# Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
## 1 Introduction  
The International Telecommunication Union (ITU) and the Third-Generation Partnership Project (3 GPP) have identified the Internet of Things (IoT) as a key application for the fifth-generation (5G) and beyond-5G (B5G) cellular systems [1]. The IoT represents a communication paradigm that promotes machine-to-machine (M2M) interaction, allowing diverse wireless devices to connect and interact, seamlessly. IoT systems are primarilyimplemented using wireless technologies for flexibility; however, wired solutions are also still employed in scenarios demanding high reliability and low latency. This integrated environment enables automated systems, such as sensors and actuators, to effectively communicate and collaborate, resulting in advances in smart technology across multiple industries. Despite the IoT potential, combining IoT networks with current and future cellular networks, such as 6G, poses substantial architectural hurdles [2]. IoT systems enable enhanced decision making through real-time data acquisition, processing, and actionable insights derived from interconnected devices. IoT technology is a critical technology for daily life, with numerous applications in many fields [3]. The IoT holds great promise for creating novel intelligent applications across a wide range of fields, from private and social to medical and industrial. As a result of IoT contextual sensing capabilities, the number of application domains has grown rapidly. Information about the environment, natural occurrences, medical data, and user habits can be collected using IoT networks to provide individualized services [4]. Unlike traditional Internet and network systems, IoT frameworks, architectures, and technologies are specifically designed to meet the needs of these interconnected objects, which frequently operate under varying limits and requirements. The development and deployment of IoT networks are especially complicated in the light of the severe requirements established for next-generation networks. According to the 3 GPP specifications for 5G and its core use cases, IoT networks must meet several stringent requirements to reach their full potential. These requirements include increasing data capacity per area by 1000 times, multiplying the number of connected devices by 10 to 100 times, and raising the average user data rate by a similar factor [1; 5]. Furthermore, IoT networks must double the battery life of low-power massive machine communication (MMC) devices, cut end-to-end (E2E) latency by ten times, and achieve near-complete network availability and dependability, with targets set at 99.999%. Furthermore, these networks must include extremely strong security measures to protect the massive amounts of data generated and sent inside the IoT ecosystem. Meeting these requirements is critical for the successful integration of IoT into the growing landscape of cellular networks, ensuring that the benefits of IoT are fully realized in the 5G era and beyond [5; 6]. To overcome the design challenges of next-generation IoT networks and handle the expected massive number of IoT devices, novel approaches should be introduced to traditional IoT networks. Edge computing is a promising paradigm for assisting IoT network evolution and meeting dense deployment demands [7]. Distributed computing schemes provide computing facilities within the radio access network, which increases network efficiency in terms of scalability, availability, reliability, latency, and energy [8]. It also enables implementing artificial intelligence (AI) algorithms over IoT in many places over the network [9]. Fog computing is a type of distributed decentralized computing that provides resources near end devices. Deploying fog servers in the path between end devices and the core network reduces communication latency. It also provides a path for data offloading and network algorithm implementation [10].