Smart Resource Research Articles

Value capture in IoT-driven business models: considerations about smart resources and isolating mechanisms in networked environments

PurposeThis conceptual paper aims to contribute to a better understanding of value creation and value capture with smart resources in the Internet of Things (IoT)-driven business models against the backdrop of an increasingly networked and connectivity-based environment. More specifically, the authors screen strategic management theories and adapt them to the specificities of new types of smart resources by focusing on a conceptual analysis of isolating mechanisms that enable value creation and value capture based upon different types of smart resources.Design/methodology/approachBy adapting the state of the art of the contemporary resource-based discussion (resource-based view, dynamic capabilities view, relational view, resource-based view for a networked environment) to the context of IoT-driven business models, the paper typifies valuable intra- and inter-organisational resource types. In the next step, a discursive discussion on the evolution of isolating mechanisms, which are assumed to enable the translation of value creation into value appropriation, adapts the resource-based view for a networked environment to the context of IoT-driven business models.FindingsThe authors find that connectivity shapes both opportunities and challenges for firms, e.g. focal firms, in such business models, but it is notably social techniques that help to generate connectivity and transform inter-organisational ties into effective isolating mechanisms.Originality/valueThis paper lays a foundation for a theoretically underpinned understanding of how IoT can be exploited through designing economically sustainable business models. In this paper, research propositions are established as a point of departure for future research that applies strategic management theories to better understand business models that work with the digitisation and connectivity of resources on different levels.

Discussion on the Construction of SWPU Smart Library Evaluation Indicator System

With the rapid development of information technology, especially the wide application of artificial intelligence, big data and cloud computing and other technologies, the management mode of libraries is also undergoing a profound change. From the traditional management mode based on paper books, it is gradually transformed into a smart library mode characterized by digitization and intelligence. In this context, the construction of SWPU smart library has become an important issue. In order to ensure that the construction of SWPU smart library can keep pace with the times and meet the needs of teachers and students, this paper adopts the Delphi method to construct the evaluation indexes as well as the weights of the indexes, aiming to provide scientific and reasonable guidance for the construction level of SWPU smart library. In this paper, we first determined the evaluation index system of SWPU smart library, including smart technology and facilities, smart resources, smart services and other aspects. Then, we invited experts in the fields of library management and information technology to evaluate these indicators and their weights using the Delphi method. Through several rounds of solicitation and feedback, the experts reached a relatively unanimous opinion on the evaluation indicators and their weights of SWPU's smart libraries. On this basis, we conducted a comprehensive evaluation of the construction level of SWPU Smart Library and put forward corresponding suggestions for improvement. These suggestions will provide strong support for the future development of SWPU Smart Library so that it can better serve the students and faculty and promote the development of university education.

Smart materials for sustainable energy

In the new era of technologies, different smart or responsive materials are used which can respond to external stimuli, such as a specific amount of mechanical stress, pressure, temperature, pH, or sunlight radiations, by modifying their shape or dimensions or mechanical properties. As global temperatures rise and weather patterns become more erratic and severe. The risk to communities with energy-passive structures is growing, smart materials such as smart rooftops, thermoelectrics, photovoltaics, pyroelectrics, chromoactive, photoluminescent, as well as other innovations, help to conserve renewable energy smartly and sustainably. This review paper reflects on the applications of different smart materials as renewable smart resources. Intelligent quantum dot solar cells, Schottky solar cells, organic thin-film photovoltaic cells, dye-sensitized solar cells (DSSCs), and organic-inorganic heterojunction solar cells have high conversion efficiency, low cost, and possess wide absorption spectra that reach the near-infrared range. Biomechanical energy can be transformed into green energy using triboelectric and piezoelectric-based smart nanogenerators such as zinc oxide nanowires. Shape memory materials such as Nitinol (NiTi) have been embedded in the wind turbine blades to enhance aerodynamic efficiency. Piezoelectric nanogenerators can convert mechanical energy directly into electrical energy by using wireless sensors to harvest energy from moving water in hydroelectric power plants. Thermoelectric materials such as silver antimony telluride (AgSbTe2) offer a sustainable energy option, which employs industrial waste heat to produce power. Smart materials like graphene are more reliable and effective than carbon nanotubes for storing green hydrogen. Smart non-enzymatic biofuel cells are used in biomedical gadgets such as anesthesia machines and pacemakers, which are self-powered and have great sensitivity. Shape memory materials (SMM) are introduced in natural gas and oil reservoirs because they offer exceptional qualities such as the shape memory effect (SME), lightweight, corrosion resistance, and superelasticity, which enhance the performance and robustness of offshore industries. These new advancements, challenges, and applications of smart materials for renewable energy are covered in this review paper.

