*4.4. Organ Development*

Coordinated cell growth, di fferentiationand morphogenesis are the three fundamental aspects of development that cause an organism to procure its shape and an intricate cascade of gene regulatory networks comprising RLKs are known to be implicated in this. In higher plants, all the aerial organs develop from shoot apical meristem (SAM). The maintenance of undi fferentiated cells of SAM and organ formation through di fferentiation from the progeny cells are two processes maintained in a balance during the common developmental process. Interestingly, di fferent RLKs are known to su ffice this role. In Arabidopsis, CLAVATA1 or CLV1 (RLK), CLV2 (RLP) and CLV3 (secreted polypeptide) perform a pivotal role in meristem and organ development [17,104,105]. The CLV3 polypeptide acts as a ligand for CLV1 and CLV2 complex. This ligand-receptor binding promotes the activation of cytosolic kinase domain of CLV1 and subsequently, it initiates a signal transduction cascade to control gene expression and stem cell fate in the SAM by elevation of cytosolic calcium as secondary messengers [17,106,107] (Figure 4A). Meristematic receptor-like kinase (MRLK), a LRR-RLK expressed in shoot and root apical meristems, interacts with and phosphorylates a MADS-box transcription factor, AGL24, to regulate floral transition [108] (Figure 4A). Another LRR-RLK, ERECTA, which is expressed in the entire shoot apical meristem and developing organs, monitors organ shape and inflorescence architecture, upon the perception of epidermal patterning factors (EPFs)/EPF-like proteins (EPFLs) [109] (Figure 4A). Moreover, mutants of ERECTA-family LRR-RLKs conferred extreme dwarfism and abnormal flower development, suggesting that ERECTA-family RLKs control cell proliferation as well as organ growth and patterning like stomata formation, the shoot apical meristem (SAM) and flower development [110]. ERECTA can form complexes with a range of co-receptors like SERKs and transmembrane receptor-like proteins like Too Many Mouths (TMM) to activate the signaling pathway [111,112]. Botrytis-induced kinase 1 or BIK1, an RLCK, interacts and phosphorylates ER-family proteins to modulate leaf morphogenesis and inflorescence architecture [113] (Figure 4A).

**Figure 4.** Arabidopsis RLKs in the regulation of growth and development. A few examples of RLKs that regulate (**A**) shoot development, (**B**) root development, (**C**) vascular tissue development, and (**D**) organ abscission.

Similar to aboveground organ development, several studies demonstrated the utmost importance of multiple RLKs in root development. Arabidopsis CRINKLY 4 (ACR4) is involved in the formation of proper lateral roots and columella stem cell differentiation in the root apical meristem [114,115]. ACR4 can regulate root meristem maintenance in response to the CLE4 peptide by forming heterodimers with CLV1 [116] (Figure 4B). Besides, ACR4, abnormal leaf shape 1 (ALE1) (a member of subtilisin-like serine protease family), and ALE2 (RLK) have been known to share partial overlapping roles in the formation of leafy organs [103] (Figure 4A). Similar to ACR4, cysteine-rich receptor-like kinases (CRKs), a member of one of the largest RLK families, is involved in root organogenesis. The *crk28* mutants have displayed longer and branched roots, while CRK28 overexpression lines have shown the contrasting phenotype, i.e., delayed root growth and reduced lateral root formation [117] (Figure 4B).

Plasmodesmata are microchannels between two cells, through which trafficking of molecules occur. STRUBBELIG (SUB) is a RLK involved in inter-cell layer signaling which is required for tissue

morphogenesis. The *sub* mutants have shown defects in floral organ shape, integument initiation, and outgrowth, asymmetry in leaf shape and stem morphology, as well as a reduction in plant height. This indicates the functional role of SUB across several cells in the floral meristem, ovule, and shoot apex [118,119]. Further genetic screening has led to the identification of a putative membrane-anchored C2-domain protein, encoded by QUIRKY (QKY), which is known to act as a downstream component of SUB signaling [120]. SUB and QKY interact in plasmodesmata to promote tissue morphogenesis (Figure 4A). Apart from aerial organs, SUB or SCRAMBLED (SCM) also regulates cell-type patterning in the root epidermis [121] (Figure 4B). The BAM1 (barely any meristem 1), a member of CLV1 class LRR-RLKs, is expressed preferentially in the quiescent center and its surrounding stem cells at the root tip and known to bind to the CLE peptide. BAM1 is capable of forming heteromeric complexes with RPK2 and inhibit cell proliferation in the root meristem [122] (Figure 4B). Inflorescence and root apices receptor kinase (IRK), a typical meristematic LRR-RLK, is known to be expressed in the outer plasma membrane of root endodermal cells and negatively regulates cell division to maintain tissue organization [123] (Figure 4B). Further, FERONIA (FER) receptor-like kinase functions upstream of Rho-like small G-protein or RAC/ROP during reactive oxygen species (ROS)-mediated root hair development. The FER activates RAC/ROP by GDP-GTP exchange to stimulate NADPH oxidase for ROS formation [25] (Figure 4B).

#### *4.5. Vascular Tissue Development*

The development of xylem and phloem from the vascular meristem is a multifaceted process. The RLK, phloem intercalated with xylem (PXY), maintains cell polarity during vascular development, which is ascertained by the presence of partially interspersed xylem and phloem, and irregular vascular development in *pxy* mutants [124]. The ligand for PXY receptor is tracheary element di fferentiation factor (TDIF), a peptide, which is encoded by CLAVATA3/ESR 41/44 (CLE41/44) genes [125]. The PXY-TDIF interaction activates the WUSCHEL-related homeobox 4 (WOX4) signaling pathway to regulate cell division in the procambium. Another LRR-RLK, PXY/TDR-CORRELATED (PXC1), acts as a positive regulator of secondary cell wall formation in xylem fibers [126] (Figure 4C). The CLE41/PXY/WOX4 cascade is antagonistically directed by the LRR-RLK more lateral growth 1 (MOL1), via regulating the stem cell homeostasis within the cambium. This MOL1 also attenuates ethylene and jasmonic acid hormone signaling pathways that positively influence cambium activity [127] (Figure 4C). The maintenance of the cell morphology organization during vascular development is accomplished by a RLK, xylem intermixed with phloem 1 (XIP1). Genetic evidences also unveil that XIP1 prevents ectopic lignification in phloem cells [128] (Figure 4C).

#### *4.6. Regulation of Organ Abscission*

Arabidopsis LRR–RLK HAESA (formerly named RLK5) exhibits developmentally regulated expression in the abscission layers of floral organs. The antisense suppression of the HAESA is known to delay the abscission of floral organs such as sepals, petals, and stamens [19]. Inflorescence deficient in abscission (IDA) and IDA-Like (IDL) proteins are considered as the ligands of HAESA (HAE) and HAESA-Like RLKs [129] (Figure 4D). The phenotypic analysis of *ida* mutant and overexpression of *IDA* gene validates the role of HAE in floral organ abscission via IDA/IDL perception. A phosphorylation-based activation mechanism of HAE leads to the stimulation of a MAP kinase-signaling cascade and initiates cell wall hydrolysis at the base of the abscising organs. SERK1 acts as a co-receptor of HAE and allows the binding of IDA, eventually leading to floral abscission pathway [130,131]. In contrast, an early leaf senescence phenotype observed in *serk4-1* knockout mutant indicates that SERK4 acts as a co-receptor in negatively regulating leaf senescence, as well [132] (Figure 4D).

