System Architecture Research Articles

Developing a custom communication protocol for UAVs: Ground control station and architecture design

Robots have become a prominent research topic across various application domains in recent decades. Additionally, unmanned aerial vehicles (UAVs) are extensively used in both the military and commercial sectors, substantially reducing transaction costs and enhancing safety. Researchers have addressed secure control and communication protocols between software, firmware, and hardware components. This study focuses on the design of three critical elements: the hardware architecture, the software ground control station (GCS), and the firmware tasks within the UAV embedded system. These components are interconnected via an enhanced MAVLink protocol (EMP). Furthermore, various sensors are integrated into the UAV's peripheral devices. We discuss flight control (FC) approaches, such as proportional-integral-derivative (PID) control and the Kalman filter (KF), detailing the process of the hovering algorithm. Additionally, we explain how access is messaged and how message commands are implemented at the protocol layer. We propose a large-scale UAV system architecture suitable for commercial and military applications, supported by a real-life scenario. Experimental results demonstrate the effectiveness and efficiency of the UAV in outdoor activities. Our findings confirm that the proposed UAV architecture is a robust and efficient system in practical applications.

An efficient blockchain-based framework for file sharing

File sharing, being the foundation of the Internet, has traditionally relied on a centralized service architecture resulting in significant maintenance costs. Moreover, due to the lack of an effective file management system, instances of sensitive information going out of control and loss of confidentiality in file sharing have occurred frequently. In order to address the difficulty of tamper detection and the lack of supervision in the entire process of file transfer in the current Internet environment, this paper designs a blockchain-based system architecture for secure sharing of electronic documents. An efficient blockchain model is used in our framework, and with the help of distributed storage system and asymmetric encryption technology, file sharing can be controlled, reliable and traceable in the transfer process. Referring to existing consensus mechanisms, e.g., Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT), we propose a new consensus for efficient and secure file sharing. Our experimental results show that our framework can maintain a higher throughput than existing schemes

Earth-like planets hosting systems: Architecture and properties

The discovery of Earth-like planets is a major focus of current planetology research and faces a significant technological challenge. Indeed, when it comes to detecting planets as small and cold as the Earth, the cost of observation time is massive. Understanding in what type of systems Earth-like planets (ELPs) form and how to identify them is crucial for preparing future missions such as PLATO, LIFE, or others. Theoretical models suggest that ELPs predominantly form within a certain type of system architecture. Therefore, the presence or absence of ELPs could be inferred from the arrangement of other planets within the same system. This study aims to identify the profile of a typical system that harbours an ELP by investigating the architecture of systems and the properties of their innermost detectable planets. Here, we introduce a novel method for determining the architecture of planetary systems and categorising them into four distinct classes. We then conduct a statistical study to identify the most favourable arrangements for the presence of an ELP. Using three populations of synthetic planetary systems generated using the Bern model around three different types of stars, we studied the `theoretical' architecture (the architecture of a complete planetary system) and the `biased' architecture (the architecture of a system in which only detectable planets are taken into account after applying an observation bias) of the synthetic systems. To describe a typical system hosting an ELP, we initially examined the distribution of ELPs across different categories of architectures, highlighting the strong link between planetary system architecture and the presence of an ELP. A more detailed analysis was then conducted, linking the biased architecture of a system with the physical properties of its innermost observable planet to establish the most favourable conditions for the presence or absence of an ELP in a system. First, using synthetic systems, we successfully reproduce the distribution of observed multi-planet systems within the five different architectural classes. This demonstrates the relevance, at the system level, of populations of the synthetic systems derived from the Bern model and the observational bias applied. Secondly, the biased architectures (with observation bias) correspond for the most part to the theoretical architectures (without bias) of the same system. Finally, the biased architecture of a system, studied in conjunction with the mass, radius, and period of the innermost detectable planet, appears to correlate with the presence or absence of an ELP in the same system. We conclude that the detections of ELPs can be predicted thanks to the already known properties of their systems, and we present a list of the properties of the systems most likely to host such a planet.

