Blue Brain Based Clustering Protocol for Mobile Wireless Sensor Networks

doi:10.21203/rs.3.rs-2084034/v1

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Blue Brain Based Clustering Protocol for Mobile Wireless Sensor Networks

https://doi.org/10.21203/rs.3.rs-2084034/v1

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Blue Brain based clustering have emerged as one of the most popular methodologies in a variety of applications, like farming, smoke alarms, medical, and industrial surveillance e.t.c. Blue Brain based mobile wireless sensor networks offers a variety of advantages, like affordability, portability, versatility, self-organization, and the capacity to route traffic using Blue Brain algorithms. Increasingly practical applications than it's ever been utilize Blue Brain. However, among the most serious issues is the lack of energy. It prevents the full use of Blue Brain technology. Normally, battery packs have a limited life are used to power sensors. Wireless sensor networks always have opportunity for efficient energy usage even when renewable resources (like solar or electromechanical technologies) are being used as supplemental energy. In this research work point out the flaws in the existing approaches, offer a fresh technique that strengthens it, and contrast it with ICRA strategy. This investigation largely relies on the constraints of an ICRA as well as its associated algorithms in order to enhance network longevity by minimizing energy usage.

Clustering

Blue brain

virtual brain

Decision making

Lifespan

Efficiency

In blue brain environment something distinguishes humanity from those other creatures is his capacity to influence the surroundings wherein he exists. He is at the highest rank of animals due to his mental ability. Therefore, intelligence's fundamental qualities are what underlie all individual talents. The ability to grasp, analyze, react, analyze, and anticipate the future in order to attain and maintain relationships, thoughts, etc. is referred to as intelligent. It supports learning, thinking, conflict resolution, and judgement. Thus, intellect is essential for surviving and moving past the current. Because human brains are being intentionally manufactured throughout the scientific knowledge of cognitive computing, a testament to how far technologies has come. Artificial intelligence (AI) is the replication of blue brain in devices, which causes them to act like humans. An expert system is a technology that can understand its conditions and manage measures to increase its likelihood of succeeding. This field involves the study and creation of autonomous algorithms.

Low-power, inexpensive, and multifunctional blue brain nodes that really can conduct detecting, wireless connectivity, and processing make up a blue brain network. The placement of such blue brain nodes is arbitrary. After being distributed, these blue brain nodes must put themselves in a position into a wireless connection and carry out a specific mission. Most crucial blue brain component is power since blue brain nodes have a limited lifespan. A hierarchical routing protocol is proposed in this research work that is based on the blue brain based clustering protocol. The blue brain based clustering protocol has several purposes, including surveillance, domestic surveillance, medical applications, military uses, battleground surveillance, asset preservation, smart directing, and much more. Improving the networks longevity and reducing the energy consumption of the blue brain network are the key challenges facing blue brain technologies.

Blue brain nodes are frequently dispersed around the sensing range can able to detect quakes, wars, industrial facilities, domestic surveillance [1], cultivated lands [2], physiological weather systems, and smart homes. Blue brain nodes use a wireless network to gather data from their environment and send it to the base station (BS). Blue brain implementation is extensive, making it difficult to charge node batteries so that it obtains energy from human body by electromagnetic field. As a consequence, one of the main goals of blue brain technology is to decrease blue brain node power utilization in order to prolong the lifetime of the system. There have been numerous clustering-based techniques suggested [3]. A few of these are identified through literature review. By lowering the sensor communication range, grouping is an effective way to manage network energy utilization. In just this method of thought, collective contact with BS is controlled by the Cluster Head (CH). The CH gathers all of the information from the blue brain nodes, combines it, and then sends it to the BS; individual blue brain nodes need not send information straightforwardly to BS. Information is sent to the relevant CH from each of the cluster nodes. To minimize disruptions, the CH distributes the TDMA schedule to every blue brain node. As per the specified distribution scheme, every blue brain node of the cluster primarily transfers its data to CH. As a result, blue brain node will turn off its transmitter if there is no time interval. The utilization of blue brain nodes is prioritized via TDMA sequencing, allowing these member blue brain nodes to operate for long durations as well.

