Enhancing Traffic Routing Inside a Network through IoT Technology & Network Clustering by Selecting Smart Leader Nodes

Enhancing Traffic Routing Inside a Network
through IoT Technology & Network Clustering by
Selecting Smart Leader Nodes
Radwan S.Abujassar
Department Information Technology and Computing,
Arab Open University Kuwait,Alardiya Industrial, Kuwait
Abstract. IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate net-
working. Internet of Things (IoT) networks have a lot of control overhead messages because devices are
mobile. These signals are generated by the constant flow of control data as such device identity, geograph-
ical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead
communication management method. Many cluster-based routing methods have been developed to address
system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster
all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN
cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads
these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in
IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based
on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This
will facilitate the secure and reliable interchange of healthcare data between professionals and patients.
NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based
on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option
from a range of choices and preparing for likely outcomes problem addressing limitations on activities is
a primary focus during the review process. Predictive inquiry employs the process of analyzing data to
forecast and anticipate future events. This document provides an explanation of compact units. The Pre-
dictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a
routing information table for each intelligent node, resulting in higher routing performance. Both principal
and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outper-
form CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition,
the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets
compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.
Keywords: Optimized Link State Routing Protocol (OLSR) ,Internet of Things (IoT)* , Smart designated
node (SDN)* , Predictive Inquiry Small Packets (PISP), Nodes Clustering-Based on IoT (NCIoT)
1 Introduction
This section will elucidate the conceptualization of the Internet of Things (IoT) and its
consequentiality within the framework of smart cities. The phrase ”IoT,” short for the
Internet of Things, refers to a network consisting of interconnected items, cars, and ap-
pliances that interact and share data over the Internet. In the context of smart cities,
the Internet of Things (IoT) plays a crucial role in the efficient management of resources,
enhancing the quality of life for residents, and promoting sustainability. The Internet of
Things (IoT) allows for the acquisition, dissemination, and examination of data by various
devices, therefore improving the automation and enhancement of several urban services,
such as transportation, energy management, waste management, and public safety. This
section will examine the significance and ramifications of the Internet of Things (IoT) in
the progression of intelligent urban environments. By leveraging Internet of Things (IoT)
technology, metropolitan areas can actively monitor and efficiently manage their resources
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DOI: 10.5121/ijcnc.2024.16201
International Journal of Computer Networks & Communications (IJCNC) Vol.16, No.2, March 2024

in real-time, hence leading to improved efficiency in the allocation and utilization of these
resources. The adoption and execution of these measures not only serves to improve the
overall welfare of residents but also aids in the reduction of waste and gates environmental
consequences, promoting enduring urban sustainability. Moreover, the data generated by
Internet of Things (IoT) devices holds the potential to provide substantial insights for
urban planners and politicians. This significant knowledge enables individuals to make
educated decisions and implement targeted solutions that effectively address specific chal-
lenges in urban settings. The Internet of Things (IoT) allows for the seamless integration
of various devices and systems, enabling the real-time monitoring and administration of
urban infrastructure. The interconnectivity between various components improves the ef-
ficiency of resource use and promotes the effectiveness of decision-making processes. In
addition, the Internet of Things (IoT) enables the development of innovative applications
and services that enhance the overall quality of life in metropolitan areas. These include
intelligent housing, sophisticated healthcare systems, and efficient transit options. The in-
corporation of Internet of Things (IoT) technology holds significant significance in the es-
tablishment of sustainable and resilient urban environments due to the ongoing expansion
and growing challenges faced by cities. The main aim of the proposed NCIoT protocol is
to efficiently distribute information over two distinct paths. It is advisable to prioritize the
primary choice, while considering the alternative option as a backup plan in case of any
unforeseen complications. The use of predictive inquiry small packets (PISP) messages
into cluster topologies enhances the routing protocol’s ability to gather supplementary
information on node distances and surrounding nodes. The NCIoT protocol is responsi-
ble for initiating the primary route selection process, which has been previously defined
through the routing protocol. Consequently, the proposed approaches would utilize the
Nodes Clustering-Based on IoT (NCIoT) to collect extensive data pertaining to the nodes
from the routing table. Following this, the nodes will be programmed to function in an
intelligent manner, allowing them to effectively reroute traffic through other pathways as
required. The strategy that has been provided places emphasis on not only the occurrence
of failure, but also the possibility of congestion and overload inside the networks. In the
present day, the network is vulnerable to the impact of malicious individuals, unwanted
messages, and several other obstacles that impede users’ access to necessary services. The
analysis involves considering a set of possible routes, followed by a detailed evaluation and
assessment of the selected route based on certain constraints. The establishment of the
Node Clustering on IoT (NCIoT) has been shown to contribute to the improvement of
stability. By selecting a route that maximizes the lifetime (LT) as recommended by the
Smart designated network (SDN), while also choosing the fastest path to reduce latency.
An effective DNA The proposed methodology presents a selection procedure that consid-
ers the mobility characteristics of the node, including its velocity and the comparative
velocity of the nearby nodes within the cluster as shown in Figure 1.
The structure of this article is as follows: In section 2, we discussed the impact of the
Internet of Things (IoT) on networks and proposed solutions through the development
of PISP messages. The purpose of these messages is to reduce the frequency of using
”HELLO” messages, as they contribute to increased latency in checking the connectivity
between nodes within a clustered grid Internet of Things (IoT) network. In section 3, an
overview of relevant literature and research on enhancing node clustering in the context of
the Internet of Things (IoT) for smart cities is provided. In this study, we will discuss many
CBR protocols that have been developed or improved for implementation in a clustered
grid topology, including LEACH, LEACH-e, and RCBRP. In the aforementioned section
4, our novel algorithm, along with its associated methodologies, has been presented and
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Fig. 1. Clustering Characteristics
juxtaposed against other relevant studies. Furthermore, we have presented the theoretical
modeling equations together with a portion of the code implemented in the suggested
protocol. Section 5 provides an overview of the simulation parameters, along with a
comprehensive presentation and analysis of the obtained results. Lastly, section 6 provides
the concluding remarks.
2 Improved node clustering by using IoT
Improving the clustering of nodes within the Internet of Things (IoT) framework, es-
pecially in the domain of healthcare applications within smart cities, has considerable
importance. The integration of Internet of Things (IoT) device clusters inside healthcare
settings facilitates improved examination and exploitation of gathered data. The applica-
tion of clustering methods enables improved surveillance of patient welfare and permits
healthcare professionals to swiftly respond to emergency circumstances. Nevertheless, this
particular approach also engenders concerns regarding privacy and security due to its
propensity to increase the probability of unauthorized persons acquiring personal patient
information. Further investigation is necessary to emphasize the importance of data anal-
ysis and monitoring in healthcare settings, particularly about Internet of Things (IoT)
devices, to enhance patient outcomes and overall population health. Investigating the spe-
cific approaches utilized for node clustering in Internet of Things (IoT) devices within
healthcare settings can provide readers with a deeper understanding of the operating pro-
cesses and potential benefits of this technology. The examination of measures implemented
by healthcare organizations and specialists to safeguard privacy and security in response
to escalating concerns stemming from the aggregation of Internet of Things (IoT) devices
is of utmost importance. The analysis of the measures taken by healthcare organizations
and specialists to protect privacy and security in response to increased risks resulting
from the aggregation of Internet of Things (IoT) devices is highly significant. In summary,
the optimization of node clustering within the Internet of Things (IoT) realm has a no-
table impact on advancing healthcare outcomes and facilitating the development of a more
sustainable urban environment, as seen in the accompanying figure 2.
The main purpose of this research is to investigate and analyze the phenomena under
investigation to achieve a more thorough understanding of its underlying components and
consequences. The study’s importance lies in its ability to enhance the existing body of
knowledge in the field, as well as its potential to offer insights and guidance for future
research efforts and practical applications [1]. The optimization of network performance
is facilitated by enhanced node clustering in the context of the Internet of Things (IoT),
3

