ABSTRACT Mining ecological environment monitoring systems require the deployment of numerous sensor nodes. However, the complex terrain of mines and the limited energy supply of nodes cause uneven energy consumption and a short network life cycle in traditional routing protocols. This study applies Internet of Things (IoT) technology to enable wide‐area sensing and cloud‐based data fusion of mine environmental parameters. An improved ant colony optimization (ACO) algorithm is proposed to enhance the robustness and sustainability of the monitoring system by optimizing sensor node deployment and data transmission paths. Experimental results show that in a large‐scale mining scenario, the optimal path length generated by the improved ACO is 80.46 m, which is 4.4% shorter than that of the traditional ACO. The algorithm convergence speed increases by 63.3% in small‐scale mining area and 30.9% in large‐scale mining area. The average daily energy consumption per node decreases from 2.13 to 1.21 J, extending the network lifetime from 57.42 to 92.15 days. These results demonstrate that combining a cloud–edge–end collaborative architecture with dynamic routing optimization effectively improves energy balance, network lifespan, and monitoring efficiency, providing valuable real‐time data support for ecological restoration of abandoned mines.

The article is devoted to the study of the automation of management processes of information and communication networks using network programming tools. The work substantiates the relevance of automation as a key direction in the development of modern information and communication systems, driven by the growth in traffic volumes and the complexity of network architecture. It is shown that automation allows for increasing operational efficiency, reducing task execution time, and minimizing the human factor. An analysis of existing approaches has shown that automation covers the entire network lifecycle: from initial configuration and commissioning to ongoing support and optimization. Depending on the application area, it is implemented both in corporate infrastructures and in service providers' environments. At the same time, it is essential to choose the optimal tools, in particular the command line interface, which allows you to create scripts for repetitive tasks, and graphical web interfaces that provide convenience and clarity. Special attention is paid to the Perl language, which demonstrates broad opportunities for integration with network devices and the creation of management scripts. For the client side of web applications, it is advisable to use AJAX and JavaScript/DOM, which provide interactivity and dynamic data updates. The server side can be implemented using Node.js, which is characterized by scalability and event-driven architecture. The article presents an applied solution in the form of developed software for managing and monitoring IP/MPLS routers. Its architecture includes a core, client, and server parts, providing an integrated platform for automating operational tasks. Practical tests have confirmed the ease of use of the application, reduced operation time, and fewer errors. Prospects for further research are related to the integration of the latest protocols, the unification of interfaces, and the creation of scalable network management systems.

In wireless rechargeable sensor networks (WRSNs), high node mortality rate severely constrains the network performance. To address this problem, this paper proposes an innovative charging strategy based on dynamic inhomogeneous clustering (DICCS). The core of this strategy lies in dynamically adjusting the network clustering structure, which combines the dynamic changes of node energy, position and energy consumption rate to achieve the optimal division of clusters. Firstly, the improved k-means algorithm is used to perform dynamic inhomogeneous clustering of the network, determine the optimal number of clusters through iterative optimization, and introduce a weight function to synthesize the node's initial energy, residual energy, and the average intra-cluster distance to select the cluster head in order to balance the energy consumption. On this basis, DICCS plans efficient charging paths for mobile charging carts (MCs), designs charging dwell point selection mechanisms for single-node and multi-node clusters respectively, and dynamically adjusts the charging sequence based on the mixed priorities (distance, residual energy, and energy consumption rate). Simulation experiments show that DICCS significantly reduces the node mortality rate (only 4.3%) and charging waiting time, while optimizing the mobility cost of MCs, compared to strategies such as SAMER, VTMT, and FCFS. Its dynamic inhomogeneous clustering mechanism effectively mitigates the energy consumption imbalance problem, improves the network life cycle and stability, and provides an efficient solution for charging scheduling in heterogeneous dynamic WRSNs.

