EEDLABA: Energy-Efficient Distance- and Link-Aware Body Area Routing Protocol Based on Clustering Mechanism for Wireless Body Sensor Network

Abstract

In medical environments, a wireless body sensor network (WBSN) is used to operate remotely, and sensor nodes are employed. It consists of sensor nodes installed on a human body to monitor a patient’s condition, such as heartbeat, temperature, and blood sugar level, and are functionalized and controlled by remote devices. A WBSN consists of nodes that are actually sensors in nature and are operated with a short range of communication. These sensor nodes are fixed with limited computation power and the main concern is energy consumption and path loss. In this paper, we propose a new protocol named energy-efficient distance- and link-aware body area (EEDLABA) with a clustering mechanism and compare it with the current link-aware and energy-efficient body area (LAEEBA) and distance-aware relaying energy-efficient (DARE) routing protocols in a WBSN. The proposed protocol is an extended type of LAEEBA and DARE in which the positive features have been deployed. The clustering mechanism has been presented and deployed in EEDLABA for better performance. To solve these issues in LAEEBA and DARE, the EEDLABA protocol has been proposed to overcome these. Path loss and energy consumption are the major concerns in this network. For that purpose, the path loss and distance models are proposed in which the cluster head (CH) node, coordinator (C) node, and other nodes, for a total of nine nodes, are deployed on a human body. The results have been derived from MATLAB simulations in which the performance of the suggested EEDLABA has been observed in assessment with the LAEEBA and DARE. From the results, it has been concluded that the proposed protocol can perform well in the considered situations for WBSNs.

1. Introduction

A wireless body sensor network (WBSN) is a type of wireless sensor network (WSN) in which the sensor nodes are deployed on a surface of the human body [1,2]. It is also known as a wireless body area network (WBAN). In the recent era, its applications, as well as demand, are increasing day by day. The sensor nodes of a WSN are constrained by limited energy; therefore, minimizing the network lifetime while using less energy is a research challenge [3,4]. The proposed EEDLABA routing protocol, which combines the above schemes, will reduce energy consumption, and delay and improve network lifetime. The advanced nodes deployed in the proposed work have the features and ability to transfer data without path loss and consume less energy in WBSN scenarios. Due to the rapid growth and increasing demand for sensor networks, it is said that each piece of equipment will be embedded with sensor networks [5,6]. For medical conditions and environments, the WBAN is the best idea for installing such a network without the fuss of physical cables, etc. All the communication will take place by using wireless sensor devices [6].

WBANs face numerous challenges when being designed for a healthcare environment. Some of the challenges are the design of the antenna, transmission data, signal processing, the sensors of the body, privacy and security [7], path loss, heterogeneous environments [8], overheating, radiation absorption [9,10], quality of service (QoS) [11], limited resources [12], partitioning of topology formation, the topology of the network, and so on. Many challenges have been witnessed in the environment of Internet of things (IoT)-enabled WBANs [13,14]. However, the most important and crucial challenge that WBANs face is the energy consumption of the sensor nodes [15]. Many studies have been proposed and are still in their infancy in terms of overcoming this issue and designing an energy-efficient routing mechanism for WBANs.

Energy and routing at the same time in WBANs is the current research topic. The majority of the studies are in their infancy in terms of improving the performance of WBANs by focusing on energy consumption and using the best routing path. It has been already mentioned that the nodes in WBAN have limited battery computational power, thus energy is the main concern and challenge. Not only energy, but the routing with appropriate procedure with the best and highest priority selection can keep and network on the right track. This level can increase the network performance and it can shorten the time of transmission. Multiple nodes are used for the routing procedure, which can avoid hurdles and other transmission impairments. The basic motive is the energy level and routing the data in a WBAN with appropriate routing techniques.

Sensors are put on patients’ bodies in a conventional electronic healthcare system (e-health). These gadgets capture data such as blood pressure, heart rate, movement, and so on and communicate it to the center via network connections. Due to the massive volume of data collected, it will be kept on the cloud for analysis and decision-making. One disadvantage of WBAN-based applications is the long service response time. In ambulance emergency scenarios, when associated data such as hospital location, distance, ambulance vehicles, medical personnel, and so on are collected, decisions are made in seconds and have a direct impact on the patient’s life. Patients’ lives might be saved by computational technologies with true real-time reaction times. WBAN-enabled technology with an emphasis on IoT through 5G has recently been a hot issue in healthcare research.

