Communication Radius Research Articles

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.

Modern Energy Optimization Approach for Efficient Data Communication in IoT-Based Wireless Sensor Networks

Many researchers are drawn to mobile wireless sensor networks (WSN) and the Internet of Things (IoT) because of the significant challenges of power consumption and network connectivity. A technique that takes into consideration the characteristics such as network probability, the identified region of individual nodes, and the radius of the whole identified region is presented in this article. Free-space propagation is carried out in the region of interest. This approach assures network connection, long-term communication sustainability, and maximum energy efficiency. It was discovered that a mathematical network model can be built using the probability theory. It has been possible to examine and evaluate the changes in sensor nodes as a function of distance from the detection region using this approach. As a result, a correlation has been established between a network’s communication radius and the identified region. Additionally, a novel method has been developed to reduce energy consumption and sustain connectivity through boosting the connectivity feature. Notably, IoT-based WSN architectures require more energy optimization than any other network, because they have resource-limited nodes. Also, a simulation plot of the proposed approach’s mathematical network scheme is shown to show if it works. The proposed method consumes much less energy averagely 40% compared to the existing methods, which are LEACH, ZTR, and DSR when the radius is 100.

Explosive synchronization of weighted mobile oscillators

In recent years, explosive synchronization (ES) has attracted extensive interest due to its great significance in the study of epileptic seizures and cascading failures in the real world. In this paper, a weighting method based on the frequency mismatches and distances between linked oscillators is proposed to construct a Kuramoto-like model to study ES of mobile oscillators. An interesting phenomenon that the weighting of oscillators can induce a sudden and irreversible synchronization transition with hysteresis loop is revealed. The transition between continuous synchronization and ES can be tuned by the weighting factors. Moreover, it is found that the critical coupling strength and hysteresis loop width are affected by the initial frequency distribution, the number of oscillators, the communication radius, the setting of boundary conditions and the constant absolute velocity of oscillators.

Dynamic Vehicular Communication using Gaussian Interpolation of Cluster Head Selection (GI-CHS)

Decentralized and centralized vehicular communication is investigated in this work using Gaussian interpolation function with cluster head (CH) selection technique. The work uncovered that the best communication approach is to use both centralized and decentralized vehicular communication as combining them will achieve a much more uniform results as a function of communication radius values and vehicular speed. It is also found that vehicular speed contributes negatively to the efficiency of data communication if the relative speed of the vehicles to the communication radius is limited by their ratios. Mathematical expression is presented that relates probability of successful transmission to communication radius for both centralized and decentralized techniques with data proving the importance of the spread parameter within the Gaussian interpolation in a tabulated form, and explained to prove the adaptability of the function used. It is also shown in this work that weights affecting CH selection, thus using Gaussian interpolation is proved to be important as a weighting function in an a adaptive and dynamic vehicular ad-hoc networks (VANETS) covering both vehicle to vehicle (V2V), and vehicle to infrastructure (V2I) communication through cluster head selection.

Distance Vector Hop Positioning Based on Double Communication Radius and Improved Gray Wolf Algorithm

Distance Vector Hop Positioning Based on Double Communication Radius and Improved Gray Wolf Algorithm

Topology characteristic analysis of vehicular ad hoc network based on time-varying complex network

The dynamic network topology of the Vehicular Ad Hoc Network (VANET) directly affects network performances. The VanetMobiSim software was used to study the dynamic topology of VANET in detail. A time-varying model of VANET was built. First, the information transmission model of VANET was established on the basis of the time-varying network theory. The evolution of VANET characteristic parameters in a time-varying process was stimulated by using the information transmission time τ and the maximum tolerated delay time δ accessibility graph. Then, we perform mathematical analysis of the relations of (τ, δ) to the out-degrees, average packet accessibility, and asymmetry ratio. Finally, the relations of the node communication radius to the global efficiency and the average harmonic mean of the distances were analyzed. The results indicated that the time-varying accessibility graph made VANET information transmission more efficient; reducing the information transmission time τ or increasing the maximum tolerated delay time δ tends to bring out the improving temporal connectivity of the whole network, thus improving the efficiency of packet transmission.

Construction of node deployment model for wireless sensor network based on fuzzy data fusion

With the goal of increasing the coverage of wireless sensor network nodes and reducing node deployment time, a model of wireless sensor network node deployment based on fuzzy data fusion is designed. According to the wireless sensor network structure, the communication range and communication radius of the node are determined, so as to obtain the coverage of the node. Establishing a wireless sensor network coverage perception model to obtain the perception probability and joint perception probability of the target point. Finally, through fuzzy data fusion and fuzzy fusion rules, the fusion of the target node is realized, and the judgment criterion is obtained, and the deployment of the wireless sensor network node is finally completed under constraints. Experimental results show that this method effectively improves node coverage and reduces node deployment time, indicating that this method is beneficial to enhance the perception quality of wireless sensor networks.

