Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks

Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: Comparison

Please note this is a comparison between Version 2 by Peter Tang and Version 1 by Abdullah Umar.

The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS).

WSNs
congestion mitigation
artificial intelligence
game theory
IoT

1. Introduction

Wireless data traffic has increased due to exponential growth in the use of Internet of Things (IoT) devices. It is predicted that more than 29 billion IoT devices will be in use by 2030 ^[1]. The extremely high density of IoT device connectivity will bring several problems. Congestion is one of the major problems of IoT networks powered by wireless sensor networks (WSNs) [2,3,4,5]^[2][3][4][5].

The United States of America’s (USA) military first proposed the idea of wireless sensor networks based on the Internet of Things (IoT) in 1950. The first program, called the Sound Surveillance System (SOSUS), was developed to use acoustic sensors to detect and track sound waves emanating from submarines in the Pacific and Atlantic oceans. WSNs are interdisciplinary technologies that combine the comprehensive techniques of wireless communications, pervasive computing, networking, and signal processing ^[6]. WSNs have evolved continuously and incrementally since the 1960s ^[6]. However, the Defense Advanced Research Projects Agency (DARPA) launched a brand-new initiative called Distributed Sensor Networks (DSNs) in the 1970s ^[7]. The development of DSNs had a positive impact on academic study and scientific research. In the last 50 years, it has also attracted customers and researchers.

WSN-based IoT networks consist of widely distributed sensor nodes that allow us to track and respond to events and outcomes at a remote location [8,9]^[8][9]. The Internet of Things gained the attention of the scientific community in the late 20th century due to advances in a number of important areas, including communication technology, small hardware, security monitoring, etc. These technological developments made it possible to build low-cost, compact, and multifunctional WSN nodes ^[10]. Today, the wireless IoT network has evolved into an intelligent, self-healing, extremely dynamic, and distributed system ^[11]. Due to its efficiency and adaptability, the IoT is currently playing an important role in real-time monitoring and data acquisition.

In recent decades, IoT-based WSNs have been developed on a large scale, mainly for heavy industry and military applications. It is often referred to as the Internet of Everything (IoE) due to the widespread use of IoT devices. In this scenario, the IoE offers tremendous potential for the future of the smart world. However, the widespread use of wireless sensor devices will lead to wireless network congestion. When a large number of IoT devices attempt to access network resources that are bandwidth-constrained and lack network traffic management, congestion occurs in communication networks, especially WSNs. This negatively impacts the entire network by reducing network performance and quality of service (QoS), increasing the energy consumption, leading to packet loss and causing security concerns ^[12].

2. Why Use an Artificial Intelligence-Assisted Solution?

The development of artificial intelligence is often considered essential to the development of engineering and technology. In the wireless communication network of the near future based on the Internet of Things, artificial intelligence will play an essential role in meeting the requirements of the communication system. Smart infrastructures, including smart grids, smart homes, smart cities, smart meters, and a globally interconnected smart grid, will proliferate in the coming era. Internet of Everything (IoE) communications technology will be used to make the concept of a smart world a reality. To realize a smart society, a wireless network based on the IoE must also be built. It is critical to make communication systems and other key components intelligent to improve their functionality. It is crucial to find an artificial intelligence (AI)-based solution to meet the future needs of a technologically advanced and environmentally friendly society [19]^[13]. The list of Nomenclature is shown in Table 1.

Table 1.

Nomenclature in the survey.

Acronyms	Definition

Comparison of the survey with contribution related to congestion mitigation in IoT.

Main Domain	Ref	Type	Year	Contribution	Scope of the Work
Main Domain	Ref	Type	Year	Contribution	Congestion	IoT	AI
IOT	Internet of Things
Game theory application security prospect	[23]^[17]	Survey	2008	Survey on Game theory to solve challenges relevant to energy efficiency and security

Considering the significance of the topic, the research community has proposed many congestion control techniques.

3.1. Previous Work on WSN Congestion Mitigation Schemes

Previous work in the domain of WSN congestion mitigation can be divided into four main categories, as shown in Figure 1.

Figure 1.

An overview of congestion mitigation AI algorithms in the literature for WSN.