#### *4.7. Modulation of Phytohormone Signaling*

Brassinosteroids (BRs) are essential polyhydroxylated steroidal phytohormones crucial for plant development. The developmental defects of BR biosynthetic and signaling mutants are mostly similar, which include dwarfism, severely stunted and rounded leaf with a shorter petiole, delayed flowering, photomorphogenic malfunctions as well as senescence and reduced male fertility. The first BR signaling gene, whose mutation showed these phenotypes, has been named as brassinosteroid insensitive 1 (BRI1) [133]. BAK1 (BRI1-associated receptor kinase 1), a co-receptor of BRI1, is involved in BR perception and signaling via heterodimerization with BRI1 [59,134]. In addition, a close homologue of BRI1, BRI1-like receptor kinase (BRL1) is also responsible for BR perception [135] (Figure 5A). BAK1-associating receptor-like kinase 1 (BARK1), a LRR-RLK, specifically binds to BAK1 and its homologs. Overexpression of BARK1 enhances primary root growth and these roots are hypersensitive to BR-induced root growth inhibition, suggesting the role of BARK1 in BR-mediated lateral root development via auxin signaling [136] (Figure 5A). Apart from these, evidence achieved from *bir1* mutants helps us to comprehend how it modulates immune response pathways and plant architecture as an interacting partner of BAK1 [137]. A member of somatic embryogenesis receptor, SERK3 acts as a co-receptor, which directly interacts with BRI1 [64] (Figure 5A).

**Figure 5.** Arabidopsis RLKs in brassinosteroid (BR) and abscisic acid (ABA) signaling. RLK-mediated phosphorylation-based signaling circuits regulate BR (**A**) and ABA (**B**) responses.

Abscisic acid (ABA) is yet another vital phytohormone involved in the regulation of plant abiotic stress-related phenotype as well as developmental processes. Unlike BR, in Arabidopsis, RLKs are not accountable for direct ABA perception. A LRR-RLK, receptor dead kinase 1 (RDK1) is involved in ABA signal transduction via interaction with abscisic acid insensitive 1 (ABI1), a type 2C protein phosphatase, in the plasma membrane. Predominantly, this interaction is enhanced by exogenous application of ABA, underpinning the involvement of RDK1 to recruit ABI1 to the plasma membrane [138] (Figure 5B). Most recently, a cysteine-rich receptor-like kinase, CRK28, has shown an indirect relationship with ABA. The *CRK28* overexpression lines have displayed slow root growth, reduced lateral root formation, and also ABA hypersensitivity; thereby being an important modulator of ABA signaling [117] (Figure 5B). PERK4 is also known to play an important role in ABA response. The *perk4* mutants have shown reduced sensitivity to ABA concerning seed germination, seedling growth, and primary root tip growth. Moreover, *perk4* mutant cells have retained lower cytosolic calcium concentration and Ca2+ channel currents. These results suggest that PERK4 contributes to the early stage of ABA signaling and inhibits root cell elongation via intracellular calcium signaling [139] (Figure 5B). Other RLKs like CRK5, CRK36, LRK10L1.2, and RPK1 are also known to be involved in ABA signaling during response to drought and oxidative stresses.

#### **5. RLKs in the Regulation of Plant Biotic Interactions**

#### *5.1. RLKs in Pathogen Triggered Immunity*

Plants sense the invasion of pathogens through the perception of pathogen and host-derived elicitors, like MAMPs, PAMPs, DAMPs, and HAMPs (herbivore associated molecular patterns). To

combat the attack of invading pathogens, RLK-mediated signaling boosts transcriptional activation of multiple defense and pathogenesis-related genes to eliminate the adversity caused by the pathogens. These kinds of RLKs are also termed as 'pattern recognition receptors' (PRRs) and the resulting immune response is called pathogen-triggered immunity (PTI). Predominantly, RLK-derived signals activate defense responses like hypersensitive response, stimulation of ion fluxes, ROS (reactive oxygen species) production, synthesis of phytoalexins, salicylic acid (SA) accumulation, and cell wall reinforcement [6,140,141]. Some important examples of Arabidopsis RLKs involved in defense responses are discussed here.

The flagellin sensitive 2 (FLS2) preferentially recognizes a PAMP, the flagellin epitope of bacteria (flg22), to trigger the recruitment of co-receptors or adaptor proteins and subsequent phosphorylation [20]. Usually, FLS2 heterodimerizes with BAK1 or its homolog BAK1-like kinase 1 (BKK1) and undergo transphosphorylation [72,142–144]. Subsequently, botrytis-induced kinase 1 (BIK1) (RLCK) is phosphorylated and released from the FLS2-BAK1 or FLS2-BKK1 complex. This is followed by rapid bursts of calcium and reactive oxygen species (ROS), activation of MAPKs and/or CDPKs, in order to regulate the PTI [145] (Figure 6). In contrast, BIR2 is an atypical LRR-RLK or pseudokinase, which competes with FLS2 for BAK1 and negatively regulates BAK1 mediated immune signaling and cell death responses [5,146,147] (Figure 6). The *bak1* mutants display enhanced susceptibility to the most commonly encountered necrotrophic pathogens *Alternaria brassicicola* or *Botrytis cinerea* and thus, BAK1 and its co-receptors are considered as important regulators of plant immunity [148]. Further, BAK1 is also involved in temporary desensitization of signaling as it promotes the ubiquitination and proteosomal degradation of FLS2 through phosphorylation of U-Box E3-ubiquitin ligases, PUB12 and PUB13 [149].

Another PAMP known as bacterial elongation factor Tu (EF-Tu) is perceived by an LRR-RLK, EF-Tu receptor (EFR), which activates plant defense responses, thereby reducing the e fficiency of *Agrobacterium* transformation [150]. EFR physically interacts with BAK1 in a ligand-dependent manner and establishes the PTI signaling [151] (Figure 6). Another group of LRR-RLKs, PEPR1 (perception of the Arabidopsis danger signal peptide 1) and its close homolog PEPR2 stimulate the innate immune responses upon the perception of wound-induced or plant-derived peptides, PEP1 (perception of the damage-associated molecular pattern peptide 1) and PEP2 [152,153]. Unlike FLS2 and EFR, the signaling molecules of PEPR1 and PEPR2 are DAMPs, which are produced due to wounding, PAMP treatment, or microbial infection, at the early stage of invasion. Both PEPR1 and PEPR2 associate with BAK1 to activate downstream signaling for enhancing plant immunity [63,154] (Figure 6). RLK902 is also linked with plant immunity as it phosphorylates brassinosteroid-signaling kinase 1 (BSK1) and plays an essential role in conferring resistance to the bacterial pathogen *Pseudomonas syringae*. Enhanced disease resistance 4 (EDR4), a protein involved in endocytosis, regulates sub-cellular tra fficking of RLK902 for proper modulation of plant immunity [155] (Figure 6).

