Multiobjective-energy centric honey badger optimization based routing for wireless body area network

Abstract

Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR.

Keywords

Energy efficiency life expectancy multiobjective-energy centric honey badger optimization time division multiple access wireless body area network

1 Introduction

In recent times, an ever-increasing growth of the population leads to various health diseases such as cardiovascular diseases, cancer, asthma and different chronic fatal diseases because of this millions of people losing their life. The patients might get cured, when the diseases are diagnosed at the right time. Hence, the WBAN is mandatory for the continuous observation of the human body [1 –3]. WBAN is a connection of tiny bio-sensors that are installed in / on the several parts of the human body. This WBAN is used to observe the information related to health such as electrocardiogram, blood glucose level, electro-myography, blood pressure and heartbeat of humans and this information is transmitted to the real-time health monitoring systems [4 –6]. In WBAN, biomedical sensors are categorized into three types such as in-body, on-body, and off-body sensors. The In-body sensors are fixed inside of body for broadcasting the observed data to the sink whereas the on-body sensors are located over the body for monitoring purposes. Further, the off-body sensors are placed some centimetres far from humans to collect the observed information [7].

The information collected by the WBAN is saved and it is examined to reply to the body requirements received from any remote location [8 –10]. The sensor nodes with limited battery energy are one of the specific issues in the WBANs. Because, the restocking or uninstalling of batteries becomes a challenging task, once the sensors are installed in humans or worn by people [11, 12]. The most challenging issue in the WBAN is restricted energy resources. The sensors used in the WBAN are smaller than the sensors of typical wireless sensor networks that restrict the battery capacity. Hence, an energy-efficient transmission is required to be utilized for enhancing the battery life of the WBAN [13 –15]. The sensor’s energy usage is small while observing and processing the data, but the energy usage is high while transmitting the data. Moreover, direct data transmission creates higher energy consumption over the network [16]. Therefore, the multi hop data broadcasting is accomplished to transmit the data to the sink via intermediate nodes. More specifically, energy efficient routing is required to be developed for effective communication [17, 18]. The main motivation behind this research is to develop an effective routing to ensure reliable data transmission from the sensor to the central node. Specifically, the issues caused by the restricted battery capacity are considered in this research to avoid node failure and minimize data loss.

The research contributions are concise as follows:

The M-ECHBA is developed from the conventional Honey Badger Algorithm (HBA) to select the optimal route for WBAN. Due to an efficient traversing of search space, the HBA provides an effective balance between exploration and exploitation which leads to choosing HBA for this research.

The developed M-ECHBA based energy efficient routing optimization is done using four cost metrics such as energy, distance, delay and node degree. This M-ECHBA is used to enhance the energy efficiency of the WBAN.

Therefore, the M-ECHBA improves the life expectancy of the WBAN while minimizing the delay during communication.

The remaining research is sorted as follows: Section 2 delivers the related works about routing over WBAN. The problems found from the research along with its solutions are provided in Section 3 and the preliminaries of this research are given in Section 4. Section 5 explains the M-ECHBA based routing over the WBAN. The M-ECHBA outcomes are given in Section 6 whereas the Section 7 concludes the research.

2 Related Work

This section provides the related works about the routing over WBAN along with its benefits and limitations.

S. Sharma et al. [11] developed a NEEMA for an effective data routing in WBAN. The clustering approach developed in NEEMA was used to minimize the energy usage in network. But, this NEEMA was not considered the impact of delay during the routing process.

Basha and Manoharan [19] presented the Energy Proficient Reliable Multihop Routing (EPMR) approach to improve the lifetime of the WBAN. In the first phase of EPMR, the modified conditional spider optimization was used to accomplish an optimal clustering whereas the modified flower bee approach was used in the next phase for identifying an appropriate node for broadcasting the data. The data loss was reduced by avoiding the continuous transmission of data among the nodes and BS. This work doesn’t consider the delay that occurred during the communication.

Bilandi et al. [20] developed the hPSO-SA for WBAN to reduce energy consumption. In this hPSO-SA, the single optimal outcome from the PSO was processed by SA where it exploited the search space and offered the optimal solution. The hPSO-SA was used to identify an appropriate relay node over the network which was used to minimize the energy consumption. Here, the developed hPSO-SA was considered only energy as a fitness function during relay node selection.

Panhwar et al. [21] presented the meta-heuristic Genetic Algorithm (GA) for choosing the optimal transmission path in the WBAN. This GA-based energy-efficient routing was selected as the best path based on the calculation of the shortest route among different routes. Hence, the developed routing using GA was used to save the node’s energy. However, the GA was considered the only distance as a major parameter during routing which might lead to the data loss over the WBAN.

