Internet of Things最新文献_第5页

Securing SCADA systems in smart grids with IoT integration: A Self-Defensive Post-Quantum Blockchain Architecture 通过物联网集成确保智能电网中 SCADA 系统的安全：自防御后量子区块链架构

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-09-24 DOI: 10.1016/j.iot.2024.101381

Mohammad Tazeem Naz, Wael Elmedany, Mazen Ali

The rapid development of smart city infrastructures has brought increased attention to the security of critical systems such as Supervisory Control and Data Acquisition (SCADA) systems, which are central to smart grids. SCADA systems are highly susceptible to cyberattacks, particularly false data injection attacks that can lead to catastrophic failures. While blockchain technology has been explored as a means to secure SCADA systems, traditional blockchain models are vulnerable to the emerging threat of quantum computing, which can break classical cryptographic algorithms.

This paper proposes a Self-Defensive Post-Quantum Blockchain Architecture (SD-PQBA) specifically designed to protect SCADA systems within smart cities from both classical and quantum cyber threats. The SD-PQBA framework introduces a novel Proof of Derived Authority (PoDA) consensus mechanism and a post-quantum 3-key cryptography scheme that ensures the immutability and integrity of data in the SCADA system. By addressing the limitations of existing blockchain solutions in the context of quantum computing, this architecture provides a robust, future-proof layer of defense, enhancing the resilience of SCADA systems against advanced cyber attacks. This approach not only strengthens SCADA security but also highlights critical gaps in the literature at the intersection of quantum computing, SCADA, and blockchain technology.

智能城市基础设施的快速发展使人们越来越关注关键系统的安全性，如智能电网的核心系统--监控与数据采集（SCADA）系统。SCADA 系统极易受到网络攻击，特别是可导致灾难性故障的虚假数据注入攻击。虽然区块链技术已被探索作为保护 SCADA 系统安全的一种手段，但传统的区块链模型很容易受到量子计算这一新兴威胁的影响，因为量子计算可以破解经典加密算法。本文提出了一种自防御后量子区块链架构（SD-PQBA），专门用于保护智慧城市中的 SCADA 系统免受经典和量子网络威胁。SD-PQBA 框架引入了一种新颖的 "衍生授权证明"（PoDA）共识机制和一种后量子 3 密钥加密方案，可确保 SCADA 系统中数据的不变性和完整性。通过解决量子计算背景下现有区块链解决方案的局限性，该架构提供了一个稳健、面向未来的防御层，增强了 SCADA 系统抵御高级网络攻击的能力。这种方法不仅加强了 SCADA 的安全性，还突出了量子计算、SCADA 和区块链技术交叉领域文献中的关键空白。

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引用次数: 0

Load-balanced offloading of multiple task types for mobile edge computing in IoT 为物联网移动边缘计算提供多种任务类型的负载平衡卸载

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-09-24 DOI: 10.1016/j.iot.2024.101385

Ye Zhang , Xingyun He , Jin Xing , Wuyungerile Li , Winston K.G. Seah

The continuous development of mobile networks poses new challenges for end devices with limited computing power. Mobile or multi-access edge computing (MEC) has been proposed for providing the computing resources close to the end devices that need them. However, in real network environments, MEC servers also have limited computing resources that need to be shared among many devices and efficient resource allocation is critical to ensure that the limited resources are optimally used. In view of this, we propose the Balanced Offload for Multi-type Tasks (BOMT) algorithm. The tasks to be offloaded are first prioritized according to their type, size and maximum tolerable delay; then different offloading algorithms are executed for different priority tasks according to the level of the priority and the current load on the MEC server. Following which, the optimal offloading policy is determined iteratively. Simulation results show that BOMT can effectively reduce system delay, increase user coverage and offload task completion rates.

