Obstacle Aware Clustering-Based Energy Efficient Data Collection Scheme Using UAV Data Collector In Large Wireless Sensor Networks

Unmanned Aerial Vehicles (UAVs) are now employed in various domains, including precise agriculture, power line inspection, environmental monitoring, and safety surveillance [1–3]. Given that each application has unique requirements such as long endurance, low energy consumption, high maneuverability, and stability it is challenging to incorporate all these benefits into a single Unmanned Aerial Vehicle (UAV) [4], [5]. Consequently, the development of various drone types has emerged, including Ver-tical Takeoff and Landing (VTOL), Horizontal Takeoff and Landing (HTOL), hybrid designs, and bio-inspired UAVs. UAV-based target positioning methods fall into two main categories: active and passive [6]. Active methods involve equipping UAVs with professional sensors like laser range finders and radars to directly measure the distance to the target. Passive methods rely on optical sensors, such as multi-spectral or infra-red cameras, and can be further divided into monocular and stereo vision approaches [7]. Monocular vision methods utilize a single camera and calculate the target’s position relative to the UAV using techniques like triangulation or depth estimation with neural networks [8]. Stereo vision typically involves binocular cameras or simulates them using photos captured by monocular cameras from various positions. By analyzing parallax information, the position coordinates of the target can be determined [9]. Active target positioning methods, which require UAVs to carry high-precision sen-sors, significantly increase costs and power consumption while limiting flight range [10]. These methods are unsuitable for large-scale target positioning. Additionally, stereo vision localization using binocular cameras faces limitations related to base-line length. A shorter baseline cannot accurately calculate depth for distant targets, while a longer baseline affects UAV flight stability and feasibility [11]. Still, every approach comes with its own set of limitations. Active target positioning methods, which require UAVs to carry high-precision sensors, significantly increase costs and power consumption while limiting flight range [10, 12]. These methods are unsuitable for large-scale target positioning. Additionally, stereo vision localization using binocular cameras faces limitations related to baseline length [13]. A shorter baseline cannot accurately calculate depth for distant targets, while a longer baseline affects UAV flight stability and feasibility. The stereo vision localization method based on monocular cameras relies on UAV displacement to achieve stereo vision [14]. However, it does poorly for moving object localization due to significant time differences in acquiring stereo vision images [15]. Similarly, the monocular camera-based triangulation method requires known reference scales for constructing spatial triangles, and monocular depth estimation depend on scene similarity and suitable scale information. Consequently, both methods are unsuitable for complex target localization [16]. These algorithms should predict low-activity periods without compromising the net-works responsiveness to sudden changes or data requests. Another technique involves energy harvesting, where UAVs and sensors extract energy from environmental sources like solar, wind, or vibrations. While this offsets energy usage, it may add weight and complexity to the UAVs [17].

Over time, WSNs have been extensively utilized to collect data across various application domains. [18]. Employing UAVs in conjunction with a WSN architecture can enable accurate monitoring services in many different types of economic and human interaction domains, such as environment monitoring, security, surveillance, and search and rescue operations [19, 20]. The results are better for both amount of data collected and time for data collection [21]. Literatures referred it by a variety of names, including aerial robots, drones, UAVs, and aerial platforms. There are several constraints to overcome when integrating UAV with WSN, such as data gathering, trajectory planning, and obstacle avoidance strategies [22]. In the presence of obstacles, optimal selection of stoppage points and route finding are primary goals during UAV trajectory planning and data acquisition. Diverse tasks can be performed using this integrated system, but this will lead to concerns about consistent functioning and mission accomplishment in a complex environment (even with the obstacles) [20]. This makes it difficult to meet the operational limitations [23]. It is both vital and challenging to find an efficient path in a scenario with full of obstacles and to make sure the path can cover the designated stop sets in an efficient manner. Battery life is a key domain in the design of WSNs, hence it is difficult to handle both data transmission and extend the life of WSNs. Data collection in WSNs poses challenges due to the difficulty and cost associated with maintaining an existing communication infrastructure or establishing a new communication zone [24]. Data communication significantly contributes to energy consumption in WSNs, resulting in a costly data delivery process characterized by excessive delay in large WSNs [25, 26]. UAVs has a feature of unrestricted mobility, due to which it is frequently utilized to fly and operate over a large monitoring network region and gather effective data from the terrestrial WSN [27–29]. UAV-aided WSNs utilize UAVs as sinks to collect data from terrestrial WSN nodes. The advantages of utilizing collaborative systems for large-scale monitor-ing have been emphasized, since they enhance mobility, accessibility, and efficiency of data aggregation. The sensor nodes (SNs) are deployed to accumulate data about the monitoring area and communicate to the UAV. The intelligent cooperation of UAV with WSN can result in parameter optimization, including data gathering, obstacle-aware communication, and energy consumption [28, 30].

