V2X-Based Mobile Localization in 3D Wireless Sensor Network

Javed, Iram; Tang, Xianlun; Shaukat, Kamran; Sarwar, Muhammed Umer; Alam, Talha Mahboob; Hameed, Ibrahim A.; Saleem, Muhammad Asim

doi:10.1155/2021/6677896

Cited by 37 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many schemes are proposed to extract secret keys from RSS, as it is supportive for most devices. In [16], a scheme is proposed which suggested two-level execution for generating security bits for this CIR, and RSS is used to perform validation. Now, the results show the feasibility of this scheme.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

Monga

Gupta

Prasad³

et al. 2022

Sustainability

View full text Add to dashboard Cite

The security framework in Ad-hoc Networks (ANET) continues to attract the attention of researchers, although significant work has been accomplished already. Researchers in the last couple of years have shown quite an improvement in Identity Dependent Cryptography (IDC). Security in ANET is hard to attain due to the vulnerability of links (Wireless). IDC encompasses Polynomial Interpolations (PI) such as Lagrange, curve-fitting, and spline to provide security by implementing Integrated Key Management (IKM). The PI structure trusts all the available nodes in the network and randomly picks nodes for the security key generation. This paper presents a solution to the trust issues raised in Lagrange’s-PI (LI) utilizing an artificial neural network and attribute-based tree structure. The proposed structure not only improves the trust factor but also enhances the accuracy measures of LI to provide a sustainable network system. Throughput, PDR, noise, and latency have been increased by 47%, 50%, 34%, and 30%, respectively, by using LI and incorporating the aforementioned techniques.

show abstract

Section: Related Workmentioning

confidence: 99%

“…This review has been compiled in Table 1. [15] RSS 3 [16] two-level execution using CIR and RSS 4 [17] Blocks and a two-level quantifier 5 [18] Key generation ranking mechanisms and interpolation 6 [19] Technique based on the KLT (Karhunen-Loeve Transform) 7…”

Section: Related Workmentioning

confidence: 99%

Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

Monga

Gupta

Prasad³

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…Nowadays, Internet of Things (IoT) and cloud computing networks are dominant in the Internet. The fast development in wireless sensor network technology has made the IoT network becomes more complex in term of applications [2] and routing [3]. Naturally, security turns out to be extremely important, especially in cloud environment [4].…”

Section: Related Workmentioning

confidence: 99%

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

Budiarto¹,

Alqarni²,

Alzahrani³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

As nearly half of the incidents in enterprise security have been triggered by insiders, it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software (malware) in real-time. Failing to do so may cause a serious loss of reputation as well as business. At the same time, modern network traffic has dynamic patterns, high complexity, and large volumes that make it more difficult to detect malware early. The ability to learn tasks sequentially is crucial to the development of artificial intelligence. Existing neurogenetic computation models with deep-learning techniques are able to detect complex patterns; however, the models have limitations, including catastrophic forgetfulness, and require intensive computational resources. As defense systems using deep-learning models require more time to learn new traffic patterns, they cannot perform fully online (on-the-fly) learning. Hence, an intelligent attack/malware detection system with on-the-fly learning capability is required. For this paper, a memory-prediction framework was adopted, and a simplified single cell assembled sequential hierarchical memory (s.SCASHM) model instead of the hierarchical temporal memory (HTM) model is proposed to speed up learning convergence to achieve onthe-fly learning. The s.SCASHM consists of a Single Neuronal Cell (SNC) model and a simplified Sequential Hierarchical Superset (SHS) platform. The s.SCASHM is implemented as the prediction engine of a user behavior analysis tool to detect insider attacks/anomalies. The experimental results show that the proposed memory model can predict users' traffic behavior with accuracy level ranging from 72% to 83% while performing on-the-fly learning.

show abstract

“…A mamdani fuzzy logic system is proposed to process the sensor readings and output flood warnings, with a 96.96% accuracy achieved experimentally. Another recent work [23] implemented a novel localization technique named C-CURVE to localize sensor nodes in a WSN, achieving high accuracy in comparison to other state-of-the-art methods. The monitoring of WSN systems is also a point of interest for researchers.…”

Section: Wireless Sensor Network Vs Virtual Sensor Networkmentioning

confidence: 99%

Self-Protected Virtual Sensor Network for Microcontroller Fault Detection

Sternharz

Skackauskas

Elhalwagy

et al. 2022

Sensors

View full text Add to dashboard Cite

This paper introduces a procedure to compare the functional behaviour of individual units of electronic hardware of the same type. The primary use case for this method is to estimate the functional integrity of an unknown device unit based on the behaviour of a known and proven reference unit. This method is based on the so-called virtual sensor network (VSN) approach, where the output quantity of a physical sensor measurement is replicated by a virtual model output. In the present study, this approach is extended to model the functional behaviour of electronic hardware by a neural network (NN) with Long-Short-Term-Memory (LSTM) layers to encapsulate potential time-dependence of the signals. The proposed method is illustrated and validated on measurements from a remote-controlled drone, which is operated with two variants of controller hardware: a reference controller unit and a malfunctioning counterpart. It is demonstrated that the presented approach successfully identifies and describes the unexpected behaviour of the test device. In the presented case study, the model outputs a signal sample prediction in 0.14 ms and achieves a reconstruction accuracy of the validation data with a root mean square error (RMSE) below 0.04 relative to the data range. In addition, three self-protection features (multidimensional boundary-check, Mahalanobis distance, auxiliary autoencoder NN) are introduced to gauge the certainty of the VSN model output.

show abstract

V2X-Based Mobile Localization in 3D Wireless Sensor Network

Cited by 37 publications

References 29 publications

Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

Self-Protected Virtual Sensor Network for Microcontroller Fault Detection

Contact Info

Product

Resources

About