4. Scope of AI-Based Solutions in WSNs

What limitations might be encountered when using AI-based algorithms to reduce congestion in WSN? This is a crucial question. In answering this question, we must consider the limitations of wireless sensors and IoT devices. Resources, such as memory, power, and processing speed, are scarce on the IoT side, while they are more readily available on the tower side. The likelihood of deploying certain AI-based solutions on both sides depends on the resource allocation. Deep learning (DL), deep reinforcement learning (DRL), and other approaches that require more resources can be deployed on the tower side, where resources, such as memory, processing power, and bandwidth, are abundant. In contrast, due to resource constraints, only AI algorithms with lower power and bandwidth requirements can be used on the IoT side; examples include Reinforcement Learning (RL) and Federated Learning (FL). It is possible to use RL and FL on both the tower and IoT sides. Figure 2 illustrates the range of AI methods available to reduce congestion in WSNs.

Figure 2.

Scope of various AI techniques in congestion mitigation in WSNs.

Significance of Proposed Work in Smart and Green World

Another crucial question is where this work can be practically applied. This research is useful wherever we use wireless networks for IoT. Smart homes, smart grids, smart cities, and even the broader idea of a “smart and green world” are all scenarios we can consider. The use of IoT and AI-based technologies to control our homes, communities, power grids, and, ultimately, the entire planet is referred to as “smart” technology. The term “green”, in turn, refers to the commitment to a sustainable environment achieved by minimizing carbon emissions, reducing energy consumption, and effectively managing resources.

The futuristic “smart and green world” will certainly include the integration of IoT and artificial intelligence. The proposed efforts have the potential to contribute significantly to the realization of this dream [87]^[25].

5. Potential Future Emerging AI-Based Solutions for Congestion Mitigation in Ultra-Dense WSNs

Machine Learning (ML) for WSNs

Traditionally, it has been assumed that machine learning (ML) can be used to solve problems only when a large amount of training data is available, no precise mathematical model for the system is available, and only numerical analysis over time is acceptable. Modern solutions based on ML are widely used to solve a variety of problems in both WSNs and modern communication systems. Due to adaptive communication networks that are self-healing, self-maintaining, highly dynamic, self-learning, and highly intelligent for the evolution of IoE network communication, there is a huge potential for reducing congestion in WSNs through ML-based solutions. Moreover, the algorithms of ML have a very high potential to replace the current approaches to congestion in WSNs and other problems. In this preseaper, werch, the researchers explain the basics of ML as well as the scope of ML in IoT networks and congestion avoidance in IoT networks [88]^[26]. The algorithms discussed in this preseaperrch are compared in Table 3.

Table 3.

Summary of algorithms in congestion mitigation for IoT existing techniques.

Algo.	Ref.	Solution Technique				Packet Loss Type		Limitation
Algo.	Ref.	Traffic Control	Resource Control	6LoWPAN	WSN	Buffer Loss	Channel Loss	Limitation
Back pressure	[67]^[27]		X		✓		X ✓	✓	X	✓ ✓	X
IP
Simulation not verified in real WSN simulators like cooja			Internet Protocol
Role of LTE-A in Emerging Machine Type Category (CAT-M) and Narrowband IoT Category (CAT-N).	[
GTCCF	24]^[18]	[38 Survey	] 2017	^[28]	✓ Up-to-date and comprehensive survey on (CAT-M) and (CAT-N).	X	✓ ✓		X ✓	✓ X	X	6LoWPAN
	High energy consumption, low throughput		Machine learning applications in the IoT domain	IPv6 over Low-Power Wireless Personal Area Network