Assessing the Acceptance of a Mid-Air Gesture Syntax for Smart Space Interaction: An Empirical Study

Mid-gesture interfaces have become popular for specific scenarios, such as interactions with augmented reality via head-mounted displays, specific controls over smartphones, or gaming platforms. This article explores the use of a location-aware mid-air gesture-based command triplet syntax to interact with a smart space. The syntax, inspired by human language, is built as a vocative case with an imperative structure. In a sentence like “Light, please switch on!”, the object being activated is invoked via making a gesture that mimics its initial letter/acronym (vocative, coincident with the sentence’s elliptical subject). A geometrical or directional gesture then identifies the action (imperative verb) and may include an object feature or a second object with which to network (complement), which also represented by the initial or acronym letter. Technically, an interpreter relying on a trainable multidevice gesture recognition layer makes the pair/triplet syntax decoding possible. The recognition layer works on acceleration and position input signals from graspable (smartphone) and free-hand devices (smartwatch and external depth cameras), as well as a specific compiler. On a specific deployment at a Living Lab facility, the syntax has been instantiated via the use of a lexicon derived from English (with respect to the initial letters and acronyms). A within-subject analysis with twelve users has enabled the analysis of the syntax acceptance (in terms of usability, gesture agreement for actions over objects, and social acceptance) and technology preference of the gesture syntax within its three device implementations (graspable, wearable, and device-free ones). Participants express consensus regarding the simplicity of learning the syntax and its potential effectiveness in managing smart resources. Socially, participants favoured the Watch for outdoor activities and the Phone for home and work settings, underscoring the importance of social context in technology design. The Phone emerged as the preferred option for gesture recognition due to its efficiency and familiarity. The system, which can be adapted to different sensing technologies, addresses the scalability concerns (as it can be easily extended for new objects and actions) and allows for personalised interaction.

Designing Effective Pedagogical Strategies for Fostering Meta-Competence Using Smart Resources in Language Training

Designing Effective Pedagogical Strategies for Fostering Meta-Competence Using Smart Resources in Language Training

The Current Development and Optimization of Smart Public Transportation in Taiyuan City

"With the development of technology and the smartification of cities, the urbanization process is accelerating rapidly. The population and the number of motor vehicles in cities are increasing rapidly, leading to evident issues such as traffic congestion and air pollution. Smart public transportation, as an integral component of smart cities, can reduce passengers' travel costs, improve travel efficiency to some extent, and provide big data support for the development of smart transportation and even smart cities. It is a cost-effective, convenient, and environmentally friendly mode of transportation.This article first analyzes the current situation of smart public transportation in Taiyuan city. It points out issues such as uneven allocation of public transportation resources, prolonged travel times for passengers using public transportation, and overcrowding during peak hours. Drawing on the development experiences of smart public transportation in cities both domestically and internationally, the article proposes optimization suggestions for the current state of smart public transportation in Taiyuan. These include refined management of public transportation, skip-stop operations, smart resource allocation, and improvement of infrastructure construction. The aim is to provide theoretical guidance for the optimization and improvement of smart public transportation in Taiyuan, enabling more efficient development of 'smart public transportation' in the city.".

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users’ context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

Smart Resource Allocation at mmWave/THz Frequencies with Cooperative Rate-Splitting

Smart Resource Allocation at mmWave/THz Frequencies with Cooperative Rate-Splitting

An Investigation of School leadership in the Effective Implementation of Bilingual Education in Indonesia: A Case Study

This study explores how school leadership impacts the successful implementation of bilingual education in Indonesia, focusing on three schools. Bilingualism offers many benefits, including better cognitive skills, cultural understanding, and economic opportunities. Effective school leadership is crucial in creating supportive policies, providing resources, and fostering environments where bilingual education can thrive. Through interviews with three principals and two heads of departments, the study examines their leadership practices, challenges, and strategies. The analysis identified key themes: inclusive and resilient leadership, proactive community collaboration, overcoming challenges like limited resources and teacher shortages, and maintaining a clear vision for bilingual education. The findings show that good leadership involves ongoing professional development, inclusive decision-making, and smart resource management. These practices help tackle obstacles and improve bilingual education programs. The study highlights the importance of visionary leadership in promoting language diversity, educational fairness, and social harmony in implementing bilingual education in Indonesia..