Chitin, a fungal cell wall derivative, is recognized as a MAMP by a receptor complex comprising of chitin elicitor receptor kinase 1 (CERK1), LysM receptor-like kinase 1 (LYK1) and LYK5 [61,156]. CERK1 directly interacts and phosphorylates PBL27, an RLCK, to regulate chitin-induced defense gene expression and accumulation of callose [157]. Predominantly, PBL27 phosphorylates MAPKKK5, which activate MKK4/5 and MPK3/6 cascades for triggering defense responses (Figure 7) [158]. CERK1 is also involved in the perception of bacterial peptidoglycans (PGNs) and thereby, activate resistance against bacterial infections [30,159]. In addition to chitin, fungal 1,3-β-D-glucan oligosaccharides are perceived by LYK1 [160]. LYK4 augments chitin-induced signaling by acting as co-receptor or sca ffold protein of LYK5 [161] (Figure 6). The homologues of LYKs in other angiosperms are involved in the maintenance of symbioses with beneficial mycorrhizal fungi and nitrogen-fixing bacteria [56,162,163]. In some instances, heterotrimeric G-protein components are known to participate immediately downstream to the PRRs. G-protein subunits G α, G γ1, and G γ2 physically interact with the defense-related RD-type receptor-like kinases CERK1, BAK1, and BIR1 [67]. The Gβ, G γ1, and G γ2 are required for FLS2, EFR

and CERK1-mediated PTI responses, because flg22, elf18 and chitin induced resistance is known to be compromised in *G*β single mutant (*agb1*) and *G*γ*1* and *G*γ*2* double mutant (*agg1agg2*) [164] (Figure 6).

**Figure 6.** Role of RLKs in Arabidopsis biotic stress responses. This cartoon is representing a few examples of RLKs that regulate pathogen-triggered immunity (PTI), effector-triggered immunity (ETI) or both. G, heterotrimeric G-protein.

Cell wall damage (CWD) triggers cell wall integrity (CWI) maintenance and immune signaling systems to control stress responses. Multiple RLKs like FERONIA (FER), THESEUS 1 (THE1), Male discoverer 1 (MDIS1)-interacting receptor-like kinase 2 (MIK2), WAK1, and WAK2 are known to be involved in CWI maintenance [165–167]. Amongst them, FER, THE1, and MIK2 aid in conferring resistance to the plant against *Fusarium oxysporum*, a fungal pathogen [168,169] (Figure 6). In addition, BAK1, BIK1, BKK1, PEPR1, and PEPR2 modulate responses to CWD controlled by the CWI mechanism [23]. Both PEPR1and PEPR2 perceive DAMPs, like plant elicitor peptides (AtPeps). These AtPeps (AtPep1 and AtPep3) precursor peptides are encoded by the *PROPEP* (*PROPEP1* and *PROPEP3*) genes, which are induced by pathogen infection, wounding and CWD. Although the application of AtPep plant elicitor peptides enhances expression of their corresponding PROPEP genes, these peptides also inhibit CWD-induced Jasmonic acid (JA) and salicylic acid (SA) accumulation in a concentration-dependent manner. These results suggest that both PTI signaling and CWI maintenance mechanism contribute to biotic stress responses, coordinately [170].

**Figure 7.** Role of RLKs in Arabidopsis abiotic stress responses. This cartoon is representing a few examples of RLKs that regulate various abiotic stresses in plants (drought, osmotic, oxidative, salt, cold, and heat).

#### *5.2. RLKs in E*ff*ector Triggered Immunity*

Effectors are the compounds secreted by bacterial and fungal pathogens, which translocate into the host cell for attenuation of the host's defense system (PTI). Impeding the formation of PRR complex is one of the key mechanisms of effectors to suppress immunity and in accordance with this, plants have evolved resistance (R) proteins to recognize pathogen effector proteins to establish effector-triggered immunity (ETI). AvrPto A and AvrPto B are the two types of effectors produced by *Pseudomonas syringae* to suppress the flagellin-induced PTI in Arabidopsis, by interacting with the cytosolic domain of BAK1 and thus, preventing FLS2-BAK1 heterodimerization [171,172]. BAK1-interacting RLK 1 (BIR1) is known to associate with BAK1 *in planta*. The *bir1-1* mutants display extensive cell death and activation of constitutive defense responses. Moreover, these *bir1-1* mutants show enhanced resistance to biotrophic oomycete, *Hyaloperonospora arabidopsidis*. These responses are similar to hypersensitive cell death (HR) observed during ETI, suggesting that BAK1 functions together with BIR1 to negatively regulate multiple plant resistance signaling pathways [173].

Genetic screening for suppressors of the *bir1-1* has led to the identification of *SOBIR1* gene, whose mutation showed impaired cell death in the *bir-1-1* mutant. However, in contrast, *SOBIR1* overexpression resulted in the activation of cell death, thereby indicating the role of SOBIR1 as a positive regulator of cell death [173]. The LRR-RLK, SOBIR1 also triggers defense responses by forming a complex with certain LRR-RLP like immune receptors. For example, RLP23 forms a complex with SOBIR1 and the perception of a necrosis and ethylene-inducing peptide-like 1 protein (NLP)

initiates recruitment of BAK1 to the LRR-RLP/SOBIR1 complex, thereby activating LRR-RLP-mediated immunity [174] (Figure 6). A recent investigation has revealed that auto or transphosphorylation events between SOBIR1 and BAK1 are crucial for this ETI signaling [175]. Interestingly, G-protein β subunit mutant (*agb1-2*) has seemed to reduce the cell death and defense responses in *bir1-1* mutant as well as transgenic plants overexpressing *SOBIR1*. Furthermore, *agg1agg2* double mutant has shown suppression of cell death phenotype in the *bir1-1* mutant. These results exhibit the contribution of heterotrimeric G-protein subunits (AGB1, AGG1, and AGG2) in SOBIR1-mediated ETI signaling [164].

#### *5.3. CRKs in Defense and Hypersensitive Responses*

Cysteine-rich receptor-like kinases (CRKs) are one of the largest RLK groups, which are transcriptionally induced during pathogen attack, oxidative stress, and also by the application of salicylic acid (SA) [176]. Recent studies have demonstrated the implications of CRKs in the regulation of defense responses and programmed cell death by guiding both PTI and ETI [10,177,178]. For example, constitutive over-expression of *CRK5* and inducible expression of *CRK13* leads to enhanced defense against *Pseudomonas syringae* via up-regulation of defense-related genes, like *PR1* (*pathogenesis related protein 1*), *PR5*, and *ICS1* (*isochorismate synthase 1*). Similarly, overexpression of *CRK45* results in enhanced resistance to *P. syringae*, whereas *crk45* mutants display more sensitivity to *P. syringae* by attenuating the expression of defense-related genes [179]. In addition, the induced expression of *CRK4*, *CRK5*, *CRK19,* and *CRK20* triggered hypersensitive response-like cell death in transgenic plants [28,180,181]. Recently, a physical interaction study has established that CRK36 preferentially interacts with and phosphorylates BIK1 (RLCK) and boosts plant immunity in response to flg22 treatment by regulating stomatal defense against pathogens [182] (Figure 6).

