The wide-spread Internet of Things (IoT) utilization in almost every scope of our life made it possible to automate daily life tasks with no human intervention. This promising technology has immense potential for making life much easier and open new opportunities for newly developed applications to emerge. However, meeting the diverse Quality of Service (QoS) demands of different applications remains a formidable topic due to diverse traffic patterns, unpredictable network traffic, and resource-limited nature of IoT devices. In this context, application-tailored QoS provisioning mechanisms have been the primary focus of academic research. This paper presents a literature review on QoS techniques developed in academia for IoT applications and investigates current research trends. Background knowledge on IoT, QoS metrics, and critical enabling technologies will be given beforehand, delving into the literature review. According to the comparison presented in this work, the commonly considered QoS metrics are Latency, Reliability, Throughput, and Network Usage. The reviewed studies considered the metrics that fit their provisioning solutions.
<p>Internet of Things (IoT) systems usually produce massive amounts of data, while the number of devices connected to the internet might reach billions by now. Sending all this data over the internet will overhead the cloud and consume bandwidth. Fog computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications. This technology complements the cloud computing by providing processing power and storage to the edge of the network. However, it still suffers from performance and security issues. Thus, machine learning (ML) attracts attention for enabling FC to settle its issues. Lately, there has been a growing trend in utilizing ML to improve FC applications, like resource management, security, lessen latency and power usage. Also, intelligent FC was studied to address issues in industry 4.0, bioinformatics, blockchain and vehicular communication system. Due to the ML vital role in the FC paradigm, this work will shed light on recent studies utilized ML in a FC environment. Background knowledge about ML and FC also presented. This paper categorized the surveyed studies into three groups according to the aim of ML implementation. These studies were thoroughly reviewed and compared using sum-up tables. The results showed that not all studies used the same performance metric except those worked on security issues. In conclusion, the simulations of proposed ML models are not sufficient due to the heterogeneous nature of the FC paradigm.</p>
Flooding is one of the most prevalent types of natural catastrophes, and it can cause extensive damage to infrastructure and the natural environment. The primary method of flood risk management is flood susceptibility mapping (FSM), which provides a quantitative assessment of a region’s vulnerability to flooding. The objective of this study is to develop new ensemble models for FSM by integrating metaheuristic algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), and harmony search (HS), with the decision table classifier (DTB). The proposed algorithms were applied in the province of Sulaymaniyah, Iraq. Sentinel-1 synthetic aperture radar (SAR) data satellite images were used for flood monitoring (on 27 July 2019), and 160 flood occurrence locations were prepared for modeling. For the training and validation datasets, flood occurrence data were coupled to 1 flood-influencing parameters (slope, altitude, aspect, plan curvature, distance from rivers, land cover, geology, topographic wetness index (TWI), stream power index (SPI), rainfall, and normalized difference vegetation index (NDVI)). The certainty factor (CF) approach was used to determine the spatial association between the effective parameters and the occurrence of floods, and the resulting weights were employed as modeling inputs. According to the pairwise consistency technique, the NDVI and altitude are the most significant factors in flood modeling. The area under the receiver operating characteristic (AUROC) curve was used to evaluate the accuracy and effectiveness of ensemble models. The DTB-GA model was found to be the most accurate (AUC = 0.889), followed by the DTB-PSO model (AUC = 0.844) and the DTB-HS model (AUC = 0.812). This research’s hybrid models provide a reliable estimate of flood risk, and the risk maps are reliable for flood early-warning and control systems.
Child drawing development optimization (CDDO) is a recent example of a metaheuristic algorithm. The motive for inventing this method is children’s learning behavior and cognitive development, with the golden ratio being employed to optimize the aesthetic value of their artwork. Unfortunately, CDDO suffers from low performance in the exploration phase, and the local best solution stagnates. Harmony search (HS) is a highly competitive algorithm relative to other prevalent metaheuristic algorithms, as its exploration phase performance on unimodal benchmark functions is outstanding. Thus, to avoid these issues, we present CDDO–HS, a hybridization of both standards of CDDO and HS. The hybridized model proposed consists of two phases. Initially, the pattern size (PS) is relocated to the algorithm’s core and the initial pattern size is set to 80% of the total population size. Second, the standard harmony search (HS) is added to the pattern size (PS) for the exploration phase to enhance and update the solution after each iteration. Experiments are evaluated using two distinct standard benchmark functions, known as classical test functions, including 23 common functions and 10 CEC-C06 2019 functions. Additionally, the suggested CDDO–HS is compared to CDDO, the HS, and six others widely used algorithms. Using the Wilcoxon rank-sum test, the results indicate that CDDO–HS beats alternative algorithms.
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