# A Hybrid Biologically-Inspired Optimization Algorithm for Data Gathering in IoT Sensor Networks

Gérard Kponhinto, Sondès Khemiri-Kallel, Ado Adamou Abba Ari, Abdelhak

Mourad Gueroui, Ousmane Thiare, Junseok Hwang

► To cite this version:

Gérard Kponhinto, Sondès Khemiri-Kallel, Ado Adamou Abba Ari, Abdelhak Mourad Gueroui, Ousmane Thiare, et al.. A Hybrid Biologically-Inspired Optimization Algorithm for Data Gathering in IoT Sensor Networks. Journal of Network and Systems Management, 2025, 33 (2), pp.1-32. 10.1007/s10922-025-09899-z. http://hal-04895836

HAL Id: hal-04895836

https://universite-paris-saclay.hal.science/hal-04895836v1

Submitted on 27 Jan 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# A Hybrid Biologically-Inspired Optimization Algorithm for Data Gathering in IoT Sensor Networks

Gérard Kponhinto¹,² ID · Sondès Khemiri-Kallel¹ ID ·

Ado Adamou Abba Ari¹,³,⁴ ID · Abdelhak Mourad Gueroui¹ · Ousmane Thiare² ID ·
Junseok Hwang⁵

Received: 29 November 2023 / Accepted: 2 January 2025

© The Author(s) 2025

## Abstract

The wireless sensor networks are autonomous Ad hoc Networks that form the backbone of IoT technology. Their vital role is to collect data from their physical environments and transfer these data to the Base Station (BS). This task implies high energy consumption when a suitable data routing policy is not applied. Then, the data collection method determines the efficiency of WSNs. Moreover, the IoT-based applications that require the mobility of the IoT sensor nodes introduce a new challenge regarding network connectivity. The IoT sensor node's mobility leads to regular changes in the network topology, resulting in high packet loss during communication. The management of network energy use in order to prolong the battery life of IoT sensor nodes is a crucial issue, as IoT sensor nodes possess limited battery capacity. This paper addresses these issues by proposing an efficient Hybrid Biologically-Inspired Optimization Algorithm, named HBIP, for Data Gathering in IoT Sensor Networks. The flagship idea of the HBIP algorithm is to incorporate the swarming stage of the Bacterial Foraging Optimization (BFO) into the Artificial Bee Colony (ABC) Optimization's exploitation phase. Simulation results show that the HBIP protocol outperforms the LEACH protocol and ABC and BFO metaheuristics regarding network energy consumption, lifetime, and data packets received at the BS. In practice, we demonstrated that the HBIP protocol increases the data collection by 84.40% over LEACH, 19.43% over BFO, and 7.26% over ABC in the network scenario considered. Further on, we extend the study of the HBIP algorithm on Mobile IoT Sensor Networks by proposing a Multi-Objective Optimization-based Data Gathering Protocol (MOO-DGP). The MOO-DGP protocol employs the HBIP algorithm to determine the best solution that balances Velocity, Cluster Stability, Link Quality, and Energy Consumption. An extensive evaluation of the MOO-DGP protocol shows it outperforms the existing Mobility-Based Clustering (MBC).

<footnote>Sondès Khemiri-Kallel, Ado Adamou Abba Ari, Abdelhak Mourad Gueroui, Ousmane Thiare, and Junseok Hwang have contributed equally to this work.</footnote>

<footnote>Extended author information available on the last page of the article</footnote>
protocol regarding effective data packet delivery rate, average control overhead, network lifespan, and energy consumption.

**Keywords** Data Gathering · IoT · Mobile Wireless Sensors Networks (MWSN) · Bio-inspired algorithm

# 1 Introduction

Significant progress has been made in the past decade regarding investigating and advancing IoT sensor networks. Wireless Sensor Networks are increasingly more prevalent in several domains of human activity, including agriculture, livestock management, healthcare, military operations, transportation systems, industrial settings, and environmental monitoring. Data gathering is an essential task that has drawn significant interest from researchers and several experts. The current method of data collection uses stationary IoT sensor nodes that gather data and transfer it to the base station via a routing protocol [1, 2]. The Cluster-tree model, which has been developed for the purpose of data gathering, combines the benefits of cluster and tree-based approaches. Clustering is well recognized as a technique that facilitates load balancing and enhances energy efficiency. Its primary use is in achieving energy-efficient data transmission, which lowers energy usage and improves the network's lifespan [3]. The sensors are organized into clusters in the clustering process, each managed by a specific node called the Cluster Head (CH). The primary responsibility of the cluster head is to govern the data transmission operations of the cluster's nodes and the merging of the collected data to convey to the base station. Considering their role in the network, the cluster head expends more energy throughout the process of transmitting data. Thus, one of the main challenges is to find the appropriate Cluster Heads throughout the sensor networks [1] that minimize the network energy consumption. The challenges of Cluster Head choice and cluster building are well recognized as being Non-Polynimial-hard (NP-hard) problems as demonstrated in Ref. [4]; the conventional approaches fail to find an effective and optimal solution as the size of the network increases. In this context, metaheuristic-based techniques offer a near-optimal solution and demonstrate great performance [5]. Metaheuristics allow for exploration (global search) and exploitation (local search). Exploration involves the ability to explore various areas of the search space, whereas exploitation involves the ability to find high-quality solutions within those areas. Many metaheuristic algorithms, including Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Ant Colony Optimization (ACO), frequently become stuck in local optima because they struggle to strike the correct balance between exploration and exploitation. Metaheuristic algorithms are, therefore, either strong for exploration and weak for exploitation, or strong for exploitation and weak exploration. Thus, we may deduce that the effectiveness of a metaheuristic mostly relies on the balance it provides between the exploration and exploitation phases. From the literature and to the best of our knowledge, there is no metaheuristic that offers a good exploration

<figure><img src="image_2.png" /></figure>
and exploitation search at the same time. The shortcomings in these algorithms lead to a sub-optimal solution when they are used to solve the problems of cluster head election and cluster formation. This has led researchers to search for increasingly optimal algorithms, and one of the most promising solutions is hybridization to make a trade-off between exploration and exploitation. The Artificial Bee Colony (ABC) is a metaheuristic with strong exploration (global search) abilities. It gave better performance for clustering [5]. Recently, biologically inspired metaheuristic algorithms like Bacterial Foraging Optimization (BFO) were applied in addressing routing and clustering challenges in wireless sensor networks [1]. The Bacterial Foraging Optimization (BFO) algorithm is recognized for its robust exploitation (local search) capabilities, which is achieved by its swarming process that imitates the foraging activity of bacteria. This feature enhances its efficacy in refining solutions and reaching local optima. The Artificial Bee Colony (ABC), in addition to the aforementioned issues for metatheuristics, suffers from a slow convergence problem. To get around these problems and get better performance than the original ABC for data collecting in IoT Sensor Networks, this work aims to improve the local search of the Artificial Bee Colony (ABC) by using the powerful exploitation ability of BFO to find better solutions and speed up convergence. In light of this, we proposed in Ref. [6] a Hybrid Biologically-Inspired metaheuristic algorithm (HBIP) to optimize the cluster head selection and the construction of clusters for data collection in wireless sensor networks. This approach's primary objective is to minimize individual nodes' energy consumption and prolong the network lifetime. Swarm intelligence approaches, such as the Artificial Bee Colony and Bacterial Foraging Optimization algorithms, are used in the algorithm. This article is an extended version of the work realized in Ref. [6]. The innovative aspects can be summarized as follows:

(a) Design of a Multi-Objective Optimization function for energy-efficient mobile IoT sensor network clustering as well as the optimization problem solving using HBIP metaheuristic.

(b) Proposal of a clustering algorithm for Multi-Objective Optimization-based Data Gathering Protocol (MOO-DGP).

(c) Consideration of the CC2538 RF radio transceiver model [7], a realistic low-power radio model based on the IEEE802.15.4 standard, to calculate the energy dissipation of IoT nodes.

(d) Extensive evaluation of the MOO-DGP protocol to explore its effectiveness on large-scale networks and various network densities.

(e) Comparative analysis of the MOO-DGP protocol with the existing Mobility-Based Clustering (MBC) protocol by considering different performance metrics.

## 2 Related Works

A wide number of data collection protocols for WSNs have been presented in recent years. The protocols outlined in the existing literature cover static and mobile sensor networks. Many protocols were proposed to enable mobility in wireless sensor
Networks. In Ref. [8], the LEACH-Mobile introduces mobility in the traditional LEACH protocol. The flag idea in that proposition is the membership declaration of IoT sensor nodes, where each node must confirm its entry in a specific cluster during the steady phase. Moreover, if a node doesn't get a request data message from its cluster leader during two successive frames in one round, it is considered out of the cluster. It must send a join request message to avoid packet loss to connect with a new cluster. Thus, the LEACH-Mobile trades the packet delivery ratio against the control message overhead. CBR-Mobile was proposed in Ref. [9]. The CBR-Mobile introduces an adaptive TDMA schedule and round-free schedule wherein a cluster head gets data from its members during the allotted timeslot and from the nodes that enter the cluster when some free timeslot is available. Due to its adaptive TDMA schedule, the CBR-Mobile has considerably reduced the loss of packets in the network in comparison to LEACH-Mobile. Nevertheless, the overhead due to control messages is higher than LEACH-Mobile's. LEACH-Mobile and CBR-Mobile advocate for increased packet delivery success while communication overhead is higher. This can be costly in terms of energy usage and network longevity. In Ref. [10], the MBC protocol is proposed to address these problems by considering the estimated link connection time to construct a reliable path between the nodes and their cluster head. In the MBC protocol, the IoT sensor nodes join a cluster by considering the node's link connection time, remaining energy, and degree. The protocol outperforms LEACH-Mobile and CBR regarding successful data delivery, control message overhead, and energy utilization. However, the MBC protocol suffers frequent link disconnection in highly mobile environments. Moreover, the drawback of this protocol is that the overhead in the clustering process is still high. In these classical-based techniques, CHs are not equally distributed throughout the network. Also, the cluster heads' total energy use isn't balanced under the context of node mobility. In these approaches, the CHs are chosen randomly.

