A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks

Introduction

The latest technology development in wireless communication, sensing devices, and microelectronics have opened new frontiers in wireless sensor networks (WSNs). Critical WSNs applications include environmental monitoring (Lanzolla & Spadavecchia, 2021; Sampathkumar et al., 2020), transport (Sant et al., 2021; Kumar et al., 2020; Andrushia et al., 2021), surveillance systems (Garg et al., 2020a; Chukwu, Garg & Zahra, 2021), healthcare (Chakraborty et al., 2020b, 2020a; Agrawal et al., 2019; Bonello et al., 2018; Audu et al., 2017b, 2017a; Bugeja, Garg & Audu, 2016; Bugeja & Garg, 2015; Agrawal, Garg & Dauwels, 2013), emotions recognition and monitoring (Salankar, Mishra & Garg, 2021; Jayalakshmi et al., 2021; Bhattacharyya et al., 2021; Mangion et al., 2020), home automation (Garg et al., 2020b), battlefield monitoring, and industrial automation and control (Smaragdakis, Matta & Bestavros, 2004). WSNs contain more sensor nodes capable of sensing the physical phenomenon, packet forwarding, and communicating the packets to the destination. However, each sensor node has limitations, such as limited memory, restricted processing capability, short-range transmission, finite energy resources, and low storage capability. WSNs are becoming a good network for protecting, controlling, and facilitating real-time applications (Qing, Zhu & Wang, 2006). The primary constraint of the WSNs is the limited, non-rechargeable battery-powered sensor nodes, and these nodes have used their energy for sensing, sending, and receiving the data. When the sensor battery is drained, several areas in the sensor field will lack coverage, and valuable data from these areas will not reach the sink. Using energy among the nodes and prolonging the network’s lifetime are considered the primary challenge for HWSNs.

Various routing and clustering algorithms have been addressed to minimise average energy consumption in WSNs (Akyildiz et al., 2002; Tyagi & Kumar, 2013; Heinzelman, Chandrakasan & Balakrishnan, 2000; Younis & Fahmy, 2004; Shang, 2013; Yu et al., 2012; Amgoth & Jana, 2015; Jin et al., 2013; Mahajan, Malhotra & Sharma, 2014; Kumar, Aseri & Patel, 2009). CHs in WSNs allocate the energy to their member nodes to maintain the load-balancing of a cluster (Saini & Sharma, 2010). This protocol utilises local coordination to enhance scalability and reduce the large number of data packets communicated to the sink. The clustering protocol splits up the network structure into many clusters, and every cluster contains CH and cluster members (CMs). The CM node collects and sends information to the respective CH in each cluster. Each CH gathers the data packets from the CMs and carries out the data aggregation process. Eventually, the aggregated data is communicated to the sink. The protocol in Capone et al. (2019) selects CHs periodically using residual energy and the degree of the nodes. This approach has a low overhead and performs CH selection uniformly across the network. In Wei et al. (2011), the proposed algorithm analyses the routing algorithms in WSNs to extend the network lifetime, save power, and maintain load-balancing. In Tanwar, Kumar & Rodrigues (2015), the energy consumption and nonuniform node distribution in WSNs have been discussed. An energy-aware routing algorithm is presented for cluster-based WSNs to stabilise the energy consumption of the CHs (Shang, 2013). Quality of service (QoS) and energy-efficiency requirements are satisfied in practical WSNs (Yu et al., 2012). Amgoth & Jana (2015) highlights the optimal CH selection cluster formation and maintains the load-balanced network. For a given distance, energy efficiency improves if the data is transmitted through multi-hop. By properly selecting the CHs and the next-hop nodes for multi-hop routing, energy spent by the sensor nodes can be reduced.

