Flying ad hoc sensor network (FASNET) for Internet of Things (IoT) consists of multiple unmanned aerial vehicles (multi-UAVs) with high mobility, quick changes in topology, and diverse direction. The flying multi-UAVs were operated remotely by human beings or automatically by an onboard system. The applications of multi-UAVs are remote sensing, tracking, observing, and monitoring. It has a different nature compared to ordinary ad hoc network. The speed and diverse directions of multi-UAVs make it harder to route information in a desired way. Different issues may arise due to differences in unmanned aerial vehicle mobility, speed, diverse direction, and quick changes in topology. The researchers proposed conventional ad hoc routing protocols which has poor aspects for the flying ad hoc networks. They tried to tackle the issue by using the clustering approach that divides the network structure into multiple clusters, each with its own cluster head (CH). During the selection of CH and balance cluster formation, they consider only location awareness, neighborhood range, residual energy, and connection to the base station (BS) while ignoring the multi-UAVs distance, speed, direction, degree, and communication load. In this paper, we proposed bioinspired mobility-aware clustering optimization scheme based on bee intelligence foraging behavior for routing, considering relative mobility, residual energy, degree, and communication load during CH selection and balanced cluster formation. First, the clustering problem in network is formulated to dynamic optimization problem. An algorithm is designed based on bee intelligence, applied to select optimal UAVs CH and balanced cluster. The simulation results show that the proposed BIMAC-FASNET scheme performs better among existing clustering protocols in terms of link-connection lifetime, reaffiliation rate, communication load, number of UAVs per cluster, CH lifetime, and cluster formation time.
Flying ad hoc sensor network (FASNET) for Internet of Things (IoT) consists of multiple flying nodes, i.e., unmanned aerial vehicles (UAVs) and ground segments (GSs) [
In early stages of flying ad hoc sensor networks, a single UAV was used to monitor, control, observe, and sense the objects or environment, but due to failure of a single UAV, no other UAV was available to maintain communication. Today with the use of multi-UAVs, the failure of single UAV does not disturb the communication because multiple UAVs are used to reconfigure and maintain communication among UAVs and GS. Due to the dynamic nature of flying ad hoc sensor network, the UAVs may have the issue of mobility, energy, etc. The advantages of multi-UAV network are that it works in coordination and collaboration, which does not affect the overall performance of network. In multi-UAV system, the base station or controller or server receives information from root UAVs. The root UAVs have more power and feature to obtain information from the member UAVs in the range.
The exchange of information among multi-UAVs is based on the cluster approach that divides the network structure into multiple clusters, each with its own head, called cluster head (CH). The cluster member nodes send their information to the CH in an aggregate manner with proper synchronization or organizational structure. Cluster-based routing avoids the collision during communication of nodes. Multihop networks with CH provide low latency as compared to flat routing. The topology changes in this scheme are adjusted locally within the cluster and do not influence the whole network. In this way, the network becomes more scalable; the topology information aggregates, achieves the routing efficiency, and forms stable and balance network [
Cluster-based routing is suitable to provide reliable communication between UAVs and GS. The main objectives are to dynamically update network topology, resolve the issues such as updating routing tables, and deal with high-speed UAVs, which means high mobility.
Cluster-based routing in FASNET is very constructive with following advantages [ Use aggregation approach to obtain information from the multi-UAVs in the cluster by CH instead of from the whole flying ad hoc network. Due to small size of clusters, the intercluster or intracluster movement of nodes updates their routing information locally about the change in topology that show stability and efficiency at node and cluster level. Use of clusters reduces the routing overhead at CH level as compared to flat network structure in which all the nodes communicated through router. UAVs under the cluster heads, only communication with their CH or sometimes with their gateway CH, and the communication of flying UAVs are constrained to preserve communication bandwidth. Reuse of frequency in the clustering approach that provides communication without collision and nonoverlapping clusters. The unused bandwidth can be utilized for some other beneficial purposes.
The applications of this emerging area are unlimited. In early stages, the technology that was used to destroy in battlefield is now applied for human being betterment in the variety of fields to minimize the trespass of human. The rising interests of users in the multi-UAVs technology have developed new era of applications.
