Efficient Data Delivery Research Articles

Supporting critical downlink traffic in LoRaWAN

LoRaWAN, a low-power wide-area network (LPWAN) technology, has been successfully used in the Internet of Things (IoT) industry over the last decade. It is an easy-to-use, long-distance communication protocol combined with minimal power consumption. Supporting critical downlink traffic in LoRaWAN networks is crucial for ensuring the reliable and efficient delivery of essential data in certain actuating applications. However, challenges arise when prioritizing critical downlink traffic, including commands, alerts, and emergency notifications that demand immediate attention from actuating devices. This paper explores strategies to improve downlink traffic delivery in LoRaWAN networks, focusing on enhancing reliability, fairness, and energy efficiency through prioritization techniques and network parameter configurations in the EU868 spectrum. Theoretical as well as simulation results provide insights into the effectiveness of the available solutions for supporting critical downlink traffic in LoRaWAN networks.

A secured routing algorithm for cluster-based networks, integrating trust-aware authentication mechanisms for energy-efficient and efficient data delivery

A secured routing algorithm for cluster-based networks, integrating trust-aware authentication mechanisms for energy-efficient and efficient data delivery

Optimizing energy-efficient cluster head selection in wireless sensor networks using a binarized spiking neural network and honey badger algorithm

Wireless Sensor Networks (WSNs) play a vitalparton some applications, like environmental monitoring, healthcare, industrial automation. Optimizing the performance of WSN is essential to ensure efficient data delivery while conserving energy resources. This manuscript addresses the challenges of Multi-Objective Cluster Head Selection and Energy-Aware Routing in WSNs through the development of the Binarized Spiking Neural Network optimized with the Honey Badger Algorithm (EACHS-BzSpNNHBA-WSN). The appropriate cluster heads selection is vital for the overall presentation of WSNs, influencing data aggregation, routing, and energy consumption. Conventional approaches often struggle to balance the conflicting objectives of maximizing network lifetime, minimizing energy consumption, and ensuring timely data delivery. Therefore, there is a pressing need for innovative solutions capable of addressing these multi-objective optimization challenges. In this study, the EACHS-BzSpNNHBA-WSN framework is proposed, leveraging the computational power of Binarized Spiking Neural Networks and the optimization capabilities of the Honey Badger Algorithm to identify optimal cluster heads in WSNs. By formulating the cluster head selection as a multi-objective optimization problem, the aim is to achieve robust data delivery while minimizing energy consumption and extending the network lifetime. The proposed framework is implemented in MATLAB platform. The performance assessment is conducted using various metrics, including the number of active nodes, packet drop rate, energy consumption, network lifetime, delay, throughput, packet delivery ratio. Comparative analysis with existing models demonstrates the effectiveness of the EACHS-BzSpNNHBA-WSN approach.

LCTCS: Low-Cost and Two-Channel Sparse Network for Hyperspectral Image Classification

Using convolutional neural networks (CNNs) in classifying hyperspectral images (HSIs) has achieved quite good results in recent years. It is widely used in agricultural remote sensing, geological exploration, environmental monitoring, and marine remote sensing. Unfortunately, the complexity of network structures used for hyperspectral image classification challenges the efficient delivery of HSI data extremely, and existing methods suffer from a large amount of redundancy in the network weight parameters during training, as they either require huge computational resources or make inefficient use of storage space when designing the network structure, and many of the parameters that waste computational resources contribute less to the rich spectral and spatial information transfer in HSI. So we introduce LCTCS, a better low-memory and less-parametric network approach. LCTCS aims to improve the efficiency of computational resource utilization with advanced classification performance and lower levels of computational resources. Unlike the conventional 2D and 3D convolution used previously, we use simple and efficient 3D grouped convolution as a vehicle to convey the semantic features of HSIs. More specifically, we design a novel two-channel sparse network to classify HSIs since grouped 3D convolution conveys the properties of hyperspectral data well in the time and space domains.We have compared LCTCS with eight widely used network methods on four publicly available hyperspectral datasets for learning HSI information. A series of experiments shows that the model architecture designed has 65.89%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$65.89 \\%$$\\end{document} less storage space than the DBDA method, consumes 67.36%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$67.36 \\%$$\\end{document} fewer computational resources than the SSRN method on the IP dataset, and accomplishes a highly accurate classification task with the number of parameters accounting for only 1.99%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.99 \\%$$\\end{document} that of the DBMA method.

