A PLS blockchain for IoT applications: protocols and architecture

The Internet of Things (IoT) is a disruptive technology that underpins Industry 5.0 by integrating various service technologies to enable intelligent connectivity among smart objects. These technologies enhance the convergence of Information Technology (IT), Operational Technology (OT), Core Technology (CT), and Data Technology (DT) networks, improving automation and decision-making capabilities. While cloud computing has become a mainstream technology across multiple domains, it struggles to efficiently manage the massive volume of OT data generated by IoT devices due to high latency, data transfer costs, limited resilience, and insufficient context awareness. Fog computing has emerged as a viable solution, extending cloud capabilities to the edge through a distributed peer-to-peer (P2P) network, enabling decentralized data processing and management. However, IoT networks still face critical challenges, including connectivity, heterogeneity, scalability, interoperability, security, and real-time decision-making constraints. Security is a key challenge in IoT implementations, including secure data communication, IoT edge and fog device identity, end-to-end authentication, and secure storage. This paper presents an efficient blockchain-based framework that creates a secure end-to-end communication cooperative flow IoT network. The framework utilizes a hybrid blockchain network that collaborates to offer a collaborative flow of end-to-end secure communication from end devices to cloud storage. The fog servers will maintain a private blockchain as a next-generation public key infrastructure to identify and authenticate the IoT’s edge devices. The consortium blockchain will be maintained in the cloud and integrated with the permission blockchain system. This system ensures secure cloud storage, authorization, efficient key exchange, and remote protection (encryption) of all sensitive information. To improve the synchronization and block generation, reduce overhead, and ensure scalable IoT network operation, we proposed the threshold signature-based Proof of Stake and Validation (PoSV) consensus. Additionally, lightweight authentication protects resource-constrained IoT nodes using an aggregate signature, ensuring security and performance in real-time scenarios. The proposed system is implemented, and its performance is evaluated using key metrics such as cryptographic processing overhead, consensus efficiency, block acceptance time, and transaction delay. The findings show that threshold signature-based Proof of Stake and Validation (PoSV) consensus, reduces the computational burden of individual signature verification, which results in an optimized transaction latency of 80–150 ms, compared to the previous 100–200 ms without Non-PoSV. Additionally, aggregating multiple signatures from different authentication events reduces signing time by 1.98 ms compared to the individual signature time of 2.72 ms and the overhead of verifying multiple individual transactions is 2.87 ms is significantly reduced to1.46 ms along with authentication delay ranges between 95–180 ms. Hence, the proposed framework improves over existing approaches regarding linear computing complexity, increased cryptographic methods, and a more efficient consensus process.

STMIK Lombok is an institution engaged in education that process data, about students, student practical data, lecturer data, and correspondence. Media file storage applied STMIK Lombok still on a separate computer, both academic data and practical data, so to access the file must be done on the computer where the storage. Based on the capability of Network Attached Storage in local network, by looking at the need of data storage media on the network at STMIK Lombok and the observation result then designed a network architecture by utilizing Network Attached Storage device with focus on fast storage and data transfer process. Stages of research include Data collection, design, and methods of testing To be able to help solve the problem of separate data access then implemented Network Attached Storage (NAS) which is a network to perform distribution of storage assets owned by the server of a network system that has the advantage of faster access to data stored through Local Area Network, minimal cost and cheap and easy maintenance, from setup to configuration, and available Open Source Software. After doing the testing phase it can be concluded that FreeNas as an operating system used for the processing of network storage media is very capable of handling the task well and the facilities that exist in it is very supportive in storage and accessing files. FreeNas web is very helpful and allows users to configure it other than it can be used without having to spend a large cost, and with the facilities in the FreeNas.

Energy-limited devices and connectivity in complicated environments are two main challenges for Internet of Things (IoT)-enabled mobile networks, especially when IoT devices are distributed in a disaster area. Unmanned aerial vehicle (UAV)-enabled simultaneous wireless information and power transfer (SWIPT) is emerging as a promising technique to tackle the above problems. In this article, we establish an emergency communications framework of UAV-enabled SWIPT for IoT networks, where the disaster scenarios are classified into three cases, namely, dense areas, wide areas and emergency areas. First, to realize wireless power transfer for IoT devices in dense areas, a UAV-enabled wireless power transfer system is considered where a UAV acts as a wireless charger and delivers energy to a set of energy receivers. Then, a joint trajectory planning and resource scheduling scheme for a multi-UAVs system is discussed to provide wireless services for IoT devices in wide areas. Furthermore, an intelligent prediction mechanism is designed to predict service requirements (i.e., data transmission and battery charging) of the devices in emergency areas, and accordingly, a dynamic path planning scheme is established to improve the energy efficiency (EE) of the system. Simulation results demonstrate the effectiveness of the above schemes. Finally, potential research directions and challenges are also discussed.

