Electrical power network analysis and computation play an important role in the planning and operation of the power grid, and they are modeled mathematically as differential equations and network algebraic equations. The direct method based on Gaussian elimination theory can obtain analytical results. Two factors affect computing efficiency: the number of nonzero element fillings and the length of elimination tree. This article constructs mapping correspondence between eliminated tree nodes and quotient graph nodes through graph and quotient graph theories. The Approximate Minimum Degree (AMD) of quotient graph nodes and the length of the elimination tree nodes are composed to build an Approximate Minimum Degree and Minimum Length (AMDML) model. The quotient graph node with the minimum degree, which is also the minimum length of elimination tree node, is selected as the next ordering vector. Compared with AMD ordering method and other common methods, the proposed method further reduces the length of elimination tree without increasing the number of nonzero fillings; the length was decreased by about 10% compared with the AMD method. A testbed for experiment was built. The efficiency of the proposed method was evaluated based on different sizes of coefficient matrices of power flow cases.

Trees are arguably one of the most important data structures widely used in information theory and computing science. Different numbers of intermediate nodes in wireless broadcast trees may exert great impacts on the energy consumption of individual nodes, which are typically equipped with a limited power supply in a wireless sensor network; this limitation may eventually determine how long the given wireless sensor network can last. Thus, obtaining a deep understanding of the mathematical nature of wireless broadcast trees is of great importance. In this paper, we give new proof of Cayley’s well-known theorem for counting labeled trees. A distinct feature of this proof is that we purely use combinatorial structures instead of constructing a bijection between two kinds of labeled trees, which is in contrast to all existing proofs. Another contribution of this work is the presentation of a new theorem on trees based on the number of intermediate nodes in the tree. To the best of our knowledge, this work is the first to present a tree enumeration theorem based on the number of intermediate nodes in the tree.

This paper proposes a distributed averaging iteration algorithm for energy sharing in microgrids of Energy Internet based on common gossip algorithms. This algorithm is completely distributed and only requires communi-cations between neighbors. Through this algorithm, the Energy Internet not only allocates the energy effectively based on the load condition of grids, but also reasonably schedules the energy transmitted between neighboring grids. This study applies theoretical analysis to discuss the condition in which this algorithm can finally reach supply-and-demand balance. Subsequently, the related simulation validates the performance of the algorithm under various conditions.

Bitmap indexing has been widely used in various applications due to its speed in bitwise operations. However, it can consume large amounts of memory. To solve this problem, various bitmap coding algorithms have been proposed. In this paper, we present COMbining Binary And Ternary encoding (COMBAT), a new bitmap index coding algorithm. Typical algorithms derived from Word Aligned Hybrid (WAH) are COMPressed Adaptive indeX (COMPAX) and Compressed “n” Composable Integer Set (CONCISE), which can combine either two or three continuous words after WAH encoding. COMBAT combines both mechanisms and results in more compact bitmap indexes. Moreover, querying time of COMBAT can be faster than that of COMPAX and CONCISE, since bitmap indexes are smaller and it would take less time to load them into memory. To prove the advantages of COMBAT, we extend a theoretical analysis model proposed by our group, which is composed of the analysis of various possible bitmap indexes. Some experimental results based on real data are also provided, which show COMBAT’s storage and speed superiority. Our results demonstrate the advantages of COMBAT and codeword statistics are provided to solidify the proof.

The concept of Cyber-Physical Systems (CPSs), which combine computation, networking, and physical processes, is considered to be beneficial to smart grid applications. This study presents an integrated simulation environment to provide a unified platform for the investigation of smart grid applications involving power grid monitoring, communication, and control. In contrast to the existing approaches, this environment allows the network simulator to operate independently, importing its results to the power system simulation. This resolves conflicts between discrete event simulation and continuous simulation. In addition, several data compensation methods are proposed and investigated under different network delay conditions. A case study of wide-area monitoring and control is provided, and the efficiency of the proposed simulation framework has been evaluated based on the experimental results.