Jin et al. (2013) analysed the impact of heterogeneity in WSNs, energy level, and hierarchical cluster structures. Smaragdakis, Matta & Bestavros (2004) proposed a protocol that prolongs the stability period of sensor nodes in heterogeneous WSNs (HWSNs). In Qing, Zhu & Wang (2006), Saini & Sharma (2010), average network energy and the nodes’ residual energy select the optimal CHs. In Capone et al. (2019), the proposed algorithm minimises delay based on signal-to-interference-and-noise-ratio (SINR) in WSNs. Wei et al. (2011) found optimal cluster sizes based on the hop count to the sink node. It is also used to extend the network lifetime and minimise energy consumption. Several heterogeneous routing protocols in WSNs (Tanwar, Kumar & Rodrigues, 2015) are reviewed and analysed with performance metrics. The algorithm in Bandyopadhyay & Coyle (2003) organises the nodes into several clusters in WSNs and generates a hierarchy of CHs. A genetic algorithm (GA) is a meta-heuristic algorithm used to solve optimisation problems (Pantazis, Nikolidakis & Vergados, 2013; Pal & Saraswat, 2017). GA is an appropriate scheme for solving any clustering problems in WSNs. It is also used to resolve persistent optimisation problems (Mehta & Pal, 2017). In this article, HWSNs use GA for solving the multi-hop clustering based on the newly defined fitness function (Kachitvichyanukul, 2012).

Existing solutions have the advantage of cluster formation done through the residual energy and prolonging the lifetime of WSNs. However, re-clustering consumes more energy while the end-to-end delay is not minimised. This motivates us to devise an approach for designing energy-aware multi-hop clustering for HWSNs. WSNs with heterogeneous nodes result in better network stability and extend the network lifetime. Energy consumption has been minimised using GA by selecting the optimal CHs during the re-clustering. The main contributions of this article are specified as follows:

• A GA-based energy-aware multi-hop clustering algorithm (GA-EMC) is proposed for selecting the optimal number of CHs dynamically during the re-clustering.

• A framework for optimised transmission scheduling and routing is formulated to reduce the delay under the SINR model for HWSNs.

• A combination of weak and robust sensor nodes using their residual energy mitigates the re-clustering issues.

• For optimising cluster construction, the GA maintains the stability of the nodes in a network.

• A dynamic power allocation scheme for sensor nodes is proposed to have a guaranteed QoS for nodes.

The structure of this article starts with the introduction related to the wireless sensor network, genetic algorithm, and multi-hop clustering paradigms. The following section describes the existing multi-hop clustering algorithms and their issues. In the next section, we present the GA-EMC algorithm, followed by the section which addresses the experimental results and analyses the performance of GA-EMC. The following section is a discussion, and finally, the last section discusses the conclusion.

Related works

This section presents the various modern and advanced multi-hop clustering schemes in WSNs. Many researchers have done some work in multi-hop clustering algorithms based on GA, and an overview of that work is given here. Saini & Sharma (2010) developed the probability-based CH selection and decreased the average CHs energy consumption. In Capone et al. (2019), Wei et al. (2011), the spatial distribution of CHs in WSNs by constructing a multi-hop table. It is also used to decrease the CHs when directly transmitted to the sink or base station (BS). The algorithm of Tanwar, Kumar & Rodrigues (2015) selects the CHs with higher residual energy and achieves better load-balancing among CHs. In Bandyopadhyay & Coyle (2003), CHs energy consumption was minimised during the data routing process and achieved better time complexity. The procotol of Pantazis, Nikolidakis & Vergados (2013) satisfies the QoS requirements in WSNs, and (Amgoth & Jana, 2015) addresses the cluster formation and CH selection using weight metrics in HWSNs.

In general, sensor networks can be heterogeneous regarding the initial energy, computational ability of the WSN nodes, and the bandwidth of the links (Jin et al., 2013). Designing WSNs with heterogeneous nodes increases the reliability and network lifetime. Computational and link heterogeneity reduces the latency in data transmission (Smaragdakis, Matta & Bestavros, 2004; Qing, Zhu & Wang, 2006). Various parameters are used to classify the nodes in HWSNs (Saini & Sharma, 2010). Capone et al. (2019) studied transmission scheduling and multi-hop routing to minimise delay using SINR. The initial energy varies according to the node’s distance from the sink to overcome the energy hole problem in multi-hop networks (Wei et al., 2011). Tanwar, Kumar & Rodrigues (2015) categorised several heterogeneous routing protocols with predefined parameters by enhancing network lifetime and node heterogeneity in WSNs.

