Theoretical Analysis of the Area Coverage in a UAV-based Wireless Sensor Network

Node placement for connected target coverage in wireless sensor networks with dynamic sinks

Many wireless sensor network applications must resolve the inherent conflict between energy efficient and nodes of coverage in wireless sensor networks to achieve desired quality of service (QoS). The wireless sensor networks (WSNs) have been the main focus in the past few years, and it is becoming a special issue. We study the problem of controlling the node locations and energy within ad-hoc sensor networks. Network survivability is a very important measure for deciding the efficiency of network protocols. It includes a measure of the network lifetime as well as the quality of service. In fact, these dynamics are very important in evaluating networks. The wireless network applications can be found in battlefield communication, homeland security, pollution sensing and traffic monitoring. As such, there has been an increasing need for defining and developing simulation frameworks for carrying out high-fidelity WSN simulation. In this cause, we present a simulation by including factors such as energy-consumption, Bandwidth, nodes, and coverage and provides an object-oriented definition of target, sensor and sink nodes, sensor and wireless communication nodes, and physical radio such as relating of nodes, mobility model and power model both energy-producing and energy-consuming components, the simulation was developed using C#.

To satisfy the increasing demand of wireless sensor network (WSN) distribution in the different monitoring area and enlarge the coverage of the WSNs in various scenes of application, in this paper, we constructed a new type of mobile wireless data sinking platform for collecting the data of WSN based on the technology of unmanned aerial vehicle (UAV). First, the authors studied a wireless environmental monitoring system containing the UAV platform and WSN is constructed and both test on wireless communication capacity and data collecting are carried out. The communication capacity test show that when the RF modules are operated with transmission power greater than 1 dBm and communication distance less than 100 m, the UAV wireless sink node can maintain a high quality communication data link which has a lower PLR (less than the 5 %) and better signal level (RSSI greater than −90 dBm) with transmission data rate of 250 kbps, using this RF transceiver a simple communication & data collection protocol between UAV and ground WSN sink node was proposed. The whole work of this paper should provide some experience for building a UAV cooperative WSN system in the future.

With the development of Internet of Things (IoT) technology, wireless sensor networks (WSNs) composed of multiple sensor nodes (SNs) have been able to provide a large number of high-quality services, such as continuous environmental monitoring, automatic control, and transmission of data for intelligent decision-making. SNs in WSNs are typically energy limited and often exist in large numbers, which are widely distributed in hard-to-reach areas. Hence, it is difficult to collect data from SNs without infrastructure support such as base stations (BSs). The low cost and high maneuverability of unmanned aerial vehicles (UAVs) provide an efficient solution for data acquisition in WSNs but with the challenge of control system of the UAV. In this paper, we proposed a deep reinforcement learning based UAV control system to intelligently solve the data harvesting problem in WSNs. Our optimization target is to jointly optimize the trajectory of the UAV and the bandwidth resources of the airborne base station to maximize the data acquisition success rate when the UAV is limited in energy. The objective function and constraints of this problem are highly non-convex. To solve this problem, we first modeled the movement of the UAV and the bandwidth allocation of the airborne base station in each time slot as a Markov decision process (MDP). Then we designed a deep reinforcement learning architecture to solve the above MDP. Compared with the existing data harvesting algorithms, the proposed method has a great improvement in data acquisition success rate.

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

Wireless sensor networks (WSNs) are an important means of collecting data in a variety of situations, such as the monitoring of large or hazardous areas. The retrieval of WSN data can yield better results through the use of unmanned aerial vehicles (UAVs), for example, concerning the increase in the amount of data collected and the decrease in the time between the collection and use of the data. In particular, disaster areas may be left without communication resources and with high residual risk to humans, at which point a WSN can be quickly launched by air to collect relevant data until other measures can be established. The set of rules of each problem’s component (e.g., number of UAVs, UAVs dislocation control, sensors, communication) is considered the approaches to solve the problem. In this meaning, some studies present approaches for the use of UAVs for the collection of WSN data, focusing primarily on optimizing the path to be covered by a single UAV and relying on long-range communication that is always available; these studies do not explore the possibility of using several UAVs or the limitations on the range of communication. This work describes DADCA, a distributed scalable approach capable of coordinating groups of UAVs in WSN data collection with restricted communication range and without the use of optimization techniques. The results reveal that the amount of data collected by DADCA is similar or superior to path optimization approaches by up to 1%. In our proposed approach, the delay in receiving sensor messages is up to 46% shorter than in other approaches, and the required processing onboard UAVs can reach less than 75% of those using optimization-based algorithms. The results indicate that the DADCA can match and even surpass other presented approaches, since the path optimization is not a focus, while also incorporating the advantages of a distributed approach.