OHCA	[25]^[19]		[66]^[29] Survey	✓ 2018	Comprehensive Survey on ML techniques and applications in IoT		✓ X	✓	X ✓
	✓			X			High energy consumption, low throughput		QoS	Routing Protocol low-power and lossy networks (RPL) by contiki operating system (OS)	[26]^[20]
NCGEE	Quality of Service
	Survey			[77]^[30] 2018	First Survey that categories RPL via contiki OS	✓	✓	X
	✓		✓	✓	X	✓	✓	-	I-IOT	Intelligent Internet of Things
Deep Transfer Learning	[
Game Theory	27]^[21]	[23^31] Survey	, 2018	75 Review latest work on transfer learning via DNNs as well as their application.	]	✓ X	^[	X X		✓	X✓		✓	X	-	RACH
Application Deep reinforcement learning (DRL) in IoT and UAV	Random Access Channel
	[27]^[21]	Survey
DCCC6	2019		[89]^[32]	X	✓ Review on Deep reinforcement learning in network different prospects.	✓ X	X ✓	✓ ✓		X	-	ROC
WSN recourses allocation by DL and ML.	Receiver Operating characteristic
	[28]^[22]
GTCC	[41]^[33	Survey	^], 2020	This work comprehends the DL and ML based techniques for resource allocation in WSN in Heterogeneous Networks (HetNets), NOMA, D2D communication prospective.	X	✓ ✓	✓		X	✓	X	✓	X	-	SGNANs
¹⁷		Various congestion mitigation algorithms are reviewed.	Smart Grid Neighborhood Area Networks
	[29
RPL	]^[23]	[39]^[34] Survey	✓ 2020		X This review is based on different techniques to control congestion as well a novel taxonomy has been proposed.	✓ ✓	X X		✓✓		SoNCF
^]		X			-			Transfer Learning
This work	Self-organizing network coordination framework
	[30]^[24]	- Survey	✓ 2021		✓Transfer learning and different machine learning techniques relationships.	X	✓	✓ ✓	✓ X	✓	TCP/IP
^[			Congestion mitigation AI algorithms in nature	Transmission Control Protocol IP
	-		[24]^[18]	Survey	2023	Reviewed AI algorithms exist in nature.	✓	X	✓	TARA	Topology Aware Resource Adaptation (TARA)
AI based algorithms review to solve congestion in WSN.	This work	Survey	2023	The novel review on congestion mitigation based on AI based solution in WSN.	✓	✓		LTE-A	Long-Term Evolution-Advanced
LSTM	Long Short-Term Memory
ML	Machine Learning
M2M	Machine to Machine
✓		MAC	Media Access Control
CAT-M	Machine type category
mMTC	Massive Machine Type Communication
MADM	Multi-Attribute Decision Making
CAT-N	Narrowband IoT Category
OS	Operating system	Optimization-based Hybrid Congestion Alleviation
		✓
OHCA	PB-ALOHA	Pseudo Bayesian ALOHA
PRA	Prioritized Random Access
PSO	Particle Swarm Optimization
RPL	Routing Protocol low-power and lossy networks
SOSUS	Sound Surveillance System
SDN-IoT	Software-Defined Networking based on IoT
SDRs	Software-Defined Routers
TR	Technical Report
UDP	User Datagram Protocol
USA	United States of America
WSNs	Wireless Sensor Networks

Why Use an ML-Based Congestion Mitigation Solution for WSNs?

A subfield of artificial intelligence (AI) called machine learning (ML) is concerned with using machine learning algorithms to help computers acquire new information and skills. These algorithms make it easier for a computer to evolve into an intelligent being. WThe researchers will explore the many types of machine learning and their associated algorithms in later sections. It is predicted that advanced systems will emerge in the near future that have the ability to self-heal, be highly dynamic, and be self-preserving. In thesae systems, machine learning is of critical importance. Future wireless networks will be intelligent enoaugh to meet the above requirements, with DL and ML algorithms playing a crucial role [19,20]^[13][14]. It is expected that resource management in the extremely dense IoT network will be significantly affected by deep learning (DL) and machine learning (ML) [21]^[15]. A comparison of the contributions made by researchers in the field of IoT network congestion mitigation is shown in Table 2. The utilization of deep neural networks (DNNs) in WSN devices facilitates the capability of these IoT devices to perform intricate sensing tasks and foster collaboration between the environment and humans [22]^[16].

Table 2.

3. Congestion Mitigation Algorithms for WSNs

6. Fundamentals of ML and Taxonomy of Applications

Figure 3 shows the taxonomy of ML and DL algorithms.

Figure 3.

Overview of ML and DL algorithms green boxes for DL algorithms.

7. Learning Capabilities and Requirements

The ML learning models for ML learning algorithms are based on the nature and size of the data for training. The algorithms based on batch learning are applied in the application and have large amounts of prior available data in for training. The batch learning algorithm works on the assumption of unlimited computing time availability and searches all available data. These offline learning schemes normally face issues in practical applications in terms of the limited amount of data. Hence, an algorithm based on batch learning is not suitable for processing real-time data. For the real-time training of data, online training/learning is a feasible solution for data applications with streaming data.

