Journal Description
Future Internet
Future Internet
is an international, peer-reviewed, open access journal on internet technologies and the information society, published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, Inspec, and other databases.
- Journal Rank: CiteScore - Q1 (Computer Networks and Communications)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 11.8 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.4 (2022);
5-Year Impact Factor:
3.4 (2022)
Latest Articles
Design and Implementation of a Low-Cost, Linear Robotic Camera System, Targeting Greenhouse Plant Growth Monitoring
Future Internet 2024, 16(5), 145; https://doi.org/10.3390/fi16050145 - 23 Apr 2024
Abstract
Automated greenhouse production systems frequently employ non-destructive techniques, such as computer vision-based methods, to accurately measure plant physiological properties and monitor crop growth. By utilizing an automated image acquisition and analysis system, it becomes possible to swiftly assess the growth and health of
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Automated greenhouse production systems frequently employ non-destructive techniques, such as computer vision-based methods, to accurately measure plant physiological properties and monitor crop growth. By utilizing an automated image acquisition and analysis system, it becomes possible to swiftly assess the growth and health of plants throughout their entire lifecycle. This valuable information can be utilized by growers, farmers, and crop researchers who are interested in self-cultivation procedures. At the same time, such a system can alleviate the burden of daily plant photography for human photographers and crop researchers, while facilitating automated plant image acquisition for crop status monitoring. Given these considerations, the aim of this study was to develop an experimental, low-cost, 1-DOF linear robotic camera system specifically designed for automated plant photography. As an initial evaluation of the proposed system, which targets future research endeavors of simplifying the process of plant growth monitoring in a small greenhouse, the experimental setup and precise plant identification and localization are demonstrated in this work through an application on lettuce plants, imaged mostly under laboratory conditions.
Full article
(This article belongs to the Section Smart System Infrastructure and Applications)
Open AccessArticle
Anticipating Job Market Demands—A Deep Learning Approach to Determining the Future Readiness of Professional Skills
by
Albert Weichselbraun, Norman Süsstrunk, Roger Waldvogel, André Glatzl, Adrian M. P. Braşoveanu and Arno Scharl
Future Internet 2024, 16(5), 144; https://doi.org/10.3390/fi16050144 - 23 Apr 2024
Abstract
Anticipating the demand for professional job market skills needs to consider trends such as automation, offshoring, and the emerging Gig economy, as they significantly impact the future readiness of skills. This article draws on the scientific literature, expert assessments, and deep learning to
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Anticipating the demand for professional job market skills needs to consider trends such as automation, offshoring, and the emerging Gig economy, as they significantly impact the future readiness of skills. This article draws on the scientific literature, expert assessments, and deep learning to estimate two indicators of high relevance for a skill’s future readiness: its automatability and offshorability. Based on gold standard data, we evaluate the performance of Support Vector Machines (SVMs), Transformers, Large Language Models (LLMs), and a deep learning ensemble classifier for propagating expert and literature assessments on these indicators of yet unseen skills. The presented approach uses short bipartite skill labels that contain a skill topic (e.g., “Java”) and a corresponding verb (e.g., “programming”) to describe the skill. Classifiers thus need to base their judgments solely on these two input terms. Comprehensive experiments on skewed and balanced datasets show that, in this low-token setting, classifiers benefit from pre-training and fine-tuning and that increased classifier complexity does not yield further improvements.
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(This article belongs to the Collection Innovative People-Centered Solutions Applied to Industries, Cities and Societies)
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Open AccessArticle
Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
by
Bing Zhang, Hui Li, Shuai Zhang, Jing Sun, Ning Wei, Wenhong Xu and Huan Wang
Future Internet 2024, 16(4), 143; https://doi.org/10.3390/fi16040143 - 22 Apr 2024
Abstract
Path hopping serves as an active defense mechanism in network security, yet it encounters challenges like a restricted path switching space, the recurrent use of similar paths and vital nodes, a singular triggering mechanism for path switching, and fixed hopping intervals. This paper
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Path hopping serves as an active defense mechanism in network security, yet it encounters challenges like a restricted path switching space, the recurrent use of similar paths and vital nodes, a singular triggering mechanism for path switching, and fixed hopping intervals. This paper introduces an active defense method employing multiple constraints and strategies for path hopping. A depth-first search (DFS) traversal is utilized to compute all possible paths between nodes, thereby broadening the path switching space while simplifying path generation complexity. Subsequently, constraints are imposed on residual bandwidth, selection periods, path similitude, and critical nodes to reduce the likelihood of reusing similar paths and crucial nodes. Moreover, two path switching strategies are formulated based on the weights of residual bandwidth and critical nodes, along with the calculation of path switching periods. This facilitates adaptive switching of path hopping paths and intervals, contingent on the network’s residual bandwidth threshold, in response to diverse attack scenarios. Simulation outcomes illustrate that this method, while maintaining normal communication performance, expands the path switching space effectively, safeguards against eavesdropping and link-flooding attacks, enhances path switching diversity and unpredictability, and fortifies the network’s resilience against malicious attacks.
