Data Overhead Research Articles

New Trust Based Smart Data Forwarding Mechanism in Vehicular Delay Tolerant Network

Vehicular Delay Tolerant Network (VDTN) is the growing field with a considerable possibility to handle future wireless application’s requirements. Using vehicles for the data communication purpose can be contemplated as a substitute for the wired and wireless systems. This paper proposes a trust-based data forwarding mechanism for Vehicular Delay Tolerant Network (VDTN). Data forwarding in VDTN requires every vehicular or stationary node to participate in data forwarding. But some time malicious nodes show non-cooperative behaviour in data forwarding. Therefore, malicious nodes must be identified specifically to accelerate the data forwarding. A threshold based social skeleton membership process along with new trust-based data forwarding mechanism is proposed in this paper. We reveal that the social skeleton members perform better in data forwarding in terms of data delivery ratio, data delay and data overhead. Simulation results demonstrate that the trust-based data forwarding improves the data delivery ratio with trust-based data forwarding approach. After simulation a comparative analysis of two different scenarios is presented with epidemic routing, prophet routing and spray and wait routing.

A Dynamic Trust evaluation and update model using advance decision tree for underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) are an emerging research area that is rapidly gaining popularity. However, it has several challenges, including security, node mobility, limited bandwidth, and high error rates. Traditional trust models fail to adapt to the dynamic underwater environment. Thus, to address these issues, we propose a dynamic trust evaluation and update model using a modified decision tree algorithm. Unlike baseline methods, which often rely on static and generalized trust evaluation approaches, our model introduces several innovations tailored specifically for UWSNs. These include energy-aware decision-making, real-time adaptation to environmental changes, and the integration of multiple underwater-specific factors such as water currents and acoustic signal properties. Our model enhances trust accuracy, reduces energy consumption, and lowers data overhead, achieving a 96% accuracy rate with a 2% false positive rate. Additionally, it outperforms baseline models by improving energy efficiency by 50 mW and reducing response time to 20 ms per packet. These innovations demonstrate the proposed model’s effectiveness in addressing the unique challenges of UWSNs, ensuring both security and operational efficiency goals. The proposed model effectively enhances the trust evaluation process in UWSNs, providing both security and operational benefits. These key findings validate the potential of integrating modified decision tree algorithms to improve the performance and sustainability of UWSNs.

Linear Photodiode Arrays For Time-Domain FLEET Velocimetry In Hypersonic Flows

Linear photodiode arrays are applied to Femtosecond Laser Electronic Excitation Tagging (FLEET) to determine the stream-wise velocity of flow at Mach 8 in the Sandia Hypersonic Wind Tunnel (HWT). Incident light emitted by nitrogen gas upon FLEET excitation is collected by intensifying optics that amplify and transmit the light to the photodiode elements as the gas advects downstream. Two photodiode array configurations are considered: one aligned with the flow direction and one that is perpendicular to it. Conventional FLEET using a CMOS image sensor is also performed for quantitative comparison. The configurations are tested for purely free-stream cases as well as the wake of a seven-degree half-angle cone model at various Reynolds number. The influence of the bi-exponential decay of the FLEET line and the transfer function imposed by the intensifying optics are identified as key considerations on the time-domain method accuracy. Overcoming these effects leads to promising application spaces due to the compact nature of photodiodes and low data overhead.

Receive wireless sensor data through IoT gateway using web client based on border gateway protocol

One of the significant topics in the field of the Internet of Things (IoT) pertains to the interaction and information sharing among people. The utilization of the Border Gateway Protocol (BGP) stack enhances the integration of web protocols and sensor networks, leading to greater accessibility. However, the BGP protocol stack introduces substantial overhead to messages transmitted at each layer, resulting in increased data overhead and energy consumption in networks by several orders of magnitude. This paper proposes a method to reduce the overhead on small and medium-sized packets. In multi-temporal networks utilizing BGP, scheduling and aggregating BGP packets at sensor nodes help achieve specific objectives. Various research methodologies and measures are employed to facilitate this, including request classification, BGP response prioritization within the network, determination of maximum acceptable delay, and overall network management. Synchronization and temporal integration of received messages at sensor nodes are performed, considering the maximum allowable delay for each message and the availability of the destination to process the accumulated messages. The evaluation results of the proposed method demonstrate a significant reduction in energy consumption and network traffic, particularly in monitoring applications within multi-stage networks. The protocol stack used is derived from the BGP standard.