GA has been used for the CHs’ optimal selection in recent research. The main focus of the GA-based clustering algorithms is the fitness function. The fitness function determines the goodness of an individual to be selected for the next generation (Bandyopadhyay & Coyle, 2003). Pantazis, Nikolidakis & Vergados (2013) critically analysed the energy-efficient routing protocols for WSNs. The method in Pal & Saraswat (2017) is based on biogeography-based optimisation in HWSNs. The fitness value is modified further by incorporating the residual energy of the remaining nodes that enhances the performance. It prolongs the network lifetime (Mehta & Pal, 2017). Meta-heuristics techniques are widely applied to solve several clustering problems in WSNs (Kachitvichyanukul, 2012; Bhushan, Pal & Antoshchuk, 2018). Fanian & Rafsanjani (2019) reviewed the various protocols and their properties in WSNs. Afsar, Mohammad & Tayarani (2014) investigated and presented more clustering approaches.

Bari, Jaekel & Bandyopadhyay’s (2008) approach formulates clusters and considers the relay nodes as CHs in two-tiered sensor networks that prolong the relay node lifetime. The method in Younis, Youssef & Arisha (2003) extends the network lifetime dynamic route selection and reduces energy consumption. Liu & Lin (2005) critically investigated and addressed the power-conserving issues in WSNs, and the algorithm in Zhang et al. (2017) solves the energy balance problem in WSNs. Gupta & Pandey (2016) have considered the location of BS and residual energy as clustering parameters to solve an energy hole problem in HWSNs. Darabkh, Zomot & Al-qudah’s (2019) scheme minimises the average energy consumption and prolongs the lifetime of WSNs.

Javaid et al.’s (2013a) technique for HWSNs dynamically elects the CH. It extends the network lifetime. Pal et al. (2015a) analyses the heterogeneous node locations and selects optimal CH based on the distance between the clusters. The algorithm in Sarkar & Senthil Murugan (2019) improves the energy and lifetime of both nodes and networks by choosing the optimal CHs. Kumar & Kumar (2016), Mann & Singh (2017) maximise the network energy and extend nodes’ network lifetime by selecting optimal CH in WSNs. Fan’s (2013) method investigates several issues such as energy consumption, coverage, and data routing in WSNs. This method improves the coverage ratio and prolongs network lifetime. Javaid et al.’s (2013b) scheme increases the node stability period and sends more packets to BS. Singh & Lobiyal (2012a) designs an energy-aware cluster by selecting optimal CHs in WSNs.

Ali, Shahzad & Khan’s (2012) algorithm optimises the clusters in a network and minimises the data traffic and energy dissipation among nodes. Rakhee & Srinivas (2016) continuously monitors patients’ data by selecting an optimal path in the body area network. It also enhances network lifetime, load balancing, and energy on the overall network. Pal et al.’s (2015b) method achieves a load-balanced network. It prolongs the lifetime of WSNs by optimising CH selection approach (Hoang et al., 2014) that reduces the distance between the CH and CMs in WSNs to improve energy conservation. Lin et al.’s (2012) approach maximises the lifetime of heterogeneous nodes based on sensing coverage and network connectivity. The approach in Singh & Lobiyal (2012b) selects energy-aware clusters and optimal CH based on hop count and locations. Pal et al. (2020) considers the GA parameters for enhancing the CH performance in WSNs. Mhemed et al.’s (2012) approach investigates the cluster formation that reduces energy consumption. Haseeb et al.’s (2019) method increases energy efficiency and data security against malicious activities. Zhang et al. (2019), Abo-Zahhad et al.’s (2014) algorithms prolong the network lifetime by selecting the optimal CHs and reducing average energy consumption.

Delavar & Baradaran (2012) algorithm reduced energy consumption by selecting chromosomes in different states. Bayrakli & Erdogan (2012), Liu & Ravishankar (2011), Yu et al. (2011), Zhou et al. (2010) studied the optimal selection of clusters to extend the WSNs’ lifetime. Bencan et al. (2013) analyses the spatial distribution of heterogeneous nodes in WSNs and effectively avoids the energy hole problem. Jin et al.’s (2005) algorithm enhances the reported sensitivity of the nodes and optimises the solution quality in HWSNs management. Attea & Khalil (2012) compares the various evolutionary algorithms with network lifetime, node stability period, and energy efficiency. Elhoseny et al.’s (2015) method optimises heterogeneous sensor node clustering. It dramatically extends the network lifetime. Huang et al.’s (2021) method was used to minimise the delay and collision and reduce the energy consumption in WSNs. The protocol in Deng et al. (2021) improves energy utilisation and minimises the delay in sensor networks. Chauhan & Soni (2021) minimises the energy holes and prolongs the network’s lifetime in WSNs. In this approach, the network is divided into unequal clusters, and it considers the node’s residual energy and distance to the base station for cluster formation in WSNs. Nodes with the highest energy are considered CH for WSNs (Toor & Jain, 2019). Kamil, Naji & Turki’s (2020) techniques change the WSNs’ sink node position dynamically to increase residual energy and prolong the network lifetime.

