Performance evaluation and comparative analysis of CrowWhale-energy and trust aware multicast routing algorithm

Abstract

Multipath routing helps to establish various quality of service parameters, which is significant in helping multimedia broadcasting in the Internet of Things (IoT). Traditional multicast routing in IoT mainly concentrates on ad hoc sensor networking environments, which are not approachable and vigorous enough for assisting multimedia applications in an IoT environment. For resolving the challenging issues of multicast routing in IoT, CrowWhale-energy and trust-aware multicast routing (CrowWhale-ETR) have been devised. In this research, the routing performance of CrowWhale-ETR is analyzed by comparing it with optimization-based routing, routing protocols, and objective functions. Here, the optimization-based algorithm, namely the Spider Monkey Optimization algorithm (SMO), Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO) algorithm, Water Wave Optimization (WWO) algorithm, Crow Search Algorithm (CSA), and, routing protocols, like Ad hoc On-Demand Distance Vector (AODV), CTrust-RPL, Energy-Harvesting-Aware Routing Algorithm (EHARA), light-weight trust-based Quality of Service (QoS) routing, and Energy-awareness Load Balancing-Faster Local Repair (ELB-FLR) and the objective functions, such as energy, distance, delay, trust, link lifetime (LLT) and EDDTL (all objectives) are utilized for comparing the performance of CrowWhale-ETR. In addition, the performance of CrowWhale-ETR is analyzed in terms of delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput, and it achieved better values of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using EDDTL as fitness.

Keywords

Spider monkey optimization dolphin echolocation water wave optimization crow search algorithm whale optimization algorithm

1. Introduction

IoT is a very significant part of our day-to-day life. It is an emerging application in current years in which the devices connected in IoT are linked through the internet to provide convenience and efficiency in industries, human lives, and academia [4,34]. The generation of wireless communication schemes in obedience to complicated approaches for security [12,39]. Moreover, these devices have been broadly utilized in big IoT infrastructures where a huge amount of smart as well as sensing devices are linked to manage and establish communication [18,33]. The linked devices are interconnected with one another for the purpose of broadcasting information, which is highly placed in low-power and lossy networks (LLN) [6,32]. The LLN is a network class in which the devices are linked based on memory, power, and processing, which are attained by low data rates, high loss rates, and instability in communication links [1,24,25]. One of the important issues in IoT networks is traffic, which cannot be resolved by all the conventional routing protocols [20,35].

The LLN routing protocol, proactive protocol, and lightweight IPv6 network protocol provide a better solution for traffic issues [35]. Energy availability and Energy efficiency (EE) are the two significant factors in Wireless Sensor Networks (WSNs). In addition, an efficient routing protocol, namely EE media access control (MAC) has been devised for the progression of energy-efficient multihop WSNs. The major functioning of routing protocols is that they not only rely on transmitting the information from source to destination, but it is also utilized to reduce energy consumption to maximize the lifetime of the network [8,16,28] [11,17]. Recently, IoT assisted WSN system has been introduced, which receives a significant focus from the research community. In IoT-WSN, the heterogeneity of WSN and energy-efficient ability from various processing conditions is adopted [29]. Heterogeneity has been deliberated from energy consumption, initial energy, residual energy, sensing capacity, link capacity, and transmission range [26,27,40]. It is necessary to construct the routing protocols for the applications in WSN-based IoT [22].

In IoT, the data is communicated from one to multiple nodes by multicast transmissions [3]. Generally, the routing protocols are partitioned into geographic-based and non-geographic protocols. In non-geographic-based protocols, the request packets are broadcasted from the multicast source to entire destination nodes [19,36,37]. Likewise, in geographic-dependent multicast routing, the nodes recognize the position based on Global Positioning System (GPS) devices [23]. Multichannel routing approaches minimize congestion and security [15] as well as interference for enhancing the data rates and minimizing the power utilization, thereby the QoS constraints are improved [2,10]. Thus, there is a necessity for extremely efficient multicast routing to resolve the necessities of multimedia communications in extremely dynamic IoT environments. The foremost intention of multicast routing algorithms is that upsurges resource utilization as well as decrease network energy utilization [13]. WSNs offer highly effective monitoring as well as control in effectual cost as they are autonomous and infrastructure-less [30,31].

The main aim of this research is to analyze and determine the effectiveness of CrowWhale-ETR with other routing techniques and objective functions. The major contributions of the research are:

The effectiveness of CrowWhale-ETR is examined by comparing it with other routing protocols, such as optimization-based routing, routing protocols, and objective functions.

The entire processing is carried out on the IoT-WSN model for transmitting the information.

The effectiveness of the CrowWhale-ETR is compared with optimization-based algorithms, routing protocols, and objective functions.

The organization of this manuscript is explained in this section. Section 2 portrays the system model of WSN-assisted IoT, Section 3 illustrates the performance evaluation and comparative analysis of CrowWhale-ETR, Section 4 describes the result and simulation outcome of CrowWhale-ETR, and Section 5 is the conclusion of the research.

2. IoT-WSN model

With the existence of energy constraint issues in WSN, the sensor nodes are combined into a single cluster such that the optimal node is deliberated as Cluster Head (CH). In WSN-assisted IoT, each of the sensor nodes is called as IoT nodes, which is responsible for gathering the details, and then it is sent to the base station through the optimal CH. Meanwhile, the CH node considers the limited energy to broadcast the information since the selected CH node has a minimum distance than the others. The node with minimal distance is elected as an optimal CH. Let us assume the area A with dimension $u \times v$ in which the WSN comprises of base station C and the IoT nodes B with cluster head G. Every IoT node $b_{a}$ , $1 < a < B$ is placed at $(U_{a}, V_{a})$ such that the position of base station is indicated as $(u^{B} \times v^{B})$ . Figure 1 shows the system model of IoT-WSN. Here, each node in the cluster transmits the information to CH node.