移动网络的不断发展给计算能力有限的终端设备带来了新的挑战。人们提出了移动或多接入边缘计算（MEC），以便在需要计算资源的终端设备附近提供计算资源。然而，在实际网络环境中，MEC 服务器的计算资源也是有限的，需要在许多设备之间共享，而有效的资源分配对于确保有限资源得到最佳利用至关重要。有鉴于此，我们提出了多类型任务均衡卸载（BOMT）算法。首先，根据任务的类型、大小和可容忍的最大延迟对需要卸载的任务进行优先级排序；然后，根据优先级的高低和 MEC 服务器当前的负载情况，对不同优先级的任务执行不同的卸载算法。然后，反复确定最佳卸载策略。仿真结果表明，BOMT 能有效减少系统延迟、提高用户覆盖率和卸载任务完成率。

引用次数: 0

Computation and transmission adaptive semantic communication for reliability-guarantee image reconstruction in IoT 用于物联网图像重建可靠性保障的计算与传输自适应语义通信

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-09-24 DOI: 10.1016/j.iot.2024.101383

Chen Lin, Yijun Guo, Jianjun Hao, Zhilong Zhang

Semantic communication can significantly compress source data, improving transmission efficiency. However, semantic communication systems under varying channel condition have not been well studied, especially for tasks that require reliability guarantee. This paper focus on the reliability-guarantee image reconstruction tasks, and study the computation and transmission adaptive semantic communication. First, a computation and transmission adaptive semantic communication (CTASC) system is proposed. It is able to adjust the computation load and transmission load of an image reconstruction task adaptively while guaranteeing the reconstruction reliability. Specifically, a semantic encoder with multiple convolutional neural network (CNN) slices with different network depths is designed to adjust the transmission load and computation load. Second, a joint computation and transmission resource allocation problem aimed at minimizing the maximum delay within system is formulated. To solve this problem, we decompose it into two nested sub-problems and propose a Simulated Annealing with Re-perturbation Mechanism (SA-RPM) algorithm and an Alternating Optimization (AO) algorithm to solve these sub-problems, respectively. Simulation results demonstrate that compared to variable code length enabled DeepJSCC (DeepJSCC-V), our system can achieve higher compression ratio(CR) with similar LPIPS performance. Simulation results also show that our resource allocation scheme can obtain an approaching value to the optimal maximum delay, with an average difference not exceeding 0.2%.

语义通信可以大大压缩源数据，提高传输效率。然而，对于不同信道条件下的语义通信系统，尤其是需要可靠性保证的任务，研究还不够深入。本文将重点放在可靠性保证的图像重建任务上，研究计算与传输自适应语义通信。首先，本文提出了一种计算与传输自适应语义通信（CTASC）系统。它能在保证重建可靠性的同时，自适应地调整图像重建任务的计算负载和传输负载。具体来说，设计了一个具有多个不同网络深度的卷积神经网络（CNN）切片的语义编码器，以调整传输负载和计算负载。其次，我们提出了一个联合计算和传输资源分配问题，旨在使系统内的最大延迟最小化。为了解决这个问题，我们将其分解为两个嵌套子问题，并分别提出了模拟退火与再扰动机制（SA-RPM）算法和交替优化（AO）算法来解决这些子问题。仿真结果表明，与支持可变代码长度的 DeepJSCC（DeepJSCC-V）相比，我们的系统可以在类似 LPIPS 性能的情况下实现更高的压缩率（CR）。仿真结果还表明，我们的资源分配方案可以获得接近最优最大延迟的值，平均差异不超过 0.2%。

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引用次数: 0

An empirical investigation into the enhancement of decision-making capabilities in corporate sustainability leadership through Internet of Things (IoT) integration 通过物联网（IoT）整合提高企业可持续发展领导力决策能力的实证调查

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-09-23 DOI: 10.1016/j.iot.2024.101382