WBANs [8,16] evolved from the WSN, which uses several sensor nodes to construct a sensor network [9,17]. Because of their ease of installation and low cost, the applications of these networks are rapidly evolving. These sensors are capable of sensing, aggregating, processing, and communicating the reading environment [10,18]. These networks are linked in an ad hoc or stable infrastructure-based network, with a central body that makes decisions and functions. The nodes are installed in such a way that the major duty is handled by the sink nodes, which are the network’s cores. There are also cluster heads (CHs) and different types of coordinator (C) nodes deployed. Sensor networks are employed in agriculture, industrial automation, home automation, intelligent transportation, healthcare, habitant monitoring, seismic sensing, and target tracking [12,19]. The core contributions of the proposed work are as follows.

• In this article, we present the EEDLABA routing strategy for WBAN.
• Path loss and distance models, as well as an energy consumption mechanism, have been developed with the improvement and placement of nodes.
• To be properly functionalized, the suggested protocol must consume less energy and transport data with the best stability.
• We designed the WBAN plan to reduce energy usage as much as possible.
• The EEDLABA routing protocol was designed using system models that included route loss and distance models in equation form.
• A total of nine nodes are installed on a human body, including the coordinator nodes, which can interact only when an active transmission line is available.
• Finally, the newly proposed routing protocol was compared and tested with existing systems to reveal the best results.

The rest of the article is organized as follows. The introduction and related work are given in Section 1 and Section 2. Section 3 describes the core methodology for the proposed work. The results and discussion are discussed in Section 4 with graphical and tabular illustrations. Finally, a conclusion and future works are discussed in Section 5.

2. Literature Review

The authors of [17,18,20] proposed the routing approaches for the improvements in wireless networks. They have focused on the hybrid routing protocols to deliver their best solution in routing protocols for the designing purpose. The core concept and idea were the energy and routing in multiple nodes. The authors of [21,22] proposed an energy-efficient routing mechanism for WBAN. The focus was kept on the healthcare domain where the patients will have the ability to be operated using an IoT-enabled WBAN. With the energy consumption models, they have proved to be an energy-efficient protocol framework for the sake of network enhancement in WBANs. The authors of [23] suggested a novel routing approach with a focus on energy efficiency in inter-WBAN and intra-WBAN in healthcare environments. The authors of [24] paved the path of routing in WBAN with the idea of energy conservation and delivering an energy-efficient routing protocol in WBAN settings. Blockchain and energy harvesting models were introduced. The primary goal was to obtain high throughput and keep minimum transmission loss and keep a high network stability period. The authors of [25] introduced a routing system with an advanced relay scheme and energy consumption models. They have proposed a model named CM3A with the cooperating routing in WBAN. The energy optimizations have been evaluated. The authors of [26] presented the scavenging method to improve the energy level in WBANs within the routing layers. The authors of [27] proposed a wireless energy model for WBANs to improve network performance. By doing so, they have presented a novel hybrid routing approach. The authors of [28] introduced an analysis base optimization model for WBANs to enhance the energy level among the nodes. By doing so, they evaluated their work with WBAN routing protocols named ITAEO and TAEO. The authors of [29] proposed a strategy named reinforcement energy-based routing scheme for WBANs. The energy harvesting and energy consumption scheme were proposed in their work. The authors of [30] designed the highest standard-level energy-based routing protocol for WBANs. They have used the PDR, throughput, network stability, and jitter as the core evaluation metrics. The authors of [31] have focused on energy in routing for WBANs but they have also focused on security in WBANs. Since the network is wireless, it is extremely vulnerable to threats and perpetrators. The key establishment schemes were introduced with the encoding and decoding combination. The authors of [32,33,34,35] proposed an energy-based routing protocol for WBANs with a slight change of the path loss but most importantly focused on the clustering mechanism. They have focused on reliable data transfer and secure communication in WBANs. With the highest priority-based routing, the priority-based tree was introduced in their work. The authors of [36,37,38] proposed WBAN energy-level routing and threshold measuring models. The primary goal was for the network performance to survive for a long time. The stable routing approach was introduced in WBANs in which the stability and threshold measuring parameters were deployed [39,40,41]. The majority of the related work has been done for the sake of enhancement in WBANs, especially on the basis of routing and energy. The previous works have some limitations from the perspective of the said issues and challenges. Routing and energy are the core issues and challenges in WBANs [42]. The previous authors have suggested and introduced different techniques and approaches for healthcare environment networks. With their limitations and some issues that have not been properly addressed and by using different techniques, the proposed EEDLABA routing protocol differs from their work. With the advancement of the clustering approaches as well as with the deployment of the coordinator (C) node, the proposed work has achieved the best results as contrasted with the existing WBAN approaches especially in the concept of energy and routing. The proposed EEDLABA is a hybrid in nature as this protocol is the combination of DARE and LAEEBA which possess the most proficient and positive aspects of them. Hence, the proposed protocol has overcome many limitations such as routing as well as being energy-efficient.

