# A High-Performance Energy-Balanced Forwarding Strategy for Wireless Sensor Networks

Authors: Zhangxiang Hu; Xiaodan Jiang; Xiajun Ding; Kai Fang; Xiaolong Zhou
Journal: Mobile Information Systems
 (2022)

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## Abstract

The wireless sensor network (WSN) is composed of several sensor nodes organized by multi-hop self-organization, which is a typical network for the industrial internet in industrial application. However, the energy using and processing capacity of each node are greatly limited. Therefore, it is of great significance to study energy-saving and efficient communication protocols for WSN. To prolong the lifetime of WSN and improve network throughput, a high throughput routing protocol with balanced energy consumption is proposed. The designed protocol first employs theK-means clustering algorithm to cluster the nodes, then calculates the weights based on the residual energy of and distance between the nodes, and finally selects the best node as the cluster head. Moreover, the optimal size of the package is determined by the parameters of the wireless transceiver and the channel conditions. In the data transmission stage, the Dijkstra algorithm is used to calculate the multi-objective weight function as the link cost. Experimental results demonstrate the superior performance of the proposed protocol over the CERP and TEEN routing protocols in terms of energy saving of network nodes, so as to improve the throughput and survival time of the entire system.


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## Body

## 1. Introduction

Wireless sensor networks (WSNs) are broadly applied in the Industrial Internet of Things to enhance the productivity and efficiency of existing and prospective manufacturing industries. WSN is composed of a large number of low-power, micro-smart sensor nodes that are randomly deployed to perform sensing tasks in the monitoring area. In recent years, WSNs have played an important role in the production of life, environmental monitoring, national security, and other fields [1–5]. Because sensor nodes are usually powered passively with limited energy and weak computing power, it is important to build an energy-efficient WSN. However, how to construct an excellent WSN remains a challenging problem. Network topology control provides an effective way to solve this problem. Generally speaking, topology control refers to an underlying network topology conversion technology that can enhance system performance or minimize routing costs [6]. Clustering is an effective and widely used network topology scheme among topology control technologies. In addition, the clustering-based routing protocol helps to save the energy of sensor nodes, thereby prolonging the network lifetime [7, 8].A typical clustering topology structure is shown in Figure1. Each cluster contains a cluster member node (CM) and a cluster head node (CH). The CM is used to collect data and transmit the results to the CH, and then the CH merges the data within the cluster and forwards it to the remote base station (BS) through multi-hops transmission afterwards. The process is mainly divided into two stages: (1) the network topology construction stage, which carries out network clustering and cluster head node screening; and (2) the steady-state stage, which carries out data communication, data fusion, and data transmission.Figure 1
The structure of WSN clustering topology.Currently, many researchers have focused on research of clustering routing protocols. For example, Fan and Song [9] proposed an improved low-energy adaptive clustering hierarchy (IM-LEACH), which was the most popular active routing protocol based on clustering. This protocol was composed of self-configuration and random adaptive methods, and provided local and low-energy access control for data transmission. Razzaq et al. [10] put forward an optimal K-means clustering scheme for data packets, which was another K-means clustering protocol based on high-energy efficiency. The protocol used a weighted function for cluster head selection to minimize node energy consumption and enhance system performance. Additionally, Liu et al. [11] proposed a multi-channel AODV routing protocol based on the Dijkstra algorithm, which both the energy consumed by nodes and the energy consumed during the data transmission were premeditated. Furthermore, the Dijkstra algorithm was used to select the path with the least energy consumption from the multi-data transmission channels. A clustering routing protocol based on the Dijkstra algorithm was suggested hereafter by Abderrahim et al. [12]. It was a centralized routing protocol in which the BS assigned a weight matrix to the network and then applied the Dijkstra algorithm to calculate the optimal data path from the source to the destination node. This approach was suitable for schemes that require periodic or query-based data reporting. Chen et al. [13] improved the traditional LEACH protocol, which considered load balance in the clusters. The main disadvantage of this scheme was that the residual energy of the node was neglected when selecting the relay nodes in the data transmission stage.To ensure the high throughput of the WSN cluster communication protocol, a high throughput routing protocol with balanced energy consumption is proposed in this paper. TheK-means algorithm is employed to achieve WSN clustering when exchanging data between nodes. For the CH node selection, we calculated two weight functions and opted the node not only containing the largest residual energy but also distancing the least to the initial cluster center accordingly. Since CH node is the only node that needs to aggregate data from CM nodes and relay it to BS, the proposed method guarantees a balanced energy consumption in the network. Moreover, the traditional Dijkstra algorithm is employed and the multi-objective weight function is used as link cost, which helps to refine the energy efficiency of the cluster data communication and eliminate the premature death of some nodes. The main contributions of this paper are those given here.
(1)
The best cluster head is selected according to the remaining energy, and the size of the cluster is reasonably controlled according to the channel state. It is helpful to reduce the communication energy consumption of the nodes in the cluster and balance the energy consumption between the WSN clusters(2)
The distance between cluster heads and the remaining energy of cluster heads are fully taken into consideration when routing data. Compared with the conventional methods, the energy between cluster heads is well counterpoised and the network throughput is greatly amelioratedThe rest of this paper is organized as follows. In Section2, related models include WSN topology model and energy consumption model of wireless communication are introduced. In Section 3, the proposed high throughput routing protocol for nodes with balanced energy consumption is presented in detail. The experimental results and analysis are discussed in Section 4, followed by concluding remarks in Section 5.


