International Journal of System of Systems Engineering (8 papers in press)
AN EMPIRICAL HYBRID DBN-EL SYSTEM MODEL FOR STOCK MARKET PREDICTION WITH BIG DATA
by Ishwarappa K, Anuradha Jagadeesan
Abstract: Recent years, big data analytics have become the prominent aspect for different sectors for the prediction of large datasets. The stock market is also the important exertions in the field of business in which big data play an important role for the prediction of stocks. For this reason, the big data and hybrid Deep Belief Network-Ensemble Learning (DBN-EL) model is proposed for analysis of large stock market data in order to improve the performance of its prediction. Here, the Hive based distributed database with MapReduce technique for storing and mapping of data into other for fast processing. Furthermore, the hybrid DBN-EL system model will be used as classifier for stock market prediction. The stock futures dataset is used for analysis. The simulation results shows that the proposed model outperforms by predicting the stock futures trend upto 98% when compared with other existing techniques in terms of precision, recall, and F-Measure.
Keywords: Stock Market Prediction; Deep Belief Network (DBN); Ensemble Learning (EL); National Stock Exchange (NSE); Big Data; MapReduce.
A Comparison of the System Engineering Standard and INCOSE Complexity Primer
by Samuel Kovacic, Andres Sousa-Poza
Abstract: Complexity is frequently used to describe the environment in which a Systems Engineering must work as well as the nature of the systems that they must design. Relevant publications establish a relationship between complexity and understanding, however, it is questionable whether system engineering methodologies are well suited to deal with the nuances of complexity and potential lack of understanding. This paper provides an analysis between the System Engineering standard and the INCOSE Complexity Primer to assess the state of the standard against the nature of complexity in systems. The analysis conducted shows that present descriptions of how complexity affects Systems Engineering present a problems space that is distinctly different to the problem space that can be addressed by our present practices. Furthermore, it is found that System Engineering methodology will encounter significant challenges in dealing with the attributes of complexity documented in the INCOSE Complexity Primer.
Keywords: System Engineering; Complexity; Complexity Primer; System; Methodology; Complex Systems; SEBOK; Observer; System Perspective; Analysis; Understanding; IEEE; ISO; INCOSE.
Ten Views: Towards a Process Meta-Model in a System of Systems Context
by Ikram Akkiyat, Nissrine Souissi
Abstract: Improvement and risk management plans are essentials and required in any process lifecycle, particularly when the process is linked to a System of Systems (SoS). The first step in the process lifecycle is process modeling. The ISO 9001 Standard recommends the design of improvement and risk plans in the process modeling step. Thus, this paper proposes a process meta-model which takes into account the SoS context and integrates the improvement and risk management processes, based on ISO standards. It also introduces knowledge bases related to improvement and risks in order to ensure continuous improvement of the process and anticipate the malfunctions. The proposed meta-model is an extension of Seven Views, which is a process modeling approach, by three additional views, Risk view, Improvement view, and SoS view.
Keywords: System of Systems; SoS; UML; Seven Views; Process modeling; Process improvement; Process meta-model; Risk management; ISO 9001:2015; ISO 31000:2018.
PERFORMANCE ANALYSIS OF HYBRID CLASSIFICATION SYSTEM MODEL FOR BIG DATA STREAM USING INTERNET OF THINGS
by Gayathri Devi N, Manikandan K
Abstract: Big data and IoT have become the worlds prominent technology and reached a high impact on millions of peoples daily life that helps in managing environmental and physical systems processed in real-time. In this research, we proposed a hybrid classification system model named Multi-Output Regression with Deep Belief Networks (MOR-DBN) to improve the performance of the classifier when huge amount of streaming data is transferred from IoT devices. Moreover, the improved Privacy-Preserving Rotation based Condensation Algorithm (P2RoCAl) with Geometric Transformation is also used for obtaining high utility for data streaming to protect from various kinds of attacks during data reconstruction. The simulation results show that proposed framework obtains a high accuracy when compared with other existing algorithms in terms of precision, recall, and F-measure. Overall the proposed MOR_DBN model outperforms by obtaining a 96.21% for SSDS dataset, 97.89% for FRDS dataset, 95.7% for HPDS dataset, and 99.23% for ESDS dataset.
