Non-Intrusive Load Monitoring (NILM), also known as energy disaggregation, aims to infer appliance-level energy consumption from aggregated readings obtained from a single meter measuring a household’s total electricity demand. The increasing availability of large-scale and fine-grained household electricity usage datasets has facilitated the adoption of deep learning techniques for NILM, leading to state-of-the-art performance. However, a key limitation of current approaches is the requirement to train separate models for each appliance, which impedes both model scalability and the ability to integrate new appliances. To address this limitation, we propose a query-based NILM model that leverages appliance embeddings as queries, enabling joint training for multi-appliance disaggregation. By encoding appliance energy usage patterns as embeddings, the proposed model can be queried with these embeddings to extract the energy consumption of corresponding appliances from the aggregated readings and facilitate zero-shot disaggregation. Experimental results on the UK-DALE dataset demonstrate the efficacy of the proposed query-based NILM model.

The continuous expansion of network data presents a pressing challenge for conventional routing algorithms. As the demand escalates, these algorithms are struggling to cope. In this context, reinforcement learning (RL) and multi-agent reinforcement learning (MARL) algorithms emerge as promising solutions. However, the urgency and importance of the problem are clear, as existing RL/MARL-based routing approaches lack effective communication in run time among routers, making it challenging for individual routers to adapt to complex and dynamic changing networks. More importantly, they lack the ability to deal with dynamically changing network topology, especially the addition of the router, due to the non-scalability of their neural networks. This paper proposes a novel dynamic routing algorithm, DRAMA, incorporating emergent communication in multi-agent reinforcement learning. Through emergent communication, routers could learn how to communicate effectively to maximize the optimization objectives. Meanwhile, a new Q-network and graph-based emergent communication are introduced to dynamically adapt to the changing network topology without retraining while ensuring robust performance. Experimental results showcase DRAMA’s superior performance over the traditional routing algorithm and other RL/MARL-based algorithms, achieving a higher delivery rate and lower latency in diverse network scenarios, including dynamic network load and topology. Moreover, an ablation experiment validates the prospect of emergent communication in facilitating packet routing.

The development of the Industrial Internet of Things (IIoT) is impeded by the lack of unknown protocol specifications. Protocol Reverse Engineering (PRE) plays a crucial role in inferring unpublished protocol specifications by analyzing traffic messages. Since different types within a protocol often have distinct formats, inferring the protocol type is essential for subsequent reverse analysis. Natural Language Processing (NLP) models have demonstrated remarkable capabilities in various sequence tasks, and traffic messages of unknown protocols can be analyzed as sequences. In this paper, we propose a framework for clustering unknown industrial control protocol types. Our framework utilizes a transformer-based auto-encoder network to train corresponding request and response messages, leveraging intermediate layer embedding vectors learned by the network for clustering. The clustering results are employed to extract candidate keywords and establish empirical rules. Subsequently, rule-based re-clustering is performed, and its effectiveness is evaluated based on previous clustering results. Through this re-clustering process, we identify the most effective combination of keywords that define the type. We evaluate the proposed framework using three general protocols that have different type rules and successfully separate the protocol internal types completely.

Efficient leak detection and localization in water distribution networks (WDNs) are essential for mitigating disastrous losses from leak incidents. Current unsupervised learning-based methods excel in detecting single leaks but struggle with multi-leak scenarios, limiting their practical applicability. Additionally, leak localization methods relying on simulation optimization can be computationally intensive for large-scale WDNs. Meanwhile, purely data-driven approaches struggle to leverage network topology and hydraulic characteristics, hinde ring accurate leak localization. To address these challenges, we propose a leak detection and localization framework that employs online change-point detection and leak sensitivity modeling to achieve real-time detection and localization in multi-leak scenarios. For leak detection, the framework utilizes an unsupervised One-dimensional Convolutional Auto-encoder (1D-CAE) to reconstruct the pressure data collected from sensors deployed over a WDN. The residuals between the reconstructed values and the observed values, also known as reconstruction errors, are then analyzed using a Sequentially Discounting Normalized Maximum Likelihood (SDNML) change-point detector. Equipped with a customized scoring function designed for robust multi-leak detection, this detector computes anomaly scores in real-time at each time step, facilitating effective leak detection. Furthermore, we simulate leaks at different nodes of the WDN to quantify their impact on the sensor nodes using pressure residuals-based leak characteristic factors. By comparing reconstruction errors with these factors, we identify the nodes that best match the current leak event, achieving precise leak localization. An evaluation on the L-Town dataset demonstrates the superior performance of our method for multi-leak detection and localization.