The application of multi-UAVs is to observe the particular area safe and crime free. The UAVs with thermal of hyperspectral sensors capture and monitor the area of interest, objects, and events during patrolling. These UAVs periodically patrol in the given area to observe, inspect, or secure from doubtful or unfamiliar activity. It can detect any type of change in the given range such as weapons, crime, and drugs [
The UAV technology is used to search or rescue the lost personnel and hostages in the area that are not easily accessible for human being. The flying UAVs are integrated with thermal or spectral sensors to find the position of lost person [
The UAVs have capability to carry payloads. Many of the companies use UAV technology in electronic commerce for shipping and delivering products to improve the quality of their services and decreasing the costs. People get instant services for the ordered products and pizza at homes [
The UAV technology in disaster relief management obtains the required information quickly to predict the incidence of disaster. The human may face difficulties or obstacles that prevent analysis of the entire affected area [
The multi-UAV technology is used in most of the engineering firms to manage and monitor the projects like installation of transmission line, industry, and airport planning infrastructure and oil pipelines and maintain inspection activities. The unmanned aerial vehicles are used to analyze construction and verify their progress and quality. It is also used to evaluate the conditions of the environment in order to prevent possible calamities [
The application of this emerging area is to monitor or resolve the issue of traffic jam and numerous accidents in complex infrastructure of urban and metropolitan areas [
FASNET is an emerging area that builds an interconnection among flying sensors (UAVs) and GS. Routing of information in flying node is challenging task and hot issue that took the attention of the research community in the recent years. Although a number of researchers contributed in the reliable communication [
The rest of the paper is organized as that how to develop mobility-aware cluster-based routing, which are suitable for maximum scenarios and conditions of FASNET. In Section
In this section, the categories of swarm intelligence-based optimization schemes identified are used for mobility-aware clustering in flying ad hoc sensor network. The existing clustering protocols are analyzed with different perspectives, and also the mobility model used for multi-UAVs in flying ad hoc sensor networks is discussed in this section.
The exploration and investigation of multiple UAV clustering scheme is an emerging area for future advancement of UAVs technology. The use of multi-UAVs matches with the concept of swarm, which comes from the nature such as the coordination of ants, bees, particles, firefly, wolfs, and frogs. Swarm intelligence means that the intelligent behavior is exhibited by social creatures [
Taxonomy of mobility-aware clustering protocols based on swarm intelligence optimization.
Physarum-inspired network algorithm (PAs) was first time described by Howard in 1931 [
The GSO is an optimization algorithm based on swarm intelligence first time introduced by Krishnan and Ghose [
GWO inspired by the grey wolf is metaheuristic introduced by Mirjalili et al. [
The optimization technique ACO is used to discover the shortest path of target by evaluating the ant colony foraging behavior. ACO for the first time was proposed in 1991 by Colorni et al. [
The optimization technique, bee colony algorithm, is used to find quickly the best food source by swinging or dancing (waggle dance) in the range of food source. During the waggle dance of bees, it makes a colony and exchanges information about the food source. The most common algorithm used for optimization is honey bee mating optimization (HBMO) based on the bee breeding behavior [
The mobility of UAVs in FASNET is very high, and its model is based on UAVs location, speed, direction, and velocity. The mobility models are planned to provide the complete pattern of mobile UAVs and how these UAVs location, speed, direction, and velocity change over specific time interval. It is used for simulation purposes during evaluation of new protocol. It plays an important role in the performance of multi-UAVs mobility-aware clustering protocols. The mobility models are necessary to match the movement pattern of real-life applications in practical environment. Otherwise, the simulation result and conclusion drawn from analysis for the proposed scheme may be not effective. Some of the mobility models for FASNETs are discussed in this section, each with its own pattern of mobility that will affect the performance of the mobility-aware clustering protocols [
We classify the existing mobility models for UAVs protocol simulation in five categories, as shown in Figure
Mobility models for flying ad hoc sensor network.
