# Optimizing Quality of Service of Clustering Protocols in Large-Scale Wireless Sensor Networks with Mobile Data Collector and Machine Learning

Authors: Rahma Gantassi; Bechir Ben Gouissem; Omar Cheikhrouhou; Salim El Khediri; Salem Hasnaoui
Journal: Security and Communication Networks
 (2021)

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## Abstract

The rise of large-scale wireless sensor networks (LSWSNs), containing thousands of sensor nodes (SNs) that spread over large geographic areas, necessitates new Quality of Service (QoS) efficient data collection techniques. Data collection and transmission in LSWSNs are considered the most challenging issues. This study presents a new hybrid protocol called MDC-K that is a combination of the K-means machine learning clustering algorithm and mobile data collector (MDC) to improve the QoS criteria of clustering protocols for LSWSNs. It is based on a new routing model using the clustering approach for LSWSNs. These protocols have the capability to adopt methods that are appropriate for clustering and routing with the best value of QoS criteria. Specifically, the proposed protocol called MDC-K uses machine learning K-means clustering algorithm to reduce energy consumption in cluster head (CH) election phase and to improve the election of CH. In addition, a mobile data collector (MDC) is used as an intermediate between the CH and the base station (BS) to further enhance the QoS criteria of WSN, to minimize time delays during data collection, and to improve the transmission phase of clustering protocol. The obtained simulation results demonstrate that MDC-K improves the energy consumption and QoS metrics compared to LEACH, LEACH-K, MDC maximum residual energy leach, and TEEN protocols.


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## Body

## 1. Introduction

Over the past few years, wireless technology has continued to grow through technological developments in various fields related to the microelectronics field. Simultaneously to WSN, new fields have emerged and challenges have been taken up to meet the needs of people and the demands of various application areas such as the environment, industry, and culture. These new applications have motivated researchers to sought solutions to achieve application requirements. Indeed, in spite of the outstanding achievements in this area, many problems still remain to be solved. For instance, some new protocols for the WSN have been proposed in order to handle media access control, routing protocols, data aggregation, and so on. Many research efforts have been carried out on sensor network energy consumption minimization, but only a few have focused on improving clustering protocol based on Quality of Service (QoS) criteria. As a result, clustering protocols have become the key target for sensor optimization studies.The adoption of machine learning, such as the K-means algorithm, with the LEACH protocol [1] during cluster head (CH) election yields significant gains in network lifetime, throughput, CH number, latency, and power consumption [1]. Nevertheless, multihop routing adopted by LEACH-K [1] can influence energy consumption, throughput, reliability, and latency time. Therefore, it is very important to address the problem of poor stability and latency that save energy and all QoS essentially in terms of throughput. On the other hand, mobile data collector (MDC) is used as an intermediate between the CH and the Base Station (BS) to further improve the QoS criteria of WSN, to minimize time delays during data collection, and to extend the lifespan of the network in WSN. Therefore, this study proposed a new protocol, which is a combination of the LEACH-K approach and MDC, to improve the QoS of clustering protocols. Specifically, the purpose of the proposed protocol is to prolong the lifetime of the network and enhance its QoS criteria. As a result, an enhancement of the clustering protocol is proposed, which relies on network division in clusters before the election of the CH according to the machine learning K-means algorithm. An efficient mobile data collection system is advocated as well based on data aggregation that operates as clusters on the basis of the WSN that efficiently uses mobile nodes for data collection. Specifically, the contribution of this document is as follows: (i)
We have proposed a new routing protocol called MDC-K. The proposed algorithm first applies the machine learning K-means algorithm to split the network into K clusters. After that, it determines the CH of each cluster which will be the closest node to the centroid of each K-means cluster. Assign each CH to the sensor node (SN) having the minimum distance to the CH. The CH can broadcast data to MDC when MDC visits the nearest CH. MDC completes its collection and finally delivers the data to the BS. The MDC uses the location of each CH to calculate the distance between it and all the CHs and moves to the CH having the minimum distance.(ii)
We compared the proposed protocol with existing solutions and proved its performance.The remainder of the paper is arranged as follows. Following a brief introduction to the study proposed in Section1, some related works are outlined in Section 2. Next, in Section 3, the proposed approach is explained. Section 4 gives several simulation scenarios with simulation parameters and evaluates the performance of the proposed approach. Section 5 summarizes the main contribution of this work and gives the perspectives.


