| Journal of Smart Sensors and Computing
Received: 22 July 2025; Revised: 12 September 2025; Accepted: 22 September 2025; Published Online: 23 September 2025.
J. Smart Sens. Comput., 2025, 1(2), 25210 | Volume 1 Issue 2 (September 2025) | DOI: https://doi.org/10.64189/ssc.25210
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
Internal Route Optimization in IoT-Enabled Wireless
Sensor Networks Using Cluster-Based Architecture
and Adaptive Cluster Head Communication
Minal Jain,
1
,* Khushbu
1
and Arun Vaishnav
2
1
Faculty of Computer and Application, Madhav University, Abu Road, Pindwara, Rajasthan, 307032, India
2
Faculty of Computing and Informatics, Sir Padampat Singhania University, Udaipur, Rajasthan, 313601, India
*Email: minalshah004@gmail.com (Minal Jain)
Abstract
Energy-efficient and intelligent routing strategies have become essential as wireless sensor networks are
increasingly incorporated into Internet of Things environments. For IoT-enabled Wireless sensor networks
(WSNs), this study suggests an improved internal routing framework with an emphasis on cluster head to sink
communication optimization following cluster head selection. Based on a strong cluster formation procedure
that employs hybrid fuzzy C-Means and K-Means algorithms, a multi-objective Mother-Inspired Adaptive
Optimization method is used to choose the cluster heads. Next, energy- and distance-aware routing paths
between cluster heads are dynamically constructed using the Pelican Optimization Algorithm. Key issues in
Internet of Things-based deployments, such as limited energy, data latency, and communication reliability, are
address by the suggested approach. The results show from prior hybrid optimization- based WSN Studies that
the hybrid approach is effective in managing large-scale, resource-constrained sensor networks within IoT
infrastructures by significantly extending network lifetime, improving packet delivery ratio, and lowering end-
to-end delay. The results are analyses based on key performance indicators such as routing efficiency, energy
consumption, network lifetime, end-to-end delay, and packet delivery ratio. Comparative visualizations
illustrate the routing paths optimized by POA, highlighting its effectiveness in minimizing transmission
distances and balancing energy usage among Cluster heads (CHs).
Keywords: Wireless sensor networks; Internet of things: Cluster-based routing; Cluster head; Data aggregation;
Pelican optimization algorithm; Energy efficiency.
1. Introduction
The convergence of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has catalyzed a
revolution in how data is sensed, transmitted, and acted upon in real-time environments.
[1-4]
WSNs, comprising
numerous spatially distributed autonomous sensor nodes, act as the sensory backbone of IoT systems.
[5]
These
networks capture vital information from the physical environment-such as temperature, humidity, pressure,
motion, and location-and transmit it to centralized processing units or cloud platforms via the Internet. In IoT
applications
[6]
like smart cities,
[7]
precision agriculture,
[8]
environmental surveillance, healthcare monitoring,
[9]
and industrial automation,
[10]
WSNs form the first layer of data acquisition. The seamless interaction between
WSNs and IoT facilitates context-aware decision-making, automated control, and intelligent responses.
However, integrating WSNs into the IoT ecosystem imposes stringent requirements on scalability, real-time
communication, energy efficiency, and reliability, thus demanding advanced network design strategies.
As the scale of IoT deployments continues to grow, clustering mechanisms have emerged as essential strategies
for achieving network efficiency and manageability. In a clustered WSN, sensor nodes are organized into logical
groups called clusters, each led by a Cluster Head (CH).
[11]
The CH is responsible for aggregating data from
member nodes and relaying it to the Sink or Base Station.
[12]
This hierarchical model minimizes direct
transmissions to the base station, significantly reducing redundant communication and conserving energy-a
critical requirement in battery-powered IoT devices. Clustering not only enhances scalability but also improves
load balancing, latency management, and fault tolerance, making it highly suitable for large-scale,
heterogeneous IoT networks. When paired with adaptive clustering algorithms, the system can dynamically
adjust to changes in topology and energy levels, ensuring prolonged and stable operation in dynamic IoT
environments.
While clustering optimizes intra-cluster communication, a major bottleneck remains in inter-cluster routing,
particularly the communication between CHs and the central Sink. This phase, often referred to as internal
routing, presents multiple challenges. First, energy disparity among CHs due to variable workloads can lead to
premature node failures. Second, communication overhead increases when CHs must coordinate multi-hop
transmissions without a centralized routing controller. Third, topology changes-due to energy depletion or
environmental disruptions-introduce instability and require frequent route recalculations. Lastly, latency-
sensitive IoT applications cannot tolerate long delays or packet losses, necessitating robust, low-latency routing
mechanisms. Therefore, the internal routing layer is a critical focus area for performance enhancement in
clustered IoT-enabled WSNs.