Metaheuristic algorithms have been used in the literature to select efficient cluster heads under cluster-tree data collection context. Researchers in Ref. [5] proposed an ABC metaheuristic algorithm for energy-efficient clustering named ABC-SD. The proposed bio-inspired algorithm is used in clustering and routing and involves a compromise between the nodes' energy and the reliability of the communication link. The proposed ABC-SD protocol relies on the ABC optimization algorithm's searching capability in the clustering phase. Simulations demonstrate that it performs well in terms of network lifetime and packet delivery ratio. In Ref. [11], the authors studied the performances of ABC optimization on the LEACH algorithm in various WSNs. In Ref. [12], another energy-efficient artificial bee colony is designed for the Internet of Things. In the first step, the ABC algorithm is utilized to select the near-optimal cluster heads. The method covers many variables, including the remaining energy of the devices, the number of neighboring devices, and the distance between devices and the sink, as well as the Euclidean distance between each device and its respective neighbors. The second phase of the process focuses on clustering, wherein the Euclidean distance between the cluster head and its members and the data volume produced by the clusters are considered. In Ref. [13], a Bacterial Foraging Optimization (BFO) algorithm has been proposed for clustering and routing in wireless sensor networks. In this approach, both cluster head selection

<figure><img src="image_3.png" /></figure>
and routing algorithms use a novel fitness function that considers the energy and distance of nodes. In Ref. [14] Bacterial Foraging Optimization is used to enhance the cluster construction in wireless sensor network by minimizing unclustered nodes. It is shown that BFAO outperforms Particle Swarm Optimization (PSO) and LEACH in terms of the network lifespan and throughput. Authors in Ref. [15] proposed a Hybrid HACO-ABC-CHs for cluster head selection in IoT. The HACO-ABC-CHs proposed algorithm effectively selects Cluster Head by finding a trade-off between ACO's exploitation capacity and ABC's exploration abilities. The cluster head is chosen depending on factors including delay, energy, distance, temperature, and load. The trend in recent works is to hybridize these algorithms to achieve better performance. This approach adds more time and computational complexity to the algorithms, which is not good for the systems that use them. It's, therefore, important to genuinely decide on which ones to combine to keep a trade-off between the solution and the system's complexity. Table 1 presents a qualitative review of hybrid schemes proposed for various WSN issues in some recent research works. Most of them undergo cascade hybridization, which adds computational and time complexity. So, to take full advantage of the hybrid metaheuristics algorithms, it is preferable to improve either their local or global search abilities or both. This allows to reach the near-optimal solution while keeping a low computational and time complexity, which is good for systems and applications based on IoT sensor networks.

# 3 Background

## 3.1 Artificial Bee Colony Optimization (ABC)

The ABC [24] is a metaheuristic approach that draws inspiration from the foraging activity of honeybees. The ABC algorithm emulates bees' foraging behavior in their food search efforts. Artificial bees are organized into colonies consisting of the following three different groups: employed bees, onlookers bees, and scout bees. The employed bees are the group of bees that go in search of food. They are linked to a certain food source that they are currently consuming or are "working" at. They keep track of that specific source, including its distance and direction from the nest, as well as its profitability. They communicate this information to others waiting in the hive (Onlookers and Scout bees) with a given likelihood. The group of unemployed bees is formed by the onlooker and scout bees. They are constantly seeking a food source to exploit. Onlookers stay in the nest and rely on employed bees, which provide information about food sources. Scouts explore the area around the nest to find new sources of food. It is worth noting that the number of employed bees is equal to the number of onlookers bees inside these colonies. The foraging behavior of bees requires a search for an optimal food source. The position of the food source inside the search space is regarded as the solution to the optimization issue, while the fitness of the solution represents the profitability of the food source.

Let $\mathcal{B}$ be a swarm of bees having the size $\mathcal{N}$ where each bee is associated with a food source of position $\mathcal{B}_i$, ($i = 1, 2, ..., \mathcal{N}$). The position of the $i^{th}$ food source represented as $\mathcal{B}_i = [x_{i1}, x_{i2}, ..., x_{iD}]^T$ is a vector in $\mathcal{D}$ dimensions, consisting of real
<table><caption>**Table 1** Qualitative Review of Some Recent Hybrid Metaheuristics Solutions</caption><thead><tr><th>Ref. no</th><th>References</th><th>Objective</th><th>Methodology</th><th>Avantages</th><th>Disadvantages</th></tr></thead><tbody><tr><td>1</td><td>[16]</td><td>Minimize energy consumption in the network</td><td>HSA and PSO are hybridized for optimal Cluster Head Selection</td><td>Energy-Efficiency</td><td>High time and computational complexity</td></tr><tr><td>2</td><td>[17]</td><td>Reduce the energy consumption for WSN</td><td>ABC is used for the clustering process and ACO for data routing to the BS</td><td>Cluster stability<br>Good Throughput</td><td>High time and computational complexity</td></tr><tr><td>3</td><td>[18]</td><td>Maximize the lifetime for WSN</td><td>ACO and PSO are concatenated for data routing</td><td>Network Lifetime</td><td>High time and computational complexity</td></tr><tr><td>4</td><td>[19]</td><td>Prolong the lifetime for WSN</td><td>PSO and FA are chained for data routing</td><td>Network lifetime</td><td>High time and computational complexity</td></tr><tr><td>5</td><td>[20]</td><td>Minimize energy consumption in the network</td><td>FA and PSO are hybridized for optimal Cluster Head Selection</td><td>Good Throughput</td><td>High time and computational complexity</td></tr><tr><td>6</td><td>[21]</td><td>Optimize Cluster Head Selection</td><td>Dragonfly and Firefly are hybridized for optimal Cluster Head Selection</td><td>Network energy</td><td>High time and computational complexity</td></tr><tr><td>7</td><td>[22]</td><td>Minimize energy consumption and extend the network lifetime</td><td>Combining the crowd factor in AFSA and the pseudo-random route selection in ACO</td><td>Routing convergence<br>Network lifetime</td><td>High time and computational complexity</td></tr><tr><td>8</td><td>[23]</td><td>Improve Connectivity and Coverage of WSN</td><td>ABC improved with Search Algorithm pheromone sensitivity model</td><td>Good Throughput</td><td>High time and computational complexity</td></tr><tr><td>9</td><td>HBIP</td><td>Data Collection in IoT Sensor Networks</td><td>BFO Swarm Step integrated within the exploitation and exploration phase of ABC</td><td>Maximized Data Collection Energy-Efficiency</td><td>—</td></tr></tbody></table>

<figure><img src="image_4.png" /></figure>
components, i.e., $\mathcal{B}_i \in \mathbb{R}^D$. Let also $\mathcal{B}_i^{min}$ and $\mathcal{B}_i^{max}$ be, respectively, the vectors of the lower and upper limits of each dimension of the position vector $\mathcal{B}_i$ in the swarm $\mathcal{B}$.

The respective roles of each group of bees are delineated in the subsequent sections as follows:

*Employed bees* are arbitrarily linked to the generated food sources. During this stage, each employed bee arbitrarily hunts for a fresh food source location $\theta_i$ near its present food source according to Eq. 1:

$$ \theta_{ij} = \mathcal{B}_{ij} + \phi_{ij} * (\mathcal{B}_{ij} - \mathcal{B}_{kj}) \quad (1) $$

where $\phi_{ij}$ is a random value in the range $[-1, 1]$, $\mathcal{B}_k$ represents a surrounding solution, $k \neq i$, $j \in \{1, 2, ..., D\}$ is the dimension index chosen randomly.

Upon discovering the rich source of food, they exploit and calculate the novel fitness value following the Eq. 2:

$$ fitnessFunc(\mathcal{B}_i) = \begin{cases} \frac{1}{1+g(\mathcal{B}_i)}, & \text{if } g(\mathcal{B}_i) \ge 0 \\ 1+ g(\mathcal{B}_i), & \text{if } g(\mathcal{B}_i) < 0 \end{cases} \quad (2) $$

where $i$ is the $i^{th}$ food source and $g(\mathcal{B}_i)$ represents the value of the fitness function $g$ determined at the position $\mathcal{B}_i = [x_{i1}, x_{i2}, ..., x_{iD}]^T$.

In the event that the newly calculated fitness value (see Eq. 2) is higher than the prior value, the employed bee will migrate to the new position of the food source and disregard the previous position. Otherwise, it stays in its previous position. The Onlooker bees in the beehive are informed about the nectar quantity through the performance of a waggle dance. The provided data includes the quantity of nectar, the distance to the beehive, and the orientation of the food source.

Onlooker bees wait for the dancing area for the food. They select better food sources (the best fitness function value) from those discovered by the employed bees according to Eq. 3. An abundance of nectar will tempt more bees to arrive, and many more will follow. After all the onlooker bees arrive at their corresponding food sources, they will make the exploitation the same manner as the employed bees.

$$ p_i = \frac{fitnessFunc(\mathcal{B}_i)}{\sum_{n=1}^{N} fitnessFunc(\mathcal{B}_n)} \quad (3) $$

where $p_i$ is the probability an *Onlooker bees* chooses the $i^{th}$ food sources discovered.

*Scout bees*: the employed bees that can't improve the food source through the limited time called *limit* abandon and become *Scout bees* and randomly move throughout the searching space, seeking new food sources to explore and calculate their fitness function. Let $\mathcal{B}_i$ be an abandoned solution, then a new solution determined randomly by Eq. 4 would replace $\mathcal{B}_i$:

$$ \mathcal{B}_{ij}^{new} = \mathcal{B}_{j}^{min} + rand(0, 1) * (\mathcal{B}_{j}^{max} - \mathcal{B}_{j}^{min}) \quad (4) $$

where $\mathcal{B}_j^{min}$ and $\mathcal{B}_j^{max}$ are respectively the lower limit and upper limit on the $j^{th}$ dimension.
## 3.2 Bacterial Foraging Optimization (BFO)

The Bacterial Foraging Optimization (BFO) is classified under the swarm intelligence algorithm family. The BFO imitates the foraging behavior of *Escherichia coli* bacteria in the human gut. The *E. coli* bacteria move and split thanks to their flagella. During the foraging, an E. coli bacterium executes only two modes of movement: swimming and tumbling. The *E. coli* bacterium alternates between these two modes of movement during their whole lifespan. When bacteria find a nutrient-rich environment, they keep swimming (and reproduce due to a favorable environment) and release cell-to-cell attractant signals to indicate that others should come in the same environment. On the other hand, when bacteria discover a noxious substance in their current direction, they tumble and head in a new direction (others died and were eliminated due to noxious substance). The group of bacteria that detect the noxious environment releases cell-to-cell repellant signals that tell other bacteria not to enter that environment. It's the swarming effect. The swarming effect gives the BFO a strong local search capability. The Bacterial Foraging Optimization presents a similar behavior. The bacterium is a solution in the search space, and the amount of nutrients determines the fitness value [25]. To model the foraging behavior of *E. coli* bacteria, four operations were identified: *Chemotaxis*, *swarming*, *reproduction*, and *elimination-dispersal*.