Rahman et al. [22] developed the Dual Forwarder Selection Technique (DFST) to improve the performance of WBAN. The grouping of nodes was performed in the DFST where both forwarder nodes were chosen to broadcast the data to the sink. Here, the node’s grouping was done based on the position where the nodes deployed in/on the body. The developed DFST reduced energy consumption and increases the stability period of WBAN. Moreover, the selection of the forwarder node in DFST was done using the energy and distance.

Navya and Deepalakshmi [23] presented T-EERPDCT approach to perform the data broadcasting over the WBAN. The coordinator-based routing was used for saving energy where the cost relies on energy and distance among the nodes. The aforementioned cost metric was used to perform reliable data broadcasting to the BS. Moreover, this threshold-based coordinator node selection is used to reduce path loss. The changes in the link quality created a higher packet drop in the WBAN.

Bilandi et al. [24] developed the PSO based energy efficient routing to discover the optimal routes for establishing communication. This PSO was optimized using the energy and distance for selecting the relay node and broadcasting the data packets using the multi-hop fashion. Accordingly, the selection of multiple hop nodes was used to enable load balancing over the network. The packets received by the BS were fewer while initializing the communication.

Ullah et al. [25] presented the Energy efficient and Reliable Routing Scheme (ERRS) for routing the data in WBAN. The ERRS was comprised of two distinct phases such as forwarder node discovery and forwarder node rotation. Accordingly, the rotation of the forwarder uniformly distributed the load between the nodes. Hence, each node has the possibility of a forwarder node which resulted in uniform energy consumption. This ERRS considered energy as the main parameter while choosing the relay node.

Yang et al. [26] implemented an Energy Balanced Routing (EBR) in the WBAN to accomplish the communication between the nodes. The EBR was used to select the route according to the node position, channel condition and type of node. Further, the time slot was assigned to each node based on the priority and data necessity was used to improve the communication. However, the EBR was considered only the energy metric during the route discovery that doesn’t consider the distance between the nodes.

Shunmugapriya and Paramasivan [27] presented the fuzzy based relay node selection to perform reliable broadcasting in WBAN. Two different measures such as distance and direction were considered fuzzy during relay node selection. Here, the distance was computed using the received signal strength indicator and direction was computed using MUSIC algorithm. This fuzzy based relay selection was mainly concentrated on distance and direction, it failed to consider the energy measures.

Sharma et al. [28] developed an I-RAW to develop clustering and routing in WBAN. In I-RAW, the cluster heads were chosen using a tunicate swarm algorithm for decreasing the individual broadcasting of sensors. The data was directly broadcasted from cluster head to sink, once CH was selected from the WBAN.

Bilandi et al. [29] presented the hybrid approach of Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to choose the relay sensor. The hybrid AHP-TOPSIS considered six different values for choosing the relay node such as node criticality, signal-to-noise ratio, distance, node density, traffic load, and energy. The average energy usage was better only when the routing is performed by using distance.

Aryai et al. [30] developed a swarm intelligence multi objective fuzzy approach namely SIMOF was developed as a routing approach for WBAN. The developed SIMOF was included Fuzzy Inference System (FIS) and Whale Optimization Algorithm (WOA) based automatic rule tuning during the route discovery. Here, the FIS was used for discovering appropriate relay nodes over the network. Further, an optimal performance of SIMOF was achieved by tuning the FIS’s Mamdani rules using WOA. The increment of nodes in the analysis was affected the reliability of the SIMOF.

Saxena and Patel [31] presented the CCRP to transmit the data over WBAN. In CCRP, the cluster heads were chosen for aggregating the data to broadcast the data to sink. Nodes in the clusters were broadcasts the data either as direct transmission or by forwarder node that was mainly based on energy distribution. The distance and residual energy factor were used to select the forwarder node. Further, the energy of battery was restored by using the solar energy harvesting approach. However, the direct transmission of data was caused higher energy consumption.

Javaid et al. [32] developed the Adaptive Threshold-based Thermal-aware Energy-efficient Multi-hop ProTocol (ATTEMPT) and mobility support with ATTEMPT to perform the reliable communication over the WBAN. The ATTEMPT with mobility support was used to avoid the issues related to the disconnection. Ahmad et al. [33] presented the Reliability Enhanced ATTEMPT (RE-ATTEMPT) that integrated the merits of both the single and multi-hop data transmission. Here, the selection of route with lesser relay nodes was used to reduce the energy consumption. Further, the Stable Increased throughput Multi hop Protocol for Link Efficiency (SIMPLE) was developed to increase the stability period [34]. An appropriate fitness measures were required to be incorporated for achieving the lesser energy consumption.

Arafat, M.Y et al. [35] presented the distributed energy-efficient two-hop-based clustering and routing namely DECR to perform the routing over wearable internet of things (WIoT) enabled WBAN. Each node was obtained the information about adjacent nodes in the two-hop range during the cluster generation. The CH discovery and routing optimization were done by using the grey-wolf optimization. The CH from every cluster was defined by using the remaining energy and node connectivity which used to support energy efficient data delivery. The developed DECR was required to consider the distance factor for further lessening the energy.