## 2. Related Models

### 2.1. WSN Topology Model

The WSN consists ofN wireless communication nodes, numbered as Si (i =1,2, ….., N). The nodes are randomly distributed in a rectangular area of L∗L; the position of BS is fixed and far away from the sensing area. Nodes are then grouped into clusters, within which contains a CH node and several CM nodes. Due to the infinite energy and high computational capacity of the BS, the network clustering and the selection of CH nodes are carried out in the BS, and the selected CH information is notified to the CM nodes in the cluster. This process can reduce the energy consumption of the network topology construction. When constructing the network clustering topology, the nodes wait until a sensing event occurs, and the CM node that is close to the occurring position in the cluster will sense the environment and transmit data to the CH node. The CH node then fuses collected information and uses the Dijkstra’s shortest path algorithm to efficiently transmit data to the BS in multi-hops.


### 2.2. Energy Consumption Model of Wireless Communication

Wireless communication between sensor nodes involves many aspects of energy consumption, such as signal amplification energy consumption, signal transmission energy consumption, and data processing energy consumption. In this paper, a classical energy consumption model [14] is utilized to evaluate the energy consumption of nodes. As shown in Figure 2, a large amount of energy is consumed in the data transmission stage in WSN. Theoretically, when sending the same data packet, the total energy consumption depends on the size of the data packet. To minimize the energy consumption of sending packets, the optimal packet size Popt is considered to diminish energy consumption during data transmission. Popt has been proved in [10], and its calculation can be formulated in Eq. (1).
(1)Popt=C02−4C0/ln1−p−C02,where C0=+K1, α represents the header size (unit: bit), K1 represents the energy consumed by the payload in communication, K2 represents the energy consumed by the node to start up, and p represents the bit error rate (BER) of the channel. Thus, the energy ETX and ERX exploited by nodes to send and receive Popt bit data are formulated in Eq. (2) and Eq. (3), respectively.
(2)ETXPopt,x=Popt⋅Eelec+Popt⋅εamp,(3)ERXPopt=Popt⋅Eelec,where x represents the distance between sensor nodes, Eelec represents the energy consumed by sending or receiving unit bit, and εamp represents the energy consumed by signal amplification of sending nodes as in Eq. (4).
(4)εamp=εfs⋅x2ifx<xthεmp⋅x4ifx≥xth,where xth denotes the distance threshold, and εfs and εmp denote the amplification energy and multipath fading parameters of the free space signal, respectively. If the distance x between the sending node and the receiving node is greater than or equal to xth, the multipath fading channel model will be used. Otherwise, the free space propagation model will be used. Thus, a Popt bit size packet is sent to and received, and the total energy consumed is Etotal as in Eq. (5), which can be expanded to Eq. (6).
(5)Etotal=ETX+ERX+EDA,(6)Etotal=Popt⋅2⋅Eelec+εamp+EDA,where EDA represents the energy consumed by data in the process of CH node fusion. Suppose that xCH denotes the distance between the CM node and the CH node, and xBS denotes the distance between the CH node and the BS, then the residual energy of CM node after CM transmits Popt bit data to CH can be formulated in Eq. (7).
(7)ECM=Einit−ETXPopt,xCH,Figure 2
Energy consumption model of wireless communication.After receiving the data from the CM, the CH performs data fusion, and finally transmits the fused data to BS. The residual energy of CH node is then can be calculated in Eq. (8).
(8)ECH=Einit−ERXPopt−EDA−ETXPopt,xBS.


