3D Wireless Sensor Networks Research Articles

An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization

An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization

A multi-strategy surrogate-assisted social learning particle swarm optimization for expensive optimization and applications

Evolutionary algorithms (EAs) require extensive fitness evaluations, which constitutes a barrier to solving computationally complex problems. In contrast, surrogate-assisted evolutionary algorithms (SAEAs) have the potential to solve complex expensive optimization problems. This paper proposes a surrogate-assisted social learning particle swarm optimization (SASLPSO) to handle expensive optimization problems. An adaptive local surrogate (ALS) strategy introduced in SASLPSO is introduced to accurately fit the landscape near the global optimum. ALS consists of two layers of surrogate, the part of the particles closest to the optimal particle and the closest to the optimal particle among the particles that have been eliminated historically. The proposed SASLPSO effectively combines global surrogate (GS) and adaptive local surrogate (ALS) to balance global exploration and local exploitation. Furthermore, a novel random group-based pre-screening (RGBPS) strategy is proposed to screen promising particles for real function evaluation. The proposed SASLPSO is compared with four other state-of-the-art SAEAs on 30D, 50D, and 100D benchmark functions. In addition, the significance of the SASLPSO algorithm was also verified using the Wilcoxon rank test. The test results on the benchmark function show that the SASLPSO algorithm performs better than other comparison algorithms, especially when dealing with high-dimensional benchmark functions. To further validate the effectiveness of the SASLPSO algorithm in solving expensive optimization problems, it was also applied to feature selection problems and real-world engineering problems. In addition, in the real application of node deployment in 3D wireless sensor networks, the highest coverage rate of the SASLPSO algorithm can reach 99.96%, confirming its performance advantages in solving real application problems. Finally, in the application of network intrusion detection, SASLPSO has shown more advantages in multiple metrics, proving its versatility.

An Improved 3D-DV-Hop Localization Algorithm to Improve Accuracy for 3D Wireless Sensor Networks

An Improved 3D-DV-Hop Localization Algorithm to Improve Accuracy for 3D Wireless Sensor Networks

Localization in 3D Wireless Sensor Networks with Obstacle Consideration

Wireless sensor network (WSN) refers to the network formed by sensor nodes for communicating sensed data over the wireless medium. These sensor nodes are mostly deployed randomly in the target area, and hence estimating the coordinates of these sensor nodes through the localization process is an important activity. Majority of the localization algorithms existing in the literature assume the target area to be an obstacle-free 2D terrain. However, such algorithms are not suitable for real-life scenarios where WSNs are actually deployed on 3D terrains that may also have obstacles that hinder the radio signals from the deployed sensor nodes. Hence, factors such as the height of the terrain, the presence of obstacles, etc., require significant considerations while designing algorithms for WSN. The proposed research presents a localization scheme for 3D WSN where the sensor nodes are randomly deployed in a 3D area having large obstacles. The proposed scheme adopts a distributed approach for calculating the virtual coordinates of the sensors using four beacon nodes. The deployment area is divided into regions, and each node computes its virtual coordinates with respect to the obstacle. Simulation results indicate an average localization error of 6.93 m. The proposed scheme requires very less computational effort and can be easily adapted in different scenarios.

Grundy Number-based optimal sensor placement in 3D wireless sensor network

Grundy Number-based optimal sensor placement in 3D wireless sensor network

Adaptive mean center of mass particle swarm optimizer for auto-localization in 3D wireless sensor networks