## 2. Literature Study

Several studies have been conducted to develop MDC in clustering protocols [2–15] where they have focused on the maximization of WSN’s lifespan, while other studies have been conducted in order to assess the impact of the K-means algorithm on the clustering performances in WSNs [1, 16–20]. Particularly, some studies have integrated MDC into clustering protocol, whereas others have applied the machine learning K-means algorithm in the clustering protocol. The objective of this section is to review and summarize the recent studies on both K-means and MDC approaches in routing protocols. Some studies used MDC in clustering protocol; others used MDC in Large-Scale Wireless Sensor Networks (LSWSNs), whereas the rest of the studies used MDC in WSN in general. Table 1 summarizes some literature works.Table 1
Comparative study of the literature.
StudyApproachAdvantagesChallenges[2]The authors proposed distance energy evaluated (DEE) approach:(i) Introducing the stratified network topology arrangement that provides resiliency in multihop communication data to the base stationThe simulation shows that protocol behavior is significantly enhanced for reliability, lifetime, and energy savings(i) In this approach, the authors did not focus on the QoS improvement[3]The author approved the steering convention based mainly on the multichannel plan MDC at the lower station:(i) This allocates channel to MDCs instead of the single channel(i) This protocol demonstrates meaningful changes from LEACH and a single application-specific network protocol for WSNs. (ii) The conventions for using sensor hub vitality and improving system lifetime and efficient information assembly due to less use of vitality in the middle of information transmission(i) Despite the good finding of this study, it did not improve QoS performance[4]MEACBM protocol addresses heterogeneous clustering taking into account 3 SNs levels of multihop for intercluster communication and connectivity of SNs throughout the network areaSimulation results indicate the performance of MEACBM protocols compared to other contemporaneous routing protocols based on clusters regarding network lifetime, stability, throughput, channels, and dead nodes(i) MEACBM protocol improves QoS performance[5]The proposed approach used MDC minimum distance leach which is based on the multihop communication between BS, SNs, and MDCThe MDC minimum distance leach approach outperforms the hybrid multihop LEACH approach regarding energy consumption and greatly improves lifetime(i) Despite the good finding of this study, it did not improve QoS performance[6]The authors proposedanalytic methods to compute node power consumption and identify the optimum node clusters to be used multihop and single-hop modelsThis approach can model large-scale networks (10,000 nodes and more) and test various network scenarios in little time.(i) For multihop, it demonstrated that the increase of clusters does not always reduce the energy consumption of the nodes owing to the DREQ problem ﬂooding.(ii) For single hop, sensor nodes would have to spend a significant quantity of energy to send to the mobile access point when the number of clusters is lower in LSWSNs(i) In this approach, the authors did not focus on the QoS improvement[7]The authors used the multilevel MDC method to assess the lifespan of the network in an open and dense agricultural area. Specifically, this study provided an effective method for extending the lifespan based on automaton learning at WSN.(i) Experimental results showed a significant improvement in network lifetime extension compared to previous methodologies(i) This approach did not ameliorate the QoS[8]The authors proposed MDC maximum residual energy protocol:(i) The authors presented a comparative study between MDC maximum residual energy routing protocol and LEACH multihop hybrid routing protocol(i) Simulation results showed significantly better performance of the proposed maximum residual energy MDC protocol than the LEACH multihop hybrid routing protocol regarding the lifetime of the network(i) The simulation parameters were very limited as in [14]. Except in [14], the MDC moves toward the nodes with the minimum distance appearing at the MDC, while in [8], MDC moves toward the nodes with maximum energy.[9]In the proposed approach:(i) The network is divided into equal clusters. (ii) In particular, [9] has integrated the fine-tuning of the node transmission range and the adjustment of the MDC speed(i) Simulation findings showed significant reductions in the energy consumption of the CH(i) In this approach, the authors did not focus on the QoS improvement.