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(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy II)
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Open AccessArticle
Edge Federated Optimization for Heterogeneous Data
by
Hsin-Tung Lin and Chih-Yu Wen
Future Internet 2024, 16(4), 142; https://doi.org/10.3390/fi16040142 - 22 Apr 2024
Abstract
This study focuses on optimizing federated learning in heterogeneous data environments. We implement the FedProx and a baseline algorithm (i.e., the FedAvg) with advanced optimization strategies to tackle non-IID data issues in distributed learning. Model freezing and pruning techniques are explored to showcase
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This study focuses on optimizing federated learning in heterogeneous data environments. We implement the FedProx and a baseline algorithm (i.e., the FedAvg) with advanced optimization strategies to tackle non-IID data issues in distributed learning. Model freezing and pruning techniques are explored to showcase the effective operations of deep learning models on resource-constrained edge devices. Experimental results show that at a pruning rate of 10%, the FedProx with structured pruning in the MIT-BIH and ST databases achieved the best F1 scores, reaching 96.01% and 77.81%, respectively, which achieves a good balance between system efficiency and model accuracy compared to those of the FedProx with the original configuration, reaching F1 scores of 66.12% and 89.90%, respectively. Similarly, with layer freezing technique, unstructured pruning method, and a pruning rate of 20%, the FedAvg algorithm effectively balances classification performance and degradation of pruned model accuracy, achieving F1 scores of 88.75% and 72.75%, respectively, compared to those of the FedAvg with the original configuration, reaching 56.82% and 85.80%, respectively. By adopting model optimization strategies, a practical solution is developed for deploying complex models in edge federated learning, vital for its efficient implementation.
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(This article belongs to the Special Issue Software-Driven Federated Learning for/in Smart Environment)
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Open AccessCorrection
Correction: Li et al. A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation. Future Internet 2023, 15, 395
by
Zuopeng Li, Hengshuai Ju and Zepeng Ren
Future Internet 2024, 16(4), 141; https://doi.org/10.3390/fi16040141 - 22 Apr 2024
Abstract
In the original publication [...]
Full article
(This article belongs to the Section Internet of Things)
Open AccessArticle
SUDC: Synchronous Update with the Division and Combination of SRv6 Policy
by
Yuze Liu, Weihong Wu, Ying Wang, Jiang Liu and Fan Yang
Future Internet 2024, 16(4), 140; https://doi.org/10.3390/fi16040140 - 22 Apr 2024
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With the expansion of network scale, new network services are emerging. Segment Routing over IPv6 (SRv6) can meet the diverse needs of more new services due to its excellent scalability and programmability. In the intelligent 6-Generation (6G) scenario, frequent SRv6 Traffic Engineering (TE)
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With the expansion of network scale, new network services are emerging. Segment Routing over IPv6 (SRv6) can meet the diverse needs of more new services due to its excellent scalability and programmability. In the intelligent 6-Generation (6G) scenario, frequent SRv6 Traffic Engineering (TE) policy updates will result in the serious problem of unsynchronized updates across routers. Existing solutions suffer from issues such as long update cycles or large data overhead. To optimize the policy-update process, this paper proposes a scheme called Synchronous Update with the Division and Combination of SRv6 Policy (SUDC). Based on the characteristics of the SRv6 TE policy, SUDC divides the policies and introduces Bit Index Explicit Replication IPv6 Encapsulation (BIERv6) to multicast the policy blocks derived from policy dividing. The contribution of this paper is to propose the policy-dividing and combination mechanism and the policy-dividing algorithm. The simulation results demonstrate that compared with the existing schemes, the update overhead and update cycle of SUDC are reduced by 46.71% and 46.6%, respectively. The problem of unsynchronized updates across routers has been further improved.