Alqahtani, H et al. [36] developed the Modified Power Line System (M-POLC) for improving the lifetime of 5G networks. The data broadcasting with best hop count was used to perform the energy balancing among the 5G networks which used to minimize the usage of energy. The developed M-POLC was mainly considered hop count which affected the data communication.

3 Problem statement

The problems found from the related work along with the solution given by the proposed M-ECHBA are given in this section.

An appropriate cost metric selection is required for an effective routing among the biosensors of the WBAN. If the routing approach considered only distance as the main parameter [21], then there is a possibility of node failure during the route discovery which creates data loss. The changes in the route quality create data loss in the network [23]. The relay node selection with inadequate cost functions [22 , 26] degrades communication performances.

Solution:

The solutions given by the proposed method for the aforementioned issues are given as follows: The M-ECHBA is optimized with four distinct cost metrics such as 1) Energy, 2) Distance, 3) Delay and 4) Node degree that are used to discover an optimal routing path over the WBAN. The developed M-ECHBA is used to enhance life expectancy by decreasing the energy consumption of WBAN. The higher network life expectancy returns in high packet delivery. Additionally, the delay in the broadcasting is minimized by choosing the route which takes less delay to broadcast the data.

4 Preliminaries

The preliminaries such as network model, channel model and work process considered for this M-ECHBA are given in this Section.

4.1 Network model

Figure 1 shows the sample network model which is considered for this research. WBAN includes sensor nodes and one central node namely BS. BS has higher reserved energy and enhanced calculating proficiency, as well as each node, comprises a transmitter and receiver. The sensors of the WBAN sense the data about the human body and broadcasts it to the BS by using the M-ECHBA routing method. Subsequently, the BS is linked to the Internet using a gateway or wireless access point. The assumptions considered for this network model are given as follows:

All the sensors are static and utilized the bi-directional link in the network.

The energy given to each sensor is equal.

Fig. 1

Network model.

4.2 Channel propagation model

In WBAN, the signals aren’t broadcasted only in the free space, they are also broadcasted via the human body. The energy expenditure of data via free space depends on the distance among the nodes whereas the data broadcasting over WBAN mainly depends on the distance and path loss coefficient. The first-order radio model is utilized to compute energy utilization. If the sensor is required to send k bits of data to another sensor over the distance d, then the energy expenditure during data transmission is calculated as shown in equation (1). $E_{Tx} (k, d) = E_{TX - elec} \times k + E_{amp} \times k \times d^{2}$ (1)

Where E_TX-elec is the energy spent by the transmitter and E_amp is energy for transmitting amplifiers. On the contrary, the communication medium is the human body that leads to the radio signal attenuation, therefore energy expenditure during transmission is computed by incorporating path loss coefficient (n) as shown in equation (2). $E_{Tx} (k, d) = E_{TX - elec} \times k + E_{amp} \times n \times k \times d^{n}$ (2)

Equation (3) shows the energy spent while receiving the data packets. $E_{Rx} (k, d) = E_{RX - elec} \times k$ (3)

Where E_RX-elec is the energy spent by the receiver.

4.3 Work process

The following factors are required to be considered for ensuring reliable communication over the WBAN. 1) Each sensor broadcasts the data to the BS by selecting an appropriate relay node using the M-ECHBA, 2) The effectiveness of energy is considered for improving life expectancy.

5 M-ECHBA based energy efficient routing optimization

The M-ECHBA based route discovery is proposed to increase the energy efficiency of the WSN. The M-ECHBA discovers the optimum route for broadcasting information about the human body to the BS. Here, the conventional HBA [37] is modified into M-ECHBA for effective communication over the WBAN.

Generally, the HBA mimics the searching behaviour of the honey badger whereas the honey badger either follows the honeyguide bird or digs and smells to identify the food source. HBA is stated as a global optimization algorithm, because it is developed with both the exploration and exploitation phases. The conventional HBA considers only distance whereas the proposed M-ECHBA considered four cost measures such as energy, distance, delay and node degree for enhancing the searching process. The flowchart for the M-ECHBA is shown at the below Fig. 2.

Fig. 2

Flowchart for the M-ECHBA.

5.1 M-ECHBA Representation and Initialization

Initially, each honey badger has denoted as the transmission route from a transmitter node to the destination BS. A dimension of each solution is equivalent to the total sensors exists in the respective route. Consider, the i^th honey badger of M-ECHBA is x_i = (x_i,1, x_i,2, … , x_i,m) and a dimension of the honey badger is m that equals to amount of relay nodes. The routing paths are randomly generated while starting the route discovery. From these routing paths, the best route x_prey is selected based on the derived cost metric values. Further, the routing paths are processed under the M-ECHBA’s iterative process to find the optimal path.

