Will Serrano

Data is powering the fifth industrial revolution via Artificial Intelligence (AI) and robotics in a human centric approach that will transform the workforce and workplace while driving innovation and economic growth. However, this dependency on data has generated two vulnerabilities that challenge the sought-after benefits. If data is intentionally or accidentally incorrect, then the generated wealth may not be optimum or even reversed. In addition, cyber attackers attempt to retrieve or impact any valuable data by any possible method. To mitigate these risks, cryptography provides data confidentiality, integrity, and authentication services based on large prime numbers with specific applications such as Distributed Ledger Technologies (DLT), Transport Layer Security (TLS) and Data Marketplaces. This article presents the Random Neural Network (RNN) as a mathematical model to provide the three cryptographic services based on a symmetric key algorithm for data confidentiality, a hash function for data integrity, and a digital signature for data authentication in a Real Estate Data Marketplace. Experimental results based on 1.4 million tests cover a proof of concept validation, with random values, a static validation with data from a commercial Real Estate Database, CoStar, and live validation with IoT data from a smart asset, the Marshgate at University College London (ULC) East Campus. The performance of the algorithm is benchmarked against two major AI algorithms, the Long Short-Term Memory (LSTM) and the Transformer networks. In addition, the performance of the RNN as symmetric key algorithm for data confidentiality is compared against the Advanced Encryption Standard (AES), as a hash function for data integrity is compared against Secure Hash Algorithm (SHA) and as digital signature for data authentication is compared against the Digital Signature Algorithm (DSA), Rivest-Shamir-Adleman (RSA) and Elliptic Curve Digital Signature Algorithm (ECDSA) respectively. Validation results demonstrate that neural encryption can be successfully applied to provide data confidentiality, integrity, and authentication services in data marketplaces with no loss of binary information. When a bit is tampered with in the encrypted message by swapping its value, the three neural networks detect this modification. On average, the tampered ciphertext increments the decryption Root Mean Square Error (RMSE) from a magnitude of E-25 to E-1, whereas the decryption Cross Entropy Loss (CEL) increases from 0.00 to 36.04. Furthermore, the Transformer is 13.24 times faster than the LSTM and 664.59 times quicker than the RNN in the message encryption, decryption, hashing, key pair generation, signature, and verification time; however, the Transformer requires 2.69 more iterations and the RNN 29.39 more iterations than the LSTM.

Digital or Technology strategies are the first step of the Digital Transformation. The main risk is that information and assessments not included in the strategy and left to be confirmed and managed at later stages have the potential to negatively affect the successful implementation of the Digital Transformation, therefore negating sought-after business benefits. To mitigate this risk, this article proposes DigStratCon, a Digital or Technology strategy framework that generalises the Digital Transformation, detaching it from its specific functional application, such as marketing, products, Information Technology (IT), and Operational Technology (OT). Therefore, DigStratCon applies to any area within a government, organisation or infrastructure, including Data and Artificial Intelligence (AI). DigStratCon defines seven components within a Digital or Technology strategy, specifically (1) market research, (2) target state, (3) current state, (4) roadmap, (5) risks, (6) supply chain, and finally (7) enablers. A qualitative analysis of several United Kingdom (UK) government digital strategies assesses their completeness against the DigStratCon model. On average, UK digital strategies score 6/7 with an innovative and ambitious vision; however, they generally lack a common or standardised structure and wider international benchmark and alignment.

Digital or Technology Strategies are the first step of a Digital Transformation. The main risk is that information and assessments not included in the Strategy and left to be confirmed and managed at later stages have the potential to negatively affect the successful implementation of the Digital Transformation, therefore, negating the sought after business benefits. To mitigate this risk, this article proposes DigStratCon, a Digital or Technology Strategy Framework that generalises the digital Transformation detaching it from its specific functional application, such as marketing, products, Information Technology (IT) and Operational Technology (OT). Therefore, DigStratCon applies to any area within an organisation or infrastructure including Data and Artificial Intelligence (AI). DigStratCon defines seven key components within a Digital or Technology Strategy, specifically 1) Market research, 2) Digital vision, 3) Current state, 4) Roadmap, 5) Risks, 6) Enablers, and finally 7) Supply Chain. A qualitative analysis of several United Kingdom (UK) government digital strategies assesses their completeness against the DigStratCon model. On average, UK digital strategies score 6/7 being innovative and ambitious in their vision; however, they generally lack a common or standardised structure and wider international benchmark and alignment.