Hence, it is vital to explore these concerns and to suggest appropriate data gathering schemes. This helps not only in enhancing the efficiency of whole operation but also increases the lifetime of WSN. Over all, it can be established that the key challenges for achieving UAV-aided data gathering in WSN comprises the UAV route designing, UAV flight time, impact of obstacles, consumption rate of energy for both UAV and sensor nodes, communication among the sensor nodes and amidst the ground sensor nodes and UAV.

1. 1. Motivation

In several literatures, it is presented that UAV helps in extending the range of monitor-ing area and enhances the output of whole network. In this respect, several researchers proposed methods for employing UAVs to collect WSN data, with a primary empha-sis on path-planning algorithms to optimize the path that a UAV will follow [31–33]. However, the practice of employing UAVs is not effortless since the energy efficiency amongst the UAV and the terrestrial WSN has to be tuned carefully. The task at hand involves maintaining a stability between the energy consumption and data gath-ering efficiency of UAVs from WSNs by implementing solutions that optimize UAV trajectory, minimize flight time, and minimize delay in data transfer. However, the UAV’s trajectory planning and WSN organization have a considerable impact on the system’s performance and energy efficiency [34]. Although scholars have keen interest in WSN and UAV, work on trajectory planning and optimization is still underway. These studies differ over both the optimization scheme used as well as the objec-tive function exploited. Furthermore, in WSN monitoring area, various obstacles may exist and these obstacles can significantly affect the performance of the network [20, 29, 31, 35, 36]. Still, there remains an open challenge in determining the path for UAV in 3D scenario with obstacles such that performance of the terrestrial WSN is up to the mark. It is critical to find a solution to this issue in a dynamic environment with obstacles and achieve better service lifetime. In the UAV–assisted WSN data col-lection, it is again a naïve method to gather data from each sensor that is deployed in a large area. It is both troublesome and time consuming for the UAVs.

In this study, we suggest a collaborative UAV–WSN system driven by the present changing needs for monitoring and data collecting from large-scale geographical areas. The following factors need to be taken into account while designing a trajectory for a UAV in order to ensure effective data collecting from WSN. To gather WSN data, the UAV must fly above the selected rendezvous sites (RPs). Optimization of the trajectory length can further enhance the efficiency of data collection while lowering energy usage. Moreover, the path should be obstacle-free and each RP guarantees that effective WSN data is gathered from these RPs.