(ii) This approach did not ameliorate the QoS[10]In the proposed approach [10], the MDC begins its data collection journey from the data sink by getting a signal of the CH beacon, then collects the data directly at the CH, and conducts the data to the BS(i) With this method, the lifetime of the sensors can be extended because the method is reactive(i) In [10], the authors however did not make any simulation to validate the proposed approach[11]In [11], MDC can interrogate one by one the sensors located nearby upon receipt of the acknowledgment; a sensor loads the data directly into the MDC and does not transmit the data back to another SN(i) This approach has the potential to significantly extend the network lifetime relative to the schematic with a static data collector and greatly reduce the length of displacement relative to the coverage line protocol(i) This approach did not ameliorate the QoS[12, 13]The authors focused on the design of an LWSN-based MDC, in which the mobile node tours each SN within the network, collects the data from the SNs, and transports the collected data to the BS [12, 13](i) For the multihop approach, researchers in [12] decrease the network distance while utilizing several well nodes. Thus, the problem of hot spots is prevented and the number of hops can also be reduced, resulting in a reduction of energy consumption(i) This approach did not ameliorate the QoS[14](i) The authors proposed,analyzed, and validated the MDC using the LEACH protocol in an environmental application(i) This protocol shows considerable progress in the energy consumption of the sensor nodes. (ii) This protocol improves the life of the networkThe simulation parameters were very limited(i) The maximal number of the used SNs was only 40 and the simulation area was equal to 1 Km2[15]To avoid the premature death of sensors and avoid the problem of hot spots, a dynamic clustering approach is proposed in [15]. Furthermore, the technique of ant colony optimization (ACO) is used for the efficient selection of the mobile sinks path.(i) The results of the simulation demonstrate that the CH election strategy based on the COA-based MDC greatly improves the WSN's lifespan.(ii) ACO was compared to current GA, PSO, and LEACH(i) This approach did not ameliorate the QoS[16](i) The author applied the K-means approach in WS(i) The number of clusters is increasing.(ii) WSN’s energy consumption is reduced(i) The limitation to this approach is that a minimum number of parameters are used. For example, during the simulation phase, the authors focused only on 2 clusters with 10 nodes.(ii) This approach did not ameliorate the QoS.[1]In [1], the K-means algorithm was applied in LEACH protocol prior to the election of CH to reduce energy consumption and enhance the Quality of Service criteria(i) The results showed that the proposed protocol decreased power consumption and latency and improved network stability, lifetime, and throughput.(ii) The mechanisms of aggregation and LEACH-K deteriorate the QoS leading to reduced stability and latency(i) This approach did not ameliorate the QoS(ii) This approach did not apply the MDC (iii) This approach had not been tested in LSWSNs[17]The authors analyzed the routing protocol and started from the point of view of cluster routing in order to investigate the classical ACH protocol and evaluate its advantages and drawbacksBased on this analysis, an aggregation process based on the K-means algorithm is provided and a verification function is appended to the K-means algorithm for dynamically adjusting the system to obtain stable system operation and transmitting information while minimizing the system’s energy consumption(i) This approach did not ameliorate the QoS(ii) This approach did not apply the MDC (iii) This approach had not been tested in LSWSNs[18]The authors analyzed the performance of KM and FCM and investigated which of them is best suited to build balanced clusters, allowing researchers to choose the right algorithm to improve network lifetimeSimulation results show that FCM is more superior to KM by producing balanced clusters with the random distribution manner for SN(i) This approach did not ameliorate the QoS(ii) This approach did not apply the MDC(iii) This approach had not been tested in LSWSNs[19, 20]The authors of [19, 20] proposed two hybrid clustering algorithms, K-means particle swarm optimization (KPSO) and K-means genetic algorithms (KGAs), which show improvements compared to traditional LEACH in energy savingThe results showed that the proposed protocol reduced energy consumption(i) This approach did not ameliorate the QoS(ii) This approach did not apply the MDCBesides, many studies used the machine learning K-means clustering algorithm in WSN. In particular, the majority used the K-means algorithm in clustering protocol. The K-means approach can make the cluster of nodes close to the centroid, which decreases the energy consumption and increases the throughput. Furthermore, the integration of MDC into the routing protocol can reduce the data collection times and extends network life in WSNs. Moreover, clustering protocols contain two phases, namely, the CH election phase and the data transmission phase. As previously discussed, some studies have integrated the MDC in clustering protocol, whereas others have applied the machine learning K-means algorithm in clustering protocol. To the best of our knowledge, this work presents the first attempt to combine both MDC and K-means algorithm to reduce the energy consumption in clustering protocol.


