In the network field, Wireless Sensor Networks (WSN) contain prolonged attention due to afresh augmentations. Industries like health care, traffic, defense, and many more systems espoused the WSN. These networks contain tiny sensor nodes containing embedded processors, Tiny OS, memory, and power source. Sensor nodes are responsible for forwarding the data packets. To manage all these components, there is a need to select appropriate parameters which control the quality of service of WSN. Multiple sensor nodes are involved in transmitting vital information, and there is a need for secure and efficient routing to reach the quality of service. But due to the high cost of the network, WSN components have limited resources to manage the network. There is a need to design a lightweight solution that ensures the quality of service in WSN. In this given manner, this study provides the quality of services in a wireless sensor network with a security mechanism. An incorporated hybrid lightweight security model is designed in which random waypoint mobility (RWM) model and grey wolf optimization (GWO) is used to enhance service quality and maintain security with efficient routing. MATLAB version 16 and Network Stimulator 2.35 (NS2.35) are used in this research to evaluate the results. The overall cost factor is reduced at 60% without the optimization technique and 90.90% reduced by using the optimization technique, which is assessed by calculating the signal-to-noise ratio, overall energy nodes, and communication overhead.
As an expeditious development in wireless communication technology, wireless sensor networks are gaining more importance in this era. Wireless sensor networks consist of several tiny nodes [
The main aim of this study is to propose an incorporated technique to provide an efficient routing mechanism with minimal overhead and maintain QOS parameters. In this paper, an Incorporated lightweight security model (ILSM) is proposed in which random waypoint mobility (RWM) model for path selection is used and manages node movements pattern to get efficient routing and minimal overhead in communication and grey wolf optimization (GWO) technique used to the optimized quality of service parameters.
The rest of the paper is organized as follows: Section 2 describes the proposed methodology, which is based on a hybrid model (RWP + GWO), and detail of the proposed algorithm. Section 3, named results, includes the performance of the proposed model and a comparative analysis with previous results. In the last Section, 4, the conclusion and future work are provided.
After the brief introduction of WSN, the background of wireless sensor networks cover many aspects related to sensor networks and their parameters in different domain. Here are some highlights of WSN; where it started over time, ARPA (Advanced Research Project Agency)-Net was introduced; this agency was operational with 200 hosts in research institutes known as the first internet (World Wide Web). In contrast, the markets demand a more robust and extensive network covering the vast area network in wired architecture. Beyond these technologies, wireless networks were introduced as distributed/wireless sensor networks [
The Internet of Things (IoT) is a network of globally recognizable physical entities, their Internet integration, and their virtual or digital representation. A wide range of technologies is used to develop the Internet of Things. It is one of the most significant technological shifts in history, aiming to make everything intelligent, provide more innovative services, and develop new goods. IoT is a network of interconnected machines that exchange data and perform tasks by interacting with one another and with other items. This innovative technology was created to increase people’s quality of life and eliminate their direct interaction with the environment [
Wireless sensor networks are also implemented in agriculture for various reasons. Suitable sensor collection assisted in the resolution of numerous agricultural issues. To regulate the irrigation process, assess the weather impact on the crops, and test the soil fertility using wireless sensor networks and devices. With WSN, agriculture now moved into a new age of farming and other related processes [
WSNs are also used in military and security systems to provide high safety and accurate operation levels for such essential organizations. The sensors may detect mines, find injured individuals, regulate various components, and ensure the proper operation of military equipment [
The proposed methodology aims to provide an incorporated Quality of Serve (QoS) strategy in package delivery dependability, living nodes, energy cost, dead nodes, throughput, and power consumption in WSN is being provided; for this purpose, a random waypoint mobility model is used. The routing algorithm aims to strengthen the durability and trustworthiness based on WSNs where the sensors are placed from source to destination and protected data communication. Then used, the Optimal Path Routing Algorithm based on Grey Wolf Algorithm for security as an enhanced lightweight security model.
In previous papers, the Random Waypoint (RWP) Simulation as a Mobility Model begins informally in a simulated field. This research helps us determine the ideal path by applying Grey Wolf Optimization with RWP Model. In the random mobility point model, the mobility parameter’s uniform distribution differs significantly from a stationary distribution. Nodes are concentrated in the stationary distribution around the network center. Thus, the point selected by the node along the travel is longer. There are three different ways to initialize. After the simulation is performed, the first approach stores the position file. For each simulation, it must create a position file such as each simulation starts with the stationary distribution. The second technique indicates that the time for simulation starts at 1000 s to prevent the problem of creating. The situation in both cases is, how long do we have to throw them away.? The stationary distribution chooses the third approach position and node speed. Across this article, nodes are distributed in a two-dimensional area using the random mobility model. Due to its simplicity, this is the most popular form of mobility. Each node randomly selects a path, destination, and speed for its mobility pattern and pauses after its destination (called pause time), and selects a random path and destination again until the simulation stops. This pause is irrespective of direction and tempo (vary from [1, vmax]). There are three primary components in the RWP models: speed(s), direction(s), and break time (p). In the RWP model of the distribution of mobility nodes, mobility and direction parameters are executed alone.
