A Development of Edge Computing Method in Integration with IOT System for Optimizing and To Produce Energy Efficiency System Renas Rajab Asaad Department of Computer Science, Nawroz University, Duhok, Iraq Renas.rekany@nawroz.edu.krd Ahmed Alaa Hani Department of Computer Science, College of Science, Nawroz University, Duhok, KRG-Iraq Ahmed.alaa@dpu.edu.krd Amira Bibo Sallow Department of Computer Science, College of Science, Nawroz University, Duhok, KRG-Iraq Saman Mohammed Abdulrahman Department of Computer Science, Nawroz University, Duhok, Iraq saman.almofty@gmail.com Hawar Bahzad Ahmad Department of Computer Science, Nawroz University, Duhok, Iraq Subhi R. M. ZeebareeEnergy Engineering Department, Technical College of Engineering, Duhok Polytechnic University, Duhok, Iraq Abstract: The number of the Internet of Things (IoT) devices is growing, leading to increased generation of data at a level that has never been witnessed from the network edge. These pervading are through different industries: in the likes of healthcare, transportation, manufacturing, and smart cities, explicit needs call for effective data processing and analysis at the edge. This paper discusses the idea of edge computing in an IoT context, focusing on how this can best be optimized in terms of minimizing the two aforementioned areas to improve overall system performance. The traditional approach to data processing in IoT systems, which is cloud-centric,json, often experiences latency problems because data has to travel over distances to servers that are centralized for analysis. More so, the continuous transmission of voluminous raw data into the cloud is very consuming of the energy and weighs down the network bandwidth. That is where the edge computing comes into place, filling this gap through the relocation of computational tasks near the source, thereby reducing latency that translates to relieving the network from congestion. This paper tries to reflect key principles and benefits of edge computing in an IoT environment based on a comprehensive review of existing literature and relevant case studies. Near IoT devices, edge nodes enable computational capabilities. Keywords: Edge Computing, IoT, Latency, Energy Efficiency, Cloud-Centric I. INTRODUCTION The number of devices for the Internet of Things (IoT) has grown so rapidly in the past years that it has, in reality, redefined the scenario of modern computing paradigms. This immense adoption of IoT cuts across diverse domains: healthcare, transportation, manufacturing, and smart cities, underlining the ubiquitousness of the devices in our daily lives. At the same time, this ultrahigh pace of development of IoT devices has also introduced new challenges to effectively process and analyze such huge flows of data, issuing action- generating insights in real-time. In some respect, the traditional cloud-centric approaches towards IoT data management are good, but they, in general, miss the point when it comes to dealing with inbuilt problems related to issues of latency and energy efficiency in decentralized IoT ecosystems. Data has to travel over long geographical distances to be processed in centralized cloud servers; thus, it brings with it latency issues that compromise not only the required responsiveness but also the reliability of time-critical applications. Moreover, the constant transmission of raw data to the cloud, being energy expensive and overloading network bandwidth, is limiting for IoT solution deployments in terms of scaling. In response to these challenges, edge computing has emerged as a promising paradigm shift—emphasizing the decentralized approach toward data processing and analysis by bringing computational resources closer to the source of data. In other words, edge computing seeks to take advantage of edge nodes having computational capability for reduced latency, increased responsiveness, and lowering energy expenditure in data handling. The paper explores the prospects for edge computing in IoT, specifically in optimizing latency and energy efficiency. The aim of the present article will be to showcase the principles, the advantages, and the challenges that have to be put forth into computing on the edge in IoT environments, illustrating that with case studies and state-of- the-art techniques, through a detailed review of already available literature. Furthermore, we provide various optimization techniques used, including energy-aware scheduling algorithms and predictive analysis, to maximize improved energy efficiency with performance requirements for different IoT applications. Finally, we discuss security and privacy issues related to edge computing for IoT, among other things, new cryptographic solutions and practices on how sensitive data ought to be maintained in decentralized-edge environments. This paper will attempt to summarize insights from academia, industry, and real-world deployments to summarize a holistic understanding of edge computing in IoT and bring out its transformative potential to optimize latency, conserve energy, and enable innovative applications of IoT in diverse domains. II. LITERATURE REVIEW A. The Emergence of Edge Computing in IoT: They argue that this kind of computing has actually answered the limitations pointed out through traditional, cloud-centric approaches. Research shows that edge computing is paramount to reduce latency and boost responsiveness by moving the computational resources nearer to the data source (figure 1). Moreover, edge computing architectures have currently drawn attention as a new practical technology for the realization of low latency required IoT services. Distributed processing nodes are deployed at the edge of the network. 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) 979-8-3503-6016-5/24/$31.00 ©2024 IEEE 835 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) | 979-8-3503-6016-5/24/$31.00 ©2024 IEEE | DOI: 10.1109/ICACITE60783.2024.10617436 Authorized licensed use limited to: UNIVERSITY TEKNOLOGI MALAYSIA. Downloaded on August 12,2024 at 10:12:29 UTC from IEEE Xplore. Restrictions apply.