Wireless Sensor Networks (WSNs) have become instrumental in environmental monitoring, healthcare, agriculture, and industrial automation. In WSNs, the precise localization of sensor nodes is crucial for informed decision-making and network efficiency. This study explores localization in the context of WSNs, focusing on the 6LoWPAN and Zigbee protocols. These protocols are vital for integrating WSNs into the Internet of Things (IoT). We highlight the significance of spatial node distribution and WSNs' challenges, such as resource limitations and signal interference. We emphasize range-based methods due to their accuracy. We propose the Adaptive Mean Center of Mass Particle Swarm Optimizer (AMCMPSO) to address these. Inspired by the center of mass principle, this algorithm adapts parameters for enhanced localization on regular and irregular surfaces. AMCMPSO leverages the principle of the center of mass and mean values to enhance the efficiency of sensor node localization. The algorithm incorporates adaptive parameters, including inertia weight and acceleration coefficients, to improve search efficiency and convergence speed. Our simulations demonstrate the superior performance of AMCMPSO, with an average improvement rate of 99.86%. Moreover, the localization error is consistently below 1.34 cm, ensuring precise spatial awareness. In 3D environments, AMCMPSO consistently delivers coverage rates exceeding 87%, even in challenging scenarios.

An adaptive learning grey wolf optimizer for coverage optimization in WSNs

Wireless sensor networks (WSNs) are widely used emerging technologies, but they also face many challenges in practical applications. One of the most critical challenges is the coverage problem. The size of the sensor nodes’ coverage determines the service quality of WSNs. Therefore, an adaptive learning grey wolf optimizer (ALGWO) is proposed to optimize the coverage problem for 2D and more complex 3D regions. In ALGWO, a dynamic opposite learning strategy with dynamic, asymmetric search is employed to prevent premature convergence and improve the exploration capability. Adaptive dimensional learning provides information on the neighborhood dimension for individuals to overcome the dependence on the first three wolves, thus improving the diversity of the population. Meanwhile, the dimensional learning of each individual adaptively performs exploration and exploitation. The performance of ALGWO is evaluated in 2D and 3D WSNs scenarios through simulations. Compared with GWO and its advanced variants, the simulation results show that the ALGWO algorithm has better coverage. In 2D scenes, ALGWO achieved the average coverage of 95.5% and the maximum coverage of 96.93%. In 3D scenes, ALGWO achieved the average coverage of 94.32% and the maximum coverage of 95.13%.

Research on Optimization of 3D Location Algorithm Based on Non-ranging Wireless Sensor Network

In order to solve the problem of node location in 3D space WSN (Wireless sensor network), this article summarizes the structure and characteristics of WSN, then uses weighted centroid algorithm to estimate the location, designs a new non-ranging 3D node location algorithm for wireless sensor network, and gives the theoretical basis and implementation steps of this algorithm design. The algorithm does not need to measure the actual distance between nodes, but only needs the anchor nodes to broadcast their own beacon information, and the unknown nodes within the one-hop communication range of the anchor nodes receive and store the monitored beacon information, and estimate their own positions according to these information. The final simulation results show that, compared with the existing 3D localization algorithm based on non-ranging, there is no need for unknown nodes to communicate with each other, which has less communication overhead and does not depend on the proportion of anchor nodes. And it is robust to the network topology. As a non-ranging positioning algorithm, its positioning error is only about 15% in the set 3D space.

Optimized Approach for Localization of Sensor Nodes in 2D Wireless Sensor Networks Using Modified Learning Enthusiasm-Based Teaching–Learning-Based Optimization Algorithm

Wireless Sensor Networks (WSNs) have a wonderful potential to interconnect with the physical world and collect data. Data estimation, long lifespan, deployment, routing, task scheduling, safety, and localization are the primary performance difficulties for WSNs. WSNs are made up of sensor nodes set up with minimal battery power to monitor and reveal the occurrences in the sensor field. Detecting the location is a difficult task, but it is a crucial characteristic in many WSN applications. Locating all of the sensor nodes efficiently to obtain the precise location of an occurrence is a critical challenge. Surveillance, animal monitoring, tracking of moving objects, and forest fire detection are just a few of the applications that demand precise location determination. To cope with localization challenges in WSNs, there is a variety of localization algorithms accessible in the literature. The goal of this research is to use various optimization strategies to solve the localization problem. In this work, a modified learning enthusiasm-based teaching–learning-based optimization (mLebTLBO) algorithm is used to cope with a 2D localization problem applying the notion of an exclusive anchor node and movable target nodes. A modified LebTLBO algorithm seeks to increase overall efficiency by assessing the exploration and exploitation abilities. The computational results reveal that this technique outperforms others with respect to localization errors in a 2D environment of WSN.

