Unequal sized cells based on cross shapes for data collection in green Internet of Things (IoT) networks

The smart city concept originated a few years ago as a combination of ideas about how information and communication technologies can improve urban life. With the advent of the digital revolution, many cities globally are investing heavily in designing and implementing smart city solutions and projects. Machine Learning (ML) has evolved into a powerful tool within the smart city sector, enabling efficient resource management, improved infrastructure, and enhanced urban services. This paper discusses the diverse ML algorithms and their potential applications in smart cities, including Artificial Intelligence (AI) and Intelligent Transportation Systems (ITS).The key challenges, opportunities, and directions for adopting ML to make cities smarter and more sustainable are outlined.

Section: A Overview Of Smart Citiesmentioning

confidence: 99%

Machine Learning for Smart Cities: A Comprehensive Review of Applications and Opportunities

Dou,

Chen,

Zhu

et al. 2023

“…The convergence of Internet of Things (IoT), smart grids, meta-heuristic algorithms, machine learning, Artificial Intelligence (AI), association rule mining, and urban public transportation plays a pivotal role in revolutionizing the landscape of cloud computing. IoT sensors and devices generate an unprecedented volume of data, which smart grids harness to optimize energy distribution [11][12][13]. Meta-heuristic algorithms are essential for efficiently allocating resources in cloud data centers to manage this influx of data [14,15].…”

Section: Introductionmentioning

confidence: 99%

Virtual Machine Allocation in Cloud Computing Environments using Giant Trevally Optimizer

Zhang

2023

Cloud computing has gained prominence due to its potential for computational tasks, but the associated energy consumption and carbon emissions remain significant challenges. Allocating Virtual Machines (VMs) to Physical Machines (PMs) in cloud data centers, a known NP-hard problem, offers an avenue for enhancing energy efficiency. This paper presents an energy-conscious optimization approach utilizing the Giant Trevally Optimizer (GTO) which is inspired by the hunting strategies of the giant trevally, a proficient marine predator. Our study mathematically models the trevally's hunting behavior when targeting seabirds. The trevally's approach involves strategic selection of optimal hunting locations based on food availability, including pursuing seabird prey in the air or seizing it from the water's surface. Through extensive simulations, our method demonstrates superior performance in terms of skewness, CPU utilization, memory utilization, and overall resource allocation efficiency. This research offers a promising avenue for addressing the energy consumption challenges in cloud data centers while optimizing resource utilization for sustainable and cost-effective cloud operations.

“…Notably, studies have delved into the analysis of Android ransomware using hybrid approaches [11] and explored machine learning-based network slicing for efficient 5G network management [12]. Additionally, predictive models and probabilistic neural networks have been harnessed to forecast idle slot availability in wireless local area networks [13], while innovative cell designs have been introduced to enhance data collection efficiency in green IoT networks [14]. The integration of machine learning into data conditioning and forecasting methodologies has showcased its potential in optimizing wellpad operations [15,16].…”

Section: Introductionmentioning

confidence: 99%

A QoS-aware Mechanism for Reducing TCP Retransmission Timeouts using Network Tomography

2023

A wide range of web-based applications uses the Transmission Control Protocol (TCP) to ensure network resources are shared efficiently and fairly. As wired and wireless networks have become more complex, various end-to-end Congestion Control (CC) schemes have been developed, offering solutions through their proposed TCP variants. Network tomography, a powerful analytical tool, offers a unique perspective by measuring end-to-end performance to estimate internal network parameters, including latency. This estimation capability proves valuable, especially in cases where precise protocol performance evaluation is essential. TCP protocol can be improved significantly by properly estimating RTT time. It has resulted in better network conditions and improved reliability, as well as a higher level of user satisfaction. In this study, we propose a method to infer the link delay using network tomography and then adjust the RTT based on the delay estimation obtained in the previous step. Simulation results performed using the NS2 software show that the proposed method significantly improves the TCP protocol's Round-Trip Time (RTT) estimation by more than 15%. It reduces congestion, improves information transfer efficiency, and ensures the highest level of service in the network.