Beyond the worst-case analysis of algorithms, the learning-augmented framework considers that an algorithm can leverage possibly imperfect predictions about the unknown variables to have guarantees tied to the prediction quality. We consider online non-clairvoyant scheduling to minimize the max-stretch under this framework, where the scheduler can access job size predictions. We present a family of algorithms: Relaxed-Greedy (RG) with an $O(\eta^3 \cdot \sqrt{P})$ competitive ratio, where $\eta$ denotes the prediction error for job sizes and $P$ the maximum job size ratio; Adaptive Relaxed-Greedy with an $O(\lambda^{0.5} \cdot \eta^{2.5} \cdot \sqrt{P})$ competitive ratio, where $\lambda$ denotes the error for the minimum job size; Predictive Relaxed-Greedy with an $O(\lambda^{0.5} \cdot \varphi^{0.5} \cdot \eta \cdot \max \\\{ \eta, \varphi \\\} \cdot \sqrt{P})$ competitive ratio, where $\varphi$ denotes the error for the maximum job size. We also present *${RG}^x$*, an algorithm that represents a trade-off between consistency and smoothness, with an $O(\eta^{2+2x} \cdot P^{1-x})$ competitive ratio. We introduce a general method using resource augmentation to bound robustness, resulting in *RR*-augmented *RG*, with a $(1 + \epsilon)$-speed $O(\min \\\{ \eta^3 \sqrt{P}, \frac{n}{\epsilon} \\\})$ competitive ratio. Finally, we conduct simulations on synthetic and real-world datasets to evaluate the practical performance of these algorithms.

Pain, a primary reason for seeking medical help, requires essential pain assessment for effective management. Studies have recognized electrodermal activity (EDA) signaling's potential for automated pain assessment, but traditional algorithms often ignore the noise and uncertainty inherent in pain data. To address this, we propose a learning framework predicated on data uncertainty, introducing two forms: a) subject-level stimulation-reaction drift; b) ambiguity in self-reporting scores. We formulate an uncertainty assessment using Heart Rate Variability (HRV) features to guide the selection of responsive pain profiles and reweight subtask importance based on the vagueness of self-reported data. These methods are integrated within an end-to-end neural network learning paradigm, focusing the detector on more accurate insights within the uncertainty domain. Extensive experimentation on both the publicly available biovid dataset and the proprietary Apon dataset demonstrates our approach's effectiveness. In the biovid dataset, we achieved a 6% enhancement over the state-of-the-art methodology, and on the Apon dataset, our method outperformed baseline approaches by over 20%.

Non-intrusive load monitoring (NILM) is a cost-effective energy disaggregation means to estimate the energy consumption of individual appliances from a central load reading. Learning-based methods are the new trends in NILM implementations but require large labeled data to work properly at end-user premises. We first formulate an unsupervised multi-source domain adaptation problem to address this challenge by leveraging rich public datasets for building the NILM model. Then, we prove a new generalization bound for the target domain under multi-source settings. A hybrid loss-driven multi-source domain adversarial network (HLD-MDAN) is developed by approximating and optimizing the bound to tackle the domain shift between source and target domains. We conduct extensive experiments on three real-world residential energy datasets to evaluate the effectiveness of HLD-MDAN, showing that it is superior to other methods in single-source and multi-source learning scenarios.

As a critical infrastructure of urban water systems, sewer networks suffer from serious corrosion and odor problems, which can be controlled by chemical dosing. It is a challenging task to optimize chemical distribution in such a hybrid system with continuous hydraulic flow, discrete pump operations, and dynamic constraints. In this article, we study real-time control of multiple pumps to achieve the desired chemical distribution in a sewer network. A novel hybrid optimization approach is developed, which involves an event-triggered scheme triggered by predicting proper pumping events at uncontrolled pumping stations, and an improved nature-inspired elephant herding optimization (iEHO) algorithm for scheduling pumping at controllable pumping stations. The proposed method is validated through simulation studies of a real-life sewer network using real measured data. Our strategy significantly improves chemical distribution with reduced costs, despite an astronomic searching space. The iEHO algorithm outperforms the genetic algorithm in terms of the quality of solutions and convergence efficiency.

Sustainable energy management systems have been increasingly studied in recent years. Non-intrusive load monitoring (NILM), as a key component, estimates the power consumption of individual appliances from the main readings only. However, most NILM approaches are computationally expensive, and their generality is negatively affected by the data drift occurred when the models are used across domains. Besides, the threats of privacy violation will rise in the model transfer due to the possible leakage of the personal information of the users from the source domain. To address all these challenges, we designed a cost-efficient learning method using LightGBM for energy disaggregation. We also proposed a model-based transfer learning algorithm using feature importance analysis, which enhances the generalisation capability of tree-based ensemble models applied in different domains while protecting privacy. We conducted experiments with real-world data sets. The performance of our approach is superior to the state-of-the-art solutions.