Learning Distribution Grid Topologies: A Tutorial

Unveiling feeder topologies from data is of paramount importance to advance situational awareness and proper utilization of smart resources in power distribution grids. This tutorial summarizes, contrasts, and establishes useful links between recent works on topology identification and detection schemes that have been proposed for power distribution grids. The primary focus is to highlight methods that overcome the limited availability of measurement devices in distribution grids, while enhancing topology estimates using conservation laws of power-flow physics and structural properties of feeders. Grid data from phasor measurement units or smart meters can be collected either passively in the traditional way, or actively, upon actuating grid resources and measuring the feeder’s voltage response. Analytical claims on feeder identifiability and detectability are reviewed under disparate meter placement scenarios. Such topology learning claims can be attained exactly or approximately so via algorithmic solutions with various levels of computational complexity, ranging from least-squares fits to convex optimization problems, and from polynomial-time searches over graphs to mixed-integer programs. Although the emphasis is on radial single-phase feeders, extensions to meshed and/or multiphase circuits are sometimes possible and discussed. This tutorial aspires to provide researchers and engineers with knowledge of the current state-of-the-art in tractable distribution grid learning and insights into future directions of work.

Healthcare access and equity in Nigeria

This study examines government health finance, which requires patients to pay out-of-pocket even in the presence of a universal healthcare program, and how it affects Nigerians' life expectancy. Numerous investigations have indicated that socioeconomic factors impact healthcare results as well as the dispersion of medical facilities and resources throughout Nigeria. Government spending and gender life expectancy were used as indices to look at healthcare access and equity among socioeconomic determinants in Nigeria. In order to determine the impact of government health expenditure on life expectancy in relation to healthcare and equity in Nigeria, time series data was sourced from the Central Bank of Nigeria (CBN) statistical bulletin and World Population Prospects. A combination of descriptive statistics, Unit Root (ADF) test, Vector Regression (VAR), Autoregressive Distributed Lag (ARDL), Bounds test and residual diagnostics and stability test were used in the analyses. The study demonstrated a long-term, statistically significant association between the independent factors, such as male and female life expectancy, and the dependent variable, government health expenditure. Recurrent and capital health expenditures in Nigeria have been greatly impacted by the insufficient financial allocation to healthcare. It is important to remember that any health system's capital expenditures will continue to be greatly impacted by the inadequate funding. It is important to reform the healthcare finance system, ensure smart resource allocation, and shift the focus away from out-of-pocket healthcare expenses in order to improve the lives of both sexes and extend the existing life expectancy.

Reliability-Guaranteed Uplink Resource Management in Proactive Mobile Network for Minimal Latency Communications

Proactive Mobile Network (PMN) has been proposed to support extremely low latency communications with multi-tier computing architectures, machine-centricity, and data-driven operation features. Nevertheless, the communication reliability of the PMN introduces new substantial technological challenges. As PMN employs unique proactive open-loop communication, any feedback-based control is avoided to enhance end-to-end latency. This paper focuses on machine-initiated uplink transmission in PMN and proposes a reliability-guaranteed resource management scheme. Without requiring feedback control information, our scheme uniquely decomposes conventional resource management into two collaborative decision processes: predictive resource allocation suggested by network anchor nodes (ANs) and proactive smart resource utilization by smart equipment (SE). These two decision-making processes are constructed as independent reinforcement learning (RL) problems, but implicitly share the states of radio resources according to operating environments. Different algorithms for different operating scenarios have been investigated for this dual-decision solution. Simulation results in various scenarios show that our scheme enables PMN’s reliability close to the theoretical optimal value and successfully serves radio resource utilization in the PMNs.

