Search Results (61)

In many distributed applications such as the Internet of Things (IoT), large amounts of data are being generated that require robust storage solutions. Traditional cloud solutions, although efficient, often lack trust and transparency because of centralized management. To address these issues, we present Robust-DSN, a distributed storage network leveraging the hybrid distributed replication and encoding network (HYDREN) and the distributed swarm workflow scheduler (DSWS) as its main components. Our system uses an interplanetary file system (IPFS) as an underlay storage network and segments it into multiple regions to distribute the failure domain and improve the data’s proximity to users. HYDREN incorporates Reed–Solomon encoding and distributed replication to improve file availability, while DSWS optimizes resource allocation across the network. The uploaded file is encoded into chunks and distributed across distinct optimal nodes leveraging lightweight multithreading. Additionally, Robust-DSN verifies the integrity of all chunks by preserving the hashes when uploading and validating each chunk while downloading. The proposed system provides a comprehensive solution for resilient distributed data storage, focusing on the key challenges of data availability, integrity, and performance. The results reveal that compared with a state-of-the-art system, the proposed system improves file recovery by 15%, even with a 50% peer failure rate. Furthermore, with replication factor 4 and the same failure resilience as IPFS, it saves 50% storage and enhances file recovery by 8%. Robust-DSN acts as a distributed storage platform for emerging technologies, expanding storage system capabilities in a wide range of distributed applications. Full article

The Internet of Things (IoT) consists of millions of devices deployed over hundreds of thousands of different networks, providing an ever-expanding resource to improve our understanding of and interactions with the physical world. Global service discovery is key to realizing the opportunities of the IoT, spanning disparate networks and technologies to enable the sharing, discovery, and utilisation of services and data outside of the context in which they are deployed. In this paper, we present Decentralised Service Registries (DSRs), a novel trustworthy decentralised approach to global IoT service discovery and interaction, building on DSF-IoT to allow users to simply create and share public and private service registries, to register and query for relevant services, and to access both current and historical data published by the services they discover. In DSR, services are registered and discovered using signed objects that are cryptographically associated with the registry service, linked into a signature chain, and stored and queried for using a novel verifiable DHT overlay. In contrast to existing centralised and decentralised approaches, DSRs decouple registries from supporting infrastructure, provide privacy and multi-tenancy, and support the verification of registry entries and history, service information, and published data to mitigate risks of service impersonation or the alteration of data. This decentralised approach is demonstrated through the creation and use of a DSR to register and search for real-world IoT devices and their data as well as qualified using a scalable cluster-based testbench for the high-fidelity emulation of peer-to-peer applications. DSRs are evaluated against existing approaches, demonstrating the novelty and utility of DSR to address key IoT challenges and enable the sharing, discovery, and use of IoT services. Full article

Integration of the Internet of Things (IoT) into the fields of wastewater treatment and water quality prediction has the potential to revolutionize traditional approaches and address urgent challenges, considering the global demand for clean water and sustainable systems. This comprehensive article explores the transformative applications of smart IoT technologies, including artificial intelligence (AI) and machine learning (ML) models, in these areas. A successful example is the implementation of an IoT-based automated water quality monitoring system that utilizes cloud computing and ML methods to effectively address the above-mentioned issues. The IoT has been employed to optimize, simulate, and automate various aspects, such as monitoring and managing natural systems, water-treatment processes, wastewater-treatment applications, and water-related agricultural practices like hydroponics and aquaponics. This review presents a collection of significant water-based applications, which have been combined with the IoT, artificial neural networks, or ML and have undergone critical peer-reviewed assessment. These applications encompass chlorination, adsorption, membrane filtration, monitoring water quality indices, modeling water quality parameters, monitoring river levels, and automating/monitoring effluent wastewater treatment in aquaculture systems. Additionally, this review provides an overview of the IoT and discusses potential future applications, along with examples of how their algorithms have been utilized to evaluate the quality of treated water in diverse aquatic environments. Full article

