Network Life Cycle Research Articles

Coopetition Networks for Small and Medium Enterprises: A Lifecycle Model Grounded in Service-Dominant Logic

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.

Algorithm for Target Coverage Problem Based on Deep Learning in Wireless Sensor Networks

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

Buildings in urban areas significantly contribute to global energy consumption, necessitating solutions to reduce energy demands and mitigate environmental impacts. This study investigates district heating networks in urban areas, using Nottingham as a case study. A literature review addresses key barriers to decarbonising building energy in the UK, focusing on: (1) Information, engagement, and behaviour, (2) Strategy, policy, and regulation, (3) Infrastructure, (4) Supply chain and skills, and (5) Distribution of costs and impacts. The study examines the network's location, current processes, energy performance, and potential future impacts. Heat mapping and energy flow assessments identify areas for energy savings, guiding system optimisation. The findings suggest that increasing the energy output from the heat station to the network can enhance performance. The analysis highlights the potential of a life cycle perspective for district heating networks, proposing a framework that considers all stages from material sourcing to system utilisation. This approach helps assess environmental impacts, including greenhouse gas emissions and resource depletion, identifying opportunities to improve resource efficiency and reduce environmental impacts. By evaluating current sustainability goals and regulatory compliance, the study provides insights into the environmental and social implications of the district heating network's operations, pinpointing areas for improvement. It emphasises the need for continuous optimisation and innovation throughout the network's life cycle. The potential of a full life cycle assessment (LCA) approach is highlighted to evaluate four main future advancements: (1) heating network with added energy storage, (2) advanced system operations through model predictive control (MPC) for demand prediction, (3) general network improvements, and (4) network expansion. Overall, this study aims to enhance the sustainability, efficiency, and resilience of district heating networks, contributing to the transition towards sustainable energy systems while ensuring environmental and economic viability over time.

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

Nowadays, smart environmental monitoring devices are widely used in various fields, and one of the most representative tools is the wireless sensor network (WSN). WSNs are easy to deploy and provide real-time information feedback, which is very suitable for environmental monitoring. As we all know, the environmental monitoring network because of the special nature of its work requirements, the need for uninterrupted and real-time transmission of monitoring data, which leads to energy consumption is extremely large, and this cannot meet the needs of its long-term work. Existing traditional routing has problems such as unscientific cluster head election and high redundancy in data transmission, which usually lead to a large amount of energy consumption, which is not conducive to the long-term stable operation of sensor networks. In this paper, we improve the traditional routing protocol and design a cluster head election method based on the genetic algorithm, which proposes a new fitness function in terms of energy, distance, and the number of nodes in the cluster, and performs the selection of cluster head nodes based on this method. In addition, we propose a new grey prediction model, which can realize the real-time update of data queues, and optimize the data transmission process of traditional WSNs based on this prediction model to reduce the amount of intra-cluster data transmission. Combining these improvements, a grey cluster prediction (GCP) model is proposed, and the performance of the model is tested based on real mine and soil data sets. The simulation results show that the model significantly reduces energy loss and extends the network life cycle while ensuring the integrity of data transmission. It can also meet the requirements of long-term stable operation of environmental monitoring equipment.

Applications and challenge of digital twin in life cycle of municipal pipe networks

Applications and challenge of digital twin in life cycle of municipal pipe networks

