A Review on Machine Learning Techniques for QoS in WSN

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A Review on Machine Learning Techniques for QoS in WSN

S.Venkatasubramanian¹ Dr. A. Suhasini² Dr.C.Vennila³

1Associate Professor in Department of CSE at Saranathan college of Engineering, Trichy, India.

2 Professor in the Department CSE, Annamalai University, India

3Professor in department of ECE at Saranathan college of Engineering Trichy, India.

Abstract

WSN is circulated, self-directed and distributed in nature. WSN is a kind of network consist of multiple nodes which are wireless sensors connected to the base station. In recent days WSN is widely used for sensing vital information and communicate to the destination by means of the base station. In this transmission, it is essential to use the best efficient path and proper utilization of the available resources. Generally, nodes in the WSN are energy constraints, on the absence of efficient path it leads to lengthening of network lifetime and results in severe causes. The wide growth of WSN and its importance in used application increase its attention in the researched area. There are several traditional approaches were designed for WSN in the motto of limited energy usages.

Most of the existing methods are one -size-fits-all approaches which are reactive and centrally-managed. But these are not properly fit for satisfying and serving the future complex networks on the aspect of cost-effective as well as optimization. On this way, Hierarchical routing protocols result effective on the concern of energy efficiency. These hierarchical protocols utilize clustering approach in collecting and disseminating the data. The need for huge data to be processed on WSN during sending and receiving makes this approach still in development stage. The important factor to be considered during the WSN process is bandwidth, sensor energy consumption, and time consumptions. To overcome these issues an ML (machine learning) based effective algorithm need to develop in order to improvise the WSN characteristic in all manner.

The intention of this survey is to establish Machine Learning as an applied methodology in overcoming the WSN problems especially in the term of energy efficient routing.

Keywords: WSN, Next-generation wireless, evolutionary algorithm, reinforcement learning, fuzzy logic, machine learning, neural network, routing and artificial intelligence.

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1. Introduction

In WSN, there are several distributed sensors are known as nodes. These network of devices are well connected with each other by means of the wireless link. It mainly uses for communication, sending data from source to destination and receiving in the same manner. As it is wireless in nature it selects the nearest neighbor node and passes the data till it reaches the destination. These WSN's are widely used for two application known as monitoring and tracking [13]. In tracking applications, WSN is mainly used the sensor nodes to track the animal, moment of enemies, human, and also in tracing car/bus, traffic etc. In monitoring applications, the sensor nodes are responsible for monitoring animal and patient movements from one place to another within the range. In addition it is also effective in security detection, monitoring the environment etc. Fig 1 shows the WSN structure in detail manner.

Fig1: Wireless sensor network

WSN process the communication by means of sending and receiving the data between the nodes. To do this process successfully a prominent routing protocol is essential in selecting the best routing path. The network connection can be built by means of topology. The popular topologies used in WSN are Star, tree, and mesh. Based on the dependency of the process the topology is selected for communications. In WSN the environment decides that the network to deployed on land, underwater, underground and so on. WSN types are listed below;

 Terrestrial WSNs

 Underground WSNs

 Underwater WSNs

 Multimedia WSNs

 Mobile WSNs WSN Architecture:

The entire WSN architecture has two form combined, that is with five layers and next form with three cross layers. In WSN’s, these five layers are very important and are namely transport layer, network layer, physical layer, application layer and data link layer. The major responsibility of the three cross planes are managing the task, mobility and power. The overview WSN architecture is described below;

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Fig 2: WSN Architecture

Application layer: This layer is manages traffic and allows multiple applications in transmitting clear form of data in order to gain maximum valid information.

Transport layer: This layer maintains the plan for connection in the network. It works by means of a protocol that perform where to send and receive the data. Transport segment the data into Packet driven and Event-driven. The utmost familiar protocols in the industry for the transport layer are PORT (Price-Oriented Reliable Transport Protocol, STCP (Sensor Transmission Control Protocol) and PSFQ (pump slow fetch quick).

Network layer: Network layer is responsible for performing routing and according to which a several tasks to be performed. Some of the important task performed by this layer are power conserving, partial memory, buffers, and sensor self-organizing.