An optimized cooperative data transmission scheme in relay‐enabled cellular communication networks

SummaryCooperative transmission is of great significance in data transmission and resource schedule in mobile cellular communication networks. To attain the desired network transmission performance objectives over the whole cellular network system, we propose an appropriate cooperative data transmission scheme to optimize the data delivery efficiency in terms of the transmission time consumption for each and every transmission group. The proposed transmission optimization scheme includes the tasks of determining the transmission group, allocating the channel rate resource, specifying the cooperative transmission mechanism, and performing resource allocation and schedule as well. Firstly, we propose a utility function based rate allocation scheme to optimize the channel rate resource utilization for all those transmission groups over the cellular communication network. Based on it, we further present a cooperative data transmission scheme to deal with the data delivery and reception for all those subgroups with high efficiency over the whole network system. Particularly, we propose a resource efficiency function‐based coordination scheduling (EFCS) scheme to conduct the cooperative data transmission and resource schedule as well for each transmission group within the network. The EFCS scheme facilitates to determine the subgroups for schedule over each scheduling cycle, which can produce the best data transmission efficiency across both the direct and cooperative transmission hop. Both the theoretical analysis and the simulation evaluation results verify the superior performance of our proposed cooperative data transmission scheme.

Efficient Time-Series Data Delivery in IoT With Xender

Efficient Time-Series Data Delivery in IoT With Xender

A comprehensive survey of performance, security and privacy issues in the network interface layer of the TCP/IP

The network interface layer of the TCP/IP protocol suite, primarily comprised of the Internet Protocol (IP), serves as the backbone of modern internet communication. With its efficient data delivery, The network interface layer, presents key challenges in terms of performance, security, and privacy. This comprehensive survey delves into these three crucial aspects, analyzing the inherent vulnerabilities, limitations of the interface layer, and provide solutions of the related problems. The performance analysis explores throughput, latency, and bandwidth constraints, along with solutions such as bandwidth allocation and optimization techniques. Vulnerabilities within Network Interface Layer, including denial-of-service attacks and MAC address spoofing, are discussed, along with a review of existing security mechanisms. Privacy flaws are examined, covering MAC address tracking, profiling risks, and anonymization techniques, while also addressing privacy considerations on the Internet of Things. The survey analyzes several case studies providing comparative analysis of the network interface layer protocols, with support of the real world scenarios including performance analysis in high density environment, and security and privacy risks in smart homes networks. The findings provide a comprehensive understanding of the complexities surrounding performance, security, and privacy issues future directions and potential solutions.

Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things

<div align="center"><span>The internet of things (IoT) underscores pivotal real-world applications ranging from security systems to smart infrastructure and traffic management. However, contemporary IoT devices grapple with significant challenges pertaining to battery longevity and energy efficiency, constraining the assurance of prolonged network lifetimes and expansive sensor coverage. Many existing solutions, although promising on paper, are intricate and often impractical for real-world implementations. Addressing this gap, we introduce an energy-efficient routing protocol leveraging reinforcement learning (RL) tailored for wireless sensor networks (WSNs). This protocol harnesses RL to discern the optimal transmission route from the source to the sink node, factoring in the energy profile of each intermediary node. Training of the RL algorithm is facilitated through a reward function that includes energy outflow and data transmission efficacy. The model was compared against two prevalent routing protocols, LEACH and fuzzy C-means (FCM), for a comprehensive assessment. Simulation results highlight our protocol’s superiority with respect to the active node count, energy conservation, network longevity, and data delivery efficiency.</span></div>

A metaheuristic MAC protocol for safety applications in cognitive vehicular networks

In a dynamic cognitive vehicular network (CVN) environment, ensuring reliable data delivery of safety messages (sm) is crucial. However, fast-moving vehicles and rapidly changing channel accessibility make sm broadcasting challenging. To address this issue, we propose a cooperative make-up technique, which leverages the 2-Hop Harris Hawks Optimization (HHO) algorithm to choose an optimal helper for sm retransmission in case of failed direct transmission. Our simulations using the Network Simulator version 2 with SUMO demonstrate that the proposed approach achieves cooperative communication within the tight 100ms sm broadcast time frame and outperforms contemporary CVN protocols regarding throughput and packet delivery ratio. Our results suggest that our approach holds great promise for reliable and efficient data delivery in CVNs.

