3D Sensor Networks Research Articles

Estimation of Spatial Features in 3-D-Sensor Network Using Multiple LiDARs for Indoor Monitoring

In recent years, smart city has been attracting attention as a sustainable urban development. Smart monitoring is one of the key components of a smart city, and Light-Detection-and-Ranging (LiDAR) sensor is used as one of the sensors for smart monitoring. A 3D sensor network using multiple LiDAR sensors can be constructed to eliminate blind spots. Prior work presented a system that prioritizes transmission in important regions to prevent data loss in the case of bandwidth limitation. However, prior work has only presented transmission methods and has not shown how to estimate important regions in the point cloud space. This paper proposes a system to estimate important regions using spatial features created based on multiple spatial metrics. The important regions vary from task to task, and in particular, for the task of detecting moving objects, the important regions are the spaces where the moving objects may be located. The accuracy of estimation can be improved by creating spatial features based on more spatial metrics. This paper addresses two kinds of spatial metrics, temporal metric and statistical metric. In this work, we collect point-cloud data using multiple LiDAR sensors and produce datasets with labels for evaluation. Using datasets, we validate the proposed system in terms of the accuracy of spatial feature estimation.

3D Deformation Capture via a Configurable Self-Sensing IMU Sensor Network

Motion capture technologies reconstruct human movements and have wide-ranging applications. Mainstream research on motion capture can be divided into vision-based methods and inertial measurement unit (IMU)-based methods. The vision-based methods capture complex 3D geometrical deformations with high accuracy, but they rely on expensive optical equipment and suffer from the line-of-sight occlusion problem. IMU-based methods are lightweight but hard to capture fine-grained 3D deformations. In this work, we present a configurable self-sensing IMU sensor network to bridge the gap between the vision-based and IMU-based methods. To achieve this, we propose a novel kinematic chain model based on the four-bar linkage to describe the minimum deformation process of 3D deformations. We also introduce three geometric priors, obtained from the initial shape, material properties and motion features, to assist the kinematic chain model in reconstructing deformations and overcome the data sparsity problem. Additionally, to further enhance the accuracy of deformation capture, we propose a fabrication method to customize 3D sensor networks for different objects. We introduce origami-inspired thinking to achieve the customization process, which constructs 3D sensor networks through a 3D-2D-3D digital-physical transition. The experimental results demonstrate that our method achieves comparable performance with state-of-the-art methods.

Efficient approaches to optimize energy consumption in 3D wireless video sensor network under the coverage and connectivity constraints

Two advanced approaches (PA_1, and PA_2) based on a realistic 3D model of video sensor nodes (VSNs) deployed randomly over a 2D target area are proposed to minimize energy consumption in the network maintaining area coverage and connectivity. The reduction of the number of active VSNs decreases energy consumption but at the same time reduces the area coverage and connectivity. These conflicting issues are resolved and an optimal solution is obtained by using an integer linear programming-based approach (PA_1). But PA_1 is not tractable for large instances as the problem is NP-Hard. Hence, a heuristic approach (PA_2) based on an advanced genetic algorithm is also proposed in the present work for obtaining a near-optimal solution. Simulation studies are carried out to compare the performance of PA_1 and PA_2 with the other three state-of-the-art approaches (APP_5, APP_6, and ET_3). Among the three existing approaches, APP_6/(ET_3) is the best in energy consumption/(area coverage). It is observed that for the same simulation environment, both PA_1 and PA_2 guarantee higher network services, by reducing energy consumption by 40.85% and 33.34% respectively compared to the best existing approach APP_6; and as well as by increasing area coverage by 0.94% than the best existing approach ET_3 for the node density 150 on the target area of size 75x75 square meter. Between PA_1 and PA_2, PA_2 generates a suboptimal solution and PA_1 substantiates its superiority by reducing energy consumption by 11.26% than PA_2 without losing area coverage for the same simulation environment.

