Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization

Abstract

FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated than MANETs or VANETs. Clustering approaches based on artificial intelligence (AI) approaches can be used to solve complex routing issues when static and dynamic routings fail. Evolutionary algorithm-based clustering techniques, such as moth flame optimization, and ant colony optimization, can be used to solve these kinds of problems with routes. Moth flame optimization gives excellent coverage while consuming little energy and requiring a minimum number of cluster heads (CHs) for routing. This paper employs a moth flame optimization algorithm for network building and node deployment. Then, we employ a variation of the K-Means Density clustering approach to choosing the cluster head. Choosing the right cluster heads increases the cluster’s lifespan and reduces routing traffic. Moreover, it lowers the number of routing overheads. This step is followed by MRCQ image-based compression techniques to reduce the amount of data that must be transmitted. Finally, the reference point group mobility model is used to send data by the most optimal path. Particle swarm optimization (PSO), ant colony optimization (ACO), and grey wolf optimization (GWO) were put to the test against our proposed EECP-MFO. Several metrics are used to gauge the efficiency of our proposed method, including the number of clusters, cluster construction time, cluster lifespan, consistency of cluster heads, and energy consumption. This paper demonstrates that our proposed algorithm performance is superior to the current state-of-the-art approaches using experimental results.

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

“…The authors of [57] unveiled a new concept for cloud computing energy efficiency that tracks how the emissions of different greenhouse gasses from data centers have caused, how much ozone depletion, and how eco-friendly our technology can be [69][70][71][72][73][74][75][76][77][78][79]. It also addresses the numerous main problems when these energy-saving strategies are applied in cloud data centers.…”

“…A manually verified referenced list of the 119 relevant articles collected after refining automated and manual search outcomes was then used to conduct a reference snowballing independently by the authors. Additionally, regards to the previously released cloud computing energy-efficient technologies SLRs [18,76,100,122]. We investigated to reduce the probability that related papers cannot be neglected.…”

Efficient Middleware for the Portability of PaaS Services Consuming Applications among Heterogeneous Clouds

“…Moreover, one platform could supply some assistance that is not offered by another platform [10,[43][44][45][46][47][48][49]. So, application portability among different clouds or from legacy enterprises to the cloud becomes essential to preclude users from vendor lock-in [50][51][52][53][54][55]. A vast amount of the literature on application portability solutions was reviewed, and a comparative analysis is presented in Table 1.…”

“…We defined the structure of our configuration file so that the middleware layer handles and implements the cloud services even with the different designs of the configuration variables. The configuration is not hardcoded in the middleware code [48][49][50][51]. Instead, it is provided in a JSON file that can be changed to alter the design even if the application runs.…”

A Novel Optimization for GPU Mining Using Overclocking and Undervolting

“…Mining is computationally demanding and demands the solving of mathematical problems, as previously stated. Let us take a short look on the hardware that these computations need [24]. Mining was performed exclusively on the common end by the processors (CPUs) which were only used to run programs in the first generation [25].…”