Implementation of Digitization Audit Reporting for Supports Performance LPSQ Function at PT.XXX

Loss Prevention Safety and Quality/Designed Person Ashore (LPSQ/DPA) is responsible for ensuring safety elements on Pertamina International Shipping vessels and ensuring the fulfillment of maritime-worthy documents before ships set sail. This research aims to identify the challenges faced by PT.XXX in implementing digital audit reporting to support the performance of the LPSQ Function and to determine the benefits of implementing digital audit reporting on the performance of the LPSQ Function at PT.XXX. The research method used in this study is a qualitative descriptive approach. Data collection techniques include interviews with internal and external sources, observations, and documentation. Data analysis techniques involve data reduction, data presentation, and drawing research conclusions. Data validity testing includes credibility testing, source and technique triangulation, and member checking. This research reveals several challenges in the data grouping process, human errors, and server downtime. The benefits of implementing digital audit reporting on the performance of the LPSQ Function at PT.XXX include improved audit planning compliance and audit execution fulfillment, resulting in improvements in the audit reporting phase. The improvements obtained from using this application include easy presentation of audit results and more effective and efficient data delivery to audit leaders.

Service Continuity Based Data Delivery Optimization in Satellite-Terrestrial Networks

The satellite-terrestrial networks (STNs) enable global data delivery with low latency and high transmission rate utilizing the Low-Earth-Orbit (LEO) satellite constellation, especially for users in remote regions. However, the dynamic nature of STN presents two challenges to the quality of experience (QoE): 1) the dynamic topology in different time slots; 2) the dynamic path planning for each user. These raise unique problems for the optimization of data delivery paths in STN with service continuity guarantee. In this paper, we first model the STN by a time-varying graph to describe the data delivery in a certain service duration. An indicator called user satisfaction is constructed to measure delivery performance. Aiming at maximizing total user satisfaction, we reformulate the STN path planning problem by utilizing the spatial-temporal decomposition. Considering the path planning in each time slot, we modify the heuristic algorithm to optimize the allocation of transmission resources for different users. In the temporal dimension, we show that the path allocation results in previous time slots directly affect subsequent path calculation then propose an active time slot division algorithm to improve the STN service continuity. Excellent STN transmission performance is matched to the best fit time slot duration to obtain efficient and continuous data delivery. Experimental results show that the proposed STN data delivery optimization scheme achieves superior transmission performance with better service continuity.

MP-VRCR: A Multi-Dimension and Priority-Based Vehicle-Road Collaborative Routing Protocol

With the requirements of time-sensitive and highly reliable applications, it is difficult to implement emerging communication technologies in internet of vehicles. Literature works are dedicated to improving the reliability and efficiency of data delivery, to address problems such as high and unstable End-to-End (E2E) latency, high network overhead. This paper explores the routing protocol with assistance of core network to provide high-reliability and low-latency data delivery services. We propose a Multi-dimension and Priority-based Vehicle-Road Collaborative Routing (MP-VRCR) protocol to improve the efficiency and reliability of data delivery. Specifically, MP-VRCR utilizes multi-dimension indicators to evaluate the data delivery capability of candidate devices, considering trajectory similarity of data, predicted buffer time, buffer occupation and distance cost. Furthermore, MP-VRCR evaluates device priority based on geographical information, selects optimal next-hop device, and evaluates data priority to order data forwarding. Then, MP-VRCR updates the congestion status of network relay devices according to buffer occupation. Vehicles carry data bypass congested network relay devices to reduce the E2E latency. Extensive simulation results show that MP-VRCR significantly outperforms other baseline algorithms regarding delivery ratio, overhead, average delivery latency.

Intelligent and trusted metaheuristic optimization model for reliable agricultural network

Artificial intelligence (AI) is gaining demanding growth in the field of smart cities, agriculture, food management, and weather forecasting due to the lack of computing power on sensing devices. The applications of artificial intelligence are integrated with various Internet of Things (IoT) and ubiquitous sensors for the improvement of the agriculture sector and to decrease its management cost. Due to the bounded resources of wireless technologies, most of the solutions are designed for efficient delivery of agriculture data to cloud systems, however, still optimizing the resources management and data load for forwarding nodes, especially those closest to edge boundaries is a challenging issue. Moreover, due to the collection of incorrect environmental data, the decision-making process leads to a decrease in the productivity of the optimization process. To overcome such issues, this work proposes a trustworthy and intelligent agricultural model that uses metaheuristic optimization to enhance resource management to address these problems. The proposed model approach employs the decision-making function to overcome information loss and inconsistency. Moreover, it builds trust in agricultural data collection by using secure IoT devices and facilitating reliable communication. In terms of performance metrics, the proposed model is simulated to assess its importance in comparison to state-of-the-art solutions. It not only collects updated data from agricultural land but also uses artificial intelligence's lightweight optimization technique to reduce the overheads on IoT devices. The experiment findings demonstrate the importance of the proposed model for resource monitoring and overheads on the IoT system.