The mobility model selection is required to develop any network protocol or algorithm. The synthetic mobility model is suitable for the actual behavior of a network Random mobility point model is one of the models of synthetic mobility. To prevent the initialization problem, randomly select the speed and direction. Pause time is taken to ensure the stability of the network throughout the movement; see
The key source of electricity reduction in the network is data transmission and reception. Sensory nodes consume substantially less energy for detection and processing. In this work, we track the original radio-a model of energy reduction in the transfer of raw data as indicated below:
1. To simulate the proposed model, the random mobility model is used. Predefined ranges are utilized to move the nodes and speeds randomly. Procedure Node Selection and Routing Phase (Number of Nodes N, Cluster Heads CHs, Cluster Coordinators CCs, Numberof Clusters C)
2. Network Deployment in the Static Area (Random Distribution of Nodes).
3. Division of area into a number of Clusters C
4. Declare Cluster ID (C0, C1, C2, … Cc)
5. Distribute IDs to every cluster
6. Declare Sensor Nodes (SN)
7. Declare Local Cluster (C)
8. Declare Cluster Head (CH)
9. Declare Cluster Coordinator (CC)
10. Declare T as Node traveling time in the network
11. Declare Ø as the direction of a node in the network
12. Declare V as velocity
13. Step-1% Mbl (moving) Phase%
14. X coordinaten = X coordinaten n-1 + Vn x T x cos Ø n
15. Yn = Yn-1 + Vn x T x sin Ø n
16. Step-2% Selection of (RN) %
17. For each, cluster_id ≤ M do
18. Random N Selection
19. End for
20. Step-3% Cluster Head & Coordinator Cluster Selection
21. For each cluster, cluster_id ≤ M do
22. elect CH and CC randomized in the local cluster, where Ch. Energy >= energy. Threshold
23. The Number of CC in every cluster is >= cluster_id-1
24. ch ɛ CH and cc ɛ CC
25. End for
26. Step-4% Route selection %
27. For every cluster, cluster_id <= M do
28. Transfer the packets from the local station to Base Station
29. Send packets to the energy level
30. End for
31. Step-5 For each cluster, cluster_id ≤ M do
32. If rn_en < en_Threshold then
33. Go to R-N selection.
34. If CH_energy and CC_energy < en_Threshold then
35. Move to cluster H and CC selection.
36. Else
37. Go to Route Selection
38. End if
39. End for
40. End Procedure
The mobility models outlined in this study are carried out in 03 parameters: source-to-destination (E-to-E) delay, throughput, and packet transfer ratios. All these parameters and mathematical equations are covered below, which are determined.
Valid data provided to sinks from sensor nodes at a specific moment is called their (network) output. It will be quite crisp to start rounds in every protocol. Later the processing is minimized when the information is repeatedly reused. The performance S can be computed as:
S is throughput, where S (k) is the total nodes, and g (k) is the total data sent from nodes.
The period a node occupies for data transmission to the destination node is called an E-to-E delay time. A high source-to-destination delay value refers to the optimal cluster head selection. It can be calculated as:
The Packet transfer ratio is defined as the number of packets transferred successfully to the destination; see
The mobility models discussed in this paper are built and evaluated using MATLAB simulation. Consequently, many scenarios have been provided to mimic the outcome of each mobility performance. Different network sizes are explored in various situations. To put it another way, network performance is assessed for each mobility model in this study utilizing a variety of network sizes. The 1st category contains static sensor nodes, whereas the 2nd comprises a mobile sink node that gathers data from the static sensor nodes. As a result, the total number of nodes in each scenario is n + 1 node. To be clear, the network size, in our case, is 26 nodes. As a result, 25 fixed sensor nodes and 1 mobile sink node exist. The mobile sink movement is investigated at three different speeds utilizing three other mobility models and varied network sizes. The simulation metrics are shown; see
Components | Values |
---|---|
Total time required for the simulation | 1 k s |
Number of Nodes | ‘26’. ‘51’. ‘76’. ‘101’. |
Total time of pause per node | 5 s |
Size of the complete simulated area | 1k*1k |
Traffic type | Constant bit rate |
Speed | 5 m/s, 10 m/s, 15 m/s, 20 m/s |
.1,.2,.3 (checked on different values) |
In the first step, each node in the network is chosen to be a cluster head (CH). Each node selects a value in each round (0–1). It is compared to