Summary of the mobility models and their network characteristics [
Mobility model | Category | Smooth curve | Smooth acceleration | Microvariation | Connection awareness | Collison avoiding |
---|---|---|---|---|---|---|
RWP [ | Random | ✗ | ✗ | ✗ | ✗ | ✗ |
RW [ | Random | ✗ | ✗ | ✗ | ✗ | ✗ |
RD [ | Random | ✗ | ✗ | ✗ | ✗ | ✗ |
MG [ | Random | ✗ | ✗ | ✗ | ✗ | ✗ |
GM [ | Time dependent | ✗ | ✓ | ✓ | ✗ | ✗ |
BSA [ | Time dependent | ✓ | ✓ | ✓ | ✗ | ✗ |
GM [ | Time dependent | ✗ | ✓ | ✓ | ✗ | ✗ |
ST [ | Time dependent | ✓ | ✗ | ✗ | ✗ | ✗ |
SRCM [ | Path planned | Partially | ✗ | ✗ | ✗ | Partially |
PPRZM [ | Path planned | Partially | ✗ | ✗ | ✗ | ✗ |
CLMN [ | Group | ✗ | ✗ | ✓ | ✓ | Partially |
NC [ | Group | ✗ | ✗ | ✓ | ✓ | ✗ |
PRS [ | Group | ✗ | ✗ | ✓ | ✓ | ✗ |
DPR [ | Topology control | ✓ | ✗ | ✗ | ✗ | ✗ |
SDPC [ | Topology control | ✗ | ✗ | ✗ | ✓ | Partially |
MPB [ | Time dependent | ✗ | ✓ | ✗ | ✓ | ✗ |
The most commonly used category by the research community for network simulation is the pure randomized mobility models in which the multiple UAVs movement are independent and random. The applications of this category are an environmental sensing, traffic, and urban monitoring. The pure randomized mobility models are as follows: Random waypoint The random waypoint (RWP) mobility model was first time proposed in 1996 by Johnson and Maltz [ Random walk Random walk (RW) mobility model was first time introduced in 1905 by Pearson [ Random direction The mobility model, random direction (RD), was introduced to resolve the issue in the random waypoint mobility model, i.e., only focusing UAVs in the center part of simulation area [ Manhattan grid The mobility model Manhattan grid (MG) is proposed to describe the movement of UAVs in an urban area using grid road topology [
The time-dependent mobility (TDM) models use different mathematical equations to change the speed and direction smoothly instead of sharp speed and direction changes. The applications of this category are environmental sensing and search and rescue operations. These mobility models are as follows: Gauss–Markov Liang and Haas introduced Gauss–Markov (GM) mobility model for the protocol simulation in the wireless ad hoc network [ Boundless simulation area The boundless simulation area mobility model relates the current speed and direction with previous UAVs speed and direction. In this model, the UAVs smoothly change the speed and direction. The UAV moving from one side reaches the target area of simulation and then continues its movement and reappears in the opposite direction of that simulation area. In this mobility model, during simulation, the undesirable effects occur; i.e., the UAVs moving out from an edge and entering from another edge [ Smooth turn In smooth turn (ST) mobility model, the UAVs select a point in the space and move around it until the UAV chooses another turning point. To ensure smooth movement on the trajectories, the selected point must be perpendicular to the UAV direction. The time spent by the UAV to move around the current turning point is modeled to be exponential distributed. This model allows the mobile UAVs to move freely across spatial and temporal coordinates. This model captures accurately the smooth movement pattern of UAVs without enforcing redundant constraints. The main issue with this model is the lack of method for collision avoidance and the reflection effects from the boundary, i.e., the impact of mobile UAV to enforce for sudden change in its direction on the boundary [
The path planned (PP) mobility models work on the preplanned paths to be followed by UAVs during the simulation. Each UAV movement in these models is based on some specific patterns to move from one point to another, and the pattern may be changed or it follows the same pattern [ Semirandom circular movement Semirandom circular movement (SRCM) mobility model is developed for UAV-based ad hoc networks in which the UAV movements are in a circular form or curving manner [ Paparazzi The Paparazzi (PPR) mobility model is with a stochastic versatility that imitates properties of paparazzi in the unmanned aerial vehicle system [