The increasing use of renewable energy sources and electrical energy storage systems creates a new energy paradigm for residential houses and buildings. Such design reduces carbon footprint, but it also introduces a new challenge for minimizing the electricity bill while still meeting users’ needs. This challenge is often accompanied by deep uncertainty in user load demands, electricity tariffs, and renewable energy generations. To address this challenge, we propose an online algorithm, Virtual Algorithm-based Lightweight Online Scheduling (VALOS), to manage electricity purchasing and battery operations. Our solution does not use any prediction components in dealing with uncertainties. Instead, our algorithm employs a lightweight routine, Virtual Algorithm (VA), for making critical decisions to manage uncertainty. We prove that VA achieves an expected probability of 1/e for choosing the optimal purchasing timing online and incurs only a logarithmic computational cost. With VA, VALOS decides how the energy storage reacts to the load demands, which incurs only a linear-logarithmic online computational cost while achieving optimal performance under the specified conditions. Finally, we conduct extensive trace-driven simulations on real-world datasets to confirm the theoretical results and demonstrate the potential of VALOS.

With the Internet of Things continuously penetrating into all spheres of our daily lives, the increasing use of smart devices enabled the emergence of the edge computing paradigm. To meet the needs of saving energy and reducing electricity bills for each household, solar energy is exploited by using photovoltaic (PV) panels that can be integrated into an edge computing platform based on a cost-effective scheduling scheme. However, it is still a major challenge to determine the optimal energy allocation of renewable energy due to the intermittent nature of renewable energy generation. In this paper, we propose a unified clustering-based prediction framework with two tree-based algorithms to provide short-term prediction of PV power output. We also provide the in-terpretability analysis for our approach to reveal the features that are important for the prediction. The experimental results show our proposed framework is superior to other benchmark machine learning algorithms.

With the continuous growth in population and energy demands more attention has been paid to energy consumption issues in residential environments. At the user-end, the home energy management system (HEMS) has been proposed as a cost-effective solution to reduce the electricity cost in households, while maintaining users' comfort and reducing the pressure on energy providers. However, it is a challenge to design a cost-effective scheduling strategies for HEMS which takes many objectives into consideration while potentially benefiting both users and providers. In our work, we propose a new approach named adaptive multi-objective salp swarm algorithm (AMSSA) based on traditional multi-objective salp swarm algorithm (MSSA) to realise a multi-objective optimisation approach for the power scheduling problem. AMSSA not only fulfils the trade-off among users' comfort, electricity cost and peak to average ratio (PAR), but also enhances the convergence speed for the overall optimisation process. Moreover, we also set up a testbed by using smart appliances and implemented our design on an edge-based energy management system. The experiment results demonstrated a reduction in both electricity cost (47.55%) and PAR (45.73%), compared with the case without a scheduling scheme.

There is a growing trend for employing cyber-physical systems to help smart homes improve the comfort of residents. However, a residential cyber-physical system is different from a common cyber-physical system since it directly involves human interaction, which is full of uncertainty. The existing solutions could be effective for performance enhancement in some cases when no inherent and dominant human factors are involved. Besides, the rapidly rising interest in the deployments of cyber-physical systems at home does not normally integrate with energy management schemes, which is a central issue that smart homes have to face. In this article, we propose a cyber-physical-system-based energy management framework to enable a sustainable-edge computing paradigm while meeting the needs of home energy management and residents. This framework aims to enable the full use of renewable energy while reducing electricity bills for households. A prototype system was implemented using real-world hardware. The experiment results demonstrated that renewable energy is fully capable of supporting the reliable running of home appliances most of the time and electricity bills could be cut by up to 60% when our proposed framework was employed.

There is a growing trend for engaging edge computing to help smart homes to improve the living comfort of residents. However, the rapidly rising interest in such deployments does not normally integrate with energy management schemes, which is a central issue that smart homes have to face. In this paper, we propose a unified energy management framework for enabling a sustainable edge computing paradigm while meeting the needs of home energy management and smart home applications. This framework aims to enable the full use of renewable energy while reducing electricity bills for households. A prototype system was implemented by using low-cost and easy-to-get hardware. The experiment results demonstrated that renewable energy is fully capable of supporting the reliable running of edge computing devices and electricity bills could be cut by up to 86% when our proposed framework was employed.

To solve the energy limitation problems of wireless sensor networks used in forest fire detection applications, a new algorithm based on a fire probability threshold switch is proposed. The method uses the weighted average method to obtain the weights of each sensor node, and then calculates the fire probability for each node based on a logistic regression to obtain the fire probability threshold, based on this, the numbers of sensor nodes sending data to the sink node are obtained. The proposed algorithm is applied to detection of a wood fire in a simulated situation. The results show that the proposed method reduced the transmission energy used by 34%. These results indicate that the method can reduce the unnecessary energy usage effectively while guaranteeing the reliability of the transmission data.