## 2.1. WSN Topology Model

The WSN consists ofN wireless communication nodes, numbered as Si (i =1,2, ….., N). The nodes are randomly distributed in a rectangular area of L∗L; the position of BS is fixed and far away from the sensing area. Nodes are then grouped into clusters, within which contains a CH node and several CM nodes. Due to the infinite energy and high computational capacity of the BS, the network clustering and the selection of CH nodes are carried out in the BS, and the selected CH information is notified to the CM nodes in the cluster. This process can reduce the energy consumption of the network topology construction. When constructing the network clustering topology, the nodes wait until a sensing event occurs, and the CM node that is close to the occurring position in the cluster will sense the environment and transmit data to the CH node. The CH node then fuses collected information and uses the Dijkstra’s shortest path algorithm to efficiently transmit data to the BS in multi-hops.


## 2.2. Energy Consumption Model of Wireless Communication

Wireless communication between sensor nodes involves many aspects of energy consumption, such as signal amplification energy consumption, signal transmission energy consumption, and data processing energy consumption. In this paper, a classical energy consumption model [14] is utilized to evaluate the energy consumption of nodes. As shown in Figure 2, a large amount of energy is consumed in the data transmission stage in WSN. Theoretically, when sending the same data packet, the total energy consumption depends on the size of the data packet. To minimize the energy consumption of sending packets, the optimal packet size Popt is considered to diminish energy consumption during data transmission. Popt has been proved in [10], and its calculation can be formulated in Eq. (1).
(1)Popt=C02−4C0/ln1−p−C02,where C0=+K1, α represents the header size (unit: bit), K1 represents the energy consumed by the payload in communication, K2 represents the energy consumed by the node to start up, and p represents the bit error rate (BER) of the channel. Thus, the energy ETX and ERX exploited by nodes to send and receive Popt bit data are formulated in Eq. (2) and Eq. (3), respectively.
(2)ETXPopt,x=Popt⋅Eelec+Popt⋅εamp,(3)ERXPopt=Popt⋅Eelec,where x represents the distance between sensor nodes, Eelec represents the energy consumed by sending or receiving unit bit, and εamp represents the energy consumed by signal amplification of sending nodes as in Eq. (4).
(4)εamp=εfs⋅x2ifx<xthεmp⋅x4ifx≥xth,where xth denotes the distance threshold, and εfs and εmp denote the amplification energy and multipath fading parameters of the free space signal, respectively. If the distance x between the sending node and the receiving node is greater than or equal to xth, the multipath fading channel model will be used. Otherwise, the free space propagation model will be used. Thus, a Popt bit size packet is sent to and received, and the total energy consumed is Etotal as in Eq. (5), which can be expanded to Eq. (6).
(5)Etotal=ETX+ERX+EDA,(6)Etotal=Popt⋅2⋅Eelec+εamp+EDA,where EDA represents the energy consumed by data in the process of CH node fusion. Suppose that xCH denotes the distance between the CM node and the CH node, and xBS denotes the distance between the CH node and the BS, then the residual energy of CM node after CM transmits Popt bit data to CH can be formulated in Eq. (7).
(7)ECM=Einit−ETXPopt,xCH,Figure 2
Energy consumption model of wireless communication.After receiving the data from the CM, the CH performs data fusion, and finally transmits the fused data to BS. The residual energy of CH node is then can be calculated in Eq. (8).
(8)ECH=Einit−ERXPopt−EDA−ETXPopt,xBS.


## 3. Routing Protocol

### 3.1. Network Deployment Stage

The sensor nodes are randomly deployed in the monitoring area. Considering the nodes’ inability to form a routing table, data transmission cannot be realized. Therefore, the sensor nodes in the network broadcast beacon to their neighbors in the communication range. The beacon frame includes the node number and node position. After all nodes in the network broadcasting beacon frame, all nodes can set up neighbor list, and routing tables can be easily generated in accordance with the list. Given to the high power of the BS, it can broadcast an initialization request data frame to all nodes in the network in a single hop. The nodes in the network will reply to the data immediately after receiving the message frame. According to the routing table inside the node, the response data frame is sent back to the BS along the shortest path provided by the Dijkstra algorithm in the form of multi-hops. The response data frame contains the residual information of the node residual energy and node position, assuming that the node position in the network has been obtained by GPS or positioning technology [15, 16]. So far, the deployment of WSN has been completed.


