A computationally-efficient sandbox algorithm for multifractal analysis of large-scale complex networks with tens of millions of nodes

Multifractal analysis (MFA) is a useful tool to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. One of the widely used methods for fractal analysis is box-covering. It is known to be NP-hard. More severely, in comparison with fractal analysis algorithms, MFA algorithms have much higher computational complexity. Among various MFA algorithms for complex networks, the sandbox MFA algorithm behaves with the best computational efficiency. However, the existing sandbox algorithm is still computationally expensive. It becomes challenging to implement the MFA for large-scale networks with tens of millions of nodes. It is also not clear whether or not MFA results can be improved by a largely increased size of a theoretical network. To tackle these challenges, a computationally-efficient sandbox algorithm (CESA) is presented in this paper for MFA of large-scale networks. Our CESA employs the breadth-first search (BFS) technique to directly search the neighbor nodes of each layer of center nodes, and then to retrieve the required information. Our CESA's input is a sparse data structure derived from the compressed sparse row (CSR) format designed for compressed storage of the adjacency matrix of large-scale network. A theoretical analysis reveals that the CESA reduces the time complexity of the existing sandbox algorithm from cubic to quadratic, and also improves the space complexity from quadratic to linear. MFA experiments are performed for typical complex networks to verify our CESA. Finally, our CESA is applied to a few typical real-world networks of large scale.Comment: 19 pages, 9 figure

Modern computing: vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Data-driven analysis of electricity use for office buildings: a Norwegian case study

Buildings are major consumers of primary energy and main contributors to carbon emission. To improve energy efficiency, it is essential to understand the characteristics of energy use in buildings. This study uses an in-use office building with digital systems for monitoring and control in Trondheim, Norway, as the study case. Based on data collected from this office building, a data-driven analysis was conducted to capture the characteristics of electricity use of different parts in the office building. The approaches used in this study included statistical analysis and polynomial regression. The impact of occupancy level on the total electricity use, the electricity use in office areas, and that in corridors & meeting rooms was also studied. The hourly electricity use profiles were obtained for ventilation fans and the cantina. In the end, the electricity use characteristics and existing issues in this office building were discussed

Data-driven analysis of electricity use for office buildings: a Norwegian case study

Buildings are major consumers of primary energy and main contributors to carbon emission. To improve energy efficiency, it is essential to understand the characteristics of energy use in buildings. This study uses an in-use office building with digital systems for monitoring and control in Trondheim, Norway, as the study case. Based on data collected from this office building, a data-driven analysis was conducted to capture the characteristics of electricity use of different parts in the office building. The approaches used in this study included statistical analysis and polynomial regression. The impact of occupancy level on the total electricity use, the electricity use in office areas, and that in corridors & meeting rooms was also studied. The hourly electricity use profiles were obtained for ventilation fans and the cantina. In the end, the electricity use characteristics and existing issues in this office building were discussed

A Crowdsourcing-based Localization Scheme with Ultra-Wideband Communication

With the development of mobile computing, crowd-sourcing has become one of the key technologies supporting collaborative tasks. It has been widely used in areas of real-time localization, such as data collection and fingerprint calibration. Currently, crowdsourcing-based localization are mainly designed for WiFi signals. However, due to the excellent performance on real-time positioning, Ultra-Wideband (UWB) has attracted the attention of major smart device makers. It is highly expected that UWB will be supported by smart devices in the near future. With this consideration, a crowdsourcing localization scheme is introduced based characteristics of UWB signals. Compared to existing positioning technologies, the proposed scheme does not require direct connection between UWB anchors and smart devices. Instead, the smart devices collaborate with peers to complete the positioning process. Finally, a simulation example is provided as a demonstration and to evaluate the performance

DHW tank sizing considering dynamic energy prices

Due to the rapid development of the building stock in Norway, the energy use in this segment is drastically increasing. Therefore, improving the energy performance of buildings becoming an urgent problem. Among technical systems in buildings, domestic hot water (DHW) systems have still significant untapped potential for energy saving. Storage tanks enable us to change DHW demand in buildings in a more energy-efficient and cost-effective way. However, to achieve this effect, the proper sizing and operation of the storage tanks are required. The aim of this study was to define a method for the DHW tank size optimization considering dynamic electricity prices and to assess how different electricity pricing methods would influence the DHW tank size. A dynamic discretized model of the DHW tank was used as a DHW tank model. Dynamic optimization was implemented as the optimization method to find the optimal tank charging rate based on the different pricing methods. Two pricing methods were considered in this study: 1) the current method with the fixed grid fee and 2) the power extraction method with the pricing of the maximum power extraction. The results showed that the electricity pricing pattern had significant impact on the DHW charging heating rate. In the case of the extraction fee pricing method, the charging rate was more stable over the day than in the case of the fixed grid fee. This stable charging rate gave stable DHW tank temperature over the day and the highest decrease in the total cost. A general conclusion was that the extraction grid fee pricing method would promote for stable charging over the day.publishedVersio

The Integration of Spatial Analysis and GIS: The Development of the Statcas Module for ARC/INFO (91-5)

It seems widely expected that future GISs will have increased analytical capabilities that will take them beyond being efficient display and database management devices. Several attempts have already been made to link existing analytical software to various GISs. However, a problem with all of these attempts is that the user is forced to switch back and forth between the GIS operating environment and the analytical software. In this paper we present a statistical analysis package, STACAS, that runs totally within the operating environment of a GIS and utilizes a command structure that makes running the package transparent to the user of the GIS

Research on the Influence of Sensor Network Communication in the Electromagnetic Environment of Smart Grid

Smart grid adopts wildly various sensors for lots of applications to sense work environment, monitor production process and realize the automation control, and so forth. However, due to the wireless and open communication, the electromagnetic phenomena in the communication and the electric network of the sensor network usually produce the mutual interference. Meanwhile, electrical equipment and sensors are usually in high pressure electromagnetic environment. Therefore, it is very necessary and important to ensure the reliability and stability in smart grid applications. And the sensing and communication device must be after equal parameter simulation environment under strict evaluation and verification can be put to use in actual production operation system. In this paper, we analyze the application of wireless sensor network in smart grid and propose the test method of the interaction between WSN and smart grid

Improved Multitarget Tracking in Clutter Using Bearings-Only Measurements

Multitarget tracking in clutter using bearings-only measurements is a challenging problem. In this paper, a performance improved nonlinear filter is proposed on the basis of the Random Finite Set (RFS) theory and is named as Gaussian mixture measurements-based cardinality probability hypothesis density (GMMbCPHD) filter. The GMMbCPHD filter enables to address two main issues: measurement-origin-uncertainty and measurement nonlinearity, which constitutes the key problems in bearings-only multitarget tracking in clutter. For the measurement-origin-uncertainty issue, the proposed filter estimates the intensity of RFS of multiple targets as well as propagates the posterior cardinality distribution. For the measurement-origin-nonlinearity issue, the GMMbCPHD approximates the measurement likelihood function using a Gaussian mixture rather than a single Gaussian distribution as used in extended Kalman filter (EKF). The superiority of the proposed GMMbCPHD are validated by comparing with several state-of-the-art algorithms via intensive simulation studies