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5G Edge-Based Video Surveillance in Smart Cities
Published in Zoran S. Bojkovic, Dragorad A. Milovanovic, Tulsi Pawan Fowdur, 5G Multimedia Communication, 2020
Edge computing is an architecture that distributes resources and services of computing, control and storage, anywhere along the route from cloud to edges of the network. Edge computing concept reduces the amount of data transferred from an edge to cloud, and most of the computations and storage are performed closer to the IoT terminals, either within the edge or near the edge. That way, it is possible to use video resolutions higher than in the case of core cloud solution, and we can move to 4K and 8K resolution, if edge computing is used. Thus, the main idea of edge computing is to have computing facilities between the IP camera and the current cloud. Bringing computing and storage to the edge of the network reduces the latency and jitter [23], which are very important communication characteristics for real-time applications such as video surveillance. IP cameras are connected to an edge server, which itself is connected to the cloud through the rest of the network. The edge server is at a fixed physical location and has relatively high computational power, but it is less powerful than a conventional data center used in the cloud. There is a clear distinction between the device (camera) level and the edge (server) level.
Proficient Prediction of Acute Lymphoblastic Leukemia Using Machine Learning Algorithm
Published in K. Gayathri Devi, Mamata Rath, Nguyen Thi Dieu Linh, Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches, 2020
M. Sangeetha, K.N. Apinaya Prethi, S. Nithya
Machine learning (ML) [2] provides many algorithms to process, discover, identify, suggest and predict future actions. ML techniques may become coercion when the entire world starts producing big data streams in daily life through social media applications like WhatsApp, Facebook, Instagram, etc. [3]. However, ML algorithms have to overcome several challenges when they works on data which are huge in size [4]. Data storage and manipulation will be major challenges which can be resolved by parallelizing the process [5]. In cloud computing, server may go down due to huge amounts of data being processed at a same time. Data storage and security is also a major issue. This will lead to many problems like higher response time and high bandwidth consumption. To overcome this, data processing is being pulled closer to the user with edge computing. Edges may be a gateway, router or any network device that is located near the user and responds to the user in a short period of time. The response will get stored in the cloud if needed for further prediction processes. Edge computing also has many challenges, such as connectivity, power management and security.
Smart Energy Management on the Wearable Devices Based on Edge Computing
Published in Nishu Gupta, Srinivas Kiran Gottapu, Rakesh Nayak, Anil Kumar Gupta, Mohammad Derawi, Jayden Khakurel, Human-Machine Interaction and IoT Applications for a Smarter World, 2023
Aileni Raluca Maria, Suciu George, Poenaru Carmen, Anghel Madalina, Mocanu Cristian, Subea Oana, Orza Oana
Edge computing is a networking philosophy focused on bringing computing as close as possible to the source of data to reduce latency and bandwidth use. In less complicated terms, edge computing means running fewer procedures in the Cloud and moving those procedures to nearby places, for example, on a client's PC, an IoT gadget, or an edge server. Carrying calculation to the system's edge limits the measure of long-separation correspondence that needs to occur between a customer and server. It aims to deliver context-aware storage and distributed computing at the edge of the networks.
Evolutionary PSO-based emergency monitoring geospatial edge service chain in the emergency communication network
Published in International Journal of Digital Earth, 2023
Sheng He, Xicheng Tan, Yanfei Zhong, Meng Huang, Zhiyuan Mei, You Wan, Huaming Wang
Meanwhile, the development of edge computing technology provides a new way for geographic information services to support DER in emergency communication networks. Different from the centralized and unified processing model of cloud computing, edge computing is a new model that performs computing tasks decentralized at the edge nodes of the network (Shi et al. 2016). Without uploading all the data to the cloud computing center, edge computing has higher data processing efficiency and is more suitable for time-sensitive application scenarios. Moreover, edge computing well evades and solves the problems of over-reliance on communication capabilities and data security in the cloud computing model (Liao and Wu 2020; Wang et al. 2020). In emergencies, edge nodes in LAN can still operate in an orderly manner, and almost no communication with the cloud is required to complete the computing task, which is of great practical importance. However, how to build efficient and reliable geospatial edge service (GES) chains for emergency monitoring tasks in the edge computing environment based on emergency communication networks is still a big challenge because of the spatiotemporal dynamicity of the edge computing environment and the emergency communication networks.
A cyber physical production system framework for online monitoring, visualization and control by using cloud, fog, and edge computing technologies
Published in International Journal of Computer Integrated Manufacturing, 2023
Rishi Kumar, Kuldip Singh Sangwan, Christoph Herrmann, Sachin Thakur
The necessity for implementing edge computing comes from the fact that due to digitalization, an increasing amount of data is generated at the edge of the network. If these data get processed at the edge of the network, then it would be more efficient, particularly for applications such as autonomous driving, healthcare monitoring, and process control systems where service latency management is significant for providing high quality-of-service and experience to terminal users (Shi et al. 2016; Cao et al. 2021; Blesson et al. 2016). Therefore, offloading some computing tasks at the edge of network is more efficient in terms of handling enormous raw data, energy savings, and reduced response time. Edge computing, where data processing is performed at the closer proximity to data sources, ensures shorter response time, cost saving with shorter bandwidth usage, data safety and privacy, better battery life, and energy saving (Shi et al. 2016). These three innovative and complementary computing technologies (cloud, fog, and edge) can be instrumental in utilizing the full potential of CPPS implementation by complementing to meet the specific requirements like latency, bandwidth, energy efficiency, security, etc., for online monitoring, visualization, and control.
Algorithm for operating an ordinary engineering system as a quantum bit
Published in SICE Journal of Control, Measurement, and System Integration, 2022
Teturo Itami, Nobuyuki Matsui, Teijiro Isokawa, Noriaki Kouda, Takanori Hashimoto
“Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth ” [11]. The problems of cloud networks are as follows. For example, car collision avoidance has too much delay over the cloud. For analysis of large amounts of video data on the cloud, it is necessary to reduce the amount of communication data in order to prevent pressure on the network bandwidth. The computational speed will eventually reach a plateau. Quantum computer that fully uses linear parallelism becomes one powerful possibility of a breakthrough in high-speed computing. Under these circumstances, the idea of mounting quantum computers on the edge naturally emerges. Quantum mechanical linearity is vulnerable to noise. Peripheral equipment that maintains the superposition is indispensable for the computation system. For example, the superconducting type requires a cooling device. The equipment has to be large-scale. In another model using light, since the light goes straight, the circuit is inevitably large-scale apart from the question of whether to keep the overlay. It would be just a dream that is difficult to realize to install such large-scale quantum computer to the edge.