### 3.2. Network Clustering Stage

In this stage, theK-means clustering algorithm is employed for clustering the whole sensor network, which is an unsupervised learning algorithm that collects data into K clusters. Sunil et al. [17] have made a detailed study on the clustering problem which was most suitable for WSNs, and the value of K was calculated in Eq. (9). When the network is clustered into K clusters, the cluster similarity between clusters is low, while the intra-class similarity is high.
(9)K=N2π⋅εfsεmp⋅FxBS2where N represents the number of sensor nodes in the network, F represents the dimension (two-dimensional plane) of a given network, xBS represents the average distance between sensor nodes and BSs in the network, and K represents the most suitable class number divided from the original clusters.The pseudo-code ofK-means WSNs clustering algorithm is detailed in Algorithm 1. After the WSN has run for a fixed number of rounds, Algorithm 1 is re-executed to update the network clustering.Algorithm 1:K-means WSNs clustering algorithm.
K-means WSNs clustering algorithmStep 1: Using Eq. (9) to calculate the value of the optimal cluster number K. Then, randomly select K nodes as the initial cluster head nodes.Step 2: Calculating the Euclidean distance from each node to the clustering center and assigning the node to the nearest clustering center.Step 3: Calculating the centers of all nodes within a particular cluster and updating the centers of the clusters.Step 4: Repeating Step 2 for the new clustering center. If the cluster to which the node belongs changes, repeat Step 3 or stop the algorithm.


### 3.3. CH Node Selection Stage

Once a WSN is divided intoK clusters, one CH node is selected from each clustering. The main function of the CH node is to aggregate the data of the CM nodes in the clustering, and forwards the data to the server by multi-hop after data fusion. Because the CH node needs to bear greater load, from the perspective of network energy balance, nodes with more residual energy and closer to the average distance of the CM node should be selected as the CH node. By doing so, the premature death of the CH node can be avoided and the energy consumption of the data transmission of the CM nodes can be decreased. In this paper, a weight function is designed to help select CH nodes, as in Eq. (10).
(10)Wi=C1⋅Ei+C2⋅DC2iwhere i =1, 2, 3, 4 … N, C1 and C2 are constants, Wi represents the weight value of each node in the cluster, 𝐷𝐶2𝑖 represents the distance between the ith node and the center of the cluster, and Ei represents the residual energy of the ith node.After obtaining the weight of all the sensor nodes in the cluster, a criterion is needed to determine which nodes are more suitable as CHs. In this paper, the evaluation function is designed based on energy consumption balance and energy-saving goals, as in Eq. (11).
(11)Wstd=C1⋅Emax+C2⋅avgDC2iwhere Wstd represents the standard weight, C1 and C2 are constants, avgDC2i represents the average distance from all nodes in the cluster to the center, and Emax represents the highest residual energy of nodes in the cluster.The weightWi of each node in the cluster is compared with the standard weight value Wstd, and node i corresponding to the minimum value of Wi−Wstd is used as the CH. To ensure that the energy consumption of intra-cluster nodes is balanced, the CH nodes are re-selected as described above for each fixed number of data acquisitions.


### 3.4. Data Transmission Stage

So far, the clustering and CH node selection of WSNs have been completed, at which time sensor nodes can collect and transmit data. Since most of the data collected by sensor nodes are basically meaningless, only when the sensing value exceeds a certain threshold value can the sensing data be meaningful. Therefore, this paper proposes an event-driven routing protocol. When the sensed data is larger than the threshold valueT, the data is transmitted to the CH node. The CH node fuses together the data into a plurality of member nodes in the cluster, and then sends the data to the BS. The CH node is also selected from the same kind of nodes, and their communication distance is limited as well. So multi-hop transmission between cluster heads is crucial to transmit data to the BS. Therefore, the Dijkstra algorithm is employed in this paper to find the best path from the source CH node to the target BS according to the link cost. The main steps are detailed as follows.Step 1: Create a setS, which contains only the source node NS and the link cost WNS,NA from NS to the adjacent node NA at the initial time.
(12)S=Ns.The weight function proposed in this paper, as link costWNS,NA, contains two factors: the residual energy of the next hop node (the node to receive data) and the distance from the source node to the next hop node, as in Eq. (13),
(13)WNS,NA=d1⋅ENA+d2⋅DS2A,where WNS,NA represents the link cost between the source node NS and the neighboring node NA, and DS2A represents the distance between the source node NS and the neighboring node NA. d1 and d2 are adjustable parameters and satisfy 0≤d1<d2 and d1<d2≤1.Step 2: If the source nodeNS and its neighboring node NA are not in the set S, then adding the node to S, and subsequently continue to find the node NC adjacent to the source node NS. If the node is not in the set S either, then adding the NC to S, and the link cost is WNS,NC.Step 3: Compare the link costWNS,NA with WNS,NC, and then select a neighboring node with the least link cost as the next hop node of the source node, as in Eq. (14).
(14)WNS,NN=minWNS,NA,WNS,NC.Step 4: Use the neighboring node selected in Step 3 as the source node, and then repeat Steps 2 and 3 until the minimum cost of data transferring from the CH to the BS is found, that is, finding a transmission path that minimizes the Eq. (15). When data is transmitted to the BS, the algorithm ends.
(15)Cmin=∑i=1m−1WNs,ND,where ND represents the BS and m represents the number of nodes included in the transmission path.The minimum cost path selection process for transmitting data to the BS by multi-hop between CHs is shown in Figure3. Finally, the path with the minimum energy consumption is selected to transmit data from CH to BS.Figure 3
Minimum energy consumption path selection.
(a)
Cluster head neighbor topology(b)
Minimum energy consumption transmission path