Ming Yuan Hsieh

This research investigates the impact of Internet of Things (IoT) applications on decision-making capacity (DMC) development in Corporate Sustainable Leadership (CSL). The research focuses on how IoT enhances Environmental, Social, and Governance (ESG) practices within organizations, addressing key sustainability challenges and supporting Sustainable Development Goals (SDGs). Through quantitative and qualitative analyses, the study identifies three primary findings: (1) IoT applications directly enhance power source management in the environmental domain, improving Business Strategy and Long-term Growth (BSLG) and Measurement and Reporting (M&R) capabilities. This is achieved through real-time monitoring, smart building management, and predictive maintenance. In the social sphere, IoT strengthens supply chain transparency, bolstering Stakeholder Management (SM) through real-time tracking and blockchain-integrated systems for product authenticity verification. IoT comprehensively empowers performing risk management in the governance realm, improving Organizational Culture (OC) through early warning systems and predictive analytics for risk mitigation. (2) IoT integration facilitates Data-Driven Decision Making (DDDM), Real-Time Responsiveness (RTR), and Business Risk Management (BRM), key components of IoT-DMC. Furthermore, it supports the development of Effective Stakeholder Management (ESM), Resource Efficiency (RE), and Sustainable Competitive Outcomes (SCO) within IoT-CSL. (3) While highlighting the potential of IoT in advancing CSL, the research also acknowledges challenges such as initial investment costs, data privacy concerns, technological complexity, energy consumption of IoT devices, and electronic waste management. The study concludes that successful IoT implementation in CSL requires careful planning, robust data management, and a holistic approach considering both benefits and potential drawbacks.

本研究调查了物联网（IoT）应用对企业可持续领导力（CSL）中决策能力（DMC）发展的影响。研究重点是物联网如何加强组织内的环境、社会和治理（ESG）实践，应对关键的可持续发展挑战并支持可持续发展目标（SDGs）。通过定量和定性分析，该研究确定了三个主要发现：（1）物联网应用直接增强了环境领域的电源管理，改善了业务战略和长期增长（BSLG）以及测量和报告（M&R）能力。这可以通过实时监控、智能楼宇管理和预测性维护来实现。在社会领域，物联网增强了供应链的透明度，通过实时跟踪和区块链集成系统进行产品真实性验证，加强了利益相关者管理（SM）。物联网全面增强了治理领域的风险管理能力，通过预警系统和预测分析改善组织文化（OC），从而降低风险。(2) 物联网集成促进了数据驱动决策（DDDM）、实时响应（RTR）和业务风险管理（BRM），这些都是物联网-DMC 的关键组成部分。此外，它还支持在 IoT-CSL 中发展有效利益相关者管理（ESM）、资源效率（RE）和可持续竞争成果（SCO）。(3) 研究在强调物联网在推进 CSL 方面的潜力的同时，也承认了一些挑战，如初始投资成本、数据隐私问题、技术复杂性、物联网设备的能耗以及电子废物管理。研究得出结论，在 CSL 中成功实施物联网需要精心规划、健全的数据管理以及考虑到好处和潜在弊端的整体方法。

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引用次数: 0

A review of LoRaWAN performance optimization through cross-layer-based approach for Internet of Things 通过跨层方法优化物联网 LoRaWAN 性能综述

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-09-23 DOI: 10.1016/j.iot.2024.101378

Melchizedek Alipio , Carl Christian Chaguile , Miroslav Bures

The Internet of Things (IoT) is proliferating in technology and automation. Some popular IoT applications include environmental monitoring, home automation, agriculture, aquaculture, healthcare, transportation, and logistics. The construction of the IoT system is determined by where it will be used. The design can be tailor-made for short-range or long-range communication, rural or urban areas, indoor or outdoor applications, real-time or delay-tolerant communication, and much more. Low-Power Wide Area Networks (LPWAN) are frequently used for long-distance, energy-efficient, cost-effective communication. In LPWAN technology, Long-Range Wide Area Network (LoRaWAN) is one of the most popular choices because of its remarkable features and openness, making it highly suitable for IoT applications. However, despite its exceptional features, there are still ways to optimize the system to become more efficient and address several challenges during runtime. One possible way to address issues and challenges in LoRaWAN is through cross-layer optimization. This optimization technique violates the restrictions set by the Open System Interconnection (OSI) model and gives freedom to its protocol layers to communicate depending on the intended purpose. This paper surveys the state-of-the-art cross-layer approaches that optimize LoRaWAN for IoT applications. The cross-layer approaches were categorized according to the layer combinations and architecture. In addition, this paper provided observations on the effects of cross-layer optimization in LoRaWAN. Lastly, possible issues and solutions, challenges, and future directives from cross-layer optimization approaches were included.

