Internet of Things (IoT) provides a generic infrastructure for different applications to integrate information communication techniques with physical components to achieve automatic data collection, transmission, exchange, and computation. The smart grid, as one of typical applications supported by IoT, denoted as a re-engineering and a modernization of the traditional power grid, aims to provide reliable, secure, and efficient energy transmission and distribution to consumers. How to effectively integrate distributed (renewable) energy resources and storage devices to satisfy the energy service requirements of users, while minimizing the power generation and transmission cost, remains a highly pressing challenge in the smart grid. To address this challenge and assess the effectiveness of integrating distributed energy resources and storage devices, in this paper we develop a theoretical framework to model and analyze three types of power grid systems: the power grid with only bulk energy generators, the power grid with distributed energy resources, and the power grid with both distributed energy resources and storage devices. Based on the metrics of the power cumulative cost and the service reliability to users, we formally model and analyze the impact of integrating distributed energy resources and storage devices in the power grid. We also use the concept of network calculus, which has been traditionally used for carrying out traffic engineering in computer networks, to derive the bounds of both power supply and user demand to achieve a high service reliability to users. Through an extensive performance evaluation, our data shows that integrating distributed energy resources conjointly with energy storage devices can reduce generation costs, smooth the curve of bulk power generation over time, reduce bulk power generation and power distribution losses, and provide a sustainable service reliability to users in the power grid1.
In Cyber-Physical Networked Systems (CPNS), attackers could inject false measurements to the controller through compromised sensor nodes, which not only threaten the security of the system, but also consumes network resources. To deal with this issue, a number of en-route filtering schemes have been designed for wireless sensor networks. However, these schemes either lack resilience to the number of compromised nodes or depend on the statically configured routes and node localization, which are not suitable for CPNS. In this paper, we propose a Polynomial-based Compromised-Resilient En-route Filtering scheme (PCREF), which can filter false injected data effectively and achieve a high resilience to the number of compromised nodes without relying on static routes and node localization. Particularly, PCREF adopts polynomials instead of MACs (message authentication codes) for endorsing measurement reports to achieve the resilience to attacks. Each node stores two types of polynomials: authentication polynomial and check polynomial derived from the primitive polynomial, and used for endorsing and verifying the measurement reports. Via extensive theoretical analysis and simulation experiments, our data show that PCREF achieves better filtering capacity and resilience to the large number of compromised nodes in comparison to the existing schemes.
The exponential increase of cyber security has led to an ever-increasing accumulation of big network data for cyber security applications. The big data analysis for cyber security management presents challenges in data capturing, storing and processing. To address these challenges, in this paper we develop a cloud computing based system for cyber security management to fasten the analysis process of big network data. Our developed system is built on the MapReduce framework and consists of end-user devices, cloud infrastructure and a monitoring centre. To make our proposed system efficient, we introduce two key function modules of our system: data storage module and task scheduling module. We conduct the system implementation using Apache Hadoop, and our implemented system consists of data collection, data normalisation, data computation and data visualisation. Using ranking and aggregation as primitives for performing cyber security management, we conducted extensive experiments to show the effectiveness of our developed system. We also discuss how to extend our proposed system to other applications.
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