Data link layer: This layer took the responsibility of maintaining reliability of point–

point (or) point-multipoint where it performs data streams, error control MAC and multiplexing data frame detection.

Physical layer: In this layer data encryption, signal detection, frequency selection and modulation are performed which reflects in WSN's cost power consumption as well as density which improves battery life.

In order to improvise the network, a single infrastructure need to fulfill the expanded services like enhanced mobile broadband, low-latency communications, massive machine type and ultra-reliable communications. The various challenges involved developing an application using sensor network technology are clustering, scheduling, security, design and deployment, energy-aware routing, sensor fusion, data aggregation, localization and quality of service etc. The technology advancement had implemented the large-scale sensors in this field an enormous volume of data to get processed, received and communicated. It is impossible to process those applications with limited sensor energy and bandwidth restraints. Machine Learning algorithms having the ability to beavering rapidly according to the environment. On the same basis neural network, evolutionary algorithm, reinforcement learning, swarm intelligence and fuzzy logic were also considered for WSN utilizations [14].

The major factors that affecting in building a prominent routing protocol are minimal computational and memory requirement, energy efficiency, traffic patterns, automaticity, self-organization, scalability and network support. In addition, another important factor that affects routing is limited battery power and their exchangeability energy. In WSN, for maximizing the lifetime Energy Efficient Routing (EER) is very important. To achieve this the routing protocol uses cluster and popular cluster based energy efficient protocols are LEACH, PEGASIS, and TEEN.

Common limitations in WSN:

 Possess very little storage capacity

 Consumes modest processing power

 Process in minimum communication range – which consumes heavy power

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 Protocol constrains

 Needs heavy amount of energy

 Lack of batteries with finite lifetime

 Passive devices delivers minimum level of energy

This work is structured as follow: The related work part and EER in WSN is discussed on section2. Section 3 presents the various EER algorithms in WSN. Section 4 presents various factor affecting EER protocols along with the tables demonstrating an overview of existing methods, algorithm's, characteristic and section 5 holds the conclusion part.

2. Related work

Mingzhe Chen el at [1] presented a deep study about the QOS in next-generation wireless networks. The author stated that due to the increase of IoT devices in real time, the quality of service can be attained by means of AI (artificial intelligence) and ML (machine learning). In his research, the author demonstrates various key terms about artificial neural networks (ANNs) as well as machine learning, especially on wireless network applications. His work briefly explains neural network type's basic architecture, training procedure along with its associated challenges and opportunities. The author concludes that for variety of wireless communication problem ANN (Artificial neural network) will be a finite solution.

X. Cheng el at [2] presented his research work on the new domain called Mobile Big data (MBD). The author research area covers heterogeneous mobiles enormous data consumptions by means of their sources like smartphones, sensors and Internet of Vehicles. The author elaborates the challenges in mobile environment, security, reliability, scalability analytics, and cost-effectiveness. The author suggested thematic taxonomy approach in classifying MBD according to its data type, source, characteristics, applications, analytics, and security.

Raouf Boutaba at al [3] presented a broad survey on machine learning for WSN.

The author coveys that ML (machine learning) has the ability to overcome the real-time issues in WSN and enables automation. In his survey, the author gives a brief overview of intricate problems which took during network operations and management. Additionally a clear learning paradigm along with ML techniques to overcome the fundamental problems like QoS and QoE management, resource and fault management, congestion control, routing, traffic prediction, classification and network security.

Energy Efficient Routing:

In WSN, computing a valid path for transmission is very important. Generally, routing protocol performance depends according to the network architecture. The absence of reliable protocol effects heavily in energy consumption during the data transmission.

The common factors that WSN devices suffering in real time are limited resource constrained, low storage capacity, limited communication bandwidth, low processing speed, and limited battery power.

K. Akkaya et. Al [4] presented the major three routing protocol approaches such as proactive, reactive and hybrid. The proactive strategy describes the routing information by means of the routing table of entire sensor nodes in the network. The reactive approach is an on-demand strategy, by which a protocol counts minimum hop to reach the destination from the desired node sending the packet.The hybrid strategy combines and implements the best features of proactive and reactive strategies. Hybrid strategy cab is used within as well as across the clusters.