The method in Prabaharan & Jayashri (2019) selects the smart CH in WSNs to prolong the network lifetime. Optimal CH selection is performed for extending the network lifetime of WSN by using various attributes of sensor nodes (Kalaimani, Zah & Vashist, 2020; Rajpoot & Dwivedi, 2019). Kashyap et al.’s (2019) algorithm performs load-balancing among sensor nodes for WSNs. Also, it balances the optimal number of CHs and evenly distributes the load among nodes. The technique of Zhang et al. (2016) performs that the sensors are scheduled into several disjoint complete cover sets in WSNs and activates them in batch for energy conservation. The algorithm in Chang et al. (2018) is suitable for small-scale WSNs and suffers from high network latency due to multiple forwarding operations. Chang et al. (2019), Wang et al. (2018b), Wang et al. (2020), Wang et al. (2018a), Wang et al. (2022), Tabatabaei (2020) and Vijayalakshmi & Anandan (2020) discussed the various energy management challenges for WSNs.

Many research proposals exist in the related works addressing the energy-efficient hierarchical clustering issues, but node heterogeneity of WSN nodes has not been exploited to its full potential.

Energy efficiency is the essential component in extending the life of WSN systems that are resource-constrained, particularly in terms of energy. The energy-aware clustering algorithms become a significant factor in WSNs since multi-hop clustering methods relate to the network’s communication operations. The energy, computation, and link are the three broadly divided basic types of heterogeneity of WSN. Another vital factor to consider is the heterogeneity of data creation rates, which considers nodes with varying data transmission requirements. As a result, distinct performance evaluation parameters must be used to categorise sensor nodes. So there is a necessity to categorise sensor nodes based on different performance evaluation metrics. Motivated by the above facts, in this article, we provide a genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous WSNs. Table 1 shows the Summarisation of Related Works.

The proposed method is similar to Capone et al. (2019) and Pal et al. (2020). Compared to existing works (Capone et al., 2019; Zhang et al., 2017; Darabkh, Zomot & Al-qudah, 2019; Javaid et al., 2013a; Sarkar & Senthil Murugan, 2019; Kumar & Kumar, 2016; Mann & Singh, 2017; Ali, Shahzad & Khan, 2012; Pal et al., 2020), our study is distinguished by the type of algorithm. In this approach, two methods are investigated in HWSNs. The first method uses GA to enhance performance by selecting the optimal CHs during the clustering and re-clustering phases. The second method extends the first method by featuring optimal transmission scheduling. In this method, we carefully analyse the transmission scheduling and communication among CHs. As a result, we address various properties and analyses of node strategies to minimise the end-to-end delay, extend the network lifetime, and improve energy efficiency. However, this is the first article presenting a GA-based energy-aware multi-hop clustering to minimise end-to-end delay, expand the network lifetime, and enhance energy efficiency in HWSNs.

Materials and Methods

In HWSNs, clusters are formed based on GA. GA finds optimal CHs by considering the network coverage and its energy level. The CHs perform data aggregation and transmit the combined data packets to the sink. A multi-hop network is used to send packets from CHs to the sink. Neighbouring sink nodes consider regular, advanced, and supernodes. These nodes have different initial energy. Regions near the sink have a more significant number of supernodes than other regions. The next-hop CH is selected with the distance between the CHs, the residual energy, the number of CMs, and the neighbouring CHs associated with the given CH in routing. Various symbols and notations used in the proposed work are mentioned in Table 2.

Clustering formulation phase

In this phase, more clusters are formed in HWSNs. It also contains two other sub-phases, namely CHs Selection and CM Association phases. The CHs selection phase selects an optimal CH. Each CM is associated with any one of the nearest energy-efficient CH in the CM association phase.