In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications. Full article

Natural disasters, such as wildfires, can cause widespread devastation. Future-proofing infrastructure, such as buildings and bridges, through technological advancements is crucial to minimize their impact. Fires in disasters often stem from damaged fuel lines and electrical equipment, such as the 2018 California wildfire caused by a power line fault. To enhance safety, IoT applications can continuously monitor the health of emergency personnel. Using Bluetooth 5.0 and wearables in mesh networks, these apps can alert others about an individual’s location during emergencies. However, fire can disrupt wireless networks. This study assesses Bluetooth 5.0’s performance in transmitting signals in fire conditions. It examined received signal strength indicator (RSSI) values in a front open-fire chamber using both Peer-to-Peer (P2P) and mesh networks. The experiment considered three transmission heights of 0.61, 1.22, and 1.83 m and two distances of 11.13 m and 1.52 m. The study demonstrated successful signal transmission with a maximum loss of only 2 dB when transmitting through the fire. This research underscores the potential for reliable communication in fire-prone environments, improving safety during natural disasters. Full article

The widespread adoption of distributed energy resources (DERs) and the progress made in internet of things (IoT) and cloud computing technologies have enabled and facilitated the development of various smart grid applications and services. This study aims to develop and implement a peer-to-peer (P2P) energy trading platform that allows local energy trading between consumers and prosumers within a microgrid which combines IoT and blockchain technologies. The proposed platform comprises an IoT-cloud home energy management system (HEMS) responsible for gathering and storing energy consumption data and incorporates a blockchain framework that ensures secure and transparent energy trading. The proposed IoT–blockchain architecture utilizes a Chainlink oracle network and a private Ethereum blockchain. Through the use of smart contracts, consumers and prosumers can participate in an open auction to trade energy, while the settlement process involves acquiring external energy data from an API through the oracle network. The performance of the platform is evaluated through a testbed scenario using real-world energy data from a real house in Valparaiso, Chile, while storing those measurements in AWS cloud, validating the feasibility of the proposed architecture in enabling local energy trading. This work contributes to the development of energy management systems by providing a real-world implementation of an IoT–blockchain architecture for local energy trading. The integration of these technologies will allow for a more efficient and secure energy trading system that can benefit prosumers, consumers, and utilities. Full article

The internet of things (IoT) is a complex system that includes multiple technologies and services. However, its heterogeneity can result in quality-of-service (QoS) issues, which may lead to security challenges. Software-defined network (SDN) provides unique solutions to handle heterogeneity issues in large-scale IoT networks. Combining SDN with IoT networks has great potential for addressing extreme heterogeneity issues in IoT networks. Numerous researchers are investigating various techniques to resolve heterogeneity issues in IoT networks by integrating SDN. Our study focuses on the SDN-IoT domain to improve QoS by addressing heterogeneity. Heterogeneity in SDN-IoT networks can increase the response time of controllers. We propose a framework that can alleviate heterogeneity while maintaining QoS in SDN-IoT networks. The framework converts m heterogeneous controllers into n homogeneous groups based on their response time. First, we examine the impact of the controller’s bandwidth and find that the system throughput decreases when the controller’s bandwidth is lowered. Next, we implement a simple strategy that considers both the bandwidth and service time when selecting the peer controller. Our results show some improvement in the framework, indicating its potential to alleviate heterogeneity while maintaining QoS and other metrics. Full article

The current trends in 5G and 6G systems anticipate vast communication capabilities and the deployment of massive heterogeneous connectivity with more than a million internet of things (IoT) and other devices per square kilometer and up to ten million gadgets in 6G scenarios. In addition, the new generation of smart industries and the energy of things (EoT) context demand novel, reliable, energy-efficient network protocols involving massive sensor cooperation. Such scenarios impose new demands and opportunities to cope with the ever-growing cooperative dense ad hoc environments. Position location information (PLI) plays a crucial role as an enabler of several location-aware network protocols and applications. In this paper, we have proposed a novel context-aware statistical dead reckoning localization technique suitable for high dense cooperative sensor networks, where direct angle and distance estimations between peers are not required along the route, as in other dead reckoning-based localization approaches, but they are obtainable from the node’s context information. Validation of the proposed technique was assessed in several scenarios through simulations, achieving localization errors as low as 0.072 m for the worst case analyzed. Full article

