An Energy-efficient Task Offloading Solution for MEC-based IoT in Ultra-dense Networks

Sparse code multiple access (SCMA) is a kind of code-domain non-orthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multi-access edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. The integration of these two technologies can efficiently improve the computation service. In this paper, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. To minimize the total overhead of devices, the communication and computation resources allocation, as well as computation offloading are jointly considered. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed to maximize the uplink transmit rate. Moreover, we put forward CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and computation offloading can achieve a good compromise between time delay and energy consumption for IoT devices.

The design problem associated with robust downlink beamforming in multicast, multigroup, multicell wireless systems is addressed. The channel state information (CSI) of users is assumed to be imperfect and the uncertainty of CSI is modelled using the Frobenius norm. The objective is to optimise the signal-to-interference-plus-noise ratio over all users with a constraint on the maximum total transmitted power. This was achieved through a robust solution using the successive convex approximation (SCA) method. The beamforming problem is treated as a bi-convex problem, which is solved using the iterate-alternative convex technique. Here, the CSI uncertainty is addressed using a convex package through the non-monotone spectral projected gradient method and the beamforming vector is extracted using the SCA method. Also, the authors offer the required condition to extract the beamform vector using the SCA method through a suboptimal solution that always addressed before using different beamforming methods. Their simulation results examine all proposed system parameters in order to show convergence and feasibility of the solution. They also compare the solution with a suboptimal solution and the quality of service method for imperfect CSI in downlink beamforming. Numerical results show that the robust solution achieves the best power efficiency for practical solutions.

Multi-access edge computing (MEC) plays a key role in fifth-generation (5G) networks in bringing cloud functionalities at the edge of the radio access network, in close proximity to mobile users. In this paper we focus on mobile-edge computation offloading, a way to transfer heavy demanding, and latency-critical applications from mobile handsets to close-located MEC servers, in order to reduce latency and/or energy consumption. Our goal is to provide an optimal strategy to associate mobile users to access points (AP) and MEC hosts, while contextually optimizing the allocation of radio and computational resources to each user, with the objective of minimizing the overall user transmit power under latency constraints incorporating both communication and computation times. The overall problem is a mixed-binary problem. To overcome its inherent computational complexity, we propose two alternative strategies: i) a method based on successive convex approximation (SCA) techniques, proven to converge to local optimal solutions; ii) an approach hinging on matching theory, based on formulating the assignment problem as a matching game.

Heterogeneous ultra dense network (UDN) emerges as one of the potential techniques to tackle the $1000\times $ data challenge in 5G network. However, by cell densification, backhaul becomes one of the key issues in 5G heterogeneous UDN. In this paper, the cooperative wireless backhaul scheduling is concerned. First, a novel two-layers hierarchical model is proposed for the cooperative wireless backhaul, based on which energy efficiencies (EEs) of the cooperative orthogonal multiple access (C-OMA) and the cooperative non-orthogonal multiple access (C-NOMA) schemes are derived and analyzed. Moreover, based on the proposed model, a cooperative wireless backhaul optimization which maximizes EE is formulated. To solve this mixed integer non-linear optimization problem, the greedy algorithm-based C-OMA, C-NOMA and joint C-OMA and C-NOMA (JCC) schemes for cooperative wireless backhaul are proposed. Due to the benefits of both the C-OMA and C-NOMA, the proposed JCC scheme shows its superiority over C-OMA, C-NOMA and the traditional OMA schemes, which is validated by the simulation results.

Third party session control can be useful in many Internet of Things (IoT) use cases including monitoring and assistance of elderly and mission critical communications. Enhanced session control with media playing, collecting and recording interaction adds value and enables more information to be provided and gathered. The Multi-access Edge Computing (MEC), which brings the cloud computational and storage power at the network edge, can address many IoT use cases. The paper studies the capabilities of exposing media control to MEC applications. MEC Application Programming Interfaces that enable third party application to play media message, to collect and record media interactions within ongoing session, are presented. Media control data model is proposed. As a proof of the concept, state models supported by the network and MEC application are presented, formally described and verified.