## 3. The MC-K Proposed Protocol

To the best of our knowledge, no study has used the combination of K-means and MDC in clustering protocol, which is the aim of this study. Specifically, the K-means algorithm was integrated into the election step and MDC in the transmission step of the clustering protocol in order to improve the QoS metrics.During data collection, the MDC follows a greedy approach, each time selects the nearest CH, and moves to it to collect its data. Afterward, the MDC sends all collected data to the BS. Our proposed protocol contains two phases, the initialization phase and the transmission phase.


### 3.1. Initialization Phase

This phase starts with the application of the K-means algorithm [21, 22] to the WSN. Then, the SNs are divided into K groups.The output of the K-means algorithm isk centroids. The BS selects the SN with the minimum distance to these centroids to be the CH. After CH assignment, an advertisement message containing the CH identifier is broadcast. Once receiving this message, SNs send a request message containing their coordinates to the CH to join the cluster. Each CH chooses its member nodes that are closest to it. Specifically, the CHs select the nodes belonging to their K-means cluster. Then, the BS knows the coordinates of the CHs. Suppose that the SNs denoted by ni are divided into k clusters {C1, C2, C3, C4, C……., CK}. Assume ck to be the centroid of the cluster CK. Therefore, (1)C1∪C2∪…∪CK=∑i=1Nn,where N is the total number of nodes in the network.The goal of K-means is to reduce the overall Euclidean distance between the CH and all the members of the cluster. It reduces the sum of the distances between SNs and the cluster centroid as shown in Figure1.Figure 1
Initialization phase of the MDC-K.The BS decides the nodes to be CH, based on two conditions: the closer node from the centroid and the total number of CHs should not be greater than the probability value.(2)Pt=KN−K.So, if the energy of the nodes differs, the probability Pi (t) relates to the remaining energy at each node. This probability will therefore be equal to (3)Pit=EitEtotaltK,where Eit is the residual energy relating to each node ni. This probability favorises the nodes with more energy resource to become CHs. Etotalt is the total energy available in the network.


### 3.2. Data Transmission Phase

The MDC moves toward the nearest CH and collects its data. MDC maintains a set of already visited CHs and then goes to the nearest CH from the remaining CHs until it collects all data from all CHs. Afterward, it sends all the data to the BS. Afterward, it sends all the data to the BS. Figure2 explains the flowchart of data transmission from CH to MDC.Figure 2
Data transmission from CH to MDC.


## 3.1. Initialization Phase

This phase starts with the application of the K-means algorithm [21, 22] to the WSN. Then, the SNs are divided into K groups.The output of the K-means algorithm isk centroids. The BS selects the SN with the minimum distance to these centroids to be the CH. After CH assignment, an advertisement message containing the CH identifier is broadcast. Once receiving this message, SNs send a request message containing their coordinates to the CH to join the cluster. Each CH chooses its member nodes that are closest to it. Specifically, the CHs select the nodes belonging to their K-means cluster. Then, the BS knows the coordinates of the CHs. Suppose that the SNs denoted by ni are divided into k clusters {C1, C2, C3, C4, C……., CK}. Assume ck to be the centroid of the cluster CK. Therefore, (1)C1∪C2∪…∪CK=∑i=1Nn,where N is the total number of nodes in the network.The goal of K-means is to reduce the overall Euclidean distance between the CH and all the members of the cluster. It reduces the sum of the distances between SNs and the cluster centroid as shown in Figure1.Figure 1
Initialization phase of the MDC-K.The BS decides the nodes to be CH, based on two conditions: the closer node from the centroid and the total number of CHs should not be greater than the probability value.(2)Pt=KN−K.So, if the energy of the nodes differs, the probability Pi (t) relates to the remaining energy at each node. This probability will therefore be equal to (3)Pit=EitEtotaltK,where Eit is the residual energy relating to each node ni. This probability favorises the nodes with more energy resource to become CHs. Etotalt is the total energy available in the network.


## 3.2. Data Transmission Phase

The MDC moves toward the nearest CH and collects its data. MDC maintains a set of already visited CHs and then goes to the nearest CH from the remaining CHs until it collects all data from all CHs. Afterward, it sends all the data to the BS. Afterward, it sends all the data to the BS. Figure2 explains the flowchart of data transmission from CH to MDC.Figure 2
Data transmission from CH to MDC.