Let us suppose we want to determine, using the BFO algorithm, the minimum of a fitness function $Z(Θ)$ where $Θ = [Θ_1, Θ_2, ..., Θ_D]^T$ is a vector in $D$ dimensions, consisting of real components, i.e., $Θ \in \mathbb{R}^D$. Following the BFO idea, $Θ$ represents the location of a bacterium in the search space. Let $\mathcal{N}$ represent the size of the population of bacteria; $m, n$, and $l$, denote the indexes of the chemotaxis event, reproduction event, and elimination-dispersal event, respectively. Let $Θ^i(m, n, l)$ be the location of the $i^{th}$ bacterium at $m^{th}$ chemotaxis event, $n^{th}$ reproduction event, and $l^{th}$ elimination-dispersal event. Finally, let $P(m, n, l) = \{Θ^i(m, n, l), i = 1, 2, ..., N\}$ represents the set of positions of bacteria in the swarm. The description of the four operations of bacterial foraging behavior are as follows:

**Chemotaxis:** In search of nutrients, bacterium make two movements via their flagella during their entire life. A bacterium swims in the nutrient-rich environment or tumbles in the nutrient-poor environment. The chemotactic step is a tumble followed by a tumble or a tumble followed by a swim. $Θ^i(m, n, l)$ being the location of the $i^{th}$ bacterium, $Θ^i(m, n, l) \in \mathbb{R}^D$, where $(i = 1, 2, ..., N)$, let $M(i) > 0$ denote the chemotaxis step's unit. The equation that characterizes a bacterium's tumbling or swimming motion is expressed as follows (see Eq. 5).

$$ \Theta^i(m+1, n, l) = \Theta^i(m, n, l) + M(i) * \frac{\Delta(i)}{\sqrt{\Delta(i)^T \Delta(i)}} \quad (5) $$

where $\Delta(i) \in \mathbb{R}^D$ is an arbitrary generated vector in the range of $[-1, 1]$.

**Swarming:** The bacteria *E. coli* engage in social interaction. Bacteria traveling in a high-nutrient space emit an attractant signal, allowing other bacteria to come closer, whereas the cell emits a repellent signal, causing other bacteria to flee. The

<figure><img src="image_5.png" /></figure>
mathematical modeling of the cell-to-cell attractant-repellant to replicate swarming behavior for the $i^{th}$ bacterium is as follows (see Eq. 6):

$$ Z_{cc}^{i}(\Theta, \Theta^{i}(m, n, l)) = \sum_{i=1}^{N} \left[ -d_{at} \times \exp\left(-\omega_{at} \sum_{r=1}^{D} (\Theta_{r} - \Theta_{r}^{i})^{2}\right) \right] + \sum_{i=1}^{N} \left[ +h_{rep} \times \exp\left(-\omega_{rep} \sum_{r=1}^{D} (\Theta_{r} - \Theta_{r}^{i})^{2}\right) \right] \quad (6) $$

where $d_{at}$ and $\omega_{at}$ are the depth and the width of attractant signal released by the cell, $h_{rep}$ and $\omega_{rep}$ are the height and the width of the repellant signal released by the cell. Thus, the cell-to-cell signaling for the swarm consisting of $N$ bacteria is given by the function $Z_{cc}(\Theta, P(m, n, l))$ as follows:

$$ Z_{cc}(\Theta, P(m, n, l)) = \sum_{i=1}^{N} Z_{cc}^{i}(\Theta, \Theta^{i}(m, n, l)) \quad (7) $$

The cell-to-cell attractant-repellant value $Z_{cc}(\Theta, P(m, n, l))$, mimicking the swarming effect, is added to the objective function to be minimized, to present time-varying.

Let $\tilde{Z}(i, m, n, l)$ be the cost at the position $\Theta^{i}(m, n, l)$ of the $i^{th}$ bacterium. $Z(i, m, n, l)$ is calculated by adding on the previous value $Z_{last}$ of $Z(i, m, n, l)$, the cell-to-cell attractant-repellant $Z_{cc}^{i}$ to simulate the swarming effect:

$$ Z(i, m, n, l) = Z(i, m, n, l) + Z_{cc}^{i}(\Theta, \Theta^{i}(m, n, l)) \quad (8) $$

When the fitness value obtained is better than the one at the last position, another chemotactic step of size $M(i)$ is taken, and this process is repeated until the maximum step $N_s$ (pre-determined) is reached. The parameters $d_{at}$, $\omega_{at}$, $h_{rep}$, and $\omega_{rep}$ are crucial factors that need to be carefully chosen for the attractant and repellent signals according to the problem at hand.

**Reproduction:** After the number of chemotaxis steps $N_c$ has been reached chemotaxis, the bacteria reproduce. The bacteria that have performed well throughout their lifetime may contribute to the next generation, while the bacteria with the lowest health will eventually die (higher fitness value). To keep the swarm size constant, the healthy bacteria will split into two. $Z_{health}^{i}$ expresses the amount of nutrients the $i^{th}$ bacterium got during his entire lifetime:

$$ Z_{health}^{i} = \sum_{n=1}^{N_c+1} Z(i, m, n, l) \quad (9) $$

The half of bacteria $\frac{N}{2}$ with the highest $Z_{health}$ die, and the remaining healthier bacteria split, the copies are put at the same location as their parent.

**Elimination-Dispersal:** Changes like local rise of temperature occur in the environment where bacteria might be murdered or dispersed to a newer location. To
replicate this occurrence in BFO, a few bacteria were discarded with a certain probability, $P_{ed}$, as well as new random samples were introduced over the search space.

# 4 Methodology

This section presents the Hybrid Bio-Inspired Optimization Algorithm for Data Gathering in static and mobile IoT Sensor Networks. Firstly, we describe the Hybrid Bio-Inspired Optimization for Data Gathering in Static IoT Sensor Networks. Secondly, the Multi-Objective Optimization-based Data Gathering Protocol (MOODGP) for mobile IoT Sensor Networks is introduced.

## 4.1 Hybrid Bio-Inspired Optimization for Data Gathering in Static IoT Sensor Networks

HBIP is a hybrid metaheuristic algorithm proposed in Ref. [6]. HBIP uses the hybridization of the ABC and the BFO's swarming behavior. The HBIP algorithm aims to correct the problem of slow convergence and trapping in the local optimum solution present in the original ABC algorithm. HBIP is successfully applied to the clustering technique for data collection in wireless sensor networks. The flagship idea behind the HBIP algorithm is to include the BFO's swarming approach into the exploitation phase of the ABC algorithm covered by the *Employed Bee* and *Onlooker Bee* phases to strengthen the local search. The data collection technique is implemented through the cluster-tree architecture depicted in Fig. 1.

A set of L IoT sensor nodes, denoted as $\mathcal{X} = \{x_1, x_2, ..., x_L\}$, is distributed arbitrary throughout an area of square field. Every node in the system is assigned a distinct identification number, denoted by $i \in [1, L] \cap \mathbb{N}$. The ability of nodes to connect with each other depends upon their transmission range. In addition, the nodes possess knowledge of the location of the base station. The energy spent by IoT sensor nodes during the data transfer and reception process is evaluated using the first-order radio model, as presented in Ref. [26]. The model calculates the energy consumption ($E_{TRX}$) required for transmitting k data packets over a distance r as follows (Eq. 10):

$$E_{TRX} = \begin{cases} k * (E_{electr} + E_{fs} * r^2), & r \le d_{thres} \\ k * (E_{electr} + E_{amp} * r^4), & r > d_{thres} \end{cases} \quad (10)$$

Meanwhile, receiving the same data packet requires an energy expenditure denoted as $E_{RX}$, as calculated in Eq. 11:

$$E_{RX} = r * E_{electr} \quad (11)$$

where $E_{electr}$ represents the electrical energy required for one-bit transmission or reception, $E_{fs}$ is the free space factor, $E_{amp}$ is the energy dissipated by the amplifier for data forwarding, and $d_{thres}$ represents the distance threshold and is computed as follows: (see Eq. 12):
<figure><img src="image_6.png" /><figcaption>Fig. 1 Cluster-Tree Data Collection Networks</figcaption></figure>

$$d_{thres} = \sqrt{\frac{E_{fs}}{E_{amp}}} \qquad (12)$$

### 4.1.1 Proposed Data Gathering Protocol in Static IoT Sensor Networks

The aim of our Data Gathering Scheme is to combine the ABC algorithm with the BFO's swarming behavior. This hybridization is meant to ease the network clustering, with the ultimate objective of lowering individual node energy usage. The hybrid approach, known as HBIP, combines the traditional characteristics of ABC and BFO swarming step algorithms. This scheme is executed within a centralized Base Station (BS) management system.

#### 4.1.1.1 Cluster-Tree-based Data Gathering Architecture

The architecture for data gathering in cluster-tree-based networks comprised a setup and a steady phase. The setup phase consists of the creation of clusters and the formation of a hierarchical tree structure. The steady phase comprises intra-cluster communication and inter-cluster communication. The cluster formation process is carried out by electing cluster heads (CH) and assigning normal IoT nodes to their corresponding cluster heads.
## Election of Cluster Heads (CH)

Upon network initialization, every node collects energy level information and distance from neighboring nodes. Every node transmits an advertising packet ($P_{ADV}$) to the network, which includes its unique identifier and location. Each node that captures the ($P_{ADV}$) packet calculates the distance from the source node and generates a data table to store the source's ID along with the corresponding distance calculated. The table is updated whenever a ($P_{ADV}$) is arrived. After completing their tasks, every node utilizes the flooding method to transmit their respective table, residual energy, and identification number to BS. Hence, depending on the remaining energy, the BS establishes the set $P_{CH}$ comprising the eligible cluster head (Eq. 13). Only nodes with enough remaining energy are considered suitable to serve as cluster heads. The value of the threshold $\epsilon_{thr}$ is determined by the remaining energy of the network's active nodes at the beginning of the communication round.

$$P_{CH} = \{x_i, i \in [1, L] \cap \mathbb{N} | E(x_i) \ge \epsilon_{thr}\} \quad (13)$$

where $i$ represents the identifier of the IoT sensor node $x_i$ and $E(x_i)$ represents the remaining energy of the IoT sensor node $x_i$.