1. 2. Contributions

 A well-curated trajectory for UAV-aided data collection can significantly enhance the performance along with energy efficiency of the system. This work proposes a UAV-based data collection algorithm for WSN with Obstacles termed as Obstacle-Aware Clustering-based Energy Efficient Data Collection (OA-CBEEDC) to optimize the energy dissipation, data collection time and enhance network sustenance time simultaneously through the construction optimal clusters along with valid and obstacle-free route for UAV. The core uniqueness of the work is cooperative approach for UAV–WSN data gathering, which incorporates a convenient method for cluster formation and UAV route selection in three-dimensional environment with obstacles. The work proposes a hybrid Fire-Hawk and Lyrebird optimization approach for clustering purpose and meta-heuristic approach based on lyrebird algorithm to select obstacle-free as well as optimal path such that both UAV flight time and sensor node lifetime can be enhanced. The UAV collects data from the RPs keeping communication constraint in consideration and RPs are designated point where UAV hovers and collects data from the set of CHs. The UAV travels from one RP to another avoiding obstacle such that the path is obstacle-free as well as optimal. This approach enhances network lifetime, flight time as well as reduces data latency. Similar to other swarm-based algorithms, the proposed algorithm is a population-based approach that comprises key phases including initialization of population, computation of the cost fitness of individuals, search space exploration, exploitation and termination. The pro-posed optimization method is still different from the present optimization approaches. Among the recent algorithms, most of them largely employ the direct and shortest flight path length as the primary objective to model the ideal UAV route for data collection purpose [20, 21, 29, 37–39]. The key concept behind using this objective is that the shortest path will lead to minimum movement of UAV, this in turn leads to minimum energy consumption of UAV-WSN. But this approach ignores the fact that UAV consumes energy when head direction deviates [21, 29, 36, 38–40]. This work focusses on reducing both the length and angle cost of the path, minimize the energy consumption, and data latency. It includes operations like, decision making, fleeting and hiding, substitution and tuning and finally fine-tuning operation to discover an optimal route that is obstacle-free as well. The hiding phase is further divided into two stages where the algorithm decides either to explore or exploit the search space. This results into global optimum result and algorithm refrains from producing local optimum result. The best trajectory of UAV is obtained such that the path length is both obstacle-free and optimal, while reducing the data latency and optimizing the network lifetime along with flight time. The outcomes demonstrate the success of OA-CBEEDC method in terms of path length and data gathering efficiency when compared to other equivalent approaches. The major objectives of the proposed work are:

• To design a system model of UAV enabled WSN platform for enhancing the    efficiency of data gathering with higher network lifetime.
• To propose a new hybrid meta-heuristic optimization approach for clustering and selecting the optimal UAV path for making efficient data gathering by considering varied factors.
• To analyze the strength of proposed study by evaluating varied metrics and   comparing the results with other existing methods.

The organization of the research is: Section 2 and 3 details the related works with the problem statement and the detailed proposed methodology is presented in Section 4. The simulation result with analysis and discussion is presented in Section 5 and finally, the conclusion and future direction is presented in Section 6.