A Champernowne Adaptive Filter-Based Control of Grid Interactive Solar PV with Power Quality Enrichment

This work presents a variable step Champernowne Adaptive Filter (VS-CAF) based control architecture for a grid-integrated solar photovoltaic system. The control structure is implemented to successfully synchronize the two-stage solar photovoltaic at the point of common coupling where the source and domestic loads are connected. The control architecture ensures solar power injection and improvised power quality at the unity power factor. Moreover, the control structure provides only the balanced positive sequence fundamental sinusoidal source currents at solar irradiation alteration and load disturbances. The VS-CAF is exploited to estimate the peak of the fundamental component out of the harmonics-rich domestic load currents. To examine the efficacy of the suggested control technique, a 40 kW solar photovoltaic is simulated using MATLAB/Simulink. The performance assessment of the grid-tied solar generation system is carried out at solar irradiation alteration, zero solar irradiation, and detachment of one phase of the three-phase load. Various simulation results are carried out to verify the successful integration of solar photovoltaics into the three-phase grid following the IEEE-519 standard and satisfactory performance during various working conditions. Moreover, the simulation results are also verified with real-time results taken from the Opal-RT simulator.

Cyber-Physical Distributed Intelligent Motor Fault Detection.

This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results offers reliable diagnosis of industrial motor faults. The proposed methodology involves the development of a cyber-physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber-physical system, incorporating the dynamic interactions between distributed motors and their edge controllers. Fast Fourier transform is employed for signal processing, enabling the extraction of meaningful frequency features that serve as indicators of potential faults. The artificial neural network based fault detection system is integrated with the solution, utilizing its ability to learn complex patterns and adapt to varying motor conditions. The effectiveness of the proposed framework and model is demonstrated through experimental results. The experimental setup involves diverse fault scenarios, and the system's performance is evaluated in terms of accuracy, sensitivity, and false positive rates.

Design of control system for seawater total alkalinity in-situ analyzer

Abstract To address the inadequacies of traditional seawater total alkalinity detection methods, which fail to meet the demands for continuous on-site monitoring at sea, a seawater total alkalinity analyzer based on single-point titration and spectrophotometry has been developed. This paper focuses on the design of the analyzer’s system architecture, hardware, and control systems. The instrument adopts a modular design concept and utilizes microfluidic technology for detection, with the STM32 serving as the control core. Through the design of the control system for the detection instrument, accurate in-situ automatic measurement of seawater total alkalinity is achieved. Laboratory testing and analysis demonstrate that the instrument’s measurement relative error is less than 1%, satisfying the accuracy requirements for seawater total alkalinity measurement. This research provides critical monitoring data support for a timely understanding of marine ecological environments and the marine carbon cycle.

Enhancing 5G network performance through effective resource management with network slicing

The immense growth of mobile networks leads to versatile applications and new demands. The improved concert, transferability, flexibility, and performance of innovative network services are applied in diversified fields. More unique networking concepts are incorporated into state-of-the-art mobile technologies to expand these dynamic features further. This paper presents a novel system architecture of slicing and pairing networks with intra-layer and inter-layer functionalities in 5th generation (5G) mobile networks. The radio access network layer slices and the core network layer slices are paired up using the network slicing pairing functionalities. The physical network elements of such network slices will be logically assigned entities called softwarization of the network. Such a novel system architecture called network sliced softwarization of 5G mobile networks (NSS-5G) has shown better performances in terms of end-to-end delay, total throughput, and resource utilization when compared to traditional mobile networks. Thus, effective resource management is achieved using NSS-5G. This study will pave the way for future softwarization of heterogeneous mobile applications.

Photo-dynamical characterisation of the TOI-178 resonant chain

Context. The TOI-178 system consists of a nearby, late-K-dwarf with six transiting planets in the super-Earth to mini-Neptune regime, with radii ranging from to 2.9 R⊕ and orbital periods between 1.9 and 20.7 days. All the planets, but the innermost one, form a chain of Laplace resonances. The fine-tuning and fragility of such orbital configurations ensure that no significant scattering or collision event has taken place since the formation and migration of the planets in the protoplanetary disc, thereby providing important anchors for planet formation models. Aims. We aim to improve the characterisation of the architecture of this key system and, in particular, the masses and radii of its planets. In addition, since this system is one of the few resonant chains that can be characterised by both photometry and radial velocities, we propose to use it as a test bench for the robustness of the planetary mass determination with each technique. Methods. We performed a global analysis of all the available photometry from CHEOPS, TESS and NGTS, and radial velocity from ESPRESSO, using a photo-dynamical modelling of the light curve. We also tried different sets of priors on the masses and eccentricity, as well as different stellar activity models, to study their effects on the masses estimated by transit-timing variations (TTVs) and radial velocities (RVs). Results. We demonstrate how stellar activity prevents a robust mass estimation for the three outer planets using radial velocity data alone. We also show that our joint photo-dynamical and radial velocity analysis has resulted in a robust mass determination for planets c to 𝑔, with precision of ~ 12% for the mass of planet c, and better than 10% for planets d to 𝑔. The new precisions on the radii range from 2 to 3%. The understanding of this synergy between photometric and radial velocity measurements will be valuable for the PLATO mission. We also show that TOI-178 is indeed currently locked in the resonant configuration, librating around an equilibrium of the chain.

