Development and Assessment of Internet of Things-Driven Smart Home Security and Automation with Voice Commands

Netinant, Paniti; Utsanok, Thitipong; Rukhiran, Meennapa; Klongdee, Suttipong

doi:10.3390/iot5010005

Cited by 4 publications

(3 citation statements)

References 51 publications

(76 reference statements)

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“…The proposed system utilizes Raspberry Pi as a control device for processing natural language input. Netinant et al, in [16], developed an IoT-driven smart home security and automation framework with voice Node labels [11] were introduced in Hadoop version 3. They allow administrators to categorize nodes based on specific attributes, such as hardware capabilities, geographical location, or other intended uses.…”

Section: Relevant Research Workmentioning

confidence: 99%

“…The proposed system utilizes Raspberry Pi as a control device for processing natural language input. Netinant et al, in [16], developed an IoT-driven smart home security and automation framework with voice commands. The proposed framework ensures the incorporation of components, including Raspberry Pi, relays, motion sensors, etc.…”

Section: Relevant Research Workmentioning

confidence: 99%

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Optimizing Hadoop Scheduling in Single-Board-Computer-Based Heterogeneous Clusters

Qureshi

2024

Computation

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Single-board computers (SBCs) are emerging as an efficient and economical solution for fog and edge computing, providing localized big data processing with lower energy consumption. Newer and faster SBCs deliver improved performance while still maintaining a compact form factor and cost-effectiveness. In recent times, researchers have addressed scheduling issues in Hadoop-based SBC clusters. Despite their potential, traditional Hadoop configurations struggle to optimize performance in heterogeneous SBC clusters due to disparities in computing resources. Consequently, we propose modifications to the scheduling mechanism to address these challenges. In this paper, we leverage the use of node labels introduced in Hadoop 3+ and define a Frugality Index that categorizes and labels SBC nodes based on their physical capabilities, such as CPU, memory, disk space, etc. Next, an adaptive configuration policy modifies the native fair scheduling policy by dynamically adjusting resource allocation in response to workload and cluster conditions. Furthermore, the proposed frugal configuration policy considers prioritizing the reduced tasks based on the Frugality Index to maximize parallelism. To evaluate our proposal, we construct a 13-node SBC cluster and conduct empirical evaluation using the Hadoop CPU and IO intensive microbenchmarks. The results demonstrate significant performance improvements compared to native Hadoop FIFO and capacity schedulers, with execution times 56% and 22% faster than the best_cap and best_fifo scenarios. Our findings underscore the effectiveness of our approach in managing the heterogeneous nature of SBC clusters and optimizing performance across various hardware configurations.

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Section: Relevant Research Workmentioning

confidence: 99%

“…The proposed system utilizes Raspberry Pi as a control device for processing natural language input. Netinant et al, in [16], developed an IoT-driven smart home security and automation framework with voice commands. The proposed framework ensures the incorporation of components, including Raspberry Pi, relays, motion sensors, etc.…”

Section: Relevant Research Workmentioning

confidence: 99%

Optimizing Hadoop Scheduling in Single-Board-Computer-Based Heterogeneous Clusters

Qureshi

2024

Computation

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“…al. in [7] developed an IoT-Driven Smart Home Security and Automation framework with Voice Commands. The proposed framework ensures the incorporation of components, including Raspberry Pi, relays, motion sensors, etc.…”

Section: Introductionmentioning

confidence: 99%

Towards Improving YARN performance for Frugal Heterogeneous SBC-based Edge Clusters

Qureshi

2024

Preprint

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In the dynamic landscape of sustainable computing, use of edge devices is paramount for reducing the need for large-scale centralized data centers. By processing data locally, edge devices minimize the energy-intensive computing in data centers, improving the overall performance, cost-effectiveness whereas reducing the environmental impact. Edge devices may constitute edge clusters composed of resource frugal Single Board Computers (SBC) such as Raspberry Pi etc. The small form-factor and energy efficiency of these computers makes them ideal for processing large data on the edge. Despite their potential, traditional Hadoop configurations struggle to optimize performance in heterogeneous SBC clusters due to disparities in computing resources. Consequently, we propose modifications to the Yet Another Resource Negotiator (YARN) scheduling mechanism to address these challenges. Our proposed changes include the introduction of a Frugality Index and an adaptiveConfig policy. The Frugality Index categorizes SBC nodes based on their capabilities, enabling intelligent resource allocation. The adaptiveConfig policy dynamically adjusts resource allocation in response to workload and cluster conditions, enhancing system efficiency. Additionally, we introduce a fetch_threshold for reduce tasks to improve task prioritization based on locality and data processing efficiency. We evaluate our approach using a 13-node SBC cluster and conduct experiments with CPU-intensive and IO-intensive Hadoop benchmarks. The results demonstrate significant performance improvements compared to native YARN settings, with execution times 4.7 times faster than the worst_native and 1.9 times faster than the best_native scenarios. Furthermore, the proposed adaptiveConfig policy implementing the frugality index and a fetch_threshold outperforms the native YARN by 5.86 times and 1.79 times in Terasort and wordcount executions respectively. Our findings underscore the effectiveness of our approach in managing the heterogeneous nature of SBC clusters and optimizing performance across various hardware configurations. The adaptive policies prove well-suited to the frugal SBC-cluster context, yielding enhanced outcomes and paving the way for sustainable big data processing initiatives.

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Architecture and Applications of IoT Devices in Socially Relevant Fields

Lakshman,

Akash,

Cynthia

et al. 2024

SN COMPUT. SCI.

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Development and Assessment of Internet of Things-Driven Smart Home Security and Automation with Voice Commands

Cited by 4 publications

References 51 publications

Optimizing Hadoop Scheduling in Single-Board-Computer-Based Heterogeneous Clusters

Optimizing Hadoop Scheduling in Single-Board-Computer-Based Heterogeneous Clusters

Towards Improving YARN performance for Frugal Heterogeneous SBC-based Edge Clusters

Architecture and Applications of IoT Devices in Socially Relevant Fields

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