To address the complex demands of IoT-based WSNs, intelligent and adaptive routing mechanisms are
essential. Traditional static or distance-based routing methods fail to account for real-time network dynamics
such as fluctuating energy levels, node mobility, or environmental changes.
[13]
Therefore, in this work, we follow
the intelligent routing structure as shown in Fig. 1. Bio-inspired algorithms, metaheuristic optimization
techniques, and machine learning-based approaches offer the flexibility and intelligence required to make
optimal routing decisions under such conditions. An ideal routing protocol for IoT-based WSNs should be
energy-aware, delay-sensitive, load-balanced, and scalable across diverse topologies. Adaptive mechanisms like
the Pelican Optimization Algorithm (POA) and Mother-Inspired Adaptive Optimization (MIAO) can dynamically
evaluate multiple objectives and select the most efficient routing paths, enhancing network longevity,
reliability, and communication quality.
Fig. 1: Importance of intelligent routing.
This research aims to address the internal routing challenges of IoT-integrated WSNs by proposing an
optimized cluster-based communication architecture. The three key contribution of the proposed work are
integration of cluster-based architecture with adaptive CH Communication, application of POA for route
selection and comprehensive performance evaluation. By combining energy-efficient clustering with intelligent
CH-to-Sink routing, the network achieves improved structural organization and efficient data delivery. POA is
employed to discover optimal CH-to-Sink routes based on real-time energy levels, transmission distances, and
delay constraints. This hybrid approach enables dynamic and intelligent routing decisions. The proposed model
is validated through simulation, and results are analyses in terms of core IoT network metrics such as energy
consumption, network lifetime, packet delivery ratio, end-to-end delay, and routing scalability.
2. Literature survey
Chandrasekaran et al. explains how to maximize mobile safe routing in an intrusion detection system (IDS) for
WSNs using the POA.
[14]
Through dynamic path optimization and the use of swarm intelligence, POA successfully
mitigates security threats and improves network resilience. With an energy consumption of 0.08J and 12% of
the average energy usage of 0.05J, an active node of 92%, and a dead node of 10%, numerical validation confirms
POAs.
[14]
Suggests using the Multi-objective-Trust Aware Improved Pelican Optimization Approach (M-TAIPOA)
to cluster and route data in a secure and energy-efficient manner. Sine chaos mapping, a combination of Levy
Flight Strategy and Sine Cosine Optimization (CSO), is used to improve POA in order to jump out of local optima,
increase search variety, and improve convergence accuracy. Using an upgraded Artificial Bee Colony (EOR-
iABC), M-TAIPOA uses less energy than the current method, Energy Optimization Routing, by 4.5J for 10 cycles
for scenario 3.
[15]
To solve these problems and improve cluster head selection for the best clustering, suggest the
Enhanced Pelican Optimization Method for Cluster Head Selection (EPOA-CHS). By combining the Levy flight
process with the conventional POA algorithm, this approach guarantees the selection of the best cluster head
while simultaneously enhancing the program's optimization level. The EPOA-CHS approach ultimately performs
better in these areas than the SEP, DEEC, Z-SEP, and PSO-ECSM procedures, according to extensive experimental
study.
[16]
Provides a novel method for extending the network lifetime of WSNs through energy conservation and
node activity maintenance: the Modified Pelican Optimization Algorithm (MPOA). By the 400th iteration, the
total amount of live nodes had dropped from 100 to 70, indicating that PSO had significantly reduced node
survival. At the conclusion of the simulation, SFO maintained 78 nodes, demonstrating a somewhat improved
performance. On the other hand, after 400 cycles, MPOA shown a significant improvement, keeping 85 live
nodes.
[17]
Intends to increase the network's efficacy by putting forth the Energy Efficient Yellow Saddle Goatfish
Pelican Optimization algorithm (EEYSGPO), a hybrid optimization method inspired by nature that employs the
Yellow Saddle Goatfish Algorithm to determine the best cluster head among a group of nodes. According to
simulation results, the EEYSPO method's optimal cluster head and route selection fixed the problems associated
with premature convergence or extended the WSN's lifetime or scalability. Network stability is increased by
57.28%, 324.5%, 571.72%), and 91.37%, respectively, using the suggested methods. In order to maintain energy
stability and increase network lifetime longevity by resolving issues in the CH selection process, the golden
eagle optimization algorithm (GEOA) as well as improved grasshopper optimization algorithm (IGHOA), which
are based on the energy efficient cluster-based routing protocol (GEIGOA), are suggested. In comparison to the
competing CH selection schemes, it was also established that the computational cost imposed by the suggested
GEIGOA with varying numbers of sensor nodes was reduced by 14.98%, 17.21%, 19.76%, and 21.62%.