An efficient caching security approach for content‐centric mobile networks in internet of things systems

AbstractWith the expansion of internet of things (IoT), the IP‐based network architecture has been difficult to support the development of IoT. Content‐centric mobile networking (CCMN) models are based on naming the content to get rid of address‐space scarcity, caching the content at intermediate nodes to provide efficient data delivery, which can solve the development bottleneck of IoT. The in‐network caching is a key factor to enable practical deployments of CCMN. And it is also subject to serious security threats of cache pollution attacks (CPA), which can tamper the distribution of content and reduce the advantages of built‐in cache. In addition, the identity and trust mechanism of mobile devices are also important factors hindering the deployment of CCMN. However, existing caching security mechanisms cannot be applied directly, because the features of the built‐in cache, including ubiquity, mobility, and heterogeneity, bring new challenges on designing the caching security mechanism against CPA. In this article, we propose a lightweight CPA detection and defense approach for CCMN. We first model the attack and defense process of CPA as a dynamic game, which can optimize the performance of the attack detection. Then, we adopt popularity analysis to detect CPA, and accurately estimate the current network and attack state by a logical regression algorithm. Our design can also intelligently maintain the identity and trust mechanism. Finally, we design a punishment mechanism to deal with the attackers. Extensive simulations demonstrate that our approach can detect CPA efficiently, mitigate the impact of CPA effectively. Our game model can reduce the impact of locality‐disruption attack and false‐locality attack by 60% and 30%, respectively, compared with the state‐of‐the‐art methods. When the game reaches Nash equilibrium, our model can reduce the impact of CPA on cache hit ratio by 55% on average.

Security paradigm for remote health monitoring edge devices in internet of things

The tremendous growth in the internet of things (IoT) technology has provided great support for e-healthcare applications. The remote health monitoring of patients at home is gaining popularity with the increase in design of number of IoT enabled wearable devices. The secure and efficient health data retrieval during continuous uploading of huge data on network is a challenge. In addition, another challenge exists due to limited resources available with IoT device to proceed efficient data retrieval. On the other hand, the edge cloud in edge computing technology provide abundant resources to its devices for various computations, storage and processing. The integration of IoT with edge would solve the resource constraint issue. However, efficient retrieval of health data from large pool of data would still be an issue due to IP based solutions offered by IoT. The Named Data Networking (NDN) is a promising solution for efficient delivery of data based on its name based content searching. This paper proposed NDN communication based secure remote healthcare monitoring framework by integrating IoT with edge computing where security to the each individual content is provided through hashing and encryption mechanism. The performance of the proposed framework is evaluated against its companion in Icarus simulator. The results proved the efficiency of our framework for content retrieval delay and cost associated in accessing the required data.

Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment

The edge computing paradigm has emerged as a new scope within the domain of the Internet of Things (IoT) by bringing cloud services to the network edge in order to construct distributed architectures. To efficiently deploy latency-sensitive and bandwidth-hungry IoT application services, edge computing paradigms make use of devices on the network periphery that are distributed and resource-constrained. On the other hand, microservice architectures are becoming increasingly popular for developing IoT applications owing to their maintainability and scalability advantages. Providing an efficient communication medium for large-scale microservice-based IoT applications constructed from small and independent services to cooperate to deliver value-added services remains a challenge. This paper introduces an event-driven communication medium that takes advantage of Edge–Cloud publish/subscribe brokers for microservice-based IoT applications at scale. Using the interaction model, the involved microservices can collaborate and exchange data through triggered events flexibly and efficiently without changing their underlying business logic. In the proposed model, edge brokers are grouped according to their similarities in event channels and the proximity of their geolocations, reducing the data delivery latency. Moreover, in the proposed system a technique is designed to construct a broker-based utility matrix with constraints in order to strike a balance between delay, relay traffic, and scalability while arranging brokers into proper clusters for efficient data delivery. Rigorous simulation results prove that the proposed publish/subscribe model can provide an efficient interaction medium for microservice-based IoT applications to collaborate and exchange data with low latency, modest relay traffic, and high scalability at scale.