The heating network of the heat supply system, which has exhausted its resource, needs reconstruction. At the stage of choosing the method of relaying the heating network, there is a need to determine the most profitable reconstruction option in terms of capital and operating costs. The article proposes a methodology based on the comparison and contrast of the most significant types of costs and losses during the life cycle of heat networks: during construction, during the transportation of heat carrier, in case of leaks of heat carrier and through thermal insulation. A comparative analysis of possible options based on relative performance indicators determined as a result of processing initial, standard and calculated data is carried out using ZuluThermo and MS Excel programs. The distribution heating network and possible options for its reconstruction are considered and calculated. For each option, a design calculation was carried out to determine the diameters of pipelines in the sections and an adjustment calculation to determine the calculated values of the characteristics of the heating network. A comparison of the effectiveness of the options was performed. The most effective option is with duct-free piping in bitumen-perlite insulation with pumping mixing for the consumer, but due to the fact that the heating main passes through an industrial area, a slightly less efficient reconstruction option with above-ground piping on low supports in polyurethane foam insulation with pumping mixing for the consumer is recommended. The methodology can be used to substantiate the choice of the option for the reconstruction of the heat supply network.

To address the issues of high energy consumption, data collisions, and invalid wake-ups of nontarget nodes in large-scale node-deployment scenarios of long-range (LoRa) star networks, this paper proposes an energy-saving wake-up scheme that combines (i) time-division multiple access (TDMA) slot allocation based on bidirectional timestamp correction with (ii) a sensing and communication integrated (ISAC) scheme proposed for physical address identification of LoRa nodes operating on the same channel. The scheme incorporates parameter estimation of the LoRa channel, which effectively enhances the identification accuracy and improves the system’s robustness. The proposed scheme consists of two parts: First, in case nodes briefly lose power, a bidirectional timestamp calibration algorithm and GPS-assisted timing are used to synchronize the gateway and each node with high precision, ensuring the accurate scheduling of the TDMA mechanism. Second, based on time synchronization, a “slot–LoRa module address” mapping table is constructed to set the communication time points between the gateway and each node. The gateway can wake the target nodes at specific, precise communication time points. Experimental results show that the proposed method maintains the error range within ±1 ms. The significant decrease in the rate of unnecessary node wake-up decreases data collisions and energy waste in the same channel environment. Energy savings scale with network size, thereby significantly extending the network life cycle.

The exponential growth of artificial intelligence (AI) and machine learning (ML) has significantly transformed the requirements for cloud infrastructure, demanding advanced networking solutions capable of handling the unique challenges posed by AI workloads. Traditional networking systems fall short when dealing with the bursty traffic patterns, extreme latency sensitivity, and massive data throughput needed for modern AI operations. Software-defined networking (SDN) offers a crucial solution by providing flexible, programmable, and dynamically scalable network infrastructure. This guide outlines four core pillars necessary for AI-ready network architectures: automation, performance optimization, resilience, and security. Automation spans the entire network lifecycle, including infrastructure provisioning, virtual network configuration, rapid regional deployment, and consistent configuration management through distributed state systems. Performance optimization involves leveraging AI for network path tuning, hardware acceleration with specialized units like SmartNICs and FPGAs, kernel bypass techniques for software modules, and dynamic latency-throughput balancing. Resilience mechanisms focus on device discovery, self-healing agents, redundant traffic paths, and automated troubleshooting. Security measures emphasize identity-based authentication, microsegmentation, modern protocol support (e.g., IPv6), regulatory compliance through automated audits, and advanced threat detection using behavioral algorithms. The integration of zero-trust principles within cloud-native architectures ensures robust security while maintaining optimal performance. This guide provides actionable strategies based on real-world deployments, combining theoretical concepts with practical insights for building scalable, high-performance AI cloud services, essential for organizations aiming to stay competitive in the evolving AI landscape.