CH selection phase

This phase uses the GA for selecting optimal CHs and their location. GA is working on natural genetics and natural selection principles and is used to optimise various parameters. GA is applied in multiple fields for solving constrained and unconstrained optimisation problems (Bandyopadhyay & Coyle, 2003).

CM association phase

Each CH sends a CH advertisement $(C{H}_{ADV})$ message containing its identifier, location, initial and residual energies and starts a clustering timer with a predefined value in the CM association phase. When a CM receives $C{H}_{ADV}$, it stores the information in the cluster table (CT). A CM may receive $C{H}_{ADV}$ from one or more CHs. For each CH entry stored in the CT, CM calculates the cost as given by Eq. (8). CM selects their respective CH with low cost and sends JOIN message to the optimal CH.

(8) $$Cost=c_1\times f_1+(1-c_1)\times f_2$$

Here $c_1$ is a constant $0\le c_1\le 1$. By setting proper value for $c_1$, we can decide how much importance to give to distance and energy in the CH selection. The terms $f_1$ and $f_2$ are calculated as provided by Eqs. (9) and (10).

(9) $f_1={\displaystyle \frac{d(C{M}_i,C{H}_i^j)}{d_{max}}}$

(10) $$f_2={\displaystyle \frac{{\lambda }_i^j}{{\phi }_i^j}}$$where, ${\lambda }_i^j$ and ${\phi }_i^j$ represents the initial and residual energies of $C{H}_i^j$ respectively. $C{H}_i^j$ is a CH present in the cluster table of $C{M}_i$ and $d_{max}$ is the distance among the CH and its CMs, and it is calculated by Eq. (11),

(11) $$d_{max}=max\left\{d(C{M}_i,C{H}_i^j)\right\}$$

The CHs collect the JOIN message from the CMs until the clustering timer expires. Upon the expiry of the timer, CHs create a dynamic time division multiple access (TDMA) scheduling for the packet transmission and send it to the CMs. The GA-based clustering algorithm shows the various steps involved in forming clusters and optimal CH selection.

Next-hop neighbour selection phase

Each CH broadcasts a neighbour advertisement message that contains information like identifier, location, initial and residual energies, distance to sink, and the size of CMs associated with it. When a CH receives a neighbour advertisement message, it adds the information contained in the packet to the neighbour. As shown in Fig. 1, CHs use multi-hop paths to communicate the data packets to the sink. The next-hop CHs are chosen based on the distance, residual energy, and the size of CMs associated with the next-hop CH, the number of CHs that have reached via the next-hop CH. When more CHs can be reached via a CH, the CH will help forward the packet reliably. CH with more residual energy, less distance, smaller CMs, and more neighbouring CHs prefer the next-hop CH.

For each CH node in the neighbour table, a merit value (MV) is calculated based on the above factors. Equation (12) shows the calculation of MV.

(12) $MV={\theta }_1\times {\delta }_1+{\theta }_2\times {\delta }_2+{\theta }_3\times {\delta }_3+{\theta }_4\times {\delta }_4$

(13) ${\theta }_1+{\theta }_2+{\theta }_3+{\theta }_4=1$

(14) $${\delta }_1={\displaystyle \frac{(d(C{H}_i,C{H}_j)+(C{H}_j,S)}{d_{{max}_i}}}$$

(15) $$d_{{max}_i}=max\{d(C{H}_i,C{H}_j)+(C{H}_j,S)\},\mathrm{j}\in \theta$$

Here $\theta$ represents the neighbouring CHs of $C{H}_i$.

(16) $${\delta }_2={\displaystyle \frac{{\rho }_j}{{\phi }_j}}$$

(17) $${\delta }_3={\mathrm{N}}_{C{H}_j}^{CM},\mathrm{j}\in \theta$$

(18) $${\delta }_4={\displaystyle \frac{1}{{\mathrm{N}}_{C{H}_j}^{CM}}\mathrm{j}\in \theta }$$

In Eq. (13), ${\theta }_1,{\theta }_2,{\theta }_3,$ and ${\theta }_4$ represents weights associated with different factors.