Anomaly detection in the Industrial Internet of Things (IIoT) is a challenging task that relies heavily on the efficient learning of multivariate time series representations. We introduce Skip-patching and Spatial-Temporal discrepancy mechanisms to improve the efficiency of detecting anomalies. Traditional feature extraction is hindered by redundant information in limited datasets. The situation is that feature generation from stable operational processes results in low-quality representations. To address this challenge, we propose the Skip-Patching mechanism. This approach involves selectively extracting features from partial data patches, prompting the model to learn more meaningful knowledge through self-supervised learning. It also effectively doubles the training sample size by creating independent sub-groups of patches. Despite the complex spatial and temporal relationships in IIoT systems, existing methods mainly extracted features from a single domain, either temporal or spatial (sensor-wise), or simply cascaded two features, i.e., one after one, which limited anomaly detection capabilities. To address this, we introduce the Spatial-Temporal Association Discrepancy component, which leverages discrepancies between spatial and temporal features to enhance latent representation learning. Our Skip-Patching Spatial-Temporal Anomaly Detection (SSAD) framework combines these two components to provide a more diverse and comprehensive learning process. Tested across four multivariate time series anomaly detection benchmarks, SSAD demonstrates superior performance, confirming the efficacy of combining Skip-patching and Spatial-Temporal features to enhance anomaly detection in IIoT systems.

The number of natural gas users has been growing rapidly in China due to the promotion of clean energy and the economic benefits of natural gas, especially in businesses and industries. Though the infrastructures for gas supplies have been highly improved, gas providers are still suffering from various problems such as malfunctioning gas meters, gas leakage, gas theft etc. With the development of the Internet of Things, smart gas meters have been widely adopted by gas providers to collect real-time gas consumption data for billing purposes which can also serve as a basis for anomaly detection. One challenge of using such data for anomaly detection is that it is difficult to obtain sufficient labelled data for model training. To address this challenge, we propose DAE, a data distillation enhanced autoencoder for detecting anomalous gas consumption, which consists of three modules. The first module preprocesses the raw meter readings and carries out a rule-based anomaly detection. The second module extracts the normal gas usage patterns via an integration of correlation and clustering based consistency evaluation methods. The extracted normal usage patterns are then used in the third module to train an autoencoder for anomaly detection. DAE intends to provide a method to detect anomalous gas consumption induced by various causes such that manual inspection can be largely reduced. Moreover, DAE does not require user-specific information and can be applied to different types of gas users. Based on a real-world gas consumption dataset, we carry out a set of experiments and show that DAE outperforms the existing and improves the F1 score by an average of 7.4% for restaurant users and 5.7% for canteen users.

Energy disaggregation, a.k.a. Non-Intrusive Load Monitoring, aims to separate the energy consumption of individual appliances from the readings of a mains power meter measuring the total energy consumption of, e.g., a whole house. Energy consumption of individual appliances can be useful in many applications, e.g., providing appliance-level feedback to the end users to help them understand their energy consumption and ultimately save energy. Recently, with the availability of large-scale energy consumption datasets, various neural network models such as convolutional neural networks and recurrent neural networks have been investigated to solve the energy disaggregation problem. Neural network models can learn complex patterns from large amounts of data and have been shown to outperform the traditional machine learning methods such as variants of hidden Markov models. However, current neural network methods for energy disaggregation are either computational expensive or are not capable of handling long-term dependencies. In this article, we investigate the application of the recently developed WaveNet models for the task of energy disaggregation. Based on a real-world energy dataset collected from 20 households over 2 years, we show that WaveNet models outperforms the state-of-the-art deep learning methods proposed in the literature for energy disaggregation in terms of both error measures and computational cost. On the basis of energy disaggregation, we then investigate the performance of two deep-learning based frameworks for the task of on/off detection which aims at estimating whether an appliance is in operation or not. The first framework obtains the on/off states of an appliance by binarising the predictions of a regression model trained for energy disaggregation, while the second framework obtains the on/off states of an appliance by directly training a binary classifier with binarised energy readings of the appliance serving as the target values. Based on the same dataset, we show that for the task of on/off detection the second framework, i.e., directly training a binary classifier, achieves better performance in terms of F1 score.

There has been increasing interest in deploying Internet of Things (IoT) devices to study human behavior in locations such as homes and offices. Such devices can be deployed in a laboratory or “in the wild” in natural environments. The latter allows one to collect behavioral data that is not contaminated by the artificiality of a laboratory experiment. Using IoT devices in ordinary environments also brings the benefits of reduced cost, as compared with lab experiments, and less disturbance to the participants’ daily routines, which in turn helps with recruiting them into the research. However, in this case, it is essential to have an IoT infrastructure that can be easily and swiftly installed and from which real-time data can be securely and straightforwardly collected. In this article, we present MakeSense, an IoT testbed that enables real-world experimentation for large-scale social research on indoor activities through real-time monitoring and/or situation-aware applications. The testbed features quick setup, flexibility in deployment, the integration of a range of IoT devices, resilience, and scalability. We also present two case studies to demonstrate the use of the testbed: one in homes and one in offices.