[18]
Suggested an enhanced version of the GWO (EECHIGWO) algorithm for energy-efficient cluster head selection
in order to mitigate the imbalance among exploration and exploitation, the lack of population diversity, and the
early convergence of the standard GWO algorithm. By employing minimum energy levels in WSNs, the
simulation results have resolved premature convergence, validated the best choice of cluster heads with the
least amount of energy consumption, and improved the network lifetime. In comparison to the SSMOECHS,
FGWSTERP, LEACH-PRO, HMGWO, and FIGWO protocols, the suggested method improves network stability by
169.29%, 19.03%, 253.73%, 307.89%, and 333.51%, respectively.
[19]
Here, cluster heads or non-cluster heads
are chosen using the Genetic algorithm (GA) in conjunction with the modified particle swarm optimization (M-
PSO) technique. The GA is used to find the best shortest route, & the suggested method calculates the likelihood
of selecting the best nodes to be cluster chiefs. Furthermore, the suggested approach performs better than
current state-of-the-art methods like GAPSO-H, EC-PSO, and NEST. Overall, though, DMPRP outperforms NEST,
EC-PSO, and GAPSO-H by 12%.
Provided a thorough analysis of BOAACO, DEEC, LEACH, & Airproofed. Their
impact on network efficiency, including energy consumption and network longevity, is being examined by the
CHS and routing methods. The simulation results show that it greatly increases the overall efficiency and
robustness of WSNs comparing the suggested system to LEACH, DEEC, and BOAACO. To improve the network
lifetime of the systems created for Internet of Things applications, the energy-saving CH selection (ESCHS)
approach is proposed. For cluster formation, this approach uses the concept of uniform clustering. The node
selected to be a CH has residual energy greater than the average residual energy of the corresponding cluster.
The results show that the recommended approach outperforms the current approaches in terms of network
longevity and energy savings.
[20]
Suggested an osprey optimization technique to select the optimal CH in a
wireless sensor network-based Internet of Things system, based on energy-efficient cluster head selection
(SWARAM). The MATLAB2019a tool is used to simulate the suggested SWARAM technique. The SWARAM
method's effectiveness in comparison to the current EECHIGWO CH, HSWO, and EECHS-ARO selection
algorithms. The proposed SWARAM increases network lifetime by 10% and packet delivery ratio by 10%.
Suggest a novel method that uses improved crow swarm optimization (ECSO), updated fuzzy logic, or the Whale
optimization algorithm (WOA) to optimize the CH selection and path selection. According to the results, the
suggested method performs better than the current methods in terms of throughput, delay, packet delivery
higher.
[21]
The multi-objective seagull optimization method (CAR-MOSOA) is used in collision-aware routing to
achieve scalable WSN efficiency. The suggested CAR-MOSOA for 400 nodes has better simulation outcomes than
the FDEAM, EOMR, TSGWO, and CoCoA. These findings include energy consumption of 33 J, end-to-end delay of
29 s, packet delivery ratio of 95%, and network lifetime of 973 s.
[22]
Introduces the multipath routing protocol
in the IoT-assisted WSN network utilizing the suggested optimization technique known as the Tunicate Swarm
Grey Wolf Optimization (TSGWO) method. With a maximum average residual energy of 2.161 J, a maximum link
lifetime of 0.075 s, a maximum PDR of 96.38%, and a maximum throughput of 429.49 Kbps, the suggested
TSGWO performed better than alternative techniques.
[23]
Creates an energy-efficient path planning technique
that is optimized to increase the network's connection and lifespan. Stable election algorithms (SEA), a novel
heuristic clustering technique, is presented to reduce the amount of information exchanged among sensor
nodes and avoid frequent cluster head rotation. When compared to current routing techniques, it was
successful in extending the network lifetime by up to 66%.
[24]
Create an energy-efficient routing protocol for
Internet of Things applications based on wireless sensor networks that are unfair in networks with a lot of
traffic. Three factors like lifetime, reliability, and traffic intensity at the next-hop node are taken into account by
the suggested protocol while choosing the best course of action. NS-2 has been used for rigorous simulation.