Data transmission phase

It involves communication within the cluster and communication between sink and CH. In intra-cluster communication, the CH receives packets from their CMs per the dynamic TDMA scheduling. CM also senses the data from the surroundings and sends them to the concerned CHs during a particular time. The CMs turn off their radio in the remaining time to save the energy wasted during idle listening. Each CH has many next-hop CH neighbours, and the best neighbour node is selected in the next-hop neighbour selection phase.

Genetic algorithm

In GA, each result to a specific problem is denoted by a chromosome using a binary coding scheme. A group of chromosomes constitutes the population. The initial population consists of randomly selected chromosomes, and each bit in the chromosome is called a gene. For each chromosome, a fitness value is calculated, and it evaluates the effectiveness of the chromosome. Chromosomes with high fitness values will get more chances to create new chromosomes. The GA involves three basic operations: selection, crossover, and mutation to select the best chromosome. The selection process duplicates good chromosomes and eliminates the poor ones, and there are many selection methods like tournament selection, ranking selection, and roulette wheel selection. The crossover operation selects two parents, recombines them, and creates two children. Crossover can be either single-point crossover or multi-point crossover. Crossover does not introduce any new genetic properties. Mutation operation introduces new genetic properties. These operations are repeated for a given number of generations (Bandyopadhyay & Coyle, 2003; Pantazis, Nikolidakis & Vergados, 2013). The implementation of various GA operations is explained below.

• i. Binary Coding: Binary coding scheme represents each chromosome for the given sensor scenario as a string of $0_s$ and $1_s$. a chromosome of length $\mathrm{N}$ bits signifies HWSNs with $\mathrm{N}$ nodes. The chromosome size is the same as the size of the network. In the chromosome set, value 1 and 0 represents the CH and CM, respectively. Figure 2 shows the chromosome representation of a network with 20 sensor nodes. Nodes $S_2,S_3,S_{11}$ are CHs, and the remaining nodes are CMs.

• ii. Objective Function: The objective function ( $\delta$) is used for selecting optimal CHs. In designing $\delta$, the following facts are considered. The optimal CH consumes more energy than the CM, so the number of CHs must be minimised. The power required for intra-cluster communication depends on the distance between CHs and CMs, and the power required for inter-cluster communication depends on the distance between two CHs. To save power, we have to reduce the size of optimal CHs ( $\varpi$), the distance between CHs and CMs ( $\vartheta$), and the two CHs distance ( $\tau$). By selecting CHs with higher residual energy, we can deliver packets reliably. The $\delta$ selects the CHs by considering the above factors, and it is a minimisation function as given in Eq. (19).

(19) $$\delta =\left({\displaystyle \frac{1}{\phi }}\right)+\vartheta +\tau +\varpi$$where, $\phi$ represents the sum of the residual energy associated with the CHs.

(20) $$\phi =\sum _{i=1}^{\varpi }{\phi }_i$$

Eq. (21) determines the sum of the distance between CMs from their respective CHs.

(21) $$\vartheta =\sum _{i=1}^{\varpi }\sum _{i\in C_i}d(C{M}_i^j,C{H}_i)$$where, $C{H}_i$ represents the $i^{th}$ CH, $C_i$ denotes the set of CMs associated with $C{H}_i$ and $C{M}_j$ represents $j^{th}$ CM node associated with $C{H}_i$.

In Eq. (22), $\tau$ represents the total distance between all the CHs in the $i^{th}$ level to the parent CH nodes in the $i-1^{th}$ level. The node level is considered to find out the parent CH nodes. All the CHs in level 1 send packets to the destination directly. CHs in the remaining level send packets to their parent CHs in a multi-hop fashion to the sink, and CHs in $i-1^{th}$ level is the parent of CHs in the $i^{th}$ level.

(22) $$\tau =\sum _{i=1,j=i-1}^{\varpi }d(C{H}_i,C{H}_j)$$where $j\in P_i$ and $P_i$ represents the set of parent CHs associated with $C{H}_i$.

• iii. Fitness Function: GA is generally suitable for solving the maximisation problem. Since our aim is minimising $\delta$, this problem is transformed into maximising the fitness value $f_v$. For each chromosome in the population $\delta$ is used to calculate the $f_v$ as given by Eq. (23).

(23) $$f_v={\displaystyle \frac{1}{1+\delta }}$$

• iv. Selection: It is used to select chromosomes with higher $f_v$ to join the mating pool to form a new population for the subsequent generations. The proposed method uses the Roulette wheel selection method.