3D sensor network location spatial positioning technology based on machine learning

Abstract The purpose of this paper is to combine machine learning to locate the 3D sensor network space. Real life is mostly a three-dimensional environment. Whether it is a factory in manufacturing or a vegetation base in agriculture, it needs to be monitored and positioned. In this paper, the localization algorithm is discussed to a certain extent. This paper firstly introduces the relevant background and organizes related work. It also wrote related algorithms, such as ranging-based positioning algorithms in the free space of wireless sensors. It shows the positioning link by introducing the wireless sensor network structure system and node structure. And this paper summarizes the Bounding-box Method positioning principle, TDOA algorithm principle, and TDOA positioning principle. It then describes the gradient boosting tree classification algorithm based on machine learning, and focuses on the admiral boosting tree classification algorithm related to the experiment. This paper also describes the ranging technology combining RSSI algorithm and DV-Hop algorithm in three-dimensional space, and mentions two algorithms of RSSI and DV-Hop. In the fourth part, the machine learning coordinate prediction accuracy improvement experiment and the three-dimensional space positioning algorithm optimization experiment and result analysis are carried out. It is proved by experiments that the model evaluation effect of the gradient boosting tree classification algorithm in machine learning is the best. It can be applied to the calculation of relative position coordinates of label nodes. It then carried out the three-dimensional positioning effect test experiment of IDV-Hop algorithm. This shows that when the network density in the experimental environment reaches more than 12, the localization coverage of IDV-Hop algorithm and DV-Hop algorithm are both higher than 91%. Finally, the hybrid algorithm of RSSI and DV-Hop algorithm is used to compare the positioning accuracy, positioning coverage and bad node rate with these two algorithms. It draws the stability of the hybrid algorithm and its effects, and finally discusses and summarizes the experiments.

Construction of an Innovative Development Model of Intelligent Media under the Coverage of a Wireless Sensor Network

A distributed network composed of sensor nodes with limited computing and communication capabilities is a wireless sensor network, which is a system that can autonomously and intelligently complete target tasks according to the surrounding environment. With its wide application in the fields of smart medical care, smart home, networks of vehicles, and smart media, its communication performance requirements are also increasing. According to the needs of different application scenarios, the first key problem to be solved in wireless sensor networks is what optimization coverage strategy to adopt, which will have a direct impact on the optimal allocation of very limited resources such as node energy, communication bandwidth, computing power, and other network services’ quality. Nodes adopt a probability-based joint perception model, and its algorithm is a node scheduling mechanism based on the connected dominating set to construct a tree. Each node sets the waiting time and becomes a candidate node priority according to the remaining energy and the distance from the parent node. In this paper, the issue of energy fairness is considered in heterogeneous wireless sensor networks. In order to prevent the occurrence of “energy holes,” this paper proposes a node deployment model similar to cellular networks. By deploying heterogeneous sensor nodes in this way, energy saving can be achieved. Based on this model, game theory is used to simulate the data packet transmission process between sensor nodes, an energy consumption mechanism suitable for each stage of data transmission is proposed, and Nash equilibrium is finally obtained by rationally designing the measurement function. The node deployment scheme proposed in this paper not only balances the energy consumption of the network but also prolongs the life cycle of the network. Aiming at the three-dimensional (3D) space environment, this paper studies a node deployment mechanism that guarantees connectivity and has a perceptual coverage degree of k with the help of the Reuleaux tetrahedron model. In this paper, a node deployment strategy is proposed and compared with the node deployment algorithm for 3D sensor network based on truncated octahedron. The simulation experiment results show that the wireless sensor network with artificial intelligence can not only optimize the production process of intelligent media but also improve each link of the media industry chain and further catalyze the development of new formats in the media industry. The experimental results also show that the algorithm can effectively meet the requirements of sensing coverage and connectivity coverage, the number of working nodes is small, the network life cycle is significantly prolonged, and the overall energy consumption of the network is reduced. The study results of this paper provide a reference for follow-up research on the construction of innovative development model of intelligent media under the coverage of wireless sensor network.

Spatial-Importance-Based Computation Scheme for Real-Time Object Detection From 3D Sensor Data

Three-dimensional (3D) sensor networks using multiple light-detection-and-ranging (LIDAR) sensors are good for smart monitoring of spots, such as intersections, with high potential risk of road-traffic accidents. The image sensors must share the strictly limited computation capacity of an edge computer. To have the computation speeds required from real-time applications, the system must have a short computation delay while maintaining the quality of the output, e.g., the accuracy of the object detection. This paper proposes a spatial-importance-based computation scheme that can be implemented on an edge computer of image-sensor networks composed of 3D sensors. The scheme considers regions where objects exist as more likely to be ones of higher spatial importance. It processes point-cloud data from each region according to the spatial importance of that region. By prioritizing regions with high spatial importance, it shortens the computation delay involved in the object detection. A point-cloud dataset obtained by a moving car equipped with a LIDAR unit was used to numerically evaluate the proposed scheme. The results indicate that the scheme shortens the delay in object detection.