3. Methodology

This section consists of the proposed methodology and implementation in which various steps are involved for the novel energy-efficient distance- and link-aware body area (EEDLABA) routing protocol. The procedure deals with the practical implementation as well as the proposed approach. Every subsection has an alternative and diverse approach in perspective of the methods and tools used.

Table 1. Node Nomenclatures based on Their Functionality.

Name	Function
UC H-N1	Cluster head I
UC N-3	Sense of Sight, Reflex Action, and ECG Sensor
UC N-4	Heartbeat
UC N-5	Blood Pressure, Temperature Sensor
UC N-6	Sense Stimuli, Pulse Rate
LCH N-2	Cluster head II
LC N-7	EMG-sensor
LC N-8	Glucose
LC N-9	Motion

and

Table 2. Simulation Setup.

Parameter	Value
Tool for Simulation	MATLAB
Performance Parameters	Average Path Loss, Average Residual Energy Average End-to-End delay, and Average Throughput
Type of Channel	Wireless 802.11
No of Node(s)	9
Routing Protocol(s)	EEDLABA, LAEEBA, and DARE
Initial Energy (Eo)	0.357 J
Minimum Voltage Supply	1.9 V
Reception Energy Erx–elec	36.1 nJ/bit
Transmission Energy Etx–elec	16.7 nJ/bit
Amplifier (Eamp)	1.97 nJ/bit
DC Current (TX)	10.5 mA
DC Current (RX)	18 mA
EDA	5 nJ/bit
Wavelength (λ)	0.125 m
Frequency (f)	2.4 GHz

show different values for simulation.

Table 1 represents the terminologies for the proposed EEDLABA routing protocol. The term UC stands for upper cluster, and N denotes the nomenclatures. In total, nine nodes are used to assign each node to UC. Similarly, LC stands for lower cluster, in which the clusters are divided into two main clusters, UC and LC. The function of each cluster is illustrated in the table with its functionality. The clustering mechanism is deployed in EEDLABA; therefore, Table 1 represents those clusters with their functionalities and number of nodes.

A total of 9 nodes have been deployed in the design of the EEDLABA routing protocol for WBANs. These 9 nodes are also mentioned in the proposed mechanism in Figure 1 (Flow Chat of Proposed EEDLABA Routing Protocol for WBANs.) In Table 2, the simulation setup has been corrected with the 9 nodes.

The EEDLABA routing protocol consumes less energy as the protocol is energy efficient and only 9 nodes have been deployed because the smaller number of nodes consumes less amount of energy. Additionally, these nodes have been deployed in combination with the coordinator node, as the advanced nodes have been used in the EEDLABA routing protocol for WBSNs. So, that is the actual reason for using the 9 sensor nodes. Each spot has been paved in a human body by placing each sensor node at the exact place where they all are directed to communicate with the cluster head and the coordinator (C) node.

The network layer has been used for the purpose of communication and since the proposed EEDLABA protocol is based on LAEEBA and DARE, the proposed protocol is neither reactive nor proactive. In fact, the proposed protocol is a hybrid.

3.1. The Proposed EEDLABA Protocol

In the proposed model, the first phase is system initialization, also known as the system configuration and initialization phase, as shown in Figure 1, followed by the deployment phase of the nodes in diverse positions on a human body. This scheme is divided into clusters and nodes in which the sensor is connected with these CHs, as shown in Figure 2. Multiple conditions have been illustrated here where the conditional phases use the yes/no statements. The CHs and body sensor space have been measured in the given scenario. Doing so comes the phase of path loss (PL) in which two PL models are used with conditional statements; it says if the distance D1 is greater than D2, then follow up the PL1 model or else follow up the PL2 if the distance D2 is greater than D1. Finally, transmit the data in a mutual perspective and store all the measured parameters. Table 2 illustrates that the concerned research is used in different factors for simulation.

In total, 9 sensor nodes have been deployed in the design of the proposed EEDLABA protocol. The clustering mechanism has been done on the said sensor nodes. Mostly, the clustering is done in situations where there are so many nodes in the network to overcome the routing and flooding mechanism among the sensor nodes. In the EEDLABA protocol, the clustering has been done for a mere 9 sensor nodes. The most important and basic motive for using the clustering approach for this scenario is that the nodes have been placed in an appropriate position where the coordinator and the BS node are. If all the nodes communicate directly with the C node and BS, then these will cause flooding and have an issue among all the nodes and then it will be difficult to avoid some such circumstances. The reason for clustering is that the nodes have limited sources of energy and this work is based on energy efficiency. So, to deal with the energy consumption and to deliver an energy-efficient protocol, the clustering approach is the best way to avoid path loss and delays and also to increase the network lifetime.

There are a total of 9 nodes that are deployed on a human body. Nine clusters are also introduced, such as the upper and lower clusters. Thus, the nodes are divided into clusters, i.e., distributed into the cluster, and each group possesses one node.