## 3.1. Network Deployment Stage

The sensor nodes are randomly deployed in the monitoring area. Considering the nodes’ inability to form a routing table, data transmission cannot be realized. Therefore, the sensor nodes in the network broadcast beacon to their neighbors in the communication range. The beacon frame includes the node number and node position. After all nodes in the network broadcasting beacon frame, all nodes can set up neighbor list, and routing tables can be easily generated in accordance with the list. Given to the high power of the BS, it can broadcast an initialization request data frame to all nodes in the network in a single hop. The nodes in the network will reply to the data immediately after receiving the message frame. According to the routing table inside the node, the response data frame is sent back to the BS along the shortest path provided by the Dijkstra algorithm in the form of multi-hops. The response data frame contains the residual information of the node residual energy and node position, assuming that the node position in the network has been obtained by GPS or positioning technology [15, 16]. So far, the deployment of WSN has been completed.


## 3.2. Network Clustering Stage

In this stage, theK-means clustering algorithm is employed for clustering the whole sensor network, which is an unsupervised learning algorithm that collects data into K clusters. Sunil et al. [17] have made a detailed study on the clustering problem which was most suitable for WSNs, and the value of K was calculated in Eq. (9). When the network is clustered into K clusters, the cluster similarity between clusters is low, while the intra-class similarity is high.
(9)K=N2π⋅εfsεmp⋅FxBS2where N represents the number of sensor nodes in the network, F represents the dimension (two-dimensional plane) of a given network, xBS represents the average distance between sensor nodes and BSs in the network, and K represents the most suitable class number divided from the original clusters.The pseudo-code ofK-means WSNs clustering algorithm is detailed in Algorithm 1. After the WSN has run for a fixed number of rounds, Algorithm 1 is re-executed to update the network clustering.Algorithm 1:K-means WSNs clustering algorithm.
K-means WSNs clustering algorithmStep 1: Using Eq. (9) to calculate the value of the optimal cluster number K. Then, randomly select K nodes as the initial cluster head nodes.Step 2: Calculating the Euclidean distance from each node to the clustering center and assigning the node to the nearest clustering center.Step 3: Calculating the centers of all nodes within a particular cluster and updating the centers of the clusters.Step 4: Repeating Step 2 for the new clustering center. If the cluster to which the node belongs changes, repeat Step 3 or stop the algorithm.


## 3.3. CH Node Selection Stage

Once a WSN is divided intoK clusters, one CH node is selected from each clustering. The main function of the CH node is to aggregate the data of the CM nodes in the clustering, and forwards the data to the server by multi-hop after data fusion. Because the CH node needs to bear greater load, from the perspective of network energy balance, nodes with more residual energy and closer to the average distance of the CM node should be selected as the CH node. By doing so, the premature death of the CH node can be avoided and the energy consumption of the data transmission of the CM nodes can be decreased. In this paper, a weight function is designed to help select CH nodes, as in Eq. (10).
(10)Wi=C1⋅Ei+C2⋅DC2iwhere i =1, 2, 3, 4 … N, C1 and C2 are constants, Wi represents the weight value of each node in the cluster, 𝐷𝐶2𝑖 represents the distance between the ith node and the center of the cluster, and Ei represents the residual energy of the ith node.After obtaining the weight of all the sensor nodes in the cluster, a criterion is needed to determine which nodes are more suitable as CHs. In this paper, the evaluation function is designed based on energy consumption balance and energy-saving goals, as in Eq. (11).
(11)Wstd=C1⋅Emax+C2⋅avgDC2iwhere Wstd represents the standard weight, C1 and C2 are constants, avgDC2i represents the average distance from all nodes in the cluster to the center, and Emax represents the highest residual energy of nodes in the cluster.The weightWi of each node in the cluster is compared with the standard weight value Wstd, and node i corresponding to the minimum value of Wi−Wstd is used as the CH. To ensure that the energy consumption of intra-cluster nodes is balanced, the CH nodes are re-selected as described above for each fixed number of data acquisitions.


