Localization Techniques Using Machine Learning Algorithms

Abstract

Wireless sensor networks monitor environments that amendment apace over time. This dynamic behavior of the networks is either caused by external factors or initiated by the system itself. Machine learning techniques help us to work with extreme conditions and assist in avoiding the redesign of the network. The prominent feature of training the machine or network itself to modify according to such kinds of environments is being introduced in the sensor networks using machine learning techniques. However, the performance of the sensor networks has many constraints like energy efficiency, information measure or bandwidth, etc. Localization of nodes is one of the major issues that have to be worked on, as proper placement of nodes solves above-mentioned performance issues. The sensors in wireless networks gather knowledge regarding the objects they are to be sensed by which machine learning algorithms conjointly evoke several sensible solutions for localization of nodes that maximize resource utilization and prolong the lifetime of the network. The machine learning algorithms are categorized into three categories, namely supervised learning, unsupervised learning and reinforcement learning algorithms. As localization is the method of deciding the geographic coordinates of network’s nodes and its relevant components as position awareness of sensing element of every sensor nodes plays a vital role in network communication for further process. In this chapter, we are going to focus on how the localization issue in wireless sensor networks can be solved using the three categorized machine learning algorithms.

1 Introduction

Machine learning(ML) is raised out of artificial intelligence (AI). Humans are intelligent species on earth, they learn from past experiences and act accordingly. Introducing this concept in machine learning, the machine to learn from past experiences and act accordingly is known as machine learning. Here, when related to computer previous experiences are termed as data, we are introducing the intelligence in the computer is termed as AI. Applying AI, we tend to needed to create higher and intelligent machines. However, aside from a few minor tasks like finding the shortest path between purpose A and B, we tend to were unable to program a lot of complicated and perpetually evolving issues. There was a realization that the sole thanks to being able to accomplish this task were to let machine learn from itself. This is similar to the scenario compared with a child learning itself. Therefore, machine learning was developed as a replacement capability for computers. And currently, machine learning is the gift in such a significant amount of segments of technology that we tend to do not even realize it. Machine learning is an inspiration to be told from examples and knowledge, while not being expressly programmed. Rather than writing code, you feed information to the generic formula, and it builds logic supported the info given [1]. On the other side, wireless sensor networks(WSNs) are extraordinarily popular and are ubiquitous at present, and they have gained popularity over a decade now. These wireless sensor networks became the key to the formation of the Internet of things. They both collaboratively work in making a country digitization. WSN, a big element of comprehensive computing that is presently being employed on an oversized scale to supervise period environmental standing further on stimulate the gathered results for future analysis. The main focus of the research in WSNs is the deployment of nodes in the network to maximize its life and minimize energy consumption in the system during communication. Sensors are mainly utilized under extreme energy constraints, i.e., human intervene highly impossible. To overcome the above scenario, creating a new wireless sensor node [2] is incredibly a tough task and involves a range of different parameters of accessibility to the required application which includes various ranges, transmitter/transceiver type, target technology, components, collective memory, storage space, power, lifetime, security and safety, quantum capability, inter/intra-communication technology, energy and resources, etc. So, we can train the sensor networks to act accordingly to the environment or according to the need by introducing machine learning algorithms(MLA) into wireless sensor networks [3,4,5]. Figure 1 shows the sensor network structure layout with a sensing field, communication lines, nodes and base station sensor network.

Fig. 1

Wireless sensor network layout

1.1 Problem Formulation

In wireless sensor networks, node deployment and node localization are the primary issue, and it is the root cause for most of the challenges/issues related in wireless sensor networks. The sensor’s position may be known or not. To get the correct details about the target area, we should accurately define the location of each node in the network. The actual localization problem can be resolved using two phases. Phase one is called the assessment/estimation phase and phase two is the testing phase. In three simple steps, this two-phase localization can be developed.