Fig. 2. Smart Cities with Node Connections via Different Connection
provide a notable advantage. The enhancement of communication and data transmission
within a network may be attained by the arrangement of nodes into clusters based on
their closeness and functionality. This methodology enables the implementation of efficient
procedures, leading to enhanced response times and reduced latency. Furthermore, the use
of increased node clustering enables improved resource allocation and utilization. This is
achieved by enhancing the collaboration among nodes, which leads to a more efficient
distribution of processing power and storage capacity. The aforementioned enhancement
not only contributes to the improved performance of the Internet of Things (IoT) system
but also plays a substantial part in attaining energy efficiency and reducing costs.
2.1 IoT Challenges and Prospects
The academic community has shown considerable interest and conducted extensive search
on the difficulties and potential associated with the Internet of Things (IoT). To ensure
the optimal performance of Internet of Things (IoT) nodes, it is crucial to develop a ro-
bust and interconnected network architecture. The successful execution of this objective
requires the establishment of comprehensive service frameworks that conform to mutually
agreed-upon criteria. By implementing this approach, the functionalities and capabilities
of different applications, situations, and user requirements may be greatly improved. The
development of Internet of Things (IoT) applications, albeit an ongoing process, poses
several obstacles that need solutions. The difficulties at hand cover several factors like
cost, power consumption, processing capacity, low latency, self-organization, distributed
intelligence, and systems technology. The Internet of Things (IoT) presents a variety of
issues. Despite the multitude of connections and opportunities presented by the Internet
of Things (IoT) for end users and industry insights across many application fields, there
exists a discernible lack in the construction of an effective architecture and standardized
technological framework. The lack of this element poses a barrier to the smooth incorpora-
tion of the physical and virtual domains within a cohesive structure [1]. Several significant
concerns have been discovered as follows. The discipline of architecture has a significant
issue with the proliferation of various types of sensors, such as physical, chemical, bi-
ometric, and photo sensors. These sensors, in conjunction with networked smart devices
and sophisticated technologies have a pivotal impact on the advancement of Internet of
Things (IoT) applications.
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These developments are highly dependent on several disciplines and their respective
architectural designs or circumstances. In addition, the interrelationships among these
devices enable wireless, spontaneous, and automatic communication. The efficacy of ar-
chitectural services is augmented by the use of decentralization, mobility, and heightened
complexity. Technical challenge: The wide range of application areas within the Internet of
Things necessitates the creation of different scenarios and architectures. This, in turn, re-
quires the development of numerous technologies to support the complex services provided
by the Internet of Things. The presence of heterogeneity poses a significant challenge in the
implementation of intelligent Internet of Things (IoT) systems. The subject matter under
consideration refers to wireless networks, with a special emphasis on the idea of Wireless
Networks[2][3][4]. An obstacle encountered within the realm of hardware for the Internet
of Things (IoT) pertains to the incorporation of intelligent devices into intelligent systems.
The optimization of inter-device communication is crucial for the successful deployment
and supply of services in Internet of Things (IoT) applications. Scholars place a high em-
phasis on the advancement of hardware design, specifically focusing on the creation of a
wireless trackable system that demonstrates qualities of being cost-effective, compact in
size, and highly functional.
3 Background and Related Work
This paper provides an overview of node clustering in the context of the Internet of Things
(IoT) and highlights the issues associated with this approach. Node clustering in the con-
text of the Internet of Things (IoT) pertains to the systematic procedure of categorizing
IoT devices into distinct groups, taking into consideration specific criteria or attributes.
This technique has the potential to streamline network management, enhance communi-
cation efficiency, and optimize system performance. Nevertheless, node clustering in the
context of the Internet of Things (IoT) several number of issues. The issues encompassed
in this context are to the scalability of the clustering algorithm, the dynamic character-
istics of IoT networks, and the heterogeneity exhibited by IoT devices. The resolution of
these difficulties is of utmost importance in order to attain efficient and dependable node
clustering in the Internet of Things (IoT). An illustrative counterexample highlighting the
issues pertaining to node clustering in the Internet of Things (IoT) can be observed in a
hypothetical situation where the clustering method exhibits inadequate scalability. In the
context of a vast Internet of Things (IoT) network with numerous devices, the clustering
algorithm may have difficulties in managing the substantial volume of data and process-
ing demands [2]. Consequently, this might result in delayed reaction times and potential
system malfunctions. In the given situation, it is possible for the clustering algorithm to er-
roneously cluster nodes, leading to ineffective distribution of resources and inferior system
performance. Moreover, if the clustering algorithm is dependent on a centralized method-
ology, it has the potential to serve as a singular point of vulnerability, so compromising the
overall functionality and resilience of the Internet of Things (IoT) network. In addition, in
the event that the clustering algorithm lacks robustness in dealing with outliers or noisy
data, there is a possibility of misclassifying significant nodes, hence resulting in severe sys-
tem malfunctions. Moreover, the clustering algorithm’s centralized approach may create a
notable bottleneck in communication and decision-making procedures, impeding the abil-
ity to respond in real-time in dynamic Internet of Things (IoT) settings[3]. Nevertheless,
the utilization of decentralized clustering algorithms in Internet of Things (IoT) networks
present a comprehensive counterexample to the concern of a single point of failure. The dis-
tribution of the clustering process among numerous nodes enhances the network’s resilience
5