Fig. 1.

System model of WSN-assisted IoT.

3. Performance evaluation and comparative analysis of CrowWhale-ETR

The aim of the research is to analyze and justify the effectiveness of CrowWhale optimization in comparison with the existing optimizations for IoT energy-aware multicast routing. By considering these parameters, a routing framework, called CrowWhale-Energy Trust Routing (CrowWhale-ETR) is compared with several optimization-based routing algorithms, such as SMO [7], DEO algorithm [14], WWO algorithm [41], CSA [5], and WOA [21]. Moreover, the routing protocols, such as AODV [9], CTrust-RPL [35], EHARA [22], light-weight trust-based QoS routing [38] is compared with CrowWhale-ETR [31]. In addition, the objective functions, like energy, distance, delay, trust, and LLT are considered fitness for comparing the effectiveness of CrowWhale-ETR.

3.1. Optimization-based routing algorithms

In this paper, the effectiveness of CrowWhale-ETR is compared with several conventional optimization-based routing algorithms, such as SMO, DEO, WWO, CSA, and WOA in IoT. All these algorithms are briefly explained in the next section.

3.1.1. SMO

SMO [7] is a stochastic optimization algorithm, which is based on the foraging aspects of spider monkeys (SMs). Moreover, the fission-fusion social structure (FFSS) dependent foraging aspects of SMs inspire the researchers to develop the SMO algorithm. Generally, the SMs are live in a group of 40–50 persons, and they explore their food in FFSS manner. The FFSS of the swarm diminished the food-searching competition between the members in a group by partitioning the entire group into smaller clusters. In a spider group, the female is normally used to lead the group and is responsible for examining the food, hence the female SM is called as global leader. If the female SM or global leader does not catch the food, then the female SM categorizes the group into smaller sub-groups in order to explore the food. Likewise, the sub-groups are also led by the female SM who is responsible for taking decisions for exploring the food in an effective foraging path. The female leader of the subgroup is called as a local leader. Moreover, the members present in the subgroups are communicated within or outside of the subgroup based on the existence of food.

In the algorithmic process of SMO, the location of SM is updated by adapting the foraging aspects of SM. The location update of SM is done by considering four steps. In the first step, the spider group initiates the food search and computes the distance from a food source. In the second step, the group members renew their locations based on the computed distance, and then re-evaluate the distance from a food source. In the third step, the local leader renews the optimal locations within the group. In the fourth step, the global leader in the group updates the ever-best location. In case, the location of the global leader is not renewed, then every member in the cluster is again partitioned into smaller sub-groups for searching the food in distinct locations. All the above-mentioned four steps are processed continuously till the desired solution is attained.

Moreover, the control parameters utilized to search the location in SMO is ‘GlobalLeaderLimit’ and ‘LocalLeaderLimit’. Here, these two parameters are used to help the SM for taking suitable decisions to explore the food. Here, the parameter “LocalLeaderLimit” is employed to evade stagnation, which means that, if the local group leader does not renew herself in a stated iteration count, then the corresponding group is re-planned to search the food in various directions. Here, the maximum count of iteration performed by the local leader is termed as LocalLeaderLimit. Likewise, the maximum count of iterations conducted by the global leader is termed GlobalLeaderLimit. If the global leader does not renew the optimal location in a certain iteration, then the global leader split the group into a smaller number of sub-classes. The pseudocode of SMO is explained in Algorithm 1.

Algorithm 1

Pseudocode of SMO

3.1.2. DEO

The word “echolocation” was invented by Griffin, which was derived based on the principle of sound generated by the flying bats [14]. Generally, the flying bats utilize echo for identifying the objects and prey by observing the returning high-frequency echo. Normally, an echo is reflected back from the restricted object. The bats identify the objects by observing the high-frequency clicks, which are reflected from objects and back to them. Some of the mammals as well as birds utilized the echolocation principle. In the marine environment, an echolocation principle is utilized by bottlenose dolphins. The dolphin has the ability to make the sound in click form. The clicks of various species differ to each other, and if the clicks frequency is higher than the frequency of sound then communicate the information between the species. If the sound restricts an object while making the sound, then some quantity of energy is reflected back from the sound-wave to the dolphin. When the reflected echo is received, then the dolphin makes another click. Thus, the time gap among echo as well as click is used to assist the dolphin to identify the spacing from dolphin to object. The changing signal strength received from two sides of dolphin’s head is used to identify the direction of object. Moreover, the location of the object is tracked by measuring the distance among the repeatedly generating clicks and receiving the echo. The short sequence of clicks generated by the dolphin is termed a click train.

The click rate upsurges when impending an object of interest. In the sonar system, the echolocation of bats is different from dolphins. Bats utilize their sonar system at small ranges of up to around 3–4 m, whereas dolphins can identify objects at ranges changing from several tens of meters to above a hundred meters. Numerous bats search for insects, which dart quickly to and-fro, creating it very dissimilar from the seepage performance of a fish pursued by dolphin. The velocity of sound in free space is around one-fifth of that of water, so the data transferal rate throughout the sonar broadcast for bats is considerably smaller than the dolphins. Based on the optimization issue, it can be assumed that echolocation is comparable to optimization in certain features. The process of foraging preys based on echolocation in dolphins is alike to finding the optimum response to a problem. In DE, each of the dolphins initially explores the search space in order to recognize the prey. After that, the dolphin tries to reach the target, however the animal limits its exploration, and incrementally upsurges its clicks for focussing on the location. This method pretends the DE by controlling its exploration related to the spacing from the target. In the DE algorithm, two phases are utilized. In the first phase, each of the dolphin explores the search space to perform a global examination, thus it should look for unknown areas. This process is carried out by searching some arbitrary positions in the search space. In the second phase, it achieved a better result by considering the previous step. The pseudo-code of DE is explained in Algorithm 2.

