Multi-criteria decision support for energy-efficient IoT edge computing offloading

Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices’ resource restrictions (e.g. process- ing and memory). In the past, offloading would always utilise remote cloud infrastruc- tures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network, closer to the IoT devices. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage, based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time conditions of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network. This thesis presents a dynamic computation offloading mechanism, which improves the performance (i.e. in terms of response time) and energy consumption of IoT de- vices in a decentralised and autonomous manner. We initially propose the Multi-critEria DecIsion support meChanism for IoT offloading(MEDICI), which runs independently on an IoT device, enabling it to make offloading decisions dynamically, based on multiple criteria, such as the state of the IoT, edge or cloud devices and the conditions of the net- work connecting them. It provides mathematical models of the expected time and energy costs for the different options of offloading a task (i.e. to the edge or the cloud or the IoT device itself). To evaluate its effectiveness, we provide simulation results, by extending the EdgeCloudSim simulator, comparing it against previous families of approaches used in the literature. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, such as IoT cyber intrusion detection, and the energy consumption not only of the individual IoT devices but also of the system as a whole. Furthermore, we present an enhancement of our MEDICI mechanism, the ProbeLess Multi-critEria DecIsion support meChanism for IoT offloading (PL-MEDICI), which en- ables MEDICI to operate in real IoT environments without the need for probing or having pre-defined parameters in order to estimate or model the network conditions or the com- putation capabilities of the different devices involved. This is the first probeless dynamic and decentralised offloading decision support mechanism for IoT environments. The probeless property is achieved by combining lightweight statistical techniques with the concept of age of knowledge (AoK) to allow us to have accurate enough information to use for our estimations. We provide experimental results performed in a real IoT testbed with three real IoT applications, showcasing that PL-MEDICI outperforms existing techniques in terms of both response time and energy consumption. Finally, in order to further evaluate our PL-MEDICI mechanism, we formulate a mixed- integer linear program optimisation problem that provides the theoretical optimal cen- tralised solution to our problem. This is used to compare our PL-MEDICI against the theoretical optimum, given the same estimated input. Our results showed that our of- floading mechanism is close to the obtained optimal solution in terms of both the re- sponse time and energy consumptio

Dynamic decision support for resource offloading in heterogeneous internet of things environments

Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices' resource restrictions. In the past, offloading would always utilise remote cloud infrastructures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time condition of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network. Here, we model the expected time and energy costs for the different options of offloading a task to the edge or the cloud, as well as of carrying out on the device itself. We use this model to allow the device to take the offloading decision dynamically as a new task arrives and based on the available information on the network connections and the states of the edge and the cloud. Having extended EdgeCloudSim to provide support for such dynamic decision making, we are able to compare this approach against IoT-first, edge-first, cloud-only, random and application-oriented probabilistic strategies. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, and the energy consumption not only of the individual IoT devices but also of the system as a whole. This paves the way for future IoT devices that optimise their application response times, as well as their own energy autonomy and overall energy efficiency, in a decentralised and autonomous manner

A deep gated recurrent neural network for petroleum production forecasting

Forecasting of oil production plays a vital role in petroleum engineering and contributes to supporting engineers in the management of petroleum reservoirs. However, reliable production forecasting is difficult to achieve, particularly in view of the increase in digital oil big data. Although a significant amount of work has been reported in the literature in relation to the use of machine learning in the oil and gas domain, traditional forecasting approaches have limited potential in terms of representing the complex features of time series data. More specifically, in a high-dimensional nonlinear multivariate time series dataset, a shallow machine is incapable of inferring the dependencies between past and future values. In this context, a novel forecasting model for petroleum production is proposed in this work. The model is a deep-gated recurrent neural network consisting of multiple hidden layers, where each layer has a number of nodes. The proposed model has a low-complexity architecture and the capacity to track long-interval time-series datasets. To evaluate the robustness of our model, the proposed technique was benchmarked with various standard approaches. The extensive empirical results demonstrate that the proposed model outperforms existing approaches

Intelligent measuring for a customer satisfaction level inspired by transformation language model

The rapid growth of e-commerce has fundamentally reshaped online consumer behaviour, creating a disconnect between sellers and consumers, and potentially resulting in dissatisfaction. To address this, sentiment analysis emerges as a crucial tool for business decision-makers, providing insights into product and service preferences and a profound understanding of customer sentiments. While conventional machine learning algorithms struggle with intricate patterns, deep learning, especially transformation learning, proves to be a robust solution. Deep learning excels in intricate sentiment classification tasks, yet it demands extensive data, posing challenges for smaller databases. In this paper, we propose a customer satisfaction level framework inspired by the Bidirectional Encoder Representations from the Transformers (BERT) model, The proposed model has the capacity to process bidirectional text contexts and has catalysed a paradigm shift in sentiment analysis. The result demonstrated that our model outperforms other sentiment analysis models

Dynamic decision support for resource offloading in heterogeneous Internet of Things environments

Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices’ resource restrictions. In the past, offloading would always utilise remote cloud infrastructures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time condition of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network. Here, we model the expected time and energy costs for the different options of offloading a task to the edge or the cloud, as well as of carrying out on the device itself. We use this model to allow the device to take the offloading decision dynamically as a new task arrives and based on the available information on the network connections and the states of the edge and the cloud. Having extended EdgeCloudSim to provide support for such dynamic decision making, we are able to compare this approach against IoT-first, edge-first, cloud-only, random and application-oriented probabilistic strategies. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, and the energy consumption not only of the individual IoT devices but also of the system as a whole. This paves the way for future IoT devices that optimise their application response times, as well as their own energy autonomy and overall energy efficiency, in a decentralised and autonomous manner

Estimating the prevalence of problematic opiate use in Ireland using indirect statistical methods.

This report presents the results of a study that used the four-sample capture-recapture method, along with the multiple indicator method, to estimate the number of opiate users in Ireland in 2014, along with trend information for 2011 to 2014. There were four data sources used within the analyses, three of which were derived from the Central Treatment List (CTL). These three CTL data sources were constructed from data relating to Clinics, GPs and Prison. The fourth source was derived from Irish Probation Service data. Table 1 presents a summary of the main results of the study, stratified by age group, gender and by a Co. Dublin/Rest of State split. In total there were an estimated 18,988 opiate users in Ireland in 2014. The 95% Confidence Interval (95% CI) for this estimate is 18,720– 21,454. This corresponds to a prevalence rate of 6.18 per thousand population aged 15 to 64 (95% CI 6.09–6.98). The majority were male (69%) with approximately two thirds in the older 35 to 64 age group. The estimate for Co. Dublin (Dublin City, Dún Laoghaire-Rathdown, Fingal and South Dublin) was 13,458 (95% CI 12,564–14,220). The prevalence rate for Co. Dublin was higher than the rest of the State at 15.15 per thousand population aged 15 to 64 (95% CI 14.14–16.00). Estimates were also provide for Cork City, Galway City, Limerick City and Waterford City, with Cork having an estimated prevalence rate of 5.67 per thousand population, Galway having an estimated prevalence of 1.93 per thousand, Limerick with an estimated prevalence of 8.82 per thousand and Waterford with a prevalence of 6.72 per thousand. Estimates were also derived for Counties (n=30), Local and Regional Drugs Task Force areas (n=24) and Community Healthcare Organisation (CHO) areas (n=9) with the Drugs Task Force areas in Dublin having the highest estimated prevalence rates. Estimates for 2011, 2012 and 2013 were compared to the 2014 estimates to provide information on changes in opiate use prevalence over time. Although the overall prevalence rates remain stable, the prevalence in the older age group (35 to 64 years of age) appears to be increasing and this may be due to an ageing cohort effect where existing opiate users are getting older while fewer younger people initiate into opiate use

Effect of Long-Term Moderate Physical Exercise on Irisin between Normal Weight and Obese Men

Background. Irisin is a myokine that has a beneficial effect on obesity and glucose metabolism by increasing energy expenditure. This study aims to investigate the effect of long-term moderate physical exercise on irisin levels and its correlations with body mass index (BMI), waist circumferences (WC), and metabolic parameters in normal weight and obese males. Material and method. A follow-up case-control study of sixty male participants, comprised of thirty normal weight and thirty obese, who had undergone supervised long-term moderate physical exercises for six months. Serum irisin levels, fasting blood glucose, serum insulin, homeostatic model assessment of the insulin resistance index (HOMA-IR), and β-cell function (HOMA-B2) were assessed. Results. Long-term moderate exercise induced elevation of the irisin level significantly (P<0.0001) with significant reduction of the BMI, WC, fasting blood glucose, insulin, HOMA-IR, and HOMA-B2 levels (P<0.0001) in comparison between obese and normal weight groups. There are significant differences for each parameter in each obese and normal weight group before and after physical exercise with exception of the BMI and WC in the normal group. Significant negative correlations were shown between irisin and blood glucose and insulin and HOMA-IR levels in the obese group and normal weight group. Conclusion. Irisin improves glucose homeostasis after long-term moderate physical exercises, suggesting that irisin could have regulatory effect on glucose, insulin resistance, and obesity and it could be used as a potential therapy for obesity and insulin resistance

Highly responsive NaCl detector based on inline microfiber Mach–Zehnder interferometer

This paper details the fabrication and performance evaluation of a sodium chloride (NaCl) detector based on an inline microfiber Mach–Zehnder interferometer (IMMZI) that had been fabricated via a glass fiber processing system (Vytran GPX-3400). Spectral characteristics of IMMZIs possessing a variety of waist diameters and situated within assorted concentrations of NaCl solutions are also reported. An optimally high responsitivity to NaCl solutions of about 2913.7 nm per refractive-index unit (RIU) was exhibited by an IMMZI with a total length of 34 mm and a tapered waist diameter of 10 μm, in which the RI ranged from 1.31837 to 1.31928