In this digital era, many industries have widely adopted the Internet of Things (IoT), yet its implementation in the construction industry is relatively limited. Integration of Construction 4.0 drivers, such as business information modeling (BIM), procurement, construction safety, and structural health monitoring (SHM), with IoT devices, provides an effective framework for applications to enhance construction and operational efficiencies. IoT and Construction 4.0 driver integration research, however, is still in its infancy. It is necessary to understand the present state of IoT adoption in the Construction 4.0 context. This paper presented a comprehensive review to identify the IoT adoption status in the Construction 4.0 areas. Furthermore, this work highlighted the potential roadblocks to IoT’s seamless adoption that are unique to the areas of Construction 4.0 in developing countries. Altogether, 257 research articles were reviewed to present the current state of IoT adoption in developed and developing countries, as well as the topmost barriers encountered in integrating IoT with the key Construction 4.0 drivers. This study aimed to provide a reference for construction managers to observe challenges, professionals to explore the hybridization possibilities of IoT in the context of Construction 4.0, and laymen to understand the high-level scientific research that underpins IoT in the construction industry.

This paper demonstrates an unprecedented novel neural network (NN)-based digital predistortion (DPD) solution to overcome the signal impairments and nonlinearities in Analog Optical fronthauls using radio over fiber (RoF) systems. DPD is realized with Volterra-based procedures that utilize indirect learning architecture (ILA) and direct learning architecture (DLA) that becomes quite complex. The proposed method using NNs evades issues associated with ILA and utilizes an NN to first model the RoF link and then trains an NN-based predistorter by backpropagating through the RoF NN model. Furthermore, the experimental evaluation is carried out for Long Term Evolution 20 MHz 256 quadraturre amplitude modulation (QAM) modulation signal using an 850 nm Single Mode VCSEL and Standard Single Mode Fiber to establish a comparison between the NN-based RoF link and Volterra-based Memory Polynomial and Generalized Memory Polynomial using ILA. The efficacy of the DPD is examined by reporting the Adjacent Channel Power Ratio and Error Vector Magnitude. The experimental findings imply that NN-DPD convincingly learns the RoF nonlinearities which may not suit a Volterra-based model, and hence may offer a favorable trade-off in terms of computational overhead and DPD performance.

Sigma Delta Radio over Fiber (S-DRoF) systems are looked upon as an enabling technology due to their advantages that comes due to combination of analog and digital radio over fiber (RoF) systems. In this paper, we have proposed and experimentally demonstrated a Multi-stAge-noise-SHaping (MASH) based Sigma Delta Modulated RoF system targeting 5G C-RAN (cloud/centralized radio access network) fronthaul applications. The evaluation has been done for LTE 20 MHz signal having 256 quadrature amplitude modulation with a carrier frequency of 3.5 GHz up to 5 Km of Standard Single Mode Fiber (SSMF). Furthermore, a comprehensive analysis of the design is explained followed by the experimental setup. The performance is reported in terms of error vector magnitude (EVM) and adjacent channel leakage ratio. It is concluded that S-DRoF substantiates the desired range of the 5G C-RAN fronthaul networks.

The recent development of Big Data, Internet of Things (IoT) and 5G network technology offers a plethora of opportunities to the IT industry and mobile network operators. 5G cellular technology promises to offer connectivity to massive numbers of IoT devices while meeting low-latency data transmission requirements. A deficiency of the current 4G networks is that the data from IoT devices and mobile nodes are merely passed on to the cloud and the communication infrastructure does not play a part in data analysis. Instead of only passing data on to the cloud, the system could also contribute to data analysis and decision-making In this work, a Big Data driven self-optimized 5G network design is proposed using the knowledge of emerging technologies CRAN, NVF and SDN. Also, some technical impediments in 5G network optimization are discussed. A case study is presented to demonstrate the assistance of Big Data in solving the resource allocation problem.

Ant colony optimization (ACO), inspired by the ants' foraging behavior, is one of the most recent techniques for solving optimization problems. We present an ACO based algorithm for symbol detection in multi-input multi-output (MIMO) system. Since symbol detection is an NP-hard problem so ACO is particularly attractive as ACO algorithms are one of the most successful strands of swarm intelligence and are suitable for applications where low complexity and fast convergence is of absolute importance. Maximum Likelihood (ML) detector gives optimal results but it uses exhaustive search technique. We show that ACO based detector can give near-optimal bit error rate (BER) at a much lower complexity level. The simulation results suggest that the proposed detector gives an acceptable performance complexity trade-off in comparison with ML and VBLAST detectors.

The fusion of Artificial Intelligence (AI) and the Internet of Things (IoT) in surveillance systems, known as the Artificial Intelligence of Things (AIoT), represents a major leap in security technology, enabling advanced monitoring and real-time data processing. However, this integration also presents a new frontier of complex security challenges. This comprehensive survey examines the evolving landscape of AIoT security in surveillance, focusing on critical issues such as the integration of heterogeneous devices, vulnerabilities inherent in edge computing and distributed processing, and the increasing threat of adversarial attacks on AI algorithms. We also address regulatory and compliance challenges that arise in this domain. The survey highlights the urgent need for robust security frameworks and proposes cutting-edge solutions, including advanced encryption mechanisms, lightweight communication protocols, and specialized Intrusion Detection and Prevention Systems (IDPS). Furthermore, we emphasize the significance of ethical considerations, particularly the implementation of zero-trust architectures and the necessity for ethical oversight to maintain user trust and ensure regulatory compliance. By synthesizing the latest research and identifying future directions, this paper provides essential insights for advancing AIoT security in surveillance and offers valuable guidance for researchers and industry practitioners navigating this rapidly evolving field.