Quality attributes of software architecture in IoT-based agricultural systems

Software architecture forms the cornerstone for achieving and ensuring various software quality attributes. It encompasses the collected requirements of the product, serving as a blueprint that delineates quality features for all project stakeholders, along with methods for measurement and control. Despite the significant increase in IoT-based agricultural systems, there is a dearth of studies on the quality elements of their software architecture. To address this need, this study offers an overview of components and services tailored to address specific quality attributes pertinent to agriculture systems. It identifies, investigates, and presents quality attributes influencing the design of software architecture for IoT-based agriculture systems. This paper identified and discussed several quality attributes, including performance, scalability, flexibility, interoperability, productivity, extensibility, and security, and mapped them to corresponding components of the IoT-based agriculture software architecture. Also, several issues were identified and discussed for the software architecture quality of IoT-based agriculture systems, such as real-time processing and interoperability due to the various devices and protocols utilized in these systems. The findings of this study offer valuable insights for developing, executing, and refining IoT-based agricultural systems to fulfill the changing requirements of the agriculture industry.

VLR-BPP: An intelligent virtual location replacement based bilateral privacy-preserving architecture for edge cloud systems

Mobile Crowdsourcing (MCS) has emerged as a significant edge-cloud computing paradigm in which workers perceive data at the network edge and report it to cloud-based computing services for processing, enabling the construction of various applications. Consequently, it is imperative to achieve Bilateral Location Privacy-Preserving (BLPP) to protect the privacy of both Data Requester (DR) and workers, as disclosing location information entails many sensitive details that can result in losses for DR and workers alike. The Local Differential Privacy (LDP) approach is widely employed in Privacy-Preserving (PP) techniques due to its inherent advantages, wherein owners release data with added noise, allowing for proactive customization of privacy strength without relying on any third party. However, the current state of LDP methods presents a dilemma: when privacy protection is strong, introducing excessive location noise can lead to a decrease in the accuracy of task-worker matching, while a high rate of task-worker matching necessitates the compromise of privacy strength. In this paper, an intelligent Virtual Location Replacement based enhanced Bilateral Privacy-Preserving (VLR-BPP) architecture is proposed to improve privacy protection strength and matching accuracy in MCS simultaneously. Within the VLR-BPP architecture, a Bipartite-Graph-based Matrix Completion (BGMC) model is employed to establish the spatiotemporal correlations among data. Then, a Virtual Location Replacement (VLR) strategy is proposed to obfuscate the locations of tasks or workers to their highly correlated virtual location before publishing. Based on VLR, three preemptive location virtualization approaches are introduced: Only Task Location Virtual (OTLV), Only Workers Location Virtual (OWLV), and Both Task and Workers Location Virtual (BTWLV). For workers and DR, Randomized Response (RR) techniques and Random Matrix Multiplication Mechanism (RMM) are used to implement LDP independently. A greedy algorithm is adopted to recruit workers for tasks. In response to the data submitted by workers, BGMC imputation mechanism is utilized to enhance data quality. Finally, simulations based on real-world datasets demonstrate that the performance of our architecture surpasses existing state-of-the-art methods in privacy protection and data collection quality by 18.92∼38.17% and 15.49∼50.77%, respectively.