物联网（IoT）技术和自动化正在蓬勃发展。一些流行的物联网应用包括环境监测、家庭自动化、农业、水产养殖、医疗保健、交通和物流。物联网系统的构造取决于其使用地点。设计可根据短程或远程通信、农村或城市地区、室内或室外应用、实时或延迟通信等情况量身定制。低功耗广域网（LPWAN）经常用于长距离、高能效、高成本效益的通信。在 LPWAN 技术中，长距离广域网（LoRaWAN）是最受欢迎的选择之一，因为它具有显著的特性和开放性，非常适合物联网应用。不过，尽管其功能卓越，但仍有一些方法可以优化系统，使其更加高效，并解决运行期间的一些难题。解决 LoRaWAN 中的问题和挑战的一种可行方法是跨层优化。这种优化技术打破了开放系统互连（OSI）模型的限制，使协议层可以根据预期目的自由通信。本文研究了针对物联网应用优化 LoRaWAN 的最先进的跨层方法。跨层方法根据层组合和架构进行了分类。此外，本文还对 LoRaWAN 中跨层优化的效果进行了观察。最后，还包括跨层优化方法中可能存在的问题和解决方案、挑战以及未来指示。

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引用次数: 0

Enhancing real-time intrusion detection and secure key distribution using multi-model machine learning approach for mitigating confidentiality threats 利用多模型机器学习方法加强实时入侵检测和安全密钥分配，以减轻保密威胁

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-09-21 DOI: 10.1016/j.iot.2024.101377

Ju Lu , Arindam Bhar , Arindam Sarkar , Abdulfattah Noorwali , Kamal M. Othman

Ensuring strong security measures against intrusions is of utmost importance in the ever-changing field of information management systems. Conventional Intrusion Detection Systems (IDS) frequently have difficulties in dealing with the ever-changing and intricate characteristics of contemporary cyber threats, particularly in the realm of the Internet of Things (IoT). The current body of research emphasizes the difficulties in attaining both high precision and real-time speed while still preserving the anonymity of data. This work tackles these concerns by presenting a scalable multi-model Machine Learning (ML) technique developed to improve real-time intrusion detection and ensure safe cryptographic key distribution. The suggested solution takes use of the widespread use of IoT devices, which increases the likelihood of advanced cyberattacks. Our approach involves implementing a ML-based automated IDS specifically designed for various IoT environments. These IDS enhance adaptability and accuracy. We also utilize Maximum–Minimum (Max–Min) normalization on the UNSW-NB15 and CICIoT2023 datasets to improve the accuracy of detecting intrusions. Furthermore, we classify a wide range of contemporary threats and typical internet traffic into nine distinct attack categories. To streamline data processing and improve system efficiency, we employ Principal Component Analysis (PCA) for dimensionality reduction. Additionally, we deploy six advanced ML models to optimize detection capabilities and accurately identify threats. Finally, we develop a secure key distribution mechanism using synchronized Artificial Neural Networks (ANNs). The process of mutual learning guarantees the secure distribution of keys among IoT networks, thus reducing the risks to secrecy. This novel methodology not only reinforces the ability to identify intrusions in real-time, but also improves the overall security stance of information management systems. This work significantly contributes to the field of digital security in information management by addressing the limits of current IDS solutions and presenting a complete, multi-faceted security strategy.