Wireless sensor networks are a collection of specially designed nodes to provide support to sensor information propagation. Sensor nodes are basically designed to sense data as sensor and distribute to servers for further analysis. Based on the specific requirements, the numbers of sensor nodes can range from hundreds to thousands. All the communication will take place through predefined network characteristics. WSNs generally face energy issues. LEACH is designed for efficient cluster formation. It provides an efficient energy solution for data transmission from node to base station. Various enhanced version of LEACH has been proposed by Assuming LEACH as base protocol. The energy constraints are a critical aspect in developing the routing protocols for WSNs. Designers and researchers have contributed their effort through enhancement in energy saving procedure into WSNs. In this work, the LEACH based protocols and their performance in WSN has been analysed. This research paper evaluates recent advancement in LEACH based approaches for Wireless Wide Area Sensor Network with comparison table.

A wireless sensor network is a correspondence framework which detects and accumulates data from a specific zone and sends it to the base station or sink node. WSN can be very exceptionally valuable for target detection and recognizable proof by military and ecological applications like agrarian farm monitoring and woodland fire supervising. A genetic algorithm is a meta-heuristic approach propelled by the process of natural selection that belongs to the class of evolutionary algorithms. It is utilized to create high-quality solutions to optimization and search problem by relying on bio-inspired operators such as mutation, crossover, and selection. The goal is deploying a minimum number of sensor element nodes without overlapping each other based on antenna height and separation between sensor nodes. The experimental result shows that better coverage in heterogeneous wireless sensor network using NS2 software simulation.

Although most of the studies on coverage and connectivity in wireless sensor networks (WSNs) considered two-dimensional (2D) settings, such networks can in reality be accurately modeled in a three-dimensional (3D) space. The concepts of continuum percolation theory best fit the problem of connectivity in WSNs to find out whether the network provides long-distance multihop communication. In this paper, we focus on percolation in coverage and connectivity in 3D WSNs. We say that the network exhibits a coverage percolation (respectively, connectivity percolation) when a giant covered region (respectively, giant connected component) almost surely spans the entire network for the first time. Because of the dependency between coverage and connectivity, the problem is not only a continuum percolation problem but also an integrated continuum percolation problem. Thus, we propose an integrated-concentric-sphere model to address coverage and connectivity in 3D WSNs in an integrated way. First, we compute the critical density lambdaC con above which coverage percolation in 3D WSNs will almost surely occur. Second, we compute the critical density lambdac con above which connectivity percolation in 3D WSNs will almost surely occur. Third, we compute the critical density lambdac cov-con above which both coverage and connectivity percolation in 3D WSNs will almost surely occur. For each of these three problems, we also compute their corresponding critical network degree. Our results can be helpful in the design of energy-efficient topology control protocols for 3D WSNs in terms of coverage and connectivity.

The use of mobile nodes to collect data in a Wireless Sensor Network (WSN) has gained special attention over the last years. Some researchers explore the use of Unmanned Aerial Vehicles (UAVs) as mobile node for such data-collection purposes. Analyzing these works, it is apparent that mobile nodes used in such scenarios are typically equipped with at least two different radio interfaces. The present work presents a Dual-Stack Single-Radio Communication Architecture (DSSRCA), which allows a UAV to communicate in a bidirectional manner with a WSN and a Sink node. The proposed architecture was specifically designed to support different network QoS requirements, such as best-effort and more reliable communications, attending both UAV-to-WSN and UAV-to-Sink communications needs. DSSRCA was implemented and tested on a real UAV, as detailed in this paper. This paper also includes a simulation analysis that addresses bandwidth consumption in an environmental monitoring application scenario. It includes an analysis of the data gathering rate that can be achieved considering different UAV flight speeds. Obtained results show the viability of using a single radio transmitter for collecting data from the WSN and forwarding such data to the Sink node.