Due to geography, structural, and economic factors, California has been the site of multiple climate crises. Deaf and disabled Californians face especially high health risks in crises, partly due to inaccessible risk communications before, during, and after disasters. California's Offices of Emergency Services (OES) play an important role in ensuring inclusive emergency communication for disabled people. This study examines accessibility of climate‐related risk communications in California's OES for deaf and disabled people and is intended to provide guidance to the cities, counties, and the state to improve risk communication plans for disabled people. Key informants from county ( n = 44) and city ( n = 13) OES offices were surveyed about whether their OES provided accessible emergency communications, had barriers, and had needs to improve accessible communications. Findings reveal that urban OES offices more frequently report using or being aware of accessible communication strategies, while rural offices face challenges due to limited resources and infrastructure. Rural OES have fewer partnerships with deaf‐serving organizations and are less likely to have resources to provide captioning and American Sign Language (ASL) interpreters, compared to urban OES. The study highlights the need for improved training, resource allocation, and policy integration to address accessible communication gaps, especially in rural areas. Recommendations include increased training and funding for rural OES offices, stronger partnerships with disability advocacy groups, and the adoption of universal design principles in climate risk communications. This would ensure that all Californians—regardless of location or ability—receive the information they need to prepare for and respond to climate disasters.

The size of Internet of Things (IoT) networks is increasing exponentially, and parallelly, the security threats are also escalating. As many of the IoT devices run on limited resources, any intrusion attack based on DoS, packet flooding, man-in-middle and probing is effective to disturb, distract and defunct the networks. A intrusion detection is always a challenging task for the network administrator or any automated software system. A machine learning network-based intrusion detection system (IDS) works efficiently and detects such attacks in any type of networks. It analyzes the packets transmitting over the networks without bothering the IoT devices. Hence, IDS systems are highly crucial and important for IoT network security. This paper proposes a machine learning network-based IDS for securing IoT networks. The proposed technique uses classification techniques to classify a network packet as normal or some kind of malicious attacks. The model was trained with a dataset which is a network logs collected from a network transmitting data from NodeMCU with ESP8266 wi-fi module to a server. The data was captured from the ultrasonic sensor with Arduino and NodeMCU used to monitor a network. For choosing the best detection model out of eight classification based models were studied. The decision tree and random forest are most accurate models as compared to other classification techniques. The comparative analysis of these models is analyzed and discussed in the result section.KeywordsIoTNetwork securityIntrusion detection systemMachine learningAttacks

In the wireless Internet of Things (IoT) networks with resource-constrained devices, fog computing has been introduced to deal with the computation-intensive applications at the edges of the networks. While fog computing decreases the computation delay and fronthaul traffic data, it also brings the severe challenge on complex resource allocation of the available computation and communication resources under the stringent quality of service (QoS) requirements. In this paper, we investigate the problem of tasks scheduling and heterogeneous resource allocation for multiple devices in the wireless IoT networks. The IoT devices that collect a massive amount of data need to make proper offloading decision to transfer the data to the fog computing nodes (FNs). Moreover, to support a massive number of device connections and transfer a huge amount of data with low latency and limited resource, we consider the deployment of non-orthogonal multiple access (NOMA) in IoT networks, which enables multiple IoT devices to simultaneously transmit data to the same FN at the same time, frequency, and code domain. We jointly optimize the allocation of resource blocks and transmit power of multiple IoT devices, subject to the respective QoS requirements. Furthermore, the optimization problem is formulated as a mixed-integer nonlinear programming problem to minimize the system energy consumption. Since it is an NP-hard problem, we introduce an improved genetic algorithm (IGA) to solve it. The simulation results show that the proposed scheme achieves good performance in throughput, delay, outage probability, and energy consumption.