在瞬息万变的信息管理系统领域，确保针对入侵采取强有力的安全措施至关重要。传统的入侵检测系统（IDS）往往难以应对当代网络威胁不断变化和错综复杂的特点，尤其是在物联网（IoT）领域。当前的研究强调了在保持数据匿名性的同时实现高精度和实时速度的困难。为解决这些问题，本研究提出了一种可扩展的多模型机器学习（ML）技术，旨在提高入侵检测的实时性并确保加密密钥的安全分发。物联网设备的广泛使用增加了高级网络攻击的可能性，所建议的解决方案正是利用了这一点。我们的方法包括实施基于 ML 的自动 IDS，该 IDS 专为各种物联网环境而设计。这些 IDS 增强了适应性和准确性。我们还在 UNSW-NB15 和 CICIoT2023 数据集上使用了最大最小（Max-Min）归一化技术，以提高检测入侵的准确性。此外，我们还将各种当代威胁和典型互联网流量分为九个不同的攻击类别。为了简化数据处理并提高系统效率，我们采用了主成分分析法（PCA）来降低维度。此外，我们还部署了六个先进的 ML 模型，以优化检测能力并准确识别威胁。最后，我们利用同步人工神经网络（ANN）开发了一种安全密钥分配机制。相互学习的过程保证了密钥在物联网网络之间的安全分配，从而降低了保密风险。这种新颖的方法不仅增强了实时识别入侵的能力，还改善了信息管理系统的整体安全状况。这项工作解决了当前 IDS 解决方案的局限性，提出了一个完整的、多方面的安全策略，为信息管理领域的数字安全做出了重大贡献。

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引用次数: 0

Adaptive Single-layer Aggregation Framework for Energy-efficient and Privacy-preserving Load Forecasting in Heterogeneous Federated Smart Grids 用于异构联邦智能电网中节能和保护隐私的负荷预测的自适应单层聚合框架

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-09-20 DOI: 10.1016/j.iot.2024.101376

Habib Ullah Manzoor, Atif Jafri, Ahmed Zoha

Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating data from distributed load networks while ensuring data privacy. However, the heterogeneous nature of smart grid load forecasting introduces significant challenges that current methods struggle to address, particularly for resource-constrained devices due to high computational and communication demands. To overcome these challenges, we propose a novel Adaptive Single Layer Aggregation (ASLA) framework tailored for resource-constrained smart grid networks. The ASLA framework mitigates data heterogeneity issues by focusing on local learning and incorporating partial updates from local devices for model aggregation in adaptive manner. It is optimized for resource-constrained environments through the implementation of a stopping criterion during model training and weight quantization. Our evaluation on two distinct datasets demonstrates that quantization results in a minimal loss function degradation of 0.01% for Data 1 and 1.25% for Data 2. Furthermore, local model layer optimization for aggregation achieves substantial communication cost reductions of 829.2-fold for Data 1 and 5522-fold for Data 2. The use of an 8-bit fixed-point representation for neural network weights leads to a 75% reduction in storage/memory requirements and decreases computational costs by replacing complex floating-point units with simpler fixed-point units. By addressing data heterogeneity and reducing storage, computation, and communication overheads, the ASLA framework is well-suited for deployment in resource-constrained smart grid networks.

联合学习（FL）通过整合来自分布式负载网络的数据，提高了负载预测的准确性，同时确保了数据的私密性。然而，智能电网负荷预测的异构性带来了当前方法难以解决的重大挑战，特别是对于资源受限的设备，因为它们对计算和通信的要求很高。为了克服这些挑战，我们提出了一种为资源受限的智能电网网络量身定制的新型自适应单层聚合（ASLA）框架。ASLA 框架侧重于本地学习，并结合本地设备的部分更新，以自适应的方式进行模型聚合，从而缓解数据异质性问题。通过在模型训练和权重量化过程中实施停止准则，该框架针对资源受限的环境进行了优化。我们在两个不同数据集上进行的评估表明，数据 1 和数据 2 的量化分别导致 0.01% 和 1.25% 的最小损失函数衰减。此外，用于聚合的局部模型层优化实现了通信成本的大幅降低，数据 1 的通信成本降低了 829.2 倍，数据 2 的通信成本降低了 5522 倍。对神经网络权重使用 8 位定点表示法可使存储/内存需求减少 75%，并通过用更简单的定点单元取代复杂的浮点单元降低了计算成本。通过解决数据异构问题并减少存储、计算和通信开销，ASLA 框架非常适合部署在资源受限的智能电网网络中。