To address the localization problem, let us assume a randomly node deployment of network consists of number of predefined position nodes expressed in two-dimensional coordinates and a set of sensors with unknown locations N. The localization problem can be addressed using machine learning algorithms. Due to the increasingly widespread presence of sensitive sensors on WSNs, an overall performance location technique is insufficient for all applications. In recent years, therefore, there have been some significant advances in WSN localization techniques. Steps in Localization

• 1. Estimating distance between the nodes by time arrival of the signal and strength of the message.

• 2. Apply localization algorithms for optimizing the distance between the nodes.

• 3. Based on the result in step 2, get the position of the node which needs to be identified.

Figure 2 address the localization process. Table 1 represents the abbreviations used in this chapter and Table 2 presents the symbols and notations used in this chapter.

Fig. 2

Generalized localization process

Table 1 Abbreviations used in chapter

Table 2 Symbols and notations used in chapter

This localization is mainly concerned with minimizing the error rates of the actual node position and the position calculated when randomly deployed [6].

The rest of the chapter is organized as follows. In Sect. 2, we prioritize the role of machine learning (ML) in networking of wireless sensors. Followed by the benefits and detriments of implementing machine learning algorithms in sensor networks in Sects. 3 and 4. Then, the categorization of MLA based on localization issue in wireless sensor networks(WSNs) is mentioned in Sect. 5. In Sect. 6, the first category of MLA, i.e., supervised learning is addressed and its related algorithms are discussed. Followed by the second category the unsupervised learning is discussed along with the relevant algorithms in Sect. 7. The third categorization of MLA which is the reinforcement learning is mentioned in Sect. 8. Finally, we draw conclusion and we give future scope in Sect. 9.

1.2 Motivation and Contributions

Researchers are working on the concept of deployment of sensor in networks applicable globally for any application and localization of sensors to identify aggregate data from a particular location without any congestion, corruption, or redundancy. Machine learning is powerful tool for sensors that help to calibrate and correct sensors when connected to other sensors measuring environmental variables. WSN monitor dynamic environments that change rapidly over time. Sensor networks often utilize ML techniques to eliminate unnecessary redesign while identifying locations of sensors or during deployment of sensor nodes in the sensing area, etc. Machine learning also inspires many practical solutions to maximize resource utilization along with prolonging the survival of nodes in the network, as machine learning algorithms originated from various fields like mathematics, neurosciences, statistics, including computer science.

Our contribution in this chapter is that we gathered a few of the machine learning algorithms as categorized into three major categories, namely supervised learning, unsupervised learning, and reinforcement learning. We given insights on few of the techniques in each group which are more suitable for WSN domain. Finally, we concluded the chapter with few ideas for future scope.

2 The Role of Machine Learning in WSN

The traditional WSN approaches are programmed that create the networks firm to retort dynamically. Techniques for machine learning can be used by reacting to overcome such eventualities. The method of self-learning from the experiences and actions, while not human intervention or re-programming is machine learning. Due to its size, efficiency and simply deployability, the WSN is responsible for monitoring, gathering sure data and transferring it to the bottom of the station for a wide range of sensor applications in the post-knowledge analysis range. The WSN is one of the most promising technologies in every field. WSN has an overlay large number of sensor systems. So managing such an oversized range of nodes need scalable and efficient algorithms. In most scenarios, sensor networks adopt ML techniques to remove the need for unnecessary redesign. Applying ML algorithms to WSN, it invites you to use various sensitive solutions to maximize the use of resources and extend the network life [7].

3 Benefits of Machine Learning in Wireless Sensor Networks

There are many benefits in implementing machine learning algorithms in wireless sensor networks. Few of them are listed below with an explanation.

• (a) If we adapt machine learning techniques/algorithms in wireless sensor networks, where we train the network to monitor dynamically in environments that change over time, i.e., for example, environmental change can be erosion in soil or by turbulence in sea, and many more situations similar to it can adopt such changes dynamically and operate itself efficiently without any human intervention.

• (b) WSN is mainly used in cases where human intervention is not possible, and these sensor networks operate on behalf of human to identify the situations and gather the data from such environmental locations.

• (c) Machine learning algorithms can be used for maximum data coverage by the sensors. As WSN applications cowl minimum knowledge thanks to limited detector capability or hardware resources of the sensors.