The term in Figure 1 refers to the placement and deployment of the nodes designated to clusters to be effectively operated. The clusters perform and work on the basis of sensor nodes.

The primary energy for each node has been set to 4.5 joules. Now, if the energy of a cluster head is equal to zero, it will be directed to follow the path with high energy, or it will not be transmitted until its energy is greater than zero.

In the proposed system’s initialization phase, the deployed nodes on various body positions are divided into two clusters, and nodes are connected with cluster heads. In Figure 1, various conditions are checked. The space between the body sensor and CH is measured. Check path loss if D1 > D2, then follow PL1; else, if D2 > D1, then follow PL2, transmit the data, and store all measured parameters.

3.2. System Model

3.2.1. Proposed System

The sensor node for coordination, named the coordinator (C), has been installed in the middle of the human body in the proposed model. After that, multiple sensor nodes were deployed on the human body at different places along with two major clusters. For numerous functionalities and conditions, other sensor nodes are connected with these cluster heads (CHs). As a WBAN is a mixture of multiple sensor networks, the sensor node placement on a human body becomes an issue. For this solution, nine sensor nodes have been installed on a human body; these nodes have diverse computational (performing some processing and generating output) and power capabilities.

For continuous monitoring of the human body, nodes 3, 4, and 9 have been placed at a distance where continuous monitoring becomes feasible. Other sensor nodes are called action-oriented from the perspective of sensors. In case of an emergency, the three sensor nodes for constant monitoring are embedded with capabilities to communicate directly with the C node to transfer the data directly. The remaining action drive nodes can transmit the data to the C node via other CHs functioning as forwarding nodes, as shown in Figure 2.

A sensor node with a minimum cost function is preferred as a forwarder. All the neighbor nodes then stick to the forwarder node and transmit their data. The forwarder node aggregates data, transfers it to CH, and then to the coordinator node. This node has maximum residual energy and minimum distance to CH; therefore, it consumes minimum energy to forward data to CH. Nodes such as 3, 4, and 9 for continuous monitoring communicate directly to C in case of emergence and do not participate in forwarding data.

In the subsections that have been provided, the mathematical model for the EEDLABA suggested protocol has been used, and each step has been addressed and represented in equations. For the design of EEDLABA, the ten nodes have been used in implementation on the human body for a WBAN. These are divided into two parts, each named after the cluster. These clusters are the lower cluster (LC) and upper cluster (UC) due to the upper and lower location placement on a body. The first cluster node, one, is connected with the LC, whereas the second cluster node, two, is associated with the UC. These nodes are selected as upper cluster head (UCH) and lower cluster head (LCH). In contrast to the LCH, which is linked to the nodes in sequences 7, 8, and 9, the UCH is connected to the nodes in sequences 3, 4, 5, and 6. Apart from these, one sensor node has been installed on the human body and is named the coordinator. This node checks the other action-driven nodes as if the node is alive or dead from the perspective of energy consumption. If a node is found to be alive, then the suggested algorithm is responsible for staying alive to the CH. After that, it also checks whether the node is for measuring the threshold or monitoring continuous sensors. The overall functionalities of these nodes are illustrated in Table 1, with different values for each. For dealing with the distance, it has been measured between the coordinator and the source node. The data that pass from nodes to CHs are then checked to determine if there has been any signal propagation delay. When this CH accepts and receives the data from the sensor nodes, it then diagnoses a process known as fusion, which involves assembling and combining the received information before sending it to the C node. The PL is identified with the help of two proposed distance models, D1 and D2. For emergency data, PL1 is used in D1 > D2, and PL2 is used for multi-hop routing of data in D2 > D1. In the end, all the measured parameters are stored, and the data are transmitted.

3.2.2. Initialization Phase

This phase, in which the primary process becomes active to begin functioning completely, is also known as system configuration. Three different types of functionalities are carried out in this phase. Every potential node in the area is first notified about the locations of the CH and the C nodes for all possible pathways from source to destination. When a node broadcasts a packet, the sensor node changes the CH and C node IDs so that their own energy status and position are made public.

3.2.3. Radio Model and Equations

With the propagation of the signal, the radio model is introduced in which the amplification and the transmission power can be calculated using Equation (1) [39].

$${ \mathrm{E}}_{\mathrm{tx}}\left(\mathrm{b},\mathrm{S}\right)={\mathrm{E}}_{\mathrm{TXelec}}\times \mathrm{b}+{\mathrm{E}}_{\mathrm{amp}}\left(\mathrm{n}\right)\times \mathrm{b}\times {\mathrm{S}}^{\mathrm{n}}$$

(1)

For the reception level of energy, the equation is given as;

$${\mathrm{E}}_{\mathrm{rx}}\left(\mathrm{b}\right)={\mathrm{E}}_{\mathrm{RXelec}}\times \mathrm{b}$$