5.2 Iterative process of routing path discovery using M-ECHBA

The routing paths and optimum routes from the aforementioned step are processed under the M-ECHBA to discover the optimal path. In this phase, the intensity is linked to the prey’s attention strength, and distance among i^th honey badger and prey. The smell intensity of the prey is represented as I_i. Here, the intensity is updated based on the x_prey identified using the derived cost metric. Therefore, the calculated intensity is used to improve the route selection process. The movement is fast when the smell is high; Otherwise, the movement is slow that is given by the Inverse Square Law as defined in equation (4). $I_{i} = r_{2} \times \frac{S}{4 π d 1_{i}^{2}}$ $S = {(x_{i} - x_{i + 1})}^{2}$ $d 1 = x_{prey} - x_{i}$ (4)

Where, the random value created in between [0, 1] is depicted as r₂; concentration strength is represented as S; distance among the badger i and prey is denoted as d1 and x_prey is the location of optimum prey i.e., optimum routing path.

The conversion from exploration to exploitation is ensured by using the density factor (α) which is used to handle the time-varying randomization. This density factor α is reduced with the iterations for minimizing the randomization process with a time as shown in equation (5). $α = C \times \exp (\frac{- t}{t_{\max}})$ (5)

Where C is constant i.e., C ⩾ 1 i.e., 2; t and t_max are the iterations and maximum iterations respectively.

In the next phase, the flag F is used for changing the foraging direction to obtain higher chances for honey badgers for scanning the throughout search space. Further, the location update process of HBA is accomplished in two distinct ways as digging and the honey phase. The act identical to the Cardioid shape is accomplished at the digging phase. This Cardioid movement is depicted in equation (6).

$\begin{matrix} x_{new} = x_{prey} + F \times β \times I \times x_{prey} + F \times r_{3} \\ \times α \times d 1_{i} \times | cos (2 π r_{4}) \times [1 - cos (2 π r_{5})] | \end{matrix}$ (6)

Where, β ⩾ 1 has the capacity of honey badger for getting food; r₃, r₄ and r₅ are random numbers generated in the range of [0, 1]. Equation (7) defines the flag used to change the foraging direction.

$F = {\begin{matrix} 1 if r_{6} ⩽ 0.5 \\ - 1 else \end{matrix}$ (7)

Where, r₆ is the random value generated in the range of [0, 1]. Here, the searching mainly depends on the prey’s smell intensity x_prey, distance and density factor. On the other hand, the honeyguide bird is followed by honey badger to identify the beehive which is expressed in equation (8).

$x_{new} = x_{prey} + F \times r_{7} \times α \times d 1_{i}$ (8)

Where, r₇ is the random value generated in the range of [0, 1]. If Cost (x_new) ⩽ Cost (x_i), then x_i = x_new. Otherwise, if Cost (x_new) ⩽ Cost (x_prey), then x_prey = x_new. According to the Cost value, the optimum route over the WBAN is identified using the M-ECHBA. The computation of Cost value is detailed in the following section.

5.3 Computation of Cost Function

In this M-ECHBA, four different cost metrics are considered to enhance routing over the WBAN. The cost values considered in the route discover phase are distance, energy, delay and node degree. Equation (9) expresses the computation of the cost function.

$\begin{matrix} Cost = τ_{1} \times Energy + τ_{2} \times Distance \\ + τ_{3} \times Delay + τ_{4} \times ND \end{matrix}$ (9)

Where, τ₁, τ₂, τ₃ and τ₄ represents the weight values allocated to each cost value for converting multiple objectives into a single objective value; ND represents the node degree. The definition of each cost metric is explained below:

The energy metric is required to be high for the node in the network which denotes that the remaining energy in the node is required to be high for continuing the data communication over the WBAN. Therefore, the residual energy is considered an important metric when choosing the relay node. Because the relay node has to collect and transmit the information to the respective node. The computation of residual energy is expressed in equation (10).

$Energy = \sum_{a = 1}^{m} E_{R_{a}}$ (10)

Where the energy remained in the relay node is denoted as E_{R
_a}.

The euclidean distance expressed in equation (11) is considered as the next cost metric. As shown in section 4.2, the energy expenditure is related to the broadcasting distance of the route. If the distance is less, accordingly the energy expenditure is minimum in WBAN. Hence, it is required to discover the route with less transmission distance.

$Distance = \sum_{a = 1}^{m} dis (R_{a}, BS)$ (11)

Where dis (R_a, BS) represents the distance between the relay node and BS.

The delay is considered in the route discovery phase to select the optimal route with less transmission time. This delay is the transmission time to broadcast the data packets from the transmitter node to the BS.