Internet of Things (IoT) expects to incorporate massive machine-type (MCT) devices, such as vehicles, sensors, and wearable devices, which brings a large number of application tasks that need to be processed. Additionally, data collected from various devices needs to be executed and processed in a timely, reliable, and efficient manner. In this paper, we introduce the multi-access edge computing (MEC) service into the IoT system such that it can assist the computation offloading and resource allocation for different application tasks. Further, we define a total cost function as the weighted sum of task delay and energy consumption and propose a novel deep reinforcement learning (DRL)-based decision-making algorithm to cooperatively optimize the task offloading and resource allocation. More specifically, we transform the initial problem into a convex optimization problem with the aid of our proposed atom action generation technique (2AGT) and adaptive aggregation parameter update strategy (2APUS). Additionally, the DRL agent will be retrained periodically to adapt to different network environments. Finally, numerical simulation results validate that the proposed algorithm is nearly close to optimal performance compared with some cutting-edge schemes.

Unmanned aerial vehicle (UAV) communication has been deemed as a promising technology to collect data for the Internet of Things (IoT) in inaccessible areas. However, due to the limited UAV flight time, traditional UAV communication may not be competent for large-scale IoT data collection. This paper considers integrating non-orthogonal multiple access (NOMA) into UAV communication systems to collect data for large-scale IoT devices within UAV flight time. We aim to minimize the total energy consumption of IoT devices while ensuring data collection, by jointly optimizing UAV trajectory, IoT device scheduling and transmit power. The formulated problem is a mixed integer non-convex problem, which is challenging to solve in general. We propose a data collection optimization algorithm (DCOA) to solve it by applying the Generalized Benders Decomposition (GBD) and successive convex approximation (SCA) techniques. Then, a greedy algorithm (GA) is also proposed to reduce complexity by simplifying the optimization of UAV trajectory and IoT device scheduling. Finally, the numerical results demonstrate that, compared with traditional UAV communication systems, the NOMA-aided UAV system performs better in terms of data collection and lower total energy consumption of IoT devices can be achieved by DCOA.

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

Multi-Access edge computing (MEC) is an emerging network architecture that enables cloud computing at the edge of the network characterized by ultra-low latency, high-bandwidth, and direct access to real-time network information. However, the network heterogeneity (4G, 5G, WiFi, etc.) and quality requirement diversity (Internet of Things, Artificial Intelligence, Augmented Reality, etc.) make the deployment of MEC ever more complicated and expensive. In this paper, we introduce a novel end-to-end simulation tool- Behavior Simulation from User Equipment to Edge and Cloud (BSUEEC)- which can offload computing, communication, and traffic simulation to different simulators for the deployment and resource planning of MEC, applicable to various real world scenarios. It's a co-simulation tool that can: 1) seamlessly integrate with heterogeneous access network simulators via a self-developed adapter, 2) extend the simulation from UE(user equipments)-to-edge to UE-to-multi-layer-MEC, even the Cloud 3) assign the UE with real traffic patterns, 4) consider the interaction among access network, MEC/Cloud processing, and road traffic in one simulation. The effectiveness and applicability of our simulation framework ("BSUEEC") three Vehicle to Everything (V2X) scenarios.