Unlocking the Path toward Intelligent Telecom Marketplaces for Beyond 5G and 6G Networks

6G systems are perceived to be heavily softwarised, and therefore, the collaboration among all stakeholders, such as network and cloud providers, operators, application developers, service providers and device and equipment vendors, shall occur via software in a secured, distributed, and automated manner. This could be envisioned with a brokerless and immutable Marketplace anchored in Distributed Ledger Technologies, removing the need for a trusted party as Marketplace operator while avoiding this single point of failure, increasing transparency and flexibility of operational rules, and featuring automated contract negotiation and fulfillment supporting governance, delivery, and billing operations. This work goes a step forward and introduces a Smart Resource and Service Discovery application, based on intent recognition and Machine Learning assisted techniques, to enable the automated discovery of resources and services exposed over the Marketplace. Evaluation results demonstrate the accuracy of our approach and its suitability to provide decentralized Telecom Marketplaces with intelligent and datadriven discovery capabilities.

Smart Resource Management: An Innovative Way of Balancing Materials, Energy, and Waste for a Green Future

In an era defined by environmental consciousness and resource scarcity, the concept of smart resource management has emerged as a linchpin in achieving a sustainable and green future. This abstract explores the multifaceted landscape of resource utilization, energy management, and waste minimization, collectively shaping our journey toward a more ecologically responsible world. The manuscript delves into the critical dimensions of this paradigm shift, examining innovative practices and technologies across diverse sectors. From precision agriculture optimizing crop yields with minimal environmental impact to the integration of renewable energy sources reshaping our energy landscape, the narrative unfolds . This highlights the profound impact of circular economy principles, which prioritize resource conservation and recycling. These principles, coupled with resource-sharing platforms, redefine how we perceive and utilize materials, fostering economic growth while reducing waste. Amidst these transformative tasks, challenges and barriers to implementation come to light. The abstract acknowledges these obstacles and explores emerging technologies and trends that promise to surmount them. Ultimately, the abstract underscores the imperative of smart resource management in the pursuit of a green future. It encapsulates the essence of a global movement where sustainability, economic prosperity, and environmental stewardship converge, offering hope and inspiration for generations to come.

Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

Due to exponential increase in smart resource limited devices and high speed communication technologies, Internet of Things (IoT) have received significant attention in different application areas. However, IoT environment is highly susceptible to cyber-attacks because of memory, processing, and communication restrictions. Since traditional models are not adequate for accomplishing security in the IoT environment, the recent developments of deep learning (DL) models find beneficial. This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection (HMFS-SDLCAD) model. The major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT environment. At the preliminary stage, data pre-processing is carried out to transform the input data into useful format. In addition, salp swarm optimization based on particle swarm optimization (SSOPSO) algorithm is used for feature selection process. Besides, stacked bidirectional gated recurrent unit (SBiGRU) model is utilized for the identification and classification of cyberattacks. Finally, whale optimization algorithm (WOA) is employed for optimal hyperparameter optimization process. The experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several aspects. The simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.

Sexual mixing patterns in men who have sex with men: network approaches for smart resource allocation.

Age-based sexual mixing patterns in men who have sex with men (MSM) can greatly inform strategic allocation of intervention resources to subsets of the population for the purpose of preventing the greatest number of new HIV infections. Egocentric network data collected from MSM participating in annual HIV sentinel surveillance surveys were used to assess age-dependent mixing and to explore its epidemiological implications on the risk of HIV transmission risk (among those HIV-infected) and HIV acquisition risk (among those not infected). Mixing in this sample of 1605 Chinese MSM is relatively age assortative (the average of values expressing the degree of preferential mixing were 2.01 in diagonal cells vs 0.87 in off-diagonal cells). Expected numbers of HIV acquisition were highest in the 20-24years age group; those for HIV transmissions were highest among 25-29year olds. The risk of both acquisition and transmission was highest in age groups that immediately follow the most commonly reported ages of sexual debut in this population (i.e. age 20). These findings suggest that combination prevention resources should be targeted at younger MSM who are at higher risk of both transmission and acquisition. Programs may also do well to target even younger age groups who have not yet debuted in order to establish prevention effects before risky sexual behaviours begin. More research on optimal strategies to access these harder-to-reach subsets of the MSM population is needed. Findings also support ongoing efforts for public health practitioners to collect network data in key populations to support more empirically driven strategies to target prevention resources.

