Systemic formalisation of Cyber-Physical-Social System (CPSS): A systematic literature review

peer reviewedThe notion of Cyber-Physical-Social System (CPSS) is an emerging concept developed as a result of the need to understand the impact of Cyber-Physical Systems (CPS) on humans and vice versa. This paradigm shift from CPS to CPSS was mainly attributed to the increasing use of sensor enabled smart devices and the tight link with the users. The concept of CPSS has been around for over a decade and it has gained an increasing attention over the past few years. The evolution to incorporate human aspects in the CPS research has unlocked a number of research challenges. Particularly human dynamics brings additional complexity that is yet to be explored. The exploration to conceptualise the notion of CPSS has been partially addressed in few scientific literatures. Although its conceptualisation has always been use-case dependent. Thus, there is a lack of generic view as most works focus on specific domains. Furthermore the systemic core and design principles linking it with the theory of systems are loose. This work aims at addressing these issues by first exploring and analysing scientific literatures to understand the complete spectrum of CPSS through a Systematic Literature Review (SLR). Thereby identifying the state-of-the-art perspectives on CPSS regarding definitions, underlining principles and application areas. Subsequently, based on the findings of the SLR, we propose a domain-independent definition and a meta-model for CPSS, grounded in the Theory of Systems. Finally a discussion on feasible future research directions is presented based on the systemic notion and the proposed meta-models

Is this Digital Resilience? Insights from Adaptation and Exaptation of a Cyber-Physical-Social System

This paper is based on a qualitative case study that explores the adaptation and customisation of a Cyber Physical Social System (CPSS)-based patient monitoring solution for use during Covid19 in the Norwegian health sector. The study seeks to answer the following research questions: 1) what are the preconditions that enable the adaptive use of a CPSS in crisis response efforts? 2) what are the contributions of the adaptive use of technology in the building of digital resilience in a health organisation? The study identifies five main themes emerge as enabling factors forming a basis for the preconditions to adaptive use of the CPSS. We conclude with a discussion on the practical and theoretical implications of this research and how it contributes to crisis management and digital resilience theory

Dynamic Security Risk Evaluation via Hybrid Bayesian Risk Graph in Cyber-Physical Social Systems

© 2014 IEEE. Cyber-physical social system (CPSS) plays an important role in both the modern lifestyle and business models, which significantly changes the way we interact with the physical world. The increasing influence of cyber systems and social networks is also a high risk for security threats. The objective of this paper is to investigate associated risks in CPSS, and a hybrid Bayesian risk graph (HBRG) model is proposed to analyze the temporal attack activity patterns in dynamic cyber-physical social networks. In the proposed approach, a hidden Markov model is introduced to model the dynamic influence of activities, which then be mapped into a Bayesian risks graph (BRG) model that can evaluate the risk propagation in a layered risk architecture. Our numerical studies demonstrate that the framework can model and evaluate risks of user activity patterns that expose to CPSSs

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence

Cybersecurity and Digital Privacy Aspects of V2X in the EV Charging Structure

With the advancement of green energy technology and rising public and political acceptance, electric vehicles (EVs) have grown in popularity. Electric motors, batteries, and charging systems are considered major components of EVs. The electric power infrastructure has been designed to accommodate the needs of EVs, with an emphasis on bidirectional power flow to facilitate power exchange. Furthermore, the communication infrastructure has been enhanced to enable cars to communicate and exchange information with one another, also known as Vehicle-to-Everything (V2X) technology. V2X is positioned to become a bigger and smarter system in the future of transportation, thanks to upcoming digital technologies like Artificial Intelligence (AI), Distributed Ledger Technology, and the Internet of Things. However, like with any technology that includes data collection and sharing, there are issues with digital privacy and cybersecurity. This paper addresses these concerns by creating a multi-layer Cyber-Physical-Social Systems (CPSS) architecture to investigate possible privacy and cybersecurity risks associated with V2X. Using the CPSS paradigm, this research explores the interaction of EV infrastructure as a very critical part of the V2X ecosystem, digital privacy, and cybersecurity concerns

Resource Allocation in the Cognitive Radio Network-Aided Internet of Things for the Cyber-Physical-Social System: An Efficient Jaya Algorithm

Currently, there is a growing demand for the use of communication network bandwidth for the Internet of Things (IoT) within the cyber-physical-social system (CPSS), while needing progressively more powerful technologies for using scarce spectrum resources. Then, cognitive radio networks (CRNs) as one of those important solutions mentioned above, are used to achieve IoT effectively. Generally, dynamic resource allocation plays a crucial role in the design of CRN-aided IoT systems. Aiming at this issue, orthogonal frequency division multiplexing (OFDM) has been identified as one of the successful technologies, which works with a multi-carrier parallel radio transmission strategy. In this article, through the use of swarm intelligence paradigm, a solution approach is accordingly proposed by employing an efficient Jaya algorithm, called PA-Jaya, to deal with the power allocation problem in cognitive OFDM radio networks for IoT. Because of the algorithm-specific parameter-free feature in the proposed PA-Jaya algorithm, a satisfactory computational performance could be achieved in the handling of this problem. For this optimization problem with some constraints, the simulation results show that compared with some popular algorithms, the efficiency of spectrum utilization could be further improved by using PA-Jaya algorithm with faster convergence speed, while maximizing the total transmission rate