This article attempts to address the three main questions any asset manager would have raised since the concept of an asset as an entity to be managed was developed. Question One: Why did I not know about this problem before? followed by Question Two: Is this issue really occurring? And then, finally, Question Three: How much and how long will it take me to correct it? This article proposes a cascaded structure of Artificial Intelligence (AI) for complex management decisions in Smart Asset Management for Fault Prediction Detection and Diagnosis (FPDD). Specifically, a Deep Learning (DL) neural network first performs a regression algorithm to predict future values as Fault Prediction to address question one. This structure is followed by another DL neural network that performs a classification algorithm as the Fault Detection & Diagnosis to define faults, therefore answering question two. The key value added by this article is that the asset fault prediction, detection and classification model is based on future predictions, rather than actual values; hence, issues can be proactively managed, reducing time, energy and cost, thus supporting the third question. The practical application of this proposed model is based on AI and data-driven control and automation, specifically smart building assets and their Heating, Ventilation and Air Conditioning (HVAC) systems. The two proposed AI algorithms analysed are Transformers and Long Short-Term Memory (LSTM) networks, which are broadly deployed in positional and time series applications such as text translation and trend prediction. Supported Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) were discarded due to their learning time and convergence in a multi-classification application on this complex model. Experimental results demonstrate the proposed model successfully predicts, detects and diagnoses faults. On average, the Fault Detection precision for the LSTM is 95.30% and the Transformer is 94.43% against a 50% random precision, whereas the Fault Diagnosis precision for the LSTM is 49.57% and the Transformer is 47.89% against a 4.34% random precision for the eight-variable model. With one month of learning and prediction windows, the Fault Prediction precision of 95.43% for the Transformer and 60.37% for the LSTM against a 4.34% random precision for the four-variable model. The total number of data points ingested and analysed by the AI algorithms is two times 7.51E+09, equivalent to the return distance between the Planet Earth and the Sun in kilometres.

One of the main Frequently Asked Questions (FAQs) in project management for built environment or physical infrastructure projects is “How will this project scope be delivered on time and under budget, addressing health and safety in a sustainable way?” This article presents a parallel point of view summarised in a competing question: “Have you followed a systems engineering methodology to detail interfaces and integrations in the business case?” Megaprojects face multiple risks that incur project delays and cost overruns; hence, this article proposes a simple but nevertheless innovative model that incorporates a systems engineering framework at the start of the built environment or physical infrastructure project: the business case phase. This proposed approach seeks to derisk megaprojects composed of complex systems of systems (SoSs) in their earliest stage when financial decisions based on cost estimations have to be made. The scope of this article covers built environment and physical infrastructure projects and their associated ICT, digital, and technology programmes, rather than purely IT developments. The inconvenient truth is this additional systems engineering task embedded in the business case comes at a further project CAPEX cost that decision makers or stakeholders should be willing to accept as it provides a wider technical vision of the project and better quantifies the Return on Investment (ROI).

Smart or intelligent built assets including infrastructure, buildings, real estate, and cities provide enhanced functionality to their different users such as occupiers, passengers, consumers, patients, managers or operators. This enhanced functionality enabled by the Internet of Things (IoT), Artificial Intelligence (AI), Big Data, Mobile Apps, Virtual Reality (VR) and 5G does not only translate into a superior user experience; technology also supports sustainability and energy consumption to meet regulation (ESG, NZC) while optimising asset management and operations for enhanced business economic performance. The main peculiarity is that technology is standardised, ubiquitous and independent from the physical built assets whereas asset users including humans, machines and devices are also common to different assets. This article analyses the atomic differences between built assets and proposes an asset omni-management model based on micro-management of services that will support the macro-functionality of the asset. The proposed key concept is based on the standardisation of different assets based on common and specific functionality and services delivered by the technology stack that is supporting already the transition to Industry 5.0 based on Web 3.0 and Tokenisation.