Algorithm 2

Pseudo code of DE

3.1.3. WWO

The principle of the shallow water wave scheme is adopted by generating the WWO algorithm [41]. The WWO algorithm copies the idea from wave motions, which is handled by the interactions between wave–current–bottom to the model of searching strategy for high-dimensional global optimization issues. Various experiments demonstrate that the WWO is very competitive with some other widespread meta-heuristic schemes, such as BBO, IWO, BA, and so on. Moreover, the WWO applies in real-time high-speed train scheduling applications in China, which also ensures that the WWO is applicable in real time applications. In WWO, the algorithm explains the development of periods, wave heights and propagation directions based on the analytical approaches by deliberating the nonlinear wave interactions, wind forcing, frictional dissipation, and so on. The advantage of WWO is that the speed, generality, and accuracy of this scheme were high. It is designed by considering the benefits of shallow water in order to solve optimization issues. This method avoids the generality, thereby the maximization issue with objective function is improved. The solution space of WWO is equivalent in the seabed region, and then the fitness is computed inversely by the depth of the seabed. The minimum distance in the water level increases the fitness value. During the implementation process, there are three types of operations are performed, namely refraction, propagation, and breaking.

Propagation

For all generations, each wave requires to be broadcasted once. Here, the propagation operator generates a new wave by moving all directions of the original wave. The low fitness region is considered as deep water, whereas the high fitness region value is considered as shallow water. When a wave traverses from the minimum fitness region to the maximum fitness region, then the wave height progresses and the wavelength diminishes.

Refraction

During the propagation of sound waves, the ray of wave is vertical to isobaths, then the direction is deflected. If the ray is deviate in a deep location, then it should be converge in shallow locations.

Breaking

If the depth of water is less than the threshold value, then the water shifts to a location so that the crest velocity of wave surpasses the celerity of wave. Accordingly, the crest can be steeper and steeper, and then the wave disruptions into a sequence of solitary waves. Moreover, the wave disruption is performed only on wave that estimates the new optimal solution and performs the local search.

Furthermore, the implementation of the WWO algorithm is simple and easy to execute. WWO scheme offers a better solution with minute population size, which forms the algorithm as computationally effective and high potential. In addition, the WWO algorithm adapts the population reduction scheme that is very effective in enhancing the presentation of outcomes. The control parameters used to execute the WWO algorithm are peak wave height, breaking coefficient, wavelength reduction coefficient, and highest count of breaking directions. Here, higher the values of wave height maximize the life time of waves, and the minimum value of wave height is replaced by the new waves, thereby the diversity of the solution is improved. The pseudocode for the WWO technique is illustrated in Algorithm 3.

Algorithm 3

Pseudocode for the WWO approach

3.1.4. CSA

Crows are more intellectual birds, which has the biggest brain according to their body size [5]. Depending on the ratio of brain to body size, the brain of crow is slightly smaller than the brain of human. The cleverness of crow is verified by its plentiful behaviour. Crows have the ability to recall the face in memory and they alert each other while an unfavourable person tries to approach that. In addition, they utilize the tool for communicating each other and they remember the place where the food is hidden after few months. Moreover, crows have the ability to observe other birds to find the hidden location of their foods for stooling it after they leaves. In case, the crow is recognized as thieve by other birds, and then it takes the excess precautions, like shifting to hidden places. Indeed, they utilize their personal knowledge to calculate the behaviour of a crook, and can define the safest course to guard their caches from being stolen. Depending on these defined behaviours, the population-dependent meta-heuristic algorithm, namely CSA is modeled. Furthermore, the principle of CSA is organized as below.

Crows are live in a flock manner.

Crows remember the location of their secret areas.

Crows observe other birds to perform thievery.

Crows guard their food from being stolen.

By considering the memory of the entire crow, the best location has need to be memorized. Crows shifts location from one to other for finding the best food source.

In this algorithm, there are two assumptions are stated when the crow follows the other to find the location of the hidden area.

Condition 1 : If the crow-1 does not see the crow-2 who is following it, as a consequence, crow-2 will identify the hidden location.

Condition 2 : If the crow-1 sees the crow-2 is following. As a consequence, the crow-2 will protect their food by fooling the crow-1 to avoid the pilfered.

The CSA scheme provides the better imbalance among intensification and diversification. Moreover, the intensification and diversification are mainly controlled by the parameter awareness probability (AP). Here, less value of AP makes the intensification process as progressive. Likewise, when the value of AP increases, then the searching probability of best solution decreases, which explores the search space on global scale. In addition, larger the value of AP maximizes the diversification process. The pseudo-code of the CSA technique is depicted in Algorithm 4.

Algorithm 4

Pseudocode of CSA technique

3.1.5. WOA

Whales are fancy creatures. Whales are deliberated as the largest mammals in the globe [21]. An adult whale grows as 30 m long as well as 180 t weight. There are 7 kinds of species in giant mammals, like Minke, killer, humpback, finback, Sei, blue, and right. Whales are mostly acted as predators, which never sleep since they breathe only on the surface of the ocean. In sometimes, fifty percent of the brain only sleeps. Especially, whales are considered as intelligent mammals with feelings. As stated by Van Der Gucht and Hof, the cells in certain areas of whale brain are alike to the human brain cells, which are termed as spindle cells. The spindle cells are responsible for making decisions, social behaviours, and emotions in humans. Moreover, the spindle cells are utilized to differentiate one from other things. In whales, it doubles the amount of spindle cells than the adult human, which is the major reason behind their smartness. In addition, it validates that the whales can learn, think, communicate, judge, and become emotional as human beings, but perceptibly with a much lesser ratio of smartness. Meanwhile, the whales have the ability to construct their individual dialect as well.