## 3.4. Data Transmission Stage

So far, the clustering and CH node selection of WSNs have been completed, at which time sensor nodes can collect and transmit data. Since most of the data collected by sensor nodes are basically meaningless, only when the sensing value exceeds a certain threshold value can the sensing data be meaningful. Therefore, this paper proposes an event-driven routing protocol. When the sensed data is larger than the threshold valueT, the data is transmitted to the CH node. The CH node fuses together the data into a plurality of member nodes in the cluster, and then sends the data to the BS. The CH node is also selected from the same kind of nodes, and their communication distance is limited as well. So multi-hop transmission between cluster heads is crucial to transmit data to the BS. Therefore, the Dijkstra algorithm is employed in this paper to find the best path from the source CH node to the target BS according to the link cost. The main steps are detailed as follows.Step 1: Create a setS, which contains only the source node NS and the link cost WNS,NA from NS to the adjacent node NA at the initial time.
(12)S=Ns.The weight function proposed in this paper, as link costWNS,NA, contains two factors: the residual energy of the next hop node (the node to receive data) and the distance from the source node to the next hop node, as in Eq. (13),
(13)WNS,NA=d1⋅ENA+d2⋅DS2A,where WNS,NA represents the link cost between the source node NS and the neighboring node NA, and DS2A represents the distance between the source node NS and the neighboring node NA. d1 and d2 are adjustable parameters and satisfy 0≤d1<d2 and d1<d2≤1.Step 2: If the source nodeNS and its neighboring node NA are not in the set S, then adding the node to S, and subsequently continue to find the node NC adjacent to the source node NS. If the node is not in the set S either, then adding the NC to S, and the link cost is WNS,NC.Step 3: Compare the link costWNS,NA with WNS,NC, and then select a neighboring node with the least link cost as the next hop node of the source node, as in Eq. (14).
(14)WNS,NN=minWNS,NA,WNS,NC.Step 4: Use the neighboring node selected in Step 3 as the source node, and then repeat Steps 2 and 3 until the minimum cost of data transferring from the CH to the BS is found, that is, finding a transmission path that minimizes the Eq. (15). When data is transmitted to the BS, the algorithm ends.
(15)Cmin=∑i=1m−1WNs,ND,where ND represents the BS and m represents the number of nodes included in the transmission path.The minimum cost path selection process for transmitting data to the BS by multi-hop between CHs is shown in Figure3. Finally, the path with the minimum energy consumption is selected to transmit data from CH to BS.Figure 3
Minimum energy consumption path selection.
(a)
Cluster head neighbor topology(b)
Minimum energy consumption transmission path


## 4. Experimental Results and Analysis

In this paper, the experiment is simulated on MATLAB platform.N =50 nodes are randomly deployed in a square area with L =100 m. These nodes are divided into K clusters, and each cluster contains N/K nodes. The performance of the data protocol for the WSN is evaluated according to three parameters: residual energy, energy consumption balance, and network throughput. The residual energy is evaluated to illustrate the energy saving of the data routing protocol, the energy consumption balance is evaluated to demonstrate the energy consumption balance within the WSN, and the network throughput is evaluated to indicate the amount of data successfully transmitted to the BS per unit time. In the experiments, the concept of “round” is defined, and each “round” carries out 100 data transmissions. Each node in the WSN transmits Popt data at each time step. Due to the mobility of the nodes in the network, their distribution is not fixed. We assumed that for every 10 “rounds” of data communication, the location of the nodes will change randomly, thus forming a new distribution of the node cluster. Therefore, for every 10 “rounds” of data communication, the CH and cluster need re-selecting. The specific parameter settings of the data communication model are listed in Table 1.Table 1
Experimental parameter settings.
ParameterValueEelec50 nJ/bitEinit0.5 JEDA5 nJ/bit/signalL×L100 m ×100 mNumber of nodesN100Coordinate of BS(50 m,0 m)Optimal groupingPopt1701bitsεfs10pJ/bit/m2εmp0.0013pJ/bit/m4The optimal grouping size in Table1 has been validated in reference [10]. Experiments compare the CERP improved protocol HCERP [18] and the TEEN improved IM-TEEN [19] routing protocol. Sensor networks have the following properties.Property 1.
Being homogeneous in nature, sensor clusters equip with the same initial energy, and are randomly deployed in the monitoring area.Property 2.
The whole network contains only one BS.Property 3.
After deployment, the sensor nodes and BS locations are fixed and known.Property 4.
CH collects the information of nodes in the cluster, and then forwards it to the BS in the form of multi-hop.Property 5.
The cluster uses the Rayleigh fading channel model [18, 20].