Reference point group (RPG) mobility model forms a group of UAVs for movement in a specific domain [ Column Sanchez and Manzoni proposed column (CLMN) mobility model for multihop wireless networks where mobile UAVs exchange information with low power, low capacity of transmission channel, and shared medium [ Nomadic community The mobility model nomadic community (NC) is proposed to able the UAVs with random movement around the reference point. The reference point can also move randomly at each time interval. The UAVs in the group form can share the common space as defined by the unique reference point. To avoid the collision among UAVs, the flying space divides managing the distance among UAVs pair. In this mobility model, the unexpected movement may occur because of changes in direction, speed of mobile UAVs, and reference points. The issue is that the UAVs smooth turn and the random speed changes are not present in this model [ Pursue This pursue (PRS) mobility model is similar to the nomadic community model and shares the same features. The mobile UAVs during pursuing the target use a simple random relative motion. In this model, the UAVs follow the particular target that moves in a certain direction. It works like the police officer following and trying to catch the escaped criminals [
Topology control-based mobility model is the new category to ensure the real-time communication among UAVs during the mission. During the mobility of UAVs, it is a difficult task to maintain connectivity and exchange of information. This mobility model is able to control the network topology by reducing random movement and adding the UAVs aware movement according to the mission. Messous et al. [ Distributed pheromone repel Kuiper and Nadjm proposed mobility model named distributed pheromone repel (DPR) [ Self-deployable point coverage Self-deployable point coverage (SDPC) mobility model is proposed by Sanches-Garcia et al. [ Mission plan-based Mission plan-based (MPB) mobility model is designed for UAV-based ad hoc networks [
The communication of flying nodes (UAVs) is challenging task due to sensitivity, mobility, and dynamic topology of multiple UAVs. Due to the large number of flying nodes, the partition of the nodes into different nonoverlapping clusters becomes an optimization problem. Once the parameters are optimized, it can used for different scenarios. However, to the best of author knowledge, none of the existing techniques utilized a combination of multiple parameters for selection of cluster heads in FASNET. This section will provide a support for the necessity of the FASNET cluster optimization using swarm intelligence. It also explains how to provide optimum cluster organization in order to form balanced cluster with specified node degree, energy, control routing overhead, and provide flexibility with high mobility of nodes [
The researchers proposed mobility-aware clustering protocols for FASNET in [
BIMPC is mobility-aware clustering protocol with mobility prediction using foraging model of Physarum polycephalum [
Khan et al. in [
Khan et al. in [
The existing clustering protocols [
Summary of selected cluster-based routing protocols (CH selection parameters).
Ref. | SIO scheme | Energy | Mobility | Loc. awareness | Deg. | Dist. |
---|---|---|---|---|---|---|
[ | [ | ✓ | ✓ | — | — | — |
[ | [ | ✓ | — | ✓ | ✓ | ✓ |
[ | [ | ✓ | ✓ | — | ✓ | ✓ |
The cluster formation in the existing clustering protocols did not consider the reclustering and balance cluster formation, as shown in Table
Summary of selected routing protocols (cluster formation).
Ref. | CH election | Neighbor criteria | Parameters | Reclustering | Balanced cluster |
---|---|---|---|---|---|
[ | Distributed | 1-hop | Mobility, energy | Yes | — |
[ | Weighted metric | M-hop | Energy, distance | — | — |
[ | Weighted metric | M-hop | Neighbors, distance | Yes | — |
The performance metrics of the selected routing protocols are not tested for cluster building time, packet delivery ratio, and cluster lifetime, as shown in Table
Summary of selected routing protocols (performance metrics).
Ref. | Comparison scheme | Routing overhead | Delay | Throughput | Delivery ratio | Cluster building time | C. lifetime |
---|---|---|---|---|---|---|---|
[ | [ | ✓ | — | — | — | — | ✓ |
[ | [ | — | — | — | ✓ | ✓ | ✓ |
[ | [ | — | — | — | ✓ | ✓ | ✓ |
Summary of selected routing protocols (simulation study).