Another one significant aspect of whale is the social interaction of whales. Generally, the whales are live in a group or alone. Though, it is mostly live in groups. In addition, a few species of whales live along with their family throughout their whole life span. One of the largest whales in the ocean is humpback whale. A matured humpback whale is nearly with a size of school bus. The prey of matured humpback whales is small fish groups and krill. Moreover, an extraordinary ability of humpback whale is the special hunting scheme. Here, the process of hunting is carried out in the bubble net feeding approach. Humpback whales are more likely to eat krill school or smaller fisher comes nearer to the water surface. The foraging of these fishes is done by making different bubbles in a circular or ‘9’ shaped path. Prior to 2011, the hunting behaviour of whale is examined only from surface of water. Nowadays, the bubble net shaped hunting is observed with tag sensors. These sensors gathered the 300 feeding events of 9 humpback whales. There are 2 maneuvers linked with bubble, named as ‘doubleloops’ and ‘upward-spirals’. In the prior maneuver, humpback whales jump around 12 m downward and begin to create bubbles in a spiral manner around the prey and they move upwards to the surface. The current maneuver involves three distinct steps, namely capture loop, coral loop, and lobtail. The bubble net shaped hunting is done only by the humpback whales, and its analytical expression is utilized to conduct the optimization. The pseudocode of the WOA technique is depicted in Algorithm 5.

Algorithm 5

Pseudocode of WOA

3.2. AODV

AODV [9] is a reactive routing protocol that computes the routes based on the requirement. In AODV protocol, Hello messages are utilized to identify and investigate the connections to neighbourhoods. In case, the Hello messages are utilized, then every node continuously sends the Hello message to the neighbours. While transmitting the hello messages, if any of the nodes fails to receive the hello messages from its neighbours, then determine link breakage. When the source node needs to transfer the data to unknown destinations, then it forwards the route request (RREQ) to the corresponding destination. Then, the intermediate nodes get given the RREQ such that the route to the source node is generated. In case, the receiving node not accepts the RREQ, and then it is declared that there is no current path exists to the destination node, hence the source node retransmits the RREQ. In case, the destination node accepts the RREQ, then the receiving node transmits the Route Reply message (RREP) to the source. The RREP is a one way message in a node-by-node fashion to the source. When the RREP is propagated, then each of the intermediate nodes computes the path to the destination. Then, the source stores the path to the destination after receiving the RREP. After receiving the multiple paths by the source node, then it selects the shortest path for transmission. When the data flow from source to destination, then every node renews the timer linked with routes, and is stored in the routing table. When any of the routes have not been utilized in a certain period, then the node cannot know whether the route is still valid or inactive, thereby the node has to be removed from the routing table. In case, the link breakage is determined while performing the data flow, then the corresponding node transmits the Route error (RERR) message back to the source. While transmitting RERR to the source, then every intermediate node cancels the route of corresponding failed nodes. After receiving the RERR by the source, then the node cancels the routing and reinitializes the route discovery process.

In AODV, various events are happen, which are described by:

When to transmit a RREQ : If any of the routes to destination is not known as valid, then the AODV sends the RREQ to the corresponding node.

When and how to secure packets during the discovery of route : The packet sent to the unknown destination is line up during the path discovery. After finding the path, then the packet is to be sent.

Time to update the lifespan of active route : It is represented by the packet to be transmit or received to the known destination.

When to produce RERR in case a valid route does not occur : While receiving the data packet from other node, if there is no path that exists to the destination, then the node broadcast the RERR to the source.

When to produce a RERR during daemon restart : If no routing loops exist, then the RERR is generated

3.3. CTrust RPL

CTrust-RPL [35] is a routing mechanism, which helps to investigate the trust characteristic of IoT nodes and resolving the network management behaviours by recognizing and segregating the malicious nodes. The CTrust-RPL approach provides an extended view of data flow as well as trust-dependent data collection, propagation, isolation, and computation of suspicious nodes. In addition, the malicious nodes are detected based on the trust model.

Device layer:

In this layer, the IoT devices, such as actuators and sensors are considered as low power and lossy networks (LLN). These sensor devices and actuators are answerable for collecting, sensing, and pre-processing the gathered information from environments and broadcasting it to the root node. In order to establish the routing, the devices in IoT utilize RPL routing protocol for broadcasting the information. At the initial stage of RPL-based IoT network, there is no harmful nodes are detected. In the device layer, there are two main functions are available, such as network discovery and network establishment.

Sink layer:

Sink layer contains the sink nodes, which help in forwarding and receiving the data from control layer without processing. In the sink layer, greater than one sink node is used to interconnect the node with dissimilar destination-oriented directed acyclic graph (DODAG). Sink layer has multiple sink nodes, which is helpful for improving the bandwidth and diminishes the probability of packet drop for transmitted data from device layer. Moreover, the sink nodes accept the trust values from control layer after only by computing the trust, and then transmit back to the device layer. The major function of sink layer is the propagation of trust parameter.

Control layer:

The overall trust model is executed in control layer in order to compute the respective trust values for diminishing the memory overhead as well as energy consumption. Furthermore, the control layer in CTrust-RPL scheme involves various processes, such as aggregation, trust calculation, updating, and rating. The major functioning of the control layer is to manage the complex computations linked with the trust calculation.