## 4. Simulation Results and Analysis

In order to evaluate the performance of the proposed approach, it is compared with various existing protocols based on QoS criteria such as throughput and energy consumption in the WSN. The simulation of the proposed approach is done using MATLAB and various parameters and factors are taken into consideration while performing the simulation to enhance the network performance. In order to obtain these results, the value of K was set to 10 (10 is the optimal value ofk for the best QoS criteria values according to [1]). Table 2 describes the simulation parameters common to all scenarios.Table 2
Simulation parameters.
SettingsValuesInitial energyEelec = 0.5 J/nodeElectronic dissipation energy (sending; receiving)Eelec = 50 nJ/bitData aggregation energyEDA = 5 nJ/bit/m2Transmit amplifier if dtoBS ≤d0Efs = 10pJ/bit/m2Transmit amplifier if dtoBS ≥d0Emp = 0.0013pJ/bit/m4Number of nodes100nodesLEACH routing percentage0.05 (percentage of cluster head 5%)Simulation area(100∗ 100)mIn these simulation scenarios, we compared the MDC-K protocol with LEACH, LEACH-K, TEEN, and MDC maximum residual energy protocol based on residual energy, throughput, and latency time of network in 10000 rounds. One hundred nodes have been chosen to test the performance of the MDC-K protocol. Figure2 compares the residual energy of MDC-K against LEACH, LEACH-K, TEEN, and MDC maximum residual energy.As indicated in the above figures, the proposed solution improves the network lifetime with higher performances. It improves the SN residual energy as well and numerical results show that the proposed method is able to decrease the SN energy consumption, which will result in more network lifetime. Figure3 presents the residual energy and the throughput variations (when K = 10) of the proposed approach compared to LEACH, LEACH-K, TEEN, and MDC maximum residual energy leach. The graphs illustrated in Figure 3 depict that, by the integration of an unsupervised algorithm which is the K-means before the CH election and MDC in data transmission phase, total residual energy and throughput increase. The sensor network can be coated with an acceptable amount of energy (more than 0,027mj) in comparison to 0 of LEACH, 0 of TEEN, 0 of LEACH-K, and 0 of MDC maximum residual energy leach during 10000 rounds. Specifically, the MDC moves to its nearest CH to reduce energy consumption and consequently increases the residual energy.Figure 3
Comparison of residual energy between LEACH, LEACH-K, MDCEACH-K, TEEN, and MDC maximum residual energy leach protocols.As indicated in the above figures, the proposed solution improves the network lifetime with higher performances as the throughput is increased. Figure4 presents the throughput variations (when K = 10) of the proposed approach compared to LEACH, LEACH-K, TEEN, and MDC maximum residual energy leach. The graphs illustrated in Figure 4 depict that, by the integration of an unsupervised algorithm which is the K-means before the CH election and MDC in data transmission phase, throughput increases.Figure 4
Comparison of throughput between LEACH, LEACH-K, MDCEACH-K, TEEN, and MDC maximum residual energy leach protocols.Specifically, SNs can be coated with an acceptable amount of energy (more than 0,027J in comparison with 0 for LEACH, 0 for TEEN, 0 for LEACH-K, and 0 for MDC maximum residual energy leach during 10000 rounds) and can provide a considerable throughput (more than 37865 packets in comparison to 12009 for LEACH, 12001 for TEEN, 23300 for LEACH-K, and 17864 for MDC maximum residual energy leach during 10000 rounds). The simulation results are summarized in Table3.Table 3
Comparison of the network lifetime (residual energy), the stability, throughput, latency time, and number of CHs between the LEACH, TENN, LEACH-K, and MDC-K of 10000 rounds.
LEACHTEENLEACH-KMDC maximum residual energy leachMDC-KThroughput (packet)1200912001300172330037865Residuals energy (mJ)0000≥0,027Latency time (ms)194,86153,7496,16051,55147,33Stability (rounds)7991078139919132567As shown in Table3, the use of K-Means improved the election of the CH, whereas the use of MDC improved the routing. The effectiveness of the MDC-K protocol in LSWSNs was also tested. Specifically, we have evaluated the QoS criteria values in different area sizes. The latency time also falls down from 194,865 (ms) in LEACH, 153,74 (ms) in TEEN, 96,1602 (ms) in LEACH-K, and 51,551 in MDC maximum residual energy leach to 47,33 (ms) in MDC-K. Figure 5 presents a comparison between MDC-K, LEACH-K, TEEN, MDC maximum residual energy leach, and LEACH protocol in terms of stability, latency time, residual energy, and throughput in different area sizes.Figure 5
Comparison between MDC-K, LEACH-K, TEEN, MDC maximum residual energy leach, and LEACH protocol in terms of stability, latency time, residual energy, and throughput in different area sizes.
(a)(b)(c)(d)Figure5 showed that when the area size increases, the residual energy, throughput, and stability decreased slightly which means that the benefits of our protocol remain almost stable with a large area. For instance, the latency time also increases from 47,33 (ms) in area size (100, 100) to 50,505 (m) in area size (1000, 1000). The throughput decreases down from 37865 (packet) in area size (100, 100) to 37601 (packet) in area size (1000, 1000). According to its results, we conclude that our MDC-K is the best solution for LSWSNs. The stability decreases down from 2567 (rounds) in area size (100, 100) to 2407 (rounds) in area size (1000, 1000).We have also tested the effect of the speed of MDC on QoS criteria. Figure6 summarizes the simulation results of MDC-K with K-means by varying the speed of MDC.Figure 6
Comparison of the network lifetime (residual energy), the stability, the throughput, latency time of MDC-K with various MDC velocities during 10000 rounds.
(a)(b)(c)(d)We have also tested the effect of the speed of MDC on QoS criteria. Figure6 summarizes the simulation results of MDC-K with K-means by varying the speed of MDC.As shown in Figure6, when the speed of MDC increases, the residual energy, throughput, and stability increases. This can show the importance of MDC in routing protocols. In order to evaluate the effect of the density of nodes on the improvement of the QoS. We have simulated MDC-K with a different number of nodes. Figure 7 presents the results of the simulation.Figure 7
Comparison of the network lifetime (residual energy), stability, throughput, and latency time of MDC-K with a various number of nodes.Results showed that the variation of density of nodes keeps the same benefit and improvement with residual energy, stability, throughput, and latency time. As shown in Figure7, when the number of nodes increases, the latency slightly increases, but it is still less than the latency of LEACH and LEACH-K protocols.