Symmetric Rendezvous in Planar Environments With and Without Obstacles

We study the symmetric rendezvous search problem in which two robots that are unaware of each other’s locations try to meet as quickly as possible. In the symmetric version of this problem, the robots are required to execute the same strategy. First, we present a symmetric rendezvous strategy for the robots that are initially placed on the open plane and analyze its competitive performance. We show that the competitive complexity of our strategy is O(d/R) where d is the initial distance between the robots and R is the communication radius. Second, we extend the symmetric rendezvous strategy for the open plane to unknown environments with polygonal obstacles. The extended strategy guarantees a complete coverage of the environment. We analyze the strategy for square, translating robots and show that the competitive ratio of the extended strategy is O(d/D) where D is the length of the sides of the robots. In obtaining this result, we also obtain an upper bound on covering arbitrary polygonal environments which may be of independent interest.

Malware propagation model for cluster-based wireless sensor networks using epidemiological theory.

Due to limited resources, wireless sensor network (WSN) nodes generally possess weak defense capabilities and are often the target of malware attacks. Attackers can capture or infect specific sensor nodes and propagate malware to other sensor nodes in WSNs through node communication. This can eventually infect an entire network system and even cause paralysis. Based on epidemiological theory, the present study proposes a malware propagation model suitable for cluster-based WSNs to analyze the propagation dynamic of malware. The model focuses on the data-transmission characteristics between different nodes in a cluster-based network and considers the actual application parameters of WSNs, such as node communication radius, node distributed density, and node death rate. In addition, an attack and defense game between malware and defending systems is also established, and the infection and recovery rates of malware propagation under the mixed strategy Nash equilibrium condition are given. In particular, the basic reproductive number, equilibrium point, and stability of the model are derived. These studies revealed that a basic reproductive number of less than 1 leads to eventual disappearance of malware, which provides significant insight into the design of defense strategies against malware threats. Numerical experiments were conducted to validate the theory proposed, and the influence of WSN parameters on malware propagation was examined.

Wireless Sensor Network Node Localization Algorithm Based on PSO-MA

Aiming at the large error and low accuracy of wireless sensor node location, this paper proposes a node location method based on the fusion of Particle Swarm Optimization and Monkey Algorithm (PSO-MA). Firstly, this article describes the node location model based on DV-HOP algorithm; secondly, this article uses PSO in node location, uses place Laplace distribution for population initialization, improves population diversity, and optimizes particle weights to avoid algorithm falling into local optimality. In this paper, dynamic guidance factors are used to update individual positions to improve individual optimization capabilities, and Monkey Algorithm is used to select individuals to improve the quality of optimal solutions. In the simulation experiment, the algorithm PSO and MA of this paper are compared to achieve better positioning results in the reference node ratio, node density and communication radius indicators.

Research on the Coverage of Ultraviolet Communication Network in the Arbitrary Polygon Area

Given that the current ultraviolet (UV) networking model is established in a regular circular area, this article studies the coverage of a UV non-line-of-sight (NLOS) communication network creatively in the arbitrary polygon area. In this paper, the UV communication model and the basic concepts of network coverage are introduced first. Then the influence parameters of the UV node communication radius are studied, and the changes of the communication radius under different work patterns are analyzed. Finally, the coverage of the square target area is simulated under different communication parameters (transmitted power, data rate and node density). The results illustrate that the smaller the transceiver elevation angles are, the better the network coverage performance is. Additionally, we numerically compare the UV network models of polygonal and circular regions, which can be used as a reference for actual networking.

Wireless-Sensor Network Topology Optimization in Complex Terrain: A Bayesian Approach

Existing methods for wireless-sensor network (WSN) topology optimization employ simplifying assumptions of a fixed communication radius between network nodes, which is ill-suited for IoT networks deployed in complex terrain. This article proposes a data-driven approach to WSN topology optimization, employing a Bayesian link classifier trained on LIDAR-derived terrain characteristics and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> survey of link quality. The classifier is trained to predict where good network links (packet-delivery ratio, PDR>0.5) are likely to form in a region given complex terrain attributes. Then, given numerous candidate wireless node placements throughout the domain, the classifier is used to construct an undirected weighted graph of the potential connectivity across the domain. Edge weights in the connectivity graph are proportional to the probability of forming a good link between the nodes. A novel modified cycle-union (MCyU) algorithm for generating a 2-vertex-connected, Steiner minimal network is then applied to the undirected weighted graph of potential network element placements. This ensures a survivable network design, while maximizing the probability of good links within the final network. The total number and spatial distribution of network elements produced by the algorithm is compared to an existing WSN, deployed for environmental monitoring in remote regions. In addition, the MCyU algorithm has been evaluated in three graph test cases to compare with state-of-the-art solutions, where MCyU outperforms in terms of weight minimization and computation time.

