ALPA filtering.
\r\n\tWith this rapid transformation of computing and communication world, information-system security has moved from a largely self-contained bounded environment interacting with a generally known and disciplined user community to one of worldwide scope with a body of users that may not be known and are not necessarily trusted. Importantly, security control now must deal with circumstances over which there is largely no control or expectation of avoiding their impact. Computer security, as it has evolved, shares a similarity with liability assurance; they each face a threat environment that is known in a very general way and can face attacks over a broad spectrum of sources; however, the exact details or even time or certainty of an attack is unknown until an incident actually occurs.
\r\n\tThe purpose of this book is to discuss some of the critical security challenges in today’s computing world and to discuss mechanisms for defending against those attacks by using classical and modern approaches of cryptography and other security solutions. With this objective, the book invites contributions from researchers in the field of cryptography and its applications in network security. Some illustrative topics of interest (but not limited to) are: cryptography algorithms, authentication, authorization, integrity, confidentiality, privacy, security in wireless networks, security in wireless local area networks, wireless sensor networks, wireless ad hoc networks, vehicular ad hoc networks, security and privacy in the Internet of Things.
With the arrival of global navigation satellite systems (GNSS), in-car navigation has increasingly become an essential tool for the automotive industry. However, the performance of GNSS is compromised in harsh environments where there is not a line of sight (LOS) to satellites, e.g., tunnels, covered parking areas and dense urban canyons [1]. Hence, in-car navigation requires a localization technology that operates with robustness in such circumstances. The development of vehicular ad-hoc networks (VANETs) provides a promising platform to fulfill this requirement [2].
In VANETs, an on-board unit (OBU) inside the vehicle communicates with other OBUs or with stationary roadside units (RSUs), in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, respectively [3]. Cooperation between OBUs can provide good position estimates in V2V communication [4-5]. However, the quick topology changes required by V2V approaches make V2I communication be the preferred option for in-car navigation in harsh environments [6]. In V2I communication, the position of an OBU (the target) can be estimated from range-related measurements taken on the radio-frequency signals transmitted to and from the RSUs (the anchors) [7]. However, the changeable and unpredictable characteristics of the wireless channel in harsh environments make multipath and non-line of sight (NLOS) propagation conditions be predominant [8-9]. Therefore, conventional positioning systems designed for tractable and static signal behavior cannot guarantee an adequate performance.
The position information extracted from the radio-frequency signals varies according to the type of measurement taken. Techniques based on time of arrival (TOA) [9-10] or received signal strength (RSS) [11-12] measurements obtain range-related information, whereas techniques based on angle of arrival (AOA) or time difference of arrival (TDOA) measurements extract information related to directions or difference of distances, respectively [13-14]. AOA and TDOA measurements entail significant costs of antenna-array integration or synchronizing devices. In this chapter, we focus on RSS and TOA measurements that can provide accurate localization with an appropriate complexity.[1] -\n\t\t\t
Range or position estimation is an inference problem where the observations are the RSS and TOA measurements [15-16]. From a Bayesian perspective, determining the posterior distribution of ranges or positions from observations is the optimal approach [17-25]. Then, ranges or positions can be obtained by means of the maximum a posteriori (MAP) or the minimum mean square error (MMSE) estimators.
The optimality of the above mentioned methods depends on the fit between the model assumed for the relationship between measurements and ranges or positions (i.e., the likelihood function) and the actual behavior of the measurements. Tractable and static models for the likelihoods based on Gaussian distributions accurately explain the behavior of measurements only in open areas [26-28]. For harsh environments, several techniques have been developed to address the complex behavior of wireless signal metrics. In the TOA case, the NLOS bias causes range overestimation. Thus, a common procedure is to detect and remove NLOS measurements [29]; other techniques utilize prior knowledge about this NLOS error to subtract it and adjust the measurements to their LOS values [27,30]. In the RSS case, the performance depends on the estimation of the parameters that characterize the propagation channel at each time [12,26]. Certain approaches deal with the dynamic nature of RSS metric through fingerprinting or machine learning [11,21,31]; however, their accuracy is sensitive to fast environmental changes and they do not fuse different signal metrics.
Range and position estimation can be improved by exploiting the relationship among positions in time through Bayesian filtering. Kalman filtering techniques rely on Gaussian models that are not adequate for harsh environments. Different alternative methods based on variations of such filters, as well as on particle filters (PFs), have been proposed: low complexity non-linear/non-parametric adaptive modeling is used for filtering of RSS fingerprints in [11,21]; recursive Bayesian estimation together with multipath and NLOS propagation effects are considered in [22-23]; TOA and RSS data fusion is performed in [32-34]; hybrid information is exploited by particle filtering in [24]; and RSS/TOA Bayesian fusion for multipath and NLOS mitigation are performed in [25]. However, these methods require prior information achieved by arduous training phases or rely on assumptions non-realistic for harsh environments, such as Gaussian and static models.
This chapter presents a framework for adaptive data fusion to handle the difficulties described above, based on non-parametric dynamic modeling of the likelihood. The subsequent usage of a PF leads to the adaptive likelihood particle (ALPA) filter. As we show, the estimation can be carried out without requiring any calibration stage, thus enabling localization capabilities to pre-existing wireless infrastructures, such as VANETs based on V2I communication. The main contributions of this chapter are as follows:
We present techniques for adaptive and systematic modeling of the relationship between measurements and positions, by means of a dynamic and empirical likelihood function.
We present a model for Bayesian fusion of TOA and RSS measurements, based on nonlinear and non-Gaussian Bayesian filtering and the likelihoods derived over time.
We show the suitability of the proposed techniques by experimentation performed using common wireless local area network (WLAN) devices.
We show the near-optimality of the method by comparing its performance to the posterior Cramér-Rao lower bound (CRLB).
Both empirical and simulation results show that the proposed methods significantly improve the accuracy of conventional approaches with an important reduction on the number of measurements needed.
The structure of the rest of this chapter is as follows: Section II defines the position estimation problem; Section III addresses this problem under a hidden Markov model (HMM) and defines the dynamic and measurements models; Section IV presents the adaptive data fusion technique for likelihood modeling and the recursive Bayesian approach for solving the resulting non-linear and non-Gaussian problem; Section V shows the experimental and simulation results; Section VI includes a discussion on complexity; and finally, Section VII draws the conclusions.
Notations: The notation
In the following, we consider a two-dimensional scenario where a mobile target (e.g., a car equipped with an OBU) moves freely. To determine its position, the target communicates with several anchors (the RSUs) with known positions. Since the localization system can get measurements in discrete times
Next section establishes the probabilistic relationship between vectors
In addition to the information conveyed by the measurements, the fact that the sequence
where
The correlation in time expressed in (1) implies that
Hidden Markov Model for positional-states and measurements evolution. The relationship between yk and yk-1 and the relationship between zk and yk are the only two kinds of dependence.
The conditional independence assumptions reflected in Figure 1 lead to two kinds of dependence between the random variables [36],
Dynamic model: establishes the relationship between the state vector in time
Measurements model: establishes the relationship between the measurements and the state vector in each time, i.e.,
Then, the joint distribution of all the random variables involved in the process is given by,[1] -\n\t\t\t
The modeling as an HMM shown in (2) makes possible to infer the posterior distribution
The dynamic model of the positional-state vector can be obtained from the evolution in time given by (1), and by approximating each
where
is the transition matrix, and
where
The second ingredient to characterize the HMM is the measurements model or likelihood,
In a given specific instant and place, the RSS values are affected by the distance between emitter and receiver. The attenuation caused by the distance between two nodes is known as path-loss and is proportional to this distance raised to a certain exponent, called path-loss exponent [7,12,15,26]. However, the RSS values are likewise affected by a wide range of unpredictable factors, such as multipath propagation (fast fading) and shadowing (slow fading) [37]. By reflecting these factors in the Friis transmission equation for free-space, the relationship between the received signal strength,
where
By following the procedure described in [26] and taking logarithmic units, we obtain the measurements model for RSS values,
where
The distance between emitter and receiver also affects the time taken by the signal to be propagated from one node to the other. By assuming known the signal speed, we can infer this distance by means of a linear transformation of the TOA values. Due to the technical difficulty of synchronizing devices in a wireless network, techniques that use round-trip time estimation are the most attractive to estimate delays [10,28]. In this case, the processing time at the device that has to transmit the echo causes the relationship between TOA and distance to be affine linear (it has an intercept term). Then, we can model the relationship between the delay,
where
From the above discussion, we can notice that in all cases the expected value of the measurements is
the relationship between measurements related to distances and distances is nonlinear and non-Gaussian;
such relationship highly depends on the propagation environment that can change rapidly.
