Enterprise AI Analysis
An energy aware cluster inspired routing protocol using multi strategy improved crayfish optimization algorithm for guaranteeing green communication in IoT
Internet of things (IoT) has a significant impact on environmental and economic factors for interconnecting billions or trillions of devices that utilize various types of sensors during communications using Internet. Energy is identified as the heart of smart IoT applications since it permits the sensors to carry out their their operations. Even though, sensors necessitate a small amount of energy for operations, rapid energy drain when billions and trillions of them interconnect is determined to crumble their performance by influencing energy stability. Clustering is the potential energy managing green communication mechanism which when implemented using metaheuristic techniques helps in achieving required quality of service by facilitating near-optimal solutions. In this paper, mult strategy-improved crayfish optimization algorithm-based intelligent clustering mechanism (MSCFOAICM) is proposed as a solution to the NP-hard problem of achieving green communication in IoT with maximized network lifetime. This MSCFOAICM scheme used a multi-objective fitness function that considered factors of delay, energy, distance, jitter and packet forwarding potential into account such that energy potent nodes are selected as cluster heads (CHs) during the clustering process. It uses multi-strategy-improved crayfish optimization algorithm for CH selection for establishing a better trade-off between exploration and exploitation. It then used a hybrid BWM-TOPSIS multicriteria decision making model for determining nodes' trust using direct and indirect interaction to prevent selection of malicious nodes as CHs. This protocol also prevented low energy nodes to be selected as CHs depending on residual energy estimation during the trust computation process. The number of clusters built during the implementation is determined to be optimal as it aids in sustaining maximized energy and extending network lifetime. The results of MSCFOAICM scheme confirm better throughput of 18.14%, operating IoT nodes of 19.42% and reduced mean transmission delay of 18.42%, compared to the baseline schemes.
Executive Impact & Key Findings
Our deep analysis of the research reveals significant advancements in optimizing IoT network performance and sustainability:
Deep Analysis & Enterprise Applications
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Performance Improvements (Spotlight)
The MSCFOAICM scheme significantly boosts throughput by 18.14% compared to baseline methods. This improvement is attributed to optimal CH selection and efficient data aggregation, ensuring faster and more reliable data delivery within the IoT network. This directly translates to enhanced operational efficiency for enterprises relying on timely data.
Green Communication Protocols Comparison
| Protocol | Strengths | Weaknesses |
|---|---|---|
| Proposed MSCFOAICM |
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| ERWCOA |
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| BFABCOA |
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| HWFSOA |
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| SPCHOA |
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A comparative analysis of the proposed MSCFOAICM against existing green communication protocols highlights its superior capabilities in maximizing network lifetime and energy efficiency. While other protocols offer certain advantages, MSCFOAICM provides a more holistic approach by integrating trust management and multi-strategy optimization.
Enterprise Process Flow
The MSCFOAICM methodology involves an iterative process of population initialization, environmental updates for exploration or exploitation, and a robust trust evaluation model. This ensures optimal CH selection, preventing malicious nodes and maximizing network longevity.
Mitigating Malicious Nodes in IoT with BWM-Fuzzy TOPSIS
The BWM-Fuzzy TOPSIS model is a cornerstone of the MSCFOAICM protocol, designed to enhance the trustworthiness of Cluster Heads (CHs) in IoT networks. Malicious CHs can severely degrade network performance, leading to energy wastage and packet loss. This model systematically evaluates potential CHs using multiple criteria such as packet forwarding potential, energy utilization, and packet dropping rate. By employing fuzzy logic, it effectively handles the inherent vagueness and uncertainty in real-time trust assessment. This mechanism ensures that only the most reliable and energy-efficient nodes are selected as CHs, thereby significantly improving network security and overall operational stability. The prevention of low-energy nodes from being selected further safeguards the network's longevity, ensuring green communication.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by implementing MSCFOAICM.
Your Implementation Roadmap
A phased approach to integrate MSCFOAICM into your existing IoT infrastructure for sustainable green communication:
Phase 1: Initial Network Deployment & MSCFOAICM Integration
Deploy IoT nodes and integrate the MSCFOAICM protocol for initial clustering and CH selection, focusing on energy-aware metrics.
Phase 2: Continuous Trust Evaluation & Dynamic Re-clustering
Implement BWM-Fuzzy TOPSIS for ongoing trust assessment of CHs, enabling dynamic re-clustering to mitigate malicious or low-energy nodes.
Phase 3: Performance Monitoring & Optimization
Monitor network throughput, energy consumption, and packet delivery rates. Utilize collected data to fine-tune MSCFOAICM parameters for enhanced green communication.
Phase 4: Scalability & Long-term Network Health
Scale the IoT network while ensuring sustained performance and extended lifetime through adaptive MSCFOAICM adjustments based on network growth and evolving environmental factors.
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