A High Density WSN Cluster Positioning Approach

Node self-positioning is one of the supporting technologies for wireless sensor network applications. In this paper, a clustering localization algorithm is proposed for large-scale high-density wireless sensor networks. Firstly, the potential of the node is defined as the basis for the election of the cluster head. The distance between the nodes in the network is calculated indirectly by the relationship between the received signal strength and the communication radius. The topology information in each cluster is saved by the cluster head, and the linear programming method is used in the cluster head to implement the cluster internal relative positioning. Then, from the sink node, the inter-cluster location fusion is gradually implemented, and finally the absolute positioning of the whole network is realized. Compared with the centralized convex programming algorithm, the proposed algorithm has low computational complexity, small traffic, high positioning accuracy, and does not need to know the signal attenuation factor in the environment in advance, and there is anti-noise ability.

Research on Optimization of Distributed Node Location Algorithm in Wireless Sensor Networks

This paper studies the related theories of wireless sensor networks, and an improved DV-Hop algorithm is proposed, which used to estimate the distance error mainly based on the error of average per hop distance, unknown nodes and anchor nodes in traditional algorithm. Through the simulation analysis, it can be found that compared with the traditional Location algorithm of DV-Hop and other improved algorithm, the improved algorithm proposed in this paper can reduce the location error and improve the location accuracy when the proportion of the anchor nodes is moderate and the communication radius is small, and this algorithm is suitable for the low power and wide range wireless sensor network environment.

A comprehensive investigation on range-free localization algorithms with mobile anchors at different altitudes

In this work, the problem of localizing ground devices (GDs) is studied comparing the performance of four range-free (RF) localization algorithms that use a mobile anchor (MA). All the investigated algorithms are based on the so-called heard/not-heard (HnH) method, which allows the GDs to detect the MA at the border of their antenna communication radius. Despite the simplicity of this method, its efficacy in terms of accuracy is poor because it relies on the antenna radius that continuously varies under different conditions. Usually, the antenna radius declared by the manufacturer does not fully characterize the actual antenna radiation pattern. In this paper, the radiation pattern of the commercial DecaWave DWM1001 Ultra-Wide-Band (UWB) antennas is observed in a real test-bed at different altitudes for collecting more information and insights on the antenna radius. The compared algorithms are then tested using both the observed and the manufacturer radii. The experimental accuracy is close to the expected theoretical one only when the antenna pattern is actually omnidirectional. However, typical antennas have strong pattern irregularities that decrease the accuracy. For improving the performance, we propose range-based (RB) variants of the compared algorithms in which, instead of using the observed or the manufacturer radii, the actual measured distances between the MA and the GD are used. The localization accuracy tremendously improves confirming that the knowledge of the exact antenna pattern is essential for any RF algorithm.

Effect of Noise on Pandemic Structure for Proliferation of Malevolent Nodes in Remote Sensor Network

Remote sensor systems experience basic investigation of network security because of inexplicable operational imperatives. The challenge flinches with the fragment of worms in the remote system. A solitary contaminated hub is adequate to spread the worms over the entire system. The tainted hub hastily contaminates the contiguous hubs in a relentless way. In this paper, a scientific model depending on pandemic hypothesis is proposed which is an enhancement of the SIRS, SEIS, SEIRS models. SEIQRV (susceptible-exposed-infectious-quaraintain-recovered-vaccinated) model enriched with the additive white noise conquers the hindrances of existing models. Using Routh-Hurwitz criteria, reproduction number, existence of steady states, and stability about endemic steady state are analyzed. The proposed system’s stability is substantiated varying for different values of communication radii and node densities. The additional constituent to the proposed model is the stochastic fluctuation implemented and the examination of the stochastic variations. This model incorporates a limited correspondence span and the related hub thickness. The assessment of the proposed model with other existing models are presented and analyzed based on different executions.