by eliminating the presence of a singular point of failure capable of causing a complete
system shutdown. In addition, the algorithm can effectively classify significant nodes even
when confronted with noisy data by including robust outlier identification approaches,
hence mitigating the potential for system failures [4]. Moreover, the implementation of a
decentralized method might lead to enhanced expediency and efficiency in communication
and decision-making procedures. Nevertheless, it is important to consider a comprehensive
counterexample that illustrates a situation in which the process of distributed clustering
produces inconsistent outcomes as a result of communication delays or failures occurring
between nodes. In instances of this nature, the network’s ability to withstand and recover
from disruptions may be undermined due to potential inaccuracies in the classification of
critical nodes, hence resulting in system faults [3][5]. In addition, the decentralized ap-
proach may give rise to coordination difficulties and disagreements among nodes, leading
to longer decision-making processes and potentially affecting the overall efficiency of the
network. A Critical Examination of Current Node Clustering Techniques in the Context
of Smart Cities Healthcare systems possess the capacity to offer significant insights into is-
sues and constraints associated with existing methodologies. The author introduces a novel
Robust Cluster Based Routing Protocol (RCBRP) that aims to improve performance by
optimizing energy utilization. The clustering approach is employed to choose CH based on
predetermined criteria, and each cluster is further broken into smaller sections. This study
outlines six distinct steps, namely initialization, setup phase, distance computation, cluster
formation, selection of cluster head (CH), selection of secondary cluster head (SCH), and
energy conservation [6][7]. This section introduces a novel Robust Cluster Based Routing
Protocol (RCBRP) that aims to improve performance by optimizing energy utilization.
The clustering approach is employed to choose CH based on predetermined criteria, and
each cluster is further broken into smaller sections. This study outlines six distinct steps,
namely initialization, setup phase, distance computation, cluster formation, selection of
cluster head (CH), selection of secondary cluster head (SCH), and energy conservation.
On the other hand, the author introduces a novel Robust Cluster Based Routing Pro-
tocol (RCBRP) that aims to improve performance by optimizing energy utilization. The
clustering approach is employed to choose CH based on predetermined criteria, and each
cluster is further broken into smaller sections [8]. This study outlines six distinct steps,
namely initialization, setup phase, distance computation, cluster formation, selection of
cluster head (CH), selection of secondary cluster head (SCH), and energy conservation.[9].
The non-functional nodes are removed from the communication network. The effectiveness
of the proposed plan RCBRP is assessed by comparing its results with those of its equiv-
alents. The primary parameter of the routing protocol is to identify the sensor nodes that
are currently operational[10]. The GEEC algorithm demonstrates superior performance
compared to the previous two algorithms, since it operates for a duration of 680 rounds.
The most optimal algorithm, which has been proposed, remains active for a total of 720
rounds. The RCBRP protocol demonstrates much superior performance in comparison to
the LEECH, LEECH-C, and EECRP procedures. The first node experiences failure after
80 rounds in LEECH-C, 400 rounds in LEACH, 380 rounds in GEEC, and 380 rounds in
EECRP [11][12][13]. Future research should prioritize the development of novel algorithms
that possess the capability to adapt dynamically to the evolving network architecture and
effectively manage the diverse data demands of distinct healthcare applications [14]. The
concept of nodes clustering technology pertains to the procedure of categorizing nodes
inside a network into groups based on specific traits or characteristics. This technology
plays a crucial role in diverse domains like data analysis, machine learning, and social
network analysis. The utilization of nodes clustering technology allows researchers and
6

analysts to acquire valuable insights into the intricate systems’ structure, behavior, and
relationships by recognizing clusters or communities inside a network. The provided data
can be utilized to identify irregularities, forecasting forthcoming patterns, and formulat-
ing well-informed judgments [15] [16]. In the context of data analysis, the utilization of
node clustering technology facilitates the identification of patterns or trends inherent in
a given dataset, hence enhancing the comprehensibility and interpretability of the data.
Clustering techniques in the field of machine learning are employed to effectively group
data points that exhibit similarities, hence enhancing the precision of predictions and clas-
sifications [17]. Furthermore, within the realm of social network analysis, the utilization
of nodes clustering technology can be important in discerning communities or cohorts of
individuals that exhibit comparable interests or behaviors. This analytical approach yields
valuable insights that can be leveraged for targeted marketing initiatives or the allocation
of resources [18][19].
A multi-rate Wi-Fi network can limit the bandwidth of high-rate links to that of low-
rate links, as explained in this article. In this study, we present an MTF AP selection
technique, a refinement of Mininet Wi-Fi processes. MTF uses access point throughput
and station count as selection measures for association decisions. Simulations indicate
that MTF delivers enhanced performance, particularly in multi-rate settings [20]. Various
routing protocols have been documented for diverse contexts to enhance network per-
formance in scenarios where nodes experience mobility or failure [21–24]. In the study
conducted by the authors [25], many characteristics and settings of Mobile Ad hoc Net-
works (MANETs) were identified, including bandwidth (BW), resource availability, and
energy constraints. Several proactive routing protocols, including Destination-Sequenced
Distance-Vector Routing (DSDV), Optimized Link State Routing Protocol (OLSR), Clus-
ter HEAD Gateway Switch Routing Protocol (CGSR), and Wireless Routing Protocol
(WRP), employ message-triggered mechanisms to detect link failures [26, 27]. Based on
the aforementioned messages, it can be inferred that the routing protocol possesses the
capability to establish and uphold routes leading to its intended destination. In reactive
routing protocols such as Dynamic Source Routing (DSR), Ad hoc On-Demand Distance
Vector (AODV), and Temporally Ordered Routing Algorithm (TORA), the utilization of
network resources will be optimized due to the creation of new paths between nodes only in
the event of a failure. This approach effectively reduces the overhead associated with path
establishment. The authors of [28, 29] discovered that the Optimized Link State Routing
(OLSR) protocol employed Multi Point Relay (MPR) nodes for transmitting link state
messages in order to generate a routing table. Within the context of the Optimized Link
State Routing (OLSR) protocol, there exist two distinct categories of broadcasts that are
transmitted, namely HELLO messages and Topology Control (TC) messages. To assess
the status of connectivity, each node will periodically transmit a HELLO message to its
neighboring nodes at intervals of two seconds. This approach is adopted as a waiting pe-
riod of six seconds is deemed excessively lengthy. The transmission control (TC) message
is derived from the data gathered through the HELLO messages [30] [31]. The duration
of re-routing traffic is influenced by the intervals at which HELLO messages are sent.
Consequently, this delay results in a higher rate of data packet loss and a decrease in
overall throughput [32]. Regarding the density of nodes, Broch et al. (1998) conducted a
study in which a total of 60 nodes were generated and dispersed throughout a terrain area
measuring 1200m x 800m. The network configuration implemented the Random Waypoint
Mobility Model, which incorporated nodes with varied speeds ranging from 2.5 m/s to 15
m/s [33]. The findings of this study, however, indicate that the impact of high node density
was not statistically significant due to the presence of node mobility. This phenomenon
7