A Framework for the Analysis of Personal Learning Networks

This paper reports on research undertaken to map and analyse Personal Learning Networks (PLNs). PLNs are the total preferred connections to the different people, technological devices, services, and information resources an individual uses for learning activities and learning goals in all learning contexts. Drawing from Education, Web Science, Digital Sociology and Network Science, a Framework was developed which conceptualises PLNs as egocentric interaction networks involving a mode, purpose and endpoint. The Framework introduces the idea of measuring the frequency of interaction along paths consisting of pre-determined, generalised nodes (and node sets). This eliminate network differences at the micro level and allows meaningful comparison and aggregation of individual PLNs into groups or whole samples. Quantitative survey data was collected as part of a FutureLearn MOOC and in real-time converted by a bespoke mapping and visualisation tool into an online PLN map. Analysis indicates that regardless of any contextual factors, individuals interact nearly three quarters of the time via digital devices, and just a quarter of the time face-to-face or non-digitally. One third of those interactions are with smartphones, most often for the purpose of gathering information from web searches. Individuals also interact more frequently with non-humans than they do with humans. Chi-square significance testing to examine the effect of a range of external shaping factors found that the PLNs of apparently diverse groups display a considerable homogeneity. Gender, country of residence and position on the Digital Resident-Digital Visitor spectrum have no effect on the size and use of a PLN. Age and being a UK HE student have the most effect. There may also be evidence of a Network Lifecycle, with a critical period of PLN growth occurring during the age of 18-25. This means that universities are ideally placed, indeed may even have a duty of care, to foster PLN development in educationally and personally productive ways. If HE institutions are to respond to the networked student, living, working and learning in a network age, then no longer can the learner be considered separately from the network of people, devices, services and information resources they use for daily life. Transitioning towards a PLN-centred, networked learning HE pedagogy and learning design may arguably be the most suitable response to a study body which is increasingly and inextricably embedded in a sociotechnical reality.

Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique

A wireless sensor network (WSN) is a wireless network composed of sensor nodes, widely used in military, industrial, and agricultural fields. In these practical application scenarios, due to the limited routing search ability of wireless sensor nodes and the small coverage range of a WSN, it faces the problem of a relatively short network lifecycle. Therefore, increasing a WSN’s coverage range and reducing node energy consumption are of high value. This article proposes a method of using the ant colony algorithm (ACOD) to calculate the optimal routing of a WSN, the K-means algorithm for clustering, and the whale algorithm (WOA) and a backpropagation (BP) neural network to increase the coverage range of a WSN. After multiple experiments, the total energy consumption of WSN nodes has been effectively reduced, the coverage range of the WSN has been increased, and the lifecycle of the WSN has been extended.

Research on WSN clustering algorithm of firefly algorithm to optimize k-means

Aiming at the problem that uneven clustering of wireless sensor networks leads to large energy consumption differences between clusters and short network lifetime, this paper proposes a clustering routing algorithm of WSN based on the Firefly algorithm to optimize k-means (FAKMC). This paper mainly studies the clustering stage of the network, adopts the k-means algorithm for node partition, and uses the improved FA to optimize the sensitive problem of the initial clustering centre of k-means. Simulation results show that, compared to LEACH and k-means, the clustering result proposed in this paper is more uniform and can extend the network life cycle.

NS+NDT: Smart integration of Network Simulation in Network Digital Twin, application to IoT networks

Network Digital Twin (NDT) and Network Simulation (NS) are two paradigms leveraging virtual representations of networks to help decision-making. These tools may seem similar or interchangeable and are often confused or opposed. However, they have their respective purposes, use cases and underlying concepts, which differ and are complementary. The goal of this article is to explore and clarify the specificity, the benefits and the limits of these two decision support tools, analyze how they complement each other and can be nicely combined. We argue that a smart integration of NS in NDT, named NS+NDT, can ease, accelerate and strength decisions for network design, deployment, operations, management and evolution. To study and demonstrate this claim, we focus on the domain of Internet of Things (IoT) solutions, where wireless networks are critical for connecting the physical assets to the Internet, but are complicated to configure for meeting the requirements of a specific application. We examine how NS, coupled with NDT, can contribute to support IoT architects and operators decisions throughout the life cycle of an IoT network. We analyze the different steps required to use NS in the context of NDT and examine how this helps remove NS barriers such as credibility and reliability. In particular, we show how NDT data enable to fine tune and customize the energy consumption models, making the simulation results more context-aware and insightful. Then, addressing the often-prohibitive simulation cost for exploring a large parameter space, we propose to associate surrogate modeling to NS+NDT. As surrogate models, we first introduce a simple ML (Machine Learning)-based surrogate model and illustrate this method with two IoT network configuration optimization use cases. Secondly, we propose a Bayesian optimization approach based on Gaussian Processes as surrogate model to further accelerate the (re)configuration decisions. We show how this method enables to select simulation scenarios that converge rapidly to the optimal solution, and allows the NDT to timely perform the adaptation. The contribution of this article is threefold. It provides (i) the first systematic analysis of the differences and potential synergies between NDT and NS; (ii) a synthetic presentation of the integration of NS and associated decision algorithms within a NDT to unlock NS accessibility and credibility throughout the life cycle of an IoT solution; (iii) a proposal for a smart and cost-efficient integration of NS in NDT via surrogate modeling, for reducing evaluation and optimization cost, paving the way to NS-augmented NDT-based dynamic adaptation and real-time optimization of IoT networks.