• v. Crossover: The proposed GA-EMC scheme uses a single-point crossover. A random value (0 to 1) and two chromosomes have been selected for this operation. The crossover operation is performed only if the selected random value is less than the crossover probability $p_c$. Otherwise, no crossover is done. If it is decided to perform crossover, an arbitrary crossover point is selected. After the crossover point, the two-parent chromosomes exchange their packet to generate two child chromosomes. Table 3 shows the crossover operation.

• vi. Mutation: In bit-level mutation, a random value is chosen for every bit in a chromosome. Suppose this random value is less than $P_m$, then the mutation is performed to invert the bit. Otherwise, the bit is kept as such.

Table 3:

A single-point crossover.

Parent chromosome	Cross over point	Child chromosome
00010001110000000101	16	00010001110000001101
00001001010000101101	16	00001001010000100101

DOI: 10.7717/peerj-cs.1029/table-3

As shown in Table 4, in the first chromosome, no mutation is performed, whereas in the second chromosome, six bits are mutated.

Table 4:

Bit-level mutation.

Chromosome before mutation	Chromosome after mutation
00010001110000001101	00010001110000001101
00001001010000100101	00001100010010100010

DOI: 10.7717/peerj-cs.1029/table-4

Selection, mutation, and crossover operations are repeated for given generations. The better chromosome is selected at the end of the last generation. In the best chromosome selection, if the genome value is 1, the node becomes CH, and otherwise, it becomes CM.

Results

In this section, the performance of GA-EMC is analysed and compared with E-MDSP (Capone et al., 2019) and EEWC (Pal et al., 2020). Simulations are performed using the Network Simulator (NS2) (Issariyakul & Hossain, 2012). An HWSN consists of 400 nodes in a simulation area. To evaluate the GA-EMC performance, we have considered the metrics such as network lifetime, throughput, network stability, the number of data packets sent to the sink, and the average energy consumption in the whole network. The various parameters for simulation are presented in Table 5.

Network lifetime

To extend the HWSNs’ lifetime, we have considered the alive nodes in each round. Figure 3 illustrates that the proposed GA-EMC scheme extends the lifetime of alive nodes in every round than EEWC and E-MDSP. The proposed GA-EMC provides a better network lifetime than existing schemes.

The proposed GA-EMC uses multi-hop communication for packet delivery to extend the network lifetime. Compared to the existing schemes, the first node dies after 1,800 rounds in GA-EMC. Later, the last node remains alive for 2,100 rounds. In EEWC and E-MDSP schemes, the nodes have died after 1,000 and 1,600 rounds, respectively. Figure 3 shows that the proposed GA-EMC scheme prolongs the network lifetime and stability, and the last alive node can still respond to the network in this approach.

Throughput

In HWSNs, the proposed GA-EMC algorithm analysed the number of data packets sent, each CH sends data packets to the sink, and the CMs send data packets to their respective CHs. As shown in Fig. 4, the EEWC performs poorly with less data packet communication. Similarly, the E-MDSP gives the best behaviour than EEWC and also provides poor performance than GA-EMC. The number of data packets sent from CHs is increased significantly by the EMC-GA and achieves better throughput when compared to the other schemes.

Stability period

Figure 5 illustrates the regular time interval from the beginning of the network process until the death of the first node in HWSNs. As shown in Fig. 5, the GA-EMC has a better stability period than the other schemes. The first dead node starts at 1,800 rounds in the GA-EMC scheme, whereas the first dead node starts at nearly 1,000 and 1,600 rounds under the EEWC, E-MDSP approaches. The stability duration of GA-EMC compared with the EEWC scheme increases from 1,000 to 2,500 rounds, and the E-MDSP increases from 1,600 to 2,500 rounds. So, GA-EMC provides better stability duration and prolongs the network lifetime.

Minimising the end-to-end delay

Figure 6 displays the analysis of various approaches in terms of delay. It shows that the EEWC acquires the extreme delay of 0.04 s in 2,500 rounds. However, the delay is low compared to EEWC, and it is maximum than the GA-EMC. At the same time, E-MDSP achieves a minimum delay than EEWC, but it fails to outperform GA-EMC. GA-EMC approach achieves a low delay of only 0.02 s at the 2,500 rounds.

