Heuristic green computing based energy management with security enhancement using hybrid greedy secure optimal routing protocol

Oliver, Avital; Ravi, Bhavani; Manikandan, R.; Sharma, Ashutosh; Kim, Byung-Gyu

doi:10.1016/j.egyr.2023.01.052

Cited by 8 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2023, A. S. Oliver et al [18] provided an innovative energy management approach using heuristic green computing technology and an optimised routing protocol to improve effectiveness and security. Heuristic green computing uses energy management to boost network energy efficiency.…”

Section: Literature Surveymentioning

confidence: 99%

A Hybrid MFCM-PSO Approach for Tree-Based Multi-Hop Routing Using Modified Fuzzy C-Means in Wireless Sensor Network

Sikarwar,

Tomar

2023

IEEE Access

View full text Add to dashboard Cite

A wireless sensor network (WSN) collects data from various monitoring systems using small sensor nodes (SNs). The WSN analyses and sends found data to the BS. SNs have a short lifespan, therefore the sensor network must gather and transmit data efficiently. Energy efficiency is the main difficulty in sensor network organisation. Sensor nodes require energy efficiency due to their high cost and limited capacity. This limitation significantly compromises the longevity of the network. The anticipated demands on a node's battery determine its lifespan. Sensor networks must be resilient to sensor node lifetime reduction. Energyefficient routing algorithms may increase the lifetime of multihop networks by selecting the most energyefficient approach. Unfortunately, the present technique has performance issues. Modified Fuzzy C-Means with Particle Swarm Optimization (MFCM-PSO) is a novel hybrid clustering method that combines fuzzy cmeans with PSO to address the shortcomings of existing methods that use FCM to determine cluster centres and then optimise them using PSO. The PSO helps choose optimum no. of clusters and CHs (Cluster Heads). This method uses 100 and 200 sensor nodes. We devised a hierarchical packet routing technique by introducing the concepts of Direct Cluster Head (DCH) and PCH (Parent Cluster Head), that are chosen based on distinct FF (Fitness Functions) and act as relays for a limited number of other CHs, saving network energy. The simulation results represents that the proposed MFCM-PSO method superior performance base EEHCHR on scenario-1, with 28% for FND (First Node Dead), 23% for HND (Half Node Dead), and 19% for LND (Last Node Dead), and on scenario-2, 8% for FND, 23% for HND, and assumed 36% for LND since no node dies up to 1500 rounds. Thus, the simulation results suggest that the MFCM-PSO method improves coverage, network energy, and lifetime efficiency over the EEHCHR method.

show abstract

Section: Literature Surveymentioning

confidence: 99%

A Hybrid MFCM-PSO Approach for Tree-Based Multi-Hop Routing Using Modified Fuzzy C-Means in Wireless Sensor Network

Sikarwar,

Tomar

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Oliver et al [23] proposed a unique energy management strategy with enhanced security based on a heuristic green computing technique and an optimized routing protocol. Energy management in green computing is used to improve the energy efficiency of the network by employing a heuristic green computing technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks

2023

KSII TIIS

View full text Add to dashboard Cite

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. Velmurugan. S et al.: Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks Hong et al.: paper titleThe proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

show abstract

Brainstorming on Dataset Reduction from an Heuristic Bioinspired Green Computing Approach

Aravena-Cifuentes,

Porlan-Ferrando,

Nuñez-Gonzalez

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Heuristic green computing based energy management with security enhancement using hybrid greedy secure optimal routing protocol

Cited by 8 publications

References 14 publications

A Hybrid MFCM-PSO Approach for Tree-Based Multi-Hop Routing Using Modified Fuzzy C-Means in Wireless Sensor Network

A Hybrid MFCM-PSO Approach for Tree-Based Multi-Hop Routing Using Modified Fuzzy C-Means in Wireless Sensor Network

Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks

Brainstorming on Dataset Reduction from an Heuristic Bioinspired Green Computing Approach

Contact Info

Product

Resources

About