Developing a predictive maintenance model for the rehabilitation of roads’ assets

Maintaining road infrastructure is a significant challenge in minimizing public fund waste from reactive maintenance. Preventive maintenance is considered a new era in this field. To achieve cost savings and prolong the life cycle of road networks, it’s crucial to determine their lifespan at the time of construction. This article proposes an emerging predictive methodology to select road network rehabilitation policy. This methodology facilitates long-term planning based on technical and budgetary perspectives to identify the most cost-effective rehabilitation policy for the road network’s assets over the planning horizon. To accomplish this, the Pavement Rehabilitation Predictive Model (PRPM) is developed for long-term forecasting of future asset conditions as the network deteriorates over time. It takes into account effective planning for different rehabilitation policies predicts asset deterioration, and aids in making informed decisions by identifying the best rehabilitation policy for pavement sections. An empirical example is used to illustrate the proposed approach in detail, based on the evaluation of three scenarios of the rehabilitation policies. The results indicate that the prediction model is highly efficient, resulting in an average budget savings of up to 20% compared to other relevant maintenance methods.

An energy consumption optimization strategy for Wireless sensor networks via multi-objective algorithm

Deploying relay nodes is a significant mechanism to prolong the network lifetime of wireless sensor networks (WSNs). However, most existing studies overlook the energy consumption of relay nodes, leading to imperfections in the optimization process. Additionally, there is also a lack of analysis of conflicts between different optimization objectives. In this regard, a multi-objective antlion with fuzzy clustering algorithm (MOALO-FCM) is designed to obtain a better trade-off between different optimization objectives. And an adaptive membership function revision strategy is introduced to improve the energy balance of relay nodes. To verify the performance of the proposed algorithm, simulation experiments are set in 2-dimensional and 3-dimensional scenes with correlative algorithms, respectively. The main evaluation indexes include performance of Pareto optimal solution sets, the life cycle of network, the energy consumption of sensor nodes, the energy consumption of relay nodes, the number of living nodes, and the running time of algorithms. The results indicate that the proposed algorithm has better performance in various indexes.

A multiobjective optimization approach for wastewater network long-term rehabilitation planning: a case study

Abstract During the last decades, municipalities have increasingly invested in new approaches for rehabilitating sewerage networks. With the increasing number of rehabilitation techniques, objectives and constraints, the number of rehabilitation scenarios rises exponentially. This article proposes an asset management approach to create long-term rehabilitation plans where different budget allocations for rehabilitation techniques are considered every year depending on performance and cost indicators. It builds long-term strategies through multiobjective black-box optimization where the impact of the budget allocations over the network life cycle is part of the decision process. It employs a pipe deterioration model based on Markov chains whose transition matrices are estimated by survival curves for different pipe cohorts. The proposed approach seeks to determine the appropriate investment (CAPEX) and operational expenses (OPEX) levels in the coming decades. It was tested with real-world data from a sewerage network in Sofia, Bulgaria, and the results show that it provides efficient long-term rehabilitation plans.

Building a Network Design and Implementation Framework of a Campus Area Network for the Mariano Marcos State University

A framework for network design and implementation is imperative to ensure value in the delivery of network services. Frameworks serve as guides in identifying value propositions to address the changing demands of the organization’s stakeholders. Frameworks prescribe efficient methods and appropriate tools that can be used to ensure that the implementation of a technological innovation addresses the demands, regardless of the changing IT landscape. The demand to deliver an efficient network service requires that the network have optimized levels of scalability, availability, performance, security, and other desirable network goals. These goals have to be prioritized depending on the goals of the organization. A framework for identifying priority service delivery components must be in place in order for the governing body and IT managers of the organization to objectively prescribe appropriate technological solutions to employ. The development of the proposed framework is patterned on Plan Design Implement Operate Optimize network life cycle, anchored on the principles of the service value chain of ITIL and the principles of IT governance using the goals cascade of COBIT. A review of existing studies was carried out to determine the prevailing technical goals and technology enablers that are peculiar to IT service management in a campus environment, specifically the delivery of computer network service. A framework was drawn to serve as a guide to achieving network design goals that are aligned with business goals, compliant with standards, and provide value to customers of state universities and colleges like Mariano Marcos State University.