A universal knowledge model and cognitive architectures for prototyping AGI

The article identified 56 cognitive architectures for creating general artificial intelligence (AGI) and proposed a set of interrelated functional blocks that an agent approaching AGI in its capabilities should possess. Since the required set of blocks is not found in any of the existing architectures, the article proposes a reference cognitive architecture for intelligent systems approaching AGI in their capabilities. As one of the key solutions within the framework of the architecture, a universal method of knowledge representation is proposed, which allows combining various non-formalized, partially and fully formalized methods of knowledge representation in a single knowledge base, such as texts in natural languages, images, audio and video recordings, graphs, algorithms, databases, neural networks, knowledge graphs, ontologies, frames, essence-property-relation models, production systems, predicate calculus models, conceptual models, and others. To combine and structure various fragments of knowledge, archigraph model are used, constructed as a development of annotated metagraphs. As other components, the reference cognitive architecture being developed includes following modules: machine consciousness, machine subconsciousness, interaction with the external environment, a goal management, an emotional control, social interaction, reflection, ethics, worldview, learning, monitoring, statement problems, solving problems, self-organization and meta learning. Based on the composition of the proposed reference architecture modules, existing cognitive architectures containing the following modules were analyzed: machine consciousness, machine subconsciousness, reflection, worldview.

Multi-Derivational Parsing of Vague Languages- The New Paradigm of Syntactic Pattern Recognition.

The new paradigm of syntactic pattern recognition, SPR, which uses multi-derivational parsing of vague languages is introduced in the paper. The methodology proposed addresses the issue of the recognition of vague/distorted patterns which is one of the important open problems in the area. The concept of the vague language of patterns and the efficient parsing method based on the class of dynamically programmed grammars are introduced. A vague language is defined with vague primitives which are vectors of "neighboring" primitives associated with measures of distance, probability, fuzziness, etc. The use of vague primitives allows us to identify b best structural templates during multi-derivational parsing that can be used for getting more adequate final result. The generic architecture of SPR system based on the approach proposed together with the system's applications for short-term electrical load forecasting and for analysis of ultrasound images in order to diagnose congenital defects of fetal palates are presented. The results of the experimental studies are discussed.

The bacterial defense system MADS interacts with CRISPR-Cas to limit phage infection and escape

The constant arms race between bacteria and their parasites has resulted in a large diversity of bacterial defenses, with many bacteria carrying multiple systems. Here, we report the discovery of a phylogenetically widespread defense system, coined methylation-associated defense system (MADS), which is distributed across gram-positive and gram-negative bacteria. MADS interacts with a CRISPR-Cas system in its native host to provide robust and durable resistance against phages. While phages can acquire epigenetic-mediated resistance against MADS, co-existence of MADS and a CRISPR-Cas system limits escape emergence. MADS comprises eight genes with predicted nuclease, ATPase, kinase, and methyltransferase domains, most of which are essential for either self/non-self discrimination, DNA restriction, or both. The complex genetic architecture of MADS and MADS-like systems, relative to other prokaryotic defenses, points toward highly elaborate mechanisms of sensing infections, defense activation, and/or interference.

Architecture characterization of orchard trees for mechanical behavior investigations

Characterizing the architecture of tree root systems is essential to advance the development of root-inspired anchorage in engineered systems. This study explores the structural root architectures of orchard trees to understand the interplays between the mechanical behavior of roots and the root architecture. Full three-dimensional (3D) models of natural tree root systems, Lovell, Marianna, and Myrobalan, that were extracted from the ground by vertical pullout are reconstructed through photogrammetry and later skeletonized as nodes and root branch segments. Combined analyses of the full 3D models and skeletonized models enable a detailed examination of basic bulk properties and quantification of architectural parameters. While the root segments are divided into three categories, trunk root, main lateral root, and remaining roots, the patterns in branching and diameter distributions show significant differences between the trunk and main laterals versus the remaining lateral roots. In general, the branching angle decreases over the sequence of bifurcations. The main lateral roots near the trunk show significant spreading while the lateral roots near the ends grow roughly parallel to the parent root. For branch length, the roots bifurcate more frequently near the trunk and later they grow longer. Local thickness analysis confirms that the root diameter decays at a higher rate near the trunk than in the remaining lateral roots, while the total cross-sectional area across a bifurcation node remains mostly conserved. The histograms of branching angle, and branch length and thickness gradient can be described using lognormal and exponential distributions, respectively. This unique study presents data to characterize mechanically important structural roots, which may help link root architecture to the mechanical behaviors of root structures.

