Improving the performance of three level code division multiplexing using the optimization of signal level spacing

Recent innovations in the Internet of Things (IoT) have given rise to IoT applications that require quick response times and low latency. Fog computing has proven to be an effective platform for handling IoT applications. It is a significant challenge to deploy fog computing resources effectively because of the heterogeneity of IoT tasks and their delay sensitivity. To take advantage of idle resources in IoT devices, this paper presents an edge computing concept that offloads edge tasks to nearby IoT devices. The IoT-assisted edge computing should meet two conditions, edge services should exploit the computing resources of IoT devices effectively and edge tasks offloaded to IoT devices do not interfere with local IoT tasks. Two main phases are included in the proposed method: virtualization of edge nodes, and task scheduling based on deep reinforcement learning. The first phase offers a layered edge framework. In the second phase, we applied deep reinforcement learning (DRL) to schedule tasks taking into account the diversity of tasks and the heterogeneity of available resources. According to simulation results, our proposed task scheduling method achieves higher levels of task satisfaction and success than existing methods.

Section: Introductionmentioning

confidence: 99%

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

Yang¹

2023

“…Recent rapid growth in artificial intelligence [1,2], machine learning [3], optical networks [4,5], smart grids [6], cloud computing [7], 5G connectivity [8], Blockchain [9,10], and Internet of Things (IoT) [11,12] have resulted in an explosion of data in almost all fields of engineering and commerce. With cloud computing, large-scale applications can be deployed quickly and efficiently, affordably on a per-use basis [13].…”

Section: Introductionmentioning

confidence: 99%

Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

Wang¹,

Liu²

2023

Cloud computing involves the dynamic provision of virtualized and scalable resources over the Internet as services. Different types of services with the same functionality but different non-functionality features may be delivered in a cloud environment in response to customer requests, which may need to be combined to satisfy the customer's complex requirements. Recent research has focused on combining unique and looselycoupled services into a preferred system. An optimized composite service consists of formerly existing single and simple services combined to provide an optimal composite service, thereby improving the quality of service (QoS). In recent years, cloud computing has driven the rapid proliferation of multi-provision cloud service compositions, in which cloud service providers can provide multiple services simultaneously. Service composition fulfils a variety of user needs in a variety of scenarios. The composite request (service request) in a multi-cloud environment requires atomic services (service candidates) located in multiple clouds. Service composition combines atomic services from multiple clouds into a single service. Since cloud services are rapidly growing and their Quality of Service (QoS) is widely varying, finding the necessary services and composing them with quality assurances is an increasingly challenging technical task. This paper presents a method that uses the firefly optimization algorithm (FOA) and fuzzy logic to balance multiple QoS factors and satisfy service composition constraints. Experimental results prove that the proposed method outperforms previous ones in terms of response time, availability, and energy consumption.

“…Recent advancements in Internet of Things (IoT) [1,2], Blockchain [3], 5G connectivity [4,5], cloud computing [6], smart grids [7], optical networks [8,9], machine learning [10], and artificial intelligence [11,12] have resulted in exponential growth in data usage across a wide variety of engineering and commerce fields. Embedded systems and Internet-enabled devices have given rise to a new technology called the IoT [13].…”

Section: Introductionmentioning

confidence: 99%

A Cloud and IoT-enabled Workload-aware Healthcare Framework using Ant Colony Optimization Algorithm

Liu¹,

Deng²

2023

In recent years, smart cities have gained in popularity due to their potential to improve the quality of life for urban residents. In many smart city services, particularly those in the field of smart healthcare, big healthcare data is analyzed, processed, and shared in real time. Products and services related to healthcare are essential to the industry's current state, which increases its viability for all parties involved. With the increasing popularity of cloud-based services, it is imperative to develop new approaches for discovering and selecting these services. This paper follows a two-stage process. The first step involves designing and implementing an Internet-enabled healthcare system incorporating wearable devices. A new load-balancing algorithm is presented in the second stage, based on Ant Colony Optimization (ACO). ACO distributes tasks across virtual machines to maximize resource utilization and minimize makespan time. In terms of both makespan time and processing time, the proposed method appears to be more efficient than previous approaches based on statistical analysis.