Internet of Things最新文献

As urbanization accelerates, the demand for efficient parking surveillance solutions has increased. However, existing solutions often face challenges related to energy consumption, scalability, and reliability. This paper introduces a smart hybrid parking surveillance system integrating wireless sensor networks (WSNs) with vision based solution at the edge for resource constrained IoT devices to address these challenges. The solution leverages WSNs for periodic readings of parking space occupancy and introduces a low power sleep mode in the network for energy efficiency, along with optical verification strategies using computer vision models like R-CNN and Faster R-CNN FPN on ResNet50 and MobileNetv2 backbones for distinguishing between true and false positives in the WSN data for a greater accuracy in parking space occupancy. The system utilizes edge for computing on edge servers resulting in increased responsiveness of the system, reduced data transmission and real time processing of data. The proposed solution is formulated in such a way that it automatically switches between WSN and vision based sensing resulting in less energy consumption and longer lifespan of the system without compromising on accuracy. Through experimental results it is observed that models trained on the MobileNetv2 backbone demonstrated at least twice faster for both processing the images and training compared to those models trained on the ResNet backbone. On the other hand, both Faster R-CNN FPN (input resolution: 1440) and R-CNN (input resolution: 128) models trained on the MobileNetv2 backbone have slightly lower accuracies than the same models trained on the ResNet50 backbone.

Data is becoming increasingly pivotal and foundational in the development of smart agriculture, underscoring the importance of efficient methods for obtaining high-value data. Data sensing methods have become the key technologies and methods to realize the agricultural Internet of Things (IoT). However, in the face of the new agricultural paradigm driven by big data, traditional agricultural IoT confronts numerous challenges at the data sensing level. This article, therefore, adopts a data sensing perspective and, based on the agricultural IoT, explores the evolution of data sensing technology in the agricultural domain. Initially, it introduces a data sensing framework for the agricultural Internet of Things, which integrates cloud and edge computing. Subsequently, it reviews the sensors commonly deployed in agricultural scenarios. Then, common methods for collaborative sensing of agricultural data were discussed from three aspects: intra-node, multiple nodes, and cross-domain. At the same time, the issues of data security and privacy in data collaborative sensing were discussed. Next, we integrate multi-dimensional technology to construct an application case for data sensing in the agricultural IoT. Finally, it discusses the challenges that Collaborative sensing technology encounters within the agricultural IoT.

The Internet of Things (IoT) has emerged as one of the growing fields in digital technology over the past decade. A primary goal of IoT is to connect physical objects to the Internet to provide various services. Due to the vast number and diversity of these objects, referred to as devices, IoT must tackle both traditional and novel theoretical and practical network problems. Among these, multi-interface problems are well-known and have been extensively studied.

This research focuses on one of the newest multi-interface models that fits well within the IoT context. It is known as the Coverage in the budget-constrained multi-interface problem, where the budget represents the total amount of energy in the network, and coverage refers to the model’s goal of activating all required communications among IoT devices. Since most IoT devices are battery-powered, energy consumption must be considered to extend the network’s lifespan. This means selecting the most energy-efficient interface configuration that allows all desired connections to function. To achieve this, both global energy consumption and the local number of active interfaces are limited. Moreover, this model also incentivize devices to turn on the available interfaces to create a more performant network. Finally, this model also takes into account the performance of the networks assigning a profit to devices that activate interfaces and realize connections.

This problem can be represented using an undirected graph where each vertex represents a device, and each edge represents a desired connection. Every device is equipped with a set of available interfaces that can be used to facilitate transmission among the devices. The final goal is to activate a subset of the available interfaces that maximize the total profit, while not violating the constraints.

This problem has been recognized as NP-hard, which is why we decided to investigate the decision version from the perspective of fixed-parameter tractability (FPT) theory. FPT is an advanced area of complexity theory that aims to identify the core complexity of a combinatorial problem by incorporating parameters into the time complexity domain.

We provide two fixed-parameter tractability results, each describing an FPT algorithm. One algorithm is based on the well-known pathwidth parameter, the number of available interfaces, and the maximum available energy. The other algorithm considers pathwidth, the number of available interfaces, and an upper bound on the optimal profit. Finally, we show that these two algorithms can be applied to the maximization version of the problem.