## Formation of Clusters

The process of clustering begins with the creation of the set $P_{CH}$, which represents the eligible cluster heads. The HBIP algorithm is utilized to construct a set of $D$ clusters from the $L$ network's active nodes. The algorithm makes use of the population of bees to identify the cluster heads, where the bees navigate in a search environment of $D$-dimensions. The initial population $N$ of bees is determined by the number of eligible cluster heads, represented as $\#P_{CH}$. Each individual in the population is denoted as $B_i, i \in [1, N]$. $B_i$ consists of $D$ cluster heads, which are elected randomly. $B_i$ denotes the solution vector. Hence, each IoT sensor node denoted as $x_i, i \in [1, L] \cap \mathbb{N}$, performs the computation of the distance $d(x_i, CH_z)$. This distance is calculated between $x_i$ and every cluster heads $CH_z$ in $B_i, z \in [1, D] \cap \mathbb{N}$. Subsequently, $x_i$ is assigned to the cluster head $CH_z$ that minimizes the value of $d(x_i, CH_z)$ such as $d(x_i, CH_z) = \min_{z=1,2,...,D} \{d(x_i, CH_z)\}$. Once the cluster members have been constructed, the objective function is assessed. The HBIP algorithm is initiated to carry out the position update procedure. The optimal choice of CHs is determined based on the lowest fitness value. The HBIP algorithm is executed in order to output the list of cluster heads for the round. The algorithm for the formation of clusters, as proposed, is presented in Algorithm 1. Additionally, the HBIP algorithm is described in Algorithm 2 and the swarming algorithm in Algorithm 3.
**Algorithm 1 Formation of Clusters**

1: Produce the eligible cluster head set $P_{CH}$

2: Construct the initial swarm $\mathcal{B}_i, i \in [1, \mathcal{N}] \cap \mathbb{N}$

3: For every $CH_z$ in the vector solution $\mathcal{B}_i$, builds the clusters as follows:

* For every sensor $x_i, i \in [1, L] \cap \mathbb{N}$

* Determines the distance $d(x_i, CH_z), z \in [1, \mathcal{D}] \cap \mathbb{N}, CH_z \in \mathcal{B}_i$

* Attributes $x_i$ to $CH_z$ where $d(x_i, CH_z) = \min_{z=1,2,...,\mathcal{D}} \{d(x_i, CH_z)\}$

4: Evaluate the fitnessValue($\mathcal{B}_i$) for each vector solution $\mathcal{B}_i$ using Eq. 16

5: $\text{theBestFitnessValue} = \min_{i=1,2,...,\mathcal{N}} \{\text{fitnessValue}(\mathcal{B}_i)\}$

6: Run HBIP algorithm

**Algorithm 2 HBIP Algorithm**

**Input:** Number of CHs $\mathcal{D}$, MaxIteration, Initial Population of Clusters $\mathcal{B}_i, i = 1, ..., \mathcal{N}$

**Output:** Optimal Vector Solution $\mathcal{B}_i$ - Best Clusters in the Network

1: **for** (iter = 1, 2, ..., MaxIteration) **do**

2:   **for** (i = 1, 2, ..., N) **do**

▷ Employed Bees Phase

3:   Randomly choose the $j^{th}$ dimension to be modified.

4:   Generate novel position $\mathcal{B}_i^{new}$ based on Eq. 1.

5:   Calculate the fitnessValue($\mathcal{B}_i^{new}$) of the updated solution according to Eq. 16.

6:   **if** (fitnessValue($\mathcal{B}_i^{new}$) < fitnessValue($\mathcal{B}_i^{old}$)) **then**

▷ Greedy Selection

7:   Update the fitness value and replace the old position with the new one.

8:   **end if**

9:   Execute the BFO's swarming step.

10: **end for**

11: Calculate the probability $Prob_i$ according to Eq. 3.

12: **for** (i = 1, 2, ..., N) **do**

▷ Onlooker Bees Phase

13: **if** (rand(0, 1) < Prob_i) **then**

14:   Choose the position $\mathcal{B}_i$.

15:   Randomly choose the $j^{th}$ dimension to be modified.

16:   Generate novel position $\mathcal{B}_i^{new}$ based on Eq. 1.

17:   Calculate the fitnessValue($\mathcal{B}_i^{new}$) of the updated solution according to Eq. 16.

18:   **if** (fitnessValue($\mathcal{B}_i^{new}$) < fitnessValue($\mathcal{B}_i^{old}$)) **then**

▷ Greedy Selection

19:   Update the fitness value and replace the old position with the new one.

20: **end if**

21: Execute the BFO's swarming step.

22: **end if**

23: **end for**

24: **for** (i = 1, 2, ..., N) **do**

▷ Scout Bees Phase

25: **if** (trialNumber_i ≥ $\mathcal{D} * N$) **then**

26:   Substitute $\mathcal{B}_i$ with an arbitrary generated solution in accordance with Eq. 4.

27:   Assess the fitness value of the new randomly produced solution.

28: **break**.

29: **end if**

30: **end for**

31: Remember the best solution theBestFitnessValue so far.

32: **end for**
**Algorithm 3** Algorithm of BFO's Swarming Stage

1: Let $w = 0$

2: **while** ($w < dim(B_i)$) **do**

3: **if** ($w \neq j$) **then**

4: Generate novel position $B_i^{new}$ based on Eq. 1.

5: Calculate the fitnessValue($B_i^{new}$) of the updated solution using Eq. 16.

6: **if** (fitnessValue($B_i^{new}$) < fitnessValue($B_i^{old}$)) **then**

▷ Greedy Selection

7: Update the fitness value and replace the old position with the new one.

8: **end if**

9: **end if**

10: Let $w = w + 1$

11: **end while**

The HBIP algorithm working flowchart is presented in Fig. 2.

We integrate the swarming approach of BFO in both the employed and onlooker bee phases. The algorithm's ability to exploit solutions is limited because of the inefficiency of its local search within the search environment. The utilization of the roulette wheel method (greedy selection) for solution selection leads to optimum local solution traps and decreases population diversity. The aforementioned instances are effectively resolved by incorporating the swarming phase. This enhances the local search process for selecting the optimal cluster head.

Upon completion of the clustering procedure, the base station allocates a TDMA timetable to each member within the cluster. Subsequently, through the utilization of the flooding technique, the base station disseminates information to the cluster members regarding their respective cluster affiliation and the corresponding identification (ID) of the cluster head (CH).

#### 4.1.1.2 Optimization Fitness Function for Clustering
Energy depletion represents one of the main issues in IoT sensor networks. Therefore, to enhance the network's longevity, It is essential to select nodes with sufficient remaining energy as CHs while keeping their communication distance from their members to a minimum. $\sum_{z=1}^{D} (E_z^{CH})$ is the total residual energy of the selected nodes. In order to select nodes with sufficient remaining energy, this total residual energy should be maximized. We need, therefore, to minimize its reciprocal as formulated in Eq. 14. To reduce the energy consumption of member nodes, we need to minimize the average intra-cluster distance. This is mathematically formulated, as shown in Eq. 15.

$$g_1(B_i) = \sum_{z=1}^{D} \left( \frac{1}{E_z^{CH}} \right) \qquad (14)$$

$$g_2(B_i) = \frac{1}{M} \sum_{i=1}^{M} \sum_{z=1}^{D} d(x_i, CH_z) \qquad (15)$$
<figure><img src="image_7.png" /><figcaption>Fig. 2 HBIP Working Flowchart</figcaption></figure>

where $E_z^{CH}$ represents the remaining energy of the Cluster Head $z$, $z \in [1, D] \cap \mathbb{N}$, $D$ is number of CHs in the solution vector $B_i$, $M$ is the number of element in the cluster and $d(x_i, CH_z)$ represents the distance between the IoT sensor node $x_i$ and the cluster head $CH_z$.

The ultimate aim is to optimize the aforementioned pair of objective functions, denoted as $g_1$ and $g_2$. In order to achieve this objective, we combine, normalize, and convert two objectives into a unified form represented as $\alpha * g_1 + \beta * g_2$. Additionally, we incorporate the variable $Z_{cc}$ to replicate the BFO swarming phenomenon, as discussed earlier. The fitness function that has been proposed is presented as follows in (Eq. 16):

$$G = \alpha * \sum_{z=1}^{D} \left( \frac{1}{E_z^{CH}} \right) + \beta * \left[ \frac{1}{M} \sum_{i=1}^{M} \sum_{z=1}^{D} \left( d(x_i, CH_z) \right) \right] + Z_{cc} \quad (16)$$

where the constants $\alpha$ and $\beta$ are in the range of 0 and 1. These constants will be utilized as weights for the objective functions. The choice of $\alpha$ and $\beta$ must be done carefully and appropriately. Our objective is the minimization of the cumulative objective function.
$$g(\mathcal{B}_i) = \min\{G\} \qquad (17)$$

## 4.2 Multi-Objective Optimization-based Data Gathering Protocol (MOO-DGP) for Mobile IoT Sensor Networks

Mobility in IoT-based applications introduces a new challenge regarding network connectivity due to mobile IoT sensor nodes. The network topology changes regularly, resulting in high packet loss during communication. Controlling the network energy consumption to give the IoT sensor nodes a long battery life is a critical problem because the IoT sensor nodes have limited battery power. Designing an efficient network entails managing the mobility of IoT sensor nodes to maintain constant connectivity. The challenge is enormous, and the objectives are multiple. The classical approaches fail to solve these problems efficiently as the overhead in the clustering process is high for most of the protocols. To address these issues, we propose a Multi-Objective Optimization-based Data Gathering Protocol (MOO-DGP) that uses the HBIP algorithm to overcome the Multi-Objective Optimization formulated for the clustering.