The information is typically transferred out of each network element to the nearby CH; blue brain nodes consume the least amount of energy. The quantity of data that can only be supplied to the BS is reduced by CHs estimations on the gathered data and filtering out extra bits. As a result, the blue brain broadcast energy is significantly reduced. Energy usage is among the main problems with blue brain, which is thought to be a very resource constrained type of network. Flooding is the process of transmitting data to the entire network as opposed to the intended recipient. The LEACH protocol, which is typically focused on cycle concepts and comprises two phases setup and steady state per cycles, is unsustainable for CH. This research work main focus on to reduce sensor node energy usage by conceptually segmenting the network into different parts. The manner energy is transferred among sensor nodes in wireless sensor networks determine if they are homogeneous or heterogeneous networks. LEACH, HEED, and PEGASIS are homogenous procedures, but DEEC and SEP are heterogeneous procedures [4]. The four different types of techniques used for navigation are topology-based, communication link, system structure, and trustworthy routing. The technology is split into two procedures depending on the distribution of flattened nodes and hierarchy routing [5].

In a homogeneous network, in which every node performs the very same purpose, SNs (sensor nodes) carry out their recognizing tasks simultaneously. In a distributed system, there seem to be two different kinds of SNs: high-energy and low-energy SNs. Wireless sensor networks employ clustering to create energy-efficient procedures. Heintzelman et al. first introduced the hierarchical routing protocol LEACH [4]. The clustered process is essential of the original and most used technique [4]. To decrease delay and high power consumption in wireless sensor networks, SNs are clustered. Wireless sensor networks can be effectively organised using clustering [4]. LEACH is an adaptable, self-organizing clustering method for wireless sensor networks that distributes equivalent levels of energy consumption amongst SNs. Even though other SNs are referred to as ordinary SNs, a CH is a SN that serves as the leader. Information is compiled from the ordinary SNs and sent to the BS by a CH. But at the other side, gather information from many SNs took greater energy. Every SN is given a temporary slot by a CH, preparing them for time-division multiplexing data transfer (TDMA). TDMA is being used to lessen power consumption and data duplication [5].

The important offerings are made in this article:

(1) To enhance storage utilization among nodes by utilization of Blue brain technology.

(2) To enhance the productivity of the root cluster, suggest straight data transmission that really is close to the BS.

(3) Each cluster has a vice CH in addition to the CH to minimise information loss.

(4) The suggested method is effective and an upgraded version; it decreases the overall energy utilization and increases the lifespan of the Blue Brain.

Blue brain is an artificial brain that is created by decoding fabricated brain tissue and simulating it down to the microscopic level. Henry Markram established the blue brain theory at the EPFL in Lausanne, Switzerland, in May 2005. The study objective is to fully comprehend the brain in order to improve and encourage the growth of remedies for brain diseases. Nobody will ever fully comprehend how complicated the nervous system is. It surpasses the complexity of all world electronics. Researchers are working to develop an intelligent designer that really can think, react, make decisions, and retain all information [5–8]. Researchers are working to develop an artificial brain that really is capable of thought, emotion, decision-making, and memory storage. Uploading a brain activity to a supercomputer is the major objective. such that humanity can think and make decisions effortlessly. The artificial brain would function like the individual just after physical death. Therefore, even when an individual passes away, their skills, abilities, attitudes, emotions, and experiences which can be used to advance humanity will still be preserved [9].

