainly occur before the initial loss is announced. Firms with stronger (i.e. better quality) corporate governance have less need to alter it in response to the loss. It is also reported that initial loss firms use positive abnormal accruals in the year before the loss in an attempt to defer/avoid the loss — the weaker corporate governance the more likely is it that loss firms manage earnings in this manner. Abnormal accruals are also found to be predictive of an initial loss and when used as a conditioning variable, the quality of corporate governance is an important mitigating factor in this regard. Once the loss is reported, loss firms unwind these abnormal accruals although no evidence of big-bath behaviour is found. The extent to which these abnormal accruals are subsequently unwound are also found to be a function of both the quality of corporate governance as well as the severity of the initial loss.

Application of signal detection methods to fisheries management

The abundance of many commercially important fish stocks are declining and this has led to widespread concern on the performance of traditional approach in fisheries management. Quantitative models are used for obtaining estimates of population abundance and the management advice is based on annual harvest levels (TAC), where only a certain amount of catch is allowed from specific fish stocks. However, these models are data intensive and less useful when stocks have limited historical information. This study examined whether empirical stock indicators can be used to manage fisheries. The relationship between indicators and the underlying stock abundance is not direct and hence can be affected by disturbances that may account for both transient and persistent effects. Methods from Statistical Process Control (SPC) theory such as the Cumulative Sum (CUSUM) control charts are useful in classifying these effects and hence they can be used to trigger management response only when a significant impact occurs to the stock biomass. This thesis explores how empirical indicators along with CUSUM can be used for monitoring, assessment and management of fish stocks. I begin my thesis by exploring various age based catch indicators, to identify those which are potentially useful in tracking the state of fish stocks. The sensitivity and response of these indicators towards changes in Spawning Stock Biomass (SSB) showed that indicators based on age groups that are fully selected to the fishing gear or Large Fish Indicators (LFIs) are most useful and robust across the range of scenarios considered. The Decision-Interval (DI-CUSUM) and Self-Starting (SS-CUSUM) forms are the two types of control charts used in this study. In contrast to the DI-CUSUM, the SS-CUSUM can be initiated without specifying a target reference point (‘control mean’) to detect out-of-control (significant impact) situations. The sensitivity and specificity of SS-CUSUM showed that the performances are robust when LFIs are used. Once an out-of-control situation is detected, the next step is to determine how much shift has occurred in the underlying stock biomass. If an estimate of this shift is available, they can be used to update TAC by incorporation into Harvest Control Rules (HCRs). Various methods from Engineering Process Control (EPC) theory were tested to determine which method can measure the shift size in stock biomass with the highest accuracy. Results showed that methods based on Grubb’s harmonic rule gave reliable shift size estimates. The accuracy of these estimates can be improved by monitoring a combined indicator metric of stock-recruitment and LFI because this may account for impacts independent of fishing. The procedure of integrating both SPC and EPC is known as Statistical Process Adjustment (SPA). A HCR based on SPA was designed for DI-CUSUM and the scheme was successful in bringing out-of-control fish stocks back to its in-control state. The HCR was also tested using SS-CUSUM in the context of data poor fish stocks. Results showed that the scheme will be useful for sustaining the initial in-control state of the fish stock until more observations become available for quantitative assessments.

Risk management and the potential of cattle insurance in Tigray, Northern Ethiopia

This study explores the role of livestock insurance to complement existing risk management strategies adopted by smallholder farmers. Using survey data, first, it provides insights into farmers’ risk perception of livestock farming, in terms of likelihood and severity of risk, attitude to risk and their determinants. Second, it examines farmers’ risk management strategies and their determinants. Third, it investigates farmers’ potential engagement with a hypothetical cattle insurance decision and their intensity of participation. Factor analysis is used to analyse risk sources and risk management, multiple regressions are used to identify the determinants; a Heckman model was used to investigate cattle insurance participation and intensity of participation. The findings show different groups of farmers display different risk attitude in their decision-making related to livestock farming. Production risk (especially livestock diseases) was perceived as the most likely and severe source of risk. Disease control was perceived as the best strategy to manage risk overall. Disease control and feed management were important strategies to mitigate the production risks. Disease control and participation on safety net program were found to be important to counter households’ financial risks. With regard to the hypothetical cattle insurance scheme, 94.38% of households were interested to participate in cattle insurance. Of those households that accepted cattle insurance, 77.38% of the households were willing to pay the benchmark annual premium of 4% of the animal value while for the remaining households this was not affordable. The average number of cattle that farmers were willing to insure was 2.71 at this benchmark. Results revealed that income (log income) and education levels influenced positively and significantly farmers’ participation in cattle insurance and the number of cattle to insure. The findings prompt policy makers to consider livestock insurance as a complement to existing risk management strategies to reduce poverty in the long-run.

Integrating mobile and cloud resources management using the cloud personal assistant

The mobile cloud computing model promises to address the resource limitations of mobile devices, but effectively implementing this model is difficult. Previous work on mobile cloud computing has required the user to have a continuous, high-quality connection to the cloud infrastructure. This is undesirable and possibly infeasible, as the energy required on the mobile device to maintain a connection, and transfer sizeable amounts of data is large; the bandwidth tends to be quite variable, and low on cellular networks. The cloud deployment itself needs to efficiently allocate scalable resources to the user as well. In this paper, we formulate the best practices for efficiently managing the resources required for the mobile cloud model, namely energy, bandwidth and cloud computing resources. These practices can be realised with our mobile cloud middleware project, featuring the Cloud Personal Assistant (CPA). We compare this with the other approaches in the area, to highlight the importance of minimising the usage of these resources, and therefore ensure successful adoption of the model by end users. Based on results from experiments performed with mobile devices, we develop a no-overhead decision model for task and data offloading to the CPA of a user, which provides efficient management of mobile cloud resources.