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Open AccessReview
A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions
by
Md Motiur Rahman, Deepti Gupta, Smriti Bhatt, Shiva Shokouhmand and Miad Faezipour
Future Internet 2024, 16(4), 139; https://doi.org/10.3390/fi16040139 - 19 Apr 2024
Abstract
Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals’ health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scenarios and life-threatening situations. This task
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Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals’ health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scenarios and life-threatening situations. This task becomes notably more complex when multiple ambient sensors are deployed in homes with multiple residents, as opposed to single-resident environments. Additionally, the availability of datasets containing anomalies representing the full spectrum of abnormalities is limited. In our experimental study, we employed eight widely used machine learning and two deep learning classifiers to identify anomalies in human activities. We meticulously generated anomalies, considering all conceivable scenarios. Our findings reveal that the Gated Recurrent Unit (GRU) excels in accurately classifying normal and anomalous activities, while the naïve Bayes classifier demonstrates relatively poor performance among the ten classifiers considered. We conducted various experiments to assess the impact of different training–test splitting ratios, along with a five-fold cross-validation technique, on the performance. Notably, the GRU model consistently outperformed all other classifiers under both conditions. Furthermore, we offer insights into the computational costs associated with these classifiers, encompassing training and prediction phases. Extensive ablation experiments conducted in this study underscore that all these classifiers can effectively be deployed for anomaly detection in two-resident homes.
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(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart City)
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Open AccessArticle
SRv6-Based Edge Service Continuity in 5G Mobile Networks
by
Laura Lemmi, Carlo Puliafito, Antonio Virdis and Enzo Mingozzi
Future Internet 2024, 16(4), 138; https://doi.org/10.3390/fi16040138 - 19 Apr 2024
Abstract
Ensuring compliance with the stringent latency requirements of edge services requires close cooperation between the network and computing components. Within mobile 5G networks, the nomadic behavior of users may impact the performance of edge services, prompting the need for workload migration techniques. These
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Ensuring compliance with the stringent latency requirements of edge services requires close cooperation between the network and computing components. Within mobile 5G networks, the nomadic behavior of users may impact the performance of edge services, prompting the need for workload migration techniques. These techniques allow services to follow users by moving between edge nodes. This paper introduces an innovative approach for edge service continuity by integrating Segment Routing over IPv6 (SRv6) into the 5G core data plane alongside the ETSI multi-access edge computing (MEC) architecture. Our approach maintains compatibility with non-SRv6 5G network components. We use SRv6 for packet steering and Software-Defined Networking (SDN) for dynamic network configuration. Leveraging the SRv6 Network Programming paradigm, we achieve lossless workload migration by implementing a packet buffer as a virtual network function. Our buffer may be dynamically allocated and configured within the network. We test our proposed solution on a small-scale testbed consisting of an Open Network Operating System (ONOS) SDN controller and a core network made of P4 BMv2 switches, emulated using Mininet. A comparison with a non-SRv6 alternative that uses IPv6 routing shows the higher scalability and flexibility of our approach in terms of the number of rules to be installed and time required for configuration.