A LEACH method selects a CH out of every region after grouping the entire network into compact clusters. The node initially detects its location before sending the necessary message to the CH. The CH subsequently sends the data it has acquired to the BS. The fundamental goal of the LEACH protocol is to reduce the power consumption by implementing a revolution based CH selection procedure using a arbitrary number. The LEACH methods are made to function over several cycles. Per round, there seem to be two stages. They evaluated the technique over a 100x100 metre area, and they created a special procedure in a communication environment that aligned the results of the simulation with those of the LEACH homogeneous environment. An ideal clustering is suggested by the Dynamic K Value Protocol (DK-LEACH) [10]. This tactic reduces energy usage inside the uneven energy conversion. Really the identical cluster and various cluster lengths are important factors in this situation. In node-ranked-LEACH (NR-LEACH), the authors investigated the path price and practical connectivity among every CH and sensors [11]. The present weight has a big impact on CH assortment. But occasionally, it can increase routing latency. In LEACH-VA [12], the ideal amount of CHs is predicted based on overall energy used throughout the cycle. Ant colony optimization and the Voronoi graph paradigm are used in this technique to decrease intercluster development and enhance routing. This work demonstrated current LEACH methods, apart from higher latency. The researchers of [13] introduced BRE-LEACH, a modified LEACH technique that enhanced the cost of energy consumption, the lifespan of networks, and the consistency interval. The distances to the BS, remaining energy, and multicast propagation make up the very first three components of the BRELEACH method. Remaining energy, which is utilized to maintain low nodes functioning as a CH because they consume more energy than member nodes, is indeed the key component with in CH selection methodology. The network's lifespan is extended by this method. On the basis of the residual energy, CH is chosen. In contrast hand, clusters often does not contain the exact amount of nodes. Clustering in the IB-LEACH methodology is partitioned into intraclustering and interclustering in in order to improve the overall performance of the system. The estimated findings indicate that employing the IB-LEACH methodology lengthens the network's duration. Furthermore, the modifications necessary results in a slight rise in the traffic consumption. Depending on LEACH, the direct observation equipment, FOI-LEACH [14], is a more sophisticated methodology. This tactic prolongs the longevity of the network by choosing the right CH, delaying the early death of the CH. This method also concentrates on the range between the sensors and BS in order to solve the network connectivity issue. It is neither appropriate for smaller sensory areas because the scaling issue is not considered. In [15] proposes the route discovery method for improvement under many restraints and multiple goals. Quality factors for routing quality estimation include signal strength, remaining energy factor, and maximum throughput. It facilitates quick packet distribution. The true procedure SHE is depicted by [11]. Whenever clusters formed inside this communication, packages took various routes. Aging tags are used to achieve QoS.

A clustering method known as energy-aware QoS routing algorithms [16] contributes to something like this. Utilization was highlighted, unnecessary packet full recovery was decreased, network throughput was increased, and most importantly for WSNs, end-to-end latency was decreased. In [17] presents the bihop neighbourhood information reliant scheduling algorithm. The principles of double effect idea and energy equilibrium are both present in this single method. Utilizing QoS criteria, its appropriateness in real time is demonstrated. The LEACH enhanced version PEGASIS is described in [14] for network lifetime augmentation by lowering the distance of communication among sensor chain methodologies revised for lifelong augmentation of the system, but it symbolised more augmentation than PEGASIS by positioning the CH close to BS. The DEEC and EDDEEC techniques were created, accordingly [18]. In DEEC, the node likelihood knowledge will allow on starting vitality and the overall network efficiency are both calculated. The disadvantage is that it misses the network's vigour in each cycle. The cutoff value was expressed by EDDEEC using three segments: an enhanced node, a standard node, and a superior node [19–20].

The suggested technique discusses two concepts, a network model as well as an energetic dissipation network model, which are both employed for the creation of Blue brain, the selection of CH, the transfer of information to BS, then data integration. For energy loss brought on by the transmitting and reception of collected data, the following model is applied. When using the network, the suggested strategy takes into account the ensuing presumption.

1. Every Blue brain node is movable and has a unique ID after installation.

2. Whenever it comes to computational and communication, Node functionalities are comparable, however when it comes to energy dissipation, they are heterogeneous.

3. Data processing is a feature that allows numerous information to be condensed into a single packet.

4. According on the distance power, all Blue brain nodes are operated in control technique.

5. The residual energy of the Blue brain nodes is unequal and chargeable from human body magnetic field.

6. Because a connection among Blue brain nodes is symmetrical, the energy used for packet delivery and the maximum throughput between Blue brain node A to B are equal.

7. The central place is given to BS, which is unrestricted in terms of memory, consumption capacity, and energy.

8. The elliptical Gaussian distribution technique is used to disperse nodes inside a network.

The location of nodes inside a network is a complex issue when confidentiality, energy use, and routing are taken into account. The configuration affects the network's longevity. As a result, Blue brain nodes that really are close to the BS use up their battery capacity considerably more quickly than Blue brain nodes being further away. A dispersion known as the 2D Gaussian is employed to manage the issue of a energy gap in Blue brain technology in order to find a solution to this issue.

