Related Experiment Video
Updated: Mar 9, 2026
05:30
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
1.2K
Secure quantum-resilient smart city communication networks using QSC-Net with MF-MBO-based energy-aware task
Nalavala Ramanjaneya Reddy1, G Arul Dalton2, K Swathi3
1Department of CSE, RGM College of Engineering and Technology (Autonomous), Nandyal, A.P, 518501, India. nalavala.ramanji@gmail.com.
Scientific Reports
|March 7, 2026
Summary
A new multi-strategy fuzzy-enhanced monarch butterfly optimisation (MF-MBO) improves task management for smart cities and edge computing. This adaptable optimisation framework enhances efficiency, load balancing, and energy consumption compared to traditional methods.
Area of Science:
- Computer Science
- Artificial Intelligence
- Optimization Algorithms
Background:
- Modern task management is crucial for time-varying systems in smart cities and edge-cloud computing.
- Existing optimization techniques like genetic algorithms, particle swarm optimization, and monarch butterfly optimization (MBO) exhibit limitations in adaptability, multi-objective performance, and premature convergence.
- Virtualized infrastructures present challenges in supporting heterogeneous task types and quality-of-service demands.
Purpose of the Study:
- To introduce a novel hybrid scheduling framework, multi-strategy fuzzy-enhanced monarch butterfly optimisation (MF-MBO), designed for adaptable and efficient task management.
- To address the limitations of conservative optimization techniques in dynamic and heterogeneous computing environments.
- To enhance operational efficiency, scalability, and robustness in distributed systems.
Main Methods:
- Developed a hybrid scheduling framework (MF-MBO) integrating fuzzy dominance for multi-objective ranking.
- Incorporated self-adaptive quantum-inspired tunnelling to prevent stagnation and bounded greedy migration for local refinement and load balancing.
- Dynamically balanced exploration and exploitation to accelerate convergence and ensure task fairness across distributed virtual machines.
Main Results:
- MF-MBO demonstrated superior performance over baseline algorithms (MBO, GA, PSO) under various workload conditions.
- Achieved significant improvements: 17.4% in task execution time, 22.8% in load-balancing efficiency, and 15.6% in energy consumption.
- Exhibited increased operational efficiency, scalability, and robustness in dynamic environments.
Conclusions:
- The MF-MBO framework offers a practical and explainable optimization pipeline for smart city services, distributed edge computing, and IoT applications.
- The proposed method provides a reproducible approach to adaptable optimization in complex systems.
- Empirical results and benchmarks are presented to facilitate future research and validation.
Related Concept Videos
01:23Maximum Power Flow and Line Loadability
666
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
666
01:24Fast Decoupled and DC Powerflow
795
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
795
01:21Distributed Loads: Problem Solving
1.2K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.2K
01:25Multimachine Stability
600
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
600
01:16Maximum Power Transfer
1.0K
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
By substituting the entire circuit with...
1.0K
02:12Short-distance Transport of Resources
17.9K
Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
17.9K