## 5. Discussion

To evaluate the efficiency of the proposed protocol in enhancing QoS of clustering protocol, Table4 compares this protocol with similar protocols reported in the literature.Table 4
Comparison of the network lifetime, stability, throughput, and latency time between MDC-K and similar protocols reported in the literature.
Type of networkEnergy efficiencyStabilityCH electionThroughputNetwork lifetimeLatency timeLEACH-K [1]Homogeneous WSNHigher than LEACH and TEENMore stable than TEEN and LEACHBased on initial and residual energyMore than TEEN, LEACH, MDC minimum distance leach, and MDC maximum residual energy leachHigher than TEEN and LEACHLesser than LEACH and TEENMDC-K (this paper)Homogeneous WSNHigher than TEEN, LEACH, LEACH-K, MDC minimum distance leach, MDC maximum residual energy leach, energy-efficient cluster head LEACH, DV-MDC, and MEACBMMore stable than TEEN, LEACH, LEACH-K, MDC minimum distance leach, and MDC maximum residual energy leachBased on residual energyHigher than TEEN, LEACH, LEACH-K, MDC minimum distance leach, MDC maximum residual energy leach, energy-efficient cluster head LEACH, MEACBM, and DV-MDCHigher than TEEN, LEACH, LEACH-K, MDC minimum distance leach, MDC maximum residual energy leach, energy-efficient cluster head LEACH, and DV-MDCLesser than TEEN, LEACH, LEACH-K, MDC minimum distance leach, MDC maximum residual energy leach, MEACBM, and energy-efficient cluster head LEACHMDC minimum distance leach [5]Homogeneous WSNHigher than TEEN, LEACH, and LEACH-KMore stable than LEACH, TEEN, and LEACH-KBased on initial and residual energyMore than TEEN and LEACHHigher than TEEN, LEACH, and LEACH-KLesser than TEEN, LEACH, and LEACH-KMDC maximum residual energy leach [8]Homogeneous WSNHigher than TEEN, LEACH, and LEACH-KMore stable than LEACH, TEEN, and LEACH-KBased on initial and residual energyMore than TEEN and LEACHHigher than TEEN, LEACH, and LEACH-KLesser than TEEN, LEACH, LEACH-K, and MDC minimum distance leachDV-MDC [23]Homogeneous WSNHigher than TEEN, LEACH, LEACH-K, MDC minimum distance leach, MDC maximum residual energy leach, and energy-efficient cluster head LEACHMore stable than LEACH, TEEN, MDC minimum distance leach, MDC maximum residual energy leach, and energy-efficient cluster head LEACHBased on initial and residual energyMore than TEEN, LEACH, energy-efficient cluster head LEACH, MDC minimum distance leach, and MDC maximum residual energy leachHigher than TEEN, LEACH, LEACH-K, MDC minimum distance leach, MDC maximum residual energy leach, and energy-efficient cluster head LEACHLesser than LEACH, LEACH-K, MDC minimum distance leach, MDC maximum residual energy leach, and energy-efficient cluster head LEACHEnergy-efficient cluster head LEACH [14]Homogeneous WSNHigher than TEEN, LEACH, LEACH-K, MDC minimum distance leach, and MDC maximum residual energy leachMore stable than LEACH, TEEN, MDC minimum distance leach, MDC maximum residual energy leach, and energy-efficient cluster head LEACHBased on initial and residual energyMore than LEACH and TEENHigher than TEEN, LEACH, LEACH-K, MDC minimum distance leach, and MDCLesser than TEEN and LEACHMDC/PEQ [28]Homogeneous WSNHigher than