The variance is crucial in this calculation for network longevity and energy conservation. The MxMm² space is entirely covered by the N Blue brain nodes. Here is the probability for this feature:

F(a,b)=(1/2πσ_aσ_b) exp-{[(a-a₀)²/2πσ²_a]+[(b-b₀)²/2πσ²_b]} (1)

Variance is represented by a and b, whereas (a₀, b₀) is being used to indicate geographical parameters.

3.1. Radio Energy Structure

Energy model is inclusion of modes, transmitter fading and the free space paradigm, depends on the distance.

The following is the calculation for radio energy that is used to transport information at a rate of bit/s across a length of x (bit).

E_trans(b,d,r)= E_trans-elec(b,r) = E_trans-amp(b,d,r)

= E_trans-ele*(b/ r)+ ∈_fs*(b/r)*d² for d<d_0,

E_trans(b,d,r)= E_trans-ele*(b/ r)+ ∈_mp*(b/r)*d⁴ for d>d_0,

E_trans(b,d,r) E_rec(b,d,r)= E_rec-ele*(b/ r) (2)

For the transport of an unique packet, the equations E_trans(b,d,r) provide the overall energy dissipation, E_trans-elec(b,r) indicate the electronically digitized count, E_trans-amp(b,d,r) indicate the power amplification energy consuming modeling intended for free space, ∈_mp is utilized to indicate the multipath model, E_rec(b,d,r) indicate individual package receptions for the overall energy consuming.

In this research work the main goal is of lowering energy utilization, prolonging system reliability, prolonging lifespan of the network, storing the information in blue brain for better decision making and enhancing performance, a fresh hybrid energy-efficient blue brain (HE²B²) protocol in this article. An enhanced hybrid E³-LEACH clustering procedure with rounded stages for every iteration is the chosen method. The proposed technique features four stages for each cycle as opposed to the two levels of the LEACH algorithm, lists the algorithmic stages for various instances.

4.1. Initial Cycle

In order to avoid scheduling blue brain nodes having low energy into becoming CHs and to conserve energy, the suggested method comprises choosing CHs according to the remaining energy of blue brain nodes. Depending on the operation of LEACH with their enhanced mechanisms, every node chooses a randomly selected value Rn (0< Rn<1) there at beginning of every cycle. Each node turns into a CH inside the upcoming cycle if the R value is smaller than that of the threshold function specified in equation (3). But it returns to being a regular cycle node (BN).

T(v)= p-{p/(1-pr*(cmod(l/g)))} ,if k ∈G, (3)

0 otherwise

Wherein the probability of a blue brain node will become CH during round 0 is represented by pr as well as the proportion of all CHs to BNs. l is the present cycle count, mod(•) is the modulus operation, and G is the collection of blue brain nodes that were not chosen as a CH within the most recently 1/p cycle. Every CH establishes a TDMA schedule to collect information from BNs at respective designated duration after the CHs have been chosen. The network is then informed via an advertising message that comprises the location and ID of every CH. In response to receiving such signals, NNs create a relationship matrix to CHs. To find out where the CH it corresponds to, the BN makes a JOIN request towards the CH with smallest remoteness inside its data and below the specified threshold. Whether this CH continues to have a free schedule in the TDMA programme and has fewer blue brain nodes inside its cluster as non CHs, it becomes the comparable CH. Contrarily, the BN transmits a subsequent JOIN request to the following CH with smallest route within the table as well as a length less than the threshold remoteness, and etc. The IBRE-LEACH technique states that in order to prevent multipath, that uses more storage than that of the empty space specified in Section 4.5, total remoteness among BNs and associated CHs must be smaller than that of the threshold. Equation (4)'s variables (X, Y) are used to calculate the Euclidean remoteness among two objects:

d=√{(p₂-p₁)²+(q₂-q₁)²} (4)

The proposed methodology also reduces the number of blue brain nodes in each cluster, which equalises the leftover energy of CHs inside the system. Whenever clusters have indeed been formed, BNs that are unable to connect a network would be disregarded. Such nodes are referred to as discarded Blue brain nodes (DBNs). These DBNs can send their information to BS, like BNs and CHs, by abandoning themselves.