According to the results, the suggested protocol outperforms existing protocols in terms of energy conservation,
packet delivery ratio, end-to-end latency, and network longevity.
[25]
Presenting CBR-ICWSN, an IoT enabled
cluster-based routing (CBR) protocol for ICWSN. For the best path selection, the CBR-ICWSN approach uses a
routing process based on oppositional artificial bee colonies (OABCs). In terms of network longevity and energy
efficiency, the CBR-ICWSN methodology has demonstrated superior performance in experiments compared to
the other approaches.
3. Proposed methodology
This section outlines the design and operational workflow of the proposed energy-efficient and intelligent
routing framework for IoT-enabled WSNs. The system is structured into multiple phases that include network
initialization, clustering, intelligent CH selection, optimized internal routing, and data communication. A
detailed flow chart of the proposed methodology is illustrated in the Fig. 2.
Fig. 2: Flow of steps of proposed methodology.
3.1. Initialization phase
The network initialization phase lays the groundwork for simulating the proposed IoT-enabled WSN
architecture. The simulation is implemented using MATLAB 2018 and configure with Fig. 3 with a set of
predefined parameters representing the energy and communication characteristics of the sensor nodes. These
include energy consumption models, packet sizes, optimal cluster head probability, and the MAC protocol used
for medium access. Table 1 presents the simulation settings:
Table 1: Simulation parameters.
Parameter
Value
Network Area Size
100 100 m
Initial Energy
0.5 J
Number of Rounds
6000
Electronics energy
50 nJ/bit
Free space amplifier
10 pJ/bit/m²
Multi-path amplifier
0.0013
Data aggregation energy
5 nJ/bit
Threshold distance
87.7 m
Packet Length
4000 bits
Control Packet Length
200 bits
Optimal CH Probability (p)
0.05
MAC Protocol
IEEE 802.15.4
dynamics throughout all phases.
Fig. 3:
In this simulation, sensor nodes are randomly deployed within the 100m × 100m field, mimicking unstructured,
terrain-dependent real-world IoT deployments. Each node is considered heterogeneous, representing various
IoT sensors measuring attributes like temperature, humidity, light, soil moisture, or gas levelscommonly
found in smart agriculture, industrial safety, and environmental monitoring. A Base Station (BS) or Sink is
strategically place either within the network boundary or at its periphery, depending on the application
requirements. The BS placement is crucial, as it directly affects the energy expenditure of cluster heads during
data transmission, particularly in the CH-to-Sink communication phase. Together, these elements establish a
realistic, scalable simulation environment reflecting the complexities of modern IoT-based WSN deployments.
3.2 Cluster formation
Clustering is a crucial pre-processing step to minimize network-wide energy consumption and facilitate
hierarchical data aggregation. A Hybrid Clustering Algorithm, combining Fuzzy C-Means (FCM) and K-Means,
[26]
is employed to optimize cluster formation, where FCM enables soft membership for improved flexibility, and K-
Means ensures crisp clustering for spatial balance.
[27]
The expected number of clusters is dynamically estimated
based on IoT metrics such as node density, residual energy levels, and traffic characteristics like sensor
reporting intervals, allowing clusters to adapt to real-time network load and energy distribution. Additionally,
a Fuzzy Node Assignment strategy assigns nodes to clusters based on their proximity to CH candidates and fuzzy
membership values, ensuring balanced spatial coverage, preventing cluster overlap, and evenly distributing
communication responsibilities, which is vital for scalable IoT systems.
3.3. CH selection using Mother-Inspired Adaptive Optimization (MIAO)
Efficient CH selection is vital for maintaining the longevity and performance of a WSN.
[28]
The proposed system
employs MIAO, a bio-inspired metaheuristic algorithm that mimics maternal decision-making strategies to
enhance the selection process. This algorithm evolves CH candidates iteratively by integrating adaptive learning
and dynamic feedback mechanisms, allowing it to respond effectively to real-time network conditions.
3.3.1. Mother-Inspired Adaptive Optimization (MIAO)
MIAO is rooted in the intuitive and adaptive nature of maternal decision-making, where survival and optimal
resource distribution are prioritized.
[29]
The algorithm continuously refines CH selection through iterative
learning, ensuring that only the most suitable nodes are chosen in each round. The adaptive nature of MIAO
allows it to accommodate dynamic network conditions, mitigating challenges such as energy depletion, traffic
fluctuations, and node failures.