Ref. | Mobility model | UAV speed | Simulation area | Distance | No. of UAVs | Simul. time | Flight time |
---|---|---|---|---|---|---|---|
[ | — | 40–70 m/s | 50 ∗ 50 km2 | — | 100 | — | — |
[ | RPM | — | 1 × 1, 2 × 2, 3 × 3 km2 | 5 m | 15, 20, 25, 30, 35 | 120 s | — |
[ | RPM | — | 1.5 × 1.5 km2 2.5 × 2.5 km2 | 5 m | 15, 20, 25, 30, 35 | 120 s | — |
The clustering problem in FASNET is formulated to a dynamic optimization problem. The CH selection and cluster formation algorithm are designed based on the objective function obtained as a result of problem formulation. The foraging properties of bee intelligence are used for the CH selection and cluster formation. The honeybees select food sources with more nectar in an efficient manner. The foraging behavior of honeybees is used to efficiently select the appropriate UAVs as CHs just like the bees select the food sources with more nectar. The cluster maintenance mechanism is developed to accommodate the topology changes in an efficient manner. The efficiency of bee optimization has been tested [
Structure of UAV clusters in FASNET.
The modeling of the clustering problem in FASNET is a dynamic optimization problem, and we assume the FASNET to a graph
The clustering problem representation over a number of graphs is actually the identification of CH sets. We try to keep the same number of UAVs in each CH sets keeping the size of CH sets least possible. The structure of UAV clusters in FASNET depicted in Figure
To calculate the fitness of UAVs, the following parameters are considered.
In BIMAC, the role of CH-UAV is assigned to UAV based on the combined weights of different parameters. Mobility is one of the important parameters to consider during the CH-UAV selection process. The mobility may cause unstable clusters due to the regular link update of UAVs. Hence, the mobility of UAV needs special attention to achieve stable and long-life clusters. The UAV transmits signals in a circular area with radius
Transmission zones.
The UAV suitable to perform the duties of CH-UAV is selected based on UAV relative mobility. The relative mobility is computed by using the signal strength of UAVs. The success of hello packets between sender UAV and receiver UAV represents its distance. To calculate the relative mobility of
For each nearby
In the proposed BIMAC, the extra significance is set to the choice of network parameters. Parameters have a strong impact on the network lifetime, cluster lifetime, load balancing, and reaffiliation. The number of UAVs in FASNET plays a significant role because it ensures that the lowest number of UAVs is required to cover the targeted region. To calculate the minimum number of UAVs required to cover a certain region, the projected area and the transmission range of UAVs are required. This can be achieved by dividing the total area by the area of the hexagon. The BIMAC will manage the overlapping clusters because the hexagon is considered instead of a circle for easy calculations:
The minimum number of UAVs
Here,
In the equation above,
Hence, the projected number of neighbors of a node in the FASNET can be obtained by the following equation:
The UAV having maximum neighbors is the best candidate to become a CH-UAV.
Here,
The UAV with high energy is the most appropriate candidate for the role of UAVs CH. The UAV with low energy has minimum chances or no probability to become a CH. The percentage of CH-UAVs
Fourth, the number of clusters calculated before selecting the CH for UAVs is taken. After the computation of all UAVs weight, the fitness of CH-UAVs set is calculated by a minimization function. The minimization function used in the proposed BIMAC is discussed in the next section.
The following equation is used to compute the weight of
The number of clusters
The number of CH-UAVs in FASNET is represented by
The UAVs-CH set comprises UAVs no less than 3-hop distance. After the computation of all UAVs weight, the fitness of UAVs-CH set is tested by the minimization function (Algorithms
//initial values of UAV degrees and energies //summation of all nodes remaining energy and degree //average UAV neighbors if //compute weight of UAV w.r.t energy else if //Compute weight of UAV w.r.t UAV degree else if //compute weight of UAV w.r.t UAV mobility //calculation of weighting factor values //the weight values of UAVs and total clusters are returned
//random CH-UAVs selection CH-UAVs [ //Compute the fitness of solution // replace //visit all employee bees //select new UAVs from neighbors //calculate the probability select a set of CH-UAVs subject to the probability
The notations used in BIMAC algorithm are given in Table
Algorithm notations.