Daniel Minoli et. Al [5] presented the important factor in energy consumption of WSN such as Sensing, Processing and Communication. Sensing energy consumption is the energy taken by the node in sensing node’s operations like periodic, awake/sleep etc.

The processing energy consumption during the process of data processing, sensor

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controlling and protocol communication. The major three stages in processing energy are sleep, idle, and run. The communication process is the energy taken for transmission of data between the source and destination.

L. Junhai et. Al [6] presented his work on WSN using Machine Learning algorithms. The author concentrated on the two major issues, one is network related issue and the other is application related issue. The network related issues are invoking best node arrangement, data aggregation along with effective energy-aware routing, clustering, localization and ensuring securities. The scheduling, fusion and QoS, resource allocation are also the major network associated issues to take into the concern. Certainly, application related issue are target class identification, target tracking, event classification and information processing.

Zohre and Arabi et al [7] presented a fuzzy based algorithm for hybrid EER.

Generally, the fuzzy logic system consists of three main procedures such as inference engine, fuzzification and defuzzification. In his work the author composes two algorithms known as Source-Initiated Dissemination and Earliest-First Tree algorithms for data disseminate and choosing the cluster head. In routing the CH took the responsibility for the path by means of fuzzy variables. SID, EF-Tree, Fuzzy methods are responsible for route switching according to the conditions. The main motto of this implementation is the increase in energy efficiency and network lifetime.

Toleen Jaradat et. Al [8] presented a cross-layer based energy aware routing method. The primary goal of her work is minimizing the overall consumption of energy along with maximizing the network lifetime. In this work, the author uses nodes transmission power of local communication range for evaluating the node’s next hop relay. The author incorporates fuzzy control algorithm which is self-adaptive and effectively measure the differing parameters. The author also provides her work on improving the scalability, self-learning and focuses on entire network longevity.

Neeraj Kumar et. Al [9] presented a proposed methodology using NN to overcome EER issues. The author combines NN (neural network) with the cluster in maximizing the network lifetime. He overcomes the problem by formulating linear programming with certain constraints. In the neural network, adaptive learning is responsible for cluster head where routing and data transmission is performed.

Wenhui Zhao et. Al [10] presented an advanced self-structured NN in WSN which optimize the routing process as per the nodes energy capacity and computation power. . Here the author proposed a Hop Field Neural Network for finding the best route at minimum power. This converge cast routing algorithm is very effective on high-speed WSN’s.

Nesrine Ouferhat and Abdelhamid Mellouk et. Al [11] presented EDEAR (Energy and Delay Efficient Routing protocol) for WSN. This method minimizes the energy taken by the nodes in the network. It is an adaptive routing which chooses the best path by means of Reinforcement Learning (RL) that updates the routing table and free from traffic. In this process, routing is adopted according to the traffic condition and reduce the transmission time effectively.

Yi-ping Chen et. Al [12] presented a PSO algorithm as his proposed algorithm. It uses the search optimal inter-cluster routing path for choosing the effective path during transmission. Further, the author incorporates his proposed work with cluster-based energy efficient routing algorithm for balancing the efficiency of energy and lifetime. In this work, the problem of the hot spot is overcome by cluster distribution.

2.1 Common Challenges in Energy Efficient Routing

 Node Deployment: The node deployment can be random or deterministic which are application-based operation that affects routing.

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 Node-Link Heterogeneity: The volume of sensor node sets which are heterogeneous creates several technical based data routing issues.

 Data Reporting Model: There are four kinds of data reporting such as hybrid, query-driven, event-driven, and time-driven. According to the application, only measurement, data sensing, and reporting will be done that leads to time complexity.

 Energy Consumption without Dropping Accuracy: Making the process of energy conserving and data communication are complex.

 Scalability: Sensor nodes on huge count makes routing more delay in responding to the events.

 Network Dynamics: Sensor mobility is more essential for the applications where mostly network designs adopt that the nodes are static.

 Fault Tolerance: Failure of nodes leads to delay in WSN entire process.