can be attributed to the inherent resilience of radio connections, which exhibit a relatively
slow rate of disconnection from neighboring nodes [34].
Characterisation of Network Stability and Connections In the domain of mobile
ad hoc networks, it has been noted that every individual node exhibits a unique physical
location due to the intrinsic capacity of nodes to move freely inside the network. The
network’s stability is contingent upon the velocity of movable nodes, whether it is charac-
terized by sluggish or rapid movement. The constant alteration of the network topology
in local communities, achieved by adding or removing nodes, leads to an increased load
of control messages. The aforementioned cost stems from the necessity to periodically up-
date the routing table for proactive protocols, such as connection status or distance vector.
The transmission range of ad hoc networks might be limited as a result of the limiting
capabilities of individual nodes. The analysis of the provided information is crucial. In the
subject of ad hoc networks, two often employed conceptual models are the free space model
and the ground reflection two-way model. The employment of the free space propagation
model confers benefits owing to its dependence on a basic reference model.
4 Proposed Algorithm for the Nodes Clustering
The procedure of computing network clustering across several locations and subsequently
broadcasting PISP packets to obtain comprehensive information about each cluster is il-
lustrated in the flowchart shown in Figure 3. To choose the most appropriate local route
for establishing an alternative path,the algorithm needs to calculate the distances between
all nodes. Every individual node in the network will be assigned a specific position within
its respective zone. Consequently,algorithm 1 will be employed to calculate the distance
between nodes to identify all neighboring nodes that fall within the expected radio prop-
agation range of 250 meters. Subsequently, the algorithm will ascertain a path to the
desired destination by considering the minimum number of intermediate steps required. If
the Euclidean distance between two nodes is less than 250 meters, the algorithm will clas-
sify them as proximate nodes. In situations when there are numerous adjacent nodes, the
system initiates a process to assess and determine which neighboring node offers a viable
route to the target, therefore establishing a new major pathway. The computer algorithm
assesses a feasible alternate pathway by ascertaining the trajectory from the origin to the
destination utilizing the principal routing table. The nodes that are associated with the
primary pathway will be disregarded in the subsequent stage of the alternative pathway.
Each node is assigned a position (X, Y) inside the given terrain area’s radio propaga-
tion range, chosen randomly. Algorithm 1 incorporates the utilization of nodes inside the
topology to facilitate the dissemination of concise control messages. The purpose of these
messages is to inquire with neighboring nodes on the accessibility and dependability of
alternative routes on data transmission. The backup paths serve as supplementary options
to the primary pathway, which is stored in the main routing table and directed towards
the intended destination.
The mechanism may be categorized into two distinct components. The initial duty en-
tails the calculation of the main thoroughfare, which is established based on the positional
separation between nodes. The second responsibility entails the creation of an alternate
route, which is established by taking into account the neighboring nodes inside the iden-
tical cluster. Figure 4 illustrates the transmission behavior of each node in the network.
After the initial transmission, each node sends a condensed PISP packet to gather detailed
information about the neighboring nodes of the HEAD. The transmission of PISP packets
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Fig. 3. Diagram for Collecting Info and Network Clustering
Algorithm 1 ClusteringwithPath The algorithm generates a collection of alternative
routes for each feasible route in the routing database.
1: procedure FindingPath(Tr, s, d, edges to avoid)
2: Tr: Node available
3: V : Vertex G(V, E)
4: Γ(v): The collection of neighboring vertices to a given vertexv
5: s: Source or Head
6: d: Final Destination
7: pa(s, d) ← ∅
8: qsub ← ∅
9: Enqueue(Q, (qsub, s))
10: while Q ̸= ∅ and patha(s, d) = ∅ do
11: (qsub, x) ← Front(Q)
12: for all k ∈ Γ(r) do
13: e ← (x, r)
14: if (qsub ∪ e) ∩ edges to avoid = ∅ then
15: if Pr(Tr, k, d) ∩ edges to avoid = ∅ then
16: pa(s, NHop) ← qsub ∪ e ∪ Pr(Tr, k, d)
17: else
18: Enqueue(Q, (qsub ∪ e, k))
19: end if
20: end if
21: end for
22: end while
23: end procedure
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is initiated by the nodes within the set A, G, H to identify their surrounding nodes and
establish the membership of nodes within the same cluster. This procedure further enables
the identification of the nodes that exhibit connections with other clusters. Node A selec-
tively delivers data packets to its adjacent nodes B and C, while intentionally excluding
node D. The reason for this exclusion is the fact that node D serves as the initial hop
on the principal path and may be selected by the pre-existing protocols. The node that
encompasses components B and C evaluate to ascertain the presence of any adjacent nodes
that are not part of the primary node’s route. Furthermore, this study will investigate the
connectivity of nodes to other clusters in the scenario when the destination is situated in
a distinct cluster. Nodes E, F, and C send an acknowledgment to inform the Head that
they can relay the packets on behalf of node D in case node D encounters any more issues.
Fig. 4. SDN Head and Area clustering
The routing data displayed in Table 1 indicates that a secondary route exists between
the destination and the neighboring node, which differs from the primary path. The dense
arrow symbolizes the initial transition that takes place within the cluster, traversing Area1,
Area2, and Area3, from the origin to the endpoint. For greater specificity, the arrangement
of nodes can be denoted as follows: A->D->F->E. The purpose of a node A inquiry into
whether neighboring nodes C or B provide information regarding the accessibility of an
alternate path to the target is to ascertain whether those nodes have connections to other
clusters via distinct nodes. Subsequent to the inquiry, we shall elaborate on the Nodes
Clustering Based on the IoT (NCIoT) proposal and its algorithmic implementation for
determining the Head entity’s optimal positioning. The equations incorporate factors
such as distance and power consumption.
4.1 Theoretical Analysis for the Proposed Algorithm
In a network, packets are directed toward their intended destination by utilizing quality
criteria that favor factors such as lower prices or shorter distances. Various routing ap-
proaches might be exemplified within the given environment. Nevertheless, the network
10

- A B C D E F
A – ➠ B ➠C ➠D – –
B A – – ➠D E –
C A – – D F –
D ➠A ➠B ➠C – E ➠ F
E – ➠B – D – ➠F
F ➠ H ➠N ➠ D ➠E - -
Table 1. Checking Many Routes via Adjacent
protocols successfully guarantee the continuous maintenance of an updated routing table
by periodic updates. It is hypothesized that there is a graph, represented as G=(V, E),
where V represents the set of nodes and E represents the set of edges that link different
nodes. The establishment of a virtual connection between nodes is facilitated by utilizing
the information contained in the routing table, which is actively handled by the proposed
protocol. The suggested solution assumes that the routing between nodes has been estab-
lished, and the packet begins transmission at time RTr0. All the information regarding
the journey between the source and destination is represented as Γ(Vn). Consequently,
the pathway from the point of origin to the intended endpoint will be delineated in the
subsequent manner: The variable PathRTr(i+1)
(s, d) is started as an empty set.
The network design needs to encompass the integration of a defined quantity of nodes,
represented as N. Based on the calculation of graph trees, the number of edges may be
expressed as 2n̂. The occurrence of this phenomenon can be attributed to the existence
of several pathways for each individual node. Let V be a set comprising elements V0, V1,
V2,..., and n-Vi. It is assumed that the source node is denoted as V0. The symbol ∞ is
used to denote the initial cost of arc(i, j). At the outset, we define a collection V = {V0,
V1, V2,..., VnVi} comprising all nodes. Additionally, a set S = {V0} is utilized to hold
the nodes that possess the shortest path. The source node is represented as {V0}, and
the set G K is defined as empty, indicating a graph with K alternative edges leading to
the destination. Choose a vertex W from the set V1-S, where D[W] denotes the minimum
distance, assuming S is the beginning vertex V0. Incorporate the element W into the set
S. To update the value of D[V] for each vertex V in the set V1-S, the minimum value is
computed between the current value of D[V] and the sum of D[W] and the weight C[W, V].
The set S is to be updated by including the elements V0, V1, ..and Vd. If the primary route
becomes unavailable, the subsequent calculations will proceed according to the following
outline: Let S be the set produced by the addition of the element w and the removal of the
member K from S. Perform the following actions for each vertex v in the set V, with the
except of the items S and K. The equation may be expressed in the following manner: The
value of D[v] is equivalent to the lesser value between D[v] and the sum of D[w] and C[w,
v].Therefore, a random variable Xj ∈ {0,1} has been introduced to describe the connection
status between two nodes, referred to as A and B, in a particular subregion. The index
represented by j indicates the specific point in time when A sends the update message
to the HEAD node. The provided sequence, represented as X0,1,.....,Xj={Xj}j
∞=1, is
an example of a sequence. In this concept, the Markov chain represents the succession
of random variables {Xj}j
∞. The proposed methodology has exhibited its capacity to
calculate an alternative route that might potentially function as the most efficient path
from the origin to the destination. In some circumstances, this alternative for backup may
be considered the most optimal and advantageous strategy for the new routing table. In
Section 5, the study has shown that the proposed algorithm has effectively enhanced the
transmission of traffic from the source to the destination.
11