Internet of Things networks are the large-scale distributed systems consisting of a massive number of simple devices communicating, typically, over a shared wireless medium. This new paradigm requires novel ways of coordinating access to limited communication resources without introducing unreasonable delays. Herein, the optimal design of a remote estimation system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> sensors communicating with a fusion center via a collision channel of limited capacity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k\leq n$</tex-math></inline-formula> is considered. In particular, for independent and identically distributed observations with a symmetric probability density function, we show that the problem of minimizing the mean-squared error with respect to a threshold strategy is quasi-convex. When coordination among sensors via a local communication network is available, the online learning of possibly unknown parameters of the probabilistic model is possible, enabling each sensor to optimize its own threshold autonomously. We propose two strategies for remote estimation with local communication: 1) one strategy swiftly reaches the performance of the optimal decentralized threshold policy and 2) the second strategy approaches the performance of the optimal centralized scheme with a slower convergence rate. A hybrid scheme that combines the best of both approaches is proposed, offering fast convergence and excellent performance.

Purpose and Originality: The main problem in data acquisition from different sensor nodes is the lack of access to a local wireless network. The purpose of this paper is to present the possibilities offered by the ESP-NOW communication protocol on the development board ESP32 from Espressif (1). Method: For this paper we used quantitative method. 3 wireless communication modules are used that exchange information from 3 different sensor nodes. Results: The used system provides data retrieval from sensor nodes in conditions of non-existence of local internet network, with satisfactory communication distance (190m). Society: Тhis type of devices is especially useful for use in systems for measurement of data acquisition from environmental protection systems. Limitations / further research: Direction for further research: a system change is recommended in order to increase the achieved distances of Wi-Fi information exchange Keywords: Data acquisition, Measurement, Development board, MCU, ESP32, ESP-NOW.

NFS is considered to be the bottleneck in cluster computing environment because of its limited resources and centralized data management. With the development of hardware, NFS server has more than one I/O channel, more storage space and more powerful CPU. In this paper, we describe the design and the implementation of a new logical file system called United-FS. It can make storage devices connected to multiple I/O channels work concurrently and cooperatively. It can be exported by NFS server to provide a single file system image to clients by hiding a variety of native file systems built on different type of storage devices. This paper also compares the United-FS with the software RAID system both from theoretical analysis and experiments. The results show that United-FS is much more flexible and its performance is better than software RAID in most cases.

The energy transition presents policymakers and communities with emerging opportunities, divergent pathways and consequential choices. It is possible, on the one hand, to address climate change by moving to zero-carbon power generation while simultaneously ensuring equitable and affordable access to abundant clean energy. The transition is also an opportunity for the fossil fuel industry and other power sector actors to create new channels for profitmaking that can distract from addressing the causes of climate change, increase inequity in energy access and pose threats to safety and economic well-being, particularly for low-wealth households and environmental justice communities. This article addresses two prominent energy transition plans being pursued in the United States—carbon capture and top-down “distributed” energy. These strategies can, in varying ways, prove counter-productive to climate goals and human needs. An alternative framework is then presented: local solar generation and storage networks, structured for equitable and affordable access, reliability, local control and decarbonization.

BackgroundTotal knee arthroplasty (TKA) is a surgical procedure primarily used to treat patients with end-stage knee osteoarthritis (KOA). Postoperative physical exercise is a critical part of the overall treatment of KOA and can bring significant benefits to the patients' recovery. Wearable devices can monitor patients' exercise data and upload it to the physician's workstation. This allows the rehabilitation physician to make timely adjustments based on the patients' movement feedback, and the surgeon can be informed of the patients' functional status. Overall, this study aims to evaluate the effectiveness of using wearable monitoring devices for rehabilitation exercise after TKA, with a focus on cost, time savings, and patient outcomes.Method/designThis is a single-center, single-blinded, parallel randomized controlled trial conducted at Xi'an Honghui Hospital, a regional orthopedic medical center. Eligible patients will be recruited to participate in the study, and baseline data collection and clinical assessments will be conducted at the time of admission. Using the principle of random allocation, recruited patients will be divided into either the experimental or control group. Both groups will undergo a standard, widely promoted rehabilitation program. The patients in the experimental group will wear equipment to detect and track mobility in the lower limbs. All patients will return to the outpatient clinic for follow-up assessments at 2 weeks, 12 weeks, and 24 weeks after discharge, where outcome indicators will be measured. The primary outcome will be the cost and time after discharge, while secondary outcomes will include the 6-min walk test (6MWT), range of motion (ROM), visual analog scale (VAS), American Knee Society Score (KSS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC).DiscussionWe should encourage the adoption of novel, easy-to-use, supervised devices if they prove to be beneficial for patients in terms of cost, time, and effectiveness after TKA. This type of device is particularly important for people in remote rural areas, those with limited financial resources, and those who are reluctant to return to hospitals for follow-up care.Trial registration Chinese Clinical Trial Registry ChiCTR2300068418. Registered on 17 February 2023.