The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited distributed deep learning paradigms. Federated learning is one of the most promising frameworks, where a server works with local learners to train a global model. The intrinsic heterogeneity of IoT devices, or non-independent and identically distributed (Non-I.I.D.) data, combined with the unstable communication network environment, causes a bottleneck that slows convergence and degrades learning efficiency. Additionally, the majority of weight averaging-based model aggregation approaches raise questions about learning fairness. In this paper, we propose a peer-to-peer federated learning (P2PFL) framework based on Vision Transformers (ViT) models to help solve some of the above issues and classify COVID-19 vs. normal cases on Chest-X-Ray (CXR) images. Particularly, clients jointly iterate and aggregate the models in order to build a robust model. The experimental results demonstrate that the proposed approach is capable of significantly improving the performance of the model with an Area Under Curve (AUC) of 0.92 and 0.99 for hospital-1 and hospital-2, respectively. Full article

Cloud-computing capabilities have revolutionized the remote processing of exploding volumes of healthcare data. However, cloud-based analytics capabilities are saddled with a lack of context-awareness and unnecessary access latency issues as data are processed and stored in remote servers. The emerging network infrastructure tier of fog computing can reduce expensive latency by bringing storage, processing, and networking closer to sensor nodes. Due to the growing variety of medical data and service types, there is a crucial need for efficient and secure architecture for sensor-based health-monitoring devices connected to fog nodes. In this paper, we present publish/subscribe and interest/resource-based non-DHT-based peer-to-peer (P2P) RC-based architecture for resource discovery. The publish/subscribe communication model provides a scalable way to handle large volumes of data and messages in real time, while allowing fine-grained access control to messages, thus enabling heightened security. Our two − level overlay network consists of (1) a transit ring containing group-heads representing a particular resource type, and (2) a completely connected group of peers. Our theoretical analysis shows that our search latency is independent of the number of peers. Additionally, the complexity of the intra-group data-lookup protocol is constant, and the complexity of the inter-group data lookup is O(n), where n is the total number of resource types present in the network. Overall, it therefore allows the system to handle large data throughput in a flexible, cost-effective, and secure way for medical IoT systems. Full article

The Internet of Things (IoT) is experiencing widespread adoption across industry sectors ranging from supply chain management to smart cities, buildings, and health monitoring. However, most software architectures for the IoT deployment rely on centralized cloud computing infrastructures to provide storage and computing power, as cloud providers have high economic incentives to organize their infrastructure into clusters. Despite these incentives, there has been a recent shift from centralized to decentralized architectures that harness the potential of edge devices, reduce network latency, and lower infrastructure costs to support IoT applications. This shift has resulted in new edge computing architectures, but many still rely on centralized solutions for managing applications. A truly decentralized approach would offer interesting properties required for IoT use cases. In this paper, we introduce a decentralized architecture tailored for large-scale deployments of peer-to-peer IoT sensor networks and capable of run-time application migration. We propose a leader election consensus protocol for permissioned distributed networks that only requires one series of messages in order to commit to a change. The solution combines a blockchain consensus protocol using Verifiable Delay Functions (VDF) to achieve decentralized randomness, fault tolerance, transparency, and no single point of failure. We validate our solution by testing and analyzing the performance of our reference implementation. Our results show that nodes are able to reach consensus consistently, and the VDF proofs can be used as an entropy pool for decentralized randomness. We show that our system can perform autonomous real-time application migrations. Finally, we conclude that the implementation is scalable by testing it on 100 consensus nodes running 200 applications. Full article