Further, the node degree denotes the amount of relay nodes which exists in the transmission route. The life expectancy is high when the routing path has less amount of relay nodes whereas equation (12) expresses the node degree.

ND = \sum_{a = 1}^{m} P_{a}

(12)

Where the nodes belonging to the a^threlay node are denoted as P_a. In a nutshell, the route discovery using M-ECHBA is explained in the following steps.

At first, the routing paths between the transmitter node to BS are randomly initialized in M-ECHBA.

For the initialized solutions, the Cost is computed and the

The initialized routing paths are given as input to the iterative process for identifying the optimum route.

The location of honey badger solutions are updated by using the x_prey.

The Cost is again computed for x_new and x_prey for finding the optimum solution i.e., optimum route.

The TDMA scheduling approach is additionally used to avoid the collision during the data transmission.

The sample routing scenario is shown in the following Fig. 3.

Fig. 3

Sample scenario, a) WBAN with transmitter node, b) Route discovery.

The aforementioned process are complete steps of Routing using M-ECHBA and Pseudo code is provided in Algorithm 1. The energy and distance used in the cost metric ensure an effective load balancing between the nodes. The node failure in the network is avoided by considering the energy during routing which helps to avoid data loss whereas the distance is used to identify the shortest path. The shortest path discovery is used to minimize energy consumption which improves life expectancy. Next, the delay obtained while broadcasting the information is minimized by selecting the route with less delay. Further, the node degree is also considered in the routing for improving life expectancy. After identifying the routing path, the TDMA scheduling approach is used for avoiding the collision over the WBAN. Moreover, this TDMA scheduling make sure that the same node is not sending the information, therefore it used to avoid the issues related to the different temperature.

Algorithm 1

Routing using M-ECHBA

Initialize the total populations (N) as possible paths over the WBAN.

Compute the cost for each honey badger x_i using equation (9).

Save optimal location x_prey

for t = t_max do

Update the decreasing factor based on equation (5).

for i = 1 to N do

Compute the intensity based on equation (4).

if r < 0.5 then # r is a random value generated between[0, 1]

Location update using equation (6).

else

Location update using equation (8).

end if

Estimate new location and allocate to

t_new.

Compute the cost for each honey badger

using equation (9).

Find the best route using Cost.

end for

Return x _prey

6 Simulation Results and Analysis

A sequence of simulations is performed for investigating the performance of the M-ECHBA method. The design and simulation of M-ECHBA based routing over the WBAN are performed using the MATLAB R2018a software. The system configurations used to analyze the code are 6GB RAM and an i5 processor. The developed M-ECHBA is used for reducing the energy of the WBAN. The simulation values of this M-ECHBA are given in Table 1 whereas the information about the node is given in Table 2. The transmitting and receiving energy of the node are 16.7 nJ/bit and 36.1 nJ/bit. The receiving energy is higher than the transmitting energy because of data aggregation and data broadcasting to the BS over the network. This M-ECHBA considers 9 different node types such as Motion sensor, EMG, Pulse oximetry, Glucose, ECG (12 leads), Temperature, Blood pressure, EEG (12 leads) and Relay node are for analysis. The 10th node is BS which is installed On-Body (OB) whereas the In-Body is termed as IB. Moreover, the generation of network topology is illustrated in Fig. 4.

Table 1
Simulation Values

Parameter Value

Size of packet 4000 bits

Initial energy 0.5 J

Transmit amplifier energy (E_amp) 1.97 nJ/bit

Energy used by transmitter (E_TX-elec) 16.7 nJ/bit

Energy used by receiver (E_RX-elec) 36.1 nJ/bit

Parameter	Value
Size of packet	4000 bits
Initial energy	0.5 J
Transmit amplifier energy (E_amp)	1.97 nJ/bit
Energy used by transmitter (E_TX-elec)	16.7 nJ/bit
Energy used by receiver (E_RX-elec)	36.1 nJ/bit

Table 2

Information About Node

Parameters	Node number
	1	2	3	4	5	6	7	8	9	10
x-coordinate (cm)	–11	12	–16	–14	10	–15	20	0	–5	20
y-coordinate (cm)	6.8	65	61.6	98.3	140	115	130	170	101	99
Installation type	OB	OB	OB	IB	IB	OB	OB	OB	OB	OB

Fig. 4

Generation of network topology.

6.1 Performance Evaluation

The M-ECHBA is evaluated using life expectancy, dead nodes, residual energy, delay, packets transferred to the BS, PLR and routing overhead. The conventional approaches such as SIMPLE [34] and ATTEMPT [32] are used to evaluate the M-ECHBA where these approaches are implemented using the same specifications mentioned in Tables 1 and 2. Additionally, one more classical approach i.e., RE-ATTEMPT [33] also used for evaluation, because it searches for alternative route when there is a case of dead nodes to achieve a successful data transmission.