These two factors render the linear-Gaussian assumption inadequate for the measurements model,
Conventional non-Bayesian approaches for parameter estimation are based on maximum-likelihood (ML) estimation (in our case the maximization of
In the above mentioned context, the task is to determine the posterior distribution of positional-states given the measurements,
In the case of modeling the positional-state and measurements evolution as an HMM, the expression (2) provides a way to determine the posterior distribution iteratively,
and for
From the posterior distribution,
leading to a process called filtering.[1] - By replacing (9) in (10) we obtain,
By assuming known the posterior distribution at
Prediction: from the dynamic model we obtain the prediction of the positional-state in time
Update: from the measurements model we correct the prediction when a new set of measurements,
and the normalization constant,
Hence, the objective is to infer the hidden positional-state vector in each time,
In order to perform the described filtering process, we need the likelihood function of the measurements
Density functions involved in filtering process for range and position estimation (darker zones have higher probability): (a) the target with the OBU moves in tk with respect to its position in tk-1; (b) the posterior density in tk-1 is known; (c) from the dynamic model we perform the prediction; (d) in tk the target receives a new set of measurements; (e) from the likelihood we update the prediction to obtain the posterior density in tk.
The sets of RSS and TOA measurements obtained in each instant consist of samples from the random variable
where
By assuming that the distribution of the measurements
Proposition 1. Let
where the expectation
Proof: see [48].
The Proposition 1 enables to obtain individual likelihoods from a set of measurements. Data fusion from different signal metrics (i.e., RSS and TOA) is carried out by combining these likelihoods. Let
where the likelihood of each kind of measurement can be dynamically obtained from (17).
In order to describe how the presented adaptive data fusion operates, Figures 3-4 show the histogram of 100 RSS and 100 TOA measurements taken at a fixed distance with the measuring systems described in [12] and [10], respectively. These figures also represent the corresponding Gaussian pdf and the adaptive pdf obtained by means of the kernel-based expression given by (15) and (16).[1] - From those figures, we can point out that, despite the fact that the true density is unknown, the presented adaptive pdf can express the dynamic behavior of RSS/TOA measurements in harsh environments with better accuracy than histogram and Gaussian density estimates [49-50].
The adaptive density accurately approximates the complex randomness of RSS measurements in harsh environments.
The adaptive density accurately approximates the complex randomness of TOA measurements in harsh environments.
In Figure 5 we illustrate the RSS/TOA data fusion process by representing the adaptive likelihood function obtained by means of expressions (17) and (18).[1] -\n\t\t\t\t
The adaptive RSS/TOA data fusion, defined by Proposition 1 and (18), results, in this case, in an improvement of 0.5 meters in ML estimator compared with the Gaussian case, which is equivalent to a reduction of 18% of the error.
From Figure 5, we can point out that the adaptive likelihood function provides more information about the distance than the Gaussian model, by combining the individual adaptive likelihoods obtained with RSS and TOA measurements. Moreover, the height of both functions reflects the more reliable information obtained by adaptive estimation. From that figure, we also observe the improvement achieved by means of data fusion with respect to the individual estimates. This likelihood function leads to the ALPA filter defined in the following section.
Within the framework provided by the HMM, if both dynamic and measurements models are linear-Gaussian, all the posterior distributions are also Gaussian. In this case, all the involved density functions are completely described by their mean vectors and covariance matrices, obtained by a KF [19]. In the case of interest in this chapter, the models in the HMM are neither linear nor Gaussian, and then, the usage of KFs is suboptimal. In order to circumvent this drawback, the classical solution consists of using extended KFs (EKF) [23,25]. However, better performances can be obtained by PFs that let the usage of more general and flexible models [17,19] as the adaptive likelihood described in the previous section.
A PF represents the posterior distribution through a discrete distribution, where the support points and their probabilities are called particles and weights, respectively. To estimate the posterior distribution, we need to iteratively obtain a certain number of samples (particles) and probabilities (weights) capable of representing the posterior distribution. These particles and weights can be obtained by a method known as sequential-importance-sampling (SIS) [19,51], where the weight of the different particles can be determined by evaluating the likelihood function pointwise. Therefore, more realistic models such as the presented adaptive likelihood function for data fusion can be used, leading to the ALPA filtering algorithm describe in Table 1.
i. Initialization: ∙ Initial particles: draw ∙ Initial weights: ii. Recursive estimation: for ∙ Particles in instant ∙ From RSS measurements and Proposition 1, evaluate the weight of each particle. For | \n\t\t|
\n\t\t\t\t | \n\t\t|
∙ From TOA measurements and Proposition 1, evaluate the weight of each particle. For | \n\t\t|
\n\t\t\t\t | \n\t\t|
∙ Evaluate for | \n\t\t|
\n\t\t\t\t | \n\t\t|
∙ Normalization: for | \n\t\t|
\n\t\t\t\t | \n\t\t
ALPA filtering.
To implement the algorithm detailed in Table 1, we have to choose a proposal distribution, where the most popular choice is to use the transition prior given by the dynamic model, i.e.,
Therefore, in order to use this algorithm, we have to obtain samples from the transition prior and evaluate the adaptive likelihood function pointwise. Figure 6 summarizes how this filter works with the proposal distribution chosen. First, we generate particles from the proposal distribution, in this case, the prior distribution,
Transition prior and likelihood functions. Particles are obtained by sampling from the prior and weighting from the likelihood.
In this SIS algorithm, as
where a small
The goal of this section is to quantify the performance of the methods presented in the above sections, leading to the ALPA filter. In order to do that, we obtained experimental data in a real indoor scenario by using the systems described in [10] and [12], and we ran numerous Monte Carlo simulations. In the following, we compare the performance of the introduced techniques with conventional approaches as well as with the CRLB.
We use the dynamic and measurements models above described together with the following state vector and prior information, depending on whether we estimate ranges or positions,
Range estimation: we use a state vector
Position estimation: we use a state vector
For the experimental data, the target carried a laptop equipped with an IEEE 802.11b/g adapter and the measuring systems described in [10] and [12]. The anchors consisted of IEEE 802.11b/g access points (APs). In the RSS case, the anchors periodically sent beacon frames (at a frequency of
As mentioned above, in a realistic scenario, NLOS propagation together with multipath effects constitute the major drawback of localization in harsh environments. This section illustrates the behavior of the proposed algorithm during a typical path followed by a mobile target in an indoor scenario. We carried out a measurement campaign inside an office building cluttered with clusters of objects and people moving freely in the area of the measurements. The propagation conditions were even harsher than the ones commonly find by an OBU placed within a car. Figure 8 shows the trajectory of 65 meters as well as the position of the 4 APs. It took 100 seconds to complete the whole trajectory, receiving a new set of measurements every second (
In Table 2, we compare the error achieved with the proposed ALPA range estimation method in the presented scenario to the error obtained with conventional approaches [15,24]. We specify the results for RSS-only and TOA-only cases, and for their fusion. Specifically, we call,
ML-RSS, ML-TOA, ML-Fusion: the range estimates obtained by means of the ML estimator. We utilize as likelihood function the convolution of the likelihood reported by the measurements (log-normal in the RSS case and Gaussian in the TOA case) and a Gaussian distribution corresponding to the bias.[1] - The likelihood for the fusion is computed from (18).
AML-RSS, AML-TOA, AML-Fusion: the ranges that correspond to the result of obtaining the maximum of the adaptive likelihood computed by means of Proposition 1, and (18) in the fusion case.
EKF-RSS, KF-TOA, EKF-Fusion: the result of applying EKF and KF filters for RSS and TOA measurements, respectively, using the same bias distributions as in the ML case, and the dynamic model given by (3).