Multistrategy Integrated Marine Predator Algorithm Applied to 3D Surface WSN Coverage Optimization

Achieving maximum network coverage with a limited number of sensor nodes is key to node deployment of wireless sensor network (WSN). This paper proposes an improved marine predator algorithm (IMPA) for 3D surface wireless sensor network deployment. A population evolution strategy based on random opposition-based learning and differential evolution operator is proposed to enrich the population diversity and improve the global search capability of the algorithm. The grouping idea of the Shuffled Frog Leaping Algorithm (SFLA) is then introduced. A local search strategy based on the SFLA is proposed to replace the FADs effect of MPA and enhance the ability of the algorithm to escape from the local optimum. A quasireflected opposition-based learning strategy is also presented to improve the optimization accuracy, accelerate the convergence speed of the algorithm, and improve the quality of the solution. Fifteen benchmark functions are selected for testing. The results are compared with seven different algorithms. The results show that the improved algorithm has excellent optimization performances. Finally, the IMPA is applied to optimize WSN coverage on 3D surfaces. The experimental results show that the proposed IMPA has good terrain adaptation and optimal deployment capabilities. It can improve the coverage of the network, reduce the deployment cost, and extend the network life cycle.

Repulsion-based grey wolf optimizer with improved exploration and exploitation capabilities to localize sensor nodes in 3D wireless sensor network

Repulsion-based grey wolf optimizer with improved exploration and exploitation capabilities to localize sensor nodes in 3D wireless sensor network

Multiple objective optimization-based DV-Hop localization for spiral deployed wireless sensor networks using Non-inertial Opposition-based Class Topper Optimization (NOCTO)

The problem of localization is one of most important issues in wireless sensor networks. Furthermore, it is critical to monitor and evaluate the data gathered. For a variety of factors, such as upkeep, lifespan, and breakdown, the fixed density of these beacons may be increased or decreased. Because of its robustness, flexibility, and economic viability, a well-known technique for locating wireless sensor network nodes is the Distance Vector-Hop (DV-Hop) algorithm. As a result, researchers continue to look for ways to develop it. A new Non-inertial Opposition based Class Topper Optimization (NOCTO) based enhanced DV-Hop localization algorithm is proposed. It also focuses through an optimized formulation to compute the average hop-size with weight of beacon nodes in order to reduce the localization error with estimated distance between the beacon and the dumb node, due to improved localization accuracy. For spiral deployed 2D wireless sensor networks, this paper proposes a multi-objective NOCTO-based DV-Hop localization. The simulation results indicate that our suggested multi-objective function outperforms some existing techniques.

Optimization of the Wake-Up Scheduling Using a Hybrid of Memetic and Tabu Search Algorithms for 3D-Wireless Sensor Networks

Computation of an optimal coverage and connectivity aware wake-up schedule of sensor nodes is a fundamental research issue in a 3D-Wireless Sensor Networks. Most of the existing metaheuristic-based wake-up scheduling schemes do not make sure optimal solution and occasionally smacked in local minima. This paper propose a hybrid metaheuristic-based wake-up scheduling scheme (Memtic-Tabu-based-WS) where best feature of memtic algorithm and Tabu Search algorithm is combined. The proposed scheme has considered four parameters such as energy consumption, coverage, connectivity, and optimal size of schedule list. Performance comparison of the proposed Memtic-Tabu-based-WS scheme is performed in different network scenario and compared with three well-known state-of-art schemes in terms of coverage ratio, active sensor nodes and fitness value. The result analysis validate the superiority of the proposed scheme over the existing schemes with better coverage ratio and derivation of the optimal wake-up schedule.