A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

Location information of nodes in Wireless Sensor Networks (WSN) is essential to identify the origins of events and to act on them. Several localization algorithms are developed for this purpose. In this work, we have considered hop based localization algorithms, which are popularly used in WSN applications. These algorithms use a few reference nodes with location information and localize other nodes with reference to these nodes. But, in practical scenarios, some reference nodes may turn faulty and report incorrect location information to other nodes. This reduces the localization accuracy of the entire network. Therefore, it is essential to identify and filter out faulty reference nodes from the localization process. But, in Heterogeneous Wireless Sensor Networks (HWSN), since both faulty nodes and heterogeneous nodes modify hop distances, it becomes even more challenging to identify only faulty nodes among a set of heterogeneous nodes. In this work, we have reported a fault filtering method that can be used with any of the existing hop based localization algorithms for fault-tolerant localization. This method first normalizes the distance estimations using the communication radius of nodes and then uses the Jenks Natural Breaks algorithm for filtering out the nodes producing inconsistent distance estimations. The reported method is incorporated into existing localization algorithms and tested in 2D/3D, isotropic/anisotropic environments. The results show an improvement of 14%, 52%, and 51% in localization accuracy when tested with DV-Hop, Weighted DV-Hop, and HHO-AM algorithms, respectively.

Intelligent Logistics Tracking System Based on Wireless Sensor Network

Wireless sensor networks are widely used to collect and monitor data in the physical world and play a vital role. This article mainly studies the intelligent logistics tracking system based on wireless sensor network. In this paper, the compressed sensing theory is used to reconstruct the environment monitoring data in the cabin to verify the validity of the theory. The five types of sensors are placed in the specified position in the carriage for multi-point sampling, and each node performs data collection every 45 seconds. This paper uses the entropy weight fuzzy comprehensive evaluation method to evaluate the platform planning scheme. The wireless network adopts a multi-zone network networking method, which divides the entire coverage area into multiple sub-areas. Each wireless access point is responsible for controlling a sub-area. At the same time, each wireless access point will also become a mobile terminal and the backbone network. In the actual image transmission, there are two data transmission modes of Zigbee module involved, transparent transmission and point-to-point mode. In the transparent transmission mode, only the communication between the gateway device and the edge device can be carried out, and the communication between the edge devices cannot be carried out. In order to test that the wireless sensor node can actively track the logistics status of the logistics object, this paper uses a temperature sensor to test to test whether the wireless sensor node can actively track the logistics object at different temperatures. In order to achieve the goal of performance testing, a test program was written to simulate a large number of users logging into the system at the same time to perform operations to test the performance of the system. Starting from 15m, the communication radius increases by 5m each time to 50m, and the average positioning error is gradually decreasing. The results show that the introduction of Internet technology and mobile communication network positioning technology into the logistics tracking system can greatly improve the utilization of existing resources.

Energy efficient scheme for better connectivity in sustainable mobile wireless sensor networks

The extreme issues related to energy consumption and connectivity within the network in mobile wireless sensor network indulges so many researchers to find out the optimal solutions. This paper presents a new probabilistic algorithm to analyze the network connectivity by taking in account the parameters like network probability, detection area, radius of the individual nodes and whole detection area etc. The targated area is taken for free space propagation. This methodology ensures the network connectivity, sustainability of communication and optimizes the energy consumption at utmost level. The probability theory has been used to find out a feasible mathematical network model. In this model the variation of sensor nodes with respect to the detection area has been observed and analyzed. Also, the relationship between communication radius, sensor nodes deployed, and detection area has been identified. A new algorithm is also proposed to optimize energy and maintain the connectivity by increasing the connectivity factor. Also the simulation graph of the proposed model is presented to validate the mathematical network model.

A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave Regions

At present, range-free localization algorithm is the mainstream of node localization method, which has made tremendous achievements. However, there are few algorithms that can be used in concave regions, and the existing algorithms have defects such as hop distance error, excessive time complexity and so on. To solve these problems, this paper proposes a two-stage PSO (Particle Swarm Optimization) algorithm for wireless sensor nodes localization in “concave regions”. In the first stage, it proposes a method of distance measuring based on similar path search and intersection ratio, and completes the initial localization of unknown nodes based on maximum likelihood estimation. In the second stage, the improved PSO algorithm is used to optimize the initial localization results in the previous stage. The experimental result shows that the localization error of this algorithm is always within 10% and the execution time is maintained at about 20 s when the communication radius and beacon node ratio is changing. Therefore, the algorithm can obtain high localization accuracy in wireless sensor network with “concave regions”, requiring low computing power for nodes, and energy consumption. Given this, it can greatly extend the service life of sensor nodes.