### 4.1. Analysis of Average Residual Energy of Nodes

With the operation of the sensor network, the residual energy of the sensor nodes will gradually decrease. The residual energy of the node can effectively reflect the energy consumption of the routing protocol. This experiment verifies the average residual energy of all nodes in the network when node communicates data in different rounds. As shown in Figure4, the horizontal coordinate is the number of rounds of data communication, and the vertical coordinate is the average residual energy of nodes in the WSN.Figure 4
Average residual energy of nodes.The experimental results show that with the increase of data communication rounds, the average residual energy of nodes corresponding to the three routing protocols gradually decreases. After conducting the same number of routes of data communication, the average residual energy of the proposed routing protocol is higher than that of GCERP and IM-TEEN protocols, meaning that its performance is better than that of HCERP and IM-TEEN protocols. Because nodes in the IM-TEEN protocol keep sensing situated environment to reach the desired threshold, energy loss would be inevitable. The HCERP protocol increases energy consumption at the beginning of each round because of uneven clustering. The scheme proposed in this paper, however, takes energy and distance into account, shaping the whole network more energy-efficient and the entire network less energy-consuming.


### 4.2. Analysis of Node Lifetime

In the light of the different positions of nodes in the network, each node consumes different amount of energy in data communication and transmission, which will result in unbalanced energy consumption of the whole WSN. Theoretically, the early existence of multiple nodes in the network could risk paralyzing the whole network and subsequently lose its original function. Experiments verify the number of dead nodes in the network when three routing protocols are used to run data communication in different rounds. The experimental results are shown in Figure5.Figure 5
Number of surviving nodes.Figure5 depicts the relationship between the number of surviving sensor nodes and the data communication rounds of the sensor network. As far as the results are considered, as the data communication rounds in sensor networks increases, the number of surviving sensor nodes decreases slowly. With the increase of communication rounds, the energy consumed by the nodes increases. The farther away some nodes are from the CH, the faster the energy is consumed, resulting in the death of these nodes with the increase of communication times. Meanwhile, when communicating for the same number of rounds in the network, the number of surviving nodes in our network is higher than that of the HCERP and IM-TEEN protocols. Owning to the fact that the proposed routing protocol fully considers the residual energy of nodes and the distance between nodes and the center of the cluster, it can equally process the two factors, making the energy consumption of nodes inside the cluster more balanced.


### 4.3. Analysis of Network Transmission Rate and Throughput

Network transmission rate and network throughput are key indicators for WSNs. Network transmission rate PDR indicates the amount of data received by the BS in a unit time. The experiment verifies the network transmission rate and network throughput of the three routing protocols under the same conditions. The experimental results are shown in Figures6 and 7, respectively.Figure 6
Packet transfer rate (K/s) and node density.Figure 7
Network throughput and rounds.Figure6 shows that with the increase of the number of nodes, the PDR of IM-TEEN and HCERP ascends to a certain extent, and then begins to descend. When the number of nodes in the network is 150, the network transmission rates of the three routing protocols are the highest. Even though the number of nodes is dropping, the data transmission rate of the proposed routing protocol is still much higher than that of HCERP and IM-TEEN protocols, which indicates that the proposed routing protocol has the highest scalability out of the three.Figure7 shows the relationship between data communication rounds and overall throughput for WSNs. The throughput of WSN is calculated as the total number of data packets successfully received by the BS after each round of data communication. The experimental results show that the proposed algorithm has higher throughput than other algorithms. The main reason for the increase in network throughput is that the number of surviving nodes in HCERP and IM-TEEN protocols is less than that proposed in this paper for the same number of data communication rounds. In addition, the main reason for the significant decrease of TEEN throughput is that the node will not send data to the BS if the specific threshold is not reached.


### 4.4. Analysis of Surviving Nodes

As the number of collection rounds in the system increases, the energy of the nodes in the sensor network will also be consumed, and part of the energy in the nodes will be exhausted. This experiment uses the Average hop as an indicator of the energy consumption balance of the routing protocol. The experimental results shown in Figure8 suggest that as the number of rounds collected by the system increases, the average hops for packet transmission also increase. As a result of the climbing number of dead nodes, packet transmission is put through many unnecessary communication links. On top of that, the experimental results reveal that our algorithm has the shortest average hops under the same conditions.Figure 8
Node survival and rounds.