Distributed Cooperative Localization with Optimized Constraints for 3D WSNs

In three-dimensional (3D) Wireless Sensor Networks (WSNs), the localization suffers from localization accuracy and computation efficiency problem due to added dimension compared with 2D WSNs. Aiming at localizing WSNs in 3D way, we propose a distributed cooperative localization method with optimized constraints. The optimized constraints effectively limit the potential range where the unknown nodes are limited. By analyzing the positions of anchors which are within the communication radius of unknown node, the initial positions of unknown nodes are geometrically estimated in several situations. They link with each other and form a 3D cooperative network. Based on message passing (MP) theory, we extend the 2D cooperative localization to 3D distributed cooperative localization. In this process, optimized constraints derived from hop information and RSSI avoid the gross error and large deviation in the localization. Simulation results in various scenarios show the improvement of accuracy and efficiency compared with traditional methods.

A novel hybrid range-free approach to locate sensor nodes in 3D WSN using GWO-FA algorithm

The precise node location of the sensor nodes is an essential requirement in wireless sensor networks (WSNs) to determine the place or event occurring at a particular instant of time. In WSN, existing localization schemes consider two-dimensional (2D) space, while in actual life, sensor nodes are placed in three-dimensional (3D) space. In 3D localization, there are many research challenges, such as higher computational complexity, poor location prediction, lesser coverage, and depending only on fewer anchor nodes. To address various research issues in a 3D environment we propose a range-free technique applied in an anisotropic scenario having degree of irregularity (DOI) as 0.01 using the concepts of a fuzzy logic system (FLS). Anisotropic properties of nodes are considered to determine the efficiency of Grey wolf with the Firefly algorithm. In our proposed scenario, the received signal strength (RSS) information is necessary among the target nodes and their corresponding anchor nodes for determining the location of target nodes using the information based on edge weights. These edge weights are further modeled using Hybrid Grey Wolf Optimization with Firefly Algorithm (GWO-FA) to estimate the location of target nodes. The proposed algorithm is energy efficient as a single location-aware node is used for localization. Further, the concept of virtual anchors is introduced that helps the algorithm to determine 3D positions.

Modified Parallel Tunicate Swarm Algorithm and Application in 3D WSNs Coverage Optimization

<p>As the application of Wireless Sensor Networks (WSNs) in today’s society becomes more and more extensive, and the status is getting higher and higher, the node layout of sensors has also begun to attract social attention. In reality, the coverage of WSNs in 3D space is particularly important. Therefore, it is worth investigating an efficient way to find out the maximum coverage of WSNs. In this paper, a Modified Parallel Tunicate Swarm Algorithm (MPTSA) is proposed based on modified parallelism, which can improve the convergence of the algorithm and optimal global solution. Next, the proposed MPTSA is implemented and tested on 23 benchmark functions to verify the algorithm performance. Finally, a WSNs network layout scheme based on MPTSA is proposed to improve the coverage of the whole network. Experimental results show that, compared with the traditional PSO (Particle Swarm Optimization), improved PSO (PPSO and APSO), GBMO (Gases Brownian Motion Optimization) and traditional TSA, MPTSA family algorithms show better performance in WSNs network layout.</p> <p> </p>

Genetic Algorithm Energy Optimization in 3D WSNs with Different Node Distributions