4.2. Second Cycle

Clusters are created when the CHs and DBNs have now been adjusted, and each CH sends out an advertising message to each other CH. That message contains the position and ID of every CH (or BDN). The proposed technique then chooses a cluster center. That central blue brain would be a CH or DBN which is closer to the BS over or equivalent to the overall range of overall CHs and DBNs towards the BS and has residual energy greater than or equivalent to the median present energy of existing CHs and DBNs. After transmitting its information straight to the BS, the central blue brain node first aggregates it with information obtained from various CHs and DBNs. Its main objective is to lessen BS congestion and prevent low-energy CHs and DBNs from interacting with the BS straightforwardly that consumes a large amount of energy.

4.3. Third Cycle

During this round, each CH creates its forwarding table that contains the lengths to all other CHs, DBNs, the central Blue brain, as well as the BS. In DBNs, similar situation holds true. And use this forwarding table, each CH and DBN is aware of its subsequent hop. This approach allows other DBNs, including CHs, which prior routing algorithms have discarded, to transmit their data to the BS.

4.4. Fourth Cycle

Every CH (or DBN) can utilise its forwarding table to decide the optimal way to deliver their information to the BS throughout this stage (the transmission stage), that contains lengths to everyone CHs, DBNs, the root, as well as the BS. Figure 1 shows the layout of the proposed approach. This figure shows how the suggested scheme functions. The forwarding table that every CH (or DBN) builds contains the ranges among other CHs, DBNs, the central blue brain, as well as the BS. After that, the ranges are ranked from closest to distant. The proposed methodology then provides a set of factors for deciding which path every CH and DBN should take to get to the BS. Think about First order CH (FOCH), since it is the nearest to Second order CH (SOCH), followed by the central blue brain, the BS, etc. The suggested method contrasts dSOCHtoBS (the range between the forwarding table's initial element and the BS) and dFOCHtoBS (distance among FOCH and the BS). dFOCHtoBS is here smaller than dSOCHtoBS. Therefore, SOCH won't be FOCHs in subsequent hops. The following element in its forwarding table is then selected. It finds that dFOCHtoBS is lower to dCentral—ToBs and that the BS seems to be the third part of the forwarding table while comparing dCentral—ToBs (the length among the Centraland the BS) to dFOCHtoBS. Since it is the most effective route, the FOCH chooses to transmit directly to the BS as a consequence.

4.5 Algorithm

Algorithm 1 for Initial Cycle

Few symbols utilized in algorithm

TBN: Total Numbers of Blue brain nodes

CH: Cluster Head

BS: Base station

BN: for each Blue brain node

A: any arbitrary integer

Algorithm 1

Case 1

For all BN choose number among 0 and 1

Arbitrarily If (A< T (v))

BN be converted into CH

BN transmit its CH condition

Else

BN turn into ordinary blue brain node

BN accept information transmit by CHs

End if

End for

For each (CH)

BN picks the CH with min remoteness from BS

BN will be associated with cluster

End for

For every (CH)

TDMA Slot is created

End for

Algorithm 1

case 2

Few symbols utilized in algorithm

node[i].L

node[i].E

Electing FOCH

For node[i] in the similar cluster, the blue brain node with the highest remaining energy subsequent to the CH is FOCH.

node[i].type = ‘FOCH’

end of for loop

Algorithm 2

Case 1

Communication straightforwardly to the BS (For Steady Cycle)

Necessitate: This procedure is applicable for CHs and DBNs

R_{CH_BS}: The remoteness from CH to the BS

R_CH—NBD: The remoteness from CH to the subsequently Destination (CH or DBN)