#### **6. RLKs in the Regulation of Plant Abiotic Stresses**

Abiotic stresses, such as drought, cold, salinity, ozone, metals, and UV-B radiations, have adverse impact on plant growth and development. Plants have various tactics to survive in continuously changing environmental conditions and one such is the RLK-mediated signaling circuit [183–185].

Among the plant hormones, ABA is a crucial mediator of the abiotic stress response; it can regulate the expression of drought, salt and osmotic stress response genes [186–189]. Genetic screening in Arabidopsis has established the connection between several LRR-RLKs and ABA-mediated abiotic stress signal. The loss-of-function mutants of Arabidopsis *leaf rust 10 disease-resistance locus receptor-like protein kinase 1.2 (LRK10L1.2*) display ABA-insensitive and drought stress-sensitive phenotypes indicating that LRK10L1.2 acts as a positive regulator in response to drought tolerance, perhaps through ABA-mediated signaling [32] (Figure 7). The insensitivity to ABA and downregulation of various water stress-responsive genes are also observed in *RPK1* knockouts and further, overexpression of *RPK1* exhibits increased tolerance to both drought and oxidative stress as well as up-regulation of ROS related genes. These results indicate that RPK1 regulates water and oxidative stress response via ROS homeostasis and ABA signaling [190] (Figure 7). Another LRR-RLK, guard cell hydrogen peroxide resistant 1 (GHR1) is an early component in ABA signaling and is negatively regulated by ABI2. The *ghr1* mutants show impaired ABA and H2O2 regulated activation of S-type anion currents in guard cells. Predominantly, GHR1 physically interacts with and activates the slow anion channel-associated 1 (SLAC1) by phosphorylation, resulting in stomatal closure during drought stress [191] (Figure 7). In addition, Arabidopsis receptor dead kinase 1 (RDK1) plays an essential role in drought stress response in an ABA-dependent manner. The *rdk1* mutants are hypersensitive to drought stress as a result of down-regulation of ABA-responsive genes [138] (Figure 7).

Few CRKs are also involved in ABA-mediated drought resistance. Overexpression of *CRK5* promotes stomatal closure and inhibits stomatal opening, thereby acting as a positive regulator of drought response [192]. CRK36 physically interacts with and phosphorylates ARCK1 (RLCK) during abiotic stress. The *crk36* knockdown mutants exhibit osmotic stress response during post-seed germinative growth, increases ABA sensitivity, and upregulates ABA-responsive genes. Thus, CRK36 seems to function as a significant negative regulator of ABA and osmotic stress signal transduction [186]. Besides, CRK6 and CRK7 are essential for overaccumulation of ROS in the apoplast during exposure to O3, and therefore, their mutants show increased sensitivity to O3 [29] (Figure 7).

FERONIA (FER), a member of the CrRLK1L family, plays a crucial role in ABA and salt stress responses. FER promotes activation of ABI2, a PP2C member, and a negative regulator of ABA signaling, to attenuate the ABA signaling and it has been noticed that the *fer1* mutants show hypersensitivity to both ABA and salt. This confirms the clue that FER regulates salt stress response via ABI2-mediated ABA signaling [187,188] (Figure 7). Rapid alkalinization factor 22 (RALF22) peptides are considered as the ligands of FER, which are produced during salt stress, via S1P protease-dependent pathway. In addition, RALF22/23 physically associates with the cell-wall leucine-rich repeat extensins 3/4/5 - (LRX3/4/5), which are critical for salt tolerance. Strikingly, the *fer* mutant, *lrx3*/*4*/*5* triple mutant, and overexpressed RALF23/24 lines exhibit identical phenotypes, including increased sensitivity to salt stress and retarded growth. These results demonstrate that FER, LRXs and RALFs form a signaling network that regulate plant growth by conferring tolerance to salt stress [193] (Figure 7).

Phloem intercalated with xylem-like 1 (PXL1), a LRR-RLK, is induced by cold and heat stress. Moreover, Arabidopsis *pxl1* mutants display hypersensitive phenotypes when exposed to cold and heat during the germination stage, suggesting that PXL1 functions in the regulation of stress signaling pathways during temperature fluctuations. The downstream substrates for PXL1 are the histidine-rich dehydrin 1 (HIRD1) and light-harvesting protein complex 1 (LHCA1) [194] (Figure 7). Calcium/calmodulin-regulated RLK or CRLK1 is cold inducible and their expression is enhanced by cold and hydrogen peroxide treatments; thus, justifying the role of CRLK1 in cold-related oxidative stress signal transduction pathway. According to gene knockout studies, CRLK1 acts as a positive regulator of cold tolerance and establishes a link between calcium and cold signaling [195,196] (Figure 7).

#### **7. Conclusions and Outlook**

The cellular signaling pathway is a complex network. This review summarized how the di fferent groups of RLK signaling pathways regulate developmental and stress responses in Arabidopsis. RLKs are evolutionarily conserved from algae to angiosperms and are known to monitor a wide variety of cellular processes. The abundance and diversity of RLKs provide insight into the significance of this receptor and its role in sustaining cellular homeostasis for the e fficient survival of plants. It explains the reason for its continued expansion on par with the increasing complexity of the higher group of plants. As discussed above, RLKs perform a crucial role in almost every aspect in a plant cell, throughout its life, right from the embryonal stage to senescence. The involvement of RLKs in various developmental, as well as stress responses, can be attributed to the diversity in the architecture of their ectodomains, which aid in the recognition of a plethora of ligands. This is executed by recruiting transducers, which help in communicating the signal further downstream. One such important group of transducers belong to the RLCK family, which activate several other intermediates for establishing a successful response. Interestingly, some RLCKs are conserved between di fferent RLK-mediated signaling pathways. Sporadically, the same RLCK interacts with one of the RLKs to elicit a particular response, while expressing a contrasting response upon interaction with another RLK, by activating a di fferent downstream target. The RLKs can directly use guanosine exchange factors (GEFs) like G-proteins and ROP as transducers, or indirectly via RLCKs and other intermediates. Although di fferential phosphorylation might be one possible mechanism responsible for activating the transducers, the molecular insights of how this distinction is possible remain elusive.

Although a lot of research has been carried out on RLKs in the last few decades, the biochemical and molecular mechanisms of several RLKs modulating physiological responses are not well understood in detail. The most important challenge is to identify the range of signals for RLKs and to explain how plants integrate these signals downstream. In mechanistic concerns, the dependency of certain fully functional RLKs (like BRI1) upon another RLK (BAK1) for successful complex formation and activation is yet to be discovered. Furthermore, due to the presence of a lot of crosstalk in plants, the intermediate targets of many of the pathways tend to remain unidentified. However, irrespective of the transducer activated and the pathway used, the ultimate outcome is to express appropriate proteins and products that enable the plant to endure the environmental challenges, thus, prolonging its survival. More focus on these aspects might be beneficial for developing resistant/tolerant agronomic cultivars via plant breeding or transgenic approaches. Thus, RLKs can be considered as an inherent elixir for plants' life.