• (d) Big data, cloud computing, Internet of things, cyber systems, and communication between machinery with an intelligentsia motivation to support decisions along with the control of free networks have made a significant contribution to ML development in WSN. So, ML plays a prominent role to extract various levels of distinct abstractions necessary to perform tasks of artificial intelligence with very little human involvement [8, 9].

4 Detriments of Machine Learning in Wireless Sensor Networks

However, when using machine learning techniques in wireless sensor networks, a few disadvantages and limitations should be considered.

• (a) Multiple ML methods are costly to compute. It can have adverse effects when used on wireless sensor networks, depending on how much the measurements are performed, leading to higher power/energy consumption.

• (b) ML better perform as the amount of data you have increased. Because WSNs are commonly used in volatile settings, we cannot be sure of the data we enter (except in supervised learning).

• (c) Training the nodes in the network with a large number of samples may not be sufficient, and it cannot react automatically if any unusual thing has been sensed, i.e., it may lead to reasonably tiny error bounds.

• (d) At times it may land up with a resource crisis when training the network with high computational units to manage developed systems with centralization to perform the learning task [9].

5 Categorization of Machine Learning Algorithms Based on Localization

The primary purpose of localization is used to identify the geographical location/placement of a sensor node. When we deploy the nodes randomly in a field/environment, we are unaware of its location. When nodes are used randomly in an environment there is a chance of change in the climate dynamically, during those changes machine learning algorithms [10] help to improve the location accuracy. The existing machine learning algorithms can be categorized into three types based on the mode of the intended network structure of sensors. Here, we are primarily focusing on localization issue in wireless sensor networks, and we are categorizing the machine learning algorithms based on localization [11]. There are machine learning algorithms that are categorized into two-three classes, namely supervised learning, unsupervised learning, and reinforcement learning as shown in Fig. 3.

Fig. 3

Classification of machine learning techniques in wireless sensor techniques

6 Supervised Learning

In supervised learning, the data sets with related labels(inputs) [12] are collected and the relationships are found with the system, while training. At the end of the training method, an activity from associate grade input can be carried out with the best estimation of output y, important tasks of supervised algorithms are to develop a model that reflects links between input options and predicted objective outputs. During supervised learning, the machine learning algorithms consider two phases, i.e., training phase and prediction phase [13]. The data set which is being used represents the learned relationship between input, output, and system parameters. In this, the data set is divided into two categories, namely regression-based supervised learning and classification-based supervised learning.

A regression-based supervised learning is used when the resultant variable or output is some real or continuous value. Some value (Y) should be predicted based on a certain number of features (X) represented in Eq. (1). The variables (X) are either continuous or quantitative in the regression model to predict precise results (Y) with minimal errors.

$$\begin{aligned} Y=f(X)+\epsilon \end{aligned}$$

(1)

where Y is a dependent output variables (output), X indicates an independent input variable (input) and represents the potential random error by the f is the function making a relationship between X and Y [10].

A classification-based supervised learning attempts to draw certain conclusions (Y) from the observed values (X). Given one or more inputs (X) in a classification model will try to predict the value of one or more outcomes (Y). This classification of supervised algorithm is based on four basic ideas they are namely logic-based, instance-based, perceptron-based and statistical-based classification which can be classified into six popular algorithms.

• (a) Bayesian statistics

• (b) Neural networks(NN)

• (c) Decision trees(DT)

• (d) Gaussian process

• (e) Support vector machines(SVM)

• (f) K-nearest neighbor (KNN).