The CitySPIN Platform: A CPSS Environment for City-Wide Infrastructures

Cyber-physical Social System (CPSS) are complex systems that span the boundaries of the cyber, physical and social spheres. They play an important role in a variety of domains ranging from industry to smart city applications. As such, these systems necessarily need to take into account, combine and make sense of heterogeneous data sources from legacy systems, from the physical layer and also the social groups that are part of/use the system. The collection, cleansing and integration of these data sources represents a major effort not only during the operation of the system, but also during its engineering and design. Indeed, while ongoing efforts are concerned primarily with the operation of such systems, limited focus has been put on supporting the engineering phase of CPSS. To address this shortcoming, within the CitySPIN project we aim to create a platform that supports stakeholders involved in the design of these systems especially in terms of support for data management. To that end, we develop methods and techniques based on Semantic Web and Linked Data technologies for the acquisition and integration of heterogeneous data from disparate structured, semi-structured and unstructured sources, including open data and social data. In this paper we present the overall system architecturewith a core focus on data acquisition and integration.We demon-strate our approach through a prototypical implementation of an adaptive planning use case for public transportation scheduling

PERBANDINGAN ALGORITMA NAÏVE BAYES DAN KNN DALAM ANALISIS SENTIMEN MASYARAKAT TERHADAP PELAKSANAAN PPPK GURU

Hingga saat ini penyelenggaraan pendidikan di Indonesia tidak lepas dari kerumitan tata kelolapendidik, salah satunya guru honorer, dan reformasi birokrasi yang cukup berdampak pada kualitaspendidikan dan iklim kerja di dalamnya. Sebagai salah satu cara untuk meningkatkan kepuasanpelayanan publik melalui aparatur sipil negara (ASN), Kementerian pendidikan dan kebudayaanindonesia bersepakat dengan Kementerian Pendayagunaan Aparatur Negara dan Reformasi Birokrasidan Kementerian Keuangan untuk merubah sistem rekrutmen atau pengangkatan guru-guru pegawaipemerintah dari penerimaan calon Pegawai Negeri Sipil (CPNS) menjadi pegawai pemerintah denganPerjanjian Kerja (PPPK) yang dalam pelaksanaannya masih terdapat kendala dan pro kontra, ada yangsetuju dan ada yang tidak setuju. maka dari itu peneliti melakukan penelitian tentang sentiment analisispada data mining pada pelaksanaan PPPK guru di media sosial Twitter sebanyak 871data yangkemudian diolah menjadi 519 data. penulis menggunakan teknik Naïve Bayes, dan KNN untukmengetahui efek prediksi algoritma Naïve Bayes dan KNN terhadap opini publik pada implementasiinstruktur PPPK serta membandingkan tingkat akurasi dari 2 metode tersebut. Peneliti menggunakanperalatan RapidMiner versi sembilan.10.1. Hasil prediksi Naïve Bayes adalah 328 statistik dengansentimen positif atau setuju dan 191 informasi dengan sentiment negative , dan yang terakhir adalahhasil prediksi dari KNN yaitu 315 informasi dengan sentimen postif dan 204 fakta dengan sentimennegatif. analisis sentimen masyarakat terhadap implementasi teacher first resource di media sosialTwitter dengan algoritma Naïve Bayes mencapai akurasi sebesar 75,53%. Dan pada KNN mencapaiakurasi 73,41%. Pada penelitian ini dapat diketahui bahwa metode Naïve Bayes merupakan teknikdengan tingkat akurasi yang lebih tinggi dari KNN tersebut dengan tingkat akurasi sebesar 75,53%

A Data Storage and Sharing Scheme for Cyber-Physical-Social Systems

© 2013 IEEE. Cyber-Physical-Social System (CPSS) provides users secure and high-quality mobile service applications to share and exchange data in the cyberspace and physical world. With the explosive growth of data, it is necessary to introduce cloud storage service, which allows devices frequently resort to the cloud for data storage and sharing, into CPSS. In this paper, we propose a data storage and sharing scheme for CPSS with the help of cloud storage service. Since data integrity assurance is an inevitable problem in cloud storage, we first design a secure and efficient data storage scheme based on the technology of public auditing and bilinear map, which also ensures the security of the verification. In order to meet the real-time and reliability requirements of the CPSS, the rewards of timeliness incentive and effectiveness incentive are considered in the scheme. Secondly, based on the proposed storage scheme and ElGamal encryption, we propose a lightweight access model for users to access the final data processed by cloud server. We formally prove the security of the proposed scheme, and conduct performance evaluation to validate its high efficiency. The experimental results show that the proposed scheme has lower overheads in communication and access as compared to the technique CDS

Data Processing in Cyber-Physical-Social Systems Through Edge Computing

© 2013 IEEE. Cloud and Fog computing have established a convenient and widely adopted approach for computation offloading, where raw data generated by edge devices in the Internet of Things (IoT) context is collected and processed remotely. This vertical offloading pattern, however, typically does not take into account increasingly pressing time constraints of the emerging IoT scenarios, in which numerous data sources, including human agents (i.e., Social IoT), continuously generate large amounts of data to be processed in a timely manner. Big data solutions could be applied in this respect, provided that networking issues and limitations related to connectivity of edge devices are properly addressed. Although edge devices are traditionally considered to be resource-constrained, main limitations refer to energy, networking, and memory capacities, whereas their ever-growing processing capabilities are already sufficient to be effectively involved in actual (big data) processing. In this context, the role of human agents is no longer limited to passive data generation, but can also include their voluntary involvement in relatively complex computations. This way, users can share their personal computational resources (i.e., mobile phones) to support collaborative data processing, thereby turning the existing IoT into a global cyber-physical-social system (CPSS). To this extent, this paper proposes a novel IoT/CPSS data processing pattern based on the stream processing technology, aiming to distribute the workload among a cluster of edge devices, involving mobile nodes shared by contributors on a voluntary basis, and paving the way for cluster computing at the edge. Experiments on an intelligent surveillance system deployed on an edge device cluster demonstrate the feasibility of the proposed approach, illustrating how its distributed in-memory data processing architecture can be effective