Amid rising concerns about campus safety in Taiwan, particularly with the global trend towards smart cities, integrating the Internet of Things (IoT) into institutional security frameworks has become pivotal. The paper discusses the implementation of using iBeacons and Long Range (LoRa) technology to locating the student position and ensure his safety in the school campus. It uses Internet of Things (IoT) approach in real time to monitor and locate the student presence in the school compound. This paper unveils an innovative design for a campus security system that harnesses the LoRa technology. In the system, the students are equipped with devices containing Bluetooth Low Energy (BLE) beacons to capture and transmit real-time location data. The system response time to locating student in abnormal locations such as cornered and concealed areas is about one second. By extending this system to cover all individuals on campus, a closely monitored environment and areas is enabled that significantly bolstering the security measures. This not only furnishes a dynamic protective layer for educational institutions but also serves as a proactive deterrent against potential security breaches. Ultimately, this research underscores the transformative potential of merging IoT with campus security to ushering in a new era of student safety. LoRa technology offers advantages in battery life, cost-effectiveness, deployment flexibility, and network coverage etc. Therefore, this paper ultimately provides a method of how to utilize the LoRa technology to develop a campus security system. Unlike artificial intelligence (AI)-based image recognition, which raises concerns about privacy and human rights; the features of LoRa’s long-range communication and low power consumption make it a more suitable choice.

Wireless Sensor Networks (WSN) play a vital role in the Internet of Things (IoT) and show great potential in monitoring applications. However, due to harsh environmental conditions and unreliable communication links, WSN often encounter partial data loss during data collection, which inevitably affects the quality of service. To address this challenge, researchers have employed matrix completion techniques to recover missing data by exploiting the low-rank features in the data, but its accuracy is not satisfactory. This paper argues that the spatiotemporal characteristics of the data underlie its low-rank nature, enabling a more accurate capture of the intrinsic patterns within the data. Drawing on this insight, we propose a missing data recovery algorithm based on Temporal Smoothness and Time-Varying Similarity (TSTVS). Unlike traditional low-rank methods, the TSTVS algorithm directly utilizes the structural features of data in the spatiotemporal domain to establish a missing data completion model. Subsequently, the model is converted into an unconstrained optimization problem using the penalty function method, and the gradient descent method is applied to solve it, reconstructing the complete data matrix. Finally, simulation experiments were conducted on three real-world monitoring datasets, comparing the TSTVS with three low-rank methods, Efficient Data Collection Approach (EDCA), Matrix factorization with Smoothness constraints (MFS) and Data Recovery Based on Low Rank and Short-Term Stability(DRLRSS). The experimental results indicate that the proposed TSTVS algorithm consistently outperforms the three low-rank based algorithms in terms of recovery accuracy across different datasets and missing rate scenarios.

Reliable storage and high-speed data networks enable individuals to access high-quality Internet of Things (IoT) data for scientific research through global transactions. Blockchain technology provides transparency for institutions to securely store and manage IoT data, while cross-chain transaction mechanisms facilitate the flow of IoT data. However, fairness issues may arise when it comes to cross-chain transactions of IoT data. This paper proposes a mechanism for multi-attribute data transactions to support cross-chain. The solution utilizes Vickrey–Clarke–Groves (VCG) auction, Paillier, Intel SGX, and other technologies to design a secure and equitable data seller selection scheme. The scheme ensures that the selection process for data sellers is both informed and private. Additionally, we generate a key pair for each attribute in the dataset to produce the corresponding attribute data signature. The dataset’s legitimacy is verified through batch verification to ensure that the data seller’s purchased attributes align with their requirements. The exchange of crypto assets and private keys between data sellers and buyers is designed to achieve fair payment. Our research suggests that the scheme meets the necessary security standards, and simulation results confirm its feasibility and effectiveness.

The Social Internet of Things (SIoT) amalgamates social networks with the Internet of Things (IoT) to enable intelligent devices to form social connections analogous to human networks. This research is grounded in psychological contract theory, which examines the reciprocal mechanisms arising from diverse customer interactions to encourage user engagement and provide recommendations on social media platforms. This study in particular identifies the factors that drive customer engagement on social media. It is unique in its exploration of customer interactions within the framework of psychological contracts across multiple levels of customer engagement (through customer empowerment). The findings reveal that psychological ownership among customers is influenced by empowering interactions on social media, which ultimately drive engagement behaviors.