2. Related Works

UAVs have become a prominent area for research in recent years due to their low cost, ideal maneuverability, and flexible deployment. As collaborative UAV–WSN systems advancement, researchers are drawn to addressing new problems and analyzing the trade-off between performance and efficient use of resources [20]. The incorporation of UAVs with WSN has aided in the creation of applications that demand significant improvements in response time, data communication performance, efficiency, and flexibility [20, 29, 38, 41]. In certain geographical settings, such as remote, hostile, and harsh places, UAV-WSN collaboration is an effective means of data gathering, communication, and relay. The design of trajectory depends on the type of UAV used. Circular or rectilinear UAV routes are intended to offer extensive coverage and back-haul connections to additional nodes via multi-hop transmission [27, 42]. However, some efforts have employed UAVs that possess the ability to move in any direction and hover at a particular point. As a result, the path of these UAVs can be randomly selected over a monitoring region or by locating and connecting a group of UAV hovering locations [43, 44]. Nodes in WSN are deployed randomly over the network zone, hence UAVs can adapt more effectively considering monitoring region dimension is large. Furthermore, by concentrating on UAV hovering location, which influences UAV path distance and node energy consumption for data transmission, UAV trajectory may be improved to enhance network performance, save energy consumption, and prolong the operational lifetime of WSNs. It is always a challenge to find methods for efficiently and quickly transmitting sensor data to the sink. The literature contains variety of routing strategies for UAV-assisted WSNs [28]. Effective routing protocols is a must for improving the cooperation between UAVs and WSNs for aerial collection of data. Furthermore, the routing protocol’s energy efficiency and performance are greatly impacted by the way in which WSN is organized and UAV trajectory planning is carried out [34]. Over the past decade, the challenges of determining the best trajectory planning for UAVs under various restrictions has inspired a lot of research interest [20, 21, 29, 38, 39]. The optimization techniques and objective functions vary depending on the applications. To determine the best routes for a mobile node to gather data, several studies were conducted. For path planning, the most popular approach is to use the Traveling Salesman (TSP) formalization of the problem [45, 46]. In regard to this, path planning may be characterized as a TSP problem. It is difficult to solve a TSP, hence it can be regarded as an NP-hard problem [47]. Algorithms that solve TSP include precise, approximate and heuristic algorithms. The most popular types of these algorithms are heuristic and approximation algorithms. The work in [45] designed a model that employs UAV as data accumulator in WSN. Multi-objective function is used by the UAV to build the path, such that the energy consumption can be balanced among sensor nodes and the UAV. In [48], the authors explored different frameworks and strategies using UAVs to gather data that reduced latency in WSNs. The approach gathers data from each sensor node using UAV. This extended the network lifetime due to minimization in communication distance. However, the path length increased significantly. To moderate UAV flight length, some sensor positions were designated as UAV hovering site for data collection [43, 44]. Still, their suggested routes cannot be adopted when direct connection between sensors doesn’t seem possible, and this can significantly raise the energy use at each sensor in the UAV hovering site. Another method, as in [49], identified UAV hovering locations in a way that allowed the UAV to use time-division multiple access (TDMA) to connect with numerous nearby sensors at each hovering point. To locate UAV hovering points, Obstacles are depicted in [35] as barriers in the area; if a region has impassable barriers, the UAV is unable to fly over it. The utilized the grid approach to simulate the flight area, with the suppositions being that every step takes up the complete grid in the intended path and that the obstacle’s position and size remain constant throughout the UAV’s flight. The work [36] presented an energy optimization approach for UAV-assisted data collection in WSN. They utilized particle swarm optimization to create node clusters and extracted the best topology to minimize energy consumption. The work in [41] presents a combined optimization strategy for enhancing the lifetime and data collection latency. [34] ensured that a specific amount of data is reliably collected from each sensor node and also controlled the energy dissipation of sensor nodes. The design is expressed as a non-convex, mixed-integer optimization problem that is challenging to solve optimally. To find a suboptimal solution, the successive convex optimization technique is used, and an effective iterative algorithm is created. The information age (AoI) of the ground WSN nodes was employed by the authors of [50] to gather data via UAV. The time passed since the UAV departs from a particular node and the data uploading time are the parameters that are used to calculate the Area of Interest (AoI). They used two different methods to create the trajectories: dynamic programming (DP) and genetic algorithms (GA). The energy efficiency maximization problem in UAV-aided data gathering was presented by the authors of [51], taking into account the fairness between the cluster heads that interface with the UAV directly. In [52], UAV acts as a relay node for the ground sensor nodes. To reduce packet loss, the nodes in the UAV’s coverage area were split up into distinct groups based on their locations and variable transmission priorities. The following five elements make up the core framework for UAV-assisted data gathering described in [46] network deployment, node positioning, anchor point searching, UAV rapid path planning, and network data collection. To improve path planning efficiency while ensuring a reasonably short path length, they presented the grid-divided Fast Path Planning with Rules (FPPWR) method. Additionally, they verified the efficacy in terms of the acquired data volume, UAV path length, and data gathering time. In order to accomplish more effective data collection of the WSN, a UAV–WSN collaboration network was built in [53]. The UAV was utilized to gather data of the WSN and updated the UAV movement in accordance with the feedback data of the WSN. [47] examines and modifies UAV relay WSN based on NN-heuristic TSP algorithm (NN, DNN, and DDNN schemes) to improve data collection performance by shortening the path length, allowing for faster data delivery. [54] suggests a data gathering strategy where a UAV collects data from a group of sensor nodes that are encountered in a straight line. Their goal was to minimize the flight time, and they demonstrated that in the whole network containing numerous sensor nodes, the flight time minimization problem could be formulated as a DP problem. For each level of the DP, the subproblem becomes smaller and smaller until it can be solved with just one node. The benefits of collaborating the WSN with UAV were covered in [55][56]. Research is done on potential solutions for disaster management systems, and also presents problems and difficulties that are still to be resolved. In [57], a virtual grid point-based method is employed that is energy-efficient UAV-based data collection method. The path planning enables the UAV to visit optimal grid points for data collection purpose. Another method, known as spiral path planning, was put forth by the authors of [32] to plan the UAV path for nodes that are uniformly distributed over a circular region. They concentrated on rapid path planning using the appropriate grid division, and the outcomes demonstrated the efficacy of scheme in large-scale WSN environments. In [40], a k-means based clustering strategy is utilized, and a UAV is employed to gather data by flying over predefined cluster leaders. The best trajectory for the UAV is determined using the simulated annealing technique. [58] presents a strategy where UAV collects data from a designated point. Initially, the nodes are grouped based on neighbor distance and the protocol decides designated points such that each group leader transfer data to the UAV when UAV is in communication range. The literature also formulated a method using evolutionary algorithm to avoid obstacles such that the path is feasible and ensured optimal data latency and flight path.