3.4. EHARA

EHARA [22] is a routing algorithm, which consumes minimum energy for data transmission. The EHARA routing algorithm considers three renewable energy resources, such as vibration, RF radiation, and solar. In order to improve the quantity of harvested energy, the energy harvesting calculation, and energy harvesting progression is carried out in an adequate period of time. Depending on the battery level of sensor nodes, the battery of node is categorized into three stages and two regions. Based on the residual energy of nodes, two cases are considered.

Case 1 : If the residual energy of node lies between stage 3 and stage 2, then the corresponding node computes the energy while performing the sleeping. Thus, the harvested energy of that node is computed by multiplying the harvesting period and the harvesting rate of energy at that node.

Case 2 : If the residual energy of node lies between 0 to stage 3, then the energy is not sufficient for maintaining the usual operations. Thus, the node has to be automatically turned off the transceivers temporarily and then go into sleeping mode for reducing the power consumption and it waits for recharging till the battery level is retrieved. Hence, the sensor nodes can diminish energy consumption and accumulate a small quantity of energy to progress their lifetime.

Furthermore, the performance of routing can be increased by including the new parameter, namely ‘extra back off’. The advantage of introducing ‘extra backoff’ is to progress the backoff period in conventional IEEE 802.15.4 CSMA/CA, which permits the nodes to consume excess time to wait and produce energy from the ambient energy sources.

Here, the term ‘extra backoff’ is computed based on the cost function, which is based on the energy consumption of the destination node and the residual energy of the source node. In addition, the cost metric is computed, and the computation is based on the local information among each node and link. Furthermore, the computation of harvested energy and harvested time consume excess backoff period, and it consumes more time prior to transmission opportunity. Moreover, the backoff period permits the node to consume more time for accumulating additional energy.

3.5. Lightweight trust-based QoS routing

For estimating the global optimal solution of trust-based routing algorithm, the cost metric is utilized to perform the routing [38]. There are various conventional routing algorithms, such as distributed Bellman-Ford algorithm and Dijkstra algorithm have been utilized to establish the routing. Thus, the source nodes require transmitting the data to other nodes, which is expressively expensive, due to the usage of control packet exchanges. In order to resolve these drawbacks, the trust-based QoS routing has been introduced. Based on the major principle of various routing algorithms, trust-based QoS routing comprises of two parts, such as routing maintenance and routing discovery. Here, the route discovery process contains two phases, namely REP packet delivery and REQ packet delivery, which are utilized to establish the routing. In addition, routing maintenance is carried out to destroy the changes in network topology and the occurrence of link-broken.

The major aspects of trust-based routing algorithm are expressed as below. Here, all nodes in the network transmit the information through the shared wireless channel where all communication channels in the network are bi-directional. Every node in the network execute in a promiscuous mode. In addition, this method considers a reliable link layer protocol, which is to be in place. In addition, every node in the network is assumed as similar in their posterior aspects. If the node x is placed within a particular communication range of y, then the node y is also located within a same communication range of x. Moreover, the trust degree of an entire node is achieved using the trust record table and the values of QoS parameters. In addition, the process of route discovery is based on the REQ delivery and REP delivery, which are explained as below.

REQ delivery process

When the source node transmits a packet to destination node, then the source node checks whether the path is available amongst source and destination.

Step 1 : The source node initially investigates the path in route table. If any of the paths is found, then the process goes to step 3. If no path is found, then the source node begins the route retrieval process.

Step 2 : The source node computes the trust degree of neighbour node based on trust threshold. If the computed trust value is greater than trust threshold, then the node transmits the REQ to the neighbouring node.

Step 3 : After receiving the REQ packet from the source node by the intermediate node, then it validates whether the destination node accept the REQ packet. If the packet drop occurs, then the neighbouring node computes the lost data using the routing table along with trust record table.

Step 4 : If the intermediate node has a sufficient route to destination, then the node compute whether the cost metric is less, trust degree is greater than threshold, delay overhead is minimum. If the entire conditional are satisfied, then the node generates and sent the REP to the source node.

Step 5 : If step 4 is not fulfilled, then the route discovery process is carried out like as step 3.

REP delivery process

REP delivery process is the reverse of REQ delivery process where the processing is based on two sub-procedures, such as SendingREP and ReceivingREP. However, the REP delivery process is simple and effective.

3.6. ELB-FLR

The ELB-FLR [19] is obtained by merging two already available protocols. It is developed based on the three schemes.

ELB : Since one node selects the preferable parent by taking into account both remaining energy as well as the frequency of parents corresponding to one node, ELB seems to be more load balanced than RPL. A novel set of optimal solutions is suggested for determining rank based on both hop count and remaining energy in order to do this. Additionally, change an RPL mechanism to enable energy load balancing.

FLR : A novel protocol called FLR is suggested to help decrease the frequency of localized repairs by offering increased path redundancy to be used in emergency situations. When contrasted to RPL, FLR helps sensor nodes locate many paths and switch rapidly to those paths for local repairs.

3.7. CrowWhale-ETR

The CrowWhale ETR [31] is a hybrid optimization approach, which is fashioned by integrating WOA [21] and CSA [5]. This method effectively selects the optimal route using the optimization algorithm, namely CrowWhale. The CrowWhale algorithm pursues the processing steps of CSA. Here, multicast routing is established in which the system has a single source node and various destination nodes, which explains the data flows from one node to distinct destination through the intermediate node. The function of the CrowWhale algorithm is to select the optimal routes for establishing multipath communication. The CrowWhale algorithm pursues the optimization processes of CSA with the updated function of WOA. The merit of CSA is thatit provides a proper balance among the intensification as well as diversification, which is controlled using the awareness probability. The decreasing value of awareness probability increases the solution of local search and the increasing value of awareness probability establishes the global search solution. Here, the WOA is combined with the CSA, which exhibited an adaptive nature as well as finally, produces the optimal convergence of global solutions. The pseudocode of CrowWhale is portrayed in Algorithm 6.