Symbols | Definition |
---|---|
Total UAVs in FASNET | |
Clusters in FASNET | |
Projected/average value of UAV degree | |
UAV [ | Array of UAV IDs |
Fitness value of CH set | |
Sum of all UAVs degrees | |
Sum of all UAVs energy | |
Degree of UAV | |
CHs | Cluster heads |
Vector of UAV weights | |
Sum of UAV weights | |
eb | Employee bee |
Patch size | |
Random variable [ | |
Probability of UAV |
The proposed solution is validated via a series of simulation experiments, and the results are compared with [
Clustering schemes in FASNET based on bee intelligence refer how to form and maintain mobility-aware balanced clusters and how to select optimum CHs in each cluster. Due to the large number of flying UAVs, the partition of the UAVs into diverse nonintersecting clusters becomes an optimization issue [
The focus in this section is on cluster-based routing with optimization schemes using bee intelligence showing how to provide optimum cluster organization in order to form a balanced cluster with dynamic node degree, control communication load, and provide flexibility with a high mobility of UAVs.
The frequent change in the FASNET topology brings an additional challenge of mobility. Due to the autonomous system in most scenarios, the path selection is based on the previous speed and direction. In flying ad hoc networks, the random waypoint mobility model is used in which flexibility for paths selection is favored to flying UAVs. The bioinspired mobility-aware clustering scheme having a low reaffiliation rate is considered better among others. The mobility during CH selection and cluster formation is considered.
The flying sensor nodes mobility in FASNET is very high compared to other ad hoc networks [
To select the CH-UAVs in BIMAC, a random set of UAVs is selected initially, as shown in the flow chart in Figure
Flowchart of BIMAC algorithm.
The fitness of the solution is evaluated on the basis of the following equation:
Here,
After the completion of the initial phase, the local search of bees will be initiated once the optimal set of CH-UAVs has not been found. The scout bees perform the local search in the neighborhood and proposed new solutions. Hence, the employed bee’s memory will be updated based on available information either visual or local. The new information will be overwritten on the old information of the employed bee’s memory. The memory may be updated on the basis of tests carried out via equation (
In this scenario, if the volume of CH-UAVs previous nectar is greater than volume of CH-UAV new calculated nectar, then the bee maintains the previous information in her memory. If the volume of the new nectar is grater, the bee remembers the novel nectar volume and disremembers the earlier (stowed in bee’s memory).
When the employed bee arrives to the hive after completing the search process, it performs a special type of dance on the dancing floor. The dance is performed to communicate the information of CH-UAVs nectar volume and direction. The direction of dance also shows the food source direction. The dance may be performed on a different pattern to communicate different types of information with other bees.
The onlooker bees watch the dance performance of employed bees on the dance floor in a careful way to find the direction and volume of nectar.
The novel CH-UAV will be chosen on the basis of their possibility associated with the volume of nectar
In the equation above, the patch size to search neighbors is
Once the CHs are selected, the result will be stable cluster formation. To minimize the reclustering for the formation of stable clusters, relative UAVs mobility is considered during CHs selection. The CH election, neighbor criteria, and clustering parameters must be kept in mind during the process of balanced cluster formation. The summary is given in Table
Once the CH-UAVs are selected, it broadcasts a message in the FASNET. The message contains information about their location, status, and ID. The UAVs that receive the message will join the cluster. The cluster member communicates this information with CH-UAV. If a UAV receives the membership message from more than one CH-UAV, the UAV will join the nearest CH-UAV. In case of tie, the UAV will choose randomly to join a CH-UAV.
This section provides simulation study and performance evaluation of the proposed bioinspired mobility-aware clustering (BIMAC) algorithm compared for the first time with the existing UAVs clustering algorithms BIMPC [
Simulation parameters.