ALPA-RSS, ALPA-TOA, ALPA-Fusion: the range estimates obtained by the ALPA filtering described in Table 1, where
We summarize for all these methods the quartiles of the absolute error in range estimates as well as the root mean squared error (RMSE), which incorporates both systematic (bias) and random errors. In order to study the influence of the number of measurements,
Figure 7 depicts the pdf of the absolute error in range estimation after applying AML-Fusion and ALPA-Fusion methods, taking
\n\t\t\t\t | \n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t||||
\n\t\t\t\t\tQuartiles\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tRMSE\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tQuartiles\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tRMSE\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tQuartiles\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tRMSE\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tQuartiles\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tRMSE\n\t\t\t\t | \n\t\t\t|
ML-RSS | \n\t\t\t1.64-3.12-5.45 | \n\t\t\t7.01 | \n\t\t\t1.28-2.94-4.96 | \n\t\t\t5.32 | \n\t\t\t1.36-2.72-4.68 | \n\t\t\t4.34 | \n\t\t\t1.27-2.74-4.74 | \n\t\t\t4.58 | \n\t\t
ML-TOA | \n\t\t\t2.09-3.92-7.68 | \n\t\t\t6.42 | \n\t\t\t1.55-3.40-5.64 | \n\t\t\t5.00 | \n\t\t\t1.26-2.69-4.40 | \n\t\t\t3.87 | \n\t\t\t1.12-2.44-4.00 | \n\t\t\t3.55 | \n\t\t
ML-Fusion | \n\t\t\t1.52-3.16-5.87 | \n\t\t\t5.25 | \n\t\t\t1.26-2.66-4.73 | \n\t\t\t4.23 | \n\t\t\t1.09-2.24-3.89 | \n\t\t\t3.55 | \n\t\t\t0.87-2.18-3.61 | \n\t\t\t3.26 | \n\t\t
AML-RSS | \n\t\t\t1.69-3.25-5.27 | \n\t\t\t5.64 | \n\t\t\t1.44-2.92-5.06 | \n\t\t\t4.71 | \n\t\t\t1.32-2.74-4.64 | \n\t\t\t4.27 | \n\t\t\t1.31-2.70-4.50 | \n\t\t\t4.20 | \n\t\t
AML-TOA | \n\t\t\t2.06-3.74-7.38 | \n\t\t\t6.28 | \n\t\t\t1.52-3.31-5.57 | \n\t\t\t4.93 | \n\t\t\t1.18-2.61-4.27 | \n\t\t\t3.81 | \n\t\t\t1.03-2.38-3.86 | \n\t\t\t3.48 | \n\t\t
AML-Fusion | \n\t\t\t1.38-2.91-5.19 | \n\t\t\t4.49 | \n\t\t\t1.15-2.32-3.65 | \n\t\t\t3.49 | \n\t\t\t0.86-1.91-3.39 | \n\t\t\t3.06 | \n\t\t\t0.83-1.83-3.26 | \n\t\t\t2.91 | \n\t\t
EKF-RSS | \n\t\t\t0.84-2.22-4.26 | \n\t\t\t3.82 | \n\t\t\t1.06-2.59-4.21 | \n\t\t\t3.81 | \n\t\t\t1.21-2.43-4.07 | \n\t\t\t3.76 | \n\t\t\t1.17-2.55-4.04 | \n\t\t\t3.69 | \n\t\t
KF-TOA | \n\t\t\t1.11-2.37-3.95 | \n\t\t\t3.60 | \n\t\t\t1.10-2.06-3.63 | \n\t\t\t3.04 | \n\t\t\t0.81-1.76-2.97 | \n\t\t\t2.53 | \n\t\t\t0.86-1.63-2.95 | \n\t\t\t2.36 | \n\t\t
EKF-Fusion | \n\t\t\t0.93-1.90-3.24 | \n\t\t\t2.78 | \n\t\t\t0.86-1.82-3.15 | \n\t\t\t2.59 | \n\t\t\t0.82-1.62-2.62 | \n\t\t\t2.25 | \n\t\t\t0.74-1.49-2.55 | \n\t\t\t2.10 | \n\t\t
ALPA-RSS | \n\t\t\t0.82-2.33-4.63 | \n\t\t\t3.88 | \n\t\t\t1.17-2.58-4.30 | \n\t\t\t3.79 | \n\t\t\t1.20-2.48-4.18 | \n\t\t\t3.75 | \n\t\t\t1.21-2.64-4.17 | \n\t\t\t3.78 | \n\t\t
ALPA-TOA | \n\t\t\t0.94-2.04-3.33 | \n\t\t\t3.11 | \n\t\t\t0.95-1.90-3.06 | \n\t\t\t2.69 | \n\t\t\t0.72-1.48-2.63 | \n\t\t\t2.52 | \n\t\t\t0.76-1.50-2.64 | \n\t\t\t2.32 | \n\t\t
ALPA-Fusion | \n\t\t\t0.84-1.72-2.95 | \n\t\t\t2.58 | \n\t\t\t0.80-1.70-2.85 | \n\t\t\t2.35 | \n\t\t\t0.69-1.37-2.36 | \n\t\t\t2.22 | \n\t\t\t0.70-1.45-2.40 | \n\t\t\t2.08 | \n\t\t
Range estimation error quartiles and RMSE obtained with different algorithms as a function of the number of measurements. All error values are in meters.
Analogously, in Figures 8-9 and Table 3, we summarize the results in position estimation. In this case, we call,[1] -\n\t\t\t\t
ML-RSS, ML-TOA, ML-Fusion: the positions obtained with the ML distances and a trilateration technique based on the radical axis of the circles drawn at each anchor’s position [10,12-13].
EKF-RSS, EKF-TOA, EKF-Fusion: the positions obtained by means of an EKF whose measurements model relates the measurements to the target’s position.
PF-RSS, PF-TOA, PF-Fusion: the result of applying the ALPA filter described in Table 1 to the positional-states, with
The height and width of the pdf corresponding to the error achieved by the ALPA filter reflect its better performance in comparison to other conventional range estimation techniques. 10 RSS and 10 TOA measurements were taken with respect to each anchor.
\n\t\t\t\t | \n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t||||
\n\t\t\t\t\tQuartiles\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tRMSE\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tQuartiles\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tRMSE\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tQuartiles\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tRMSE\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tQuartiles\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tRMSE\n\t\t\t\t | \n\t\t\t|
ML-RSS | \n\t\t\t3.83-5.91-8.49 | \n\t\t\t12.99 | \n\t\t\t3.32-5.21-7.49 | \n\t\t\t8.91 | \n\t\t\t3.35-4.94-6.98 | \n\t\t\t6.64 | \n\t\t\t3.26-5.00-6.60 | \n\t\t\t7.43 | \n\t\t
ML-TOA | \n\t\t\t3.95-6.14-8.03 | \n\t\t\t7.64 | \n\t\t\t2.80-4.05-6.64 | \n\t\t\t5.70 | \n\t\t\t2.24-3.34-5.16 | \n\t\t\t4.57 | \n\t\t\t1.63-3.20-4.71 | \n\t\t\t4.09 | \n\t\t
ML-Fusion | \n\t\t\t3.15-4.95-7.04 | \n\t\t\t6.73 | \n\t\t\t2.40-3.71-6.11 | \n\t\t\t5.10 | \n\t\t\t1.93-3.03-4.93 | \n\t\t\t4.34 | \n\t\t\t1.64-3.03-4.53 | \n\t\t\t3.89 | \n\t\t
EKF-RSS | \n\t\t\t2.94-4.46-6.18 | \n\t\t\t5.11 | \n\t\t\t3.47-4.83-6.85 | \n\t\t\t5.83 | \n\t\t\t3.02-4.12-6.33 | \n\t\t\t5.24 | \n\t\t\t3.02-4.24-6.40 | \n\t\t\t5.24 | \n\t\t
KF-TOA | \n\t\t\t1.77-2.79-4.25 | \n\t\t\t3.54 | \n\t\t\t2.05-2.80-3.64 | \n\t\t\t3.11 | \n\t\t\t1.54-2.28-3.09 | \n\t\t\t2.61 | \n\t\t\t1.50-2.22-3.14 | \n\t\t\t2.51 | \n\t\t
EKF-Fusion | \n\t\t\t2.20-3.24-4.30 | \n\t\t\t3.50 | \n\t\t\t2.08-2.99-3.90 | \n\t\t\t3.25 | \n\t\t\t1.76-2.32-3.00 | \n\t\t\t2.57 | \n\t\t\t1.71-2.13-2.98 | \n\t\t\t2.41 | \n\t\t
ALPA-RSS | \n\t\t\t1.93-3.28-5.18 | \n\t\t\t4.36 | \n\t\t\t3.16-3.91-5.18 | \n\t\t\t4.68 | \n\t\t\t2.38-3.09-4.61 | \n\t\t\t4.23 | \n\t\t\t2.72-3.65-4.97 | \n\t\t\t4.37 | \n\t\t
ALPA-TOA | \n\t\t\t1.90-2.59-3.76 | \n\t\t\t3.37 | \n\t\t\t1.63-2.54-3.63 | \n\t\t\t2.98 | \n\t\t\t1.08-1.98-3.25 | \n\t\t\t2.66 | \n\t\t\t1.35-2.18-3.05 | \n\t\t\t2.63 | \n\t\t
ALPA-Fusion | \n\t\t\t1.77-2.86-3.46 | \n\t\t\t3.14 | \n\t\t\t1.92-2.61-3.34 | \n\t\t\t2.82 | \n\t\t\t1.23-1.85-3.15 | \n\t\t\t2.49 | \n\t\t\t1.28-2.00-2.64 | \n\t\t\t2.40 | \n\t\t
Position estimation error quartiles and RMSE obtained with several algorithms as a function of the number of measurements. All error values are in meters.