### 4.2.1 System Model

#### 4.2.1.1 Network Model
Let's consider a square sensor field with $M$ randomly distributed IoT sensor nodes, denoted $S = \{n_1, n_2, n_3, ..., n_M\}$. The IoT sensor nodes are homogeneous and uniquely identified, $i \in [1, M] \cap \mathbb{N}$. At the start of the network, the sensors are animated with random speed and velocity. All IoT sensor nodes possess identical starting residual energy levels and are outfitted with a limited-range radio transceiver. Hence, the sensors in the network can only communicate with those within their communication range. However, the sensors can adjust their transmit power to reach a specific node. Each IoT sensor node knows its location, speed, velocity, and ID. We assume the medium radio ideal where the free space path loss models govern the radio signal attenuation. Finally, the base station is to be at the center of the sensor field and to have sufficient resources (energy) to coordinate the network.

#### 4.2.1.2 Energy Model
The first-order radio presented in Ref. [26] is used in most protocols described in related works and literature to evaluate the dissipation of energy of IoT sensor nodes during transmission and reception. However, while this model is simple, it is not applicable in the real world. Thus, this paper considers the CC2538 RF transceiver model [7] based on the IEEE802.15.4 standard, a realistic radio model manufactured by Texas Instruments. It is a low-power model adapted to IoT and WSN applications. In this model, IoT sensor nodes can adjust their transmit power up to 7dBm (over 100m). The energy consumption is solely determined by the state of the radio (Transmitter, Receiver, Sleep, and Idle) and its transitions (Tx-to-Rx, Tx-to-Idle, Rx-to-Idle...). Let $E_i$ be the total energy consumed by the IoT sensor node $i$ during one transmission round. $E_i$ can be calculated in Eq. 18 below:

<figure><img src="image_8.png" /></figure>
$$E_i = \sum_{State} P_{State} * T_{State} + \sum_{Trans} P_{Trans} * T_{Trans} \quad (18)$$

where *State* represents the state of the radio, *State* ∈ {Transmitter, Receiver, Sleep, Idle} of the IoT sensor node, *Trans* refers to the transitions between the radio states, *P*<sub>State</sub> and *T*<sub>State</sub> respectively the power consumed and the time spent in the particular state of the radio and *P*<sub>Trans</sub> and *T*<sub>Trans</sub> respectively the power consumed and the time spent during transitions of the radio. When an IoT sensor node assures the cluster head role, it will aggregate the data received from the cluster members. The cost of this operation is estimated to be an additional energy consumption equal to *E*<sub>a</sub> per bit.

### 4.2.2 Proposed Multi-Objective Data Gathering Protocol (MOO-DGP)

In this section, we will first discuss the different optimization objectives of our approach before formulating the objective function. Next, a detailed description of the MOO-DGP protocol will be given.

#### 4.2.2.1 Optimization Objective for the Network Clustering
The mobility of IoT sensor nodes introduces novel difficulties, especially in terms of successful packet delivery ratio rates caused by the regular disconnection of links. This makes the clusters unstable and, in turn, the data routing tree. To address these issues, the MOO-DGP protocol establishes a goal of measuring the stability of each node within its cluster and the quality of the clusters formed across the entire network.

Let *P* be a matrix representing a swarm's population in a continuous search space and *P*<sub>i</sub> the *i*-th vector solution of the population containing *K* cluster heads (CHs). Let also *N* be the population size (number of possible vector solutions). The MOO-DGP protocol formulates the non-linear constrained optimization problem following the objectives:

**Objective 1: Movement Similarity/Mobility Factor**

The cluster heads must have a similar speed to their cluster members to preserve stability and construct compact clusters. We proposed the scheme (see Eq. 19) to foster the IoT sensor nodes with the smallest deviation with its cluster members to be likely a cluster head. Let *m* be the size of a cluster.

$$f_1(P_i) = \frac{1}{V_{max}} * \sum_{r=1}^{K} \left( \left| \sum_{j=1}^{m} V_j - V_r \right| \right) \quad (19)$$

where $V_r, r \in [1, K] \cap \mathbb{N}$ indicates the speed of the *r*-th cluster head, $\sum_{j=1}^{m} V_j$, the average speed of the *r*-th cluster members and $V_{max}$, the maximal speed of IoT sensor nodes in the network.
## Objective 2: Maximize the Cluster Lifetime

In a mobile environment, [27] defines the "Link Expiration Time" (LET) between two mobile IoT nodes as the duration during which both nodes remain connected and within the transmission range.

Let's consider a mobile IoT sensor node $n_i$ and the cluster head $CH_r$ moving with speed $V_i$ and $V_r$ in the directions $\theta_i$ and $\theta_r$. Let $(x_i, y_i)$, $(x_r, y_r)$ respectively the coordinates of the mobile IoT sensor node $n_i$ and the cluster head $CH_r$, and $R_{trans}$ the transmission range of the two IoT sensor nodes. According to Ref. [27], the "Link Expiration Time" (LET) between the IoT sensor node $n_i$ and the cluster head $CH_r$ is given by the expression below (see Eq 20):

$$LET(n_i, CH_r) \le \frac{-(ab + cd) + \sqrt{C * R_{trans}^2 - (ad - cb)^2}}{C} \quad (20)$$

with $C = b^2 + d^2$ where: $a = x_i - x_r$ and $c = y_i - y_r$, $b = V_i \cos \theta_i - V_r \cos \theta_r$ and $d = V_i \sin \theta_i - V_r \sin \theta_r$.

To avoid a quick disconnection of the links between IoT sensor nodes and their CHs, the BS should associate an IoT sensor node to a CH with a high LET to organize a stable cluster. Let $Avg(LET_{CH_r})$ be the average lifetime of the cluster $r \in [1, K] \cap \mathbb{N}$. $Avg(LET_{CH_r})$ is the average connection time of the cluster head $CH_r$ with its members. $Avg(LET_{CH_r})$ is expressed as (see Eq. 21):

$$Avg(LET_{CH_r}) = \frac{1}{m} * \sum_{j=1}^{m} LET(n_j, CH_r) \quad (21)$$

We look for maximizing the cluster lifetime in the whole network (see Eq. 22):

$$f_2(P_i) = \frac{1}{LET_{max}} * \sum_{r=1}^{K} Avg(LET_{CH_r}) \quad (22)$$

where $LET_{max}$ is the maximum "Link Expiration Time" between two mobile nodes leaving the same position in the same direction with a maximum speed.

## Objective 3: Maximize the Cluster Quality

The cluster quality depends on the quality of the communication links between the cluster head and its members. Several studies have shown that the RSSI can quickly and accurately estimate communication link quality. We use the simplified path-loss model provided in Ref. [28] to estimate the RSSI of the mobile IoT sensor nodes:

$$RSSI_d = RSSI_{d_0} + K(dB) - 10 * \gamma * \log\left(\frac{d}{d_0}\right) \quad (23)$$

In Eq. 23, $\gamma$, $d_0$ and $RSSI_{d_0}$ are respectively the path-loss coefficient, a distance of reference, the RSSI for the reference distance $d_0$ and $K(dB) = 20 * \log(\frac{\lambda}{4\pi d_0})$ is a unitless constant. We could then calculate the RSSI at a distance $d$.

<figure><img src="image_9.png" /></figure>
Let $LQI(n_i, CH_r)$ be a Link Quality Indicator between the mobile IoT sensor node $n_i$ and the $CH_r, r \in [1, K] \cap \mathbb{N}$ and $P_i^r$, the $r^{th}$ cluster in the vector $P_i$ led by the cluster head $CH_r$. $LQI(n_i, CH_r)$ can be calculated as follows:

$$LQI(n_i, CH_r) = \frac{RSSI(n_i, CH_r)}{maxRSSI(P_i^r) - minRSSI(P_i^r)} \quad (24)$$

$maxRSSI(P_i^r)$ and $minRSSI(P_i^r)$ are the best and the worst RSSI value in the cluster $P_i^r$ while $RSSI(n_i, CH_r)$ is the RSSI value between the mobile IoT sensor node $n_i$ and the cluster head $CH_r$. The higher the value of $LQI$, the better the link quality. Then, to maximize the quality of clusters, we need to maximize the best link quality among all clusters:

$$f_3(P_i) = \frac{1}{m} * \sum_{j=1}^{m} \sum_{r=1}^{K} LQI(n_j, CH_r) \quad (25)$$

### Objective 4: Optimize the Energy Consumption

Since the power consumption of the CC2538 RF transceiver model depends on the state of the radio, the dissipation of energy of the mobile IoT sensor nodes is based on the radio state and the time spent in this state. Due to the TDMA schedule used for the intra-cluster communication where the timeslots are consecutive, the time spent in Sleep state is negligible. So, a mobile IoT sensor node transmits data to the CH in its timeslot for $T_d$. It passes in Idle state, that's TransmitterIdle state, for the remaining timeslot before switching to Receiver state. Then, energy consumed by cluster members node $E_{MN}$ in the $r^{th}$ cluster can be calculated as follows (Eq. 26):

$$E_{MN}^r = m * (T_d * P_{TX} + (T_{slot} - T_d) * P_{Idle}) \quad (26)$$

On the contrary, the CH will receive $m$ data packets and perform the data aggregation task. Then, energy consumed by any CH during a cluster data frame can be calculated as follows (Eq. 27):

$$E_{CH}^r = m * (T_d * P_{RX} + (T_{slot} - T_d) * P_{Idle} + L_d * E_a) \quad (27)$$

We define the network energy consumption cost as in Eq. 28 that's to be minimized:

$$f_4(P_i) = \sum_{r=1}^{K} (E_{MN}^r + E_{CH}^r) \quad (28)$$

where $P_{TX}, P_{RX}$, are power consumed when transmitting and receiving, $L_d$ the size of the packet, $T_d$ the data duration and $T_{slot}$ the duration of a TDMA timeslot. Finally, we formulated the optimization objective function as follows (Eq. 29):

$$F = \alpha * f_1(P_i) + \beta * \frac{1}{f_2(P_i)} + \varphi * \frac{1}{f_3(P_i)} + \psi * f_4(P_i) \quad (29)$$
where $\alpha, \beta, \varphi$ and $\psi$ are constants such as $\alpha + \beta + \varphi + \psi = 1$ and determine the significance of the sub-objective functions. Our objective is the minimization of $F$.

$$f(P_i) = \min\{F\} \qquad (30)$$

#### 4.2.2.2 MOO-DGP Protocol Description
Our MOO-DGP protocol, like most network protocols that utilize clustering designed for Wireless Sensor Networks [8–10], is split into *Set-up Phase*, in which the network is clustered, the cluster head is elected, and the TDMA timetable for their cluster members is generated; and the *Steady-Phase*, wherein data is transmitted to the base station via intra-cluster communication and inter-cluster communication. To maximize overall network resources, we employ a centralized control mechanism through which the algorithm is executed at the base station to elect the cluster head. To that end, we introduce a third phase, the *Initialization Phase*, initiated by the BS and executed at the start of each new transmission round.

