Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

Ashraf, Shahzad; Alfandi, Omar; Ahmad, Arshad; Khattak, Asad Masood; Hayat, Bashir; Kim, Kyong Hoon; Ullah, Ayaz

doi:10.1155/2020/8833767

Cited by 35 publications

(21 citation statements)

References 34 publications

(31 reference statements)

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“… The Medium-Access Control (MAC) Layer has the purpose of managing medium access through OEC devices that implement different PHY layers. Thus, this layer is responsible for identifying the communicating nodes within the coverage area, which can be enhanced by using cost-effective algorithms in deployments with mobile and static nodes [ 28 ]. The Transport Layer isolates the upper layers from any changes that may occur in terms of hardware in the lower layers (since PHY and MAC layers are usually implemented directly by specific hardware).…”

Section: Design and Implementation Of The Proposed Oec Systemmentioning

confidence: 99%

Section: Design and Implementation Of The Proposed Oec Systemmentioning

confidence: 99%

See 1 more Smart Citation

Practical Latency Analysis of a Bluetooth 5 Decentralized IoT Opportunistic Edge Computing System for Low-Cost SBCs

Niebla-Montero

Froiz-Míguez

Fraga‐Lamas

et al. 2022

Sensors

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IoT devices can be deployed almost anywhere, but they usually need to be connected to other IoT devices, either through the Internet or local area networks. For such communications, many IoT devices make use of wireless communications, whose coverage is key: if no coverage is available, an IoT device becomes isolated. This can happen both indoors (e.g., large buildings, industrial warehouses) or outdoors (e.g., rural areas, cities). To tackle such an issue, opportunistic networks can be useful, since they use gateways to provide services to IoT devices when they are in range (i.e., IoT devices take the opportunity of having a nearby gateway to exchange data or to use a computing service). Moreover, opportunistic networks can provide Edge Computing capabilities, thus creating Opportunistic Edge Computing (OEC) systems, which deploy smart gateways able to perform certain tasks faster than a remote Cloud. This article presents a novel decentralized OEC system based on Bluetooth 5 IoT nodes whose latency is evaluated to determine the feasibility of using it in practical applications. The obtained results indicate that, for the selected scenario, the average end-to-end latency is relatively low (736 ms), but it is impacted by factors such as the location of the bootstrap node, the smart gateway hardware or the use of high-security mechanisms.

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Section: Design and Implementation Of The Proposed Oec Systemmentioning

confidence: 99%

Section: Design and Implementation Of The Proposed Oec Systemmentioning

confidence: 99%

Practical Latency Analysis of a Bluetooth 5 Decentralized IoT Opportunistic Edge Computing System for Low-Cost SBCs

Niebla-Montero

Froiz-Míguez

Fraga‐Lamas

et al. 2022

Sensors

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“…Those signals can also be detected and achieved through a wireless system (Ashraf, 2020). Bodacious-instance Coverage Mechanism (tuned BiCM) and FOA algorithm can be used to detect wireless data (Ashraf et al 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

CNN Model for Sleep Apnea Detection Based on SpO2 Signal

Albuhayri¹

2022

CIS

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Sleep Apnea-Hypopnea Syndrome (SAHS) is one of the common sleep disorders which cause hypertension, coronary artery disease, stroke, and diabetes mellitus, as well as the increment of vehicle collisions. Polysomnography is a traditional way of diagnosing sleep disorder which requires multiple sensors for producing multiple physiological signals. Traditional Polysomnography causes huge costs for diagnosing SAHS because it requires numerous sensors as well as time. This study has developed a model by using deep learning techniques to minimize the cost and time for SAHS diagnosing. This study has utilized the SpO2 signal by using a Convolutional Neural Network (CNN) as a deep learning technique to detect SAHS in any individuals. The sleep disorder depends on the amount of blood in the body which is detected by the SpO2 signal.  The proposed CNN model consists of eight layers: three convolution layers, three max-pooling layers, one fully connected layer, and one softmax layer. Two datasets were used: the Apnea-ECG and UCD databases; the first has eight subjects, and the last has 25 subjects. In carrying out the tests, our model achieved an accuracy of 95.5% with the Apnea-ECG database and 90.2% with the UCD database. The suggested technique has provided a cost-effective and efficient way of identifying SAHS in any individual. 

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“…But in the article [12], there is no specific study on how to increase perturbation to improve the success rate of transfer attack and which part the model pays more attention to when identifying. BiCM was proposed in [13] to improve the coverage of the network. A new multilevel network structure was proposed by Wang et al [14], which uses the pyramid features of different levels to extract the general and transferable features of the perturbation.…”

Section: Introductionmentioning

confidence: 99%

FAPA: Transferable Adversarial Attacks Based on Foreground Attention

Yang

Gao

et al. 2022

Security and Communication Networks

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Deep learning models are vulnerable to attacks by adversarial examples. However, current studies are mainly limited to generating adversarial examples for specific models, and the migration of adversarial examples between different models is rarely studied. At the same time, in only studies, it is not considered that adding disturbance to the position of the image can improve the migration of adversarial examples better. As the main part of the picture, the model should give more weight to the foreground information in the recognition. Will adding more perturbations to the foreground information of the image result in a higher transfer attack rate? This paper focuses on the above problems, and proposes the FAPA algorithm, which first selects the foreground information of the image through the DINO framework, then uses the foreground information to generate M, and then uses PNA to generate the perturbation required for the whole picture. In order to show that our method attaches importance to the foreground information, we give a greater weight to the perturbation corresponding to the foreground information, and a smaller weight to the rest of the image. Finally, we optimize the generated perturbation through the gradient generated by the dual attack framework. In order to demonstrate the effectiveness of our method, we have conducted relevant comparative experiments. During the experiment, we used the three white-box ViTs models to attack the six black-box ViTs models and the three black-box CNNs models. In the transferable attack of ViTs models, the average attack success rate of our algorithm reaches 64.19%, which is much higher than 21.12% of the FGSM algorithm. In the transferable attack of CNN models, the average attack success rate of our algorithm reaches 48.07%, which is also higher than 18.65% of the FGSM algorithm. By integrating ViTs and CNNs models, the attack success rate of transfer of our algorithm reaches 56.13%, which is higher than 1.18% of the dual attack framework we refer to.

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Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

Cited by 35 publications

References 34 publications

Practical Latency Analysis of a Bluetooth 5 Decentralized IoT Opportunistic Edge Computing System for Low-Cost SBCs

Practical Latency Analysis of a Bluetooth 5 Decentralized IoT Opportunistic Edge Computing System for Low-Cost SBCs

CNN Model for Sleep Apnea Detection Based on SpO2 Signal

FAPA: Transferable Adversarial Attacks Based on Foreground Attention

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