A new strategy named an incorporated lightweight model, “GWA’s RMW” model, is recommended to accomplish QoS for packet transferred reliability and electricity use in WSN. This routing approach focuses on enhancing life and dependability in WSNs. It relies on a direct connection between nodes, in which each node connects to the node next to it until it sinks. A one-hop or multi-hop from source to sink might occur depending on the distance between the source, the sink, and the transmission sensor. The sink node sends a query to each node to collect information about its (ID, position, and power condition). As a result, historical information on node distribution, field position, and power level status would be available. On their way from the source to the sinking node, nodes pass close together through nearby nodes. GWO algorithm is an intriguing algorithm because of the approach of group hunting. Based on Muro et al., grey wolf shooting is separated into three steps: to track, chase and approach the prey, (ii) pursue & encircle the prey until they stop; and (iii) attack the prey. In GWO, alpha, beta, and deltas are symbolically represented as and. Optimizing Grey Wolf contributes to both the investigation and utilization phases. It is used to seek the optimum result in a native hunt area. In grey wolves, the prey surrounding & beast attack are in phases of exploitation to investigate the ideal result in a small hunt area. This hunt acts as the phase of exploration during which the grey wolves search in a global search area for prey. The general mechanism of the Grey Wolf Algorithm is described in
The fitness of a chromosome controls how much energy consumption is reduced and how much coverage is maximized [
DDBS: it denotes the sum-of-neighbor distance between all sensor nodes and the BS represented by di as:
CD: total no. of CHs and BS distance and the sum of the distance b/w the resolute member nodes and their cluster heads:
The location of sensor nodes on a controlled field can impact the network’s overall performance. The placement of the deterrence node (grid), semi-deterministic nodes, and non-set nodes in the field are three major types of placement nodes in the network. A long-range sensor node’s transmission is inefficient because it requires more energy than a linear transmission distance function. Node density is only one aspect of network design, but it is essential in determining where a node should be placed.
The common advantages of proper sensor propagation are provided in WSNs below:
A relay (active node) from the cluster architecture is designated the CH, and data is transferred to the spare nodes. Replacement nodes shall be utilized as replacement nodes if the following node or link fails. The following equation can find the radius of the district.
where Rr is the neighborhood radius, M BM is the distribution area of SNs, and c is the number of clusters. The SN with more neighbors is picked as relay nodes during the selection procedure in the GWA. If the node fails, a previous node saves data in the buffer and chooses the next node to build a new path, as illustrated in the flowchart below. It depends on numerous criteria, such as the most significant residual energy for the next Hop SN, good connection quality, buffer size, and the lowest hops.
When selecting relay nodes from the GWA, each SN creates an arbitrary integer 0–1. The random number is matched the enhanced threshold when the random number is below the T (n); the SN is selected as an active node so that G is set to be stopping it from being reelection in this round as an active node [
Suppose
We performed these experiments on Network Simulator 2 (NS2). NS2 is used to validate the Lightweight Security model performance based on GWO. This simulator is object-oriented programming based. We have added the protocol detail to initiate system programming that will change the bytes and packet headers and implement the algorithm, which will run on the human body sensor values dataset. In this system, we will check the cluster head selection and calculate the energy consumption between nodes. Exponentially EWMA (Exponentially Weighted Moving Average) is used:
Tool Command Language sets up different topologies, varying the number of nodes in a network.
In this paper, we developed a Model in NS2 by following these steps:
written the TCL code Make the entry of each event (transmission of sensed data from the node) that occurred during simulation execution into a trace file. Display a graphical form of the simulator model during execution using name trace. Generate graphs based on trace file analysis and implement c++ codes for analysis & graph generation based on the dot.tr file.
In this research, we evaluated our model by calculating the following numbers of nodes:
These nodes have energy, but they become dead by supplying energy. These tell us the total energy consumption in the VANETs.
These nodes have energy, and they supply energy. After supplying energy, they have enough energy to survive. Alive node numbers are used to attain energy for the whole VANETS.
The optimum cluster head has been selected based on the information about energy loss, current energy, and other parameters. We calculate the total energy loss from GWO as follows:
In these following energies and losses are calculated:
where
First, we have executed random waypoint mobility in the wireless sensor network; see
The mobility model selection is required to develop any network protocol or algorithm. The synthetic mobility model is suitable for a network’s actual behavior. The random mobility point model is one of the models of synthetic mobility. To prevent the initialization problem, randomly select the speed and direction. Pause time is taken to ensure the stability of the network throughout the movement.
Then after creating this, we calculated the performance of nodes as in E-to-E (source-to-destination delay, output, and packet transfer ratios and carried out some routing techniques which will help us in the Quality of Service.