– The networks encompassed by Smart Devices incorporate the algorithm that has been
put out.
Pr = PtGtGr(
h2
t h2
r
d4
) (1)
Where Gt represents the gain of the transmitter antenna, Gr represents the gain of the
receiver Wireless signal, d represents the distance between the antennas in meters, ht
represents the height of the transmitter antenna, and hr represents the height of the
reception antenna shown in equation 1.
d =
p
(x2 − x1)2 + (y2 − y1)2 (2)
– if (d < outbound) Add the node that is located in the nearby nodes to the list of nodes
that are adjacent for each node on the topology.
– Check the distance between the nodes in close proximity to determine which one will
serve as the primary next hop as we use the equation number 2.
– Find the alternative route to the main destination via NNHop.
This study presents an analysis of the hardware components and operational mechanisms
of the sensor node as shown in table 2. Specifically, it explores three distinct ZigBee
settings: ZigBee coordinator, ZigBee router, and ZigBee end devices, each serving unique
functions. The voltage consumption of the sensor node is divided between 3.3 volts and
5 volts. Consequently, a regulator is employed to facilitate the conversion between High
Voltage (HV) and Low Voltage (LV), as well as vice versa. In contrast to the Arduino Uno
version, the Arduino Pro Mini requires bootloader programming, necessitating the use of
an FTDI 232 as a bootloader. Zigbee is a wireless communication technology that operates
at a frequency of 2.4 GHz or 2400 MHz. Consequently, the wavelength λ of Zigbee can
be calculated using the formula λ = c/f*, where c represents the speed of light, which is
approximately 3x108 meters per second. The power received by the receiver, expressed in
decibels (dB), will consistently decrease as the distance (d) between the transmitter and
receiver increases, as indicated by equation 3. The starting power received by the receiver
(Pr0) when the distance (d) is equal to 1 meter may be observed in equation 4 and equation
5. The initial test parameter is the received signal strength (RSS) in decibels per milliwatt
(dBm) under the condition of Free Space propagation, where the exponent (n) of the Free
Space path loss model is set to 2. The Receiver Signal Strength (RSS) is determined by
comparing equation 6 with the measured values obtained during the experiment or field
measurement. On the other hand, the attenuation is calculated using equations 7, 8, and
9.
Hardware Information
Microcontroller Arduino
Pro mini
Processor, ADC, Data Serial Communica-
tion.
XBee S2c End Device Wireless sensor Network type to sending
Pulse sensor data to Coordinator node
XBee S2c Coordinator Wireless sensor Network type to receive Pulse
sensor data from ZED or ZR to Base Station
XBee S2c Router Wireless sensor Network type to sending
Pulse sensor data to Coordinator node from
ZED and Communicate between each Router
at Mesh network
Battery 3.7 Volt 1000 mAh Supply power to the sensor node
Table 2. Mobile Node H/W [35]
12

P0 = Ptx(
λ
π4
)2
GtGr
Pt
P0
=
(4πd)2
(λ)2
=
(4πfd)2
c2
(3)
Pr = P0 ÷ d2
(4)
FreeSpace = 20 logd +20 logf −27.5 (5)
FreeSpace2450MHz = −(20 log d + 40.3) (6)
The constant variable, represented as C, plays a crucial role in wireless signal attenuation
inside the Free Space scenario, with a precise numerical value of 27.5. As the distance (d)
between two points grows, there will be a proportional rise in the value of L Free Space
(-dB). Equation 4 specifies that the frequency utilized by Zigbee is 2.45 GHz. The afore-
mentioned equation facilitates the calculation of the Power Receiver in decibel milliwatts
(dBm).
Pr = Pt + Gr + Gt + L (7)
The received power (Pr) can be mathematically represented as the multiplication of
the transmitted power (Pt) and the ratio of the wavelength λ to the product of 4, and
the distance. The equation can be expressed in an alternative form as GtG2
r in equations
8 and 9 The power received, denoted as Pr, is impacted by many factors. The analysis
incorporates the components of transmitted power, denoted as Pt, which are measured
in units of either Watts or milliwatts. In addition, the carrier wavelength, represented by
the symbol λ and measured in meters, is of significant significance. An additional crucial
determinant in the equation is the spatial separation between the transmitting device and
the receiving device, denoted as d and quantified in units of meters. Furthermore, the
power received is subject to the influence of the transmitter’s antenna gain (Gt) and the
receiver’s antenna gain (Gr). The term ”antenna gain” refers to the measurement of power
radiation in a certain direction. However, the mathematical expression for calculating the
increment in decibels can be represented as:
Pdb = 10 log10(
Pout
Pin
) (8)
The equation for free space loss in an ideal omni-directional antenna can be expressed
as follows in equation 9 :
Pt =
(4πd)2
(λ)2
, Pr =
(4πfd)2
c2
(9)
where Pt represents the transmitted power, Pr represents the received power, d represents
the distance between the transmitter and receiver, λ represents the wavelength of the
signal, f represents the frequency of the signal, and c represents the speed of light. In the
proposed algorithm we have implemented the forwarding packets for checking all nodes
the networks as below equation 10 11.
∀PISP =
X
allocpkt() + HeaderIPsim ∗ sim :: access(PISP)
∀Node =
N
Y
s
find(Source, NNH) + PSequence
X
Scheduler :: instance().clock() ×
n
X
0
DestPort + TimeMax
(10)
13

X
HeaderIP = Hdr IP +
Y
Fwrd.resched(TimeMax)
∀(allocPacket, DestPort) =
X
allocPacket = (rate × 1500)/(size × 8)
(11)
∀HPacket.port = rout[Node− > addr][DestPort] ∈ Node− > queue[DestPort]
hdr PISP− > TTL = Scheduler :: instance().clock();
hdr PISP− > access(p)− > size() =
X
size (rate × 1500)/(size IP × 8);
(S, NH) =

||∀addr = org source × instance().clock()||, if time ≤ 1sec
||Rout[S][NHop] =
p
(x2 − x1)2 + (y2 − y1)2||, if NH ̸= Dest