Trajectory followed by the target and position estimates for different positioning methods. 10 RSS and 10 TOA measurements were taken with respect to each anchor.
Figure 9 depicts the pdf of the error in position estimation for the three mentioned RSS/TOA fusion algorithms, taking
The proposed ALPA filter obtains the best performance with an error lower than 3 meters for more than 63% of the positions.
Figures 8-9 and Table 3 show the better performance of the proposed ALPA filter for all the analyzed scenarios, resulting, for example, in an RMSE of 2.82 meters for the case of only using
The CRLB provides a lower bound on the minimum achievable mean squared estimation error for any unbiased estimator. In what follows, we use such metric to assess the optimality of the presented ALPA filter against such lower bound.
The Bayesian version of the CRLB is known as the Van Tress CRLB [53], or posterior CRLB, since it is obtained from the posterior distributions of the random state vector [54]. In our case, for each time instant
where
Tichavský et al. proposed a recursive formula to compute the FIM [55]. For the particular case of the linear-Gaussian dynamic model in (3), being
and
To start this recursion, we assume the initial density as Gaussian, then, the initial FIM coincides with its covariance matrix.
Figure 10 compares the RMSE obtained in range estimation by means of the proposed ALPA-Fusion filter with the RMSE obtained by applying the EKF-Fusion method, and with the square root of the CRLB.[1] - To obtain such curves, we simulated a trajectory of 85 positions and carried out
The near-optimal performance of the proposed ALPA filter in harsh environments is corroborated by comparison with the CRLB.
The key issue in PFs is the exponential growth of computational complexity as a function of the dimension of the state vector,
Moreover, from Proposition 1, the complexity of the likelihood grows exponentially with the number of samples. However, this complexity can be reduced by removing redundant components from the RSS and TOA pdfs or from the resulting fusion mixture. To this aim, different criteria such as William‘s criterion [58], Kullback-Leibler distance [59] or clustering [60] can be utilized. Therefore, considering the improvement achieved in range and position estimation with
In this chapter we have presented an adaptive likelihood function for robust data fusion in localization systems. Based on this likelihood, we have developed the ALPA filter for range and position estimation. This ALPA filter presents several advantages over conventional techniques,
it does not assume any parametric statistical model, utilizing the empirical distribution of the measurements at each time by means of Gaussian kernels;
it adaptively fuses RSS and TOA data being extensible to any other type of measurement;
it takes advantage of the relationship among positions in time by using Bayesian filtering;
it addresses the non-linear and non-Gaussian behavior of the measurements by using particle filtering.
These advantages result in a noticeable improvement with respect to other conventional techniques, as corroborated by the experimental and simulation results. Under NLOS and multipath conditions, ALPA filter obtains not only an RMSE in position estimation lower than 3 meters with only 10 RSS and 10 TOA measurements, but also an error remarkably close to the theoretical benchmark provided by the CRLB.
Therefore, ALPA filter is a valuable choice to provide localization in V2I communication systems. Its extension to cooperative localization would make this localization also possible in VANETs based on V2V communication.
In the current era of increasing uncertainties in crop production, emerging constraints and risks demand technical and technological advances in the agricultural sector, and integrative approaches, such as Climate Smart Agriculture (CSA), to address the interlinked challenges of food security and climate change. While maintaining food security is a major challenge for future, the possible solution is to enhance crop productivity along with nutritional security. However, this stance is remarkably limited by the different abiotic as well as biotic environments, where the crops grow and develop.
Drought, excess water (flooding), extremes of temperatures (cold, chilling, frost, and heat), salinity, high and/or low light, mineral deficiency, and toxicity are the common abiotic stresses for crop production. These stresses alter plant metabolism, growth, development, and in extreme cases cause the cessation of vegetative and reproductive growth. Some of the abiotic stresses such as drought, high temperature and salinity can influence the occurrence and spread of biotic agents like pathogens, insects, and weeds [1]. In crops like tomato, cucurbits and rice, temperature is one of the most important deciding factors for the occurrence of bacterial diseases [2]. Temperature can also alter the incidence of vector-borne diseases by modifying spread of vectors.
But, in their natural environment, plants face combination of stresses, especially under the changing climate scenario. The effect of stresses would be more pronounced under combined (biotic and abiotic) stresses [3], while simultaneous occurrence of abiotic and biotic stresses are more destructive to crop production [4]. Hence, there exists a need now, to look for common traits that can contribute for plant adaptation to such multifarious stressful conditions and sustain crop productivity as well. In this scenario, it is desirable to have a single trait that can confer tolerance to multiple (abiotic and biotic) stresses. Cuticular waxes, a major component of plant cuticle covering all the aerial parts of the plants, can be considered as an important trait for combined stress resistance.
The cuticle is a unique structure developed by land plants during the course of their evolution from an aquatic to a terrestrial lifestyle [5]. The primary role of this lipophilic layer, comprising cutin and cuticular waxes, was to limit non-stomatal water loss by functioning as a physical barrier between the plant surface and its external environment [6]. Development of a cuticular barrier is one of the major adaptive mechanisms for survival and growth of plants under water limiting terrestrial conditions [7]. As the primary barrier between the aerial surface of plants and the external environment, the cuticle also protect the plants from mechanical rupture or injury, toxic gases and ultra violet radiation [8, 9, 10]. The cuticle also has notable roles associated with growth and developmental processes like preventing epidermal fusion by establishing normal organ boundaries [11], and phytohormone homeostasis [12]. The cuticle and its components are known to play essential roles as signaling molecules for pathogens and for the plants themselves [13]. Another important role is in fruits, where it influences quality, defense and post-harvest shelf life [14]. In fruits, water retention [15] firmness [16] and its responses to physical and biotic stresses are also influenced by the cuticle [17].
The cuticle is composed of a covalently linked scaffold of cutin and a mixture of soluble cuticular lipids (SCL), called as waxes [10, 18]. Structurally, cutin is made of covalently cross linked C16 or C18 oxygenated fatty acids and glycerol, forming the most abundant structural component of the cuticle [19]. The waxes within the cuticle function as an actual barrier against the diffusion of water or solutes [20, 21]. The waxes occur in two layers; forming two distinct physical layers called intra- and epi-cuticular waxes [22]. The former is dispersed within the cutin polymer while the epi-cuticular wax is deposited on the outer surface as crystals or films [22, 23]. This outermost layer can be physically stripped off the surfaces using aqueous glue [23, 24]. These waxes are composed of a variety of organic solvent-soluble lipids; consisting of very-long-chain fatty acids (VLCFA) and their derivatives. The major composition of VLFCAs are alkanes, wax esters, branched alkanes, primary alcohols, alkenes, secondary alcohols, aldehydes ketones, and unsaturated fatty alcohols, as well as cyclic compounds including terpenoids and metabolites such as sterols and flavonoids [19, 25, 26, 27]. Wax composition varies with crop species and differs in their functions and responses to biotic and abiotic environments [10].
As per recent studies, intra-cuticular waxes form the primary transpirational barrier and the contribution of epi-cuticular waxes as a transpirational barrier depends on the species-specific cuticle composition [28]. In species like Tetrastigma voinierianum, Oreopanax guatemalensis, Monstera deliciosa, and Schefflera elegantissima, intra-cuticular wax pre-dominantly act as a transpirational barrier while in Citrus aurantium, Euonymus japonica, Clusia flava, and Garcinia spicata, both intra- as well as epi-cuticular waxes had equal contribution as transpirational barriers [28]. A study from Prunus suggests that intra-cuticular waxes of the cuticle form the actual transpirational barrier [29] and not epi-cuticular waxes [30].
The cuticular waxes confer diverse surface properties to plant parts, which actually play the key role in controlling non-stomatal water loss and gas exchange, and protection from external environment. Leaf cuticular wax amount and crystal morphology regulated post-harvest water loss from leaves [31]. Epi-cuticular wax films give glossy appearance to leaves and fruits, while wax crystals (β-diketones) conferred dull, glaucous appearance to leaves and stems [10]. The thickness [5] composition and properties of the waxes vary with crop species and are found to be induced under diverse stressful conditions [32]. These differences reflect their functions and responses to biotic and abiotic environments [10]. Importance of cuticular wax accumulation in plant resistance to both biotic as well as abiotic stress conditions is now well documented [12, 33, 34].