Algorithm 6

Pseudocode of CrowWhale algorithm

4. Results and discussion

This part explains the simulation outcome, comparative and performance assessment of CrowWhale-ETR-based routing with other routing techniques based on evaluation metrics.

4.1. Experimental setup

The experiment exhibited in this work is conducted on Python tool with Windows 10 OS and intel i3 core processor.

4.2. Evaluation metrics

The metrics used for evaluating the performance of CrowWhale-ETR are delay, detection rate, energy, PDR, and throughput.

Delay: The time taken to transmit a packet is referred as delay.

Detection rate: Detection rate specifies the accurate detection of unauthorized users.

Energy: Energy consumption is defined as the energy consumed by the node to transmit a packet.

PDR: PDR is defined as the proportion among number of packets successfully transmitted to the destination and total number of packets broadcasted to the destination.

Throughput: Throughput is expressed as the amount of packets successfully sent to the destination.

4.3. Comparative methods

The performance of CrowWhale-ETR routing [31] is evaluated based on various optimization-based routing techniques and some routing protocols. Here, various optimization-based routing techniques, such as SMO [7], DEO [14], WWO [41], CSA [5] and WOA [21] are utilized for comparing the performance of CrowWhale-ETR.

Likewise, the routing protocols involve AODV [9], CTrust-RPL [35], EHARA [22] and Light-weight trust-based QoS routing [38] are utilized for the comparison.

4.4. Comparative analysis

The efficacy of CrowWhale-ETR is examined by comparing it with various optimization-based routing techniques and some routing protocols with respect to time.

Fig. 2.

Evaluation of CrowWhale-ETR based on (a) delay, (b) detection rate, (c) energy, (d) PDR, (e) throughput.

4.4.1. Analysis based on optimization-based routing algorithms

The analyzed outcome of CrowWhale-ETR is pictured in Fig. 2. The examination of delay for CrowWhale-ETR with respect to time is portrayed in Fig. 2(a). Here, the CrowWhale-ETR exhibited the delay of 0.135 for the time is 15 s, and the optimization-based routing, such as SMO, DEO, WWO, CSA and WOA exhibited the delay of 0.218 s, 0.263 s, 0.256 s, 0.270 s and 0.263 s, correspondingly for the time is 15 s. The detection rate analysis is given in Fig. 2(b). Here, the detection rate of SMO is 0.713, DEO is 0.713, WWO is 0.715, CSA is 0.698 and WOA is 0.723 and the CrowWhale-ETR is 0.718 while the time is 19 s. The comparative assessment based on energy is given in Fig. 2(c). Here, the SMO measured the energy value of 81.06%, DEO measured the energy value of 80%, WWO measured the energy value of 80.38%, CSA measured the energy value of 78.92%, WOA measured the energy value of 80.47% and CrowWhale-ETR measured the energy value of 82.53% for the time is 20 s. Figure 2(d) illustrates the assessment in terms of PDR. The PDR of optimization-based routing techniques, such as SMO, DEO, WWO, CSA, WOA and CrowWhale-ETR is 0.651, 0.696, 0.722, 0.797, 0.828 and 0.857, when the time is 20 s. The throughput rate of CrowWhale-ETR is enumerated in Fig. 2(e). The throughput rate of 0.891 is achieved by the CrowWhale-ETR, whereas the throughput of 0.862, 0.863, 0.882, 0.876 and 0.859 is achieved by the current optimization-based routing techniques.

Fig. 3.

Evaluation of CrowWhale-ETR based on (a) delay, (b) detection rate, (c) energy, (d) PDR, (e) throughput.

4.4.2. Analysis based on routing protocols

The analysis of various routing protocols is illustrated in Fig. 3. The comparative assessment based on energy is given in Fig. 3(a). Here, the AODV protocol achieved the delay of 0.751 s, CTrust-RPL measured the delay of 0.670 s, EHARA achieved the delay of 0.716 s, Light-weight trust-based QoS routing measured the delay of 0.736 s, ELB-FLR measures the delay of 0.725 s, and CrowWhale-ETR measured the delay of 0.523 s at the time is 20 s. The examination of detection rate for CrowWhale-ETR with respect to time is portrayed in Fig. 3(b). Here, the CrowWhale-ETR exhibited the detection rate of 0.535 for the time is 20 s, and the routing protocols, such as AODV, CTrust-RPL, EHARA, Light-weight trust-based QoS routing, and ELB-FLR exhibited the detection rate of 0.579, 0.589, 0.590, 0.590, 0.603, and 0.535, correspondingly for the time is 19 s. The energy analysis is given in Fig. 3(c). Here, the energy values of AODV are 66.86%, CTrust-RPL is 68.80%, EHARA is 65.01%, Light-weight trust-based QoS routing is 67.67%, ELB-FLR is 68.49%, and the CrowWhale-ETR is 72.79% while the time is 20 s. The PDR of CrowWhale-ETR is enumerated in Fig. 3(d). The PDR of 0.746 is achieved by the CrowWhale-ETR, whereas the PDR of 0.735, 0.745, 0.734, 0.725, 0.746, and 0.801 is achieved by the existing routing protocols. Figure 3(e) illustrates the assessment in terms of throughput rate. The throughput rate of routing protocols, such as AODV, CTrust-RPL, EHARA, Light-weight trust-based QoS routing, ELB-FLR, and CrowWhale-ETR is 0.735, 0.745, 0.734, 0.725, 0.736, and 0.746, when the time is 20 s.

