Future Trends in 5G and 6G

Challenges, Architecture, and Applications

Mangesh M. Ghonge, Ramchandra Sharad Mangrulkar, Pradip M. Jawandhiya, Nitin Goje

• Publisher: CRC Press
• Year: 2021
• ISBN:
  9781000504095
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

This bookoffers a comprehensive overview of basic communication and networking technologies. It focuses on emerging technologies, such as Software-Defined Network (SDN)-based ad hoc networks, 5G, Machine Learning, and Deep Learning solutions for communication and networking, Cloud Computing, etc. It also includes discussions on practical and innovative applications, including Network Security, Smart Cities, e-health, and Intelligent Systems.

Future Trends in 5G and 6G: Challenges, Architecture, and Applications addresses several key issues in SDN energy-efficient systems, the Internet of Things, Big Data, Cloud Computing and Virtualization, Machine Learning, Deep Learning, Cryptography, and 6G wireless technology and its future. It provides students, researchers, and practicing engineers with an expert guide to the fundamental concepts, challenges, architecture, applications, and state-of-the-art developments in communication and networking.

1 An Organized Study of Congestion Control Approaches in Wireless Sensor Networks

Savita Jadhav¹ and Sangeeta Jadhav²

¹Department of E & TC Engineering, Dr. D. Y. Patil Institute of Technology, Pune

²Department of IT Engineering, Army Institute of Technology, Dighi Hills, Pune

DOI: 10.1201/9781003175155-1

1.1 Introduction

Wireless Sensor Networks (WSNs) find applications in intelligent homes, transportation services, precise agriculture production, environment, habitat surveillance, smart industries, structures, critical military missions, and disaster management [1]. The sensor network supervises and tracks an environment by sensing all the available physical parameters. The various challenges arising during use of WSNs are energy-aware clustering, node deployment, localization, dynamic topology, congestion control, power management, and data aggregation.

Congestion occurs when a packet appearance rate outstrips packet convenience rate [2]. The congestion arises in sensor network due to deterioration of radio link quality, multiple data transmissions over the links, fickle traffic densities, unpredictable and irregular links, and biased data rates. Hence comprehensive analysis of network congestion and local contention is required to attain maximum link usage, to increase network lifetime, to provide fairness among flows, to decrease data loss due to buffer overflow, and to diminish overhead on the network.

Congestion is broadly classified into two types—packet-based and location-based congestions, as shown in Figure 1.1. Packet-based congestion is further divided into two categories. Node-level congestion: This occurs when input load goes beyond the existing capacity, resulting in node buffer overflow. Consequently, the rate of packet service is smaller than the rate of packet arrival, which leads to increase in loss of packet and power wastage. Link-level congestion: This arises when multiple nodes use the same wireless channel, resulting in packet collision. Location-based congestion is classified into three types. First is source congestion: In this case, during the ongoing events the sensor nodes covering the same sensing field sense the event spot simultaneously. All the nodes in that area try to transmit data to sink and form a traffic hotspot near the event spot. Sink congestion: In this the nodes follow multi-hop and multipath routing which leads to an increase in traffic density at the sink node. Forwarder congestion: The presence of multiple sinks in the network leads to formation of a traffic junction, which leads to increase in traffic at the intersection nodes. The two major factors that can help manage congestion situation are: By adjusting data transfer rate and by regulating network resources. In general, efficient and effectual process should be designed for controlling congestion in WSNs. Further, based on particular applications, the data flow can be event-based, continuous, query-based, and hybrid expressed as follows.

Figure 1.1 Classification of congestion.

1.1.1 Types of Applications [3]

Event-based applications: Initially the data flow in the network is habitually small but on the occurrence of an event it abruptly increases. The congestion situation arises at the event spot due to increase in data rate.

Continuous sensing applications: The sensor nodes in the network continuously sense the environment and send sensed information to the base station. Periodic sensing is also allowed based on the network load.

Query-based applications: In contrast to event-based application where sensor nodes send information simultaneously on detection of event, in query-based application the sink node sends query messages to source nodes. The source nodes resolve these queries by answering them.

Hybrid application: This application is a combination of the above three techniques. In this periodic sensing, concurrent transmission on triggering of event and answering sink query takes place simultaneously.

1.1.2 Types of Congestion

Congestion mechanism is broadly classified into two types as packet-based and location-based congestion as shown in Figure 1.1.

Packet-based: It is further divided into two types as shown in Figure 1.2.

Figure 1.2 (a) Node level congestion (b) Link level congestion.

Node-level congestion: The node buffer overflows on exceeding its capacity as in this case, the rate of packet service is less than the rate of packet arrival. The node level congestion is shown in Figure 1.2(a). This congestion ends in packet loss. Packet loss leads to decrease network performance, decrease throughput and causes energy wastage, which directly affects the network lifetime [4].

Link-level congestion: In this technique the nodes use the same wireless link for multi-hop and multipath routing as shown in Figure 1.2(b). Competition for assessing link, pack...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• Preface
• Editors
• Contributors
• 1 An Organized Study of Congestion Control Approaches in Wireless Sensor Networks
• 2 NR: Architecture, Protocol, Challenges, and Applications
• 3 Comprehensive Survey on Device-to-Device Communication for Next- Generation Cellular Technology
• 4 Challenges, Opportunities, and Applications of 5G Network
• 5 Machine Learning and Deep Learning for Intelligent and Smart Applications
• 6 Key Parameters in 5G for Optimized Performance
• 7 Applications of Machine Learning in Wireless Communication: 5G and Beyond
• 8 GREEN-Cloud Computing (G-CC) Data Center and Its Architecture toward Efficient Usage of Energy
• 9 SDR Network & Network Function Virtualization for 5G Green Communication (5G-GC)
• 10 An Intensive Study of Dual Patch Antennas with Improved Isolation for 5G Mobile Communication Systems
• 11 Design of Improved Quadruple-Mode Bandpass Filter using Cavity Resonator for 5G Mid-Band Applications
• 12 Wavelet Transform for OFDM-IM under Hardware Impairments Performance Enhancement
• 13 A Systematic Review of 5G Opportunities, Architecture and Challenges
• 14 The Latest 6G Artificial Intelligence Network Applications
• 15 A Review of Artificial Intelligence Techniques for 6G Communications: Architecture, Security, and Potential Solutions
• 16 Layered Architecture and Issues in 6G
• 17 Artificial Intelligence Techniques for 6G
• 18 Antenna Array Design for Massive MIMO System in 5G Application
• Index