R_CH—Central: The remoteness from CH to the central blue brain

Begin

if R_{CH_BS} < R_CH—central then

if R_{CH_BS}< D_CH—NBD then

CH relays its information to the BS straightforwardly

end if

Algorithm 2

Case 2

Communication straightforwardly to the central blue brain

Begin

if R_{CH_Central}< R_CH—BS then

if R_{CH_Root} < R_CH—NBD then

CH relays its information to the central blue brain

end if

Algorithm 2

Case 3

Communication straightforwardly to subsequently destination

Begin

if R_{CH_NBD} < R_CH—BS then

if R_{CH_NBD} < R_CH—Central then

CH relays its information to subsequently destination (Cluster)

end if

4.6 Simulation Process.

In NS2, a blue brain was first built. For every choice, several starting default parameters have been established. In this simulation set N = 200 and Network size = 200 as the corresponding sides of the network's region to create a network with 200 blue brain nodes. That value of L will double if it needs to increase the system to 400 blue brain nodes. In existing a possibility that certain blue brain nodes will be chosen as CH. Here set P = 0:1 for the likelihood of being chosen as the CH during each cycle. P = 0:1 means that 20 blue brain nodes in a network of 200 blue brain nodes may be CH. Determine the starting energy for every blue brain node in the human environment. Beginning energy is Ei = 0:5. The outcomes were produced via a NS2 simulation and aggregated over 20 iterations. Table 1 provides a summary of the simulation factors.

Table 1: Simulation factors.

Factors	Value
Region	200x200
Packet Size	500bytes
Blue brain nodes	200
Control packet Size	25 Bytes
Initial Blue brain nodes energy	0.5J
Data	100bytes
Cycles	1500

Proposed HE²B² analyses the heterogeneous system of 200 blue brain nodes, distributed randomly in a 200x200 matrix. Locations for the BS are 150 and 150. Figure 2 compares the number of dead blue brain nodes over time in the ICRA [21] and HE²B² strategies. According to outcome, suggested methodology HE²B² outperforms existing strategies in regard to the number of dead blue brain nodes over duration. Figure 3 shows the initial blue brain node expires of the network. These results reveal that in regards to network lifetime, the proposed approach surpasses the examined approaches. Figure 4 illustrates how HE²B², as contrasted to ICRA enhances the durability region (number of cycles in which all blue brain nodes are active). The second vital figure is energy conservation. Final Blue brain node expires over time is shown in Fig. 5. The findings of such a graph show that the suggested method consumes less energy over ICRA as well as its improved methodology EEE-LEACH. The third significant metric taken into account in this research is performance. Figure 6 displays the delay of bytes sent to the BS. From these results, it is evident that HE²B² transmits more packages to the BS than ICRA. Figure 7 shows how the storage information retrieved in future utilization or in decision making process HE²B² performs better than existing methodologies by the utilization of blue brain technology.

When running three successive NS2 computations with different consequences and node counts and attempted to contrast ICRA and the proposed new HE²B² technique. Whenever virtually every blue brain node has perished as well as all cycles have been finished, the simulation is over. The graph represents the performance parameters of methodologies are produced once the simulation is finished. Each graph depicts the lifespan of the blue brain nodes, whereas another depicts their energy use. All simulations demonstrate that our proposed HE²B² outperforms the current ICRA methodology in terms of lifespan improvement rate, storage, decision making and energy debauchery rate. In every simulation, the HE²B² methodology is more effective in terms of life extension, storage, decision making and a slower rate of energy debauchery. Thus, the storage, decision making technique can be seen as a development of the ICRA procedure.

In this research, we tried to compare several ICRA methodology configurations, as well as their application, drawbacks, and challenges. Several improved ICRA techniques have been suggested by several authors. HE²B² methodology an effort to understand the challenges they faced in addition to the approach they took to carry out their inquiry. ICRA is the most popular protocol in blue brain technology. But it does have certain problems. As a result, came up with a strategy to fix an ICRA technique issue. CH selection in the ICRA methodology has been revised, storage, decision making by virtual brain as well as the new, modified ICRA is termed as HE²B². Only after successful implementation, noticed that the novel HE²B² methodology technique met our objectives and outperformed the current ICRA methodology. The objective of our investigation has so been met.

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Competing interests

The author declare that they have no competing interests.

Funding

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Authors' contributions

The Author contributed entire manuscript.

Acknowledgement

I thank teaching and non teaching faculties of Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai and I also thank to my friends and relatives for giving an opportunity to complete my research work.

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Blue Brain Based Clustering Protocol for Mobile Wireless Sensor Networks

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1. Introduction

2. Related Work

3. Modeling and Hypothesis for Networks

4. Proposed Tactic

5. Simulation Outcomes

6. An overview of the simulations and findings

7. Conclusion

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