**Author Contributions:** J.J. and S.G. contributed equally to this manuscript. J.J., S.G., and S.R.C. were all involved in writing and editing the review. All authors have read and agreed to the published version of the manuscript. SRC secured the funding.

**Funding:** Research in the author's lab is supported by Indian Institute of Science Education and Research (IISER), Tirupati; DBT-Ramalingaswami Re-entry Fellowship of S.R.C. and SERB Start-up Research Grant (SRG/2019/000901) of S.R.C.

**Acknowledgments:** We apologize to all authors whose work was not cited due to the length limitations.

**Conflicts of Interest:** The authors declare no conflict of interest.


**5. Conclusions**

Numerous scholars have debated the uniqueness of the characteristics of family firms in terms of performance as opposed to their counterpart firms. The perception that families possess a more conservative attitude towards the management of the business makes family firms less risky. The risk profile of family business has been argued to be one of the characteristics significantly impacting the management of family business. In the light of the above, this paper has as its main objective to predict if family firms have lower leverage and manage their operations in a less risky manner as compared to their counterpart firms. The analysis conducted in this study yields some interesting results regarding the risk profile of the family firm.

Firstly, family firms have a lower financial structure than non-family firms. This indicates that most family firms use less debt financing than non-family firms, and as such maintain a lower level of debt. Regarding the profitability measure, this result suggests that most families tend to increase their reserves during the profitable cycle of their firms and later reinvest this reserved profit during economic downturn, rather than using debt finance. In other words, they employ their equity finance rather than debt finance for investment.

Secondly, family firms demonstrate lower risk as illustrated by the Altman Z-score. The Altman Z-score captures the financial risk inherent in a firm by examining four financial ratios (such as working capital/total assets, retained earnings/total assets, EBITDA/total assets, and market value of equity/book value of total debt). The significant difference between family firms and non-family firms on the Altman Z-score scale indicates that the lower inherent risk of family firms arises from the operation aspect of the business. Family firms managed their business operation with lower risk and are generally healthier financially than their counterpart firms. This explains the uniqueness of the capital structure of family businesses.

Lastly, the robust tests for the hypotheses using a sample of bankrupt firms in SABI reveal that the proportion of failure of family firms as opposed to theircounterpart firms is relatively low. Analyzing the bankruptcy files of firms from 2002 to 2014 shows a considerably low ratio of family firms at the 5% significance level. This affirms that the low risk illustrated in the Altman Z-score regression is consistent withthe lower ratio of family firms that were declared bankrupt over the study period. Lastly, the average debt at bankruptcy was lower and statistically significant for family firms as opposed to

non-family firms. Therefore, the findings herein confirm the hypothesis that family firms have a lower capital structure and maintain more financially healthy operations than non-family firms.

**Author Contributions:** Conceptualization, methodology, software, and formal analysis, L.A.T.N.; validation, visualization, supervision and project administration, H.M.S.d.O.; formal writing, software and funding acquisition, B.M.F.d.S.; investigation and data curation and methodology, L.M.P.; visualization, formal analysis and resources, S.A.M.C.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Center for Studies in Business and Legal Sciences (CECEJ) and Research Centre for the Study of Population, Economics and Society (CEPESE).

**Acknowledgments:** We will like to give special thanks to the reviewers for their valuable comments. More thanks to Professor Paul Ntungwe Ndue for his remarkable review and assistance. Lastly, thanks to the Center for Studies in Business and Legal Sciences (CECEJ) and Research Centre for the Study of Population, Economics and Society (CEPESE) for all the financial funding and support provided by its members during this research.

**Conflicts of Interest:** The authors declared no potential conflict of interest with respect to the research, authorship, and publication of this article.


*Article* **Smart Cities Big Data Algorithms for Sensors Location**

#### **Elsa Estrada 1, Martha Patricia Martínez Vargas 2, Judith Gómez 2, Adriana Peña Pérez Negron 1,\*, Graciela Lara López 1 and Rocío Maciel 2**

Received: 29 June 2019; Accepted: 2 October 2019; Published: 8 October 2019

#### **Featured Application: Data sensors for Smart Cities are an important component in the extraction of patterns—thus, they must be placed in strategic locations where they are able to provide information as accurate as possible.**

**Abstract:** A significant and very extended approach for Smart Cities is the use of sensors and the analysis of the data generated for the interpretation of phenomena. The proper sensor location represents a key factor for suitable data collection, especially for big data. There are different methodologies to select the places to install sensors. Such methodologies range from a simple grid of the area to the use of complex statistical models to provide their optimal number and distribution, or even the use of a random function within a set of defined positions. We propose the use of the same data generated by the sensor to locate or relocate them in real-time, through what we denominate as a 'hot-zone', a perimeter with significant data related to the observed phenomenon. In this paper, we present a process with four phases to calculate the best georeferenced locations for sensors and their visualization on a map. The process was applied to the Guadalajara Metropolitan Zone in Mexico where, during the last twenty years, air quality has been monitored through sensors in ten different locations. As a result, two algorithms were developed. The first one classifies data inputs in order to generate a matrix with frequencies that works along with a matrix of territorial adjacencies. The second algorithm uses training data with machine learning techniques, both running in parallel modes, in order to diagnose the installation of new sensors within the detected hot-zones.

**Keywords:** smart cities; machine learning; big data; data analysis; sensors; Internet of Things

#### **1. Introduction**

An increasing number of people live in urban zones [1]. The United Nations organization estimates that, by the year 2030, more than 60% of the world's population will live in a city, and with the lack of regulation addressing spatial, social, and environmental aspects, this might create severe problems [2]—among them, air pollution as a source of health problems such as strokes, lung cancer, chronic and acute pneumopathies, or asthma [3,4].

In order to face the problems of diverse metropolises, such as reducing energy consumption or the negative impact of the city on the environment, the concept of Smart Cities has gained notoriety. This concept is based on the use of information and communication technologies (ICT). Here, data treatment represents the means to support decision making in order to provide citizens with a better quality of life. There are several different approaches to smart reducing that attempt to reduce

the problems inherent to urban life. A widespread practice to understand environmental factors is the use of mobile sensors to monitor the environment, a practice that generates a considerable amount of data. In order to analyze that volume of data, the implementation of big data technology is required.

Big data deals with the management of high volumes of data, as well as their storage. Big data is characterized by 10 'bigs'. These 'bigs' are classified by three levels of characteristics: fundamental, technological, and socioeconomic.  The technological level is formed by three bigs: big intelligence, big analytics, and big infrastructure. And the level of socioeconomic characteristics has three bigs: big service, big value, and big market [5]. Data recollected by sensors in the analysis of smart cites require technological achievements, supporting the primary goal of bringing smart cities to the requirements of socioeconomic characteristics. Figure 1 represents the association of the 10 bigs in a smart city.