∀addr = orgsource × Scheduler instance().clock()
∀Rout[IPsourse][NNH] =
d
Y
s
(NH)∀Rout[NodeS][IP DEST] /
∈ ∀Rout[S][NH]
PISP Header =
D
X
s
N
X
s
Routing[i][j]
When the allocate packets function initiates the distribution of inquiry packets to
adjacent nodes based on their proximity, the simulation time incorporates the instance
clock-measured durations of transmission and reception. After the collection of all per-
tinent data in accordance with Equation 10, the transmission of CBR or UDP packets
will initiate in the form of binary data. From one step to the next, these packets will be
forwarded until they reach their intended destination. The principal computational algo-
rithm is denoted by Equation 11, and it consists of monitoring any updates concerning
each cluster and calculating the distance. It has been observed that messages transmitted
from a source to a destination through an intermediary node return to the source in an
enigmatic manner. In the first scenario, the source node makes a request to its nearby
node for a path of higher quality than its own. To meet the second requirement, the origin
must discover a different path that avoids loops without casting suspicion on its nearby
nodes. The third stipulation has to do with the particulars of the situation under consid-
eration. The term ”clustering topology” describes the structure or layout of clusters inside
a system or network. We will attempt to explain in detail how our novel approach to
cluster network analysis operates in this paper. On the primary route, every node engages
in communication with multiple neighboring nodes. Then, a neighboring node within the
same cluster is selected as a secondary option. Furthermore, the complete path for each
cluster is computed by each neighboring node, enabling accessibility from across regions.
Every router along the primary path possesses the capability to autonomously ascertain a
secondary path for data forwarding. This is achieved through the utilization of the present
routing information of each node, which might comprise a pre-established path. To il-
lustrate how to discuss our protocol, we will concentrate on a particular subject within
topology. Within the existing topology, each node is assured of having a minimum of two
neighboring nodes. The neighboring nodes are cognizant of the fact that the leader node
is responsible for furnishing all the necessary information for the other nodes to select the
optimal alternate route. To reach the ultimate destination D from the starting location
S, one may choose from the following paths: S → adj → NH... → D The total weights
for all the topology
P
W0 = X; X: Total weights for all arcs on the topology.
Then: ∆W =
P
W0 = X; If we make the assumption that the algorithm has com-
puted the best second paths between S and D, it can be expressed as follows: Ps → D
International Journal of Computer Networks Communications (IJCNC) Vol.16, No.2, March 2024
14

S → NH → NNH → ..... → D. At the conclusion of each simulation period, the aggregate
count of PISP Control Overhead messages generated corresponds to the cumulative total
of all messages produced. Additionally, during this process, we have computed all PISP
packets and collected all relevant information by employing the routing mechanism and
incorporating alternative next-hop options into the updated routing table. Subsequently,
all gathered information is transmitted to the HEAD cluster node within the entirety of
the IoT network. The packet header for the PISP, as depicted below, remains consistent
with the header generated by the algorithm under consideration. The header exclusively
includes the IP source, IP destination, and acknowledgement, together with the identifier
of the subsequent hop if it has been modified. The optimization of the sending and re-
ceiving time from the head nodes is enhanced by taking into account the time it takes for
the data to leave the head node and return to it. Ultimately, all packets generated by the
PISP will gather the necessary data specific to the cluster they are producing. This part
introduces a revolutionary Node clustering based on the Internet of Things (NCIoT) rout-
ing protocol. It outlines the procedures and tactics we will employ to improve the routing
stability of an NCIoT network. First, we demonstrate how to segment the IoT network
into stationary clusters. Second, a unique distributed HEAD cluster election mechanism
is proposed, which allows for the selection of a next hop for forwarding packets through
it in the event of network problems. Clustering allows for more control and monitoring
of data packets as they travel from the source to the destination. This will cause the bf
HEAD cluster to be notified of any changes in its cluster by utilizing the PISP inquiry
packets that they are sending regularly without any effect on the network and controlling
them to be in the same cluster, rather than forwarding to any other unrelated cluster all
notation has defined in table3.
Notation Definition
NH Next Hop
ADJ Adjacent Node
Dest Destination
NNH Next Next Hop
Dist Distance
alt Alternative Node
sim simulation time
S Source
H Head Node
Point The node on the Route
Table 3. Algorithm Notation and Definition
4.2 Computing control PISP process
This study aims to evaluate the capacity of network nodes to utilize packets generated by
PISP for multiple purposes. These purposes include gathering information and delivering
it to the cluster head, as well as examining the accessibility of paths between adjacent
nodes and determining the number of feasible paths between a given source and destina-
tion. Furthermore, the objective of this study is to ascertain the most favorable quantity of
nodes within each cluster. In the context of ad hoc networks, the focus will be on examining
the requisite node mobility within the transmission range to facilitate efficient communi-
cation. When the process of node mobility commences, the nodes proceed to create direct
15

connections. The PISP message regulates the flow of data traffic by providing notifica-
tions to the central node of any updates. Consequently, when nodes receive a notification,
they react by instructing the HEAD to reroute the traffic through the next adjacent node.
Alternatively, if there are nodes with shorter distances, the packets are sent through them
to expedite delivery to the destination, thereby minimizing packet loss. In response to a
received message, each adjacent node offers an acknowledgment by broadcasting a packet
that contains a field indicating either ”0x” or ”1x”. When the acknowledgment packets
are designated as ”1x,” it indicates that the next node has an alternate routing path and
can function as a backup node in case of any modifications within the cluster. When the
acknowledgement packets are assigned a value of ”0x”, it signifies that the specific node
is incapable of operating as a backup near intended destination. After the extraction of
the packet, the source node will analyze its contents in order to ascertain whether they
include the value ”1x.” When a packet containing a ”1x” identifier is detected, the source
node will incorporate the associated node, which responds with a ”1x” as the first hop
in its alternative route using distinct NH. Upon receiving a packet with the hexadecimal
prefix ”0x,” the source node will proceed to authenticate the answers given by its adjacent
nodes. Consequently, if all acknowledgment headers received from surrounding nodes col-
lectively display a value of ”0x”, it can be deduced that the transmission of traffic is not
viable. Consequently, the third stage of the PISP control entails the identification of an
alternative pathway from the adjacent node to a distinct node, as seen in algorithm 2.The
packet header for the PISP, as depicted below, remains consistent with the header gener-
ated by the algorithm under consideration. The header exclusively includes the IP source,
destination, and acknowledgment, together with the identification of the subsequent hop
if it has been modified. The optimization of the sending and receiving time from the head
nodes is enhanced by accounting for the time it takes for the data to leave the head node
and return to it. Ultimately, the PISP packets will gather the necessary data pertaining
to the cluster they are generating.
Algorithm 2 Cluster Area Info via PISP
point = searchadj(S, NearestNode);
i ← NumberofAdjacent
while i ≥ NumberofAdjacent do
arrayrout[point][adj]! = INF
if rout[point][adj] == adjandrout[point][NHOP] ̸= PrimaryAdj then
NextHop[adj] = ararout[point + NH][adj]
i ← adj + 1
ClusterRoute[point][NH] = rout[NextHop[adj]][NH]
end if
end while
The focal point of consideration in this context is a specific node that is either the
head node or a neighboring node within the clustered region. The search adjacent method
initiates by disseminating the PISP packets to gather comprehensive data regarding the
nodes within the cluster. An array is employed as a buffer to store any adjacent nodes
that are not part of the major protocolś main channel, including LEACH, LEACH-e,
and RCBRP. Every node will initiate the process of reading the subsequent hop until
it reaches its final destination. The PISP facilitates the efficient acquisition of clustered
route information and ensures timely updates for the head nodes regarding the cluster
information. To determine the number of PISP messages transmitted throughout the
simulation period, the remaining power consumption and lifetime of the selected head
16