6.1.1 Analysis of life expectancy

Life expectancy is denoted as the network execution that is accomplished till the last node exhausts its full energy in the WBAN. Figure 5 shows the performance analysis of life expectancy for M-ECHBA, while being compared with life expectancies of ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34] approaches. From the figure, it is concluded that the M-ECHBA network life expectancy outperforms well than the ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34] approaches. The M-ECHBA achieves higher life expectancy because it minimizes the energy consumption of the sensors by selecting appropriate cost metrics. The life expectancy of the M-ECHBA is 15964 rounds whereas the ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34] obtains as 7478, 8670 and 7459 respectively. The combination of residual energy, distance and node degree (i.e., hop count) helps to reduce the energy that helps to enhance the life expectancy of M-ECHBA. On the other hand, the SIMPLE [34] and ATTEMPT considers only distance and energy during routine which leads to affect life expectancy.

Fig. 5

Life expectancy.

6.1.2 Analysis of dead nodes

A quantity of nodes which drains their entire energy in communication is dead nodes. Figure 6 illustrates the dead sensor comparison of M-ECHBA with ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34] is illustrated in. The dead node of the M-ECHBA is less than the ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34]. For instance, the dead nodes of M-ECHBA for 6000 rounds are 1, whereas the ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34] obtains as 3, 3 and 6 respectively. Initially, the dead node of the M-ECHBA is slightly high, but later the optimal shortest path selection and load balancing among the nodes lead to effectively minimising the dead nodes.

Fig. 6

Dead nodes.

6.1.3 Analysis of residual energy

A difference among the total energy and energy that exists after communication is residual energy which is employed to examine the overall energy spent by the nodes. The residual energy analysis with successive rounds is illustrated in Fig. 7, in which, the M-ECHBA is compared with ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34]. The developed M-ECHBA based routing method consumes less energy over the WBAN, hence the residual energy of the M-ECHBA is high than the ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34]. For example, the residual energy of M-ECHBA for 6000 rounds is 2.2299 J, whereas the ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34] obtains as 0.5054 J, 0.8367 J and 0.2361 J respectively. The energy usage of M-ECHBA is lessened in the following ways, 1) optimal shortest path identification, 2) load balancing between the nodes and 3) routing path with less node degree. The load balancing and route with less node degree of M-ECHBA help to perform the uniform energy consumption which helps to increase the residual energy.

Fig. 7

Residual energy.

6.1.4 Analysis of Packets received by the BS and PLR

The sum of packets successfully received at the BS and PLR are related to remaining energy. PLR is ratio among the amount of packets received and amount of packets broadcasted over the network. The analysis of total packets received at the BS and PLR are illustrated in Figs. 8 and 9 where the M-ECHBA is compared with ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34]. This analysis shows that the data delivery of M-ECHBA is higher than the ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34]. The data delivery at BS is 42191 packets, whereas the data delivery at BS of SIMPLE [34] is 37987.8 packets and ATTEMPT [32] is 10520.6 packets, RE-ATTEMPT [33] is 38615.94 packets. Moreover, the PLR of M-ECHBA is 3.99 % whereas the ATTEMPT [32] obtains as 80 %, RE-ATTEMPT [32] obtains as 21.93% and SIMPLE [34] obtains 83.33 %. The M-ECHBA with a high amount of alive nodes and residual energy leads to an increase the data transmission. Moreover, the data delivery is increased, by mitigating failure node during the route discovery.

Fig. 8

Packets received by the BS.

Fig. 9

Packet loss ratio.

6.1.5 Analysis of delay

The amount of time utilized during the communication between the transmitter node and the BS is depicted as delay. Figure 10 shows the performance analysis of delay for M-ECHBA with ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34] approaches. From the figure, it is concluded that the M-ECHBA has lesser delay than the ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34] approaches. The M-ECHBA achieves less delay, because of an optimal shortest path generation and an effective load balancing over the network.

Fig. 10

Delay.

6.1.6 Analysis of Routing overhead

Routing overhead is computed based on amount of packets lost and amount of packets received over the WBAN. The routing overhead analysis for M-ECHBA with ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34] is shown in Fig. 11.

Fig. 11

Routing overhead.

The routing overhead of the M-ECHBA is less when compared to the ATTEMPT [32], RE-ATTEMPT [33] and SIMPLE [34]. The optimal cost functions used in the M-ECHBA is used to optimize the route selection for reliable data transmission which helps to minimize the routing overhead.