Optimal node clustering in wireless sensor networks (WSNs) is a major issue in reducing energy consumption and extending network node life time and reliability measures. Many techniques for optimizing the node clustering process in WSN have been proposed in the literature. The metaheuristic algorithms are a subset of these techniques. Genetic algorithm (GA) is an evolutionary metaheuristic technique utilized to improve the network reliability and extending the network life time by optimizing the clustering process in the network. The GA dynamic clustering (GA-DC) algorithm is proposed in this paper to extend the network reliability and node life time of three dimensional (3D) WSN. The GA-DC algorithm made use of an improved fitness function that takes into account a variety of metrics such as energy expenditure per protocol round, clustering distance, and the number of long-distance wireless connections. There have been two types of simulation scenarios run. First, simulation results show that the GA-DC algorithm increases network life time by 80% and network throughput by 55% when compared to the well-known LEACH protocol. Second, simulation results show that the uniform node distribution outperforms the normal and exponential distributions in terms of network life time by 5.7% and 7%, network reliability by 4.2% and 76%, and data throughput by 10.85% and 19.54%, respectively

Incremental Linear Discriminant Analysis Dimensionality Reduction and 3D Dynamic Hierarchical Clustering WSNs

Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs. This ILDA approach consists of four major steps such as data dimension reduction, distance similarity index introduction, double cluster head technique and node dormancy approach. This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head (CH) selection technique. According to node’s position and residual energy, optimal cluster-head function is generated, and every CH is elected by this formulation. For a 3D spherical structure, under the same network condition, the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering (IDHC) is compared with Distributed Energy-Efficient Clustering (DEEC), Hybrid Energy Efficient Distributed (HEED) and Stable Election Protocol (SEP) techniques. It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput, network residual energy, network lifetime and first node death round.

Research on Clustering Routing Protocol Based on Improved PSO in FANET

In disaster response, 2D wireless sensor networks fail to be deployed since a large number of sensors cannot be placed in harsh ground environments. In this case, as the best air carrier for sensors, unmanned aerial vehicles (UAVs) are always used to construct a 3D flying ad hoc network (FANET) to realize emergency communication. In FANET, however, node localization and data transmission are great challenges due to the increase in network dimensions and the high mobility of UAVs. In this paper, a novel FANET clustering routing protocol based on nodes location is proposed. Firstly, an improved particle swarm optimization (IPSO) algorithm is designed by fusing variable neighborhood search, and IPSO is used to improve the locating precision of UAVs. Furthermore, the base station (BS) clusters UAVs evenly according to nodes location, and the cluster head is determined by weighting the intra-cluster distance and residual energy of nodes. Finally, the optimal next-hop routing is selected by IPSO according to the link reliability and the distance between nodes and the BS to ensure data are transmitted to the BS timely and reliable. The simulation results show that routing protocol based on IPSO realize more efficient performance compared with the existing schemes in terms of positioning indicators, energy consumption, delay, and clustering lifetime.

An Energy-Efficient Routing Protocol for 3D Wireless Sensor Networks

Energy optimization is a key problem of the energy-limited wireless sensor network (WSN), which is usually deployed in the three-dimensional space. In order to alleviate this problem, this paper studies the energy-efficient routing protocol for 3D WSN. The less sensor nodes work simultaneously, the more energy is saved. To minimize energy consumption on the premise of satisfying the full coverage of discrete points of interest (DPOI), finding the least and optimal node set for routing is an important problem. Through studying this problem, we propose an algorithm employing the improved genetic algorithm to find the minimum set of sensor nodes which can ensure the full coverage of DPOI. On the basis of the optimal node set, we propose a new adaptive clustering routing optimizing energy consumption, which includes three parts. The first one is selecting the cluster head according to the residual energy of the sensor node and the distance from the base station. The second one is setting the intra-cluster communication mode by considering the angle of node space. The last one is deciding inter-cluster communication mode by combining single-hop and multi-hop. To fully utilize the energy of the network, we design a node wake-up scheme to wake up the sleep node to replace the sensor nodes which runs out of energy. This policy also can make our proposed protocol suitable for the rechargeable WSN. In addition, we also consider the additional energy consumption of retransmission caused by the unreliable wireless channel. Simulation results show that our proposed scheme is obviously superior to other existing schemes in prolonging the WSN lifetime.