A Novel 3D Node Deployment Inspired by Dusty Plasma Crystallization in UAV-Assisted Wireless Sensor Network Applications.

With the rapid progress of hardware and software, a wireless sensor network has been widely used in many applications in various fields. However, most discussions for the WSN node deployment mainly concentrated on the two-dimensional plane. In such a case, some large scale applications, such as information detection in deep space or deep sea, will require a good three dimensional (3D) sensor deployment scenario and also attract most scientists’ interests. Excellent deployment algorithms enable sensors to be quickly deployed in designated areas with the help of unmanned aerial vehicles (UAVs). In this paper, for the first time, we present a three dimensional network deployment algorithm inspired by physical dusty plasma crystallization theory in large-scale WSN applications. Four kinds of performance evaluation methods in 3D space, such as the moving distance, the spatial distribution diversion, system coverage rate, and the system utilization are introduced and have been carefully tested.Furthermore, in order to improve the performance of the final deployment, we integrated the system coverage rate and the system utilization to analyze the parameter effects of the Debye length and the node sensing radius. This criterion attempts to find the optimal sensing radius with a fixed Debye length to maximize the sensing range of the sensor network while reducing the system redundancy. The results suggest that our 3D algorithm can quickly complete an overall 3D network deployment and then dynamically adjust parameters to achieve a better distribution. In practical applications, engineers may choose appropriate parameters based on the sensor’s hardware capabilities to achieve a better 3D sensor network deployment. It may be significantly used in some large-scale 3D WSN applications in the near future.

Adaptive Fuzzy Game-Based Energy-Efficient Localization in 3D Underwater Sensor Networks

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg–Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.

A Dynamic Hierarchical Clustering Data Gathering Algorithm Based on Multiple Criteria Decision Making for 3D Underwater Sensor Networks

Data gathering is the basis of monitoring applications in an underwater sensor network, and excellent network coverage and data transmission reliability are the guarantees for the quality of monitoring tasks. However, the energy consumption of the nodes is too fast due to the heavy load of the cluster heads closer to the sink when data is transmitted between cluster heads (CHs) and the sink by multihop, which leads to an energy hole problem in an underwater sensor network of clustering technology. Aiming to address this problem, we propose a dynamic hierarchical clustering data gathering algorithm based on multiple criteria decision making (DHCDGA) in a 3D underwater sensor network. Firstly, the entire monitoring network is divided into many layers. For selecting a cluster head in each layer, multiple criteria decision making of an intuitionistic fuzzy Analytic Hierarchy Process (AHP) and hierarchical fuzzy integration is adopted. Furthermore, a sorting algorithm is used to form a clustering topology algorithm to solve the problem that there is the only node in one cluster. Then, an energy-balanced routing algorithm between clusters is proposed according to the residual energy of the node, the depth, and the number of neighbor nodes. Finally, the simulation results show that DHCDGA can not only effectively balance the energy consumption of the network and prolong the network lifetime but also improve network coverage and data gathering reliability.

Multi-Objective Mapping Method for 3D Environmental Sensor Network Deployment

Effective deployment of the emerging environmental sensor network in environmental mapping has become essential in numerous industrial applications. The essential factors for deployment include cost, coverage, connectivity, airflow of heating, ventilation, and air conditioning, system lifetime, and fault tolerance. In this letter, a three-stage deployment scheme is proposed to formulate the above-mentioned considerations, and the fuzzy temperature window is established to adjust sensor activation times over various ambient temperatures. To optimize the deployment effectively, a multi-response Taguchi-guided $k$ -means clustering is proposed to embed in the genetic algorithm, where an improved set of the initial population is formulated and system parameters are optimized. Therefore, the computational time for repeated deployment is shortened, while the solution convergence can be improved.