The total working time of a network in terms of seconds is called network lifetime. It is considered the main evaluation parameter in wireless network systems in which nodes are involved as mobile sensors or battery-operated devices. The best network lifetime is responsible for the best wireless sensor network. Here in our case, we have compared the network lifetime of E-HARP with other state-of-the-art techniques, as shown in
Stability time is the time covering all the time of a network before the death of 1st sensor node. E-HARP’s 2nd protocol death occurs at the 7600th round, a value far greater than other techniques. This shows that our proposed protocol achieves the highest performance in stability. See
Residual energy is long-lasting energy at which the death of a sensor network occurs. The network energy of E-HARP shows that it attains maximum energy as compared to other techniques.
Useful data sent to sink from sensor nodes in a specific time is called its (network’s) throughput. The throughput of E-Harp has shown the best among others. From 15000th onwards, starting rounds were too sharp. Later, the processing becomes minimized as the information is reused repeatedly (see
An E-to-E (source-to-destination delay time) is the time it takes a node to transmit data to a destination node. Compared to other methods, E-HARP offers the shortest E-to-E (source-to-destination) delay time while maintaining the highest power efficiency. The best cluster head selection is determined by a high value of the end-to-end delay, as shown in
After implementing the RWP Model using Routing techniques, apply Grey Wolf Optimization to produce Lightweight Model for Quality of Service in a Wireless Sensor Network.
There is a 3-dimension surface plot of the tested grey wolf parameters (alpha, beta, and delta) functions in parameter spaces:
After applying GWO, it can be seen that the models have performed well as shown in
Optimization | Network life-time with E-HARP routing algorithm | Stability period with E-HARP Routing Algorithm | Residual energy with E-HARP routing algorithm | Throughput with E-HARP routing algorithm | End to end delay with E-HARP routing algorithm | Cost factor with minimized cost function |
---|---|---|---|---|---|---|
Grey Wolf Optimization | 30,000 rounds with 13 alive nodes | 23KJoules left after 12500 s | 3KJ Left after 12000 s | 10000 packets in 1 m s | 100 s | 12 C.F |
Without optimization | 20,000 rounds with 8 alive nodes | 12KJoules left after 12500 s | 1KJ left after 12000 s | 8000 packets in 1 m s | 200 s | 80 C.F |
In
Ref | Optimization/ technique | Network life-time | Stability period | Residual energy | Throughput | End to end delay | Cost factor |
---|---|---|---|---|---|---|---|
Our proposed | Grey Wolf Optimization | 30,000 rounds with 13 alive nodes | 23 KJ left after 12500 s | 3 KJ left after 12000 s | 10000 Packets in 1 m s | 100 s | 12 C.F |
Our proposed | Without optimization | 20,000 rounds with 8 alive nodes | 12 KJ left after 12500 s | 1 KJ left after 12000 s | 8000 Packets in 1 m s | 200 s | 80 C.F |
[ |
Efficient fog-based routing protocol | 350 rounds with 10 alive nodes | 1 KJ left after 12000 s | 0.5 KJ left after 12000 s | No throughput | 345 s | 200 C.F |
Wireless sensor networks are self-configured, and infrastructure networks have fewer resources. Sensor nodes are used to deploy these kinds of networks. Health care, Defense, IoT, smart farming, and many more organizations are used to deploy their networks using WSN. There is no fixed topology and routing mechanism due to the adaptive-algorithms nature of networks. This is also called dynamic routing. Unlike wired networks, wireless networks always face communication issues due to signal fading whenever the environmental conditions are changed. The main emphasis of this study is on improving QoS through a lightweight solution. Various applications require WSNs to meet application-specific performance targets such as efficient routing, minimal overhead, high reliability, throughput and delay, latency rate, and packet transfer ratio. To pursue the required metrics of WSN quality assurance, proposed a novel incorporated lightweight model for QoS improvements. A novel approach is used in this thesis which is integrated with a random waypoint model and grey wolf algorithm. A single algorithm has been implemented. Results show that the hybrid model is more efficient for secure and optimal routing and controlling E-to-E (source-to-destination delay, packet transfer ratio factor. Cost factors show improved results, which is compared with previous studies. Throughput and end-to-end delay due to overhead have been controlled by using RWP’s GWO model. MATLAB and NS2 simulators are used to simulate the results. As previously stated, these areas lead to additional tools (i.e., parameters) that QoS may utilize to balance costs and projected benefits for specific users/clients.
We consider that QoS mechanisms will be supplementary operative if users or network tasks can be assigned different levels of security services and requirements, such as response and image allegiance. Security choices can be made within acceptable areas where the service level can indicate security levels in terms of assurance, machine-like strength, administrative strength, and other factors. These domains, as previously shown, lead to additional tools (i.e., parameters) that QoS may use to balance costs and benefits.
The authors are thankful to the
The authors declared that they have no conflicts of interest to report regarding the present study.