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引用次数: 0

Enhancing security through continuous biometric authentication using wearable sensors 利用可穿戴传感器持续进行生物识别认证，提高安全性

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-09-20 DOI: 10.1016/j.iot.2024.101374

Laxmi Divya Chhibbar, Sujay Patni, Siddarth Todi, Ashutosh Bhatia, Kamlesh Tiwari

The paper presents a novel approach for biometric continuous driver authentication (CDA) for secure and safe transportation using wearable photoplethysmography (PPG) sensors and deep learning. Conventional one-time authentication (OTA) methods, while effective for initial identity verification, fail to continuously verify the driver’s identity during vehicle operation, potentially leading to safety, security, and accountability issues. To address this, we propose a system that employs Long Short-Term Memory (LSTM) models to predict subsequent PPG values from wrist-worn devices and continuously compare them with real-time sensor data for authentication. Our system calculates a confidence level representing the probability that the current user is the authorized driver, ensuring robust availability to genuine users while detecting impersonation attacks. The raw PPG data is directly fed into the LSTM model without pre-processing, ensuring lightweight processing. We validated our system with PPG data from 15 volunteers driving for 15 min in varied conditions. The system achieves an Equal Error Rate (EER) of 4.8%. Our results demonstrate that the system is a viable solution for CDA in dynamic environments, ensuring transparency, efficiency, accuracy, robust availability, and lightweight processing. Thus, our approach addresses the main challenges of classical driver authentication systems and effectively safeguards passengers and goods with robust driver authentication.

本文介绍了一种新颖的生物特征连续驾驶员身份验证（CDA）方法，该方法利用可穿戴光敏血压计（PPG）传感器和深度学习实现安全可靠的交通运输。传统的一次性身份验证（OTA）方法虽然对初始身份验证有效，但无法在车辆运行期间持续验证驾驶员的身份，从而可能导致安全、安保和问责问题。为解决这一问题，我们提出了一种系统，该系统采用长短期记忆（LSTM）模型来预测腕戴式设备的后续 PPG 值，并将其与实时传感器数据进行持续比较，以进行身份验证。我们的系统会计算一个置信度，代表当前用户是授权驾驶员的概率，从而在检测冒充攻击的同时确保真正用户的稳健可用性。原始 PPG 数据无需预处理即可直接输入 LSTM 模型，从而确保了轻量级处理。我们使用 15 名志愿者在不同条件下驾驶 15 分钟的 PPG 数据验证了我们的系统。系统的平均错误率 (EER) 为 4.8%。我们的结果表明，该系统是动态环境中 CDA 的可行解决方案，可确保透明度、效率、准确性、稳健可用性和轻量级处理。因此，我们的方法解决了传统驾驶员身份验证系统所面临的主要挑战，并通过强大的驾驶员身份验证功能有效地保护了乘客和货物的安全。

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引用次数: 0

iLocator—A low cost IoT-based hybrid architecture for tracking and locating objects in indoor environments iLocator - 基于物联网的低成本混合架构，用于跟踪和定位室内环境中的物体

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-09-19 DOI: 10.1016/j.iot.2024.101369

Lucas Marquezan, Elmer A. Gamboa Peñaloza, Paulo J.D. de Oliveira Evald, Marlon M. Hernandez Cely, Marcelo L. Rossi, Sigmar de Lima

The tracking of objects in external environments is well established in the literature. Various approaches are based on the data provided by satellite navigation systems. However, this type of technology does not offer the accuracy and precision needed for the location and tracking of objects inside buildings, factories, hospitals, or any other place that is not open-air. This paper presents a novel hybrid low-cost architecture for locating and tracking objects in indoor environments. The proposed solution is based on the concepts of the internet of things, modularity, and low energy consumption, aiming to enhance the usability of the tracking system by using only off-the-shelf components that are easily purchasable and inexpensive. The experimental results indicate high accuracy and the robustness in sending and receiving data, making it feasible to locate objects in indoor environments with obstacles.

