Bug2 algorithm-based data fusion using mobile element for IoT-enabled wireless sensor networks

Pasha, M. Jahir; Pingili, Madhavi; Sreenivasulu, K.; Bhavsingh, M.; Saheb, Shaik Imam; Saleh, Alaa

doi:10.1016/j.measen.2022.100548

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…The research presented in this paper leverages the "UNB IoT DDoS Data Set [9]" for the empirical analysis and evaluation of our proposed "Blockchain and Multi-Code Trust Framework" for securing IoT devices against Distributed Denial of Service (DDoS) attacks. he UNB IoT DDoS Dataset is a collection of network traffic data from DDoS attacks targeting IoT devices.…”

Section: Dataset Usedmentioning

confidence: 99%

Securing IoT Devices from DDoS Attacks through Blockchain and Multi-Code Trust Framework

Sharma,

Sarada,

Vasavi

et al. 2024

E3S Web Conf.

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In an era where IoT devices are integral components of numerous systems, their security from prevalent DDoS attacks has become imperative. The traditional security protocols are unable to withstand the sophisticated nature of these attacks, presenting an escalating vulnerability issue in the network security ecosystem. This research proposes a revolutionary approach to address these challenges through a “Blockchain and Multi-Code Trust Framework” utilizing the “UNB IoT DDoS Data Set.” Leveraging the decentralized and immutable characteristics of blockchain technology, alongside a multi-code driven trust mechanism, this framework aims to create a secure, robust, and resilient environment for IoT devices. Our methodology capitalizes on blockchain's transparency to foster trust and validation in network transactions, significantly reducing the threat surface for DDoS attacks. Furthermore, the incorporation of a multi-code system intensifies the security measures, providing several layers of protection against potential breaches. Data sets used in this study encompass diverse real-world IoT network traffic, meticulously collected from the “UNB IoT DDoS Data Set,” to facilitate a comprehensive analysis of the system's performance under various attack scenarios. Our preliminary findings indicate a remarkable improvement in the security posture of IoT devices, exhibiting a substantial reduction in successful DDoS attacks, thereby achieving a new pinnacle in IoT security. This research not only proposes a robust solution to a pressing issue but also opens avenues for further innovations in IoT device security using blockchain technology.

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Section: Dataset Usedmentioning

confidence: 99%

Securing IoT Devices from DDoS Attacks through Blockchain and Multi-Code Trust Framework

Sharma,

Sarada,

Vasavi

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…There are also studies of path planning related to IoT. For instance, EMESP [ 25 ] utilizes the Bug algorithm to find an efficient path for data transmission in a wireless environment. Follow Me-AI [ 26 ] manages resources of the smart environment for a user.…”

Section: Related Workmentioning

confidence: 99%

Dependency-aware action planning for smart home

Kim,

Lee,

Yun

et al. 2024

PLoS ONE

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How can a smart home system control a connected device to be in a desired state? Recent developments in the Internet of Things (IoT) technology enable people to control various devices with the smart home system rather than physical contact. Furthermore, smart home systems cooperate with voice assistants such as Bixby or Alexa allowing users to control their devices through voice. In this process, a user’s query clarifies the target state of the device rather than the actions to perform. Thus, the smart home system needs to plan a sequence of actions to fulfill the user’s needs. However, it is challenging to perform action planning because it needs to handle a large-scale state transition graph of a real-world device, and the complex dependence relationships between capabilities. In this work, we propose SmartAid (Smart Home Action Planning in awareness of Dependency), an action planning method for smart home systems. To represent the state transition graph, SmartAid learns models that represent the prerequisite conditions and operations of actions. Then, SmartAid generates an action plan considering the dependencies between capabilities and actions. Extensive experiments demonstrate that SmartAid successfully represents a real-world device based on a state transition log and generates an accurate action sequence for a given query.

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SMS2DC: Synchronous mobile sinks scheduling for data collection in internet of things‐enabled wireless sensor networks

Swapna,

Krishna,

Reddy

et al. 2024

Int J Network Mgmt

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SummaryEnergy‐efficient data collection in wireless sensor networks (WSNs) is crucial due to the limited battery capacity of sensor nodes (SNs). Using a mobile sink (MS) for data collection can lower the energy consumption of SNs to avoid relaying in WSNs. However, a single MS is not a feasible solution for large‐scale WSNs, so it was necessary to use multiple MSs to collect data. A synchronous MS scheduling strategy for data collection (SMS2DC) is proposed in this paper, which uses two types of MS, a local MS to collect data from SN and a global MS to collect data from local MS. In this process, we begin by partitioning the network based on chemical reaction optimization. For each partition, a MS is assigned and scheduled using a path construction strategy according to a geometric path construction approach. In addition, a global MS is scheduled based on a local MS trajectory by identifying the most appropriate collision point to collect data. As a result, the algorithm increases data collection accuracy while minimizing network data loss. The asymptotic time complexity of the proposed SMS2DC algorithm needed . The comparison results show the superiority of the proposed SMS2DC strategy under multiple scenarios under various deployment conditions.

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Bug2 algorithm-based data fusion using mobile element for IoT-enabled wireless sensor networks

Cited by 6 publications

References 30 publications

Securing IoT Devices from DDoS Attacks through Blockchain and Multi-Code Trust Framework

Securing IoT Devices from DDoS Attacks through Blockchain and Multi-Code Trust Framework

Dependency-aware action planning for smart home

SMS2DC: Synchronous mobile sinks scheduling for data collection in internet of things‐enabled wireless sensor networks

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