SUDC: Synchronous Update with the Division and Combination of SRv6 Policy

With the expansion of network scale, new network services are emerging. Segment Routing over IPv6 (SRv6) can meet the diverse needs of more new services due to its excellent scalability and programmability. In the intelligent 6-Generation (6G) scenario, frequent SRv6 Traffic Engineering (TE) policy updates will result in the serious problem of unsynchronized updates across routers. Existing solutions suffer from issues such as long update cycles or large data overhead. To optimize the policy-update process, this paper proposes a scheme called Synchronous Update with the Division and Combination of SRv6 Policy (SUDC). Based on the characteristics of the SRv6 TE policy, SUDC divides the policies and introduces Bit Index Explicit Replication IPv6 Encapsulation (BIERv6) to multicast the policy blocks derived from policy dividing. The contribution of this paper is to propose the policy-dividing and combination mechanism and the policy-dividing algorithm. The simulation results demonstrate that compared with the existing schemes, the update overhead and update cycle of SUDC are reduced by 46.71% and 46.6%, respectively. The problem of unsynchronized updates across routers has been further improved.

Improving the efficiency of network controllability processes on temporal networks

The controllability of temporal networks has become an important issue for researchers in recent years. In temporal networks, the controllability is achieved through the control of dynamics. Existing studies take only some dynamics into account when controlling such networks, e.g., only adding links, and ignore the rest. Besides, conventional controllability methods on temporal networks suffer from time complexity and data overhead. In this paper, a dynamic controllability method is proposed which considers all dynamics of temporal networks to fully control the network, including adding and removing nodes and links. The proposed controllability method finds all the minimum driver nodes sets (MDSs) of a temporal network in a polynomial time. In this regard, to maintain and track the network changes, a new tree-based model, named Controllable Dynamics Temporal Network (CDTN), is proposed by which the control process is executed with high speed and low overhead. The experimental results show that the proposed controllability method outperforms state-of-the-art methods in terms of speed (47% improvement), overhead (33% improvement), and the size of MDS (38% improvement).

Layer-wise multi-defect detection for laser powder bed fusion using deep learning algorithm with visual explanation

In Laser Powder Bed Fusion (LPBF), it is a major challenge to obtain detailed spatial information on different powder bed defects in real-time and simultaneously. Deep Learning (DL) algorithms under the field of Machine Learning (ML) have promoted the intelligent development of the powder bed defect detection method. However, they still need to be further evaluated in terms of detection accuracy and time delay, training data overhead, and model robustness under complex environments. Also, the DL model usually treated as a black-box demands further explanation. Herein, three advanced DL models are constructed using bounding boxes to locate multi-defects quickly and accurately for the powder spreading of LPBF. High detection accuracy is achieved with limited training samples through both data augmentation aiming at expanding image samples and model-based Transfer Learning (TL) used for transferring from the source domain to the target domain by reusing trained models. Further, the data augmentation method is employed to generate low-resolution images with interference to test the robustness of the detection model in harsh environments. Besides, visual feature maps and saliency maps were generated with the Detector Randomized Input Sampling for Explanation (D-RISE) method to help understand the validity of the defect detection process of the DL model. Overall, this work shows that the proposed multi-defect detection algorithms can provide comprehensive information on powder bed defects accurately and quickly.

Homomorphic Compression: Making Text Processing on Compression Unlimited

Lossless data compression is an effective way to handle the huge transmission and storage overhead of massive text data. Its utility is even more significant today when data volumes are skyrocketing. The concept of operating on compressed data infuses new blood into efficient text management by enabling mainly access-oriented text processing tasks to be done directly on compressed data without decompression. Facing limitations of the existing compressed text processing schemes such as limited types of operations supported, low efficiency, and high space occupation, we address these problems by proposing a homomorphic compression theory. It enables the generalization and characterization of algorithms with compression processing capabilities. On this basis, we develop HOCO, an efficient text data management engine that supports a variety of processing tasks on compressed text. We select three representative compression schemes and implement them combined with homomorphism in HOCO. HOCO supports the extension of homomorphic compression schemes through a modular and object-oriented design and has convenient interfaces for text processing tasks. We evaluate HOCO on six real-world datasets. The three schemes implemented in HOCO show trade-offs in terms of compression ratio, supported operation types, and efficiency. Experiments also show that HOCO can achieve higher throughput in random access and modification operations (averagely 9.18× than the state-of-the-art) and lower latency in text analytic tasks (averagely 7.16× than processing on uncompressed text) without compromising compression efficacy.