Defence against adversarial attacks on IoT detection systems using deep belief network

An Adversarial attack is a technique used to deceive machine learning models to make incorrect predictions by providing slightly modified inputs from the original. Intrusion detection system (IDS) is a crucial tool in computer network security for the detection of adversarial attacks. Deep learning is a trending method in both research and industry, and this study proposes the use of a deep belief network (DBN). DBN can recognize data with small differences, but is also vulnerable to adversarial attacks. Therefore, this research suggests an internet of things-intrusion detection system (IoT-IDS) architecture using a DBN that can counter adversarial attacks. The chosen adversarial attack for this study is the fast gradient sign method (FGSM) used to evaluate the IoT IDS using the DBN model. Testing was conducted in two scenarios: first, the model was trained without adversarial attacks; second, the model was trained with adversarial attacks. The test results indicate that the DBN model struggles to detect FGSM attacks, achieving an accuracy of only 46% when it is not trained with adversarial attacks. However, after training with the FGSM dataset, the DBN model successfully detected adversarial attacks with an accuracy of 97%.

Enhanced root system architecture in oilseed rape transformed with Rhizobium rhizogenes

Transformation of plants using wild strains of agrobacteria is termed natural transformation and is not covered by GMO legislation in e.g. European Union and Japan. In the current study, offspring lines (A11 and B3) of Rhizobium rhizogenes naturally transformed oilseed rape (Brassica napus) were randomly selected to characterize the morphological traits, and analyze the implications of such morphological changes on plant drought resilience. It was found that the introduction of Ri-genes altered the biomass partitioning to above- and under-ground parts of oilseed rape plants. Compared to the wild type (WT), the A11 and B3 lines exhibited 1.2–4.0 folds lower leaf and stem dry weight, leaf area and plant height, but had 1.3–5.8 folds greater root dry weight, root length and root surface area, resulting in a significantly enhanced root: shoot dry mass ratio and root surface area: leaf area ratio. In addition, the introduction of Ri-genes conferred reduced stomatal pore aperture and increased stomatal density in the B3 line, and increased leaf thickness in A11 line, which could benefit plant drought resilience. Finally, the modulations in morphological traits as a consequence of transformation with Ri-genes are discussed concerning resilience in water-limited conditions. These findings reveal the potential of natural transformation with R. rhizogenes for drought-targeted breeding in crops.

Design and implementation of college students’ physical education teaching information management system by data mining technology

This study intends to improve the efficiency of physical education teaching management, accelerate the normal teaching process, and meet the modern management requirements that traditional teaching management methods cannot meet. Based on data mining technology, this study designs a college student physical education teaching information management system, and makes a detailed design of each functional module. The main task of this study is to investigate how to effectively integrate data mining techniques with existing university student physical education teaching databases. Then, this study finds useful data information from massive data information to provide information support for university student physical education teaching. In order to effectively mine the relevant information of the data, the student evaluation module in the system is designed based on decision trees, and the teacher-student related data analysis module in the system is designed based on association rules. The research results indicate that 1039 records and 8205 student records are extracted from the teaching management database as mining objects. Rule 1: The support rate for “a professor's degree is a doctoral degree” is 20.4 %, indicating that there are 20.4 % of records in the teacher database that “the title is a professor and a doctoral degree”; the confidence level of Rule 1 is 78.2 %, indicating that 78.2 % of professors have a doctoral degree. Through the analysis of the rules that evaluate teaching as good, it can be found that the three attributes of professional title, education level, and teaching experience are the most important relevant factors affecting teaching effectiveness. Research has shown that the longer and richer the teaching experience, the stronger the teaching ability. Secondly, the mining results obtained through data mining techniques are analyzed. The maximum difference between the original algorithm's support mining results and the true values is 0.08, while the maximum difference between the improved algorithm's support mining results and the true values is 0.01. Compared to the original algorithm, the improved algorithm's mining results are accurate and effective. The application of data mining ideas in this system has laid a solid foundation for the development of physical education and teaching. Moreover, a three-layer system architecture model is adopted to better adapt to the development of school physical education, which is beneficial for later system maintenance and greatly reduces the work pressure of teachers. The system has been successfully launched and running in universities, and it is in good working condition.

Seaweed Extract and Microbial Biostimulants Show Synergistic Effects on Improving Organic Strawberry Production