MCR: A Motif Centrality-Based Distributed Message Routing for Disaster Area Networks

Internet of Things (IoT) enables the collection of large volumes of data by billions of pervasive intelligent devices and sharing them with remote cloud servers for processing, resulting in increasing network congestion and server response times. The advent of edge computing has addressed these challenges by introducing an intermediate edge layer comprising networked fog nodes that provide on-demand computation, caching, and communication services to meet critical Quality-of-Service (QoS) requirements. However, in a challenging environment brought by disaster and aftershocks, the QoS is hampered as several fog nodes in the edge layer are damaged. Nevertheless, the existing network infrastructure should still support the uninterrupted flow of time-critical contextual data between the survivors and rescuers for quick recovery operations. In this work, we envision that the IoT devices and existing fog nodes will collaborate to form ad-hoc networks for emergency message delivery under disaster situations. We present a distributed routing mechanism, termed motif centrality-based routing ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MCR$ </tex-math></inline-formula> ), that leverages the concept of network motifs (subgraphs) seen in social and biological networks. Specifically, the proposed mechanism addresses three QoS requirements of an ad-hoc network: 1) robustness against component failures; 2) low latency; and 3) energy efficiency. We experimentally show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MCR$ </tex-math></inline-formula> -based routing ensures high data delivery, low latency, and comparable efficiency in energy usage. Finally, an extensive simulation-based study shows that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MCR$ </tex-math></inline-formula> outperforms the related benchmarks in terms of the three QoS requirements.

SAUSA: Securing Access, Usage, and Storage of 3D Point CloudData by a Blockchain-Based Authentication Network

The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which are often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security, Blockchain has great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind the software-defined network (SDN) technology, this paper envisions SAUSA, a Blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud datasets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-inspired point cloud service architecture, which allows for efficient data processing and delivery to satisfy diverse quality-of-service (QoS) requirements. A Blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the Blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability, but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution.

IoV-SDCM: An IoV Secure Data Communication Model Based on Network Encoding and Relay Collaboration

Internet of Vehicles (IoV) is a significant 5G application scenario. As it developed rapidly, more and more vehicles are connected to Internet of Vehicles. The data security and privacy are the premises to ensure its service quality in an open communication network. This paper proposes IoV-SDCM, a secure data communication model in IoV. It includes a self-organizing relay forwarding network and an assured delivery mechanism. The relay forwarding network is used for constructing a dynamic collaboration network with the vehicle as the node. The security delivery mechanism is that network coding is used for data fragmentation and re-encoding to improve network communication reliability. Homomorphic encryption is used to encrypt and protect the encoding vector, improving information leakage and anticollusion attacks. The theoretical proof proves that the model has the ability of data transmission confidentiality and better antiattack capabilities, while it has privacy protection capabilities. Furthermore, the experiment verifies that the model also has the advantage of high and stable data delivery efficiency.

Adaptive scheduled partitioning technique for reliable emergency message broadcasting in VANET for intelligent transportation systems

This paper aims to enable accurate and reliable emergency message broadcast in Vehicular Ad hoc Network (VANET). The VANET is the most common topology used in Intelligent Transportation Systems (ITS), where changes in standard topology due to the mobility of nodes create challenges in broadcasting the emergency message and efficient data delivery in both highway and urban scenarios. The main problems in urban scenarios are channel contention, message redundancy and road structure. To obtain information, broadcast protocols for VANET typically use beacon messages, which are distributed among the vehicles. When multiple vehicles transmit messages at the same time, a broadcast storm occurs and vehicles experience message delivery failure. To address this problem, Adaptive Scheduled Partitioning and Broadcasting Technique (ASPBT) for emergency message broadcast and beacon retransmissions for message reliability were proposed. This protocol dynamically modifies several partitions and beacon periodicity to reduce the number of retransmissions. Later, the partition size is determined by estimating the network transmission density of each partition schedule via the Black Widow Optimization (BWO) algorithm is proposed. The simulation is carried out with different network densities at the vehicle speed of 110 km/h, a direct path length of 12 km under a four-way direction path and performance analysis was performed.