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Fault-Tolerant Routing Strategies in Mobile Wireless Sensor networks 2

Fault-Tolerant Routing Strategies in Mobile Wireless Sensor ...

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2nd IEEE international Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Vaddadi, Srinivas A Somanathan Pillai, Sanjaikanth E Vadakkethil University of the Cumberlands Department of Information Technology WilliamsburgKY United States University of North Dakota School of Electrical Engineering and Computer Science Grand ForksND United States

ISBN: (纸本)9798350317558

Mobile wireless sensor networks are crucial in several applications, such as environmental monitoring, disaster management, and military surveillance. Nevertheless, utilizing these devices in remote and hostile settings presents distinct obstacles that can result in node failures and subsequent disruption of community connectivity. Thus, fault tolerance is crucial for guaranteeing the dependability and resilience of MWSNs. This technical brief proposes fault-tolerant routing techniques for Mobile Wireless Sensor networks (MWSNs) to mitigate the impact of node failures and ensure reliable information delivery. Initially, we develop a distributed clustering algorithm that partitions the network into smaller clusters. Each cluster is led by a selected cluster head responsible for aggregating and transmitting data. During a node failure, the cluster head can redirect and establish a connection with the lower station by utilizing other pathways. In addition, we propose a routing strategy that utilizes mobility to allow nodes to bypass intermediate stations and directly connect to the base station in case of hyperlink failures or unavailability of cluster heads. © 2024 IEEE.

关键词： Aggregation Clusters Consequences Environments Surveillance Unavailability

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A distributed data acquisition architecture for track geometry measurement system 9

A Distributed data acquisition architecture for track geomet...

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9th international symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2024

作者： Zhang, Yi Qin, Zhe Yu, Ning Du, Xinyu Wang, Hao Hou, Zhixiong Infrastructure Inspection Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China

ISBN: (纸本)9781510683303

This paper introduces a distributed data acquisition architecture for track geometry measurement system(TGMS) to address the evolving needs in inspection environments and enhance the flexibility and scalability of the inspection system. It features a synchronization mode based on spatio-temporal tagging, which is essential for aligning raw data. The paper also outlines the design scheme for the data collection system of railway inspection equipment, incorporating data synchronization technology to enable the simultaneous acquisition of multi-port, diverse sensor data as well as data association technology to enable precise data alignment based on spatio-temporal tagging. The design of the data collection system, including CAN bus, GIGE port, and RS422 serial port interface circuits, leverages embedded technology. Empirical tests conducted on a comprehensive inspection train (CIT) show a high consistency between the results of the newly developed system and existing systems under similar conditions. The new data acquisition architecture enhances the system’s flexibility and scalability through spatio-temporal tagging for data alignment. © 2024 SPIE.

关键词： Inspection equipment

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Self-supervised Siamese Autoencoders 22nd

Self-supervised Siamese Autoencoders

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22nd international symposium on Intelligent Data Analysis (IDA)

作者： Baier, Friederike Mair, Sebastian Fadel, Samuel G. Leuphana Univ Luneburg Luneburg Germany Uppsala Univ Uppsala Sweden Linkoping Univ Linkoping Sweden

ISBN: (纸本)9783031585463;9783031585470

In contrast to fully-supervised models, self-supervised representation learning only needs a fraction of data to be labeled and often achieves the same or even higher downstream performance. The goal is to pre-train deep neural networks on a self-supervised task, making them able to extract meaningful features from raw input data afterwards. Previously, autoencoders and Siamese networks have been successfully employed as feature extractors for tasks such as image classification. However, both have their individual shortcomings and benefits. In this paper, we combine their complementary strengths by proposing a new method called SidAE (Siamese denoising autoencoder). Using an image classification downstream task, we show that our model outperforms two self-supervised baselines across multiple data sets and scenarios. Crucially, this includes conditions in which only a small amount of labeled data is available. Empirically, the Siamese component has more impact, but the denoising autoencoder is nevertheless necessary to improve performance.

关键词： Self-supervised learning representation learning Siamese networks denoising autoencoder pre-training image classification

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An Adaptive IDS Scheduling Framework for Host Based Mimicry Attack Detection in IoT networks 18

An Adaptive IDS Scheduling Framework for Host Based Mimicry ...

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18th IEEE international Conference on Advanced networks and Telecommunications systems, ANTS 2024

作者： Sanyal, Surja Khatua, Manas Chattopadhyay, Pratik Indian Institute of Technology Guwahati Department of Computer Science and Engineering Guwahati India Varanasi Department of Computer Science and Engineering Varanasi India

ISBN: (纸本)9798350391725

With the expanding applicability of the Internet of Things (IoT), novel IoT network security challenges also appear more frequently. Host-based Mimicry Attacks (HMA) are one of them that is difficult to detect by traditional Intrusion Detection systems (IDS) since they mimic legitimate network and host behaviors to evade detection. Additionally, IDSs consume host device resources when their implementations, partially or fully, are executed in the device, and thus it is needed to be scheduled properly so as to save device resources when possible. Current IDS solutions capable of detecting HMAs mostly use randombased scheduling or do not use scheduling at all and so are always executed. Random-based scheduling solutions have low HMA detection accuracy, while solutions without scheduling do not save device resources. This work proposes an adaptive IDS scheduling framework that suspends IDS execution based on device trust and resumes IDS execution when the device is under attack. Extensive experimentation using the Cooja simulator and Python script shows that the proposed framework achieves a device behavior classification accuracy of at least 97.15% when under attack while saving at least 81.98% of CPU energy when not under attack for a network of 40 devices, as compared to those when executing the IDS without any scheduling scheme. © 2024 IEEE.