**Figure 1.** Sensors monitoring events on a Smart City from a perspective of Big Data as 10 bigs.

Sensors allow collecting data from a context, detecting and responding to signals, which can be measured and then converted into understandable data through designed and developed models. Sensors can be installed both indoors and outdoors. Regarding interiors, sensors are those set in the human body, which allow collecting information about peoples' daily activities. They are usually acceleration sensors, widely used for their low cost and small size. The first works carried out for these devices focused primarily on the recognition of various modes of locomotion. Later, they were used in more complex activities such as sports, industry, gesture recognition, sign language recognition, and the human–computer interfaces (HCI) [6].

For exterior sensors, location is very relevant—they must be placed strategically in order to provide as accurate information as possible, in such a way that big data analysis can provide the identification of behavior patterns and the reduction of response times required for the smart cities.

However, it must be taken into account that, in the official information sites of quality measurement, data is not usually displayed in real-time—the update of the evaluation of events is not immediate. Sensor data is usually set to be taken over time intervals, which might lead to undetected changes. On the other hand, there is a lack of systems that face the continuous increase in the volume of catches and sensors, which support the monitoring of events of various kinds, and the readjustment of the infrastructure in sensor networks.

There is great interest in the installation of sensors in various areas of the city, human body, buildings, or houses, thus covering multiple scales. The tendency is to converge upon the Internet of Things (IoT), which is the construction of a dynamic network infrastructure capable of changing its configuration for better control of the flow of variables that circulate through a large number of interconnected sensors [7]. Thus, being able to extract patterns and relate phenomena to their causes within an environment such as a city constitutes a complex system [8].

The proliferation of sensors is increasing, and with this, the applications that perform a scientific analysis on their generated data, along with the use of different variables which, in one way or another, contribute to improving human wellbeing. Such is the case of the mobile systems, in which data comes from sensors installed inside them [9], or the systems that use sensors for environmental monitoring [10].

Also, a paradigm proposed in smart cities is the mobile crowd sensing (MCS), which focuses on using mobile integrated sensors to monitor multiple environmental phenomena, such as noise, air, or electromagnetic fields in the environment [11]. In this same topic, a system of pollution warning services was developed for smart cities [12], to notify the user of the concentration of pollution in the place where they are located, through mobile devices that measure the quality of the air. For example, a system based on crowdsourcing for mobile phones was developed to help car users to find the most appropriate places to park, in order to avoid problems of traffic congestion, air pollution, and social anxiety problems [8].

Another system of pollution warning services in smart cities was proposed to notify the user regarding the concentration of pollution, and about vehicles. In this case, mobile devices measure air quality [10]. In [11], an implementation of smart sensors was presented to monitor air quality where the tracking variables included dust particles (PM10), carbon monoxide (CO), carbon dioxide (CO2), noise level (dB), and ozone (O3), with the aim to keep people informed in real-time through the IoT.

In this context, machine learning and deep learning are two successful techniques both used for the classification of data as well as the identification of patterns. Another technology used in the same context is bio-inspired algorithms for the interpretation of information from the sensors. However, since big data faces the challenge of large volumes of information, several techniques are combined with it to provide proper solutions.

As mentioned, the constant use of sensors in smart cities led to the field of big data, which deals with processing and data analysis techniques. Within a broad set of techniques and methods for processing big data are the decision trees (DT) based on classifiers applied to large datasets [13]—DTs are also proposed to analyze large data sets for both numerical and mixed-type attributes. By processing all the objects of the training set without prior memory storage, this requires that the user define the parameters. It works by evaluating the training instances one by one incrementally, updating the DT with each revised case. With a small number of instances, the node is expanded faster than the expansion process of other algorithms. Furthermore, the instances used in the expansion of the node will be eliminated once the expansion is made avoiding in this way the storage of the training set in the memory.

In this context, in [14] was proposed the use of swarm search with accelerated particle swarm optimization. This is an algorithm capable of selecting the variables for data mining—its search achieves precision in a reasonable processing time. Further, in [15] was presented a system based on the ideas of pattern recognition that converges in the Bayes classifier. This system is scalable in data and can be implemented using structured query language (SQL) over arbitrary database tables—it uses disk storage with classification purposes. In [16], the algorithm C4.5 implemented a DT with the map-reduce model, to allow parallel computing of big data. This algorithm implements map-reduce with the 'divide and conquer' approach in order to discover the most relevant attributes of the data set for decision-making.

Importantly, neither of the two techniques using and not using discs for the training of big data ensure that there are defined patterns.

#### *Related Work*

In this paper, we propose a classification scheme based on machine learning, using big data generated to construct patterns in real-time for the fixed location of sensors. These patterns will provide the surrounding areas with a low quality of life in order to establish a better-fixed location for new sensors, or the relocation of the old sensors with the information generated by them through established hot-zones. Therefore, some related works regarding sensor locations are next discussed.

In [6] are presented different methodologies for selecting the places where the sensors should be installed to obtain information on the phenomena to be observed, ranging from the preparation of a grid of the study area to the use of complex statistical models that provide the number and optimal distribution of the sensors, but this is based strictly on the amount of information with which the model is generated.

In [17] is implemented a genetic algorithm (GA) to determine sensor locations and to establish the number of these sensors for proper coverage. As a first step, the GA randomly creates the population on the input map. This algorithm has the main feature of finding the number of optimum sensors based on the input map.

In [18] is presented an algorithm that simultaneously defines a sensor placement and a sensor scheduling. An approximation algorithm with a finite set of possible locations establishes where sensors can be placed; this algorithm selects a small subset of locations. These works focus on locating the sensors through random functions or by a set of defined positions.

There are several works related to the location of sensors for smart cities. Furthermore, some of them have a dynamic identification for sensors positions. However, few of them focus on the identification of fixed positions and, to our knowledge, none of them exploit the information acquired by the same sensors with big data techniques in order to identify their optimal location or relocation. In contrast, our proposal establishes the location of sensors in real-time using the concept of hot-zones in order to identify the sensors with significant activity regarding the phenomena observed with high precision.

We defined the concept of a 'hot-zone' as a perimeter area source of essential data for analysis. In a hot-zone can be found constant activity with substantial data to evaluate the quality of an observed phenomenon. These hot-zones are then used to locate or relocate new sensors. For that, a process with four phases is proposed. The first three phases are based on the data mining process, and the fourth phase constitutes the establishment of algorithms using selected techniques. The final aim of the process is to settle the hot-zones.

As an example, this process was implemented in the Guadalajara Metropolitan Zone (GMZ), Mexico, for air pollution. In this case, a first algorithm was designed for data training to generate the classification model with dynamic updating. A second algorithm was designed for data labeling, which is triggered after each data input. The two algorithms are independent. The classification is carried out in parallel with the training process—while some data are classified, others are used for training to observe possible changes in the patterns, or to decrease process time by a possible reduction in the number of variables of the model. In this phase, a frequency matrix is generated that works in conjunction with a neighboring sensor matrix in order to identify the hot-zones and present their visualization in a geo-referenced map [19,20].