 Connectivity: In WSN, connectivity between the nodes are randomly distributed in nature

 Transmission Media: In wireless sensors multi-hop networks, the connection is by means of a wireless medium. It has a problem of network incompatibility like IEEE 802.11.

 Coverage: The network environment where the sensor nodes connected inadequate in both range as well as accuracy, which covers only the partial range.

 Quality Of Service: Transmission time, in which the transmission need to complete within the time. On the aspect of the variance in applications, maintenance of energy the network lifetime is effected along with the data the quality.

 Data Aggregation: Combination of various sources for performing certain aggregation task.

Table 1: Parameters Consideration

Data acquisition and knowledge discovery Ref.

• Context-aware data achievement from single or numerous sources

• Coded (adaptive) caching

• Semantic-aware Ontology (KB) creation from network data

• Robust knowledge discovery from erroneous (missing) data

MACHINE INTELLIGENCE TECHNIQUES FOR NEXT-GENERATION CONTEXT- AWARE WIRELESS NETWORKS, proposed by Xianbin Wang, Long Bao Le and Tadilo Endeshaw Bogale.

ITU Journal: ICT Discoveries, Special Issue No.

1, 2 Feb. 2018 Network planning

• Deploying the node and allocating radio frequency

• Updating the content, computing placement and caching

• Prediction and modeling of energy consumption either idle or active

• Service configuration procedures and parameter

Network operation and management

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• Resource allocation: Multi-RAT packet scheduling, packet routing, distributed storage and processing, RAT and channel selection.

• Security: Intrusion detection and Spoofing attack

• Latency: Context-aware edge computing and scheduling

Table 2: Overview of WSN routing protocols that accept machine learning models

Table 3: Summary of data integrity, QoS and fault detection solutions.

REFERENCE METHODS MACHINE

LEARNING ALGORITHM(S)

COMPLICATI ON

FEATURES

[21] System’s

dependability

NNs High Metric dependability

estimation

[22] Fault detection Moderate Dynamic fault

detection model

[23] MetricMap DT Low Estimating the link

quality

[24] Assessing

accuracy and

GP Moderate Information

processing tasks REFERE

NCE

ROUTING PROTOCOLS

TOPOLOGY MACHINE LEARNING ALGORITHM(S)

OVER HEAD

SCALABILITY DELAY DISTRIBUTED /

CENTRALIZED QOS

[15] Distributed regression

Flat / multi- hop

kernel linear regression

Low Limited High Distributed No

[16] SIR Flat / multi- hop

SOM High Limited Moderate Hybrid Yes

[17] Q-MAP

multicast

Flat / multi- hop

Q-learning Low Moderate High Distributed No

[18] RLGR Hierarchical

/

geographic routing

Q-learning Low Good Low Distributed No

[19] Q-

Probabilistic

Flat / geographic routing

Q-learning Low Limited High Distributed Yes

[20] FROMS Flat / multi- hop

Q-learning High Limited Moderate Distributed No

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reliability metrics

[25] A QoS

scheduler

RL Low QoS task scheduler

for adaptive multimedia sensor

networks [26] Uncertainty

and coverage factors

Moderate Examining converge problems

[27] QoS-aware

power management

Low QoS-aware power

management in energy harvesting

sensor nodes [28] Quality of

Service provisioning

Low QoS provisioning structure modeling

tool

3. Conclusion

Comparing to other networks, wireless sensor networks are far different on the aspect of advancement and utilization. There are several tools and algorithms exists and those are still lacking with major limitations and challenges. In this survey, we have demonstrated the various algorithms and their performance in WSN briefly. QoS is one major concern to achieve in WSN according to the real-time scenario. EER- Efficient routing is the major key to attain the reliability of WSN. This survey gives various EER algorithm and performance in detail. Along with it, Table 1, table 2, and table 3 give a brief account of the other existing methodologies with their algorithms and characteristics. For innovative next-generation QoS, the major factors to get aware of their performance are scheduling, node clustering, security, real-time routing, fault detection, localization, data aggregation and data integrity. From this survey, we have discussed several approaches in WSN and find that machine learning has several techniques that will enhance its performance in achieving the QoS.

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