4.5. Performance analysis

The performance analysis of CrowWhale-ETR is done with respect to two parameters, such as the algorithmic parameter and network parameter.

4.5.1. Analysis based on algorithmic parameters

This section explains the assessment of CrowWhale-ETR by adjusting the algorithmic parameters, such as iteration and population size. Figure 4(a) shows the assessment of delay for CrowWhale-ETR. As the iteration is 10 and population size is 2, then the CrowWhale-ETR achieved the delay of 0.601 s, as the iteration is 20 and population size is 4, then the CrowWhale-ETR achieved the delay of 0.598 s, as the iteration is 30 and population size is 6, then the CrowWhale-ETR achieved the delay of 0.596 s, as the iteration is 40 and population size is 8, then the CrowWhale-ETR achieved the delay of 0.561 s and as the iteration is 50 and population size is 10, then the CrowWhale-ETR achieved the delay of 0.544 s, correspondingly. The assessment of detection rate is remunerated in Fig. 4(b). For the time is 20, the detection rate of 0.580, 0.611, 0.620, 0.622 and 0.623 are achieved by the CrowWhale-ETR, when the iteration is 10, 20, 30, 40 and 50, and the population size is 2, 4 6, 8 and 10. The energy values measured by the CrowWhale-ETR are given in Fig. 4(c). When the time is 20, then the energy of CrowWhale-ETR is 70.26 for the iteration is 10 and population size is 2, 72.13 for the iteration is 20 and population size is 4, 72.25 for the iteration is 30 and population size is 6, 74.49 for the iteration is 40 and population size is 8 and 75.28 for the iteration is 50 and population size is 10. The analysis based on PDR is given in Fig. 4(d). Here, the PDR of CrowWhale-ETR is 0.807, 0.813, 0.823, 0.843, and 0.848 for the time is 20 s, the iteration is from 10 to 50 and the population size is from 2 to 10. Figure 4(e) demonstrates the assessment of throughput. When the iteration is 10 to 50 and population size is 2 to 10, then the throughput of 0.706, 0.719, 0.720, 0.730, and 0.742 is achieved by the CrowWhale-ETR for the time is 20 s.

Fig. 4.

Evaluation of CrowWhale-ETR based on (a) delay, (b) detection rate, (c) energy, (d) PDR, (e) throughput.

4.5.2. Analysis based on network parameters

This section describes the assessment of CrowWhale-ETR by changing the network parameters, such as nodes and packet size. The analysis based on delay is given in Fig. 5(a). When the time is 20 s, the delay of CrowWhale-ETR is 0.587 s for 50th node with packet size is 2 kb, 0.578 s for 100th node with packet size is 2 kb, 0.573 secfor 50th node with packet size is 3 kb and 0.552 s for 100th node with packet size is 3 kb. As the node is 50 with packet size is 2 kb, then the CrowWhale-ETR achieved the detection rate of 0.602, as the node is 100 and packet size is 2 kb, then the CrowWhale-ETR achieved the detection rate of 0.616, as the node is 50 and packet size is 3 kb, then the CrowWhale-ETR achieved the detection rate of 0.617, as the node is 100 and packet size is 3 kb, then the CrowWhale-ETR achieved the detection rate of 0.620, correspondingly for the time is 20 s, which is given in Fig. 5(b). The energy values measured by the CrowWhale-ETR are given in Fig. 6(c). When the time is 20, then the energy of CrowWhale-ETR is 73.03% for the node 50 and for packet size 2 kb, 73.38% for the node is 100 and packet size is 2 kb, 75.25% for the node is 50 and packet size 3 kb and 77.45% for the node is 100 and packet size is 3 kb. Figure 5(d) demonstrates the assessment of PDR. When the node is 50 and packet size is 2 kb, then the CrowWhale-ETR recorded the PDR values of 0.830, CrowWhale-ETR attained the PDR of 0.846 for the node is 100 and packet size is 2 kb, 0.857 for the node is 50 and packet size is 3 kb and 0.861 for the node is 150 and packet size is 3 kb. Figure 5(e) illustrates the assessment of throughput for CrowWhale-ETR. The throughput of 0.736, 0.756, 0.762, and 0.765 is acquired by the CrowWhale-ETR when the node is 50, 100, 50, and 100, and the packet size is adjusted to 2 kb, 2 kb, 3 kb, and 3 kb for the time is 20 s.

Fig. 5.

Evaluation of CrowWhale-ETR based on (a) delay, (b) detection rate, (c) energy, (d) PDR, (e) throughput.

4.6. Analysis based on objective functions

This section explains the assessment of CrowWhale-ETR with respect to the various objective functions, such as energy, distance, delay, trust, LLT, and EDDTL (all objectives). Figure 6(a) shows the assessment of delay for CrowWhale-ETR with respect to the various objective functions. When the energy as fitness, then the CrowWhale-ETR acquired the delay of 0.608 s, when distance as fitness, then the CrowWhale-ETR acquired the delay of 0.587 s, when delay as fitness, then the CrowWhale-ETR acquired the delay of 0.579 s, when trust as fitness, then the CrowWhale-ETR acquired the delay of 0.564 s, when LLT as fitness, then the CrowWhale-ETR acquired the delay of 0.558 s and when EDDTL as fitness, then the CrowWhale-ETR acquired the delay of 0.539 s for the time is 20 s. Figure 6(b) illustrates the assessment of detection rate. While considering energy, distance, delay, trust, LLT, and EDDTL as fitness, then the CrowWhale-ETR acquired the detection rate of0.624, 0.637, 0.651, 0.670, 0.747, and 0.788 for the time is 15 s. Figure 6(c) exemplifies the assessment of energy. While considering energy, distance, delay, trust, LLT and EDDTL as fitness, then the CrowWhale-ETR acquired the energy of71.65%, 71.73%, 72.19%, 73.65%, 74.27%, and 78.42% for time is 20 s. Figure 6(d) illustrates the assessment in terms of PDR. Here, the PDR of CrowWhale-ETR is 0.810 for energy as fitness, 0.817 for distance as fitness, 0.831 for delay as fitness, 0.839 for trust as fitness, 0.842 for LLT as fitness and 0.871 for EDDTL as fitness, when the time is 20 s. Figure 6(e) shows the assessment of throughput for CrowWhale-ETR with respect to the various objective functions. Here, the throughput of CrowWhale-ETR is 0.710, 0.718, 0.729, 0.746, 0.753 and 0.759 while considering energy, distance, delay, trust, LLT and EDDTL as fitness.