One of the most important roles of the waxes is to protect the plant surfaces from excessive solar and ultraviolet (UV) radiations. Cuticular waxes scatter UV-B radiation [35] and was demonstrated in apple [36]. As per studies from model systems as well as crops, increased cuticular wax biosynthesis improves drought stress resistance [37]. In rice, wheat, barley and sorghum, grain yield under water limiting conditions have positive correlation with wax content [38, 39, 40, 41] . Hence, in crop plants, higher cuticular wax content is a promising trait for stress resistance as well as yield under water limiting conditions [27]. In mulberry, increasing wax load is useful to manage post-harvest water losses [42]. In barley, cuticular wax components act as a barrier to water loss and contribute to salt stress resistance [43]. Heat stress resistance is also positively correlated with wax accumulation in bahia grass [44]. Under heat stress, the wax load in sorghum was correlated with its ability to maintain the canopy temperature cool, resulting in reduced water loss [45]. Similarly, pea varieties with thicker wax load also exhibited lower canopy temperature, thereby limiting water loss and associated heat stress [46].
Cuticular waxes play an important role in preventing non-stomatal water loss during drought and high temperature stress, as well as enabling frost avoidance. Such climatic stressors can induce a heavier wax load and change the chemical composition of waxes by accumulating longer aliphatic compounds on plant tissues [47]. Drought increases stiffness and quality of the plant cuticle under climate change [48]. Similarly, the leaf cuticular surface is the first barrier blocking destructive ice penetration into the leaf cells in freezing avoidance mechanisms [49]. Using a hydrophobic film, Wisniewski et al. [50] showed the importance of the epi-cuticular hydrophobicity enabling avoidance of freezing in sensitive plants. The critical nature of the cuticular layer in frost avoidance of corn is also clearly demonstrated [51]. Freezing avoidance is the only mechanism of frost resistance in sensitive plants. In fact, the first demonstration of a transgenic organism in agriculture was the alteration of the cell wall protein secondary structure on ice nucleating bacteria, Pseudomonas syringae and Erwinia herbicola, which then prevented ice nucleation across the cuticle and avoided leaf damage [52, 53]. In future, injury due to frost stress will be more, not less under global warming [54]. Hence, a better understanding of stress-induced wax modification among crop plants holds promise to cope with climate change.
The cuticle and its components act as signaling molecules to favor fungal growth and development, and infections in plants [55, 56]. Surface waxes act as cues to activate fungal developmental processes like appressorium formation, pre-penetration processes, etc., in crop plants like avocado, wheat, rice, maize and peanut [13, 57, 58, 59]. However, the hydrophobic nature of the cuticle also renders it a barrier for bacterial as well as fungal pathogens [60], a desirable trait for disease resistance. Waxes are known to protect lotus from pathogen infection [61]. It repulses pathogen spores and atmospheric pollutants like acid rain and ozone [32]. Another role of waxes is in plant-insect interaction; to attract or to serve as a deterrent [62]. It prevents insect attachment to plant surface oviposition and feeding [63, 64] and hence confer tolerance to insects in crop plants [65, 66].
Studies in Arabidopsis and subsequently, barley, rice and tomato systems have significantly contributed for the elucidation of the complex regulatory pathways underlying the biosynthesis, transport and deposition of wax components on plant surfaces [26, 27, 67]. Cuticular wax biosynthesis predominantly occurs in epidermal cells. The biosynthetic pathway initiates exclusively in the outer membranes of the plastids of epidermal cells where C16 and C18 fatty acids are synthesized, exported to the cytosol as acyl-CoAs and then elongated up to C34 at the endoplasmic reticulum (ER); through a series of enzymatic reactions [19, 26]. The synthesized components are subsequently transported through the apoplastic pathway and deposited on the cuticle. The key steps involved [32] are summarized here.
The de novo fatty acid biosynthesis initiates with the synthesis of malonyl-CoA. It is initiated with the transfer of a bicarbonate derived CO2 molecule to the biotin moiety of a biotin carboxylate carrier protein (BCCP), that form N-1,2 carboxybiotin biotin carboxylate carrier protein-BCCP. The reaction is catalyzed by biotin carboxylase (BC). The CO2 is further transferred to acetyl-CoA by carboxyltransferases (CT). Acetyl-CoA carboxylase (ACCase), a multifunctional enzyme system then catalyzes the formation of malonyl-CoA, from acetyl-CoA [32], which will be subsequently used for de novo fatty acid biosynthesis.
De novo synthesis of acyl chain in the stroma of plastids is catalyzed by a series of enzymatic steps, which collectively forms fatty acid synthase complex (FAS). The series of reactions with the catalyzing enzymes are:
Condensation of malonyl-acyl carrier protein (manolyl-ACP) with acetyl-CoA to form 3-ketoacyl-ACP catalyzed by β-ketoacyl-ACP synthase (KAS III).
Reduction of 3-β-ketoacyl-ACP to 3-hydroxyacyl-ACP, catalyzed by 3-βketoacyl-ACP reductase.
Dehydration of 3-hydroxyacyl-ACP to trans-∆2-enoyl-ACP, catalyzed by β-hydroxy acyl ACP dehydratase.
Reduction of trans-∆2-enoyl ACP to Acyl-ACP by Enoyl ACP reductase.
This complex also includes an acyl carrier protein (ACP), a cofactor component of FAS to which the growing acyl chain remains esterified. These sequential reactions result in a fully reduced acyl chain, extended by two carbons in each cycle [68] through the sequential round of condensation, reduction, dehydration and second-reduction steps [69]. Repetition of the cycle for six times generates palmitoyl-ACP (16:0-ACP), where the condensation reactions are catalyzed by KAS I. One final cycle reaction between palmitoyl-ACP and malonyl-ACP utilizes KAS II to generate stearoyl-ACP (18:0-ACP). These products are further processed by stearoyl-ACP desaturase (introduce double bonds), plastidial acyltransferases, and acyl-ACP thioesterases (hydrolases). The fatty acyl-ACP thioesterases (FATA and FATB) hydrolyzes the C16-C18 acyl-acyl carrier proteins to generate fatty acids, which are then exported out of the plastids to undergo modifications in the ER [69].
The C16 and C18 compounds, hydrolyzed by acyl-ACP thiosterases are activated into C16- and C18-CoA by long chain acyl-CoA synthetases (LACSs) and exported to the ER. The C16 and C18 acyl-CoA then act as a substrate for fatty acid elongase (FAE) complex, localized on the ER, which adds two carbons successively to form VLCFAs with C26-C34 chains. FAE complex are heterotetramers of independently transcribed, monofunctional proteins. They operate a reiterative cycle of four reactions catalyzed by
β-Ketoacyl-CoA synthase (KCS) that catalyze the two carbon condensation to acyl-CoA.
β-Ketoacyl-CoA reductase (KCR) that catalyze the reduction of β-ketoacyl-CoA.
β-Hydroxyacyl-CoA dehydratase (HCD) that catalyze the dehydration of β-hydroxyacyl-CoA.
Enoyl-CoA reductase (ECR) that reduces the enoyl-CoA ultimately leading to VLCFAs [69, 70, 71].
The elongated products are further modified to produce wax components i.e., to primary alcohols, alkyl esters, aldehydes, alkanes, secondary alcohols, ketones and free fatty acids, via two pathways (i) acyl reduction pathway (generates primary alcohols and wax esters) and (ii) decarbonylation pathway (generates alkanes, aldehydes, secondary alcohols, and ketones).
Acyl-reduction pathway: fatty acyl-CoAs are converted into primary alcohols catalyzed by fatty acyl-CoA reductase (FAR) through an intermediate aldehyde [71]. A bi-functional wax synthase/acyl-CoA:diacylglycerol acyltransferase (WS/DGAT) enzyme, WSD1 condenses the generated fatty alcohols and C16:0 acyl-CoA into wax esters [26].
Decarbonylation pathway: acyl-CoAs are reduced to aldehyde intermediate by FAR, which are subsequently decarbonylated into alkanes, catalyzed by aldehyde decarbonylase. Stereospecific hydroxylation of alkanes catalyzed by midchain alkane hydroxylase 1 (MDH1) give rise to secondary alcohols, and oxidation of these alcohols form corresponding ketone [32]. Additional hydroxylation and oxidation reactions lead to the esterification of secondary alcohols with fatty acids and formation of diols, hydroxyl ketones and diketones [32].
The wax components generated are then transferred from the ER to the plasma membrane (PM) through Golgi and trans-golgi network mediated vesicle trafficking or non-vesicular trafficking [72]. Further, adenosine triphosphate binding cassette (ABC) transporters in the plasma membrane (homodimers and heterodimers) export the wax components to the epidermal surface [73]. Lipid transfer proteins (LTPs) like glycosylphosphatidylinositol (GPI)-anchored LTPs (LTPGs), attached to the outer surface of the plasma membrane are also directly or indirectly involved in wax export [74]. A brief representation of wax biosynthesis, transport and deposition with key genes, is presented in Figure 1 (adapted from [19, 26, 27, 32, 69, 71]).
Schematic representation of wax biosynthesis, transport and deposition in plants.