Aiming at the uneven random coverage distribution of wireless sensor networks sensor nodes, the sparrow search algorithm (SSA) is used to optimize the node coverage of wireless sensor networks. To improve the global search capability of SSA, the algorithm is improved and applied to the wireless sensor networks of bridge monitoring. For enhancing the coverage of the wireless sensor networks, this article uses two improved methods. One is to use the good point set theory to make the initial population evenly distributed; another is to introduce a weight factor to speed up its convergence. The experiments have proved the reliability and rationality of the algorithm. The improved algorithm is superior to other meta-heuristic algorithms and provides a new idea for optimizing bridge monitoring wireless sensor networks coverage.

Unmanned aerial vehicle (UAV)-enabled communication has emerged as an appealing technology for efficient data collection in wireless sensor networks (WSNs). This paper considers a scenario where multiple UAVs collect data from a group of sensor nodes (SNs) on the ground. We study the fundamental tradeoff between the aerial cost , which is defined by the propulsion energy consumption and operation costs of all UAVs, and the ground cost , which is defined as the energy consumption of all SNs. To characterize such a tradeoff, an optimization problem is formulated to minimize the weighted sum of the above two costs, by optimizing the UAV trajectory jointly with wake-up time allocation, as well as the transmit power of all SNs. As the formulated problem is non-convex, it is difficult to be optimally solved in general. To tackle this issue, we decouple it into two sub-problems: UAV trajectory and wake-up time allocation optimization, as well as SN transmit power optimization. We propose an iterative algorithm to solve the two sub-problems by leveraging successive convex approximation and alternating optimization techniques. In addition, a new approach is proposed to design the UAV initial trajectory with multiple travelling salesman problem (MTSP) technique. Simulations are conducted to corroborate our study and show the flexible tradeoff achieved by the proposed design for cost balance between UAVs and SNs.

The unmanned aerial vehicle (UAV) is widely used in some scenes with high requirements for information freshness. Due to the limited endurance of the UAV, especially in the scenes with large area and dense sensor nodes (SNs), it is difficult for one UAV to complete the data collection task under the condition of ensuring the freshness of SNs’ information. Therefore, multiple UAVs are required to cooperate to participate in data collection. In this paper, we study the multi-UAV assisted data collection problem to improve information freshness. We use the Age of Information (AoI) to measure the freshness of information, mainly including the SNs’ uploading time, the UAVs’ flight time and the data offloading time. The data collection problem is formulated to minimize the SNs’ peak AoI and average AoI in multi-UAV assisted wireless sensor networks. Since the problem is complex, we introduce a start-to-end strategy comprising of association and planning to minimize two SNs’ AoIs through an iterative three-step process. Firstly, the locations of data collection points (CPs) at which the UAVs hover to collect data and the SN-CP association are determined based on a density-based clustering algorithm. Secondly, the CPs are clustered to form CP clusters, and the CP-UAV association is established. Finally, based on the results of the above two steps, the flight trajectories of the UAVs are optimized by improved ant colony (ACO) algorithm subject to the limited endurance capability. The simulation results show the proposed strategy can optimize the peak-AoI and ave-AoI of SNs to improve the freshness of information.

Wireless Sensor Networks (WSNs) can be used effectively to form barriers that monitor strips of land for applicaions such as homeland security and critical infrastructure protection. Sensors are highly constrained in their energy usage, so when they harvest energy, it is neccessary that this energy be used effectively. When WSNs are used as barriers, they are typically deployed for extended period of time, so it is of great importance that the network has a long lifetime. The lifetime problem for WSNs has been studied extensively. In this paper we give an exact solution to the problem of achieving maximum lifetime for the application of k-barrier coverage in energy harvesting wireless sensor networks.

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