3.3.2. Multi-objective fitness function
To achieve optimal CH selection, the system employs a multi-objective fitness function, which evaluates CH
candidates based on multiple performance metrics. This ensures balanced decision-making, preventing
premature energy depletion while optimizing routing efficiency.
• Residual Energy: CHs must possess sufficient residual energy to sustain operations throughout multiple
communication rounds. Selecting high-energy nodes prevents early depletion, reducing the frequency of
re-clustering and prolonging network lifespan.
• Energy Consumption: The algorithm minimizes total power usage by optimizing both transmission and
reception processes. This ensures that network-wide energy utilization remains efficient, preventing
excessive resource wastage.
• Distance to BS: The proximity of a CH to the Base Station (BS) plays a crucial role in efficient data
transmission. Nodes closer to the BS are preferred to reduce long-range communication overhead, thereby
conserving energy and improving network throughput.
• Delay and Latency: Reducing communication delay is essential for real-time IoT applications, where data
must be delivered promptly. MIAO ensures that selected CHs maintain minimal latency to support time-
sensitive operations.
• Load Balancing: Unequal cluster distribution can lead to bottlenecks and congestion. The algorithm actively
prevents any single node or cluster from becoming overburdened by evenly distributing workload,
ensuring sustainable network operations.
• Communication Quality: Reliable data transmission is critical for maintaining network integrity. The
system assesses link reliability and signal drop rates to ensure uninterrupted connectivity between CHs
and the BS.
• Signal-to-Noise Ratio (SNR): High SNR values indicate stronger, clearer communication links with minimal
interference. The selection algorithm prioritizes nodes with superior SNR to enhance data transmission
quality and reduce errors.
By integrating these multi-objective criteria, the MIAO-based CH selection approach optimizes resource
utilization, extends network lifespan, and improves WSN performance under diverse IoT workloads. The
adaptive nature of this method ensures resilience against dynamic changes, supporting fault tolerance and
reliable communication in large-scale deployments.
3.4 Routing selection using POA
After CHs are selected, a routing backbone is established for inter-cluster communication by constructing a
virtual graph where vertices represent CHs and the BS, and edges define possible communication paths based
on distance and energy cost. The POA
is a heuristic algorithm
inspired by pelicans' dynamic foraging behavior,
is applied to determine the most energy-efficient and shortest multi-hop paths from CHs to the Sink, adapting
to the dynamic topology of WSNs for global optimization of routing paths.
[30,31]
POA also considers dynamic IoT
workloads and sensor reporting rates, enabling real-time adjustments to traffic patterns and node failures.
Additionally, the routing table is updated in each communication round based on current energy levels, network
topology, and traffic conditions, ensuring fault tolerance and adaptability in large-scale IoT networks.
3.5 Communication phase
This phase describes the actual transmission of data after CH selection and route optimization:
Intra-Cluster Communication: Member sensor nodes collect environmental data and send it to their respective
CHs. Data fusion algorithms (e.g., averaging, min-max, or threshold-based aggregation) are used at CHs to reduce
redundant information and minimize data payload size.
Inter-Cluster (CH-to-Sink) Communication: The aggregated data is transmitted from CHs to the Sink via the
multi-hop routes identified by POA. This ensures energy-efficient and delay-tolerant communication, especially
in sparse networks or when the BS is located far from the sensing region.
IoT Suitability: The communication architecture supports high data reliability and low power consumption,
which are essential for continuous monitoring in smart cities, industrial IoT systems, and environmental
applications.
3.6 Performance evaluation
Performance evaluation serves as a critical component in validating the effectiveness, scalability, and real-world
applicability of the proposed cluster-based and optimization-driven internal routing model in IoT-enabled
sensor networks. This section involves running detailed simulations across multiple operational rounds to
analyze how well the algorithm performs under different network conditions and workloads. The evaluation
metrics provide a multidimensional performance profile of the proposed model. Each performance metric is
selected to reflect the key quality-of-service (QoS) requirements for IoT applications, such as energy efficiency,
reliability, and latency.
3.6.1 Multi-round simulation
The simulation is executing over numerous operational rounds to capture the dynamic behavior of the network
as nodes consume energy and potentially die over time. Each round simulates the process of cluster formation,
CH selection via the MIAO algorithm, internal CH-to-Sink routing using the POA, and the communication phase.
Multi-round evaluation ensures that the proposed algorithm is assessed not just in initial conditions but
ding of its long-term
sustainability and robustness in real-time IoT scenarios.