(2)

For the energy transmission ${\mathrm{E}}_{\mathrm{tx}}$ is used in the given equation. Similarly, the energy of the receiver is represented by ${ \mathrm{E}}_{\mathrm{rx}}$. ${\mathrm{E}}_{\mathrm{TXelec}}$ and ${\mathrm{E}}_{\mathrm{RXelec}}$ illustrate the energy of the transmitter and receiver in terms of dissipation by radio channel. The term ${\mathrm{E}}_{\mathrm{amp}}$ denotes the energy that the amplifier has transmitted, the term b denotes the bits transmitted in number, and S denotes the distance. The values for these parameters, ${\mathrm{E}}_{\mathrm{TXelec}}$ and ${\mathrm{E}}_{\mathrm{RXelec}},$ are 16.7 nJ/bit and 36.1 nJ/bit. The value for ${\mathrm{E}}_{\mathrm{amp}}$ is taken as 7.79 μJ/bit.

3.2.4. Next Hop Selection Phase

The DARE protocol is based on inspecting a hospital ward under five different topologies, in which the patient’s different body organs are monitored to detect any ambiguity in their normal functioning. The hospital ward consists of eight patient beds in which one sensor node is installed on the patient’s chest. The topology of the WBAN in this scenario is kept the same for the entire time to equally transmit the data without any interruption and consume less energy [40].

For the forwarder node, the LAEEBA scheme consumes the cost function based on the remaining energy and distance of the link. In each round of the simulation, the LAEEBA scheme uses a new forward node to balance energy consumption. The node which has the information regarding the residual energy, distance, and ID is the sink node. It calculates the cost function for all nearby nodes and forwards the value to other neighbor nodes in the network. By doing so, every node regains the trust to transfer further or not and to be a forwarder node or not. In this sense, the distance term is indicated by d(i) among the nodes sink and i; for residual energy, the term E(i) is indicated. If the number of nodes is i in a scenario, then the value of the cost function is C(i) of node i and will be calculated as [41].

$${\mathrm{C}}_{\mathrm{i}}=\raisebox{1ex}{$\sqrt{\mathrm{d}\left(\mathrm{i}\right)}$}\!\left/ \!\raisebox{-1ex}{$\mathrm{E}\left(\mathrm{I}\right)$}\right.$$

(3)

In each cycle of the network’s operation, the proposed EEDLABA protocol chooses the new node as a forwarder after the specified phase. All the selected nodes close by can have their ID, distance, and amount of energy stored in the term CH. Accordingly, the cost function to compute for node I is provided as follows.

$${\mathrm{cf}}_{\mathrm{i}=}\frac{\mathrm{S}\left(\mathrm{i}\right)}{\mathrm{RI}\left(\mathrm{i}\right)\times \mathrm{PL}\left(\mathrm{i}\right)}$$

(4)

From Equation (4), the term s(i) denotes the distance between the C and i nodes. The term RI(i) indicates the initial residual energy of that node i which can be calculated by subtracting node current energy from the initial energy of that node. The term $\mathrm{PL}$ denotes the path loss between the CH and nodes. A node will be considered a forwarder node if its cost function has a minimal value. The data may subsequently be sent in accordance with all the neighboring nodes by directing it. The data are put together by the forwarder node and sent to the CH and C nodes. This node retains the shortest distance to the CH and the most residual energy. Because of this, this node uses less energy when forwarding data to the CH. The nodes in order 3, 4, and 9 continually monitor and interact with the C node directly in any instances requiring emergency communication and do not participate in data forwarding, whereas the other nodes are action-driven nodes.

3.2.5. Phase of Route Establishment

The proposed model is composed of multiple distributed mobile public BANs in which the intra-body routing mechanism is mandatory.

The next subsection gives the detail and modeling of intra-body routing for WBAN.

Phase of Intra-Body Routing

Routing is a mechanism for selecting a suitable and reliable path to transfer the data efficiently; from a philosophical perspective, sensors are mounted on a person’s body for the WBAN, where signal propagation matters and route loss predominates. For that, intra-body routing is mandatory, which can be solved by using the two links, i.e., in two hops, the intra-body routing will be installed. It can be assumed that the topology is two hops due to the use of these two hops. The nodes as a forwarder are chosen accordingly based on the δ values and the received signal strength indicator (RSSI). The proper utilization of δ helps the selected node be a more synchronous forwarder. The symbol δ indicates a partial derivative and is used when differentiating a function of two or more variables in a WBAN.