6.2 Comparative evaluation

The comparative analysis of the proposed M-ECHBA with NEEMA [11], hPSO-SA [20], T-EERPDCT [23], EBR [26], I-RAW [28], CCRP [31], DECR [35] and M-POLC [36] is given in this section. There are six different scenarios are considered to evaluate the performances of M-ECHBA. The details about the different scenarios are given below:

Scenario 1: The scenario 1 considers 8 nodes and 1 BS where the information about the coordinates are given in Table 3. Additionally, the nodes are configured with 0.6 J as initial energy. This scenario 1 is accomplished for comparing the M-ECHBA with hPSO-SA [20].

Scenario 2: Similar to the scenario 1, scenario 2 also considers 8 nodes and 1 BS. However, this scenario has different locations as shown in Table 4 and also it is considered with different initial energy of 0.8 J. This scenario 2 is accomplished for comparing the M-ECHBA with T-EERPDCT [23].

Scenario 3: It considers the same specifications mentioned in Tables 1 and 2. Scenario 3 is developed to evaluate the EBR [26] and M-ECHBA.

Scenario 4: In this scenario, a 14 sensors are considered as shown in Table 5. Scenario 4 is developed for evaluating the M-ECHBA with CCRP [31].

Scenario 5: Here, 100 sensors are randomly deployed in the area of 100m × 100m. Scenario 5 is developed for evaluating the M-ECHBA with NEEMA [11], I-RAW [28] and DECR [35]. As per [11], 20 nodes considered for each patient of network scenario.

Scenario 6: In this scenario, 200 nodes are randomly deployed in the area of 100m × 100m. Scenario 6 is developed for evaluating the M-ECHBA with M-POLC [36].

Table 3
Information about Nodes for Scenario 1

Parameters Nodes BS

1 2 3 4 5 6 7 8

x-coordinate 0.55 0.25 0.28 0.48 0.3 0.5 0.45 0.35 0.4

y-coordinate 1 1 0.2 0.25 0.5 0.5 0.13 0.9 1.1

Parameters	Nodes	BS
x-coordinate	0.55	0.25	0.28	0.48	0.3	0.5	0.45	0.35	0.4
y-coordinate	1	1	0.2	0.25	0.5	0.5	0.13	0.9	1.1

Table 4

Information about Nodes for Scenario 3

Parameters	Nodes								BS
	1	2	3	4	5	6	7	8
x-coordinate	0.6	0.8	0.2	0.4	1	0.3	0.9	0.6	0.6
y-coordinate	1.4	0.2	0.6	0.2	0.6	1.2	1.2	1.1	0.8

Table 5

Information about Nodes for Scenario 4

Parameters	Nodes
	1	2	3	4	5	6	7	8	9	10	11	12	13	14
x-coordinate	32	35	22	36	35	8	6	37	40	22	45	15	15	25
y-coordinate	177	137	135	101	21	145	98	127	101	91	45	50	45	17

Tables 6 to 12 shows the comparative analysis of the M-ECHBA with existing researches. This comparative analysis between the M-ECHBA with existing methods is analyzed in terms of rounds. Further, a comparison of residual energy for Scenario 2 is shown in Fig. 12. From the analysis, it is determined that the M-ECHBA is improved than the NEEMA [11], hPSO-SA [20], T-EERPDCT [23], EBR [26], I-RAW [28], CCRP [31], DECR [35] and M-POLC [36]. The dead nodes of the M-ECHBA for 8000 rounds are 4, but the EBR [26] achieves the dead nodes of 9. The inappropriate selection of cost function in NEEMA [11], hPSO-SA [20], T-EERPDCT [23], EBR [26], I-RAW [28], CCRP [31], DECR [35] and M-POLC [36] leads to a high amount of dead nodes during the communication. A higher the dead node is resulted in the less data delivery in the network. The changes of the link quality in the T-EERPDCT [23] creates the data loss over the WBAN. The M-ECHBA also provides improved performance even when it is analyzed in large scale scenario 5 and 6. However, the M-ECHBA has less amount of dead nodes and higher residual energy. The higher residual energy is used to increase the life expectancy of M-ECHBA for scenario 3 up to 15964 rounds. The life expectancy is increased because of an effective relay node selection over the network. The equal load balancing among the nodes is achieved based on the appropriate cost value used during the route discovery. Accordingly, the energy distribution among the nodes are minimized by achieving an effective load balancing over the WBAN. The data delivery of the M-ECHBA is increased by avoiding the failure node during the relay node discovery.