Parameters | Values |
---|---|
Field size | 50 ∗ 50 km2 |
No. of UAVs | 100 |
Distance among UAVs | 10 m |
Transmission range | 10 km–20 km |
Standard with MAC | IEEE 802.11 & 802.16 |
Spectrum | 2.3–2.5 GHz and 3.4–3.5 GHz |
Mobility model | RWP model in Section |
Speed of UAVs | 40 m/s–70 m/s |
UAVs location strategy | Random placement |
The connection between the CH and its member UAVs play a vital role due to the mobility of UAVs and communication range. The UAV’s intracluster and intercluster fast movement made frequent changes in the topological structure. The stable link connection for a long time increases the overall network lifetime and the performance of the algorithm. The simulation result in Figure
Average link connection lifetime vs UAV speed.
The duration of link connection is also dependent on the UAVs range of communication. The UAVs’ movement outside of the CH communication range decreases the probability of the link connection lifetime with its CH, which also degrades the network lifetime. The simulation result in Figure
Average link connection lifetime vs maximum communication range.
The proposed BIMAC is based on honeybees foraging model and considering the UAVs mobility, energy, degree, and communication load for selecting the optimum CH. Thus, the efficient mechanism for selecting the CH increases the CH lifetime with high mobile UAVs. The CHs in BIMAC algorithm have more stability compared to other algorithms. The simulation result in Figure
Average CH lifetime vs UAV speed.
The simulation result in Figure
Average CH lifetime versus UAVs communication range.
The number of UAVs in each cluster (UAV degree) should be approximately the same or minimum in order to balance the load on each CH. The degree of UAVs during balance cluster formation plays a vital role. The simulation result in Figure
UAVs degree versus UAV speed.
The simulation result in Figure
UAV degree versus maximum communication range.
The optimal CH selection is based on the very low reaffiliation rate. The changes in the topology are mainly due to the UAV speed. The multiple parameters, i.e., mobility, energy, degree, and communication load, are considered during the selection process of high mobility-aware CH. The simulation result in Figure
Average reassociation time versus UAV speed.
The simulation result in Figure
Average reassociation time versus UAV communication range.
The foraging behavior of honeybees used for cluster formation considers UAV mobility, energy, degree, and communication load. The total time taken to form a cluster is known as cluster formation time. The CH selection and the membership of UAVs in the clustering process of the proposed algorithm minimize the cost in terms of time for balance cluster formation. The simulation result in Figure
Cluster formation time versus UAV speed.
The simulation results in Figure
Cluster formation time versus UAV communication range.
With the advancement of sensor and flying nodes technology, the development of low-cost flying nodes becomes easy. The world is moving towards real applications of IoT network; the flying segment of IoT network is demanding area for various applications. In this paper, the recent developments in FASNET with a special focus on cluster-based swarm optimization for IoTs have been studied. The latest trends in the area have been analyzed, and the future directions are stated. A taxonomy has been presented on the basis of different swarm intelligence techniques for cluster formation. Futuristic clustering schemes have been analyzed. The major findings of each scheme have been stated. The authors focus on part of optimization problem for clustering in FASNET such as node degree or mobility or remaining energy and communication load. According to the author’s knowledge, none of the existing schemes had focused on all the parameters mentioned above. It is our recommendation that at least mobility, energy, and degree of nodes may be considered during the CH selection process.
In this paper, a bioinspired mobility-aware clustering optimization in flying ad hoc sensor network for internet of things BIMAC-FASNET has been proposed. The clusters in FASNET are formed using the honey bee optimization algorithm. The CHs are selected on the basis of UAVs relative mobility, degree, and remaining energy. The member UAVs join the nearest CHs in the vicinity. In case of tie, a UAV can join a CH randomly. The simulation results show that our proposed scheme outperforms existing schemes in terms of link connection lifetime, reaffiliation rate, communication load, number of UAVs per cluster, CH lifetime, and cluster formation time.
The research will help academia to explore new areas of research. More researchers may be attracted to unfold and solve the issues in smart agriculture, intrusion detection, smart cities, smart transportations, and smart buildings using low-cost flying sensors. The society will benefit by using advance technology and work remotely with ease.
The data used to support the findings of this research are included within the article.
The authors received no financial support for the research and publication of this article.
The authors declare that there are no conflicts of interest associated with this publication.