6.1 Bayesian Statistics

Bayesian methods adapt the distribution of probability to learn certain concepts effectively and without over-fitting. It is logical thinking desires a comparatively tiny set of coaching samples [14] in distinction to machine learning algorithms. Bayesian strategies use chance distribution operate to find out unsure ideas (e.g., $\phi$) while not over-fitting with efficiency. The algorithmic rule applies the present data, i.e., collected knowledge abbreviated as T to update previous beliefs into posterior beliefs $p(\phi |T)$ $\infty p(\phi )$ p(T|$\phi )$, wherever $p(\phi$|T) is that the posterior chance of the parameter $\phi$ given the observation T, and p(T|$\phi i)$ is that the opportunity of the observation T given the setting $\phi$. One application of Bayesian logical thinking in WSNs is to assess event consistency ($\phi$) exploitation incomplete knowledge sets (T) by investigation previous knowledge concerning the atmosphere. But, such applied math data demand restricts the full usage of Bayesian algorithms in the field of WSNs. The key issue is to use the current to update prior assumptions in background assumptions where the subsequent probabilities of the parameter given the observation are that the setting is likely to be considered. The evaluation of the consistency of events with integrated data sets using prior environmental knowledge is used to access Bayesian inference in WSNs.

In [15], the authors Nguyen et al. dealt with the multi-source location of WSN and proposed for this issue a statistical sampler solution based on new application of the Monte Carlo sequence (SMCs) with unknown quantified source data obtained in the fusion center by various sensors from anonymous wireless channels. The experimentation results show that the proposed algorithm is best when compared with classical methods. The authors [16] addressed the localization of the node, and the issue is resolved by proposing a refinement of the Bayesian algorithm and referred to as the progressive correction. The particle filtering technique is used for node localization in progressive correction using a small number of parameters, but for a large number of parameters, this technique is not implemented. So a generalization procedure is proposed to significantly find more accurate node localization. The numerical simulations demonstrated that the algorithm proposed is giving more accurate results for node localization with the moderate expense of computation

Device-free localization (DFL) techniques were proposed by authors [17] to estimate target locations by analyzing their shadowing impacts in the area of interest on radio signals. They proposed a DFL technique-based compressive sensing to use the diversity of the frequency of subcarrier information in fine grains. In this method, it is a sparse recovery problem to build dictionaries from several channels on the saddle surface model with a multi-target DFL. To estimate the location vector, a multitask Bayesian compressive sensing (MBCS) framework develops an iterative location vector estimation algorithm. In comparison with CS-based multi-target DFL approaches, the superiority of its work is demonstrated through the simulation results.

6.2 Neural Networks

This algorithmic learning rule cascades chains of decision units, i.e., perceptron’s or radial basis functions and is employed with efficiency to spot nonlinear and sophisticated features. The appliance of neural networks in WSNs in distributed manners continues to be not thus pervasive because it wants high procedure power to be told the network weights and depends on senior management overhead. In distinction, at the centralized manner, the NNs learn multiple outputs and call boundaries without delay that resolves many networks challenges victimization the identical model. Neural networks are known for various inputs and multiple outputs along with uncountable hidden layers for processing between inputs and outputs. The application of neural networks, for instance, sensor node localization downside is taken into account in WSNs. The node is located at the angle spread and distance measurement of anchor node signal received, which means arrival time (TOA) with received signal strength indicator(RSSI) received as well as arrival time distinction(TDOA) as illustrated in Fig. 4. The localization considers in three layers. The input layer is responsible for considering distance or angle estimation between the nodes. The former hidden layer it can be called as single layer or multiple layers for computing position calculation of each single node. The final stage is the output layer in which localization of whole network.