An AI Based IDS Framework For Detecting DDoS Attacks In Cloud Environment

ABSTRACT Cloud computing makes it easier for users to access resources from anywhere at any time. This is for as long as they have access to the internet connectivity by employing a “pay-as-you-use” model. Despite its merits, cloud computing faces shortcomings, notably the escalating security concerns linked with it. Distributed Denial of Service (DDoS) attack is a primary and biggest concert to the availability of the services offered by cloud. DDoS attacks use numerous machines to flood consumers with packets with high data overhead, flooding the network with unwanted traffic. Due to the obsolete datasets, many deep learning (DL) models are processing-intensive or may not successfully address new DDoS threats. This paper seeks to address this issue by proposing FEwDN, an AI-based DDoS detection framework that employs a hybrid approach, integrating machine learning and deep learning algorithms. The framework optimizes feature selection via ensemble techniques, enhancing accuracy by leveraging deep neural networks for traffic classification. The proposed framework is experimented on the CICDDoS2019 dataset and demonstrates superior performance over benchmark techniques across multiple metrics. The FEwDN outperforms well with other models against various performance metrics. This research strengthens cloud security and DDoS detection in modern clouds.

Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks

Despite encryption, the packet size is still visible, enabling observers to infer private information in the Internet of Things (IoT) environment (e.g., IoT device identification). Packet padding obfuscates packet-length characteristics with a high data overhead because it relies on adding noise to the data. This paper proposes a more data-efficient approach that randomizes packet sizes without adding noise. We achieve this by splitting large TCP segments into random-sized chunks; hence, the packet length distribution is obfuscated without adding noise data. Our client–server implementation using TCP sockets demonstrates the feasibility of our approach at the application level. We realize our packet size control by adjusting two local socket-programming parameters. First, we enable the TCP_NODELAY option to send out each packet with our specified length. Second, we downsize the sending buffer to prevent the sender from pushing out more data than can be received, which could disable our control of the packet sizes. We simulate our defense on a network trace of four IoT devices and show a reduction in device classification accuracy from 98% to 63%, close to random guessing. Meanwhile, the real-world data transmission experiments show that the added latency is reasonable, less than 21%, while the added packet header overhead is only about 5%.

Novel network representation model for improving controllability processes on temporal networks

Abstract Temporal networks are known as the most important tools for representing and storing dynamic systems. This type of network accurately demonstrates all the dynamic changes that occur in a dynamic system. In different applications of dynamic systems, different representation of network models has been used to represent temporal networks. In the last decade, controllability in dynamic systems has become one of the most important challenges in this field. Controllability means the transfer of the network from an initial state to a desired final state in a certain period of time. The most common representation of network model used in control processes is the layered model. But this model has a high overhead, and on the other hand, it slows down the network control processes. In this article, we have proposed a new model for storing and representing temporal networks, which uses a tree structure to save all dynamics of network. Considering that in the proposed model only essential network control information is stored, this model has a very low data overhead compared to the layered model, and this makes the control processes run at a higher speed.

Blockchain-Based Distributed Computing Consistency Verification for IoT Mobile Applications

The maturation of wireless connectivity, blockchain (distributed ledger technologies), and intelligent systems has fostered a comprehensive ecosystem for the Internet of Things (IoT). However, the growing volume of data generated by IoT devices creates substantial pressure on blockchain storage and computation capabilities, impeding the further development of the IoT ecosystem. Decentralizing data storage across multiple chains and utilizing cross-chain technology for data exchange eliminates the need for expensive centralized infrastructure, lowers data transfer costs, and improves accessibility. Hence, the issue of computational and storage pressure in blockchain can be improved. Nonetheless, the data of IoT devices are constantly updating, and ensuring consistency for dynamic data across heterogeneous chains remains a significant challenge. To address the aforementioned challenge, we propose a blockchain-based distributed and lightweight data consistency verification model (BDCA), which leverages a batch verification dynamic Merkle hash tree (BV-MHT) and an advanced gamma multi-signature scheme (AGMS) to enable consistent verification of dynamic data while ensuring secure and private data transmission. The AGMS scheme is reliable and robust based on security analysis while the dependability and consistency of BDCA are verified through inductive reasoning. Experimental results indicate that BDCA outperforms CPVPA and Fortress in communication and computation overhead for data preprocessing and auditing in a similar condition, and the AGMS scheme exhibits superior performance when compared to other widely adopted multi-signature schemes such as Cosi, BLS, and RSA. Furthermore, BDCA provides up to 99% data consistency guarantees, demonstrating its practicality.