跟踪外部环境中的物体在文献中已有明确记载。各种方法都是基于卫星导航系统提供的数据。然而，这类技术无法提供建筑物、工厂、医院或任何其他非露天场所内物体定位和跟踪所需的准确度和精确度。本文提出了一种新型低成本混合架构，用于在室内环境中定位和跟踪物体。所提出的解决方案基于物联网、模块化和低能耗的概念，旨在通过仅使用易于购买且价格低廉的现成组件来提高跟踪系统的可用性。实验结果表明，该系统在发送和接收数据时具有较高的准确性和鲁棒性，使其能够在有障碍物的室内环境中定位物体。

引用次数: 0

Robust and efficient three-factor authentication solution for WSN-based industrial IoT deployment 为基于 WSN 的工业物联网部署提供稳健高效的三因素身份验证解决方案

IF 6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2024-09-18 DOI: 10.1016/j.iot.2024.101372

Khalid Mahmood , Muhammad Asad Saleem , Zahid Ghaffar , Salman Shamshad , Ashok Kumar Das , Mohammed J.F. Alenazi

The relentless advancements in Cyber-Physical Systems (CPS) and Wireless Sensor Networks (WSN) have paved the way for various practical applications across networking, public safety, smart transportation, and industrial sectors. The Industrial Internet of Things (IIoT) integrates these technologies into complex, interconnected environments where vast amounts of data are transmitted between devices and systems. However, the inherent openness of communication channels in IIoT systems introduces distinctive security and privacy vulnerabilities, where malevolent entities can effortlessly intercept, forge, or delete communication messages. These vulnerabilities are exacerbated by the critical nature of industrial applications, where breaches can lead to significant operational disruptions or safety hazards To address these challenges, several authentication protocols have been proposed. Nevertheless, many of these protocols remain susceptible to various security attacks. To ensure privacy and security in IIoT environments, we introduce a robust and efficient authentication protocol for a WSN-based IIoT environment. This protocol preserves the privacy of information transmitted among all entities and provides an effective solution for securing this sensitive information. Additionally, we present a detailed security analysis of the proposed protocol to formally and informally demonstrate its security strength. The performance analysis is carried out to compare the proposed protocol against existing related protocols, with results unequivocally demonstrating that our protocol offers enhanced privacy and security with reduced costs.

网络物理系统（CPS）和无线传感器网络（WSN）的不断进步为网络、公共安全、智能交通和工业领域的各种实际应用铺平了道路。工业物联网（IIoT）将这些技术集成到复杂的互联环境中，大量数据在设备和系统之间传输。然而，IIoT 系统固有的通信渠道开放性带来了明显的安全和隐私漏洞，恶意实体可以毫不费力地拦截、伪造或删除通信信息。这些漏洞因工业应用的关键性而加剧，在这些应用中，漏洞可能导致严重的运行中断或安全隐患。为了应对这些挑战，已经提出了几种身份验证协议。然而，其中许多协议仍然容易受到各种安全攻击。为了确保物联网环境中的隐私和安全，我们为基于 WSN 的物联网环境引入了一种稳健高效的身份验证协议。该协议保护了所有实体之间传输信息的隐私，并为保护这些敏感信息提供了有效的解决方案。此外，我们还对提出的协议进行了详细的安全分析，以正式和非正式地证明其安全强度。我们还进行了性能分析，将提议的协议与现有的相关协议进行了比较，结果明确表明我们的协议在降低成本的同时增强了隐私性和安全性。

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引用次数: 0