3.2.6. Phase of Path Loss Selection

Path loss (PL) denotes when an obstacle or any other interruption occurs during the communication between nodes, and it becomes stopped or unable to send the data further. As shown in Equations (5)–(7), the PL can be calculated by using the distance in terms of D1 distance and D2 distance because of using the same two models of PL. The PL1 and PL2 models are used for distance calculation and to avoid any interruption. Devices in which Bluetooth, ZigBee, etc., are installed suffer from PL, and this PL ultimately drains the energy of the nodes by active and not communicating. From Equation (5), if the distance D1 is greater than or equal to D2, then the nodes will have to follow the PL1 model [42]. Similarly, if the distance of D2 is greater than or equal to D1’s, the nodes will have to follow PL model 2. A WBAN is composed of sensors that die due to limited energy if the nodes are active for a long time. In these nodes, the C node and CH work with the forwarder node’s collaboration. Speaking of PL and distance, it is given in the equations below.

If D1 ≥ D2, the nodes will follow the path loss model PL (S, f) given by

$$\mathrm{PL}\left(\mathrm{S}, \mathrm{f}\right)\left[\mathrm{dB}\right]=\mathrm{x}\times {\log }_{10}\left(\mathrm{S}\right)+\mathrm{y}\times {\log }_{10}\left(\mathrm{f}\right)+{\mathrm{N}}_{\mathrm{S}, \mathrm{f}}$$

(5)

For x and y values to be obtained with coefficients and ${\mathrm{N}}_{\mathrm{S}, \mathrm{f}}$, the LMS algorithm is used and its values were computed as x = (−)27.6, y = (−)46.5, and ${\mathrm{N}}_{\mathrm{S}, \mathrm{f}}$ = 157. If D2 ≥ D1, the nodes will use under the study for path loss model PL (S, f)

$$\mathrm{PL}\left(\mathrm{S}, \mathrm{f}\right)\left[\mathrm{dB}\right]={\mathrm{PL}}_0+10{\mathrm{nlog}}_{10}\left( \frac{{\mathrm{S}}_2}{{\mathrm{S}}_0} \right)+\mathsf{\sigma }$$

(6)

The PL is computed as:

$$\mathrm{PL}=10{\log }_{10}{\left(4·\mathsf{\pi }·\mathrm{Sf}\right)}^2\times \mathrm{sp}$$

(7)

From the given expressions, the term S0 denotes the reference to the distance as selected in the range of 10 cm. The coefficient of PL is n and their values are from 3 to 4 for LoS communication, and for N-LoS the values of 5–7.4 are used. Sp denotes the speed of light, and f stands for frequency in the given expression.

4. Results and Discussion

This section provides a detailed discussion of the simulations and the evaluation and comparison of the proposed EEDLABA with the LAEEBA and DARE routing protocols. The outcomes were obtained using MATLAB simulations, in which mathematical modeling was applied to provide these outcomes for a WBAN. The WBAN consists of sensor nodes in which different kinds of nodes are used for communication between the CHs, the C node, and other action-driven nodes. This paper shows the total of nine nodes that have been deployed for the simulations. These nodes are divided into two clusters in which nodes are used for cluster 1 and cluster 2 with path loss models PL1 and PL2. These PL models are linked with distance, link awareness, and energy consumption to efficiently transfer the data.

For contrast-based evaluation, five performance evaluation parameters have been used in this paper, and these are given as follows.

Average Path Loss (dB): The PL defines the loss in a communication signal due to some obstacles or other transmission loss due to the signal’s power. This can be measured by using the unit of dB (decibels).

$$\mathrm{PL}=10{\log }_{10}\left|\begin{array}{c}{\mathsf{W}}_{\mathsf{i}}\\ -\\ {\mathsf{W}}_{\mathsf{t}}\end{array}\right|\mathrm{dB}$$

(8)

Average Residual Energy (joules): The residual energy is the remaining energy in a node by subtracting the total transmitted energy from it. It can be measured in joules.

$$Average Residual Energy=Total energy-consumed energy$$

(9)

Average End-to-End Delay (sec): The total delay during data transmission from the source end to the destination end is known as E2ED. This can be measured in seconds or milliseconds.

$$Average E2ED {\mathrm{d}}_{\mathrm{end}-\mathrm{end}}=\mathrm{N}\left({\mathrm{d}}_{\mathrm{trans}}+{\mathrm{d}}_{\mathrm{prop}}+{ \mathrm{d}}_{\mathrm{proc}}+{\mathrm{d}}_{\mathrm{queue}}\right)$$

(10)

Average Throughput (bps): The sent packets from one node to another in a network are the throughput of a network. It can be measured as a percentage of sent/received packets.

$$Average Throughput=\frac{Number of packets acknowledged}{Number of packets sent}\times 100$$

(11)

Average Stability Period (alive versus dead nodes): The stability of a network can be defined as the actual operation time of a network from the perspectives of live and dead nodes. This metric can be measured as the number of active or dead nodes.

$$Average Stability Period=Alive Nodes-Dead Nodes$$

(12)

A detailed discussion of these parameters is given in the following subsections with graphical and tabular illustrations.