##### Initialization

At the launch of the network, the BS initializes the network by transmitting a Hello packet ($P_{Hello}$) to the network. For the very first time, this packet contains the BS position, its MAC, and Network addresses. If the first-ever hello packet is used to share the information of the BS and announce the beginning of the *Set-up Phase*, subsequent ones are only used to announce the beginning of a new transmission round.

##### Set-up Phase

This phase is preceded by the *Initialization Phase*. It is further divided into two steps:

## 1. Potential Cluster Heads (CHs) Selection

Each IoT sensor node in the network that receives the $P_{Hello}$ packet records the BS information and, from the $P_{Hello}$ packet's Received Signal Strength Indicator (RSSI), estimates and adjusts its transmit power to send a Discovery packet ($P_{Discov}$) to the BS. Only IoT sensor nodes within the sending nodes and the BS's range of communication could then receive that packet. This allows us to reduce the energy overhead compared to the protocols using the flooding approach for neighbor discovery, where all IoT sensor nodes receive and broadcast the packet until it reaches the destination (BS). Moreover, after getting the $P_{Hello}$ packet, the nodes switch to the Sleep state to save energy and wake up to send the $P_{Discov}$ packet when its time arrives and go again back to the Sleep state. All network nodes are fully awake a while before the BS sends the best CH elected. The $P_{Discov}$ packet contains the sender's self-information: ID, residual energy, speed, velocity, moving direction angle, its current geographical position. When all sensors have been done, the BS constructs the set $S_{CH}$ of the eligible Cluster Heads. Only the sensors with enough residual energy can ensure the CH role. The energy threshold value $E_{thr}$ (see Eq. 31) is referred to as the mean of the remaining energy of all active nodes in the network.
$$S_{CH} = \{n_i, i \in [1, M] \cap \mathbb{N} | E_R(n_i) \ge E_{thr}\} \quad (31)$$

$E_R(n_i)$ represents the remaining energy of the IoT sensor node $n_i$.

## 2. Network Clustering

After creating the set $S_{CH}$ of the eligibles $CH_s$, the Base Station proceeds with the clustering process. The BS employs the HBIP algorithm to create $K$ clusters based on the network's $M$ alive nodes. First, the BS encodes each CH candidate with an integer in the range $[0, card(S_{CH})]$ where $card(S_{CH})$ is the cardinal of the set $S_{CH}$. Next, it randomly generates the initial population $P$ of size $N$ from the candidates cluster head $\#S_{CH}$, every population member $P_i, i \in [1, N]$ is a vector solution of $K$ cluster heads. The search space is, therefore, of $K$-dimensions. To build stable clusters, the BS uses the information sent by IoT sensor nodes to compute the $LET(n_i, CH_r)$ between every IoT sensor node $n_i, i \in [1, M] \cap \mathbb{N}$ along with every cluster heads $CH_r$ in $P_i, r \in [1, K] \cap \mathbb{N}$ and allocates $n_i$ to the cluster head $CH_r$ with which $n_i$ has a maximum link connection time (see Eq. 32):

$$LET(n_i, CH_r) = \max_{r=1,2,...,K} \{LET(n_i, CH_r)\} \quad (32)$$

When the cluster formation is done, the objective function of each solution is evaluated. The CHs whose fitness value is smaller are the best chosen. The HBIP algorithm is executed to update the positions. When the formation of clusters ends, the BS notifies the elected cluster heads by publishing the status of the IoT sensor nodes via a control packet message to the network. Next, each CH attributes a TDMA timeslot to each cluster member in ascending order of their LET. The proposed MOO-DGP Network Clustering Algorithm is as follows 4:
**Algorithm 4 MOO-DGP network clustering algorithm**

**Input:** Number of CHs K, Size of population *N*, MaxIteration

**Output:** Best Clusters in the Network

1. Creates the set $S_{CH}$ of eligible cluster heads

2. From $S_{CH}$, constructs the initial population matrix P

3. For each $CH_r$ in the vector solution $P_i$, builds the clusters as follows:

* For every node $n_i$, $i \in [1, M] \cap \mathbb{N}$

* Determines the $LET(n_i, CH_r)$, $r \in [1, K] \cap \mathbb{N}$, $CH_r \in P_i$

* Associates $n_i$ to $CH_r$ such as: $LET(n_i, CH_r) = \max_{r=1,2,...,K} \{LET(n_i, CH_r)\}$

4. Evaluate the fitnessValue($P_i$) for each vector solution $P_i$ using Eq. 29

5. $\text{theBestFitnessValue} = \min_{i=1,2,...,N} \{\text{fitnessValue}(P_i)\}$

6. Run HBIP algorithm

7. Output the vector solution $P_i$ associated to theBestFitnessValue

8. END

**Steady Phase**

The *Steady Phase* is the stage where the intra-cluster and inter-cluster transfer of data occurs in the network. Our MOO-DGP protocol assumes all IoT sensor nodes start that phase simultaneously. As our MOO-DGP protocol is implemented on top of the IEEE802.15.4 standard (at the network layer), the link layer protocol provided for the Media Access Control (MAC) is the CSMA/CA. However, we used the TDMA for intra-cluster communication to prevent packet collision. Every IoT sensor node transmits data to its CH in the allotted timeslot. Once the CHs have collected data from their members, they compress the data into just one packet and prepare to forward it to the BS via other CHs. The CSMA/CA protocol governs the communication between cluster heads (inter-cluster communication). As both the CHs and IoT sensor nodes are mobile, it's hard to maintain a stable data routing tree towards the base station. For this reason, the path to the BS through other CHs that serve as relay nodes is dynamically created. We assumed each CH was aware of the other in the network. Each $CH_r$, $r \in [1, K] \cap \mathbb{N}$ chooses its best Next-Hop that's in the direction of the BS, i.e., $d(NH_{best}, BS) \le d(CH_r, BS)$. Next, the $CH_r$ that initiates the communication adjusts its transmit power to guarantee connectivity with its $NH_{best}$.

**5 Performance Evaluation and Discussion**

The efficiency of the HBIP and MOO-DGP protocols for data gathering was examined by utilizing OMNeT++ and the package INET. The INET package offers real radio signal propagation and disturbance models as well as realistic IoT sensor node characteristics. We conduct experiments for both static and mobile IoT sensor networks

<figure><img src="image_10.png" /></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 3** Iteration vs. fitness value</figcaption></figure>

## 5.1 Static IoT Sensor Networks

The evaluation of the HBIP protocol was conducted by considering the received packets at the base station, throughput, energy consumption, and network lifetime. A comparative analysis is conducted to evaluate the efficiency of the proposed Hybrid Bio-Inspired Protocol (HBIP) compared to the LEACH protocol and the ABC and BFO metaheuristics.

The simulations include randomly distributed stationary IoT sensor nodes within a sensing field measuring 100 × 100 m², with the base station stationed at the coordinates (x = 50, y = 0). Every IoT sensor node is assigned 0.5J as the initial energy value $E_o$. The IoT sensor nodes transmit a data packet containing 4000 bits. The maximum number of CHs is set to 5% of all active nodes. In this study, the network consists of 50 IoT sensor nodes. The parameters of the radio model are the following: $E_{amp} = 0.0013pJ/bit/m^4$, $E_{DA} = 5nJ/bit$, $E_{elec} = 50nJ/bit$, and $E_{fs} = 10pJ/bit/m^2$. The values of α and β in Eq. 16 are set to 0.5 each, ensuring an equal contribution from both sub-objectives.

We comprehensively evaluated the convergence rate and minimum achieved by executing the ABC, BFO, and HBIP algorithms for a total of 500 iterations to assess our proposition's validity. The findings are shown in Fig. 3.

The HBIP algorithm has a particularly rapid convergence rate, resulting in higher attainment of the global minimal output in comparison with the ABC and BFO algorithms. BFO and ABC metaheuristics exhibit vulnerability to the issue of local optima. The results produced in this study give evidence for the efficiency of our proposed algorithm, indicating that the HBIP offers more effective approaches for tackling clustering challenges in wireless sensor networks. The networks' longevity depends on the quantity of residual energy, as the sensors are outfitted with non-rechargeable batteries. One important parameter for assessing wireless sensor network efficiency is energy usage.

The energy consumption of the network is measured over time. The comparison with the various clustering protocols is depicted in Fig. 4. The findings indicate that HBIP exhibits lower energy consumption compared to LEACH, ABC, and BFO. After 3000 simulation rounds, the Hybrid Bio-Inspired Protocol (HBIP) demonstrates a notable increase in energy usage of the ABC network by
<figure><img src="image_12.png" /><figcaption>Fig. 4 Round(s) vs. energy spent</figcaption></figure>

6.42% and the BFO network by 9.02%. In contrast, the network using the LEACH protocol dies after 751 rounds. The enhanced performance of HBIP can be attributed to its ability to effectively navigate away from local minima, a result of integrating BFO's swarming approach to reinforce the ABC exploitation phase. The HBIP algorithm effectively identifies the optimal cluster head (CH) that possesses adequate energy and achieves the global minimal distance according to the fitness function. Subsequently, he successfully constructs solid clusters that exhibit an optimal distribution of cluster heads throughout the entire network. The weak results reported in the ABC and BFO algorithms may be attributed to the restricted capacity to choose the most optimum Cluster Heads (CHs) effectively, primarily due to the inefficiency of the exploitation phase. The suboptimal result observed with the LEACH can be attributed to the CH election process, which does not consider the energy levels of the nodes. Furthermore, the utilization of the likelihood criteria in the LEACH results in an imbalanced distribution of cluster heads across the network. The direct communication between cluster head nodes and the base station also rapidly drains their energy resources.

