Energy Consumption Of Mobile Users Research Articles

A DRL-Driven Intelligent Joint Optimization Strategy for Computation Offloading and Resource Allocation in Ubiquitous Edge IoT Systems

Intelligent computation offloading and resource allocation for mobile users (MUs) in ubiquitous edge Internet of Things (IoT) systems is a worthy research hotspot. To improve the latency and energy consumption of MUs in ubiquitous edge IoT systems, we propose a deep reinforcement learnin (DRL)-driven intelligent joint optimization strategy for computation offloading and resource allocation that includes relay selection, offloading decisions, and resource allocation. Specifically, according to the limited coverage of base stations and the extremely high deployment costs in actual environments, we introduce the virtual backbone architecture to provide users with efficient multi-hop offload services through a connected dominating set (CDS). Then, we propose a CDS-based deep reinforcement learning algorithm to search for the shortest path from the MUs to the multi-access edge computing (MEC) server. Furthermore, based on the highly coupled relationship between the offloading decision and resource allocation, we design a DLIO algorithm to solve for the joint optimization of computation offloading and resource allocation. The experimental results demonstrate that our proposed optimization algorithm outperforms the state-of-the-art methods in terms of total system cost, success rate, and acceptance number.

Matching Subcarrier Resource Allocation and Offloading Decision

A heterogeneous cellular network can define as a network that is composed of different cell sizes (macrocell (MeNB), small cell (SeNB), femtocell). Such heterogeneity of network is the backbone of the 5G networks where new applications on mobile devices need extensive computing power consumption and ultra-low latency constraints. Using a heterogeneous network will provide multiple paths in which the users’ data can flow through the network depending on the users’ available resources, remaining energy, etc. we study the heterogeneous network model, which contains MeNB, SeNB and femtocells. Also, we propose a matching subcarrier resource allocation and offloading decision (MSRAOD) algorithm, that depends on recourse allocation optimization. The main optimization goal has been set to minimize the total energy consumption of mobile users’ devices with acceptable latency requirements of the applications. Our proposed algorithm results show that the proposed algorithm enhances the average energy consumption of mobile users in the heterogeneous network.

Matching Subcarrier Resource Allocation and Offloading Decision

A heterogeneous cellular network can define as a network that is composed of different cell sizes (macrocell (MeNB), small cell (SeNB), femtocell). Such heterogeneity of network is the backbone of the 5G networks where new applications on mobile devices need extensive computing power consumption and ultra-low latency constraints. Using a heterogeneous network will provide multiple paths in which the users’ data can flow through the network depending on the users’ available resources, remaining energy, etc. we study the heterogeneous network model, which contains MeNB, SeNB and femtocells. Also, we propose a matching subcarrier resource allocation and offloading decision (MSRAOD) algorithm, that depends on recourse allocation optimization. The main optimization goal has been set to minimize the total energy consumption of mobile users’ devices with acceptable latency requirements of the applications. Our proposed algorithm results show that the proposed algorithm enhances the average energy consumption of mobile users in the heterogeneous network.

Joint Optimization for Traffic-Offloading and Resource-Allocation Over RF-Powered Backscatter Mobile Wireless Networks

We develop the joint optimization schemes for traffic-offloading and resource-allocation over radio-frequency (RF) powered backscatter-based mobile wireless networks, where a macro-cell base station (BS), several small-cell access points (SAPs), and multiple energy harvesting (EH) mobile users (MUs) co-exist. Optimizing MUs' network access (via traffic-offloading) and system resource-allocation, we aim to minimize MUs' energy consumption by using low-energy consumption of backscatter. First, we consider the scenarios when short-range ambient backscatter (AB) communication (e.g., over several meters) is employed, enabling MUs to access nearby SAPs via ambient backscatter RF signals for data transmission. Using the alternating directions method of multipliers (ADMM), we develop a distributed traffic-offloading and resource-allocation scheme. Then, we focus on traffic-offloading and resource-allocation by adopting the concurrent AB (CAB) transmission, where multiple MUs can backscatter data concurrently to further reduce energy consumption. Moreover, we also use long-range bi-static backscatter (BB) communication (e.g., over 270-meter distance) for data transmission between MUs and BS, and then MUs can convey data by backscattering RF signals radiated from dedicated carrier emitters. Then, we study the joint traffic-offloading and resource-allocation when AB and BB communications are adopted by MUs accessing SAPs and BS, respectively. Finally, the numerical analyses validate and evaluate our developed schemes, showing their good convergence performances and significant reduction in energy consumption of MUs through backscatter communications.