Distributed Denial of Service (DDoS) attacks have always been an important research topic in the field of information security. Regarding specialized infrastructures such as electric vehicle charging stations, detecting and preventing such attacks becomes even more critical. In the existing literature, most studies on DDoS attack detection focus on traditional methods that analyze network metrics such as network traffic, packet rates, and number of connections. These approaches attempt to detect attacks by identifying anomalies and irregularities in the network, but can have high error rates and fail to identify advanced attacks. Conversely though, detection methods based on system metrics use deeper and more insightful parameters such as processor utilization, memory usage, disk I/O operations, and system behavior. Such metrics provide a more detailed perspective than network-based approaches, allowing for more accurate detection of attacks. However, work in this area is not yet widespread enough further research and improvement are needed. The adoption of advanced system metrics-based methods can significantly improve the effectiveness of DDoS defense strategies, especially in next-generation and specialized infrastructures. This paper evaluates the applicability and effectiveness of Long Short-Term Memory (LSTM) and Feed-Forward Network (FFN) in detecting DDoS attacks against electric vehicle charging stations through system metrics using CICEV2023 dataset. Experimental results show that the LSTM based model offers advantages in terms of speed and processing capacity, while the FFN is superior in terms of the accuracy.

Sharing Cyber Threat Intelligence (CTI) enables organisations to work together to defend against cyberattacks. However, current methods often fail to adequately protect sensitive information, leading to security risks, especially in Intelligent Additive Manufacturing (IAM) systems. In these systems, the security and privacy of incident data collected by IoT devices are essential, as revealing threat information, such as types, impacts, and organisational interests, could be harmful. To address these challenges, we propose the Severity Pattern Matching for a Secure Computable Threat Information Sharing System (SPM-SeCTIS). This system is designed to maintain privacy by allowing intermediaries to pass along threat information without accessing sensitive details, such as the type or severity of the threats. SPM-SeCTIS ensures that attackers cannot determine which incidents organisations are interested in or what specific threats they monitor. The system employs Homomorphic Encryption (HE) to conduct threat pattern matching on encrypted data, keeping sensitive information confidential even during analysis. Our performance tests indicate that SPM-SeCTIS operates efficiently, requiring minimal time for encryption and decryption processes. Additionally, the system scales effectively, handling a large number of subscribers and incidents with ease. Compared to existing methods, SPM-SeCTIS provides improved security measures and better overall performance, making it a robust solution for protecting sensitive threat information.

The healthcare industry has witnessed a transformative impact due to recent advancements in sensing technology, coupled with the Internet of Medical Things (IoMTs)-based healthcare systems. Remote monitoring and informed decision-making have become possible by leveraging an integrated platform for efficient data analysis and processing, thereby optimizing data management in healthcare. However, this data is collected, processed, and transmitted across an interconnected network of devices, which introduces notable security risks and escalates the potential for vulnerabilities throughout the entire data processing pipeline. Traditional security approaches rely on computational complexity and face challenges in adequately securing sensitive healthcare data against evolving threats, thus necessitating robust solutions that ensure trust, enhance security, and maintain data confidentiality and integrity. To address these challenges, this paper introduces a two-phase framework that integrates blockchain technology with IoMT to enhance trust computation, resulting in a secure cluster that supports the quality-of-service (QoS) for sensitive data. The first phase utilizes the decentralized interplanetary file system and hashing functions to create a smart contract for device registration, establishing a resilient storage platform that encrypts data, improves fault tolerance, and facilitates data access. In the second phase, communication overhead is optimized by considering power levels, communication ranges, and computing capabilities alongside the smart contract. The smart contract evaluates the trust index and QoS of each node to facilitate device clustering based on processing capabilities. We implemented the proposed framework using OMNeT++ simulator and C++ programming language and evaluated against the cutting-edge IoMT security approaches in terms of attack detection, energy consumption, packet delivery ratio, throughput, and latency. The qualitative results demonstrated that the proposed framework enhanced attack detection by 6.00%, 18.00%, 20.00%, and 27.00%, reduced energy consumption by 6.91%, 8.19%, 12.07%, and 17.94%, improved packet delivery ratio by 3.00%, 6.00%, 9.00%, and 10.00%, increased throughput by 7.00%, 8.00%, 11.00%, and 13.00%, and decreased latency by 4.90%, 8.81%, 11.54%, and 20.63%, against state-of-the-art methods and was supported by statistical analysis.