The results that are gathered from various simulation parameters are analyzed and discussed. In WBANs, different types of nodes are used, such as sensing, forwarding, CH, and coordinator nodes. Sensing and forwarding nodes are used in WBANs as normal nodes, but CH nodes are used as advanced nodes compared to normal ones. CH nodes receive data from sensing and forwarding nodes but can only send aggregated data or a single signal to the coordinator node. CH nodes removed redundant information from data, which they can send to the coordinator node. Nine (09) nodes are used for the EEDLABA routing protocol. Proper numbering identifies nodes and colors, as shown in Figure 1. Nodes 1 and 2 are used as CH nodes. Nodes 3, 4, and 9 are for continuous monitoring, while nodes 5, 6, 7, and 8 are used for action-driven/threshold monitoring, and sensing nodes are used for source and coordinator nodes for the destination. The human body is used as a communication medium in WBANs.

4.1. Analysis and Evaluation of Average Path Loss

The average path loss of the EEDLABA routing protocol in contrast to LAEEBA and DARE is given in Figure 3a–c. From this point, it has been illustrated that the proposed EEDLABA routing protocol has achieved much better performance. Path loss denotes the communication loss that occurs during one sensor node’s transmission to another. The simulations have been run for 9000 s, in which each round represents different values. The proposed EEDLABA routing protocol has achieved improved performance in contrast to all routing protocols. The key factor for this is the advanced node deployment and the utilization of the clustering mechanism. This clustering mechanism can maintain stable communication among the sensor nodes. This value has been calculated and has been measured in decibels (dB). The path loss also deals with the power of the nodes; if they have strong and enough power from one sensor node to another, then communication may take place efficiently. Path loss, or path attenuation, is the reduction in the power density of an electromagnetic wave as it propagates through space. Path loss is a major component in the analysis and design of the link budget of a telecommunication system. This term is commonly used in wireless communications and signal propagation. The path loss of EEDLABA is calculated with the least value, as the lesser the path loss, the greater the communication will be. Hence, it has been observed that the proposed protocol has achieved better performance in path loss by introducing the lowest path loss value.

4.2. Analysis and Evaluation of Average Residual Energy

The average residual energy of the EEDLABA routing protocol in contrast to LAEEBA and DARE is illustrated in Figure 4a–c. Residual energy or decay heat is the energy produced by sensor nodes within the communication range. When sensor nodes are stopped due to reactor scram, decay heat decreases but never becomes zero. Despite being a decreasing function, decay heat could produce damage by itself. WBANs allow every individual to monitor their health independently and provide feedback, which helps them to keep their health status known. Similar to weather maps or air traffic images, the sensor network’s residual energy scan demonstrates a network’s geographical distribution of residual energy. The residual energy of EEDLABA is evaluated and contrasted with the other routing protocols in terms of the provided joules of energy. A value of 4.5 has been set as the initial energy for all the protocols. The proposed routing protocol has consumed the least or less energy. The key factor is that the proposed protocol only communicates when an active sensor is in the range. No discontinued communication takes place, which results in the consumption of less energy. As the nodes consume less energy, they have to deal with the situation in which there are some alternate routes to be used. This terminology has been given to the proposed protocol, which uses an alternative path for communication because of the advanced nodes deployed. Note that the maximum residual energy in (j) has been set at 4.5 for all the protocols.

4.3. Analysis and Evaluation of Average End-to-End Delay (E2ED)

The average end-to-end delay of the EEDLABA routing protocol in contrast to LAEEBA and DARE is illustrated in Figure 5a–c. End-to-end delay or one-way delay (OWD) refers to the time taken for a packet to be transmitted across a network from source to destination. It is a common term in IP network monitoring and differs from round-trip time (RTT) in that only path in one direction from source to destination is measured. This is the time a bit or packet takes to traverse from a transmitter to a receiver. Additionally, the ratio of the time when the data is received is given. The E2ED of all the routing protocols is calculated and has been measured in seconds. The less and lowest delay denotes higher performance. As the delay decreases, it ultimately causes the network performance to increase. It has been witnessed that the proposed EEDLABA protocol has achieved much better performance. The simulations were carried out in MATLAB version R2021a. From the simulations, it has been noticed that in all 9000 seconds, the proposed protocol gives the least delay and better performance, so the packet latency is very low. This indicates that the protocol works better and gains less delay.