#### **2. Process to Locate and Relocate Sensors**

Our proposed process comprises four phases:

The process is based on the data mining process for knowledge discovery in data bases (KDD) ] [21] and which, according to [22], should be guided by the seven phases: data integration, data selection, data cleaning, data transformation, data mining, pattern evaluation/presentation, and knowledge discovery. Data mining is the step of selection through the application of machine learning techniques, in this case, aiming to find classification patterns for hot-zone identification.

Similarly, in our process, the first phase corresponds to data integration, data selection, data cleaning, and data transformation. In our case, it consists of reviewing the quality of life in smart city models in order to select variables according to the standards, and then extracting data from open data public domains to conform the data set. Afterward, the sample is prepared by applying a filter, cleaning, and when required labeling, which constitutes data cleaning and data transformation.

Figure 2 depicts the four phases. The first phase is the preparation of the data sample, which begins with the revision of the smart city models and the compilation of standards related to the object of study selected. These activities will indicate the variables that the sensors must capture but, at the same time, will guide the search of data in public domains to form a sample data set. Here, preprocessing for filtering and cleaning is also applied. The result to be obtained is a sample of data validated by the metrics of the smart city models, and by models established by international organizations.

**Figure 2.** Phases of the proposed process that determine a scheme to design algorithms to place sensors in specific positions.

Once the data is transformed, it goes to Phase 2 for big data analysis focused on data mining, optimizing the performance of the search of the classification patterns.

In the second phase, the exploration of data analysis is performed using supervised learning techniques for classification in order to arrive at a prediction model, using as input the sample of the data set of the previous phase. It is suggested to explore techniques with parallelization capacity in order to optimize the processing time of patterns from large volumes of data. Also, independent variables must be clearly distinguished from their dependent counterparts. The output of phase two is a candidate list of techniques to be tested in the Phase 3.

In the third phase, the selected techniques are tested with the prepared data sample. Here, a plan has to be made to test and apply the candidate techniques. Then, those whose convergence obtained is equal to or greater than the desired limit are chosen. Another factor that should influence this selection is the technique's capacity to reduce variables. The convergence results are sent to the Phase 4, as the basis for the algorithm's design.

In the fourth phase are designed and implemented the algorithms. Two types of algorithm are required for the identification of the hot-zones.

The first type of algorithm is those that apply the chosen techniques in Phase 3, that is, those with better convergence results. These algorithms serve for the extraction of data patterns from the model. The second algorithm type includes those that classify and update the frequency matrix with the neighboring sensor matrix shapes of the hot-zones.

The application of the algorithms implies the existence of a set of sensors in the network, where every installed sensor has a pair of values related to its geographical localization: latitude and longitude. Further, it also emits the value of variables. Although the sensor placement points are initially selected for a better control of the areas, it does not necessarily mean that they are located in the focus of the phenomenon, especially if there are not enough sensors, or if they are dispersed.

By recursively identifying the hot-zones, that is to say, applying or reapplying Phase 4 of the process, midpoints between two sensors can be inferred—this represents the proposed fixed locations to place a new sensor. With newly located sensors, we can get new and different matrices with a reduced scope of hot-zones.

In Figure 3, an example of dispersed sensors in a geo-referenced region is observed. Two of the four sensors were identified as hot-zones: Sensor 2 (in red) and Sensor 3 (in magenta) in contrast with green sensors. In Figure 3, the scope of the hot-zone is reduced, approaching sensor 3, but it could go to the other way around, approaching sensor 2. The scope of the hot-zone will be reduced based on the sensors' data and using the analogy of a binary search, that is, taking one of two paths. This process will generate another dynamic map of the sensor.

**Figure 3.** A recursive search of midpoints within hot-zones.

In the next section, the process is applied for a specific situation.

#### **3. Process Implementation**

A critical problem in the big cities is air quality. Therefore, air pollution indicators of the Guadalajara Metropolitan Zone (GMZ) in the Jalisco State of Mexico were treated. The GMZ is the second most populous area in Mexico with more than 5 million habitants.

In order to choose our data sample, we consulted the official Mexican standards for population health [23,24], which recommended the following variables: PM10, PM2.5, O3, NO2, SO2, and carbon monoxide (CO) [23], and the World Health Organization (WHO) whose guidelines for improving air quality include the reduction of particulate matter (PM), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2).

For Phase 1 of the process (see Figure 2), we downloaded files from the information page of the Secretariat of the Environment and Territorial Development of Jalisco State [25], in Microsoft Office Excel software format. Data from 21 years of air variables, from 1996 to 2017, were recovered. Sensor data monitoring started with eight stations and, in 2012, two additional stations were added. The records contained data from every hour for each station. After analyzing the data, January to December, 2015 was selected as a sample. In this year, the 10 stations were implemented, and it presented fewer missing values when compared with other years.

For Phase 2 of the process, the dataset was cleaned—that is, null data were eliminated. Also, the variable PM2.5 was not considered because, initially, there were no sensors for it. Moreover, when sensors that could monitor it were installed, they contained a massive amount of null values.

Observations were labeled according to a certain level of air quality, as indicated by the Mexican reference Metropolitan Index of Air Quality (IMECA by its Spanish initials), as shown in Table 1.

**Table 1.** Concentration intervals for color assignment or air quality levels [24].

The variables have a different range. The variable with the highest value according to its range is the one that indicates the air quality level by color. Pollution levels are classified as: good = level 1 in green color; fair = level 1 in yellow color; bad = level 5 in orange color; very bad = level 4 in red color, and extremely bad = level 5 in magenta color. Table 2 shows an extract of registered entries in a file after being classified with the quality label in the sixth column.

**Table 2.** Fragment of the sample of observations captured by sensors classified with a quality label.

Then, data analysis algorithms were selected. As mentioned with the objective of discarding, if possible, some variables with less influence for the quality classification. Also, algorithms to train with machine learning that require fixed-size datasets and disk storage were selected, because it added

a function of partial elimination of records in the cloud. For Phases 3 and 4, the R language, kernlab, e1071, rpart, and doParallel libraries were applied to support the process.

Three prediction methods were tested: multiple linear regression (MLR), support vector machine (SVM), and decision trees implementing a classification and regression tree (CART). The first one (MLR) was chosen to explore the linear model. The second and third methods were used for testing because of the advantages they present, such as parallelization and reliability.

For the MLR test, six variables were involved—the quality label as a dependent variable, and CO, NO2, O3, PM10, and SO2 as independent variables. Once the files were cleaned and filtered, 66,880 observations formed the data sample. The data-training group contained 70% of these observations, with a total of 46,816 observations. The MLR test presented an R-squared of 58.13%, not reaching the expected 80%, indicating that the data did not fit a linear function.

For the SVM, two groups were formed: the training and testing groups. The first group contained 46,816 records, and the testing group 20,064 records, the values for the kernel parameter were rbfdot, radial, and hyperparameter sigma = 0.5. For both, the training error of 20% was obtained. The equivalent algorithm for both cases is represented by a mathematical equation in which the outputs are the Quality level; this virtually eliminates comparisons and decreases execution time.

CART showed a root node error of 16974/66881 = 0.25379, reducing the comparison variables PM10 and O3. However, the model does not classify for level 5 because there are few entries with that label, so that specific output is not explained, as shown in Figure 4.