Early studies in barley mutants with little or no wax on aerial plant parts, called glossy or glaucous were termed as eceriferum (cer), where cera means wax and ferre means to bear [75]. Subsequently, the wax defective mutants in Arabidopsis with bright, shiny, or glossy stems or leaves were also termed as eceriferum (cer) [76]. The wax locus from maize and Brassica napus is termed as glossy [68]. With the help of forward genetic screens using wax defective mutants and reverse genetic approaches [77, 78], considerable progress has been achieved in understanding wax biosynthesis, transport and deposition. Table 1 gives an overlook of the key genes involved in wax biosynthesis, transport and deposition identified from the model system Arabidopsis.
Gene | Protein type | Role | Reference |
---|---|---|---|
Cuticular wax biosynthesis | |||
ACC1 | Acetyl CoA carboxylase | Synthesis of malonyl CoA substrates | [79] |
FATB | Acyl acyl carrier protein thioesterase | Supply of saturated fatty acids for wax biosynthesis | [80] |
CUT1 /CER6/KCS6 | VLCFA condensing enzyme (β-ketoacyl-CoA synthase) | Regulation of VLCFA biosynthesis/elongation of 24C fatty acids | [81] |
CER1/CER22 | Aldehyde decarbonylase | VLC alkane biosynthesis | [82] |
KCS1 | β-ketoacyl-CoA synthase | Elongation of 24C fatty acids | [83] |
KCS20; KCS2/DAISY | 3-ketoacyl-coenzyme A synthase | Required for VLCFA elongation to C22 | [84] |
LACS1/CER8; LCAS2 | Long chain acyl CoA synthetase | Synthetase activity for VLCFAs C20-C30 | [85] |
KCS9 | 3-ketoacyl-coenzyme A synthase | Elongation of C22-C24 fatty acids | [86] |
WAX2/YRE/FLP1/CER3 | Aldehyde‐generating acyl‐CoA enzyme | Required for synthesis of aldehydes, alkanes, secondary alcohols, and ketones; biosynthesis of cuticular membrane | [76, 87] |
CER10 | Enoyl-CoA reductase | Biosynthesis of VLCFA | [88] |
CER4/FAR3 | Alcohol forming fatty acyl CoA reductase | Formation of C24:0 and C26:0 primary alcohols | [89] |
CYP96A15 (cytochrome P450 enzyme) | Midchain alkane hydrolase | Formation of secondary alcohols and ketones (stem cuticular wax) | [78] |
WSD1 | Wax ester synthase/diacylglycerol acyltransferase | Wax ester biosynthesis | [90] |
PASTICCINO2 (PAS2) | 3-hydroxy-acyl-CoA dehydratase | VLCFA synthesis in association with CER10, an enoyl-CoA reductase | [91] |
KCR1 | β-Ketoacyl-CoA reductase | Required for VLCFA elongation | [70] |
CER2 | BAHD acyltransferase | Fatty acid elongation beyond C28 | [92] |
CER17 (ECERIFERUM1) | Acyl-CoA desaturase like 4 | n-6 desaturation of very long chain acyl-CoAs | [93] |
Transport and deposition | |||
AtWBC12/CER5 | ATP binding cassette (ABC) transporter | Transport of cuticular waxes | [94] |
LTPG1 | Lipid transport protein | Cuticular wax export or accumulation | [74] |
ABCG11/WBC11/DESPERADO | ATP binding cassette (ABC) transporter | Secretion of surface waxes in interaction with CER5 | [73, 95] |
LTPG2 | Lipid transport protein | Cuticular wax export or accumulation | [96] |
GLN1, ECH | Vesicle trafficking | [72] |
Key genes involved in wax biosynthesis, transport and deposition identified from the model system Arabidopsis.
While the complex wax biosynthesis and transport pathways are well determined, the information on underlying regulatory mechanisms is still fragmentary. There is limited information that these processes and their candidate pathway genes are influenced by developmental factors. The cuticle development is an intrinsic part of cell developmental processes like organ development, cell partitioning, etc. [11]. PAS2, acy-CoA dehydratase, regulating the synthesis of VLCFA during wax biosynthesis in the epidermis is essential for proper cell proliferation during development [97]. Wax deposition is also known to occur in an organ-specific manner during its development and is influenced by diverse environmental conditions as well [17]. The available information on the exact developmental regulation of wax biosynthesis is however, limited. As per evidences from leek (Allium porrum L.), wax accumulation and elongation activities are highly induced within a defined and an identifiable region of leaf [98]. The expression of plastidial fatty acid synthase (FAS), FAEs that regulate elongation of long-chain fatty acids in the microsomal membranes and acyl ACP-thioesterases are probable targets of developmental regulation, depending upon the need to produce fatty acid precursor pools [98]. Some of the key genes involved in wax biosynthesis are also affected by defects in the organization of organelles, especially the ER. A mutation of PEX10 (peroxisome biogenesis factor 10) in Arabidopsis, which disrupted the ER network, in turn lead to mislocalization of CER4, CER1, SHN1 and WAX2, affecting cuticular wax biosynthesis [99].
There is increasing evidence to show that wax biosynthesis and its pathway genes are regulated at transcriptional, post-transcriptional and translational levels [26, 100]. A wide range of abiotic factors like light, water, temperature, salinity etc., influence wax biosynthesis and deposition. An increase in cuticular wax content is observed in bean, barley and cucumber on exposure to UV-B light [101]. In cotton, enhanced UV-B radiation specifically increased the epicuticular wax load on the adaxial surface of leaves [102]. There is an also an up-regulation of wax biosynthetic genes in salt tolerant rice genotypes under stress [103]. Although the underlying mechanisms have not been well explored in the above conditions, there is sufficient information on the influence of drought or moisture stress on wax biosynthesis in plants. A significant increase in wax load in Arabidopsis plants subjected to water stress is indicative of its regulation under drought [17]. In crops like rice, wheat, tobacco, alfalfa, peanut and cotton, etc., an increase in cuticular wax accumulation was observed under moisture stress condition [104]. Drought induced accumulation of wax biosynthesis is positively correlated with drought tolerance in crops like oat, rice, wheat and forage crops, etc. [104, 105, 106, 107].
The transcript levels of several genes involved in wax biosynthetic pathways are regulated in response to abiotic stresses. FAR5, a fatty acyl CoA reductase, in wheat responsible for accumulation of long chain primary alcohols of C26:0, C28:0 and C30:0 are regulated by drought, ABA and cold [108]. The transcripts of KCS2/DAISY, a 3-ketoacyl-coenzyme A synthase required for the elongation of VLCFA are up regulated under water deficit conditions [84]. Osmotic stress induces the expression of CER1, that regulates alkane biosynthesis; while the over expression of CER1 increased susceptibility to bacterial and fungal pathogens [109]. Hypoxia is also known to affect total wax loads on Arabidopsis. The expression of KCS, KCR1, ECR/CER10 and PAS2, components of fatty acid elongase complex in Arabidopsis stem and leaves is affected which in turn affects the production of VLCFA precursors of wax biosynthesis. The wax synthesis genes like MAH1, CER3, CER4, WSD1, etc., and several genes associated with wax and lipid transport are also affected by hypoxia [110]. There is also indication on the regulation of wax biosynthesis in response to cold. Acteyl-CoA carboxylase plays the essential role for cold acclimation in Arabidopsis. In sensitive to freezing3 (sfr3) mutants, with a missense mutation in ACC1, the long chain components of leaf cuticular wax were reduced and there was inhibition on the wax deposition on inflorescence stem, which rendered the plants sensitive to cold stress [111]. Wax biosynthesis is also reported to be regulated in response to carbon dioxide (CO2) concentration. This is mediated by HIC (High Carbon Dioxide), a gene encoding a 3-keto acyl coenzyme A synthase (KCS)-an enzyme involved in the synthesis of very-long-chain fatty acids that influences stomatal development in Arabidopsis [112].