Full article
(This article belongs to the Special Issue Edge Intelligence: Edge Computing for 5G and the Internet of Things)
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Open AccessArticle
From Seek-and-Destroy to Split-and-Destroy: Connection Partitioning as an Effective Tool against Low-Rate DoS Attacks
by
Vyron Kampourakis, Georgios Michail Makrakis and Constantinos Kolias
Future Internet 2024, 16(4), 137; https://doi.org/10.3390/fi16040137 - 19 Apr 2024
Abstract
Low-rate Denial of Service (LDoS) attacks are today considered one of the biggest threats against modern data centers and industrial infrastructures. Unlike traditional Distributed Denial of Service (DDoS) attacks that are mainly volumetric, LDoS attacks exhibit a very small network footprint, and therefore
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Low-rate Denial of Service (LDoS) attacks are today considered one of the biggest threats against modern data centers and industrial infrastructures. Unlike traditional Distributed Denial of Service (DDoS) attacks that are mainly volumetric, LDoS attacks exhibit a very small network footprint, and therefore can easily elude standard detection and defense mechanisms. This work introduces a defense strategy that may prove particularly effective against attacks that are based on long-lived connections, an inherent trait of LDoS attacks. Our approach is based on iteratively partitioning the active connections of a victim server across a number of replica servers, and then re-evaluating the health status of each replica instance. At its core, this approach relies on live migration and containerization technologies. The main advantage of the proposed approach is that it can discover and isolate malicious connections with virtually no information about the type and characteristics of the performed attack. Additionally, while the defense takes place, there is little to no indication of the fact to the attacker. We assess various rudimentary schemes to quantify the scalability of our approach. The results from the simulations indicate that it is possible to save the vast majority of the benign connections (80%) in less than 5 min.
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(This article belongs to the Section Cybersecurity)
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Open AccessArticle
Computation Offloading Based on a Distributed Overlay Network Cache-Sharing Mechanism in Multi-Access Edge Computing
by
Yazhi Liu, Pengfei Zhong, Zhigang Yang, Wei Li and Siwei Li
Future Internet 2024, 16(4), 136; https://doi.org/10.3390/fi16040136 - 19 Apr 2024
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Multi-access edge computing (MEC) enhances service quality for users and reduces computational overhead by migrating workloads and application data to the network edge. However, current solutions for task offloading and cache replacement in edge scenarios are constrained by factors such as communication bandwidth,
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Multi-access edge computing (MEC) enhances service quality for users and reduces computational overhead by migrating workloads and application data to the network edge. However, current solutions for task offloading and cache replacement in edge scenarios are constrained by factors such as communication bandwidth, wireless network coverage, and limited storage capacity of edge devices, making it challenging to achieve high cache reuse and lower system energy consumption. To address these issues, a framework leveraging cooperative edge servers deployed in wireless access networks across different geographical regions is designed. Specifically, we propose the Distributed Edge Service Caching and Offloading (DESCO) network architecture and design a decentralized resource-sharing algorithm based on consistent hashing, named Cache Chord. Subsequently, based on DESCO and aiming to minimize overall user energy consumption while maintaining user latency constraints, we introduce the real-time computation offloading (RCO) problem and transform RCO into a multi-player static game, prove the existence of Nash equilibrium solutions, and solve it using a multi-dimensional particle swarm optimization algorithm. Finally, simulation results demonstrate that the proposed solution reduces the average energy consumption by over 27% in the DESCO network compared to existing algorithms.
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Open AccessArticle
Blockchain-Enabled Provenance Tracking for Sustainable Material Reuse in Construction Supply Chains
by
Stanly Wilson, Kwabena Adu-Duodu, Yinhao Li, Ringo Sham, Mohammed Almubarak, Yingli Wang, Ellis Solaiman, Charith Perera, Rajiv Ranjan and Omer Rana
Future Internet 2024, 16(4), 135; https://doi.org/10.3390/fi16040135 - 17 Apr 2024
Abstract
The growing complexity of construction supply chains and the significant impact of the construction industry on the environment demand an understanding of how to reuse and repurpose materials. In response to this critical challenge, research gaps that are significant in promoting material circularity
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The growing complexity of construction supply chains and the significant impact of the construction industry on the environment demand an understanding of how to reuse and repurpose materials. In response to this critical challenge, research gaps that are significant in promoting material circularity are described. Despite its potential, the use of blockchain technology in construction faces challenges in verifiability, scalability, privacy, and interoperability. We propose a novel multilayer blockchain framework to enhance provenance tracking and data retrieval to enable a reliable audit trail. The framework utilises a privacy-centric solution that combines decentralised and centralised storage, security, and privacy. Furthermore, the framework implements access control to strengthen security and privacy, fostering transparency and information sharing among the stakeholders. These contributions collectively lead to trusted material circularity in a built environment. The implementation framework aims to create a prototype for blockchain applications in construction supply chains.