SNP: A 1-Manifold Skeleton-Based Navigation Protocol in 3D Sensor Networks

We consider the navigation application of 3D sensor networks that can proactively guide the movement of internal users from potential dangers to a safe exit, where a 3D sensor network serves as a reactive system, instead of a monitoring tool or a medium of data acquisition. Most if not all existing efforts in this line concentrate on 2D cases only, and none of them can be readily applied to 3D sensor networks, posing it a non-trivial challenge to design an effective and light-weight navigation protocol in 3D sensor networks. In this paper, we propose the first location-free, distributed, and scalable navigation protocol that can provide a navigation route for users inside the 3D sensor network with guaranteed safety. More specifically, we formulate the navigation problem as the minimum cumulative exposure problem, and design SNP, a navigation protocol based on the so-called 1-manifold skeleton, which offers a safe path with a near-optimal cumulative exposure to dangers. Extensive simulations validate the effectiveness and efficiency of the proposed algorithm.

An adaptive target tracking method for 3D underwater wireless sensor networks

Today, underwater target tracking using underwater wireless sensor networks (UWSNs) is an essential part in many military and non-military applications. Most of moving target tracking studies in UW...

Energy-Aware Dual-Path Geographic Routing to Bypass Routing Holes in Wireless Sensor Networks

Geographic routing has been considered as an attractive approach for resource-constrained wireless sensor networks (WSNs) since it exploits local location information instead of global topology information to route data. However, this routing approach often suffers from the routing hole (i.e., an area free of nodes in the direction closer to destination) in various environments such as buildings and obstacles during data delivery, resulting in route failure. Currently, existing geographic routing protocols tend to walk along only one side of the routing holes to recover the route, thus achieving suboptimal network performance such as longer delivery delay and lower delivery ratio. Furthermore, these protocols cannot guarantee that all packets are delivered in an energy-efficient manner once encountering routing holes. In this paper, we focus on addressing these issues and propose an energy-aware dual-path geographic routing (EDGR) protocol for better route recovery from routing holes. EDGR adaptively utilizes the location information, residual energy, and the characteristics of energy consumption to make routing decisions, and dynamically exploits two node-disjoint anchor lists, passing through two sides of the routing holes, to shift routing path for load balance. Moreover, we extend EDGR into three-dimensional (3D) sensor networks to provide energy-aware routing for routing hole detour. Simulation results demonstrate that EDGR exhibits higher energy efficiency, and has moderate performance improvements on network lifetime, packet delivery ratio, and delivery delay, compared to other geographic routing protocols in WSNs over a variety of communication scenarios passing through routing holes. The proposed EDGR is much applicable to resource-constrained WSNs with routing holes.

Topology mapping algorithm for 2D and 3D Wireless Sensor Networks based on maximum likelihood estimation

Automation of many sensor network protocols requires maps indicating sensor locations. Physical coordinate based maps capture the physical layout including voids and shapes, but obtaining the required distance values is often not feasible or economical. Alternative is to use topological maps based only on connectivity Information. Due to lack of physical distances, they are not faithful representatives of the physical layout. Here we present Maximum Likelihood-Topology Maps (ML-TM) that provide a more accurate physical representation, by using the probability of signal reception, an easily measurable parameter that is sensitive to the distance. Approach is illustrated using a mobile robot that listens to signals transmitted by sensor nodes and maps the packet reception probability to a coordinate system using a signal receiving probability function. ML-TM is an intermediate map between exact physical maps and hop- based topology coordinates. Results show that ML-TM algorithm generates maps for various network shapes with voids/obstacles in different environmental conditions with an error less than 7%. Performance of the algorithm in 3D sensor networks is also illustrated.

Enhanced reliable reactive routing (ER3) protocol for multimedia applications in 3D wireless sensor networks

Sensor networks designed especially for the multimedia applications require high data rate and better Quality of Service (QoS). Offering a reliable and energy efficient routing technique in a harsh and complex three-dimensional (3-D) environment for multimedia applications is a challenging job. Geo-routing and geometric routing have been efficient routing schemes for two-dimensional (2-D), but are unable to work properly for 3-D sensor networks. In order to enhance the resilience to link the dynamics in the 3-D sensor network, in this research an Enhanced Reliable Reactive Routing (ER3) is proposed. ER3 is an advancement to the existing reactive routing schemes, to provide energy efficient and reliable routing of data packets in the complex 3-D sensor networks for multimedia applications. The major attraction of ER3 is its backoff scheme, which occurs in the route discovery phase. In backoff scheme robust pilot paths formed between the source and destination are calculated to enable cooperative forwarding of the data packets. The data packets in ER3 are forwarded greedily to the destination from the source and doesn’t require any prior location information of the nodes. The encompassing simulations suggest that the ER3 outperforms the existing routing protocols on the basis of energy efficiency, low latency and high packet delivery ratio.

Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm

The coverage rate of the underwater sensor networks directly influences on the monitoring efficiency in underwater environment, and it can be effectively improved by adjusting the positions of the mobile nodes reasonably for a 3D underwater sensor network which consists of mobile nodes as underwater robots like Autonomous Underwater Vehicles. An optimal deployment method can quickly set up a reasonable topology of the sensor networks and achieve a higher efficiency for detecting or investigating. An optimal algorithm of coverage enhancing for 3D Underwater sensor networks based on improved Fruit Fly Optimization Algorithm (UFOA) is proposed in this paper. This method realizes the global optimal coverage based on foraging behavior of fruit flies, and it has the features of higher speed of convergence, few parameters to set up and stronger global searching ability. Simulation result indicates that the proposed UFOA method can significantly improve the effective coverage rate of the sensor networks compared with some widely studied PSO and IPSO algorithms.

Distributed self-deployment of mobile wireless 3D robotic sensor networks for complete sensing coverage and forming specific shapes

SUMMARYThis paper addresses a problem of complete sensing coverage in 3D environments. We propose a distributed random algorithm to drive mobile robotic sensors on the vertices of a truncated octahedral grid for complete sensing coverage of a bounded 3D area. Furthermore, we develop a distributed algorithm for the self-deployment of mobile sensors to form a desired 3D geometric shape on the vertices of the truncated octahedral grid. These algorithms are developed based on some consensus rules that only rely on local information. The proposed algorithms utilize 3D grids for the coverage task. Several simulations are conducted to illustrate the validity of the proposed distributed sensing coverage and formation building algorithms for a mobile robotic sensor network. Also, we give mathematically rigorous proof of the convergence with probability 1 of our proposed algorithms.

Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks.

Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency.

Towards Robust Surface Skeleton Extraction and Its Applications in 3D Wireless Sensor Networks

The in-network data storage and retrieval are fundamental functions of sensor networks. Among many proposals, geographical hash table (GHT) is perhaps most appealing as it is very simple yet powerful with low communication cost, where the key is to correctly define the bounding box. It is envisioned that the skeleton has the power to facilitate computing a precise bounding box. In existing works, the focus has been on skeleton extraction algorithms targeting for 2D sensor networks, which usually deliver a 1-manifold skeleton consisting of 1D curves. It faces a set of non-trivial challenges when 3D sensor networks are considered, in order to properly extract the surface skeleton composed of a set of 2-manifolds and possibly 1D curves. In this paper, we study the problem of surface skeleton extraction in 3D sensor networks. We propose a scalable and distributed connectivity-based algorithm to extract the surface skeleton of 3D sensor networks. First, we propose a novel approach to identifying surface skeleton nodes by computing the extended feature nodes such that it is robust against boundary noise, etc. We then find the maximal independent set of the identified skeleton nodes and triangulate them to form a coarse-grained surface skeleton, followed by a refining process to generate the fine-grained surface skeleton. Furthermore, we design an efficient updating scheme to react to the network dynamics caused by node failure, insertion, etc. We also investigate the impact of boundary incompleteness and present a scheme to extract the surface skeleton under incomplete boundary. Finally, we apply the extracted surface skeleton to facilitate the design of data storage protocol and curve skeleton extraction algorithm. Extensive simulations show the robustness of the proposed algorithm to shape variation, node density, node distribution, communication radio model and boundary incompleteness, and its effectiveness for data storage and retrieval application with respect to load balancing.

On the Distance-Sensitive and Load-Balanced Information Storage and Retrieval for 3D Sensor Networks

Efficient in-network information storage and retrieval is of paramount importance to sensor networks and has attracted a large number of studies while most of them focus on 2D fields. In this paper, we propose novel Reeb graph based information storage and retrieval schemes for 3D sensor networks. The key is to extract the line-like skeleton from the Reeb graph of a network, based on which two distance-sensitive information storage and retrieval schemes are developed: one devoted to shorter retrieval path and the other devoted to more balanced load. Desirably, the proposed algorithms have no reliance on the geographic location or boundary information, and have no constraint on the network shape or communication graph. The extensive simulations also show their efficiency in terms of sensor storage load and retrieval path length.