Fig. 4

Position estimation using supervised learning

In [17], the authors identified localization of nodes could not be determined using traditional mechanisms due to hardware restrictions of the nodes. Later, they identified soft-computing techniques like neural networks could be used as a solution for localization problem. They suggested the new technique of the neural network by minimizing the number of neurons from hidden neural network layers by an algorithm of particle swarm optimization. They proved that the unique algorithm has a lower rate of localization errors [18] and a lower storage requirement than the existing analog methods. The authors in [19] suggested a problem of range-free localization with artificial neural networks and introduced an algorithm named “the anisotropic signal attenuation robust localization” that uses distance estimation (DE) approach to effectively derive the gap in closed from in order to attain anisotropic signal attenuation for the location of the node. The simulation results prove that the proposed algorithm presents a range-free localization both in accuracy and robustness. In [20], the authors identified a solution for localization problem for application-based which uses both techniques particle swarm optimization (PSO)-based in advanced version and artificial neural networks for the application indoor and outdoor application tracking. There are two approaches to the proposed method. The first approach is based on a proposed hybrid particle swarm optimization and artificial neural networks (PSO-ANN) algorithm to improve the distance estimation between the nodes of accurate node localization by using the feed forward neural network type and the Levenberg–Marquardt training algorithm. The first approach is based on a log normal shadow models(LNSM) for canal propagation and the next strategy. The authors concluded their research in this paper by saying “there was a mean absolute error of 0.022 and 0.208 m for both the outdoor and indoor environments.” Indoor environments were investigated by the density of anchor nodes for the precision of the location. In the paper by Payal et al. [21] proposed the creation of a fast coverage and low costs neural network feed forward artificial neural networks (FFANN) to develop the wireless sensor networks (WSN) location framework. In order to build a cost-effective locale framework, this FFANN method has shown conclusively conjugation grade-based sensor nodes.

6.3 Decision Trees

Decision trees (DT) are a type of supervised ML classification method focused on if-then rules to improve readability. This algorithm predicts labels of data by iterating sample data (input) through a learning tree. In this processing, a comparison of feature property is made with decision conditions to reach a specific category (output) based on the decision condition [13]. DT offers an easy but efficient method to identify WSN connection reliability by defining a few critical features such as loss rate, corruption rate, mean failure time (MTTF), and mean restore time (MTTR). DT works with linearly separable results, however, and the process of building optimal learning trees is NP-complete [9]. Merhi et al. [22] developed a method for WSNs for acoustic target localization. Exacting locations of targets are determined using one of the two ways in decision trees is the time difference of arrival (TDOA) metric using a spatial correlation in the decision tree. They proposed the design of the protocol “event-based medium access control” (EB-MAC) to establish an acoustic localization in WSNs.

6.4 Gaussian Process

This Gaussian process is the solution in the selection optimum sensor locations to achieve resistance to node failures in the network and model ambiguity. Krause et al. [23] proposed an optimized solution algorithm called lazy learning algorithm for placement of sensors based on the application for which it is being used. One exciting feature of this solution is the development of an investigation phenomenon. Lazy learning algorithms store samples of training and delay the main workload until the request for classification is received.

A distributed node motion protocol for location in networks of wireless sensor systems was developed by the authors [24]. This approach is used to predict optimal locations of motive nodes based on their movements by distributing the Gaussian process regression (DGPR). It overcomes the disadvantage of traditional Gaussian process regression (GPR) algorithms, where N is the number for sample size, with O(N3)’s computational complexity. The algorithm proposed formed the solution to overcome the computational complexity by adopting a sparse Gauss process regression algorithm. In this process, each node will independently execute the regression algorithm using only local neighbor’s spatial time information for locating nodes.

6.5 Support Vector Machines

This method is used in each unfeasible sensor in the wireless sensor network to self-place the node in every device. The localization based on support vector machines (LSVM) method for locating the nodes in WSNs has been proposed by Tran and Nguyen [25]. LSVM adopts a number of decision metrics, including connectivity information and indicators to achieve its design goals and to produce suitable training data. Although LSVM provides a distributed localization quickly and efficaciously, its performance in training samples is still sensitive to outliers. The nonlinear SVM 2D visualization is shown in Fig. 5.

Fig. 5

Nonlinear support vector machine. Source Pandey, “Localization Adopting Machine Learning Techniques in Wireless Sensor Networks”, 2018