3. Problem statement

The primary motto of the optimal path planning algorithms is to construct low cost as well as shortest path. While some of the existing algorithms were successful in achieving this, few of them focused on increasing WSN lifetime and efficiency [20, 27, 37, 59], while others examined UAV energy consumption aspects [31, 59]. Several studies demonstrated that the data collection efficiency is significantly impacted by the UAV’s trajectory; however, few studies have assessed the impact of obstacles while developing collision free route in UAV–WSN collaboration [20, 21, 29, 31, 35, 36]. In [35], the literature performs exhaustive search in grid-based large scale WSN due to which the outcome was not efficient enough. Moreover, the existing methods [36, 40] utilized the minimum path length as the primary objective to construct the optimal UAV trajectory for data collection from sensor nodes. However, not only the path length but also the UAV head angle change impacts the energy consumption of UAV-WSN and data gathering time of UAV. Most of the existing strategies as in [35, 36, 40] have not analyzed the impact of angle change. It can be concluded that the UAV enabled data gathering in WSN comprises challenges like UAV flight time, energy efficiency, impact of obstacles, UAV trajectory designing and data exchange among the UAV and WSN. Thus, it is important to address these issues and to generate appropriate data gathering approaches for enhancing the lifetime of WSN. For making proper data gathering process, it is necessary to plan the path of UAV in optimal manner.

Recently, meta-heuristic algorithms are becoming more popular because of its efficient searching criteria for reaching the optimal target. Thus, it motivates to utilize a hybrid meta-heuristic optimization approach for clustering and UAV path planning to make efficient data gathering process. Therefore, this paper considers the issue of energy and data collection efficiency, as well as the viability of the UAV path for data gathering in an environment with obstacles, is a crucial one that requires adequate research focus. To this end, we explore the obstacle-aware data collection strategy in WSNs using a UAV as a flying data collector. This work proposes an efficient approach (OA-CBEEDC) where an improved meta-heuristic algorithm is designed to construct the best collision-free trajectory for the UAV through which it traverses and collects data. The UAV conducts traversal and collection via designated rendezvous sites (RPs) inside a three-dimensional dynamic environment containing obstacles, which is both optimal and minimal collision-aware trajectory concerning both distance and angular cost. Towards this goal, this paper devises a clustering approach and on top of that a obstacle-aware trajectory is constructed that minimizes data transmission latency, allowing the UAV to hover near the RPs for data acquisition, while optimizing the energy consumption of nodes in the wireless sensor network. The path is further tuned such that the obstacles are avoided along with optimized path length by using various stages of optimization. The protocol guarantees adequate communication time limitation as well as the obstacle avoidance constraint. The outcomes related to path length and data gathering efficacy is outstanding for OA-CBEEDC against other comparative approaches. Due to an efficient UAV trajectory and less energy 

Fig. 1 UAV-aided data collection.