A Design Study on the Design of Customer Claims Management System for Qinghai Electric Power Company

Abstract This paper utilizes the SOA framework to realize the system structure design, combined with the total flow of the claim processing, the design of the acceptance and audit processing, responsibility determination and reminder management of 4 modules, constituting the customer claim management system as a whole. The improved LEACH algorithm is utilized to control the network topology, and the cluster tree combined with data fusion is used to reduce the energy loss of the nodes in the network. Based on the weighted queuing scheduling algorithm, the feedback processing module is designed to determine the grouping rules and calculation methods, and the decision tree algorithm is used to build the demand management prediction model. The accuracy of the prediction of the decision tree algorithm is verified to realize the case information management statistics. Analyze the utility of the improved LEACH algorithm and test the system’s performance. The experimental results show that the improved LEACH algorithm is more balanced in the range of [0,100] dead nodes, and the energy consumption is well balanced. The total delay is 16.13-time slots, which is the lowest delay among the four algorithms. The node death time is 1045 rounds, having the longest network life cycle. The system performance is tested using IO and memory-intensive loads. Before writing a file of 16GB, the system time responsiveness stays around 80%, and the system CPU utilization and disk IO utilization increase as the file is written. The system performance is good.

Regional Link Routing Algorithm for Wireless Sensor Networks based on Improved Grey Wolf Optimization Algorithm

In order to improve the survival time, connectivity and the active number of nodes in the wireless sensor network, this paper proposes a regional chain wireless sensor network routing algorithm (RWA) based on improved grey Wolf optimization algorithm. The algorithm uses the method of dividing regions into chains, and in each region, chains are formed to select cluster heads before communication. The simulation results show that RWA has an improvement in avoiding too long chain, balancing energy consumption and prolonging the network life cycle.

Hierarchical energy-saving routing algorithm using fuzzy logic in wireless sensor networks

Currently, sensor energy assembly in wireless sensor networks is limited, and clustering methods are not effective to improve sensor energy consumption rate. Thus, a hierarchical energy-saving routing algorithm based on fuzzy logic was constructed by considering three aspects: residual energy value, centrality, and distance value between nodes and base stations. The remaining sensor nodes selected by fuzzy logic algorithm have a longer time to live and greater residual energy than those selected by low-power adaptive clustering hierarchical protocol algorithm, fuzzy unequal clustering algorithm, and fuzzy logic cluster head election algorithm. For network life cycle, the number of rounds in which the first dead node appears, in descending order, is studied: energy-saving routing algorithm (400 rounds) > new geographic cellular structure algorithm (300 rounds) > virtual grid based dynamic routes adjustment algorithm (100 rounds). Under the same experimental round, energy-saving routing algorithm’s remaining energy curve always reaches its maximum. The energy-saving routing algorithm by fuzzy logic constructed by this research institute can significantly improve network energy utilization, which has certain reference value.

Data Security in Healthcare Industrial Internet of Things With Blockchain

The Industrial Internet of Things has gained more attention because of the self-governing nature of system configuration with interoperable application connectivity. In the E-Healthcare environment, it cooperates with medical sensors to capture, examine, analyze, preserve, and document day-to-day transactions of patients in real-time. The integration of E-healthcare and IIoT provides an analytical platform to handle a large amount of data with a low cost of cloud-enabling scalable storage. Throughout the transformation, patients’ personal information is at risk while exchanging records over centralized server-based systems. Thus, the rate of node connectivity, failure of parallel data sharing, and deliverance-related issues increase. In this paper, we present solutions in three folds. First, the paper proposes a novel and secure architecture for E-healthcare data security using a blockchain-distributed ledger technology named BHIIoT. Second, the transformation in the lifecycle of medical wireless sensor networks for data management and optimization with a distributed layered hierarchy is developed, which enhances the network resources and increases trust in the blockchain-enabled peer-to-peer environment. Third, the proposed BHIIoT uses the NuCypher threshold re-encryption mechanism for data encryption and protects shared resources in the form of blocks preserved in a blockchain immutable storage. For instance, chain codes are deployed to automate authentication, logging, index information deliverance, and trace transactions to resist illegal activities in the E-Healthcare distributed-application. The customized lightweight blockchain multi-proof-of-work and multi-proof-of-stake are designed with a digital signature for improving the consumption of resources and reducing the load of storage while the transaction process of E-healthcare IIoT is scheduled.