The Internet of Things (IoT) devices are increasingly used in various settings, including factories, hospitals, homes, and vehicles.These IoT devices often have limited processing capabilities, which necessitate reliance on cloud servers to execute compute-intensive tasks.However, o!oading tasks to the cloud presents several challenges, primarily due to the signi"cant amount of data that must be transmitted between IoT devices and the cloud.Such high data volumes can saturate the available bandwidth, leading to network congestion, increased transmission delays, and ultimately higher end-to-end latency.These challenges are particularly pronounced in latency-sensitive and data-intensive IoT applications such as industrial monitoring and control, smart city video analytics, and continuous wearable health monitoring, where timely processing and dependable service delivery are critical.To mitigate these issues, the concept of edge computing was introduced, which brings computing resources closer to end users.Given the limited resources at edge nodes, it is essential to optimally schedule tasks among IoT devices, edge nodes, and cloud servers to ful"ll the requirements of IoT applications.This thesis presents a comprehensive framework for adaptive and delaye#cient task scheduling across the IoT-edge-cloud continuum, addressing both o!ine optimization and online learning perspectives.Speci"cally, we model the o!oading and scheduling challenges within a three-tier device-edge-cloud architecture using a Mixed-Integer Linear Program (MILP) aimed at minimizing end-to-end service delay.While CPLEX provides strong benchmark solutions for the MILP, it becomes computationally intensive for larger instances.To create scalable solutions, we develop e#cient heuristics along with two meta-heuristic approaches: a genetic algorithm (GA) and simulated annealing (SA).The GA typically improves solution quality compared to heuristics, but it requires more processing time.In contrast, SA achieves competitive accuracy, within 3-5% of the optimal MILP solution, while reducing runtime by more than an order of magnitude compared to the MILP solver, making it more suitable for large-scale o!ine planning.Building on this groundwork, we introduce two online schedulers capable of functioning e$ectively under time-varying workloads and partial system knowledge.The "rst scheduler, SATS, extends simulated annealing for online hierarchical multi-access edge computing (MEC) by performing incremental neighborhood searches at decision points and utilizing predictions of service requests.The second scheduler employs a cooperative multi-agent reinforce-iv ment learning (MARL) framework that addresses the entire device-edge-cloud stack and supports heterogeneous IoT services with interdependent tasks and varying deadlines.In this framework, IoT devices and the edge server act as agents that undergo centralized training and decentralized execution, using both Deep Q-Networks (DQN) and a variant called Double DQN to enhance stability.Extensive simulations demonstrate that the proposed online methods reduce average latency by up to 35% and improve deadline satisfaction by over 20% compared to leading state-of-the-art baselines.Collectively, these contributions advance scalable and adaptive scheduling for emerging 5G/6G-enabled IoT systems, facilitating low-latency, resource-e#cient, and resilient service delivery in domains such as smart manufacturing, intelligent transportation and smart cities, and real-time healthcare monitoring.

The ultra-dense deployment of small cells has been applied to the 5th-generation (5G) mobile networks. A large number of base stations (BSs) will lead to a dramatic increase in energy consumption, and network resources will be more difficult to fully utilize. In this paper, we propose the dynamic load adjustments (DLA) algorithm for small cells in heterogeneous ultra-dense networks. The proposed algorithm applies Q-learning to learn effective offloading policies which could combine the energy-saving function and the load balancing function. Based on the DLA algorithm, the heterogeneous ultra-dense networks could adjust the traffic load to turn off some redundant BSs or balance the load between heavily loaded BSs and lightly loaded BSs. The simulation results show that the algorithm not only improves the network energy efficiency when the average load of the networks is light, but also improves the network throughput when the average load of the networks is heavy.

Mobile Network Operators (MNO) can reduce their Capital and Operational Expenditure (CAPEX) and (OPEX) with the help of tower sharing approach by utilizing the physical infrastructure equipped by a third party tower provider to expand their network coverage. Moreover, Computing Resource Providers (CRP) are also setting up their micro-datacenters at tower stations to provide the Multi-access Edge Computing (MEC) services by cooperating with tower providers. Since both the communication and computing services contribute to the task offloading in MEC, the resource allocation has become a challenging problem. In this paper, we formulate the joint uplink, downlink, and computing resources allocation problem in which the objectives of both MNOs and CRP are to minimize their OPEX. The task offloading is modeled as a network of queues where the end-to-end latency is calculated based on the performance of the queue network. Then, the formulated problem is transformed into a Generalized Nash Equilibrium Problem (GNEP) to capture the conflicting interests in the resource allocation among MNOs and CRP. To solve the formulated GNEP efficiently, two decentralized algorithms are proposed by introducing the penalty parameters to the coupling constraints. In addition, the convergence and performance of the algorithms on different parameters are analyzed.

Chapter 28 - Moving to the cloud, fog, and edge computing paradigms: Convergences and future research direction

With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), the diverse IoT applications with requirements of ultra-low latency and heterogeneous connectivity have been increasing sharply in recent years. In this work, we consider the problem of diverse task offloading and computation resource allocation in the dynamic and heterogeneous MEC-enable network, wherein heterogeneous characterized by the MEC servers with different computing capacities and multi-type applications with different computation requirements. Firstly, to drive the long-term system-wide effectiveness towards a near-optimum, joint task offloading and computation resource allocation is proposed by invoking the Lyapunov optimization theory, and we analyze the tradeoff between task offloading revenue and latency. Besides, a heuristic task offloading methodology based on a search tree is developed to get the optimal offloading strategy in a heterogeneous environment. Moreover, to improve processing efficiency and reduce unnecessary communication overhead, the Offloading Priority Selection Criterion (OPSC) is designed. Finally, the effectiveness and rationality of the algorithm are verified by experimental simulations.

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.