In [26], the author proposed a new range-free localization algorithm based on polar coordinates sensor nodes to solve the locational problem within wireless sensor networks by support vector machine (SVM). With the WSN field boundaries, each sensor node can be located in one of the endless networks by dividing into a certain number of polar grids. Then, the center of the resident polar grid is calculated as the sensor node location. Furthermore, the authors suggested a new algorithm to enhance node accuracy. In THMSO, both neighborhood information and northern node information is used as refinement to locate the sensor node. The algorithm proposed is the THMSO two-hop mass-spring optimization (THMSO). The findings show that the algorithm proposed improves better than existing methods of localization. When the detection area is too wide, the author [27] proposed a solution that requires each sensor node to be classified several times to locate SVMs, which means that placement time is too long which hampers good SVM performance. For the similarity measure, the proposed quick-SVM uses the minimum spanning by dividing the support vectors into groups according to the minimized functions. A linear combination of “ determining factor” and “adjusting factor,” based on a similarity of classification speed, is replaced by each group of support vectors. Vector support machines provide the most popular options for resolving no convex free improvement problems for neuropathic networks. The malicious behavior of sensing element nodes, security, and location should be used in the context of the WSN for intrusion detection or police work. It is possible to reveal in knowledge with SVM the spatiotemporal correlations [28].

6.6 K-Nearest Neighbor

K-nearest neighbor is a query processing algorithm. This query is applied to the classified data and generates output values for the adjacent data samples as labels. Several functions are available to determine the closest node set. K-nearest neighbor requires a high computer capacity, as it is calculated on the basis of simple connected points. In this article [29], the authors proposed a solution for the novel spatial query question in mobile sensor networks, l distant K-nearest neighbors (l-KNN). The consequence of the question implies well-scattered objects closest to the interest field. The l-KNN method can be used in most KNN applications, whether we want the KNN result to be well-distributed or narrowly protected. l-KNN divides the search space into several track-sectors where all sides are equal or greater than the distance limit. By choosing Q-nodes in alternating track-sectors, we ensured l distances between any two Q-nodes. To keep the gap tight, we changed the track-sectors’ central angles and radius.

7 Unsupervised Learning

In unsupervised learning, there is no output (unlabeled) related to the inputs; even the model try and extract the relationships from the information. Unsupervised learning approach used as classifying the set of comparable patterns into clusters, spatiality reduction, and anomaly detection from the info. The main contributions of unsupervised learning in WSNs are to tackle different problems like property downside. In this, the output vector is not provided. Its primary goal is to classify the simple sets to different clusters or groups by investigating the similarity between the input samples. It tries to extract the relationships among the data associated with the input. Classification based on unsupervised learning: The classification of supervised algorithm is based on four basic ideas they are, namely logic-based, instance-based, perceptron-based, and statistical-based classification.

• (a) Self-organizing map (SOP)

• (b) Principal component analysis (PCA)

• (c) K-means clustering.

7.1 Self-Organizing Map

The WSN-based self-organizing maps (SOM) solution consisting of thousands of nodes was introduced by Paladina et al. [30]. The proposed solution involves the execution of each node with a simple SOM algorithm which considers three layers which have one input layer and two output layers. Here, the input layer has spatial coordinates of anchor nodes which are 8 in number. An unknown node surrounds these eight anchor nodes. After training the hidden nodes which are spatially coordinated in a 2D space by the output layer. To find the absolute locations from the nodes is difficult from traditional methods, as a solution in [31] proposed a localization algorithm based on node connectivity information and the SOM algorithm. This algorithm works well for networks with limited resources. The proposed algorithm can be termed as a centralized algorithm as each nodes information is transmitted to the central processing unit to design an adjacent matrix for identification of node location. In [32], Lee the author proposed a scheme for node localization. In this, the author presented a new way of localizing nodes without the need for the anchor nodes. This scheme also uses the SOM algorithm process efficiently without any restriction on the number of nodes. From [31, 32], we can analyze that both the authors used the SOM algorithm but in different ways. By the analysis, we can say that [31] is more efficient over [32] because it minimizes the overhead of node transmission by eliminating the need for the central unit.

7.2 Principal Component Analysis

It is essentially a technique to compress information by reducing its dimension by extracting vital info exhibited from collected information set and rework it into a brand new orthogonal variable known as principal elements. It is a dimensionality reduction with the multivariate method for compression of data, aiming to extract relevant priority-based information from gathered data in the form of orthogonal variables called principal components. The technique is used for the acoustic location of the underwater and the detection of abnormalities in wireless networks. The 2D visualization of the principal component analysis is shown in Fig. 6.

