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A survey on the scheduling mechanisms in serverless computing: a taxonomy, challenges, and trends
In recent years, serverless computing has received significant attention due to its innovative approach to cloud computing. In this novel approach, a new payment model is presented, and a microservice architecture is implemented to convert applications into functions. These characteristics make it an appropriate choice for topics related to the Internet of Things (IoT) devices at the network’s edge because they constantly suffer from a lack of resources, and the topic of optimal use of resources is significant for them. Scheduling algorithms are used in serverless computing to allocate resources, which is a mechanism for optimizing resource utilization. This process can be challenging due to a number of factors, including dynamic behavior, heterogeneous resources, workloads that vary in volume, and variations in number of requests. Therefore, these factors have caused the presentation of algorithms with different scheduling approaches in the literature. Despite many related serverless computing studies in the literature, to the best of the author’s knowledge, no systematic, comprehensive, and detailed survey has been published that focuses on scheduling algorithms in serverless computing. In this paper, we propose a survey on scheduling approaches in serverless computing across different computing environments, including cloud computing, edge computing, and fog computing, that are presented in a classical taxonomy. The proposed taxonomy is classified into six main approaches: Energy-aware, Data-aware, Deadline-aware, Package-aware, Resource-aware, and Hybrid. After that, open issues and inadequately investigated or new research challenges are discussed, and the survey is concluded.
Journal Article
Emotion recognition : a pattern analysis approach
\"Written by leaders in the field, this book provides a thorough and insightful presentation of the research methodology on emotion recognition in a highly comprehensive writing style. Topics covered include emotional feature extraction, facial recognition, human-computer interface design, neuro-fuzzy techniques, support vector machine (SVM), reinforcement learning, principal component analysis, the hidden Markov model, and probabilistic models. The result is a innovative edited volume on this timely topic for computer science and electrical engineering students and professionals\"-- Provided by publisher.
Book
A Review on Wireless Sensor Networks: Routing
Wireless sensor networks (WSNs) are networks with devices that can detect, process, store, and communicate wirelessly. Each network terminal can have multiple sensing devices that can measure physical variations such as temperature, brightness, humidity, and vibration. However, developing and implementing WSNs poses many challenges. This review presents the challenges of WSN using different routing algorithms such as geographic routing, energy-aware routing, delay aware routing, QoS aware routing, secure aware routing, and hierarchical aware routing. Another goal is to find out which WSN component automates interference and behavior. What kind of application is in the WSN depends not only on his work but also on the question of the basis, functionality, and handling of his project. The study was carefully planned, and the systematic review of the literature was set up in a strong framework according to a pre-defined protocol. Finally, we evaluate the performance parameters of previous routing algorithms with diverse routine metrics that are energy consumption, delay, packet delivery ratio, throughput, false ratio, packet loss ratio, and network overhead.
Journal Article
Measuring discrimination in algorithmic decision making
Society is increasingly relying on data-driven predictive models for automated decision making. This is not by design, but due to the nature and noisiness of observational data, such models may systematically disadvantage people belonging to certain categories or groups, instead of relying solely on individual merits. This may happen even if the computing process is fair and well-intentioned. Discrimination-aware data mining studies of how to make predictive models free from discrimination, when the historical data, on which they are built, may be biased, incomplete, or even contain past discriminatory decisions. Discrimination-aware data mining is an emerging research discipline, and there is no firm consensus yet of how to measure the performance of algorithms. The goal of this survey is to review various discrimination measures that have been used, analytically and computationally analyze their performance, and highlight implications of using one or another measure. We also describe measures from other disciplines, which have not been used for measuring discrimination, but potentially could be suitable for this purpose. This survey is primarily intended for researchers in data mining and machine learning as a step towards producing a unifying view of performance criteria when developing new algorithms for non-discriminatory predictive modeling. In addition, practitioners and policy makers could use this study when diagnosing potential discrimination by predictive models.
Journal Article
Energy-Aware Scheduling for High-Performance Computing Systems: A Survey
High-performance computing (HPC), according to its name, is traditionally oriented toward performance, especially the execution time and scalability of the computations. However, due to the high cost and environmental issues, energy consumption has already become a very important factor that needs to be considered. The paper presents a survey of energy-aware scheduling methods used in a modern HPC environment, starting with the problem definition, tackling various goals set up for this challenge, including a bi-objective approach, power and energy constraints, and a pure energy solution, as well as metrics related to the subject. Then, considered types of HPC systems and related energy-saving mechanisms are described, from multicore-processors/graphical processing units (GPU) to more complex solutions, such as compute clusters supporting dynamic voltage and frequency scaling (DVFS), power capping, and other functionalities. The main section presents a collection of carefully selected algorithms, classified by the programming method, e.g., machine learning or fuzzy logic. Moreover, other surveys published on this subject are summarized and commented on, and finally, an overview of the current state-of-the-art with open problems and further research areas is presented.
Journal Article
A review on smart home present state and challenges: linked to context-awareness internet of things (IoT)
The smart home is considered as an essential domain in Internet of Things (IoT) applications, it is an interconnected home where all types of things interact with each other via the Internet. This helps to automate the home by making it smart and interconnected. However, at the same time, it raises a great concern of the privacy and security for the users due to its capability to be controlled remotely. Hence, the rapid technologically growth of IoT raises abundant challenges such as how to provide the home users with safe and secure services keeping privacy in the account and how to manage the smart home successfully under the controlled condition to avoid any further secrecy or theft of personal data. A number of the research papers are available to address these critical issues, researchers presented different approaches to overcome these stated issues. This research review will analyze smart home approaches, challenges and will suggest possible solutions for them and illustrate open issues that still need to be addressed.
Journal Article
Resource Optimization Techniques and Security Levels for Wireless Sensor Networks Based on the ARSy Framework
Wireless Sensor Networks (WSNs) with limited battery, central processing units (CPUs), and memory resources are a widely implemented technology for early warning detection systems. The main advantage of WSNs is their ability to be deployed in areas that are difficult to access by humans. In such areas, regular maintenance may be impossible; therefore, WSN devices must utilize their limited resources to operate for as long as possible, but longer operations require maintenance. One method of maintenance is to apply a resource adaptation policy when a system reaches a critical threshold. This study discusses the application of a security level adaptation model, such as an ARSy Framework, for using resources more efficiently. A single node comprising a Raspberry Pi 3 Model B and a DS18B20 temperature sensor were tested in a laboratory under normal and stressful conditions. The result shows that under normal conditions, the system operates approximately three times longer than under stressful conditions. Maintaining the stability of the resources also enables the security level of a network’s data output to stay at a high or medium level.
Journal Article
From Proxemics Theory to Socially-Aware Navigation: A Survey
In the context of a growing interest in modelling human behavior to increase the robots’ social abilities, this article presents a survey related to socially-aware robot navigation. It presents a review from sociological concepts to social robotics and human-aware navigation. Social cues, signals and proxemics are discussed. Socially aware behavior in terms of navigation is tackled also. Finally, recent robotic experiments focusing on the way social conventions and robotics must be linked is presented.
Journal Article
Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles
Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the AV to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modeling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modeling, encompassing cognitive methods, machine‐learning approaches, and game‐theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration. Interaction‐aware autonomous driving (IAAD) is a growing research field, focusing on autonomous vehicles (AVs) safely interacting with human road users. Understanding and predicting human behavior is crucial. This review assesses current IAAD research, examining terminology, challenges, and existing models for human road user interaction. Interaction modeling techniques, including cognitive, machine learning, and game theory methods, and potential advantages are discussed.
Journal Article