4.4. Analysis and Evaluation of Average Throughput

The average throughput of the EEDLABA routing protocol in contrast to LAEEBA and DARE is illustrated in Figure 6a–c. Network throughput refers to the rate of successful message delivery over a communication channel, such as Ethernet or packet radio, in a communication network. These messages’ data may be delivered over physical or logical links, or through network nodes. It has been witnessed that the proposed protocol EEDLABA has achieved better performance in all scenarios of the throughput. In the scenarios here, it means the 9000 seconds of simulation. All the protocols have better performance, but EEDLABA has the highest ratio of network throughput. Due to the clustering mechanism and the deployment of the advanced nodes, the proposed protocol can perform well. The advanced nodes only communicate when there is an active path for communication and thus use the shortest route to deal with other sensor nodes.

4.5. Analysis and Evaluation of Stability Period

The average stability period of the EEDLABA routing protocol in contrast to LAEEBA and DARE is illustrated in Figure 7a–c. Frequent changes in next hops along routing paths between source and destination nodes can increase the undesired energy consumption of the WSN. Hence, the relative routing path usage count, usage rate of unique next hop, and switching frequency count are proposed as routing stability indicators. The network may be said to be stable at this moment because it has been steady up until the demise of the first node. There is no stability period or network lifespan after a death or communication breakdown. Be aware that the first node in DARE and LAEEBA died at 2123 and 2023 s, respectively; however, the first node in the planned EEDLABA died at 4205 s, indicating that it had a greater operating time than the other two. The proposed EEDLABA has achieved the best performance by generating the fewest dead nodes.

From the overall discussion and illustrations from

Table 3. Comparative Evaluation of EEDLABA with LAEEBA and DARE.

Protocol	Average. Path Loss (dB)	Average. Residual Energy (j)	Average. E2ED (ms)	Average. Throughput (bits/sec)	Average. Stability Period (Number of Dead Nodes)
DARE	228.189	0.889	0.843	0.792	5
LAEEBA	179.785	0.938	1.234	0.977	6
EEDLABA	35.523	1.532	0.768	1.165	3

, it can be seen that the proposed EEDLABA protocol has outstanding performance in proposed scenarios compared with the existing LAEEBA and DARE approaches for WBANs. Based on the performance evaluation parameters, the proposed EEDLABA is best for such conditions and environments where the CHs, C nodes, and other action-oriented sensor nodes can perform their functionalities. The key reason EEDLABA gives the best performance is the clustering approaches and the advanced C nodes in which the non-continuous data transmission has been avoided. The deployment of advanced sensor nodes that communicate only when there is an active path for communication and avoid any path loss during transmission is the feature of EEDLABA. No non-continuous data transmission takes place, and the network only operates when there is a free path for communication, which explains the performance of the proposed protocol.

5. Conclusions

This study uses the least amount of energy to maintain communication across all nodes by utilizing a complicated change-of-direction approach. This study will make it easier for the routing nodes in a WBAN to focus on the connection while interacting with the sensors, estimating the link, and computing the link. Technology usage, smart IoT dives, and wireless technology are rapidly increasing. Thus, WBANs have great room for innovation in such an environment. The main problem in WBANs is energy consumption because of the limited computational power used in these nodes. In contrast to the LAEEBA and DARE routing techniques currently utilized in WBANs, the novel protocol EEDLABA has been proposed in this study. The suggested approach deploys the advantageous aspects of LAEEBA and DARE in an expanded form. The EEDLABA protocol has been presented as a means of resolving the flaws that currently exist in the LAEEBA and DARE. Path loss and energy consumption are major issues in this network. For that purpose, path loss model 1 and model 2 are proposed along with distance one and distance two in which the CH node, C node, and others, for a total of nine nodes, are deployed on a human body. The outcomes were obtained using MATLAB simulations in which the proposed EEDLABA’s performance was evaluated in contrast to that of the LAEEBA and DARE routing protocols. According to the findings, the suggested protocol can function effectively in the conditions under consideration.

Future Work

QoS optimization, energy with reliability, node deployment with geographic and node scalability, and shock and heat-absorbing sensor nodes may arise. These issues need proper and robust routing techniques to be solved. It is suggested that since clustering with path loss has shown good results, in the future, congestion control be utilized along with these two parameters and tested under new conditions. Furthermore, the node scalability of the network can also be analyzed by increasing or decreasing the number of nodes, and results can be obtained through simulation. Research work on WBANs is still in its early stages, and WBANs offer many challenges/problems that require efficient solutions. Several new technologies related to sensor hardware, software, and protocols are emerging, and some may be easily adaptable to WBANs. Integrating these new technologies with the existing ones is a major research challenge. WBANs need to be open-ended and should be able to accommodate any new technology. However, routing algorithms available for wireless sensor networks adapt in good health for WBANs; some WBANs-specific issues lead to further modification of the routing algorithms. WBAN routing is always from a source sensor to a coordinator. The destination does not change, and the number of sensors is also significantly fewer. Moreover, movement is more due to postural mobility, and energy is more challenging to supply.