or designated node are divided by the duration of the message exchange period between
nodes, as seen below:
GPISP =
N
X
s
PISP ÷ Sim time (12)
G: Generated number of PISP
Sim Time : Simulation Time
When a head node is present in each cluster area, it establishes a default state for the
status of all nodes. The communication between nodes should be established by utilizing
the HELLO message over the default routing protocol. The primary objective of this pa-
per is to increase knowledge of Head, a protocol that can potentially decrease the volume
of HELLO messages created during the exchange of PISP packets. This reduction is
achieved by employing smaller and faster PISP packets, as seen in the simulation results.
Once a nearby node crosses the threshold point and enters the cluster zone, it is automati-
cally designated as a member of the cluster. Subsequently, a PISP message is transmitted
to the head, contingent upon the condition that the HEAD gets the PISP message within
a certain duration of time denoted as ”T” seconds. In this particular instance, we are
presented with two distinct scenarios. The PISP message is generated by the HEAD. In
the event that any nodes become part of the cluster area or any neighboring node leaves
the cluster, it is imperative to take into account the potential failure or disruption of con-
nections.The objective of this study is to determine the number of control PISP packets
present in each cluster through a systematic calculation process.
5 Simulation Experiment
A network simulation using NS2 was conducted to assess the performance of the proposed
upgraded NCIoT protocol in networks with high and low node densities. The simula-
tion results of the NCIoT protocol were compared with other relevant study protocols,
namely LEACH-e, LEACH, and RCBRP. The evidence obtained through NS2 simu-
lation provided strong support for the utilization of IoT technology by nodes to enhance
their responsiveness and preparedness in various connection scenarios. A radio propaga-
tion range was employed, utilizing a transmission power of 0.28 watts. The system lets
individual nodes transmit or receive data packets to or from next to them within a max-
imum range of 250 meters. The researcher employed the IEEE 802.11b protocol at the
data-link and physical levels to facilitate the sharing of multimedia content via wireless
networks. The network simulator utilized the random WayPoint mobility model, with a
roaming region measuring 1600 x 1600 m2. During the simulation, the movement or relo-
cation of each node within clusters occurred. The initial velocity, which was measured at 1
Parameter Value
Wireless LAN Medium Ac-
cess Control (MAC)
IEEE 802.11
Maximum range Distance
between Mobile Nodes
250 m
Roaming area 1600 X 1600 m2
Number of Nodes test 25,50,100 up to 500
Minimum Node Speed
Movement
0 to 1 m/s
Table 4. Simulation Parameter
17

m/s, was comparatively modest. Nevertheless, this velocity selection will become evident
and significant in subsequent analyses and correlations. The duration of each simulation
was 500 seconds. The experiment was carried out in a series of ten trials, after which the
mean value was calculated. The packet was 512 bytes in length and was limited to 1024
bytes in length; the bit rate was set to 2 MB/s. The ability of a wireless connection to
be shared within an ad hoc configuration network is a widely acknowledged fact. About
density, the initial simulated scenario comprised an aggregate of 200 to 500 nodes, evenly
distributed across each cluster region. A traffic rate of 512 kb/s was recorded between the
source and destination nodes throughout the simulation. The subsequent section presents
the simulation results, which have been formatted as line graphs. The parameters for these
graphs are specified in Table 4. The concept of ”packet loss ratio” refers to the proportion
of deleted packets about the total number of transmitted packets. The average end-to-end
delay is a metric used in statistics to quantify the mean duration of time that elapses
between the commencement of data packet transmission and its final arrival. Calculated
as follows, throughput is the quantity of packets that have been received during a specified
simulated period without interruption or interference.
Prior to assessing the performance concerns associated with network topologies in
relation to computing a backup path over a Mobile Ad hoc Network (MANET), it is crucial
to identify the network factors that may impact the Quality of Service (QoS) of video data
being broadcast. The present study centers its attention on three parameters that have
the potential to provide a more comprehensive understanding of the impact of video traffic
tactics. To assess the impact of node density, it is imperative to acknowledge that higher
densities exhibit greater longevity when contrasted with lower or moderate densities. This
phenomenon occurs because of the decreased capacity of the latter to effectively identify
and sustain novel pathways as the nodes progressively disperse over various clusters, hence
heightening the likelihood of generating a disconnected topology. Areas with nodes exhibit
more stability when they undergo slower movement, hence enabling the preservation of
services for extended durations. The packet latency, throughput, and packet delivery ratio
were tested after partitioning the networks into many clusters.
5.1 Performance and Analyses Evaluation
At each cluster, an assessment is conducted on our suggested protocol and another protocol
of a similar kind, with the focus on evaluating the stability of the route between the
source and the destination. The Node clustering based on the Internet of Things(NCIoT)
protocol determines the complete clustered region by considering the connectivity distance
between all areas. The NCIoT protocol is employed to determine the appropriate Base
Station or HEADnode by gathering information from all nodes through the dissemination
of PISP inquiry packets. The selection of the HEAD by the NCIoT is determined by
considering its historical background, database information, and lifespan. Each node within
the cluster is regarded as an intelligent device that is designed to remain within the confines
of the cluster. In the event of instability or malfunction, neighboring nodes are notified
of the issue by PISP inquiry packets. Furthermore, to guarantee the interconnectivity
and stability of all clusters along a certain route, the NCIoT protocol incorporates the
assessment of route validity. It is crucial to acknowledge that the NCIoT protocol operates
in real-time and exhibits dynamic behavior. Consequently, whenever a packet reaches
a cluster, the HEAD node initiates the NCIoT protocol recursively across all cluster
regions. This recursive application is based on the computed distance between nodes, as
demonstrated in the aforementioned equation. Based on this, the closest node will relay
the data packet until it reaches the intended destination. Numerous scholarly studies
18

are presently examining cluster routing protocols, including LEACH, LEACH-e, and
RCBRP, which are documented in the existing body of research. RCBRP employs a
sequential cluster selection approach to facilitate route construction. The selection process
is contingent upon real-time traffic conditions and node density, as well as the traffic load
and distance of the route in question relative to the intended destination.
Fig. 5. Throughput Network Fig. 6. End to End Delay
The Node clustering based on the Internet of Things NCIoT employs a node selection
mechanism that identifies nodes leading to the nearest subsequent cluster, which in turn
has the shortest path hop by hop to the target. When compared to existing (CBR) pro-
tocols, the simulation results provide evidence of the efficacy of the NCIoT algorithms.
These algorithms have been shown to enhance network throughput by 50% and reduce
end-to-end latency by 17%. In this study, we conduct a comparative analysis of the NCIoT
protocol about the LEACH, LEACH-e, and RCBRP protocols.According to the data
depicted in Figures 6 and 5, it can be observed that the NCIoT protocol exhibits superior
performance compared to the LEACH, LEACH-e, and RCBRP protocols. The selec-
tion of routes with high stability is a key factor in determining throughput and end-to-end
delay in the context of the NCIoT protocol. This selection process is based on the histor-
ical performance and available resources of the HEAD. The RCBRP algorithm finds the
cluster with the required route to the destination regardless of any event that occurred,
such as the stability of all nodes, overhead, or collision. Also, LEACH, LEACH-e, and
RCBRP selects a series of clusters by considering real-time node density with power
consumption, the traffic load on the respective road segment, and the travel distance to
the destination without considering the stability of the routes. The figures [7,8] are the
average throughput, the percentage of the throughput, the average end-to-end delay, and
the percentage of the end-to-end delay, respectively. The NCIoT algorithm significantly
improves the networks performance by increasing the throughput percentage by 50-60%
compared to LEACH, LEACH-e, and RCBRP.
The measurement of the continuity index for each scenario is depicted in Figure 9. Live
video broadcasting can be deconstructed into segments with similar dimensions. Within
the framework of a live broadcast system, it is noted that every node participates in the
presentation of similar content for a specific segment. Therefore, the continuity index can
be defined as follows:
19