Table 6

Comparative analysis for Scenario 1

Number of rounds	Residual energy (J)		Packets received by the BS
	hPSO-SA [20]	M-ECHBA	hPSO-SA [20]	M-ECHBA
2000	3.4	3.9891	0.6×10⁴	1.6×10⁷
4000	2.25	2.9720	1.2×10⁴	3.1996×10⁷
6000	1.3	1.8986	1.65×10⁴	4.7912×10⁷
8000	0.3	1.1266	2.7×10⁴	6.2524×10⁷
10000	0	0.5900	3.15×10⁴	7.7252×10⁷

Table 7

Comparative analysis for Scenario 2

Number of rounds	Packets received by the BS		Delay (s)
	T-EERPDCT [23]	M-ECHBA	T-EERPDCT [23]	M-ECHBA
2000	2225	16000000	0.27×10^-4	3.2064×10^-9
4000	4500	32000000	0.27×10^-4	3.2072×10^-9
6000	7000	48000000	0.21×10^-4	2.4044×10^-9

Table 8

Comparative analysis for Scenario 3

Number of rounds	Dead nodes		Residual energy (J)
	EBR [26]	M-ECHBA	EBR [26]	M-ECHBA
1000	0	0	3.8	4.1118
2000	0	0	3.1	3.7230
3000	0	0	2.45	3.3349
4000	1	0	1.8	2.9501
5000	1	1	1.2	2.5866
6000	1	1	0.65	2.2299
7000	2	2	0.15	1.8719
8000	9	4	0	1.5613

Table 9

Comparative analysis for Scenario 4

Number of rounds	Dead nodes
	CCRP [31]	M-ECHBA
2000	0	0
4000	0	0
6000	0	2
8000	1	3
10000	4	6
12000	8	6
13000	14	7

Table 10

Comparative analysis for Scenario 5 in terms of dead nodes

Number of	Dead nodes
rounds	NEEMA [11]	I-RAW [28]	M-ECHBA
1000	0	0	5
2000	0	0	10
3000	0	1	18
4000	0	20	29
5000	13	82	35
6000	38	100	38
7000	85	100	44
8000	100	100	46

Table 11

Comparative analysis for Scenario 5 in terms of alive nodes

Number of rounds	Alive nodes
	DECR [35]	M-ECHBA
200	95	100
400	77	100
600	53	100
800	27	100
1000	8	95

Table 12

Comparative analysis for Scenario 6

Number of rounds	Residual energy (mJ)
	M-POLC [36]	M-ECHBA
10	3.85	3.99
20	3.2	3.98
30	2.95	3.97
40	2.95	3.96
50	2.95	3.90

Fig. 12

Comparison of residual energy for Scenario 2.

7 Conclusion

In this research, the M-ECHBA is developed for balancing the energy usage of the node. The distance, energy, delay and node degree are the cost metrics used while optimizing the M-ECHBA based routing path discovery. After routing, the essential information is exchanged through the optimal route that improves the WBAN performances. The residual energy and broadcasting distance are considered in the cost metrics utilized to perform an effective load balancing among the nodes that lead to achieving uniform energy consumption. Besides, the transmission delay during the communication is minimized by discovering the shortest path using M-ECHBA. Therefore, the energy efficiency of the WBAN is improved while reducing the transmission delay. From the results, it is determined that the M-ECHBA is improved than the SIMPLE, ATTEMPT, RE-ATTEMPT, NEEMA, hPSO-SA, T-EERPDCT, EBR, I-RAW, CCRP, DECR and M-POLC. The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR. In future, the trust based routing using new optimization approach can be utilized to enhance the security of WBAN.

Declarations

Funding: This research received no external funding.

Conflict of Interest: The authors declare that they have no conflict of interest.

Data Availability: Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Multiobjective-energy centric honey badger optimization based routing for wireless body area network

Abstract

Keywords

1 Introduction

2 Related Work

3 Problem statement

4 Preliminaries

4.1 Network model

5 M-ECHBA based energy efficient routing optimization

5.2 Iterative process of routing path discovery using M-ECHBA

Table 1 Simulation Values Parameter Value Size of packet 4000 bits Initial energy 0.5 J Transmit amplifier energy (E amp ) 1.97 nJ/bit Energy used by transmitter (ETX-elec) 16.7 nJ/bit Energy used by receiver (ERX-elec) 36.1 nJ/bit

6.1.1 Analysis of life expectancy

Table 3 Information about Nodes for Scenario 1 Parameters Nodes BS 1 2 3 4 5 6 7 8 x-coordinate 0.55 0.25 0.28 0.48 0.3 0.5 0.45 0.35 0.4 y-coordinate 1 1 0.2 0.25 0.5 0.5 0.13 0.9 1.1

Declarations

References

Table 1
Simulation Values

Parameter Value

Size of packet 4000 bits

Initial energy 0.5 J

Transmit amplifier energy (E_amp) 1.97 nJ/bit

Energy used by transmitter (E_TX-elec) 16.7 nJ/bit

Energy used by receiver (E_RX-elec) 36.1 nJ/bit

Table 3
Information about Nodes for Scenario 1

Parameters Nodes BS

1 2 3 4 5 6 7 8

x-coordinate 0.55 0.25 0.28 0.48 0.3 0.5 0.45 0.35 0.4

y-coordinate 1 1 0.2 0.25 0.5 0.5 0.13 0.9 1.1