3.6.2 Evaluation metrics
3.6.2.1 Energy consumption
This metric quantifies the amount of energy consumed during each round for data transmission, reception,
aggregation, and routing. In WSNs, energy is a finite resourcethus, minimizing energy consumption is critical
to prolonging the operational period of the network. The proposed system is expected to show reduced energy
usage due to optimal CH selection and energy-aware routing paths. Energy consumption is analyzed for Sensor-
to-CH communication, CH-to-CH multi-hop routing, and CH-to-Sink transmission.
3.6.2.2 Network lifetime
Network lifetime refers to the duration (in terms of rounds) until the first node dies (FND), half of the nodes die
(HND), and the last node dies (LND). This metric reflects how well the algorithm balances the energy load across
the network. A longer network lifetime indicates better energy management, which is especially important for
IoT applications deployed in remote or hazardous environments where manual battery replacement is not
feasible.
3.6.2.3 Packet Delivery Ratio (PDR)
PDR is defined as the ratio of the number of successfully received data packets at the sink to the total number
of packets sent by the sensor nodes. This metric is essential to assess the reliability of the network. A high PDR
indicates that the routing algorithm can maintain stable and error-free communication even under dynamic
conditions, such as node failures or varying energy levels. It directly correlates with the effectiveness of
clustering and route optimization in ensuring end-to-end data integrity.
3.6.2.4 End-to-end delay
This metric measures the average time it takes for a data packet to travel from the sensor node to the sink,
including delays introduced during cluster formation, CH selection, route discovery, queuing, and transmission.
In time-sensitive IoT applications such as emergency monitoring or industrial automation, low latency is crucial.
The POA-based routing mechanism aims to reduce this delay by selecting paths that are not only energy-efficient
but also shorter and less congested.
4. Results and discussions
This section presents the visual and quantitative outcomes of the proposed intelligent routing framework,
focusing on its performance in IoT-enabled Wireless Sensor Networks (WSNs). The results are analyze based
on key performance indicators such as routing efficiency, energy consumption, network lifetime, end-to-end
delay, and packet delivery ratio. Comparative visualizations illustrate the routing paths optimized by POA,
highlighting its effectiveness in minimizing transmission distances and balancing energy usage among CHs.
Additionally, delay patterns and energy trends are discussing to evaluate the system's responsiveness and
sustainability under dynamic network conditions. The insights gained underscore the critical role of intelligent
routing mechanisms in maintaining Quality of Service (QoS) across large-scale, heterogeneous IoT deployments.
Fig. 4:
Fig. 5:
Fig. 6:
Fig. 7:
Fig. 8:
5. Conclusion and future scope
This study integrates an intelligent routing mechanism with a cluster-based architecture to provide a
comprehensive method for optimizing internal routing in IoT-enabled wireless sensor networks. For effective
cluster formation, the suggested system uses a hybrid clustering technique. For balanced and energy-conscious
CH selection, it uses the MIAO algorithm. This Study summarizing the main contributions and suggesting
potential real-world Based on this clustered framework, the Pelican Optimization algorithm is presented for
CH-to-Sink communication with the goals of lowering end-to-end latency, maximizing energy efficiency, and
improving overall routing effectiveness. The simulation results confirm that the POA-based routing strategy is
effective in extending network lifetime and ensuring dependable data transmission, which satisfies the crucial
QoS requirements in Internet of Things applications like smart agriculture, industrial monitoring, and urban
sensing. In addition to emphasizing the value of intelligent and adaptive routing in limited WSN settings, the
study shows how bio-inspired optimization methods can greatly enhance intra-network communication. Given
the dynamic and diverse nature of contemporary IoT-based WSNs, the results highlight the value of multi-
objective optimization. Future research will concentrate on implementing the suggested system in real-time
using IoT hardware platforms like Arduino and Raspberry Pi, allowing for useful validation in real-world
settings. Scalability, programmability, and data analytics capabilities will also be improved through integration
with cloud-based IoT platforms and Software-Defined Networking (SDN). To further support extremely
dynamic scenarios and lessen bottlenecks in large-scale deployments, the use of mobile sinks will also be
examined. By making these improvements, the suggested architecture should become more feasible for use in
next-generation IoT-driven wireless sensing applications.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
The datasets generated and/or analyzed during the current study that support the findings are available from
the corresponding author upon reasonable request.
Conflict of Interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,
grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical
content, experimental implementation, results, and interpretations were independently developed and verified
by the authors.
Supporting Information
Not applicable.
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