Fig. 7. Packet Over All Ratio Received
Fig. 8. Continuity Index
ContinuityIndex = Np/Ns, (13)
In this context, Np represents the quantity of blocks that are received before to the desig-
nated playback dates, whereas Ns denotes the overall count of blocks inside a single content.
A NCIoT network with dimensions of 1600m x 1600m was established, followed by the
Fig. 9. Continuity of Receiving Data Packets
partitioning of the network into area clusters with a radius of 125 meters. The cumulative
output of traffic generators supplies the network structure with intelligent nodes that are
interconnected within the Internet of Things (IoT). Multiple numbers of nodes are gener-
ated in each simulation run. Consequently, after all the nodes have been integrated into
the NCIoT network, we commence the reception of outcomes. To enhance the authentic-
20

ity of our simulation, Sumo maintains a sufficient spatial separation between neighboring
structures to mitigate the occurrence of collision scenarios.
The number of active nodes over time as determined by the NCIoT and LEACH
methodologies is depicted in Figure 10. The provided figure shows cases how NCIoT ex-
hibits a 20% increase in the mean number of operational nodes when compared to LEACH,
LEACH-e, and RCBRP protocols. resolves the routing problem by employing fuzzy logic
to model cluster-head selection, thereby surpassing competitors. In conjunction with the
quantity of operational nodes, the network lifetime exerts an impact on IoT systems.
The duration of an Internet of Things (IoT) system is ascertained by tally-marking the
number of iterations from the system’s inception until a specific proportion of the initial
operational nodes remain. In this study, we conduct a comparative analysis of the afore-
Fig. 10. Comparison Energy
Fig. 11. Hello with PISP Utilization
mentioned protocol, namely LEACH, LEACH-e, and RCBRP; To assess their simi-
larities and differences. The mean and the proportion of individuals who send a greeting
message, specifically ”HELLO,” in comparison to those who use the Predictive Inquiry
Small Packets (PISP) are provided, in that order. The calculation of the proportion of
HELLO messages transmitted by nodes is determined by dividing the entire count of
HELLO messages sent out by the overall count of messages sent out. Simultaneously, we
conduct measurements on the PISP packets and ascertain that the PISP exhibits a lower
overhead compared to HELLO, as indicated by its relatively modest size. The NCIoT
protocol effectively minimizes the transmission of HELLO messages by generating them
at a rate of only 5.9%. This reduction is achieved by selectively propagating HELLO
messages in three specific scenarios: whenever the HEAD node enters the cluster zone,
when clever node locations exit the cluster zone, and when a new HEAD node declares
itself to the cluster. In contrast, a significant quantity of greeting messages is produced by
the LEACH, LEACH-e, and RCBRP algorithms. This phenomenon can be attributed
to the regular transmission of HELLO messages by all of these protocols. In this context,
Np represents the quantity of blocks that are received prior to playback deadlines, whereas
Ns denotes the entire amount of blocks within a given content. To assess the effectiveness
of the NCIoT protocol, a comparative analysis is conducted with three alternative pro-
tocols: LEACH, LEACH-e, and RCBRP. These protocols share the characteristic of
transmitting control overhead signals either every 5 seconds or when their deviation from
the originally established motion function exceeds 10 m/s. Furthermore, the NCIoT pro-
tocol adheres to a typical practice of broadcasting HELLO messages at regular intervals
21

of 10 seconds. Additionally, we employ the tiny PISP inquiry to ensure the node database
information remains up to current. Figure 11 presents a comparison of the performance
of LEACH, LEACH-e, and RCBRP in terms of the quantity of HELLO messages
created. To provide a comprehensive analysis, we calculate the average performance and
contrast it with NCIoT. The NCIoT protocol disseminates HELLO messages in many
contexts, including when the HEAD is assigned and enters the cluster zone when the
HEAD quits the cluster zone, and when a new HEAD proclaims its presence to the
cluster. The use of NCIoT resulted in a significant reduction in the quantity of nodes
generated inside each cluster.
6 Conclusion
The current study examined the influence of dynamic node displacement and various veloc-
ities (namely, walking and cycling) on the backup path. The repositioned nodes foresee the
required movement of vehicles. The study above shows that there are numerous instances
of node failures when establishing connections. As a result, it is necessary to update the
routing table to appropriately represent the dynamic changes inside the network. This doc-
ument offers a thorough introduction and detailed elucidation of the NCIoT protocol. The
NCIoT protocol requires the head or base station to repeatedly initiate communication,
considering the stability of the path, in order to establish contact with the target cluster.
Each packet that a cluster receives undergoes examination, as previously mentioned. The
assessment of whether the durability and power efficiency of the head node have been pre-
viously improved will impact the result. Geographical regions are classified according to
their source, destination, and the route that has the highest minimum average throughput
among all possible options. The simulation findings demonstrate that the NCIoT protocol
surpasses traditional clustered routing protocols in terms of both route performance and
end-to-end latency. In addition, the NCIoT protocols improve network efficiency. The main
goal of the PISP (Protocol Independent Spanning Tree Protocol) is to reduce the number
of Hello messages that are transmitted across clusters. The aim is achieved by using a new
method to determine the best time for updating or exchanging control overhead messages
between the primary node and its nearby node. The PISP packets is specifically developed
to effectively distribute inquiry packets across networks, hence reducing the time needed
for constructing routing tables and updating network topology.
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7 Acknowledgments
This research was supported and funded by Arab Open University-Kuwait Branch under
decision number 23024
Authors
Radwan Abujassar is currently Associate professor at the Information Tecnology and
Computing (ITC) Faculty at Arab Open University which is following the OU Univer-
sity in UK. Dr Radwan was in the computer Engineering department of the faculty of
Engineering at the Bursa Orhangazi University in Turkey. Dr. Radwan received his B.Sc.
degree from Applied Science University, Amman, Jordan in 2004, and M.Sc. degree from
New York Institute of Technology in 2007, both in computer science. His Ph.D. degree in
computing and electronic in the field of IP recovery in IGP and MANET networks from
University of Essex, UK in 2012. His research interests include Network and Controls,
Routing Protocols, Cloud Computing and Network security.
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