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Enterprise AI Analysis: An energy aware cluster inspired routing protocol using multi strategy improved crayfish optimization algorithm for guaranteeing green communication in IoT

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:

0% Throughput Increase
0% Operating IoT Nodes Increase
0% Mean Transmission Delay Reduction

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

Performance Improvements (Spotlight)

18.14% Increased Throughput

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
  • Maximizes network lifetime through energy-aware CH selection;
  • Integrates trust management to prevent malicious nodes;
  • Balances exploration and exploitation in search space.
  • Requires initial parameter tuning for optimal performance;
  • Computational overhead during trust evaluation for extremely large networks.
ERWCOA
  • Sustained energy in IoT networks;
  • Addresses data transmission efficiency challenges;
  • Local escaping operator prevents local optima.
  • Restricted exploration capability;
  • Moderate performance with static IoT nodes.
BFABCOA
  • Superior data gathering;
  • Blends exploration and exploitation phases for energy management;
  • Balances link quality and cluster stability.
  • Problem of selecting worst IoT nodes as CHs;
  • Can introduce additional communication overhead due to more clusters.
HWFSOA
  • Multi-objective clustering for energy efficiency;
  • Balances network stability with extended lifetime;
  • Efficient CH identification.
  • Imbalanced exploration/exploitation;
  • Suffers from scalability issues with a greater number of IoT nodes.
SPCHOA
  • Optimizes CH selection with high fitness;
  • Decreases energy consumption;
  • Increases throughput and network lifetime.
  • Limited factors considered for CH selection;
  • Restricted exploitation in search space;
  • Can lead to energy holes if not carefully managed.

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

Initialize Search Agents (Crayfish) Population
Evaluate Candidate Solutions & Identify Global Best
Calculate Adaptive Water Flow Factor
Perform Environment Update (Exploration or Exploitation)
Determine Trust via BWM-Fuzzy TOPSIS
Select Optimal CHs & Form Clusters

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.

Estimated Annual Savings $0
Operating Hours Reclaimed 0

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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