The network lifetime, which refers to the number of rounds after which the network's First Node Dies (FND), is illustrated in Fig. 5.

The FND indicates the time all nodes are operational from the start of the network's operation. The aforementioned time duration is alternatively referred to as the Network Stability Period (NSP). The NSP is a desirable metric due to its direct correlation with the network's lifetime. The results presented in Fig. 5 demonstrate that the HBIP significantly extends the network's overall lifetime. The reason behind this is that it enables greater energy efficiency during the process of transmitting data. The algorithm proposed HBIP demonstrates a notable improvement in terms of the network lifespan over ABC by 16.91%, BFO by 35.31%, and LEACH by 69.51%. This enhancement can be observed by analyzing the number of alive nodes. The presence of an important number of alive nodes is indicative of a network that displays an effective lifespan.

The last metric in this study is the Number of Packets Received at the base station. This metric is relevant because the primary goal is data gathering. In that line, Fig. 6 illustrates the quantity of data packets received at the base station. The performance of the proposed protocol is illustrated in Fig. 6, compared to the other

<figure><img src="image_13.png" /></figure>
<figure><img src="image_14.png" /><figcaption>**Fig. 6** Packets received at BS</figcaption></figure>

<figure><img src="image_15.png" /></figure>

protocols. The successful result of HBIP is evident in its ability to transmit a greater number of data packets efficiently. This success can be attributed to several factors, one of which is the effectiveness of the proposed protocol in identifying the optimal cluster heads with greater remaining energy. The HBIP algorithm significantly improves data collection compared to the ABC algorithm, with a 7.26% increase. Similarly, compared to the BFO algorithm, the HBIP algorithm demonstrates an increase of 19.43% in data gathering. Furthermore, compared to the LEACH algorithm, the HBIP algorithm outperforms it by a remarkable margin of 84.40% in data collection. Similar to other performance metrics, the LEACH protocol demonstrates poor results compared to the ABC, BFO, and HBIP protocols.

It is worth noting that the network system model analyzed presents some limitations and can conceal the full ability of the HBIP protocol. Indeed, the performance analysis of the HBIP protocol carried out in this study is focused on small-scale ($100 \times 100 \text{ m}^2$) and low-density (50 IoT sensor nodes) networks. Furthermore, the radio model considered [26] to calculate the energy dissipation of nodes is simple but not applicable in the real world. Moreover, the static scenario of the network considered is limited, as nowadays, most IoT applications require the mobility of IoT nodes to accommodate human life. Then, in the next section of this paper, we will integrate all above into our data gathering approach with the aim of being as realistic as possible for future real-world implementation of our model. More specifically, we will include the mobile aspect of the IoT nodes deployed in a large-scale network.
Next, the scalability of the network will be investigated by varying the density of the IoT nodes in the network while considering the CC2538 RF radio transceiver model [7] based on the IEEE802.15.4 standard, a realistic Low-Power radio model manufactured by Texas Instruments, adapted to IoT and WSN applications. Finally, we will explore the capability of the HBIP protocol to enhance the efficiency of these large-scale networks.

## 5.2 Mobile IoT Sensor Networks

The MOO-DGP protocol is compared to the MBC protocol [10], which has been proven better than LEACH-Mobile [8] and CBR-Mobile [9]. We used several metrics for performance evaluation, such as Packet Delivery Ratio (PDR), average energy consumption and control overhead for the successful data packet, network energy consumption, and network lifetime. Simulations are conducted with randomly deployed mobile IoT sensor nodes on a 2000 × 2000 m² sensing field with the BS located in the center (1000, 1000). For the experiment, we vary the number of mobile IoT sensor nodes from 100 to 700 while considering the random waypoint (RWP) as a mobility model with *waitTime* equal to 0.05s. The speeds of mobile IoT sensor nodes are evenly distributed within 2 m/s and 5 m/s. The initial energy denoted as *E*₀ of each mobile IoT sensor node is set to 0.5J. The coefficient in Eq. 29 are set to α = β = φ = ψ = 0.25.

The radio model parameters and the remaining network parameters are provided in Table 2.

Figure 7 illustrates the packet delivery rate with the MOO-DGP and MBC protocol. The MOO-DGP protocol provides a higher rate of successful packet delivery than the MBC protocol because it builds a stable cluster with a stable communication link between mobile IoT sensor nodes and their cluster heads. As the network's number of mobile IoT sensor nodes increases, the PDR for both protocols decreases. This decrease is due to an increase in broken links resulting from interference from neighboring clusters and the mobility of nodes. However, it is readily apparent that the MOO-DGP protocol decreases the rate significantly more slowly than the MBC protocol.

Figure 8 depicts the average consumption of energy per data packet effectively received for the MBC and MOO-DGP protocol. It's seen that the MOO-DGP protocol consumes much less energy compared to the MBC protocol. With the MBC protocol, mobile IoT sensor nodes select the cluster head with the greatest link time for connection instead of the closest cluster head; IoT sensor nodes may not select the closest cluster head. This consumes more energy because a node can have a long connection time with a cluster head while not being in his communication range and then continues to broadcast the "JoinRequest" control packet throughout the network, which consumes a lot of energy. In addition, when a data packet is effectively received, the cluster head should send an "Ack" control packet message back to the sender node. When a mobile IoT sensor node loses contact with its cluster head, it must advertise the "JoinRequest" control packet message throughout the network so as to reconnect with a novel cluster. This process is an actual seat of energy

<figure><img src="image_16.png" /></figure>
<table><caption>**Table 2** Simulation Parameters</caption><thead><tr><td>Parameters</td><td>Value</td></tr></thead><tbody><tr><td>Maximum number of CHs</td><td>5%</td></tr><tr><td>Data Packet Length (L<sub>d</sub>)</td><td>4000 bits</td></tr><tr><td>Control Packet Length (L<sub>c</sub>)</td><td>200 bits</td></tr><tr><td>Bitrate</td><td>250 kbps</td></tr><tr><td>Transmit Power (T<sub>x</sub>)</td><td>- 16.7689 dBm</td></tr><tr><td>Sensitivity</td><td>- 97 dBm</td></tr><tr><td>Energy Detection</td><td>- 90 dBm</td></tr><tr><td>Noise Power</td><td>- 110 dBm</td></tr><tr><td>SNIR Threshold</td><td>- 8.63 dB</td></tr><tr><td>Bandwidth</td><td>2.8M Hz</td></tr><tr><td>Center Frequency</td><td>2450 MHz</td></tr><tr><td>Transmitting Power Consumption (P<sub>TX</sub>)</td><td>72 mW</td></tr><tr><td>Receiving Power Consumption (P<sub>RX</sub>)</td><td>60 mW</td></tr><tr><td>Idle Power Consumption (P<sub>Idle</sub>)</td><td>1.8 mW</td></tr><tr><td>Sleep Power Consumption (P<sub>Sleep</sub>)</td><td>0.0039 mW</td></tr><tr><td>Link Layer Protocol</td><td>IEEE802.15.4 MAC</td></tr><tr><td>Physical Layer</td><td>IEEE802.15.4 PHY/ Physical Radio</td></tr></tbody></table>

<figure><img src="image_17.png" /><figcaption>**Fig. 8** IoT nodes vs. energy/ packet</figcaption></figure>

<figure><img src="image_18.png" /></figure>
<figure><img src="image_19.png" /><figcaption>Fig. 9 IoT nodes vs. overhead</figcaption></figure>

consumption in the MBC protocol. On the contrary, the MOO-DGP protocol builds compact clusters with the IoT sensor nodes having their cluster head, low relative velocity, long link connection time, and better link quality (higher RSSI). This leads to fewer link breaks and a longer lifetime of the clusters. There is neither a membership declaration nor "JoinRequest" or "Ack" control packet message sent throughout the network; hence, energy is saved.

Figure 9 presents the MOO-DGP and MBC protocol’s control overhead per successfully received data packet. It is observed that the MOO-DGP protocol uses less control overhead, regardless of the number of mobile IoT nodes in the network. The overhead in MOO-GDP occurs only at two points: when nodes transmit their personal information (velocity, position, speed, direction, and energy) to the BS for the clustering process and when the cluster head broadcasts the TDMA timetable to their members to collect data. The increased overhead seen when the number of mobile IoT nodes equals 100 is because the network lifetime in the MOO-DGP protocol for this size is nearly double that of the MBC protocol. Therefore, more data packets are transmitted, as evidenced by the higher PDR score of 91%. In the MBC protocol, the "JoinRequest" messages are sent to avoid more packet loss, and the "Ack" control packet message is sent to acknowledge the reception of data, and frequently, membership changes due to the mobility of IoT sensor nodes induce colosyal overhead.

Figure 10 shows that in terms of average consumption of energy, the MOO-DGP protocol greatly outperforms the MBC protocol as the speed of the mobile IoT sensor node increases. The difference in energy consumed per packet between the two protocols suggests that the MOO-DGP protocol is more appropriate for an extremely mobile system while keeping network performance within acceptable limits.

Figure 11 depicts the cumulated amount of energy consumed in the network over time with the MOO-DGP and MBC protocol, respectively, in the case of 100 mobile IoT sensor nodes.

In terms of overall network energy consumption, it is clear that the MOO-DGP protocol surpasses the MBC protocol. This energy efficiency profile of the MOO-DGP protocol is due to the various optimizing parameters of the fitness function. As shown in Fig. 12, energy efficiency improves the network lifetime.