**Figure 4.** Training results using machine learning applying the CART classification technique.

#### *3.1. Algorithms for Classification and Training*

The goal of these algorithms is to classify data and each data acquisition, from the sensor a set of variables is identified as a record=(\_n 1){ variable \_i }, where n is the given number of variables of instrumentation, for example, light intensity, humidity, or movement. To get the registration form as register = |1|2||3||n| where each number is associated with a risk level of quality, as indicated by the standards. In our case of study, the record is formed by the variables register = CO, NO2, O3, PM10, SO3 to be classified according to the level of quality from the IMECA index.

According to the results of Phase 3 of the process (see Figure 2), in Phase 4, the algorithms were executed—the algorithm to classify the sensor inputs, and the algorithm for training and setting the prediction model. Here, we proposed an architecture that allows each sensor to dispatch data captured to different processing threads, using cloud computing for a distributed performance, and with access to the shared memory. Figure 5 depicts the parallel processing of the data inputs captured by the sensors, their destinations in the cloud, and a matrix with frequencies, the shared memory variable to identify the hot-zones.

**Figure 5.** A general scheme of big data analysis with classification threads, an updated matrix with frequencies of pollution levels, and cloud training.

Classifying the sensor's inputs can be made by a table in memory, in which the intervals for the variables are specified. In this case, a systematic review is carried out, starting with the values that exceed the limits in the IMECA classification. The best case is when just one comparison is needed, and the worst case is when the variable does not exceed any previous interval and must continue with the comparisons to the green level 1—see Figure 6.

This classification can be optimized through CART reducing conditions, as can be observed in Figure 7. In this case, the number of variables is two (PM10 and O3)—the input lacks some variable values (i.e. CO, SO2, or NO2 variables) that are not necessary in this case for the classification.

It is essential to highlight that, if a mathematical model presents a proper classification solution, for example, through the MLR model, that must be the less resource-consuming path to follow. Otherwise, as in this case, a classification method has to be selected. In any case, a matrix with the frequencies of sensors' events has to be updated. This update requires observing a time range. It is also necessary to label a representative sample of the big data burst, which will be used as a training group, maintaining the observation stored. The remaining registers are eliminated, avoiding the demand for storage space.

Cloud storage occurs to form a temporary dataset for training, to take advantage of resources, and to distribute tasks for the tests with machine learning techniques. In the cloud, a process is activated when a sample size or a specific period is reached. The process consists of using the training data with the selected methods in Phase 3 of the process. In this example, SVM and CART run in parallel and are distributed concurrently. The algorithm finally updates the classification model to be read by the threads that will perform the classification.

**Figure 6.** Flowchart for level classification.

**Figure 7.** CART Algorithm from data training.

#### *3.2. Matrices for the Dynamic Map*

The objective of the frequency matrix is to provide an environment variable in shared memory. This variable content is the sum of incidents specifying time intervals with high risk. Frequent readings to this matrix will be performed to draw on a georeferenced map with the current sensors.

The frequency matrix accumulates the number of quality levels labeled (see Figure 5) during a specific time. In this way, the sensors with the highest levels of pollution are easily identified—Table 3 depicts one of these periods for five sensors. In Table 3, it can be observed that sensors 3, 4, and 5 have the highest frequency in Level 5, meaning that they are classified as extremely bad.

**Table 3.** The frequency matrix of Quality levels with five sensors.

In order to locate or relocate sensors, a matrix indicating sensors in neighboring areas is required. See, for example, Table 3, where the name to identify the sensor implies its coordinates. In Table 4 can be observed, for instance, that Sensor 1 (Oblatos) in the first row is in the surrounding area with Sensors 2, 4, and 5 (Centro, Tlaquepaque, and Loma Dorada) indicated by a number '1' in those columns.

**Table 4.** Neighboring sensors matrix.

Matching frequency matrix to the neighbor sensors with high pollution levels can be deduced. Locating new sensors in the highest levels of pollution will help getting a better understanding regarding the contamination source or the type variants that are affecting the area. The dynamic model might change the location requirements by distinguishing a new sensor's location or the relocation of those that are already in the system.

In our example, new sensors could be located where the two arrows point in Figure 8. These are between sensors 3 and 4, and sensors 4 and 5 (corresponding to Miravalle, Tlaquepaque and Loma Dorada). According to the frequency matrix in Table 3, these three sensors have a high number of records at level 5, or Extremely bad, and they also correspond to neighboring areas according to the neighboring matrix in Table 4, where it can be observed that sensor 3 is adjacent to sensor 4, and sensor 4 is adjacent to sensor 5.

**Figure 8.** Georeferenced map the neighbor's sensor border on a first run of a recursive process.

#### **4. Conclusions**

The development of smart cities has led to the increase of sensors in cities—with them, it is possible to identify phenomena to meet one of its main objectives, the development of quality of life and sustainability. Therefore, it is essential to have methods that help to establish a better location for new sensors in existing critical points or hot-zones.

We propose that the analysis of the data generated by the same sensors, provides information helpful to identify the hot-zones, and to locate or relocate sensors.

Based on the data mining paradigm, we observed that this is not sufficient for a dynamic system. Thus, our process includes the last phase for the recognition and visualization of hot-zones, a perimeter with significant data related to the observed phenomenon. The information captured by the sensors determines the location of new sensors, or the relocation of those already receiving data.

The proposed process offers a scheme for data labeling, creating a dynamic classification model. The classification and training algorithms in the cloud manage an independent control. Then, prediction techniques required to be tested to get those that better fit the data.

In our case study, data regarding air pollution in the Guadalajara Metropolitan Zone in Mexico was analyzed and, for that, the SVM was selected. With the SVM, different parameters were tested, and it was adjusted to different kernels, allowing for more accurate predictions.

Finally, two algorithms were designed as a result of the application of the process. These algorithms are independent processes on threads using cloud computing. The first one classifies each one of the entries captured in order to generate a matrix of territorial adjacencies. The second one is trained with a classification and regression tree (CART) and SVM. Although the development of the algorithms was performed in the context of environmental variables, this proposal is autonomous with respect to the sensors' features. It can also be scaled to various types, including the quantity and volume of information, because it does not require data storage. It is worth mentioning that it is necessary to continuously verify the local and international standards for the algorithms since new sensors might be incorporated varying in variables and ranges.

In future work, the combinations of variables obtained from sensors, along with other retrieving variables, such as those coming from questionnaires, social media, or government data, will be included.

**Author Contributions:** Conceptualizacion, E.E. and M.P.M.V.; Methodology A.P.P.N.; Software, E.E. and J.G.; Validation G.L.L.; Formal analysis E.E. and A.P.P.N.; Investigation, E.E., M.P.M.V., J.G., and R.M.; Data curation, E.E. and J.G., Writing—Original draft preparation E.E.; Writing—Review and editing E.E., A.P.P.N. and M.P.M.V.; Visualizacion M.P.M.V.; Supervision A.P.P.N.

**Funding:** This research received no external funding.

**Acknowledgments:** We would like to thank Marco Pérez-Cisneros director of the Electronic and Computer Division of the CUCEI for his support.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).