With the identification of several transcription factors (TFs), transcriptional regulatory mechanisms are considered to be a major contributor for the wax biosynthesis [113]. WIN1/SHN1 (WAX INDUCER 1/SHINE1) is a TF from AP2/EREBP family initially reported to regulate cuticular wax and then cutin biosynthesis by regulating the expression of CER1, KCS1, CER2, LACS2, GPAT4, CYP86A4, CYP86A7 and HTH-like genes [114]. SHN1 overexpression increased drought tolerance in Arabidopsis [115]. Wax synthesis regulatory gene 1 (WR1) from rice [116] and SHN1 from wheat [117], both homologs of WIN1/SHN1 from Arabidopsis also reduced water loss and improved drought tolerance. Transcriptional repression by diurnally controlled DEWAX2 is another important for regulator of wax biosynthesis in Arabidopsis. Compared to wild type, the total wax loads in dewax2, were increased by 12 and 16% respectively in rosette and cauline leaves [118, 119]. Another candidate from AP2/ERF TF family, WRINKLED4 (WRI4) positively regulates wax biosynthesis in stems. wri4 mutants expressed 28% reduction of total wax loads in stems, although siliques and leaves were unaffected. Hence WRI4 act as a transcriptional activator to regulate the expression of LACS1, KCR1, PAS2, ECR and WSD1, to maintain the levels of 29C long alkanes, ketones and secondary alcohols in stems [113]. MYB94, regulate the expression of wax biosynthetic genes like WSD1, KCS2/DAISY, CER2, FAR3 and ECR to activate cuticular wax biosynthesis and is up regulated by drought and ABA. This also conferred tolerance to drought stress in Arabidopsis and Camelina [120]. MYB96, an ABA responsive TF also regulates wax biosynthesis under drought [121]. In Camelina, MYB96 activated the expression of wax biosynthetic genes KCS2, KCS6, KCR1-1, KCR1-2, ECR, and MAH1 which resulted in high levels of alkanes and primary alcohols and improved drought tolerance [120]. MYB96 acts as a component of plant disease resistance, through salicylic acid mediated signaling [122]. Both MYB94 and MYB96 share a common region containing MYB consensus motifs in the promoter of their target wax biosynthetic genes [123]. Hence MYB94 and MYB96 have an additive role on plant cuticular wax biosynthesis and under drought and ABA conditions.
In addition, to transcriptional regulation, wax biosynthesis is regulated by other events. Expression of CER3/WAX2/YRE, an aldehyde-generating acyl-CoA enzyme in the wax biosynthetic pathway is regulated by CER7, a core RNA processing and degrading exosomal subunit. CER7 regulates WAX2 transcript levels by degrading a specific mRNA species encoding its negative regulator [124]. Many of such regulators have been identified from model systems as well as crop species and a brief overview of the key regulatory events and their targets has been presented in Figure 2.
Brief representation of the key regulatory events in wax biosynthesis and their targets.
Under field conditions, crops encounter multiple biotic and/or abiotic stresses simultaneously at different stages of developments. Cuticular waxes have a direct role in multiple stress tolerance in crops [109]. In cucumber, wax biosynthesis has been shown to have key roles in influencing the plant responses to biotic as well as abiotic stresses [125]. In sorghum, genes regulating leaf waxes have critical role in regulating tolerance to drought and heat stress [45]. Considering the relevance of cuticular waxes under diverse biotic as well as abiotic stressful conditions, as discussed above and under combined stress conditions, it can be an ideal trait to tackle multiple stresses in crop plants.
Being the outermost layer of plant cuticle, the epi-cuticular wax can serve as a first line of physical defense against pathogens and herbivores. However, increasing thickness and hydrophobicity of the cuticle through over-deposition of the wax may not necessarily increase the resistance of the plant against biotic stresses. The composition and structure of wax in the cuticle can constitute the source of signals for the foreign invaders and for the plants themselves. Thus, the roles of cuticular wax could be multifunctional and can vary not only for various plant species but also for different kinds of pathogens. Functional study of the DEWAX gene, a negative regulator of wax biosynthesis in Arabidopsis, is a good example of this complexity. The dewax mutant line in Arabidopsis, with increased epicuticular wax and decreased cuticular permeability, showed susceptibility to the fungal pathogen Botrytis cinerea, but resistance to the bacterial pathogen Pseudomonas syringae [126]. Moreover, DEWAX overexpressing lines in Arabidopsis and Camelina showed inverse defense modulations to B. cinerea and P. syringae as compared to dewax mutant in Arabidopsis [126].
Wax and cutin components in the plant cuticle could function in pattern- and effector-triggered immunity (PTI and ETI) and could serve to generate local and systemic acquired resistance against numerous pathogens [127]. During plant-pathogen interaction, the plant cuticle can be affected by enzymes synthesized and secreted by the pathogens. Many fungal pathogens synthesize and secrete hydrolytic enzymes (for example, cutinases, esterases and lipases) at the early stage of infection that directly target the cuticle [128, 129, 130, 131]. Fusarium oxysporum secretes cutinases that degrade cutin layers in the cuticle and generates cutin monomers that support fungal adherence to the host plant and facilitate the initiation of infection [128]. Hexadecanediol, a cutin component in rice can facilitate spore germination and differentiation for pathogenic fungi Magnaporthe grisea and B. cinerea [55]. Presence of a very-long-chain C26 aldehyde (a wax component) was important for the barley powdery mildew fungus (Blumeria graminis) to initiate infection in host plant species. Germination and appressorial differentiation of B. graminis were strongly prohibited in aldehyde free glossy11 mutant in corn. Spraying of n-hexacosanal (C26-aldehyde) or wax preparation from wild-type corn can restore the conidial formation and differentiation [59].
Plant can also recognize the attachment of pathogens and activate defense responses against them, in which pathogen-infection generated plant products, such as cutin monomers or cell wall oligosaccharides, can act as signaling molecules [132]. Defense responses in plants are often manifested as alternations of the cuticle. Colletotrichum acutatum infection in citrus resulted in increased lipid synthesis in the epidermal cell and increased deposition of those lipids in cuticle, the process eventually changes the structure of the cuticle [133]. Cuticular biosynthesis was also found to be up-regulated in tomato fruit following infection by fungal pathogen C. gloeosporioides [134].
Cuticular permeability plays a vital role in almost all plant-pathogen interactions. A more permeable cuticle can lead to either resistance or susceptibility to pathogens. Elevated deposition of cuticular wax as well as the presence of hydrophobic wax components (e.g., very-long-chain alkanes or ketones) can make a cuticle less permeable. Mutation or overexpression of genes that diminish biosynthesis of various wax components can generate the opposite effect. There are number of wax-deficient mutant and transgenic lines in Arabidopsis and other plant species with diminished cuticular permeability showed resistance to the fungal pathogen B. cinerea [34, 127]. However, the phenomenon is not true for all wax deficient plant lines. Wax and cutin deficient acp4 and gl1 mutants in Arabidopsis displayed increased sensitivity to B. cinerea [135, 136]. Mutations in SHINE transcription factors in other studies also showed alteration in cuticular wax accumulation, and susceptibility to B. cinerea infection [137, 138].
Epicuticular wax also plays important roles in plant interaction with insects and herbivores. Flowering plants have evolved with cuticular wax of various forms, sizes and structures that are either enabling the attachment and movement of pollinating insects, or reducing the attachment of herbivorous insects and pests on the plant surfaces. Reducing the attachments of herbivores on plant surfaces is a part of a plant defense strategy against herbivores.
Most plant body surfaces are covered with a two-dimensional (2D) epicuticular wax film of various thicknesses. In many species, wax film is protrudes with three-dimensional (3D) wax crystals. Wax crystals can generate various shapes as revealed by electron microscopic analysis, such as rodlets, threads, platelets and tubules [61]. The complexity of these various shapes originates from the molecular self-assembly of various wax components, in which morphology of those crystals is also correlated with the presence of specific chemical components in the wax [139, 140]. Many experimental studies and reports from various plants species (for example, from genera Eucalyptus, Pisum, Brassica) have shown that 3D wax crystals have protective functions against insects, in general, including the herbivorous insects [141]. Studies with Eucalyptus species in canopy found that glaucous juvenile leaves containing high quantities of wax crystals were less prone to herbivorous infestation as compared to the glossy adult leaves [142]. Feeding rates of flea beetles, Phyllotreta cruciferae, on low-wax glossy (eceriferum, cer) Brassica napus mutant lines were much higher as compared to the wild-type B. napus [143]. Cuticular surfaces with wax crystals also interferes with the attachment, locomotion and foraging behavior of predatory insects and parasitoids [65, 144]. Pisum sativum lines with higher prevalence of crystalline epicuticular wax (CEW) were found more favorable for four predatory coccinellid species to attach, move and consume more aphids as compared to the P. sativum mutant line with reduced CEW [145]. Flowering stems with high CEW of numerous other plant species (for example, species under the genera Salix, Hypenia, Eriope) often generate slippery surfaces that prevent the movement of nectar robbers, ants and other plant pests [141, 146].
Several hypotheses have been proposed and tested on the mechanisms of wax crystal inhibition of insect attachment inhibition: (i) roughness hypothesis; (ii) contamination hypothesis; (iii) fluid absorption hypothesis [141]. Wax crystals, in general, generate a micro-rough surface on the cuticle that may prevent adhesive pads of the insects to stick, preventing them to successfully attach to the plant surface [144, 147, 148]. Contamination hypothesis proposed that detached wax crystals of the cuticular surface of some plants can adhere to the insect attachment organs (e.g., adhesive pads), contaminate those, and as such subsequent insect attachment becomes challenging and unsuccessful [147, 148, 149]. Adhesive pads of many insects secret fluids, which can also enhance wax crystal contamination to attachment organs. Fluid secretion from the adhesive pads are supposed to help insects to pursue successful attachment to the plants. However, there is evidence certain plant species have crystalline wax coverage that can absorb the fluids secreted by the adhesive pads and prevent the insects to successfully attach to the cuticle [150, 151].