Blockchain-Based Data Sharing With Key Update for Future Networks

Future networks incorporate artificial intelligence to enable smart resource management and adaptive service provisioning. With a heterogeneous architecture and a large number of users in future networks, transparent and decentralized data sharing is required to promote data circulation and break data silos, for which blockchain is a potential solution to allow intelligent access permission control. However, it remains a challenging task to achieve flexible authorization management for blockchain-based data sharing and efficient key update for multi-users in case of key exposure. In this paper, we propose an intelligent blockchain-based data-sharing scheme with key update for future networks. First, we design a new encryption scheme, where keywords of data are extracted using machine learning algorithms that are published on the blockchain. Then, keywords of data and time validity are used to encrypt different types of data for flexible data authorization. Second, using hierarchical identity-based encryption, we construct an efficient key update mechanism, where update tokens are generated by invoking a smart contract deployed on the blockchain to facilitate key and ciphertext updates. We formally prove that the proposed scheme can guarantee three essential security properties: forward security, post-compromise security, and collusion attack resistance. On-chain and off-chain experiment results are provided to demonstrate that the proposed scheme can achieve computational and communication efficiency for key and ciphertext updates.

SMART VILLAGE GOVERNANCE THROUGH THE VILLAGE INFORMATION SYSTEM IN TUBAN REGENCY

The rapid use of information technology (IT) in all aspects of life from government to the general public has encouraged this local government to integrate city elements to support information technology-based urban life or can be called a smart city. In line with the use of this concept, in the context of a smaller government, namely the village, the concept of a technology-based smart village or commonly referred to as a smart village has been applied. Indicators or basic elements of the smart village concept by looking at the indicators used in the smart city concept which are adapted to be more suitable for implementation in village government into Smart Governance, Smart Technology, Smart Resources, Smart Village Services, Smart Living, and Smart Tourism. This research uses descriptive analysis research method and combined with SWOT analysis method. The results showed that the application of the smart village concept in Tuban Regency did not have a previously designed development model. The implementation of smart villages is only focused on the development of administrative services in the form of the use of information technology which is only one indicator of the implementation of smart villages, but for other indicator elements it is still far from the general concept of what the actual implementation of smart villages is, so that various improvements are still needed, including the application of the development model. smart village, sustainable improvement of village information systems for the sake of ongoing public accountability in providing services to the community.

(Retracted) Intelligent resources based on active vision sensing strategy in the evolution of food aesthetics system

The active vision research platform allows the computer to recognize the local information around one or more images, even if the device can observe the geometric information of the objects in the area. We analyze and study the evolution of the diet aesthetics system based on the intelligent resources of active vision sensing strategies, modeling, and analysis of images with vision sensors and aim to combine the advanced vision research technology of active vision with the main body of food that is most closely related to the development of human society as the research object. The discussion makes this process more consistent with the human development process. This article is mainly divided into two parts to expand the narrative. The first part is the theoretical basis, including the construction of the active vision system, imaging applications, and these theories. The second is the specific role it plays in the promotion of dietary aesthetics as the basis for the development of intelligent resources. The article spends a lot of space on the analysis of active vision imaging. The content of this part can make the sensing strategy of active vision clearer. The experimental results show that the application of intelligent resource active visual sensing can make the sound characteristics in food aesthetics more vivid and more real. Vividness is the main indicator to reflect the beauty of food; among them, the maximum value of vividness before application is 5.97, the maximum value after application is 8.18, and even the minimum is 6.66, higher than the maximum value before smart resource application. The active visual sensing strategy of intelligent resources also has a huge impact on the aesthetics of the diet. The overall evaluation value of the audience has reached 8.41 after big data analysis.

Mitigation of Thermal Stability Concerns in FinFET Devices

Here, we developed a procedure for mitigating thermal hazards in packaged FinFET devices. A monitoring system was installed into devices, based on self-heating impact analysis in the system and device levels, to allow for the observation and alerting of chip temperature and reliability risks. A novel algorithm for reducing measurement noise by means of temperature fluctuation compensation and the filtering of invalid data is presented and demonstrated on packaged devices. The results presented in this work show that the proposed techniques make exceptional improvements to sensory accuracy. Using this methodology enables the mitigation of thermal concerns in systems, including large data servers, and accelerates development of smart resource allocation formations.