The application of seaweed extract and microbial biostimulants has been suggested as a promising approach to overcome yield-limiting factors in organic farming. Yet, information regarding their impact on organic strawberry production is limited. This 2-year field study evaluated the effect of seaweed extract and microbial biostimulants and their synergistic effects on strawberry plant growth, nutrient uptake, fruit yield, and quality under organic production. The biostimulant effects were compared on two strawberry cultivars: Sweet Sensation® Florida127 and Florida Brilliance. Over two seasons, the combination of seaweed extract plus microbial biostimulants applied biweekly consistently resulted in a significant increase of whole-season marketable and total strawberry fruit yields by 23% and 20% on average, respectively, compared with the no-biostimulant control. Application of either biostimulant alone did not consistently show positive effects on strawberry productivity. Modified strawberry root system architecture, enhanced N uptake, increased number of crowns, and higher soil respiration were observed in the biostimulant combination treatment in contrast to the no-biostimulant control. The biostimulant impact was not influenced by strawberry cultivar, but genotypic difference in yield performance under organic production was observed. ‘Florida Brilliance’ produced significantly higher total fruit number and yield than ‘Florida127’ by 26% and 12%, respectively, in the first season, and by 34% and 11%, respectively, in the second season. Marketable fruit number (by 18%) and yield (by 9%) of ‘Florida Brilliance’ were also higher in the first season, along with greater marketable fruit number (by 31%) in the second season. In addition, ‘Florida Brilliance’ showed significantly higher values of SPAD index, photosynthetic rate (early harvest), and fruit mineral contents based on dry weight (late harvest) than ‘Florida127’ in both seasons. Although the biostimulant treatments exhibited little influence on the fruit quality attributes including soluble solids content (SSC), titratable acidity (TA), SSC/TA, and total anthocyanin content, varietal differences were observed with significantly higher levels of SSC and lower contents of total anthocyanins in ‘Florida 127’ vs. ‘Florida Brilliance’ during each season. The benefits of combined application of seaweed extract and microbial biostimulants demonstrated in this study suggest the need to further elucidate their synergistic functions in promoting nutrient uptake and fruit yield in organic strawberry production systems under different soil and environmental conditions.

Battery-electric powertrain system design for the HorizonUAM multirotor air taxi concept

AbstractThe work presented herein has been conducted within the DLR internal research project HorizonUAM, which encompasses research within numerous areas related to urban air mobility. One of the project goals was to develop a safe and certifiable onboard system concept. This paper aims to present the conceptual propulsion system architecture design for an all-electric battery-powered multirotor electric Vertical Takeoff and Landing (eVTOL) vehicle. Therefore, a conceptual design method was developed that provides a structured approach for designing the safe multirotor propulsion architecture. Based on the concept of operation the powertrain system was initially predefined, iteratively refined based on the safety assessment and validated through component sizing and simulations. The analysis was conducted within three system groups that were developed in parallel: the drivetrain, the energy supply and the thermal management system. The design process indicated that a pure quadcopter propulsion system can merely be designed reasonably for meeting the European Union Aviation Safety Agency (EASA) reliability specifications. By adding two push propellers and implementing numerous safety as well as passivation measures the reliability specifications defined by EASA could finally be fulfilled. The subsequent system simulations also verified that the system architecture is capable of meeting the requirements of the vehicle concept of operations. However, further work is required to extend the safety analysis to additional system components as the thermal management system or the battery management system and to reduce propulsion system weight.

Enhancing Smart City Infrastructure: An Iot-Integrated System Approach for Real-Time Urban Management

Objectives: The primary objective of this study is to evaluate the effects of new information technologies on the workability, scalability, and reliability of urban installations. This study also aims to enhance smart city infrastructure by integrating various Internet of Things (IoT) systems, particularly for real-time smart city management. Methods : A complete experimental setup was established in a controlled urban environment encompassing numerous IoT technologies, such as environmental sensors, traffic management units, and public security systems, as well as data communication units. The Tensorflow framework, along with Python, was used to perform simulations and experiments. The system design was categorised into four key levels: data reception and transmission, computation, network and data analytics, and application. The proposed systems were subsequently tested in real-world environments, including zones frequently hosting urban events, to analyse key performance indicators. Findings : The results demonstrate significant improvements in urban management through IoT initiatives, including a 25% reduction in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) levels, leading to decreased respiratory hospital admissions and enhanced air quality and health benefits. Additionally, notable advancements were observed in noise control, energy efficiency, and emergency response. Specifically, the findings recorded a 10 dB decrease in nighttime noise, a 30% reduction in energy consumption for public lighting, and a 40% faster emergency response time. These outcomes highlight the broad impact of IoT on urban sustainability and safety. Novelty : This paper introduces a novel concept focused on the design perspective of IoT-connected systems to boost smart city infrastructure, with particular emphasis on the real-time management of urban environments. Keywords: Smart Cities, IoT Technologies, Urban Infrastructure, Real ­Time Urban Management, System Architecture