Wireless sensor network data is an important source of big data. A sensor node cooperatively transmits or forwards data through intermediate nodes to a collection center, which is then aggregated for big data analysis and application. The relay selection algorithm selects the best transmissible node among the candidate nodes to fully exploit the limited resources of the sense nodes and extend the network lifecycle. A wireless sensor network relay selection algorithm based on a fuzzy inference system often uses sorting methods or random methods as the selection mechanism to choose when the fuzzy system outputs the same result. However, in the state of communication, networks often face the retransmission of lost packets, which consumes excess electricity. This study proposes a contraindicated safety selection mechanism algorithm to address equal output values in fuzzy systems. The proposed algorithm effectively reduces the retransmission probability to achieve benefits that isolate destructive or malicious nodes, thereby maintaining a higher network lifespan and safety.

Network optimization is accomplished by integrating AI-powered technologies that are industry leading throughout the network lifecycle to optimize network performance in accordance with strategic objectives and maximize return on investment. These technologies utilize live and predictive network data to advance the network to its full potential, proactively resolving performance issues prior to the impact on subscribers. By employing predictive forecasting and active monitoring, these systems also assess future interconnection requirements and determine the optimal time and location to increase capacity to achieve the highest possible return, months in advance. This yields a network that is consistently operational and provides exceptional performance, customized to the strategic business objectives, and prepared to satisfy the growing performance requirements of future 5G use cases. Linguistic intuitionistic fuzzy sets (LIFSs) offer an adequate base to represent and manage unpredictability linked to intuitionistic assessments and linguistic structures. Aggregation operators (AOs) play a critical role in enhancing the decision-making (DM) procedure by adeptly managing preferences and uncertainties in multiple attribute decision-making (MADM) problems. This leads to decisions that are both more accurate and reliable. Dynamic AOs, which adjust to time-varying data, further improve flexibility and precision in DM. This research builds upon these concepts to develop novel AOs, including the LIF dynamic Dombi weighted averaging operator (LIFDyDWA), and the LIF dynamic Dombi weighted geometric operator (LIFDyDWG), and illustrates their key structural properties. An algorithm is also proposed to address the challenges of handling imprecise data in DM using the LIF dynamic Dombi aggregation approaches. These strategies are successfully applied to present a solution to an MADM problem concerning the selection of an optimal strategy to enhance the efficiency of telecommunication network systems to demonstrate their effectiveness and superiority. A comparative analysis is provided to validate the efficacy and advantages of the suggested methods over existing approaches.

In order to overcome the energy “hole” problem caused by uneven energy consumption of nodes in equidistant deployment, an unequal spacing optimization deployment method is adopted to deploy a chain type wireless sensor network to balance node energy consumption and extend the network life-cycle. Furthermore, a star-chain structure multi-chain wireless sensor network node optimization deployment method is adopted to deploy a two-dimensional monitoring network; Taking into account inter chain interference, network connectivity, and node energy consumption, the optimal node spacing and hop count for each chain, as well as the optimal number of chains in circular areas, are obtained. Through MATLAB simulation, it is proven that this deployment method can effectively reduce and balance node energy consumption, and maximize the network’s life-cycle.

A 3D visual presentation provides a wide spectrum of interpretive and collaborative possibilities. Building Information Modeling (BIM) is becoming increasingly popular in the AEC (Architecture, Engineering, Construction) sector. However, it mainly applies to cubic (building structures) and infrastructure projects. BIM is rarely used in the digitization of aboveground or underground networks. The purpose of this article is to fill this research gap and to demonstrate, through a case study, the real benefits of processing integrated 3D data covering civil structures, technical infrastructure and networks. The methodology of this paper included all steps towards the creation of an integrated model of a district heating network and the infrastructure located in its vicinity. The results show that integrated BIM models can help minimize or prevent design and execution collisions. This article undertakes critical inquiry and presents a unique approach to modeling urban spaces. The integrated BIM model enables management of the life cycle of a district heating network using a wide range of applications depending on the nature of the data, analysis and simulation. This article contributes to the discussion on modeling transmission infrastructure and integrating it with existing spatial models and databases. This paper presents innovative and significant interdisciplinary research.

