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Intelligent resource optimization for scalable and energy-efficient heterogeneous IoT devices

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Abstract

Due to resource shortages and device diversity, energy efficiency and scalability issues are critical in the Internet of Things (IoT) space. Managing edge resources consistently to encourage resource sharing among devices is complex, given IoT’s device heterogeneity and dynamic environmental conditions. In response to these challenges, our research presents a suite of intelligent techniques tailored for optimizing resources in IoT devices. Our solution’s core component is a thorough full-stack system architecture made to flexibly handle a diverse range of IoT devices, each of which operates under resource limitations. This paradigm centers on the deployment of multiple edge servers, strategically positioned to cater to the unique requirements of IoT devices, which exhibit compatibility with heterogeneity, high performance, and adaptive intelligence. To realize this vision, we create a clustered environment within the realm of heterogeneous IoT devices. We employ an African vulture’s optimization algorithm (AVOA), approach to establish connections between Cluster Head (CH) nodes. Following this crucial step, we meticulously select edge nodes situated in close proximity to the data source for transmission, reducing energy consumption and latency. Our proposed Multi-Edge-IoT system sets a new standard for efficiency within the IoT ecosystem, outperforming existing approaches in key metrics such as energy consumption, latency, communication overhead, and packet loss rate. It represents a significant stride towards the harmonious and resource-efficient operation of IoT devices in an increasingly interconnected world.

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Authors and Affiliations

  1. School of Computer Science and Engineering , Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India

    Shivani Gupta

  2. Assistant Professor (SG), Computer Science and Engineering , Jaypee University of Engineering and Technology, Guna, India

    Nileshkumar Patel, Ajay Kumar & Neelesh Kumar Jain

  3. Assistant Professor (SG), Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, India

    Pranav Dass

  4. Department of Computer Science and Engineering, NMAM Institute of Technology, Nitte, Karnataka, 574110, India

    Rajalaxmi Hegde

  5. Department of Electronics and Communication Engineering, EGS, Pillay Engineering College, Nagapattinam, India

    A. Rajaram

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  1. Shivani Gupta
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Gupta, S., Patel, N., Kumar, A. et al. Intelligent resource optimization for scalable and energy-efficient heterogeneous IoT devices. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18176-1

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