关键词： Network intrusion

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Broadcast and Consensus in Stochastic Dynamic networks with Byzantine Nodes and Adversarial Edges 38

Broadcast and Consensus in Stochastic Dynamic Networks with ...

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38th international symposium on distributed Computing, DISC 2024

作者： El-Hayek, Antoine Henzinger, Monika Schmid, Stefan Klosterneuburg Austria TU Berlin Germany Fraunhofer SIT Berlin Germany

ISBN: (纸本)9783959773522

Broadcast and Consensus are most fundamental tasks in distributed computing. These tasks are particularly challenging in dynamic networks where communication across the network links may be unreliable, e.g., due to mobility or failures. Over the last years, researchers have derived several impossibility results and high time complexity lower bounds for these tasks. Specifically for the setting where in each round of communication the adversary is allowed to choose one rooted tree along which the information is disseminated, there is a lower as well as an upper bound that is linear in the number n of nodes for Broadcast and for n ≥ 3 the adversary can guarantee that Consensus never happens. This setting is called the oblivious message adversary for rooted trees. Also note that if the adversary is allowed to choose a graph that does not contain a rooted tree, then it can guarantee that Broadcast and Consensus will never happen. However, such deterministic adversarial models may be overly pessimistic, as many processes in real-world settings are stochastic in nature rather than worst-case. This paper studies Broadcast on stochastic dynamic networks and shows that the situation is very different to the deterministic case. In particular, we show that if information dissemination occurs along random rooted trees and directed Erdős–Rényi graphs, Broadcast completes in O(log n) rounds of communication with high probability. The fundamental insight in our analysis is that key variables are mutually independent. We then study two adversarial models, (a) one with Byzantine nodes and (b) one where an adversary controls the edges. (a) Our techniques without Byzantine nodes are general enough so that they can be extended to Byzantine nodes. (b) In the spirit of smoothed analysis, we introduce the notion of randomized oblivious message adversary, where in each round, an adversary picks k ≤ 2n/3 edges to appear in the communication network, and then a graph (e.g. rooted tree or dir

关键词： Stochastic systems

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Advanced Computational Methods for Pelvic Bone Cancer Detection: Efficacy comparison of Convolutional Neural networks 17

Advanced Computational Methods for Pelvic Bone Cancer Detect...

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17th IEEE international symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Singh, Jagbir Patel, Priyankkumar Ingole, Balaji Shesharao Inaganti, Rambabu Ramineni, Vishnu Krishnappa, Manjunatha Sughaturu Patel, Bhushan Jayeshkumar Pittsburgh PA United States Evans GA United States Albertsons Companies PlanoTX United States Fremont CA United States

ISBN: (纸本)9798331530471

Pelvic bone cancer is a type of cancer that effects the hip(pelvis). Doctor use various methods to diagnose pelvic bone cancer, including imaging test like X-ray, CT scans, MRI scans, and PET scans. Sometimes, doctors take a small piece of bone called a biopsy to test and confirm the cancer type in a lab. However, the way doctors diagnose cancer is changing because of advancement of computers. computers can help diagnose cancer more accurately and quickly. Therefore, this study contributes to novel methods for detecting bone cancer using computer algorithms by analyzing CT scan images from over 3,000 individuals with bone tumors. These images were categorized into two groups: one containing images with cancer and the other with images without cancer. We used Convolutional Neural networks (CNNs) - to understand and analyze images. We tested five different CNNs to see which model is the best at finding bone sarcoma. Our study finds that the Xception model is the most effective at detecting bone cancer, with an accuracy of approximately 90.23%. This underscores the importance of selecting the appropriate neural network (NN) model for accurate cancer identification. This is a significant discovery in aiding doctors to detect cancer more quickly and accurately, potentially leading to earlier and more effective treatment for individuals with bone cancer. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving Network Resource Utilization for distributed Wireless Sensing Applications 24

Improving Network Resource Utilization for Distributed Wirel...