Aiming at the problems of unreasonable cluster head election, uneven energy consumption and short network life cycle in wireless sensor networks with Stable Election Protocol (SEP) cluster head election is unreasonable, uneven energy consumption and short network life cycle in wireless sensor networks, a SEP routing protocol based on hybrid strategy whale optimization (HWO-SEP) is proposed. Optimize the population initialization and enhance the population diversity by Tent chaotic mapping with adversarial learning strategy. Design nonlinear convergence factor with dynamic spiral parameters, combined with adaptive inertia weighting model to balance the ability of global exploration and local exploitation. Construct a multi-objective fitness function to optimize the cluster head election process by comprehensively considering the cluster head residual energy, intra-cluster communication cost and distance to the base station. Simulation experiments show that the improved protocol network stability is improved by 47.75%, 31.38%, and 16.35% compared with LEACH, SEP, and WOA-SEP, respectively

This paper presents an innovative approach to automating the full lifecycle management of optical networks using locally fine-tuned large language models (LLMs) and digital twin technologies. We experimentally demonstrate the integration of generative AI and digital twins to create powerful AI-Agents capable of handling the design, deployment, maintenance, and upgrade phases in the lifecycle of optical networks. By deploying and fine-tuning LLMs locally, our framework eliminates the need for public cloud services, thereby ensuring data privacy and security. The experimental setup includes a commercial-product-based testbed with eight optical multiplex sections in the C-band, showcasing the effectiveness of the AI-Agents in various automation tasks, such as API-calling for service establishment and periodic power equalization, as well as log analysis for troubleshooting. The results highlight significant improvements in operational accuracy and efficiency, underscoring the feasibility of this approach in real-world scenarios. This work represents a significant advancement toward intent-based networking, showcasing the transformative potential of AI in automating and optimizing optical network operations.

The reliability of a data center is highly dependent on its air conditioning and cooling system. This research evaluates the existing cooling system of Universitas Krisnadwipayana’s data center using the PPDIOO Network Life-Cycle approach. The study finds that the current cooling system, which relies on AC Split, fails to meet TIA-942 standards, posing significant overheating risks and increasing downtime probability. Observational analysis shows that the cooling distribution is inefficient due to inadequate airflow and the absence of a structured cooling layout. To address these issues, this research proposes an optimized cooling system design that incorporates Computer Room Air Conditioning (CRAC), hot aisle-cold aisle arrangement, and raised floor implementation. The recommended improvements also include installing temperature and humidity sensors for real-time environmental monitoring and implementing N+1 redundancy for enhanced system reliability. These solutions are expected to improve cooling efficiency, reduce energy consumption, and mitigate downtime risks. Future research should focus on evaluating the practical impact of this design by conducting real-world trials and exploring liquid cooling technology as a potential alternative for further efficiency improvements.

ABSTRACTWireless sensor networks (WSNs) is a wireless system including the set of distributed sensor nodes used for physical or environmental observation. A network energy expenditure is considered as a significant concern because of battery restricted sensors of the WSN. Clustering and multi hop routing are considered as effective approaches to enhance the network lifecycle and communication. Achieving the anticipated objective of reducing the energy expenditure, thereby increasing the network lifecycle, is considered as an optimisation issue. In recent times, a nature inspired meta‐heuristic approaches are extensively utilised for solving the different optimisation issues. In this context, this research aims to accomplish the objective by proposing the multiobjective‐perturbed learning and mutation strategy based artificial rabbits optimisation namely M‐PMARO for an optimum cluster head (CH) selection and route discovery. The proposed M‐PMARO incorporates an experience based perturbed learning (EPL) and mutation strategy to identify the capable regions over the search space for enhancing the exploration and avoiding the local optima issue. To formulate the multiobjective, the residual energy, average intracluster distance, average base station (BS) distance, CH balancing factor (CHBF) and node centrality are incorporated for optimum CH discovery while the residual energy and average BS distance are considered for multi hop routing. The M‐PMARO is analysed based on alive nodes, dead nodes, energy expenditure, throughput and data received in BS and network lifecycle. The viability of M‐PMARO is validated by comparing it with existing approaches such as fitness based glowworm swarm with fruitfly algorithm (FGF), energy balanced particle swarm optimisation (EBPSO), improved bat optimisation algorithm (IBOA), graph neural network (GNN) and fuzzy logic and particle swarm optimisation (PSO) based clustering routing protocol namely PFCRE. The alive node count of M‐PMARO is 100 for 1200 rounds, which is higher than the EBPSO.