Fig. 6.

Evaluation of CrowWhale-ETR based on (a) delay, (b) detection rate, (c) energy, (d) PDR, (e) throughput.

4.7. Comparative discussion

Table 1 shows the comparative discussion of CrowWhale-ETR with respect to the various optimization techniques, such as SMO, DEO, WWO, CSA, and WOA. From Table 1, the CrowWhale-ETR achieved the better outcome in terms of delay, detection rate, energy, PDR, and throughput. Here, the CrowWhale-ETR achieved the delay of 0.185 s, detection rate of 0.672, energy of 82.53%, PDR of 0.857, and throughput of 0.891, correspondingly. Likewise, the techniques, such as SMO, DEO, WWO, CSA and WOA achieved the delay of 0.252 s, 0.334 s, 0.337 s, 0.335 s and 0.345 s, detection rate of 0.672, 0.672, 0.672, 0.672 and 0.672, energy of 81.06%, 80%, 80.38%, 78.92% and 80.47%, PDR of 0.651, 0.696, 0.722, 0.797 and 0.828, and then the throughput of 0.862, 0.863, 0.882, 0.876 and 0.859, correspondingly.

Table 1
Comparative discussion of CrowWhale-ETR with optimized routing techniques

Metrics SMO DEO WWO CSA WOA CrowWhale-ETR

Delay (s) 0.252 0.334 0.337 0.335 0.345 0.185

Detection rate 0.672 0.672 0.672 0.672 0.672 0.672

Energy (%) 81.06 80.00 80.38 78.92 80.47 82.53

PDR 0.651 0.696 0.722 0.797 0.828 0.857

Throughput 0.862 0.863 0.882 0.876 0.859 0.891

Metrics	SMO	DEO	WWO	CSA	WOA	CrowWhale-ETR
Delay (s)	0.252	0.334	0.337	0.335	0.345	0.185
Detection rate	0.672	0.672	0.672	0.672	0.672	0.672
Energy (%)	81.06	80.00	80.38	78.92	80.47	82.53
PDR	0.651	0.696	0.722	0.797	0.828	0.857
Throughput	0.862	0.863	0.882	0.876	0.859	0.891

Table 2 illustrates the comparative discussion of CrowWhale-ETR with several routing protocols. The delay achieved by the AODV, CTrust-RPL, EHARA, Light-weight trust-based QoS routing, ELB-FLR, and CrowWhale-ETR is 0.751 s, 0.670 s, 0.716 s, 0.736 s, 0.725 s and 0.523 s, detection rate is 0.535, 0.535, 0.535, 0.535, 0.535, and 0.535, energy is 66.86%, 68.80%, 65.01%, 67.67%, 68.49%, and 72.79%, PDR is 0.651, 0.696, 0.722, 0.797, 0.828, 0.801 and 0.857, and the throughput rate is 0.862, 0.863, 0.882, 0.876, 0.859, 0.736, and 0.891, correspondingly.

Table 2

Comparative discussion of CrowWhale-ETR with routing protocols

Metrics	AODV	CTrust-RPL	EHARA	Light-weight trust-based QoS routing	ELB-FLR	CrowWhale-ETR
Delay (s)	0.751	0.670	0.716	0.736	0.725	0.523
Detection rate	0.535	0.535	0.535	0.535	0.535	0.535
Energy (%)	66.86	68.80	65.01	67.67	68.49	72.79
PDR	0.712	0.706	0.761	0.812	0.801	0.829
Throughput	0.735	0.745	0.734	0.725	0.736	0.746

5. Conclusion

This paper presents the evaluation and comparative assessment of CrowWhale with the other conventional optimization algorithms, such as SMO, DEO algorithm, WWO algorithm, CSA, and WOA, routing protocols, like AODV, CTrust-RPL, EHARA, and light-weight trust-based QoS routing, and objective functions. Here, the analysis is done to verify the effectiveness of the CrowWhale algorithm. In addition, the entire processing is carried out on the IoT-WSN model for transmitting the information. Generally, the analysis of CrowWhale is done using three algorithms, such as optimization-based approach, routing protocol-based approach, and various fitness. The objective functions, such as energy, distance, delay, trust, LLT and EDDTL (all objectives) are considered as fitness for evaluating the CrowWhale algorithm. The performance of the CrowWhale algorithm is analyzed based on the metrics, such as delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput. In addition, the CrowWhale-ETR acquired better values based on the delay, detection rate, energy, PDR, and throughput of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using all objectives as fitness. However, the security parameter is not considered in this approach. In the future, the performance of the CrowWhale algorithm is analyzed by testing it with other routing techniques, and the performance is evaluated using more metrics. Also, the security parameter will be considered in the proposed method.

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