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(This article belongs to the Special Issue Blockchain and Web 3.0: Applications, Challenges and Future Trends)
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Open AccessArticle
Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber–Physical Production Systems
by
Yuning Jiang, Wei Wang, Jianguo Ding, Xin Lu and Yanguo Jing
Future Internet 2024, 16(4), 134; https://doi.org/10.3390/fi16040134 - 17 Apr 2024
Abstract
The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable
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The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable vulnerabilities. This paper presents a novel approach to CPPS security protection by leveraging digital twin (DT) technology to develop a comprehensive security model. This model enhances asset visibility and supports prioritization in mitigating vulnerable components through DT-based virtual tuning, providing quantitative assessment results for effective mitigation. Our proposed DT security model also serves as an advanced simulation environment, facilitating the evaluation of CPPS vulnerabilities across diverse attack scenarios without disrupting physical operations. The practicality and effectiveness of our approach are illustrated through its application in a human–robot collaborative assembly system, demonstrating the potential of DT technology.
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(This article belongs to the Special Issue Digital Twins in Intelligent Manufacturing)
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Open AccessArticle
Secure Data Sharing in Federated Learning through Blockchain-Based Aggregation
by
Bowen Liu and Qiang Tang
Future Internet 2024, 16(4), 133; https://doi.org/10.3390/fi16040133 - 15 Apr 2024
Abstract
In this paper, we explore the realm of federated learning (FL), a distributed machine learning (ML) paradigm, and propose a novel approach that leverages the robustness of blockchain technology. FL, a concept introduced by Google in 2016, allows multiple entities to collaboratively train
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In this paper, we explore the realm of federated learning (FL), a distributed machine learning (ML) paradigm, and propose a novel approach that leverages the robustness of blockchain technology. FL, a concept introduced by Google in 2016, allows multiple entities to collaboratively train an ML model without the need to expose their raw data. However, it faces several challenges, such as privacy concerns and malicious attacks (e.g., data poisoning attacks). Our paper examines the existing EIFFeL framework, a protocol for decentralized real-time messaging in continuous integration and delivery pipelines, and introduces an enhanced scheme that leverages the trustworthy nature of blockchain technology. Our scheme eliminates the need for a central server and any other third party, such as a public bulletin board, thereby mitigating the risks associated with the compromise of such third parties.
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(This article belongs to the Special Issue Edge-Cloud Computing and Federated-Split Learning in the Internet of Things)
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Open AccessArticle
SeedChain: A Secure and Transparent Blockchain-Driven Framework to Revolutionize the Seed Supply Chain
by
Rohit Ahuja, Sahil Chugh and Raman Singh
Future Internet 2024, 16(4), 132; https://doi.org/10.3390/fi16040132 - 15 Apr 2024
Abstract
Farming is a major sector required for any nation to become self-sustainable. Quality seeds heavily influence the effectiveness of farming. Seeds cultivated by breeders pass through several entities in order to reach farmers. The existing seed supply chain is opaque and intractable, which
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Farming is a major sector required for any nation to become self-sustainable. Quality seeds heavily influence the effectiveness of farming. Seeds cultivated by breeders pass through several entities in order to reach farmers. The existing seed supply chain is opaque and intractable, which not only hinders the growth of crops but also makes the life of a farmer miserable. Blockchain has been widely employed to enable fair and secure transactions between farmers and buyers, but concerns related to transparency and traceability in the seed supply chain, counterfeit seeds, middlemen involvement, and inefficient processes in the agricultural ecosystem have not received enough attention. To address these concerns, a blockchain-based solution is proposed that brings breeders, farmers, warehouse owners, transporters, and food corporations to a single platform to enhance transparency, traceability, and trust among trust-less parties. A smart contract updates the status of seeds from a breeder from to . Then, a non-fungible token (NFT) corresponding to approved seeds is minted for the breeder, which records the date of cultivation and its owner (breeder). The NFT enables farmers to keep track of seeds right from the date of their cultivation and their owner, which helps them to make better decisions about picking seeds from the correct owner. Farmers directly interact with warehouses to purchase seeds, which removes the need for middlemen and improves the trust among trust-less entities. Furthermore, a tender for the transportation of seeds is auctioned on the basis of the priority location , Score, and bid_amount of every transporter, which provides a fair chance to every transporter to restrict the monopoly of a single transporter. The proposed system achieves immutability, decentralization, and efficiency inherently from the blockchain. We implemented the proposed scheme and deployed it on the Ethereum network. Smart contracts deployed over the Ethereum network interact with React-based web pages. The analysis and results of the proposed model indicate that it is viable and secure, as well as superior to the current seed supply chain system.
