Search

Purposes A load feature extraction method based on a combination of event monitoring and Gaussian mixture model clustering is proposed to explore the potential of energy saving and emission reduction at the customer side, to finely analyze and manage the customers’ electricity consumption behavior, and to improve the efficiency of electricity utilization. Methods First, the active power fluctuation of each appliance during a single operation is extracted by the event monitoring algorithm based on sliding window, and the start-up time, number of times, and operation duration of the appliance can be obtained by the event monitoring algorithm. Second, to address the problem that the same appliance often has similar power but inconsistent operation status, the Gaussian mixture model clustering algorithm with the advantages of “soft classification” and flexible class clusters is adopted to finely classify the load operating status and form a load status feature library that is consistent with the actual operation of power-using equipment. Finally, with the public data set AMPds2 as the research object, the method proposed in this paper is applied to study the energy consumption habits of residential customers, and the validation analysis is carried out. Findings The results show that the proposed method can extract load features better than other models.

This article discusses applications for monitoring and visualizing events from a cloud or fog environment. Applications where images are processed by machine learning algorithms for intelligent video surveillance systems are considered. A review of analogues of programs that implement the concept of the Internet of Things has been made. Examples of the real use of ready-made systems in various fields are considered. The problems of using systems with different architectures are discussed. A proposed solution is in the modification of the generalized architecture of the applications for monitoring and visualizing events. It allows improve events monitoring based on the ability to run applications on devices with different technical characteristics and under different operating systems. The solution is shown in the example of ensuring the safety of escalator equipment. Three applications have been developed for a smartphone, a desktop and a website for the visualization of events from a cloud or fog environment. The developed applications were tested on a large amount of data (video information). Specific examples are given.

Sensors in Cyber-Physical Systems (CPS) are typically used to collect various aspects of the region of interest and transmit the data towards upstream nodes for further processing. However, data collection in CPS is often unreliable due to severe resource constraints (e.g., bandwidth and energy), environmental impacts (e.g., equipment faults and noises), and security concerns. Besides, detecting an event through the aggregation in CPS can be intricate and untrustworthy if the sensor's data is not validated during data acquisition, before transmission, and before aggregation. This paper introduces In-network Generalized Trustworthy Data Collection (IGTDC) framework for event detection in CPS. This framework facilitates reliable data for aggregation at the edge of CPS. The main idea of IGTDC is to enable a sensor's module to examine locally whether the event's acquired data is trustworthy before transmitting towards the upstream nodes. It further validates whether the received data can be trusted or not before data aggregation at the sink node. Additionally, IGTDC helps to identify faulty sensors. For reliable event detection, we use collaborative IoT tactics, gate-level modeling with Verilog User Defined Primitive (UDP), and Programmable Logic Device (PLD) to ensure that the event's acquired data is reliable before transmitting towards the upstream nodes. We employ Gray code in gate-level modeling. It helps to ensure that the received data is reliable. Gray code also helps to distinguish a faulty sensor. Through simulation and extensive performance analysis, we demonstrate that the collected data in the IGTDC framework is reliable and can be used in the majority of CPS applications.

Monitoring agents are deployed in distributed network environments to collect data and report events to a central coordinator. A widespread event is defined as an event that is observed by all monitoring agents simultaneously. Identifying widespread events can assist in detecting problems, such as cyberattacks, network malfunctions, and abnormal usages. However, detecting widespread events is challenging and a significant communication overhead can be incurred, because every monitoring agent must report any observed event to a central coordinator. In this paper, we study the problem of detecting widespread events accurately. Our proposed schemes can significantly reduce communication overhead and achieve perfect detection accuracy, which was not achieved by any previous studies. The new schemes can also identify generalized widespread events, defined as events observed by more than a predefined number of monitoring agents.