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The Role Of Edge Computing In Enabling Low-latency Gaming Experiences

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Edge Computing Types You Need To Know

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Author: Daniel Poul Mtowe Daniel Poul Mtowe Scilit Preprints.org Google Scholar 1 and Dong Min Kim Dong Min Kim Scilit Preprints.org Google Scholar 1, 2, *

Embracing Edge Computing A Paradigm Shift In Cloud Computing

Received: June 12, 2023 / Revision: July 17, 2023 / Accepted: July 20, 2023 / Published: July 22, 2023

This study proposes a novel strategy to improve the low-latency control performance of wireless network control systems (WNCS) through edge computing integration. Traditional network control systems require receiving raw data from external sensors to enable the controller to generate appropriate control commands, a process that can result in large amounts of periodic communications traffic and lead to performance degradation in some applications. To solve this problem, we propose to use edge computing to pre-process raw data, extract essential features, and then transmit it. Furthermore, we introduce an adaptive scheme designed to reduce frequent data traffic by adaptively adjusting periodic data transfers as needed. This scheme is implemented by stopping data transfer when a comparative analysis of previously transferred data and newly generated data shows no significant changes. The effectiveness of our proposed strategy is empirically verified through experiments conducted on a testbed for a remote control system using a mobile robot that uses camera information to navigate the road. Using edge computing, only 3.42% of the original data is transmitted. Our adaptive scheme reduces the transmission frequency by 20% while maintaining acceptable control performance. Moreover, we conduct a comparative analysis of our proposed solution and the advanced communication framework WebRTC technology. The results show that our approach effectively reduces latency by 58.16% compared to using WebRTC alone in a 5G environment. Experimental results confirm that our proposed strategy significantly improves the latency performance of WNCS.

In this study, we propose an innovative technique to seamlessly integrate edge computing into wireless network control systems (WNCS), enabling a nuanced exploration of the complex relationship between improved network performance and controlled computing power. Our main goal is to achieve low latency remote control. Our approach differs from traditional WNCS strategies by utilizing edge computing to achieve low-latency communications, effectively overcoming the challenges associated with unstable communications environments. This new blend of edge computing provides a compelling solution to the inherent instabilities common in such environments, ultimately enabling optimal control performance with low latency. Furthermore, we propose an adaptive strategy that aims to alleviate the burden of frequent data traffic by sensibly adapting periodic data transmission to contextual necessity.

Edge Computing Vs Cloud Computing

Interest in smart cities is growing with advances in artificial intelligence (AI) and the Internet of Things (IoT). Much of the smart city framework is based on networked control systems. Network Control System (NCS) consists of sensors, controllers (or control servers), actuators and communication networks [1]. Sensors absorb environmental information and transmit it to the controller. The controller in turn formulates control commands and sends them to the actuators. In certain cases, the functions of sensors and actuators are combined. NCS is the embodiment of a distributed system where communication takes place over a shared network between actuators and controllers. In wireless NCS (WNCS), control loops are closed via unstable and inconsistent wireless communication links, facilitating the transfer of control and feedback signals between system components [2]. A distinguishing feature of WNCS is its ability to perform many geographically distant tasks remotely. In WNCS, the communication cycle between the controller and actuators plays a key role in maintaining optimal performance. Therefore, compared with traditional operating systems, WNCS can eliminate redundant wiring between system components, reducing the complexity and overall costs associated with designing and implementing the associated operating system. Furthermore, WNCS can be easily modified or upgraded when certain system components need to be introduced or removed, without causing major structural changes. Therefore, WNCS has been widely used, including industrial control [3]. A prominent example of a WNCS application is the Mars Exploration Rover, which was remotely operated by a National Aeronautics and Space Administration (NASA) team even during long periods of communications outage [4].

Although wireless networks are widely used for communications, they often have limitations that lead to imperfect data transmission [5]. Several challenges have arisen while using WNCS, including issues with sampling, network latency, and packet loss. Frequent periodic sampling often places a huge load on the network, which can ultimately lead to network congestion. Sending large amounts of data over communications networks, especially those with limited bit rates, is often associated with delays and packet loss. It is known that communication congestion can lead to longer delays, increased packet loss and reduced throughput, affecting the stability and reliability of the system [6, 7, 8]. This study focuses on the task of alleviating the communication overhead, which is a performance limitation in the case of low-latency remote control via WNCS.

Researchers who focus on control theory often overlook communication aspects, while those who focus on communication theory often ignore computational considerations. To improve performance from a systems perspective, both communication and computation must be considered. Guided by this basic principle, we introduce edge computing-supported WNCS and firmly believe that the application of edge computing will be optimal. The operational characteristics of WNCS are a trade-off between saving communication bandwidth and improving control efficiency. This study examines the trade-off between network performance improvements and computer energy consumption enabled by on-device edge computing.

Pdf] Edge Computing Enabled Smart Cities: A Comprehensive Survey

Traditional WNCS typically works as follows, as shown in Figure 1a. They first use sensors to identify the environment and then send the sensing data to the remote control. This remote control processes the data to formulate the appropriate control commands, which are then sent to the actuators. The executor carries out tasks based on the control commands it receives. Compared with traditional WNCS that unconditionally transmits data in a conventional way, we propose an edge computing-assisted WNCS as shown in Figure 1b. The system determines whether data needs to be transferred to a remote control, adding a layer of efficient data management to the process.

Integrating edge computing into WNCS is a key part of our proposed plan. Edge computing redistributes computing tasks from remote locations to nearby locations [9]. Many IoT devices often face limitations such as limited computing power and battery capacity, which pose an obstacle to the development of new applications and services. Edge computing can overcome the computing power and limited battery power of IoT devices [10]. Our approach to edge computing is unique: we introduce edge computing as a solution to the unstable communications environment typical of WNCS, rather than using it to overcome limited computing power.

Offloading computing tasks to remote servers can be hampered by limited bandwidth, unreliable wireless connections, or computing delays on overloaded servers. Reducing computational latency in communications is a difficult challenge due to its inverse dependence. The ultimate goal is to achieve low-latency control performance. New communication methods are needed to achieve the required control performance while reducing communication computation delays and conserving bandwidth resources.

What Is Multi Access Edge Computing?

In this study, we analyze WNCS that generate large amounts of data, which require continuous transmission and computation to estimate system status. We use edge computing to preprocess information locally and reduce the size of transmitted data, and then propose an adaptive scheme to decide when to transmit data. Compared with traditional WNCS, the adaptive scheme updates its value only when the deviation between the current state and the desired state exceeds a predetermined threshold. This approach is notable because it reduces the requirement to submit samples

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