A topology control algorithm for fusion networks based on link quality

To extend the network life, an ordinal potential game is introduced into network topology control, and a network topology control algorithm based on the Theil entropy measure is designed by improving the revenue function. The revenue function is based on the Theil entropy measure, whose factors are in the residual energy of the nodes and their neighbors. By using a primary payoff function that considers all initial network factors and a secondary one that addresses connectivity in case of reduced residual energy, a segmentation function can calculate the model's payoff. Simulation experiments show that compared to the existing game algorithms of 3DK-RNG, DEBA, and EFPC, the proposed algorithm can effectively eliminate redundant links, reduce node degree and network link length, balance node energy consumption, enhance network load, and extend the network life cycle.•A Network Topology Control Algorithm Based on the Fusion of Thiel Entropy and Ordinal Potential Games.•A network topology control method for extending the network lifecycle is successfully established.•It proposes that the performance of the fusion topology control algorithm is significantly superior to other single algorithms in the paper.

JOCP: A jointly optimized clustering protocol for industrial wireless sensor networks using double‐layer selection evolutionary algorithm

SummaryIndustrial Wireless Sensor Networks (IWSNs) have gained significant popularity for their ability to improve plant productivity and production efficiency through self‐organization and rapid deployment. However, the challenge of achieving reliable and sustainable data transmission remains due to the large amount of heterogeneous data generated by large‐scale IWSNs. In this paper, we present a systematic approach that addresses this challenge by focusing on data transmission clustering strategies, optimal cluster head selection, and routing design. We propose a novel Jointly Optimized Clustering Protocol (JOCP), which enhances cluster head selection by considering multiple critical factors that impact the IWSN life cycle. JOCP incorporates two key modules: the many‐objective clustering model and the double‐layer selection evolutionary algorithm. Specifically, the many‐objective clustering model considers cluster head selection from different perspectives, including maximum node survival cycle, minimum node distance, minimum network overall energy consumption, and balanced cluster energy consumption, with the aim of extending the network life cycle. Additionally, the double‐layer selection evolutionary algorithm optimizes the many‐objective clustering model to select appropriate cluster heads. Through performance verification, we demonstrate that the JOCP protocol effectively enhances the network life cycle and increases the number of surviving nodes compared to baseline clustering algorithms. Our research provides a comprehensive solution to the challenges associated with reliable and sustainable data transmission in large‐scale IWSNs, highlighting the potential for improved performance in industrial applications.

Multi-strategy enhanced grey wolf algorithm for obstacle-aware WSNs coverage optimization

Moving sensor nodes can mitigate the coverage problem of random deployment in wireless sensor networks. However, the movement of nodes affects the lifetime and integrity of the network. Therefore, both energy saving and efficient coverage are crucial factors. In this paper, we propose an energy-efficient coverage optimization technique with the help of the multi-Strategy grey wolf optimization (MSGWO) algorithm. This method can reduce energy consumption and improve coverage area by mixing higher-order multinomial sensing models and a sort-driven hybrid opposition-based learning. In addition, node movement and boundary strategies are proposed to help nodes jump out of obstacles when facing obstacle-aware deployments. The MSGWO is validated and compared on several classical test functions, and the results show that the MSGWO performs well. The MSGWO algorithm is applied to optimize the WSN coverage on different obstacle scenarios, the experimental results show that the algorithm helps to increase the network coverage from 84 % to 97.86 %, extends the network lifecycle by 50 %, reduces the cost of node deployment, and the network has good connectivity and scalability.