Keywords: Multi-Output Regression (MOR); Deep Belief Network (DBN); Privacy Preserving Rotation based Condensation Algorithm (P2RoCAl); Geometric Transformation; Data Streaming; Big Data; Internet of Things.
An Efficient Vote Casting System with Aadhar Verification through Blockchain
by Kuppani Sathish, Basetty Mallikarjuna, J. Gitanjali, Venkata Krishna
Abstract: In the recent past security for vote casting system plays a vital role in the autonomous constitutional authority of election. A democratic country like India highly depends on the fail proof voting system to elect the government. The many security systems have evolved to ensure the electronic voting system to avoid malpractice during election. This paper describes an electoral system for the Indian election on blockchain technology and Aadhaar verification. The proposed system provides optimal security for the voting system and preserves votes stored in the Electronic Voting Machine (EVM). The technology behind blockchain and Aadhaar verification are used to simplify the process of the voting system. The Aadhaar number has to be fetched and verified with the database after entering every vote on the machine. The information has to be verified for the vote of an individual to be accepted by EVM and stored in block chain ledger.
Keywords: Aadhar verification; Electronic Voting Machine (EVM); Cyber-attack; Blockchain; IoT.
Fog and Edge Computing Simulators Systems: Research Challenges and an Overview
by Sathish Kumar, IYAPPARAJA M
Abstract: To support the requirement of geographically data scale, low latency, heterogeneous applications two new computing paradigms have been introduced namely, fog and edge computing. Discrete Event Simulation (DES) and Discrete Time Simulation (DTS) are the two types of simulation, where DES approach is applied to the problem where the application requires more scalability and DTS is the simulation model it is applied to the system when there is a higher possibility of inaccuracy. There is a wide number of simulators available for cloud, however the simulators for fog and edge computing are limited. An overview of fog and edge computing simulators like iFogSim, CloudSimSDN, and YAFS has be analyzed in this paper. Finally resource consumption metrics and fault tolerance metrics are analyzed and results has shown that that resource consumption metrics increases when the complexity of the scenario increases.
Keywords: Fog Computing; Simulation; CDN; iFogSim; CloudSimSDN; YAFS; EMUFOG.
Military operational evaluations: credible data and decision making
by Twan Huybers, Leanne Rees
Abstract: Decision making on military capability is informed by data collected in operational evaluations. Such exercises present difficulties since a military operational environment represents a complex, socio-technical system. Based on a framework for the collection of credible data, we use an Australian case study to identify the key challenges in the execution of military operational evaluations: the purpose for data collection, its situational setting, the method of collection and the use of appropriate data sources. To address those challenges, we put forward an approach that integrates three operational research tools by applying the problem structuring method while using perceptual positions in data collection through a facilitated mode of interaction between stakeholders. We illustrate the approach with examples of the use of the operational research tools in a military operational environment. Using established requirements of military operational evaluations, we also assess the approach in terms of its validity in supporting military decision-making.
Keywords: Military operational environment; Socio-technical system; Complex systems; Operational evaluation; Defence; Decision making; Data collection; Data credibility; Case studies; Problem structuring.
Hypergraph Model for Wireless Sensor Networks Supervision Design
by MEKELLECHE FATIHA, Haffaf Hafid, Belkacem Ouldbouamama
Abstract: Security is an important condition to ensure the proper functioning of Wireless Sensor Networks (WSNs). The prevention-based security approaches as cryptographic mechanisms must be enriched by a reactive schemes such as Intrusion Detection Systems (IDSs). For safety and reliability reasons, moreover to IDSs, WSN supervision mechanisms are required. All FDI systems consist of comparing the real behavior of system with the nominal behavior given by a model: the difference leads to a fault indicator named residual. Graphical modeling is an important step in FDI realization. The innovative interest of this paper is the use of hypergraph formalism to model the interconnection between different Components Network (CNs). In our proposed approach, the hypergraph model is built from WSNs missions, such that each hyperedge is associated with a particular mission. Moreover, a Fault Signature Matrix (FSM) as a logic decision is used to provide the faults which can be detected and isolated.
Keywords: Wireless Sensor Networks (WSNs); WSN security; supervision; IDS; Fault Detection and Isolation (FDI); WSN modelling; hypergraph; hypernetworks.