Non-Fungible Token (NFT) has gained worldwide attention, relying on their superior data representation, as a solution for describing complex digital assets. However, the scale of NFT content data is so huge that we have to use P2P storage network to store it. Due to the lack of management capabilities of global resource allocation, retrieving content in existing P2P storage network relies heavily on distributed collaboration and cannot support low-latency off-line retrieval. Besides if an accident occurs on the blockchain or storage network that is different from the expectation, such as fork, the data state on-chain and off-chain would may be inconsistent. Therefore, we propose the concept of State for P2P storage network to represent the resource allocation of data and assist in the off-line retrieval of resources. We propose a state-aware model based on the permissioned blockchain and P2P storage network, which core is a data structure called StateSnap. The model supports the verification of the consistency of the on-chain and off-chain data and support State rollback and switching. Through experiments, we show that our model reduces the data retrieval time by about 78\(\%\) compared to the traditional IPFS and performs well in terms of scalability, robustness, and State switch efficiency.KeywordsNFTBlockchainP2P storage networkRetrievalMPT

We consider the Internet of Things (IoT) with malware diffusion and seek optimal malware detection strategies for preserving the privacy of smart objects in IoT networks and suppressing malware diffusion. To this end, we propose a malware detection infrastructure realized by an intrusion detection system (IDS) with cloud and fog computing to overcome the IDS deployment problem in smart objects due to their limited resources and heterogeneous subnetworks. We then employ a signaling game to disclose interactions between smart objects and the corresponding fog node because of malware uncertainty in smart objects. To minimize privacy leakage of smart objects, we also develop optimal strategies that maximize malware detection probability by theoretically computing the perfect Bayesian equilibrium of the game. Moreover, we analyze the factors influencing the optimal probability of a malicious smart object diffusing malware, and factors influencing the performance of a fog node in determining an infected smart object. Finally, we present a framework to demonstrate a potential and practical application of suppressing malware diffusion in IoT networks.

Periodic broadcasting (PB) is a scalable technique for providing video-on-demand services. It significantly reduces server input and output (I/O) and backbone network bandwidth requirements, but increases the clients' need for storage space and network bandwidth. Traditional protocols assume homogeneous clients with identical resources. In practice, however, clients have very different bandwidths, which are usually not sufficient for video-on-demand service from a PB server. Existing work on heterogeneous clients has focused on devising broadcast schedules that cater to low-bandwidth clients; these schedules inevitably require additional backbone network bandwidth between the server and the clients. In this paper, we propose a scheme to significantly reduce the waiting time of all heterogeneous clients, without the need for any additional backbone bandwidth. This scheme uses a proxy buffer within video-on-demand systems using PB. In the proposed system, the server broadcasts a video using one of the traditional PB protocols. Simultaneously, the proxy receives the stream from the server and stores it in its local buffer, then broadcasts the stored data to the clients in its local network. Because the proxy provides extra, transparent channels to the server, clients are likely to reduce their reception bandwidth requirements through the use of efficient reception schedules using the extra channels.

Introduction: Midline catheters have been reported to be an effective and safe means of providing patients with intravenous access within the hospital and community setting. With minimal experience in the introduction of a midline service across the local health network, a regional hospital pursued this task. This observational study assesses the provision of a safe clinical framework for midline insertion, and the improvement of patient care and experiences by avoiding treatment interruptions and unnecessary cannulation attempts from failed traditional peripheral vascular access devices. Methods: From the introduction of the midline service in June 2018, outcome measures of all patients who received a midline over the following two-year period were documented including rate of line success, complication rates, dwell time, and the number of insertion attempts. Results: The midline service provided 207 lines over a two-year period with a total dwell time of 1,585 days. Project goals were achieved with 85% (Aim ≥ 85%) of all lines completing treatment prior to removal. First attempt insertion was 86% (Aim ≥ 80%) with a maximum insertion attempt of two. Rates of line-related complications were less than 8%, with five documented cases of phlebitis (2.5%) and one deep vein thrombosis with no infections documented. Conclusion: Despite limited resources, a successful midline service was introduced. Future expansion will see an increase in insertor numbers providing improved access to the service.