Rice lodging seriously affects rice quality and production. Traditional manual methods of detecting rice lodging are labour-intensive and can result in delayed action, leading to production loss. With the development of the Internet of Things (IoT), unmanned aerial vehicles (UAVs) provide imminent assistance for crop stress monitoring. In this paper, we proposed a novel lightweight detection system with UAVs for rice lodging. We leverage UAVs to acquire the distribution of rice growth, and then our proposed global attention network (GloAN) utilizes the acquisition to detect the lodging areas efficiently and accurately. Our methods aim to accelerate the processing of diagnosis and reduce production loss caused by lodging. The experimental results show that our GloAN can lead to a significant increase in accuracy with negligible computational costs. We further tested the generalization ability of our GloAN and the results show that the GloAN generalizes well in peers’ models (Xception, VGG, ResNet, and MobileNetV2) with knowledge distillation and obtains the optimal mean intersection over union (mIoU) of 92.85%. The experimental results show the flexibility of GloAN in rice lodging detection. Full article

Blockchain introduces challenges related to the reliability of user identity and identity management systems; this includes detecting unfalsified identities linked to IoT applications. This study focuses on optimizing user identity verification time by employing an efficient encryption algorithm for the user signature in a peer-to-peer decentralized IoT blockchain network. To achieve this, a user signature-based identity management framework is examined by using various encryption techniques and contrasting various hash functions built on top of the Modified Merkle Hash Tree (MMHT) data structure algorithm. The paper presents the execution of varying dataset sizes based on transactions between nodes to test the scalability of the proposed design for secure blockchain communication. The results show that the MMHT data structure algorithm using SHA3 and AES-128 encryption algorithm gives the lowest execution time, offering a minimum of 36% gain in time optimization compared to other algorithms. This work shows that using the AES-128 encryption algorithm with the MMHT algorithm and SHA3 hash function not only identifies malicious codes but also improves user integrity check performance in a blockchain network, while ensuring network scalability. Therefore, this study presents the performance evaluation of a blockchain network considering its distinct types, properties, components, and algorithms’ taxonomy. Full article

The smart manufacturing ecosystem enhances the end-to-end efficiency of the mine-to-market lifecycle to create the value chain using the big data generated rapidly by edge computing devices, third-party technologies, and various stakeholders connected via the industrial Internet of things. In this context, smart manufacturing faces two serious challenges to its industrial IoT big data integrity: real-time transaction monitoring and peer validation due to the volume and velocity dimensions of big data in industrial IoT infrastructures. Modern blockchain technologies as an embedded layer substantially address these challenges to empower the capabilities of the IIoT layer to meet the integrity requirements of the big data layer. This paper presents the trusted consortium blockchain (TCB) framework to provide an optimal solution for big data integrity through a secure and verifiable hyperledger fabric modular (HFM). The TCB leverages trustworthiness in heterogeneous IIoT networks of governing end-point peers to achieve strong integrity for big data and support high transaction throughput and low latency of HFM contents. Our proposed framework drives the fault-tolerant properties and consensus protocols to monitor malicious activities of tunable peers if compromised and validates the signed evidence of big data recorded in real-time HFM operated over different smart manufacturing environments. Experimentally, the TCB has been evaluated and reached tradeoff results of throughput and latency better than the comparative consortium blockchain frameworks. Full article

Renewable energy resources have been gaining ground in recent years and we are on the verge of a decentralized energy market with consumers becoming prosumers. Platforms that facilitate peer-to-peer (P2P) sale or purchase of energy are therefore essential. This paper presents a way to trade energy across P2P networks using blockchain technology. The main server is a Raspberry Pi 4 Model B (Pi4B), on which the user interface (UI) as well as the private Ethereum blockchain are configured. The blockchain also implements a smart contract. For the purpose of developing the UI that provides assistance in conducting trading activities, an open-source Angular framework is used. Also explored in the study is the development of an Internet of Things (IoT) server using the latest ESP32-S3 microcontroller. The field instrumentation devices (FIDs) are connected to the microcontroller for the purpose of data acquisition and for subsequent transmission to an IoT server. The blockchain network maintains a record of all transactions in an immutable manner. Assuring security is achieved through a local configuration of the system, hosted on a private network with restricted access. For the purposes of information security and data integrity, additional security measures are also considered, such as a secret recovery phrase, firewalls, login credentials and private key. Among the servers and clients, there is an implementation of a Hypertext Transfer Protocol. The P2P energy trading approach involving renewable energy designed for remote communities is explained and illustrated in this paper. Full article