Abstract This paper mainly focuses on the problem of compression ratio caused by dictionary storage structure and dictionary updating method, and improves LZW. Then use the gray correlation to analyze the physical attributes between big data, select the data with strong correlation, use BP neural network to train the model, and put the trained model into the Internet terminal, so as to realize the efficient transmission for data fusion. After simulation experiments and algorithm efficiency tests, it can be seen that the compression time is reduced relative to the LZSS and LZW algorithms, and the improved LSW algorithm’s static data compression rate is compression rate of 50.75%, and the compression rate of triggering class data is 9.07%, and it is also very helpful in saving network bandwidth. With the increase of nodes, the fusion algorithm has good performance in terms of delay and network life cycle. When the nodes are 500, its life cycle reaches 1.8 × 107. Therefore, the algorithm in this paper is suitable for the application scenario of data compression and efficient transmission of big data.

Small and medium enterprises (SMEs) are vital to the European economy, but sustaining coopetition networks—collaborative arrangements between competitors—remains challenging. In this study, this gap is addressed by developing a reference model and methodology for coopetition networks explicitly designed for SMEs and grounded in the service-dominant (S-D) logic framework. The model provides a structured approach for managing coopetition across the entire network lifecycle, from initiation to dissolution, emphasizing value co-creation and resource integration. A proof of concept (PoC) was implemented in the Portuguese ornamental stone sector to validate the model, revealing significant improvements in manufacturing effectiveness and demonstrating the model’s practical applicability. The results underscore the potential of coopetition networks to boost SMEs’ competitiveness and performance while identifying key trade-offs and risks, such as knowledge sharing and market cannibalization. Although the model addresses critical challenges, in this study, limitations are acknowledged and areas for future research are suggested, particularly in relation to the long-term sustainability of coopetition and the influence of interpersonal dynamics.

Aiming at the uncertain mechanism of node activation strategies and redundancy of feasible solution sets in the process of solving target coverage problem in wireless sensor networks, we proposed a deep learning based target coverage algorithm to learn the scheduling strategies of nodes in wireless sensor networks. Firstly , the algorithm abstracted the construction of feasible solution sets into Markov decision process, and intelligently selected activated sensor nodes as discrete actions according to the network environment. Secondly, a reward function evaluated the performance of the intelligent agent in selecting actions based on the coverage capacity and its residual energy of the active node. The simulation experiment result shows that the algorithm is effective in different network environments, and the network lifecycle is superior to the three greedy algorithms, the maximum lifetime coverage algorithm and the adaptive learning automaton algorithm.

Potential use of district heating networks and the prospects for the advancements within urban areas of Nottingham as a case study

GCP: A multi-strategy improved wireless sensor network model for environmental monitoring

Abstract Digital twin is the key technology to promote the life cycle digitalization and intelligent management of municipal pipe networks. To achieve intelligent closed‐loop management for the life cycle of municipal pipe networks, this paper first analyzed the meanings of “process‐oriented” and “object‐oriented” of digital twin and gave the connotation for the life cycle management of the digital‐twin‐driven municipal pipe networks. Second, on the basis of the application paradigms in typical scenarios of design, construction, operation, and maintenance of the life cycle management of the municipal pipe networks, this paper proposed the technical framework of digital twin‐driven municipal pipe networks. Finally, the existing challenges were discussed for the digital twin in the life cycle management of municipal pipe networks. The research shows that digital twin is the technical guarantee for the life cycle management of municipal pipe networks, and high‐fidelity digital “twinning” is an important prerequisite for realizing the function of digital twin.