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(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Decentralized Edge Environments)
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Open AccessArticle
Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
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Wei Li, Mengzhen Ren, Yazhi Liu, Chenyu Li, Hui Qian and Zhenyou Zhang
Future Internet 2024, 16(4), 131; https://doi.org/10.3390/fi16040131 - 15 Apr 2024
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In order to solve the congestion problem caused by the dramatic growth of traffic in data centers, many end-to-end congestion controls have been proposed to respond to congestion in one round-trip time (RTT). In this paper, we propose a new congestion control mechanism
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In order to solve the congestion problem caused by the dramatic growth of traffic in data centers, many end-to-end congestion controls have been proposed to respond to congestion in one round-trip time (RTT). In this paper, we propose a new congestion control mechanism based on backpressure feedback (BFCC), which is designed with the primary goal of switch-to-switch congestion control to resolve congestion in a one-hop RTT. This approach utilizes a programmable data plane to continuously monitor network congestion in real time and identify real-congested flows. In addition, it employs targeted flow control through backpressure feedback. We validate the feasibility of this mechanism on BMV2, a programmable virtual switch based on programming protocol-independent packet processors (P4). Simulation results demonstrate that BFCC greatly enhances flow completion times (FCTs) compared to other end-to-end congestion control mechanisms. It achieves 1.2–2× faster average completion times than other mechanisms.
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Open AccessFeature PaperArticle
Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
by
David Todoli-Ferrandis, Javier Silvestre-Blanes, Víctor Sempere-Payá and Salvador Santonja-Climent
Future Internet 2024, 16(4), 130; https://doi.org/10.3390/fi16040130 - 12 Apr 2024
Abstract
LoRaWAN is a low-power wide-area network (LPWAN) technology that is well suited for industrial IoT (IIoT) applications. One of the challenges of using LoRaWAN for IIoT is the need to collect data from a large number of devices. Polling is a common way
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LoRaWAN is a low-power wide-area network (LPWAN) technology that is well suited for industrial IoT (IIoT) applications. One of the challenges of using LoRaWAN for IIoT is the need to collect data from a large number of devices. Polling is a common way to collect data from devices, but it can be inefficient for LoRaWANs, which are designed for low data rates and long battery life. LoRaWAN devices operating in two specific modes can receive messages from a gateway even when they are not sending data themselves. This allows the gateway to send commands to devices at any time, without having to wait for them to check for messages. This paper proposes various polling mechanisms for industrial IoT applications in LoRaWANs and presents specific considerations for designing efficient polling mechanisms in the context of industrial IoT applications leveraging LoRaWAN technology.
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(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies)
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Open AccessReview
All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
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Georgios Koukis, Konstantina Safouri and Vassilis Tsaoussidis
Future Internet 2024, 16(4), 129; https://doi.org/10.3390/fi16040129 - 09 Apr 2024
Abstract
Although several years have passed since its first introduction, the significance of Delay-Tolerant Networking (DTN) remains evident, particularly in challenging environments where traditional networks face operational limitations such as disrupted communication or high latency. This survey paper aims to explore the diverse array
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Although several years have passed since its first introduction, the significance of Delay-Tolerant Networking (DTN) remains evident, particularly in challenging environments where traditional networks face operational limitations such as disrupted communication or high latency. This survey paper aims to explore the diverse array of applications where DTN technologies have proven successful, with a focus on emerging and novel application paradigms. In particular, we focus on the contributions of DTN in the Future Internet, including its contribution to space applications, smart cities and the Internet of Things, but also to underwater communications. We also discuss its potential to be used jointly with information-centric networks to change the internet communication paradigm in the future.