The study of cuticular wax involvement in biotic stress resistance is complex with a multitude of organisms spanning insects to disease. The story is still not clear and field situations in which interactions between organisms and abiotic stresses and the role of cuticular wax needs to be evaluated. Nevertheless, certain consistencies are evident in that permeability of the cuticular layer appears to be important in pathogen invasion and wax crystals play an important role in insect intervention by the cuticular layer. These areas of research merit further investigation.
As mentioned above, abiotic stresses such as drought, extremes of temperatures, salinity, etc., cause significant losses in crop productivity. Since most of the stresses occur simultaneously, crop breeders are looking for traits contributing for multiple stress resistance. From this context, cuticular wax can serve as ideal trait. Drought stress, a major abiotic stresses in tropical regions, influences the biosynthesis and composition of cuticular wax in crops [27]. The importance of cuticular wax in desiccation tolerance is evident that, compared to gymnosperms and angiosperms, many early extant plants such as ferns, and horsetails are more sensitive to dehydration [152]. In crops like pea, cuticular wax load increases when subjected to drought stress [46]. In rice, gl1-1/wsl2 and gl1-2 loss-of-function mutants with reduced wax load exhibited sensitivity to drought compared to the wild type plants [104, 153]. Drought stress is known to increase the wax content and alter composition of cuticular wax in many plants such as pea [46], Arabidopsis [17, 115], tobacco [154], alfalfa [155]. Significant correlations between the wax content and yield, drought tolerance and water-use efficiency have been reported in different crops such as sorghum [38], barley [156], rice [41], and wheat [157, 158]. These reports demonstrate that less wax or non-waxy crops/genotypes are sensitive to desiccation with poor drought-tolerance compared to the crops having more cuticular wax [105]. The existing evidences suggests cuticular wax is responsible for reducing non-stomatal transpiration by increasing cuticular resistance [43]. The cuticular waxes also have roles in imparting resistance to salinity stress, mainly by regulating residual transpiration. A significant negative correlation observed between residual transpiration and total wax content, reports residual transpiration could be a fundamental mechanism by which plants optimize water-use efficiency under salinity stress [43]. As discussed above, wax accumulation also correlated with high temperature resistance in plants [44]. Leaf surface waxes help to maintain cooler canopy in sorghum under heat stress [45]. The cuticular waxes can further help in protecting plants from high light stress [101]. The cuticular wax has a role in protecting plants from excessive ultraviolet (UV) light and there are reports indicating that elevated UV-B radiation can affect plant cuticular wax formation [101, 159, 160]. Based on the existing information, as mentioned above, cuticular wax, can be treated as the first protective layer and an important trait contributing for both biotic and abiotic stresses.
Identification of genomic regions contributing wax traits is crucial in manipulating wax characteristics using breeding approaches. In rice, quantitative trait loci (QTL) linked to the leaf epi-cuticular layer was identified corresponding to EM15_10-ME8_4-R1394A-G2132 region on chromosome 8 [161]. In sorghum, a crop with the ability to produce profuse amounts of EW,
With the elucidation of wax biosynthetic pathways and identification of key regulators, attempts were made in crop plants to engineer cuticle properties and to enhance stress tolerance traits. One of the early reports in engineering wax traits and thereby improved stress tolerance was from Medicago sativa (alfalfa), a forage legume. WXP1, a transcriptional regulator from Medicago truncatula, upregulated by drought, cold and ABA, was over expressed in alfalfa, which significantly increased the leaf cuticular wax load, mainly contributed by the C30 primary alcohol. The transgenic plants exhibited enhanced tolerance to drought and rapid recovery under rehydration [155]. Over expression of SlSHN1, a close homolog of the WIN/SHN gene from Arabidopsis, in tomato using constitutive CaMV 35S promoter improved drought tolerance, with higher cuticular wax deposition on leaf epidermal tissue. The transgenic plants displayed delayed wilting, improved water status and reduced water status [165]. MYB96, a transcriptional regulator over-expressed in Camelina, an emerging biofuel crop, which generated plants with enhanced drought tolerance. The expression levels of CsKCS2, CsKCS6, CsKCR1-1, CsKCR1-2, CsECR, and CsMAH1 were highly upregulated in the transgenic plants which resulted in a significant increase in the deposition of epicuticular waxes and total wax loads. This gives an option to cultivate the crop on marginal lands to produce renewable biofuels and bioresource [120]. It was further demonstrated that ectopic expression of DEWAX, a negative regulator of cuticular wax biosynthesis increased tolerance to Botrytis cinerea in Camelina [126]. A study from groundnut by over-expressing the KCS1 gene from a drought tolerant genotype improved cuticular was load and drought tolerance in a susceptible genotype [166]. Likewise, several of such regulators have been identified from model systems as well as crop species and used for engineering crop plants to enhance stress tolerance.
In crop plants, due to the nature of combined stressors interactions, the stress effect is not always additive [3]. While working with glossy mutants of Zea mays (gl4), an enhanced colonization of bacteria, was observed leading to more leaf blight pathogen growth compared to the wild type [167]. The thin cuticle provided leaf blight pathogen, an easy access to nutrient and water in gl4 mutant indicating that cuticular wax thickness is a useful trait to identify plants’ resistance to combined stressors. Additionally, wax layer structure and composition are equally important in conferring defense mechanisms. As rightly pointed in Ref. [1], such combined studies allow us to understand the shared and specific effects of biotic and abiotic stressors.
Wild relatives and landraces have long been recognized as a source of genes for breeding major field and horticulture crops. During domestication of wheat, tomato, rice, soybean and corn, yield was the focus trait. This in turn narrowed the genetic diversity for other biotic and abiotic stressors [168]. For example, during domestication of modern wheat, due to a phenotyping bottleneck a largely overlooked drought trait in wheat breeding program is glaucousness [169]. Such beneficial allelic variants lost in cuticle related traits can be introgressed back by crossing an elite line with its wild relatives. Apart from genetic diversity, a mutation population (EMS or gamma irradiation) provides an alternative avenue to target crop improvement via selection of cuticle-associated trait variations [170]. In fleshy tomatoes, a mutant line underlying for delayed fruit deterioration (DFD), is characterized for minimal transpirational water-loss and enhance post-harvest shelf life [171]. A recent alternative for trait manipulation is CRISPR-Cas9 system which is a precise gene-editing technology. This new method accelerates the evaluation of beneficial cuticle-associated alleles in different genetic backgrounds [172]. In similar lines, small RNA based transgenic strategy is also emerging as a molecule of choice to deal with combined biotic and abiotic resistance in crops [173].
There is sufficient evidence to argue that cuticle and cuticular waxes are involved in the regulation of multiple biotic and abiotic interactions. The cuticular wax can be treated as an important trait contributing for multiple stress resistance. Concerted efforts have been made to elucidate the synthesis and deposition of cuticular waxes in plants. Further analysis of the key regulatory steps involved in the formation of cuticular waxes, and also the role played by diverse types of wax components and structures in stress response is needed. This information could be incorporated in crop improvement programs (via marker assisted selection for wax genes). Since there are promising options emerging to analyze the cuticular wax trait using modern synchrotron technology [174] as well as now widely recognized techniques to observe ice propagation in real time across the cuticle [175] crop breeders have the potential to improve their efficiency of selection based on these traits. Recent progress in genomics can substantially help major field and horticulture crops to buffer the impacts of climate change. In addition, new genome-editing technologies will provide interesting tools to characterize and engineer waxes in crops. Unraveling key regulators and network partners of surface wax synthesis would aid in targeted manipulation of the trait using modern biotechnological applications. There are options to analyze the cuticular wax trait using modern non-destructive approaches. Crop breeders can use these tools to improve their efficiency of selection for the trait, and effectively pyramid the trait in elite genotypes to combat combined stresses.
RYS was supported by Agriculture and Agri-Food Canada. TR and KKT research was supported by the Agriculture Development Fund (Saskatchewan Ministry of Agriculture) and the Natural Science and Engineering Research Council (NSERC) Collaborative Research and Development program, Canada. NKN would like to acknowledge the Department of Biotechnology, Government of India, New Delhi (BT/TDS/121/SP20276/2016) and UAS Bengaluru (No. DR/Prof.(S)/RKVY/Alloc./B-44/2017-18) for the partial financial support.
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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