The average energy consumption

Figure 7 displays the average energy consumption under variable simulation rounds.

Even though more packets are transmitted in the proposed protocol than in EEWC and E-MDSP, the average energy consumption till a particular round is less in the proposed GA-EMC, as shown in Figure 7. This energy-saving aims to use multi-hop communication and associate the CMs with the optimum CHs.

Impact of sink node location on the HWSNs lifetime and stability

Network stability is measured by the round when the first node died. To study the impact of sink location on the network stability and lifetime, we have considered three scenarios. In scenarios 1, 2, and 3, the sink is situated at various places such as the middle, top right corner, and outside the field, respectively. Table 6 showed the comparison of the round when a given percentage of nodes died for different sink positions. On average, the proposed protocol extends the round when the last node died by 30.94% by considering different sink positions. GA-EMC extends the network lifetime and better stability in all three cases, and it provides more significant improvement when the sink is at the corner and outside the field due to multi-hop routing.

Table 6:

Comparison of percentage of nodes died for different sink node locations.

% of nodes died	Number of rounds
	Sink located at center 100, 100			Sink located at top right corner 200, 200			Sink located outside the field 100, 300
	GA-EMC	E-MDSP	EEWC	GA-EMC	E-MDSP	EEWC	GA-EMC	E-MDSP	EEWC
10	1,049	1,019	717	710	680	401	312	282	123
20	1,192	11,62	922	824	794	564	534	504	256
30	1,412	1,382	1,028	978	948	621	678	648	384
40	1,554	1,524	1,087	1,126	1,096	789	756	726	490
50	1,693	1,663	1,157	1,267	1,237	854	869	839	587
60	1,765	1,735	1,245	1,398	1,368	981	1,023	993	712
70	1,958	1,928	1,353	1,501	1,471	1,102	1,265	1,235	891
80	2,196	2,166	1,521	1,689	1,659	1,256	1,412	1,382	1,038
90	2,458	2,428	1,702	1,876	1,846	1,411	1,623	1,593	1,214
100	2,641	2,611	1,813	2,123	2,093	1,714	1,831	1,801	1,485

DOI: 10.7717/peerj-cs.1029/table-6

Figure 8 shows the rounds when the first node died in the three scenarios. As shown in Fig. 8, the round when the first node died is postponed by 10.98%, 23.47% and 46.94% in scenarios 1, 2 and 3, respectively. This shows that the proposed protocol performs better for longer distance transmission. Compared to EEWC and E-MDSP, the GA-EMC provides a 27.13% improvement in the round when the first node died, considering the average of different sink positions.

Discussion

The proposed GA-EMC scheme outperforms the existing methods, especially EEWC and E-MDSP, in almost all aspects. It extends the lifetime of alive nodes in every round and prolongs the network lifetime and stability. Also, it significantly increases the number of data packets sent from CHs and achieves better throughput. It provides better stability duration and prolongs the network lifetime.

Furthermore, it achieves a lower delay and reduces the average energy consumption till a particular round. It extends the network lifetime and better stability in all three cases. Due to multi-hop routing, it improves when the sink is at the corner and outside the field and performs better for longer distance transmission.

Conclusions

In this article, a GA-EMC scheme is presented for extending the lifetime and minimising the delay in HWSNs. In selecting the optimal CHs, the fitness value is calculated based on cluster distances, the number of CHs, and their initial and residual energies. Each cluster selects a CH with minimum distance, higher residual energy, minimum CMs, and maximum neighbours as its next hop in inter-cluster routing. The energy hole problem created due to multipath routing is solved by deploying more higher energy supernodes in the areas closer to the sink. The mathematical model for energy consumption for clustering with multi-hop data transmission is explained. The experimental results proclaim that GA-EMC prolongs the HWSNs lifetime, minimises the delay, and maximises stability compared to EEWC and E-MDSP for various positions of the BS, primarily when the BS is situated in the network corner and outer area. The death of the first and last nodes is prolonged by 27.13% and 30.94%, respectively, compared with EEWC and E-MDSP. In the future, the simulation can be repeated to see the impact of the number of nodes in HWSNs. Also, the performance of GA_EMC can be analysed in actual (not simulated) HWSNs in some practical scenarios.

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