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(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart City)
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Open AccessArticle
A Survey on Energy-Aware Security Mechanisms for the Internet of Things
by
Peixiong He, Yi Zhou and Xiao Qin
Future Internet 2024, 16(4), 128; https://doi.org/10.3390/fi16040128 - 08 Apr 2024
Abstract
The Internet of Things (IoT) employs sensors and the Internet for information exchange, enabling intelligent identification, monitoring, and management, which has deeply impacted various sectors such as power, medical care, and security, transforming social activities and lifestyles. Regrettably, IoT systems suffer from two
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The Internet of Things (IoT) employs sensors and the Internet for information exchange, enabling intelligent identification, monitoring, and management, which has deeply impacted various sectors such as power, medical care, and security, transforming social activities and lifestyles. Regrettably, IoT systems suffer from two main challenges, namely sustainability and security. Hence, pondering how to enhance sustainable and energy-efficient practices for IoT systems to mitigate risks becomes a worthwhile endeavor. To address this issue, we conduct a survey of energy-aware security mechanisms in the Internet of Things. Specifically, we examine the challenges that IoT is facing in terms of energy efficiency and security, and we inspect current energy-saving and privacy-preserving technologies for IoT systems. Moreover, we delineate a vision for the future of IoT, emphasizing energy-aware security mechanisms. Finally, we outline the challenges encountered in achieving energy-aware security mechanisms, as well as the direction of future research. Motivated by this study, we envision advancements in the IoT that not only harness the benefits of science and technology but also enhance the security and safety of our data.
Full article
(This article belongs to the Special Issue IoT Security: Threat Detection, Analysis and Defense)
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Open AccessArticle
Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
by
Zhongcheng Wei and Yanhu Dong
Future Internet 2024, 16(4), 127; https://doi.org/10.3390/fi16040127 - 08 Apr 2024
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With the proliferation of WiFi devices, WiFi-based identification technology has garnered attention in the security domain and has demonstrated initial success. Nonetheless, when untrained illegitimate users appear, the classifier tends to categorize them as if they were trained users. In response to this
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With the proliferation of WiFi devices, WiFi-based identification technology has garnered attention in the security domain and has demonstrated initial success. Nonetheless, when untrained illegitimate users appear, the classifier tends to categorize them as if they were trained users. In response to this issue, researchers have proposed identity legitimacy authentication systems to identify illicit users, albeit only applicable to individual users. In this article, we propose a multi-user legitimacy authentication system based on WiFi, termed Multi-WiIR. Leveraging WiFi signals, the system captures users’ walking patterns to ascertain their legitimacy. The core concept entails training a multi-branch deep neural network, designated WiIR-Net, for feature extraction of individual users. Binary classifiers are then applied to each user, and legitimacy is established by comparing the model’s output to predefined thresholds, thus facilitating multi-user legitimacy authentication. Moreover, the study experimentally investigated the impact of the number of legitimate individuals on accuracy rates. The results demonstrated that The Multi-WiIR system showed commendable performance with low latency, being capable of conducting legitimacy recognition in scenarios involving up to four users, with an accuracy rate reaching 85.11%.
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Open AccessArticle
Metaverse Meets Smart Cities—Applications, Benefits, and Challenges
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Florian Maier and Markus Weinberger
Future Internet 2024, 16(4), 126; https://doi.org/10.3390/fi16040126 - 08 Apr 2024
Abstract
The metaverse aims to merge the virtual and real worlds. The target is to generate a virtual community where social components play a crucial role and combine different areas such as entertainment, work, shopping, and services. This idea is explicitly appealing in the
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The metaverse aims to merge the virtual and real worlds. The target is to generate a virtual community where social components play a crucial role and combine different areas such as entertainment, work, shopping, and services. This idea is explicitly appealing in the context of smart cities. The metaverse offers digitalization approaches and can strengthen citizens’ social community. While the existing literature covers the exemplary potential of smart city metaverse applications, this study aims to provide a comprehensive overview of the potential and already implemented metaverse applications in the context of cities and municipalities. In addition, challenges related to these applications are identified. The study combines literature reviews and expert interviews to ensure a broad overview. Forty-eight smart city metaverse applications from eleven areas were identified, and actual projects from eleven cities demonstrate the current state of development. Still, further research should evaluate the benefits of the various applications and find strategies to overcome the identified challenges.
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(This article belongs to the Special Issue Virtualization Technology: Augmented Reality, Virtual Reality, Embodied Interface)
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