A Novel Approach to Efficient Resource Allocation in NOMA Heterogeneous Networks: Multi-Criteria Green Resource Management

Hassan

IEEE Commun. Surv. Tutorials

et al. 2020

274

159

Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML-and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML-and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

Section: ML Techniques For Nomamentioning

confidence: 99%

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Hassan

IEEE Commun. Surv. Tutorials

et al. 2020

274

159

IETE Journal of Research

“…The IoT data are then copied into mini clouds based on the values existing in the designated vector. The cost function [30] (in which time is measured in seconds) is defined as (1) for every datum in the proposed approach [31]:…”

Section: Hs-based Allocation Of Replicasmentioning

confidence: 99%

Allocating Duplicate Copies for IoT Data in Cloud Computing based on Harmony Search Algorithm

Jahandideh

Mirzaei

2021

The Internet of things (IoT) generates a plethora of data nowadays, and cloud computing has been introduced as an efficient solution to IoT data management. A cloud resource administrator usually adopts the replication strategy to guarantee the reliability of IoT data. This mechanism can significantly reduce data access time, and evidently, more replicas of data increase the data storage cost. Furthermore, the process of selecting mini clouds for replica allocation and sorting replicas in mini clouds is considered an NP-hard problem. Therefore, this paper proposes an approach based on the harmony search (HS) algorithm to allocate replicas to the IoT data in the cloud computing environment in order to mitigate the data access cost. The HS algorithm was employed in the proposed approach to determine the best location for data replication in the cloud computing environment. According to the implementation results, the proposed approach outperformed the other methods and managed to significantly decrease data access time and delay as well as energy consumption.

“…n×K ), defined in (19), and in the slot assignment matrix 𝕏 (3) n×K , it holds that x i,k 1 = 1 and x j ,k 2 = 1. Then, we exchange the slots of nodes k 1 and k 2 , which leads to a new slot assignment scheme, called scheme IV below, which has channel utilization 𝜌 4 = 𝜌(𝕏 (4) n×K ).…”

Section: N×n and 𝕏mentioning

confidence: 99%

“…The drawback of inconvenient replacing battery in the traditional WSNs is remedied by energy-harvesting technique, which brings in various Energy-Harvesting WSNs (EH-WSNs) [1]. Nodes in the EH-WSNs can gain sustainable operation by scavenging energy from ambient energy sources [2][3][4], including radio frequency, sun light, wind, etc.…”

Section: Introductionmentioning

confidence: 99%

TDMA scheduling schemes targeting high channel utilization for energy‐harvesting wireless sensor networks

et al. 2021

As a contention-free channel access protocol, Time Division Multiple Access (TDMA) is widely applied in Energy Harvesting Wireless Sensor Networks (EH-WSNs) due to ease of implementation. TDMA scheduling in EH-WSNs faces the problem of low channel utilization since time slots assigned to some nodes may not be used by them due to shortage of energy or data. It is important to design TDMA scheduling schemes that enhance channel utilization. In this paper, the Fixed Frame Size Scheme (FFSS) and the Adaptive Frame Size Scheme (AFSS) are proposed to improve channel utilization of EH-WSNs. The FFSS aims at the optimal TDMA slot assignment for the TDMA scheduling with fixed frame size, and the AFSS considers the TDMA with variable frame size and targets both the optimal frame size and the optimal slot assignment. The optimization problems maximizing channel utilization are formulated for the FFSS and the AFSS, respectively, which take the upcoming energy and data into account. The optimization problems are transformed into assignment-like problems and solved by the Hungarian-based algorithm in polynomial time. Simulation results indicate that the proposed FFSS and AFSS can considerably improve channel utilization in the EH-WSNs compared with the existing TDMA scheduling schemes. INTRODUCTIONBeing main components of the Internet of Things (IoT), Wireless Sensor Networks (WSNs) have been widely deployed. The drawback of inconvenient replacing battery in the traditional WSNs is remedied by energy-harvesting technique, which brings in various Energy-Harvesting WSNs (EH-WSNs) [1]. Nodes in the EH-WSNs can gain sustainable operation by scavenging energy from ambient energy sources [2-4], including radio frequency, sun light, wind, etc. Channel access or medium access schemes in the EH-WSNs can be classified as contention-free and contention-based [5]. The representatives of contention-free and contentionbased schemes are Time Division Multiple Access (TDMA) and Carrier Sensing Multiple Access/Colission Avoidance (CSMA/CA), respectively. In addition to easy implementation, TDMA has advantages of collision-free, energy-efficient, andThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.