## 4.1. Analysis of Average Residual Energy of Nodes

With the operation of the sensor network, the residual energy of the sensor nodes will gradually decrease. The residual energy of the node can effectively reflect the energy consumption of the routing protocol. This experiment verifies the average residual energy of all nodes in the network when node communicates data in different rounds. As shown in Figure4, the horizontal coordinate is the number of rounds of data communication, and the vertical coordinate is the average residual energy of nodes in the WSN.Figure 4
Average residual energy of nodes.The experimental results show that with the increase of data communication rounds, the average residual energy of nodes corresponding to the three routing protocols gradually decreases. After conducting the same number of routes of data communication, the average residual energy of the proposed routing protocol is higher than that of GCERP and IM-TEEN protocols, meaning that its performance is better than that of HCERP and IM-TEEN protocols. Because nodes in the IM-TEEN protocol keep sensing situated environment to reach the desired threshold, energy loss would be inevitable. The HCERP protocol increases energy consumption at the beginning of each round because of uneven clustering. The scheme proposed in this paper, however, takes energy and distance into account, shaping the whole network more energy-efficient and the entire network less energy-consuming.


## 4.2. Analysis of Node Lifetime

In the light of the different positions of nodes in the network, each node consumes different amount of energy in data communication and transmission, which will result in unbalanced energy consumption of the whole WSN. Theoretically, the early existence of multiple nodes in the network could risk paralyzing the whole network and subsequently lose its original function. Experiments verify the number of dead nodes in the network when three routing protocols are used to run data communication in different rounds. The experimental results are shown in Figure5.Figure 5
Number of surviving nodes.Figure5 depicts the relationship between the number of surviving sensor nodes and the data communication rounds of the sensor network. As far as the results are considered, as the data communication rounds in sensor networks increases, the number of surviving sensor nodes decreases slowly. With the increase of communication rounds, the energy consumed by the nodes increases. The farther away some nodes are from the CH, the faster the energy is consumed, resulting in the death of these nodes with the increase of communication times. Meanwhile, when communicating for the same number of rounds in the network, the number of surviving nodes in our network is higher than that of the HCERP and IM-TEEN protocols. Owning to the fact that the proposed routing protocol fully considers the residual energy of nodes and the distance between nodes and the center of the cluster, it can equally process the two factors, making the energy consumption of nodes inside the cluster more balanced.


## 4.3. Analysis of Network Transmission Rate and Throughput

Network transmission rate and network throughput are key indicators for WSNs. Network transmission rate PDR indicates the amount of data received by the BS in a unit time. The experiment verifies the network transmission rate and network throughput of the three routing protocols under the same conditions. The experimental results are shown in Figures6 and 7, respectively.Figure 6
Packet transfer rate (K/s) and node density.Figure 7
Network throughput and rounds.Figure6 shows that with the increase of the number of nodes, the PDR of IM-TEEN and HCERP ascends to a certain extent, and then begins to descend. When the number of nodes in the network is 150, the network transmission rates of the three routing protocols are the highest. Even though the number of nodes is dropping, the data transmission rate of the proposed routing protocol is still much higher than that of HCERP and IM-TEEN protocols, which indicates that the proposed routing protocol has the highest scalability out of the three.Figure7 shows the relationship between data communication rounds and overall throughput for WSNs. The throughput of WSN is calculated as the total number of data packets successfully received by the BS after each round of data communication. The experimental results show that the proposed algorithm has higher throughput than other algorithms. The main reason for the increase in network throughput is that the number of surviving nodes in HCERP and IM-TEEN protocols is less than that proposed in this paper for the same number of data communication rounds. In addition, the main reason for the significant decrease of TEEN throughput is that the node will not send data to the BS if the specific threshold is not reached.


## 4.4. Analysis of Surviving Nodes

As the number of collection rounds in the system increases, the energy of the nodes in the sensor network will also be consumed, and part of the energy in the nodes will be exhausted. This experiment uses the Average hop as an indicator of the energy consumption balance of the routing protocol. The experimental results shown in Figure8 suggest that as the number of rounds collected by the system increases, the average hops for packet transmission also increase. As a result of the climbing number of dead nodes, packet transmission is put through many unnecessary communication links. On top of that, the experimental results reveal that our algorithm has the shortest average hops under the same conditions.Figure 8
Node survival and rounds.


## 5. Conclusion

The routing protocol in WSN has problems such as uneven energy consumption and low throughput. To remedy these, this paper proposed a high throughput routing protocol for WSN with balanced energy consumption. In the cluster head data transmission stage, this protocol used the Dijkstra algorithm to take the weight function as the communication overhead of the links between adjacent nodes. The weight function considered the energy of nodes and the distance to neighboring nodes, which increased the lifetime of nodes and the throughput of the whole network. Moreover, the improvedK-means clustering algorithm has been employed to divide the sensor nodes into K clusters, so that the energy consumption balance can be fully considered when selecting the CH. Experimental results have demonstrated that the proposed scheme has longer network life and higher network throughput. Furthermore, we can consider other indicators (such as network delay or reliability) to study the routing protocols in the future work.



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*Source: 3058499-2022-05-05.xml*