The constraint is fixed and there is time availability for each sample. Channel tracking and intelligent caching are the most common applications for online learning and batch learning (offline) in wireless WSNs to reduce congestion.

Model-based learning has high computational efficiency and uses well-known objective functions to maximize performance indices. In contrast, data-only learning uses all available data samples to extrapolate or/and interpolate the samples, which requires more time and memory. The overload of the IoT can be significantly reduced by model-based learning and learning based on samples. These two learning strategies can be used for symbol decoding and content demand, respectively. In [107]^[35], the capabilities of different algorithms of ML and the learning requirements for communication are discussed.

8. Artificial Neural Networks (ANNs) for Congestion Mitigation of WSNs

The biological processing of data in the human brain served as the basis for ANN, which aims to understand the many operations performed on observed data. The common application of ANN is the recognition of various patterns in data provided as input after they have passed through numerous ANN layers.

The ANN has three main layers of neurons; these are input layer, a hidden layer/s, and output layer. Where each neural layer performs a specific operation on given/input data to applications of ANNs, the hidden layers of neural networks are quickly increasing. There are a few well-known structures termed as Neural Turing Machine (NTM), Convolutional Neural Networks (CNN), Echo State Network (ESN), Multi-Layer Perceptron (MLP), Feed-Forward Network (FFN), Generative Adversarial Network (GAN), Hopfield Network (HN) Recurrent Neural Network (RNN), etc.

The above Neural Network (NN) topologies describe the direction of data flow in NN, with RNN neurons connecting from the output of the feedback layer to the preceding layers and FFN neurons connecting from the input layer to the output layer [107,108]^[35][36].

The process by which the connection weights between the other neurons are learned is called ANN training. The supervised learning technique is used for this purpose. Various techniques, such as Levenberg–Marquardt, Mean Square Error (MSE), Newton method, Quasi-Newton, conjugate gradient, and gradient descent, among others, are used for error reduction in the learning process. In calculating the error in each layer and correcting according to the learned/remembered weights, error reduction is an iterative process that propagates backward from the output layer to the input layer. One of the future strategies to reduce the overload of WSNs that studies need to focus on is ANN [95]^[37].

8.1. DL for Congestion Mitigation of Wireless-Based IoT Networks

A branch of ML is called DL. The deep layer of the neural network is where the input data are propagated to create the intelligent system [109]^[38]. To compute the output, the deep layers perform many different mathematical operations, including thresholding/limitation and combination [110]^[39]. A system based on DL automatically learns to map or model the already accessible data sets through significant feature extraction, either through unsupervised or/and supervised learning approaches [111]^[40]. In [112]^[41], the application of the DNN technique in wireless network-based communication was studied. The applications of DL are strongly encouraged for use in the upcoming wireless communication networks [113]^[42]. DL offers tremendous promise for wireless communications and for reducing congestion in WSNs that manage, deploy, schedule, maintain, and control resources (radio, channels, energy), among others [114]^[43]. DL in [115]^[44] and [94]^[45] was proposed as an optimization approach for downlink beamforming. The above proposal needs to be studied in the context of reducing congestion in WSN networks, as it could provide excellent results. These also offer tremendous potential for IoT congestion in the areas of data loading and caching, traffic routing, power control, resource sharing, traffic routing, dynamic spectrum access, etc. The authors of [116]^[46] give an overview of various DL applications in wireless networks. In [100]^[47], dynamic/intelligent allocation of radio resources was highlighted in a survey of wireless communication networks at the physical layer. In [117]^[48], a plan for channel characteristics for BSs with multiple antennas was presented. With only a few adjustments, this method can be used to reduce congestion in IoT networks. Due to the growth of IoT communications in the near future, ANNs have significant potential for estimation, job preservation, control, scheduling, tracking, and optimization to reduce congestion in wireless IoT networks. ANNs are important enablers in the deep learning process, as shown in Figure 4 [112]^[41]. Important aspects of data from DL as well as distribution have been learned automatically.

Figure 4.

Working principle of ANNs.

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