A Secure Data-Sharing Scheme for Privacy-Preserving Supporting Node–Edge–Cloud Collaborative Computation

The node–edge–cloud collaborative computation paradigm has introduced new security challenges to data sharing. Existing data-sharing schemes suffer from limitations such as low efficiency and inflexibility and are not easily integrated with the node–edge–cloud environment. Additionally, they do not provide hierarchical access control or dynamic changes to access policies for data privacy preservation, leading to a poor user experience and lower security. To address these issues, we propose a data-sharing scheme using attribute-based encryption (ABE) that supports node–edge–cloud collaborative computation (DS-ABE-CC). Our scheme incorporates access policies into ciphertext, achieving fine-grained access control and data privacy preservation. Firstly, considering node–edge–cloud collaborative computation, it outsources the significant computational overhead of data sharing from the owner and user to the edge nodes and the cloud. Secondly, integrating deeply with the “node–edge–cloud” scenario, the key distribution and agreement between all entities embedded in the encryption and decryption process, with a data privacy-preserving mechanism, improve the efficiency and security. Finally, our scheme supports flexible and dynamic access control policies and realizes hierarchical access control, thereby enhancing the user experience of data sharing. The theoretical analysis confirmed the security of our scheme, while the comparison experiments with other schemes demonstrated the practical feasibility and efficiency of our approach in node–edge–cloud collaborative computation.

A Comparison of Hazard Vulnerability Indexes for Washington State

Abstract Factors associated with structural racism, disenfranchisement, poverty, and other persistent sources of inequity are associated with vulnerability and exposure to environmental hazards. Social, demographic, and environmental factors associated with vulnerability to environmental hazards have been used by many researchers to produce indexes of hazard vulnerability. In preparation for a climate change health risk assessment for Washington state, we compared methods and results from six indexes designed to support environmental health risk assessment. Production of these indexes varies in the number of variables considered, calculation complexity, and exposure of local causal pathways. Results for these indexes are generally very similar, especially at the highest decile of vulnerability, the exception being the Environmental Health Disparities index, the only one to consider hazard exposure. Some indexes used methods that hide causal pathways. Those that exposed causal pathways limited model structure. Results indicate that simpler indexes may be more appropriate for use in decision support tools as they require less overhead for data updates and scenario analysis and that other methodologies may provide a more useful framework for index generation.

BP-AKAA: Blockchain-enforced Privacy-preserving Authentication and Key Agreement and Access Control for IIoT

The Industrial Internet of Things (IIoT) links multiple subnets to accomplish more real-time, efficient, and high-class production. Authentication is an essential prerequisite for secure communication and data sharing between mutually untrusted subdomains.However, solving trust issues between subnets through third-party trusted servers inevitably introduces security and efficiency bottlenecks. In addition, the issue of not compromising the privacy of mutual authentication remains a challenge. Furthermore, key agreement and access control, as two follow-up steps of authentication, is non-negligible for achieving secure and efficient data sharing. Existing authentication works either require heavy computational overhead or lack necessary features for data sharing. Therefore, this paper proposed a blockchain-enforced cross-domain private-protected authentication and key agreement scheme supporting attribute-based access control, named BP-AKAA. To the best of our knowledge, this is the first scheme that simultaneously supports privacy authentication, key agreement, and access control. Non-interactive zero-knowledge proof technology is adopted to protect the identities of devices. In addition, with the assistance of distributed blockchain, the untrust issue of cross-domain authentication is solved.Performance analysis demonstrates that our scheme satisfies multiple functions, including cross-domain, privacy-preserving, and mutual authentication, and outperforms existing schemes in terms of key generation, authentication, and access control.

Indoor 3D Point Cloud Segmentation Based on Multi-Constraint Graph Clustering

Indoor scene point cloud segmentation plays an essential role in 3D reconstruction and scene classification. This paper proposes a multi-constraint graph clustering method (MCGC) for indoor scene segmentation. The MCGC method considers multi-constraints, including extracted structural planes, local surface convexity, and color information of objects for indoor segmentation. Firstly, the raw point cloud is partitioned into surface patches, and we propose a robust plane extraction method to extract the main structural planes of the indoor scene. Then, the match between the surface patches and the structural planes is achieved by global energy optimization. Next, we closely integrate multiple constraints mentioned above to design a graph clustering algorithm to partition cluttered indoor scenes into object parts. Finally, we present a post-refinement step to filter outliers. We conducted experiments on a benchmark RGB-D dataset and a real indoor laser-scanned dataset to perform numerous qualitative and quantitative evaluation experiments, the results of which have verified the effectiveness of the MCGC method. Compared with state-of-the-art methods, MCGC can deal with the segmentation of indoor scenes more efficiently and restore more details of indoor structures. The segment precision and the segment recall of experimental results reach 70% on average. In addition, a great advantage of the MCGC method is that the speed of processing point clouds is very fast; it takes about 1.38 s to segment scene data of 1 million points. It significantly reduces the computation overhead of scene point cloud data and achieves real-time scene segmentation.