To ensure the network efficiency, information about obstacles and RPs is required so that optimal and feasible path can be constructed. In some existing optimization algorithms, energy consumption of the UAV is represented by the length of the trajectory. Different from earlier studies, we determine the optimal trajectory for the UAV to fly and collect data from the chosen rendezvous points (RPs) using an meta-heuristic approach considering the problem of energy consumption and the viability of the UAV path into consideration. Firstly, we provide the system model and the unique features of the suggested OA-CBEEDC approach. The UAV collects data by hovering close to the RPs while balancing the network’s energy consumption in an effort to decrease delay in data delivery and enhance performance. Thus, it is possible to increase the amount of data acquired from the whole WSN while decreasing the path length that the UAV moves during data collection. The initial step in this work is to create the groups of sensor nodes, select respective group heads and then based on group heads optimal hovering points are determined for energy efficient data communication with UAV. Then, a meta-heuristic method based on lyrebird algorithm is designed to plan trajectory for the UAV to improve the energy transmission efficiency and performance of the entire network. Let Plength  denotes the path length traversed by the UAV. The path length directly impacts the amount of time the UAV can fly, which in turn impacts the network longevity, energy efficiency, and data gathering efficiency. Therefore, Plength  is as given in Eq. 1. However, the cost determined is invalid if the path has obstacles or prohibited areas since it cannot be acceptable.

ETXl,s=l*Ee+l*ϵα*s2&,s<s2l*Ee+l*ϵβ*s2&,s≥s2                        (3)

However, the UAV consumes most of its energy while flying and collecting the data from a particular RP. The energy consumed by the UAV to travel from the current RP to the next RP is determined by the equation Eu=Pu*srp,rqvu where Pu  and v_u are power rate and speed of the UAV. The distance between two consecutive RPs is represented by s (r_p, r_q). The UAV may perform an angle (or head direction) change around RP or due to the influence of obstacles. This will also lead to consumption of energy of the UAV [37, 48, 61]. To optimize the energy consumption of the UAV,

Fig. 2 Flowchart for OA-CBEEDC scheme.

Stage 1: Initialization

ChanceGH=ω1*SNienergy+ω2*SNinbr+ω3*SNideg

Fig. 3 Selection of RP at safe location.

 

Fig. 4 UAV route design for data collection.

Fig. 5 Feasibility check for movement of the UAV.

Length cost and UAV path length

Fig. 6 Average Path Length Vs. Number of Nodes

Fig. 7 WSN area with 750 nodes.

Fig. 8 Instance of initial groups formed with respective group head.

Fig. 9 Instance of final cluster heads selected with respective cluster members obtained after executing hybrid Fire-Hawk-Lyrebird algorithm

Fig. 10 3D view of WSN area with 800 nodes and 5 obstacles (the rhombus denotes position of UAV).

Fig. 11 Top view of WSN area with 800 nodes and 5 obstacles.

Fig. 12 UAV changing its direction reach the node.

Fig. 13 UAV moving passes closer to the obstacle.

Fig. 14 UAV moves to the node by avoiding more than one obstacle.

Fig. 15 Average path length (with/without obstacles)

Fig. 16 Average Flight Time (with/without obstacles)

Fig. 17 Data Gathering Efficiency

Fig. 18 Average Energy Consumption

Fig. 19 Network Stability Performance

Fig. 20 Data Gathering Efficiency V/s Height of the UAV

CONCLUSION

We proposed a scheme in this work that uses UAVs to collect data from the terrestrial WSN with obstacles as an added constraint (OA-CBEEDC). An enhanced meta-heuristic algorithm is used to find the optimal trajectory for the UAV to fly and collect data from the effective rendezvous point (RPs). This obstacle-aware feasible UAV-path balances the energy consumption of nodes in the WSN and optimizes the delay of data delivery. The scheme minimizes node energy consumption by two methods, first by selecting group head and RP position in centralized and then by assisting group heads to relay data within communication range. Moreover, the optimal group head is selected on the basis of energy, number of neighbors and average distance of the neighbors. Further, the RPs are selected by simultaneously considering both obstacle and more than one group head at a time. The feasibility of the UAV path is also considered and infeasible path is substituted and regulated with desired number of deviation points. The result proves the efficacy of the OA-CBEEDC scheme against other counterpart schemes. The network stability improved significantly due to bet-ter grouping and RP selection. The data gathering efficiency of whole network also improved in case of both number of nodes and varying UAV height. Even though the performance of the proposed OA-CBEEDC is effective thereafter it has limitations. The grouping of nodes can be further improved by adopting obstacle-aware approach and by introducing WSN compatible optimization algorithms and by obstacle-aware. Hence, in future we will improve the network stability further by employing obstacle-aware optimal grouping and employ more than one UAV for performance enhancement in large WSN.

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