The technology of Ultra-High Voltage (UHV) transmission requires higher dependability for electric power grid. Power Grid Communication Networking (PGCN), the fundamental information infrastructure, severs data transmission including control signal, protection signal, and common data services. Dependability is the necessary requirement to ensure services timely and accurately. Dependability analysis aims to predicate operation status and provide suitable strategies getting rid of the potential dangers. Due to the dependability of PGCN may be affected by external environment, devices quality, implementation strategies, and so on, the scale explosion and the structure complexity make the PGCN’s dependability much challenging. In this paper, with the observation of interdependency between power grid and PGCN, we propose an electricity services based dependability analysis model of PGCN. The model includes methods of analyzing its dependability and procedures of designing the dependable strategies. We respectively discuss the deterministic analysis method based on matrix analysis and stochastic analysis model based on stochastic Petri nets.

A data center is an infrastructure that supports Internet service. Cloud computing is rapidly changing the face of the Internet service infrastructure, enabling even small organizations to quickly build Web and mobile applications for millions of users by taking advantage of the scale and flexibility of shared physical infrastructures provided by cloud computing. In this scenario, multiple tenants save their data and applications in shared data centers, blurring the network boundaries between each tenant in the cloud. In addition, different tenants have different security requirements, while different security policies are necessary for different tenants. Network virtualization is used to meet a diverse set of tenant-specific requirements with the underlying physical network, enabling multi-tenant datacenters to automatically address a large and diverse set of tenants requirements. In this paper, we propose the system implementation of vCNSMS, a collaborative network security prototype system used in a multi-tenant data center. We demonstrate vCNSMS with a centralized collaborative scheme and deep packet inspection with an open source UTM system. A security level based protection policy is proposed for simplifying the security rule management for vCNSMS. Different security levels have different packet inspection schemes and are enforced with different security plugins. A smart packet verdict scheme is also integrated into vCNSMS for intelligence flow processing to protect from possible network attacks inside a data center network.

China Unicom, the largest WCDMA 3G operator in China, meets the requirements of the historical Mobile Internet Explosion, or the surging of Mobile Internet Traffic from mobile terminals. According to the internal statistics of China Unicom, mobile user traffic has increased rapidly with a Compound Annual Growth Rate (CAGR) of 135%. Currently China Unicom monthly stores more than 2 trillion records, data volume is over 525 TB, and the highest data volume has reached a peak of 5 PB. Since October 2009, China Unicom has been developing a home-brewed big data storage and analysis platform based on the open source Hadoop Distributed File System (HDFS) as it has a long-term strategy to make full use of this Big Data. All Mobile Internet Traffic is well served using this big data platform. Currently, the writing speed has reached 1 390 000 records per second, and the record retrieval time in the table that contains trillions of records is less than 100 ms. To take advantage of this opportunity to be a Big Data Operator, China Unicom has developed new functions and has multiple innovations to solve space and time constraint challenges presented in data processing. In this paper, we will introduce our big data platform in detail. Based on this big data platform, China Unicom is building an industry ecosystem based on Mobile Internet Big Data, and considers that a telecom operator centric ecosystem can be formed that is critical to reach prosperity in the modern communications business.

With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win+Intel alliance in PC, Android+ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app’s virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework (ASEF) and Static Android Analysis Framework (SAAF), the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.

The archiving of Internet traffic is an essential function for retrospective network event analysis and forensic computer communication. The state-of-the-art approach for network monitoring and analysis involves storage and analysis of network flow statistic. However, this approach loses much valuable information within the Internet traffic. With the advancement of commodity hardware, in particular the volume of storage devices and the speed of interconnect technologies used in network adapter cards and multi-core processors, it is now possible to capture 10 Gbps and beyond real-time network traffic using a commodity computer, such as n2disk. Also with the advancement of distributed file system (such as Hadoop, ZFS, etc.) and open cloud computing platform (such as OpenStack, CloudStack, and Eucalyptus, etc.), it is practical to store such large volume of traffic data and fully in-depth analyse the inside communication within an acceptable latency. In this paper, based on well-known TimeMachine, we present TIFAflow, the design and implementation of a novel system for archiving and querying network flows. Firstly, we enhance the traffic archiving system named TImemachine+FAstbit (TIFA) with flow granularity, i.e., supply the system with flow table and flow module. Secondly, based on real network traces, we conduct performance comparison experiments of TIFAflow with other implementations such as common database solution, TimeMachine and TIFA system. Finally, based on comparison results, we demonstrate that TIFAflow has a higher performance improvement in storing and querying performance than TimeMachine and TIFA, both in time and space metrics.