引用

2024 international symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile networks and Mobile Computing, MobiHoc 2024

作者： Gayen, Snehadeep Shankar, Yamini Chakraborty, Ayon SENSE Lab IIT Madras India

ISBN: (纸本)9798400705212

Edge-assisted wireless sensing is increasingly popular, where complex neural network models perform inference tasks on wireless channel state information (CSI) data streamed from IoT devices. However large volumes of CSI data sent across the network for inference can significantly impact network bandwidth and reduce the Quality of Experience. This paper tackles the challenge of optimizing network resource utilization in wireless sensing systems by compressing and subsampling CSI streams. We evaluate methods that quantize and selectively subsample CSI data before transmission to the edge server, which is then fed to the inference models. Such approach reduces bandwidth and computational load, improving data transmission and processing efficiency. Experiments conducted in two real testbeds (indoors as well as outdoors) show how CSI compression preserves sensing information integrity while enhancing system performance in terms of latency, energy efficiency, and throughput. By integrating quantization and subsampling with edge computing, this work enhances wireless sensing systems, making them more scalable and efficient in utilizing network resources. © 2024 Copyright held by the owner/author(s).

关键词： Data transfer

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Classifying Real and Bot Users based on their News Spread for Combating Misinformation 17

Classifying Real and Bot Users based on their News Spread fo...

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17th IEEE international Congress on Intelligent and Service-Oriented systems Engineering (CISOSE)

作者： Thi Bui Pham, Anson Kulkarni, Warada Yao, Yutong Potika, Katerina San Jose State Univ Dept Comp Sci San Jose CA 95192 USA

ISBN: (纸本)9798350322392

Online social media (OSM) have become the primary global news source, but because of the distributed nature of the web, it has increased the risk of misinformation spread. Fake news is misinformation that masquerades as genuine (real) information. Consequently, it is an active topic for researchers and OSM companies to find ways to identify and flag fake news, as well as detect the responsible sources that generate them. Bots, artificial users designed for various purposes, contribute to the dissemination of information on OSM. Regrettably, bots exhibit faster propagation of information compared to real users, often leading to the spread of inaccurate and low-quality information [1]. Differentiating between bots and real users solely based only on content poses a formidable task. This paper explores and expands techniques for bot detection based on news content as well as the spread diffusion process dynamics as a countermeasure for misinformation.

关键词： Misinformation News propagation Twitter Bots Graph Classification Natural Language Processing Graph Neural networks Social networks Analysis Machine Learning

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A Novel distributed Tree-Based One-Hot Encoding (DTOHE) Technique for Intrusion Detection 6th

A Novel Distributed Tree-Based One-Hot Encoding (DTOHE) Tech...

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6th international Conference on Recent Innovations in Computing, ICRIC 2023

作者： Abirami, K. Samraj, Jasmine Chennai India

ISBN: (纸本)9789819728381

In the realm of cloud security, intrusion detection systems (IDSs) play a pivotal role as they are instrumental in identifying vulnerable activities occurring across networks and computer systems. Developing a novel IDS for Internet of Things (IoT)-enabled networks is a complex undertaking beset by numerous challenges, primarily stemming from difficulties in data access. Moreover, the specific constraints imposed by IoT devices further complicate the task of safeguarding the network against external threats and analyzing these threats effectively. In this current study, a range of existing methodologies has been explored to enhance the efficiency of intrusion detection systems, despite facing certain limitations and resource constraints. The principal objective of this research paper is to investigate the efficacy of existing intrusion detection systems integrated with genetic algorithm decision-making and coding techniques. By incorporating these techniques, the aim is to bolster the performance of intrusion detection systems and fortify them against external intrusion attempts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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Smart Flood Detection and Communication in Hazardous Zones via MANET-Based IoT and Parallely distributed Slimmable Neural networks 4

Smart Flood Detection and Communication in Hazardous Zones v...

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4th international Conference on Sustainable Expert systems, ICSES 2024

作者： Sathya, V. Bhadula, Shuchi Srinivasan, R. Bhargav, H.K. Panda, Bhavani Sankar Barve, Amit Department of Computer Science Vallam Tamil Nadu Thanjavur613403 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Avadi Tiruvallur Tamil Nadu Chennai600054 India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Karnataka Tumakuru572106 India Raghu Engineering College Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam531162 India Parul Institute of Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering P.O.Limda Gujarat 391760 India

ISBN: (纸本)9798331540364

Disasters can be mitigated by an early warning signal and proper communication within the hazardous environment using the MANET technology. However, the exact prediction of disaster situation is needed for the timely disaster management. Hence in addition to MANET technology, deep learning algorithms and Inter of Things (IoT) can also be integrated into disaster detection and communication systems. The presented research developed a novel parallely distributed slimmable neural network with orchard algorithm for the accurate flood disaster prediction. The model processed the dataset containing various measurements such as temperature, humidity, precipitation, air, soil moisture and rainfall level that are collected by the IoT sensor deployed in the various location of the hazardous environment. The gathered data are initially pre-processed using the correlation coefficient min-max normalization approach. Further, the relevant characteristics of the flood disaster from the data are extracted through spike driven transformer process. These refined and dimensionality reduced data are then entered into the proposed framework for the disaster prediction. Here the integrated orchard algorithm increased the disaster monitoring accuracy on the basis of better optimized parameters. The model resulted 95% accuracy, 93% precision, 94% recall and 96% f-score. Therefore, the presented method is an effective prediction mechanism for the disaster management in IoT MANET. © 2024 IEEE.

关键词： Soil moisture

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