An Effective Approach Based on Temporal Centrality Measures for Improving Temporal Network Controllability

The Controllability on temporal complex networks is one of the most important challenges among researchers in this field. The primary purpose of network controllability is to apply inputs by selecting minimum driver nodes set (MDS) to the network components to move the network from an initial state to a final state in a limited time. The most important challenges in the controllability of temporal networks can be mentioned the high complexity of the control algorithms used in these methods as well as the high data overhead of temporal network representation models such as the layered model. In this paper, centrality measures are used as the most important characteristics of networks for network controllability. For this purpose, centrality measures have been redefined based on temporal networks and a new controllability method has been proposed based on temporal centrality measures. Then, these properties are used for selecting the minimal driver nodes set, in such a way that the network can be fully controlled using these nodes. The experimental results demonstrate that by using temporal centrality measures the execution speed of control processes is improved (57% improvement) and the overhead is not increased and also the control process has led to the same length of MDS as other conventional controllability methods, it has even been better in some cases.

Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification

Artificial neural network (ANN) is powerful in the artificial intelligence field and has been successfully applied to interpret complex image data in the real world. Since the majority of images are commonly known as private with the information intended to be used by the owner, such as handwritten characters and face, the private constraints form a major obstacle in developing high-precision image classifiers which require access to a large amount of image data belonging to multiple users. State-of-the-art privacy-preserving ANN schemes often use full homomorphic encryption which result in a substantial overhead of computation and data traffic for the data owners, and are restricted to approximation models by low-degree polynomials which lead to a large accuracy loss of the trained model compared to the original ANN model in the plain domain. Consequently, it is still a huge challenge to train an ANN model in the encrypted-domain. To mitigate this problem, we propose a privacy-preserving ANN system for secure constructing image classifiers, named IPPNN, where the server is able to train an ANN-based classifier on the combined image data of all data owners without being able to observe any images using primitives, such as randomization and functional encryption. Our system achieves faster training time and supports lossless training. Moreover, IPPNN removes the need for multiple communications among data owners and servers. We analyze the security of the protocol and perform experiments on a large scale image recognition task. The results show that the IPPNN is feasible to use in practice while achieving high accuracy.

ZMesh: Theories and Methods to Exploring Application Characteristics to Improve Lossy Compression Ratio for Adaptive Mesh Refinement

Scientific simulations on high-performance computing systems produce vast amounts of data that need to be stored and analyzed efficiently. Lossy compression significantly reduces the data volume by trading accuracy for performance. Despite the recent success of lossy compressions, such as ZFP and SZ, the compression performance is still far from being able to keep up with the exponential growth of data. This article aims to further take advantage of application characteristics, an area that is often under-explored, to improve the compression ratios of adaptive mesh refinement (AMR) - a widely used numerical solver that allows for an improved resolution in limited regions. We propose a level reordering technique <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">zMesh</i> to reduce the storage footprint of AMR applications. In particular, we group the data points that are mapped to the same or adjacent geometric coordinates such that the dataset is smoother and more compressible. Unlike the prior work where the compression performance is affected by the overhead of metadata, this work re-generates the restore recipe using a chained tree structure, thus involving no extra storage overhead for compressed data, which substantially improves the compression ratios. We further derive a mathematical proof that lays the foundation for our method. The results demonstrate that zMesh can improve the smoothness of data by 67.9% and 71.3% for Z-ordering and Hilbert, respectively. Overall, zMesh improves the compression ratios by up to 16.5% and 133.7% for ZFP and SZ, respectively. Despite that zMesh involves additional compute overhead for tree and restore recipe construction, we show that the cost can be amortized as the number of quantities to be compressed increases.

Efficient erasure-coded data updates based on file class predictions and hybrid writes

A small update write can lead to a partial write to an erasure coding group in erasure-coded storage systems, resulting in a time-consuming write-after-read. This paper presents a data delta and logging based writing approach, named DETOG, to achieve fast partial writes. DETOG classifies files into non-write-only and write-only using machine learning, then adopts different write schemes to perform partial writes according to file classes. For non-write-only files, DETOG uses data deltas that are the differences between latest data values and original data values, instead of parity deltas to reconstruct the failed data. For non-write-only files, DETOG performs in-place data updates and log-based parity updates, so as to balance the overhead of data updates and reads. We implement DETOG on the newly designed prototype storage system to perform performance evaluation. The results of extensive experiments on real-world traces showed that DETOG significantly improved system throughput compared with state-of-the-art methods.