Fig. 6

PCA 2D visualization. Source Pandey, “Localization Adopting Machine Learning Techniques in Wireless Sensor Networks”, 2018

In [33], the authors proposed a scheme using probabilistic pattern recognition in eigenspace of PCA for underwater localization. Based on the proposed system, the information can be easily obtained by probabilistic pattern recognition of projected features in PCA space. Experimental results have shown that the proposed underwater localization scheme is efficient and accurate when compared with existing techniques.

7.3 K-Means Clustering

The k-means algorithm groups information into entirely different categories referred to as clusters. The unsupervised learning formula is widely adopted in a bunch of detector node as its implementation is smooth and has a linear process complexness. The authors [34] addressed cost-effectively to measure cost-effective K-nearest neighbor queries in a 3D sensor network using intelligent mobile data collectors.3D plane rotation algorithm that maps selected sensor nodes on different planes to a reference plane and a novel neighbor selection algorithm based on node distance and signal-to-noise parameters. We have implemented GlomoSim’s 3D-KNN algorithm and validated the cost efficiency of the proposed algorithm through comprehensive performance evaluation over well-defined device parameters.

8 Reinforcement Learning

Reinforcement learning allows a sensing element node to find out its surroundings by interacting it. The agent learns to require the most effective actions that maximize its long edges mistreatment its expertise.

8.1 Q-Learning

A widely known rule, Q-learning [35] is a form of reinforcement learning technique is explained in. As illustrated in Fig. 7, associate degree agent updates its achieved edges no inheritable due to the action taken at a given state regularly.

Fig. 7

Visualization of the Q-learning method

The entire advantages awarded called the Q-value of performing arts associate degree action ($A_{t}$) at a given state St is calculable as shown in equation (2).

$$\begin{aligned} Q(S_{t+1},A_{t+1})=Q(S_{t},A_{t}) + \alpha (b(S_{t},A_{t})-Q(S_{t},A_{t}) \end{aligned}$$

(2)

This rule is often applied only in a highly distributed design, such as a network of wireless sensing elements, when each node takes action to maximize its length. It is essential to note the extensive use of Q-learning and efficiency in WSN routing downside. where $\alpha$ $(S_{t}, A_{t})$ denotes the immediate reward of acting $A_{t}$ a given state $S_{t}$ and is the learning rate that determines how fast learning occurs (usually set to value between 0 and 1) [13].

Li et al. [36] stated that the Q-learning rules does not represent the various positions of the MB, and also the rule target is to hide all the sensors within the monitored space (i.e., all the sensors ought to hear a location update message from the MB at some stages). The entire operation is conducted within a mobile phone, which can save hidden node resources. However, the whole system can fail in case of mobile beacon defects as a centralized technique.

9 Conclusion and Future Work

In several applications of WSNs, localization of the physical/geographical location of a node is outlined. The sensor node that is placed in an excessive field without knowing its position and no other infrastructure on the market is available to track its status. The location of the sensing element, however, may be a vital task in this unique situation. This task is familiar by means that of manual assignment or geographical position system (GPS). The position of device nodes within the surroundings will support an amendment by dynamically due to some external causes. To handle such situations, machine learning algorithms can be needed to avoid the stress and strain of re-programming or reconfiguring the network and improve the accuracy of the location of nodes in wireless sensor networks.

Machine learning offers a range of techniques to strengthen the power and dynamic behavior of wireless sensor networks. Although machine learning techniques are applied to several applications in WSNs, several problems are still open, and additional analysis efforts must be put into execution to solve many issues in WSNs. Furthermore, process intelligence paradigms like neural networks and neuro-fuzzy strategies, swarm intelligence algorithms like ant colony optimization, and evolutionary formulas like the competitive imperialist algorithm might also be applied to enhance the performance of networks, and soft-computing techniques can also be used to solve the challenges of wireless sensor networks. Moreover, numerous problems are still open for future analysis like developing lightweight and distributed message passing techniques, stratified agglomeration patterns, online learning algorithms, and adopting machine learning is additionally in resource management drawback of wireless sensor networks.