TEEN, LEACH, and LEACH-KMore stable than LEACH and TEENBased on residual energyMore than TEEN, LEACH, energy-efficient cluster head LEACH, MDC minimum distance leach, and MDC maximum residual energy leachHigher than TEEN, LEACH, and LEACH-KLesser than TEEN and LEACH, LEACH-K, and energy-efficient cluster head LEACHMEACBM [14]Homogeneous WSNHigher than TEEN, LEACH, energy-efficient cluster head LEACH, MDC minimum distance leach, MDC maximum residual energy leach, and LEACH-KMore stable than TEEN, LEACH, energy-efficient cluster head LEACH, MDC minimum distance leach, and MDC maximum residual energy leachBased on residual energyMore than TEEN, LEACH, energy-efficient cluster head LEACH, MDC minimum distance leach, and MDC maximum residual energy leachHigher than TEEN, LEACH, MDC minimum distance leach, MDC maximum residual energy leach, and LEACH-KLesser than TEEN, LEACH, LEACH-K, and energy-efficient cluster head LEACHDEE [2]Homogeneous WSNHigher than LEACH, optimum LEACH, TEEN, MDC minimum distance leachMore stable than LEACH, optimum LEACH, TEEN, and MDC minimum distance leachBased on residual energy and distance to base stationMore than LEACH, optimum LEACH, TEEN, and MDC minimum distance leachHigher than LEACH, optimum LEACH, TEEN, and MDC minimum distance leachLesser than LEACH, optimum LEACH, TEEN, and MW-LEACHAs shown in Table4, several protocols were proposed for enhancing the LEACH protocol. For instance, the LEACH-K protocol is proposed for improving all the metrics of QoS. MDC-K approach also improved residual energy, latency, stability, and throughput. However, LEACH-K was not useful for LSWSNs. The MDC-K protocol is better than MDC minimum distance LEACH, MDC maximum residual energy leach, and DV-MDC because it improves all the QoS criteria. For example, MDC-K has 37865 (packet) throughput value more than 23300 (packet) in MDC maximum residual energy leach, 23422 (packet) in MDC maximum residual energy leach, and 714 (packet) in DV-MDC. The combination of the K-means and MDC approach improves all the QoS criteria of the LEACH-K protocol.


## 6. Conclusion

In this paper, we have proposed a new hybrid protocol called MDC-K, which is a combination of LEACH-K approach and MDC. Specifically, this protocol uses the K-means algorithm to reduce energy consumption in the CH election phase. In addition, MDC is used as an intermediate between the CH and the BS to further enhance the QoS criteria of WSN, to minimize time delays during data collection, and to extend the lifetime of the network in WSN. The obtained simulation results demonstrate that MDC-K has a considerable impact on energy consumption and QoS metrics. Particularly, this protocol achieves a significant improvement in terms of energy consumption, residual energy, throughput, latency time, and stability gains better than LEACH, TEEN, LEACH-K, and maximum residual energy LEACH protocols. Moreover, the simulation results show that MDC-K is more suited for LSWSNs as it maintains a good performance in energy consumption and all studied QoS criteria for the large supervised area.



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*Source: 5531185-2021-03-15.xml*