End-edge-cloud collaborative computation offloading for multiple mobile users in heterogeneous edge-server environment

With the drastic development of Internet of things, the number of connected mobile users (MUs) is increasing at an unprecedented speed. The increasing popularity of MUs has triggered more and more new mobile applications. However, these applications are sensitive to latency, which inevitably increases pressure on MUs. Fortunately, computation offloading of mobile edge computing is becoming a promising technology that can improve quality of service for MUs. However, it becomes much difficult when there are multiple edge servers (ESs) near to the MU, even to the multiple MUs. On the other hand, as the resources of ESs are heterogeneous and finite, and thus it is challenge to design effective offloading strategies for multiple MUs. To tackle the above challenges, we firstly establish a multi-objective optimization model concerning time consumption and energy consumption of MUs, and resource utilization of ESs. Moreover, we devise an end-edge-cloud collaborative computing offloading method based on improved Strength Pareto Evolutionary Algorithm 2 for addressing this mode. Finally, compared to benchmark methods, numerous experiments have proved that our proposed method is effective and efficient and can be widely used for the scenario of multiple MUs and multiple heterogeneous ESs.

Cooperative Computation Offloading in FiWi Enhanced 4G HetNets Using Self-Organizing MEC

Multi-access edge computing (MEC) is an emerging paradigm to meet the rapidly growing computation demands of mobile applications. This paper investigates the performance gains of cooperative computation offloading for MEC enabled FiWi enhanced HetNets with capacity-limited backhaul links. After presenting the envisioned two-tier MEC architecture for a FiWi based networking infrastructure, we propose a simple but efficient offloading strategy, which relies on the flexible trilateral cooperation between end-device, edge servers, and the remote cloud. We then present an analytical framework to estimate the average response time and energy consumption of mobile users for various offloading scenarios with different wireless access modes (i.e., WiFi and 4G LTE-A). The presented analysis flexibly allows for incorporating both offloaded and conventional human-to-human (H2H) traffic of mobile users as well as fixed (wired) subscribers. Finally, we present our self-organization based mechanism, which enables mobile users to make suitable energy-delay trade-offs by jointly minimizing the average task execution time and energy consumption, using only their local information. The obtained results demonstrate the feasibility of the proposed cooperative self-organizing offloading strategy and its superior performance over schemes with MEC- or cloud-only offloading strategies.

On balancing energy efficiency for network operators and mobile users in dynamic planning

The high energy consumption of network operators and mobile users has raised environmental, financial, and quality-of-experience concerns. These concerns have renewed the research efforts in developing green communication strategies for energy efficient wireless network operation. Network operators employ dynamic planning to save energy at low call traffic load by switching off some of their base stations (BSs), and mobile users are served by the remaining active BSs. The existing research investigates energy efficiency of dynamic planning approaches only from the network operator perspective. Dynamic planning, if not carefully designed, can lead to higher energy consumption for the mobile users in the uplink, which in turn degrades the uplink service quality due to mobile terminals' battery depletion. In this article we propose a dynamic planning framework with balanced energy efficiency that accounts for the energy consumption of the mobile users in the uplink as well as that of the network operators in the downlink. We discuss the associated challenges and implementation issues. A dynamic planning approach based on a multi-time scale decision process is proposed to achieve the balanced energy efficiency framework. Numerical results demonstrate the improved energy efficiency performance for the uplink mobile users as compared with the traditional dynamic planning approach.

Energy Efficient Pilot and Link Adaptation for Mobile Users in TDD Multi-User MIMO Systems

In this paper, we develop an uplink pilot and downlink link adaptation approach to improve the energy efficiency (EE) of mobile users in time division duplexing (TDD) multi-user multiple input and multiple output (MU-MIMO) systems. Assuming reciprocity between uplink and downlink channels, the downlink transmission is based on uplink channel estimation. While more uplink pilot power ensures more accurate channel estimation and better downlink performance, it incurs higher energy consumption of mobile users. This paper reveals the relationship and tradeoff among pilot power, channel estimation, and downlink link adaptation that achieves the highest energy efficiency for mobile users. We show that the energy efficiency of different users can be decoupled because the downlink average throughput of each user is independent of the pilot powers of other users and energy-efficient design can be done on a per-user basis. Based on the analysis, we propose an uplink pilot and downlink link adaptation algorithm to improve the EE of mobile users. Simulation results are finally provided to demonstrate the significant gain in energy efficiency for mobile users.