<figure><img src="image_20.png" /></figure>
<figure><img src="image_21.png" /><figcaption>**Fig. 11** Time vs. energy spent</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>**Fig. 12** Time vs. alive node</figcaption></figure>

<figure><img src="image_23.png" /></figure>

Indeed, Fig. 12 shows that the MOO-DGP protocol keeps many nodes alive over time and then prolongs the overall network lifespan. The network in the MBC protocol once again underperforms in terms of network lifetime because of its inappropriate cluster head selection that remains on a probabilistic basis.
# 6 Conclusion

The main purpose of the IoT sensor network is to gather data from its surroundings. Several unresolved transmission and routing issues remain and must be addressed in order to attain that goal. Metaheuristic-based approaches have emerged as highly promising instruments for addressing these challenges. The first part of this paper introduces a data collection protocol (HBIP) for IoT sensor networks, utilizing a metaheuristic approach. Simulations demonstrate that HBIP exhibits faster convergence and achieves a higher global minimum than ABC and BFO, given equivalent population sizes and maximum iteration counts. In addition, the HBIP protocol demonstrates greater network longevity and data transmission capabilities than LEACH, BFO, and ABC, with improvements of 84.40%, 19.43%, and 7.26%, respectively. The second part presents a Multi-Objective Optimization-based Data Gathering Protocol for Mobile IoT Sensor Networks. The proposed MOO-DGP protocol constructs stable clusters by considering mobile nodes' relative velocity, link quality, link connection time, and energy consumption. In our protocol, we formulated the problem of mobile clustering as a non-linear optimization problem and solved it thanks to a Hybrid biologically inspired metaheuristic (HBIP). Through extensive simulations, we determined that the MOO-DGP protocol shows better performance than the MBC regarding the average control overhead, energy consumption, and packet delivery ratio. The MOO-DGP protocol can better accommodate a highly mobile environment while maintaining network performance within acceptable limits.

In future studies, we intend to implement our proposal on hardware in order to proceed with a testbed to compare simulation performance to actual results.

**Acknowledgements** The authors would like to express their gratitude to the Partnership for Skills in Applied Sciences, Engineering and Technology (PASET) program.

**Author Contributions** GK, SKK, and AMG developed the idea. GK built the network model, wrote the simulation algorithms, gathered and evaluated the experimental data, and wrote the paper. Overall, advice was provided by OT, AAAA, and JH, who also refined and revised the model and the draft of the manuscript. The final manuscript was read and approved by all authors.

**Funding** Open access funding provided by Université Paris-Saclay. This research has received funding from the Regional Scholarship and Innovation Fund (RSIF), PASET's flagship program.

**Data Availability** The simulation codes and data files used in this research are accessible upon request.

# Declarations

**Conflict of interest** The authors declare that they do not have any conflict of interest.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

<figure><img src="image_24.png" /></figure>
## References

1. Pitchaimanickam, B., Murugaboopathi, G.: A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks. Neural Comput. Appl. 32(12), 7709-7723 (2020). https://doi.org/10.1007/s00521-019-04441-0

2. Effah, E., Thiare, O., Wyglinski, A.: Multi-objective optimization modeling of clustering-based agricultural internet of things. In: 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), pp. 1-5 (2020). https://doi.org/10.1109/VTC2020-Fall49728.2020.9348460

3. Effah, E., Thiare, O., Wyglinski, A.: Energy-efficient multihop routing framework for cluster-based agricultural internet of things (ca-iot). In: 2020 IEEE 92nd vehicular technology conference (VTC2020-Fall), pp. 1-5 (2020). https://doi.org/10.1109/VTC2020-Fall49728.2020.9348608

4. Rao, P.C.S., Lalwani, P., Banka, H., Rao, G.S.N.: Competitive swarm optimization based unequal clustering and routing algorithms (CSO-UCRA) for wireless sensor networks. Multimedia Tools Appl. (2021). https://doi.org/10.1007/s11042-021-10901-4

5. Abba Ari, A.A., Gueroui, A., Yenke, B.O., Labraoui, N.: Energy efficient clustering algorithm for wireless sensor networks using the abc metaheuristic. In: 2016 international conference on computer communication and informatics (ICCCI), pp. 1-6 (2016). https://doi.org/10.1109/ICCCI.2016.7480010

6. Kponhinto, G., Thiare, O., Ari, A.A.A., Gueroui, A.M., Khemiri-Kallel, S., Hwang, J.: Bio-inspired solution for cluster-tree based data collection protocol in wireless sensors networks. In: NOMS 2023-2023 IEEE/IFIP network operations and management symposium, pp. 1-6 (2023). https://doi.org/10.1109/NOMS56928.2023.10154279

7. Cc2538 powerful wireless microcontroller system-on-chip for 2.4-ghz ieee 802.15.4, 6lowpan, andzigbee®applications. In: Texas instruments products, pp. 1-25 (2015). Texas Instruments. https://www.ti.com/lit/ds/symlink/cc2538.pdf

8. Kim, D.-S., Chung, Y.-J.: Self-organization routing protocol supporting mobile nodes for wireless sensor network. In: First international multi-symposiums on computer and computational sciences (IMSCCS'06), vol. 2, pp. 622-626 (2006). https://doi.org/10.1109/IMSCCS.2006.265

9. Awwad, S.A.B., Ng, C.K., Noordin, N.K., Rasid, M.F.A.: Cluster based routing protocol for mobile nodes in wireless sensor network. In: 2009 international symposium on collaborative technologies and wsystems, pp. 233-241 (2009). https://doi.org/10.1109/CTS.2009.5067486

10. Deng, S., Li, J., Shen, L.: Mobility-based clustering protocol for wireless sensor networks with mobile nodes. IET Wireless Sens. Syst. 1(1), 39-47 (2011)

11. Gambhir, A., Payal, A., Arya, R.: Performance analysis of artificial bee colony optimization based clustering protocol in various scenarios of WSN. Procedia Comput. Sci. 132, 183-188 (2018). https://doi.org/10.1016/j.procs.2018.05.184

12. Yousefi, S., Derakhshan, F., Aghdasi, H.S., Karimipour, H.: An energy-efficient artificial bee colony-based clustering in the internet of things. Comput. Electric. Eng. 86, 106733 (2020)

13. Lalwani, P., Das, S.: Bacterial foraging optimization algorithm for ch selection and routing in wireless sensor networks. In: 2016 3rd international conference on recent advances in information technology (RAIT), pp. 95-100 (2016). https://doi.org/10.1109/RAIT.2016.7507882

14. Deepa, S.R., Rekha, D.: In: Mandal, J.K., Sinha, D. (eds.) Bacterial foraging optimization-based clustering in wireless sensor network by preventing left-out nodes, pp. 43-58. Springer, Singapore (2020)

15. Janakiraman, S.: A hybrid ant colony and artificial bee colony optimization algorithm-based cluster head selection for IoT. Procedia Comput. Sci. 143, 360-366 (2018). https://doi.org/10.1016/j.procs.2018.10.407

16. Shankar, T., Shanmugavel, S., Rajesh, A.: Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evol. Comput. 30, 1-10 (2016). https://doi.org/10.1016/j.swevo.2016.03.003

17. Kumar, R., Kumar, D.: Hybrid swarm intelligence energy efficient clustered routing algorithm for wireless sensor networks. J. Sens. 2016 (2016)

18. Kaur, S., Mahajan, R.: Hybrid meta-heuristic optimization based energy efficient protocol for wireless sensor networks. Egypt. Inf. J. 19(3), 145-150 (2018). https://doi.org/10.1016/j.eij.2018.01.002

19. Battar, S., Kumar, R.: A hybrid approach to increase network lifetime in WSN using PSO and firefly optimization. Proceedings of 2019 3rd IEEE international conference on electrical, computer and communication technologies, ICECCT 2019, pp. 1-7 (2019) https://doi.org/10.1109/ICECCT.2019.8869445
20. Pitchaimanickam, B., Murugaboopathi, G.: A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks. Neural Comput. Appl. 32(12), 7709–7723 (2019). https://doi.org/10.1007/s00521-019-04441-0

21. Alghamdi, T.A.: Energy efficient protocol in wireless sensor network: optimized cluster head selection model. Telecommun. Syst. 74(3), 331–345 (2020). https://doi.org/10.1007/s11235-020-00659-9

22. Li, X., Keegan, B., Mtenzi, F.: Energy efficient hybrid routing protocol based on the artificial fish swarm algorithm and ant colony optimisation for WSNs. Sensors (2018). https://doi.org/10.3390/s18103351

23. Yue, Y., Cao, L., Luo, Z.: Hybrid artificial bee colony algorithm for improving the coverage and connectivity of wireless sensor networks. Wireless Pers. Commun. 108(3), 1719–1732 (2019). https://doi.org/10.1007/s11277-019-06492-x

24. Karaboga, D., et al.: An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06. Erciyes University, Kayseri/Türkiye (2005)

25. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002). https://doi.org/10.1109/MCS.2002.1004010

26. Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences, pp. 10–2 (2000). https://doi.org/10.1109/HICSS.2000.926982

27. Su, W., Lee, S.-J., Gerla, M.: Mobility prediction and routing in ad hoc wireless networks. Int. J. Network Manage 11, 3–30 (2001)

28. Goldsmith, A.: Path loss and shadowing, pp. 27–63. Cambridge University Press, Cambridge (2005)

**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Authors and Affiliations

Gérard Kponhinto<fnref n="1" />,<fnref n="2" /> ID · Sondès Khemiri-Kallel<fnref n="1" /> ID ·

Ado Adamou Abba Ari<fnref n="1" />,<fnref n="3" />,<fnref n="4" /> ID · Abdelhak Mourad Gueroui<fnref n="1" /> · Ousmane Thiare<fnref n="2" /> ID ·

Junseok Hwang<fnref n="5" />

<figure><img src="image_25.png" /></figure>

Gérard Kponhinto
g-rard.kponhinto@uvsq.fr

Sondès Khemiri-Kallel
sondes.khemiri-kallel@uvsq.fr

Ado Adamou Abba Ari
adoadamou.abbaari@gmail.com

Abdelhak Mourad Gueroui
mourad.gueroui@uvsq.fr

Ousmane Thiare
ousmane.thiare@ugb.edu.sn

Junseok Hwang
junhwang@snu.ac.kr

<figure><img src="image_26.png" /></figure>

<footnote n="1">1 DAVID Lab, Paris-Saclay University - UVSQ, Versailles, France</footnote>

<footnote n="2">2 LANI Lab, Gaston Berger University, Saint-Louis, Senegal</footnote>

<footnote n="3">3 LaRI Lab, University of Maroua, Maroua, Cameroon</footnote>

<footnote n="4">4 CREATIVE, Institute of Fine Arts and Innovation, University of Garoua, Garoua, Cameroon</footnote>

<footnote n="5">5 Global R&DB Center, Seoul National University, Seoul, South Korea</footnote>