Method For Wireless Sensor Networks Research Articles

Wireless sensor network monitoring algorithm for partial discharge in smart grid

With the continuous improvement of power grid intelligent requirements, the Power Internet of Things develops rapidly. Substation is an important part of the power grid. The real-time monitoring of its equipment is conducive to the timely detection and removal of power supply faults. In this paper, a monitoring network is built for partial discharge (PD) of high voltage (HV) equipment. According to the three-dimensional characteristics of the equipment, the deployment strategy of ultrasonic sensors for PD monitoring is discussed. A virtual force algorithm based on adaptive angle adjustment is proposed. It is proved that the application of that algorithm can cover more than 94% of target in the range of controllable convergence. According to the target triggering characteristics of PD, the networking method of wireless sensor network is discussed. A sensor cooperation scheme based on the standing guard protocol is proposed, which verifies its energy saving and time delay shortening effect on the basis of ensuring the target acquisition rate. This paper describes the application of dynamic alliance routing protocol in PD information transmission, designs the process and steps of the protocol. Its performance is better than LEACH and Opportunity Routing protocol.

High-density data transmission and scheduling method in wireless sensor networks based on Wi-Fi

In wireless sensor network, the storage amount of information is high, and the transmission and scheduling of control information is reasonable. The node communication model, network structure model, and energy consumption model are constructed. On this basis, the high-density data in wireless sensor network are scheduled to optimize the time for nodes to perform tasks. The nodes in the network are fully scheduled to control the generation time of packets in the network and the generation time of packets in the network. Experimental results show that in different iterations, the proposed method has lower node delay and node energy consumption, with values less than 0.2 and 2, respectively, and the maximum data fusion quality can reach 98, with high fusion benefits, so as to improve the transmission and scheduling efficiency and quality of high-density data in wireless sensor network.

A signal reconstruction method of wireless sensor network based on compressed sensing

Compressed sensing (CS) is a new theory for sampling and recovering signal-based sparse transformation. This theory could help us to acquire complete signal at low cost. Therefore, it also satisfies the requirement of low-cost sampling since bandwidth and capability of sampling is not sufficient. However, wireless sensor network is an open scene, and signal is easily affected by noise in the open environment. Specially, CS theory indicates a method of sub-Nyquist sampling which is effective to reduce cost in the process of data acquirement. However, the sampling is “imperfect”, and the corresponding data is more sensitive to noise. Consequently, it is urgently requisited for robust and antinoise reconstruction algorithms which can ensure the accuracy of signal reconstruction. In the article, we present a proximal gradient algorithm (PRG) to reconstruct sub-Nyquist sampling signal in the noise environment. This algorithm iteratively uses a straightforward shrinkage step to find the optimum solution of constrained formula, and then restores the original signal. Finally, in the experiment, PRG shows excellent performance comparing to OMP, BP, and SP while signal is corrupted by noise.

An energy-aware clustering and two-level routing method in wireless sensor networks

Wireless sensor networks (WSN) are consisted of several sensor nodes scattered in an area to gather data from their ambient environment and send it to base station (BS). The energy of nodes in WSNs is limited. One of the most significant issues in WSNs is reducing the energy consumption of nodes, which leads to increased network lifetime. One method to reduce energy consumption in WSNs is energy-efficient routing. In energy-efficient routing, gathered data is sent to the sink in a way to save the energy of nodes. This paper proposed a cluster-based two-level routing method. In the proposed method, we seek to improve packet delivery rate and reduce energy consumption through clustering, selecting backup cluster head (BCH), layering cluster heads (CH), and dividing each cluster into four sections. The method is consisted of two phases. In the first phase, CHs and BCHs are selected, and nodes are clustered based on their residual energy, distance to BS, and centrality. To perform intra-cluster routing, each cluster is divided into four sections so that nodes directly deliver their data to CH or through the most proper node in their sections. To perform inter-cluster routing, CHs are layered based on their distance to BS. Since CHs are layered, the source CH selects the next hop from CHs in the upper layer based on their residual energy and distance to BS. The proposed method has been simulated by NS-2 software and compared with CFPT (Yarinezhad and Hashemi in J Syst Softw 155:145–161, 2019), FBCFP (Thangaramya et al. in Comput Netw 151:211–223, 2019) and DFCR (Azharuddin et al. in Comput Electr Eng, 41:177–190, 2015) methods. The results reveal that the proposed method leads to reduced end-to-end delay, number of total hops, energy consumption as well as increased packet delivery rate and network lifetime.

Energy efficiency of organized cluster election method in wireless sensor networks

<p><span lang="EN-US">In this paper, we propose a new approach to improve the protocol LEACH performance. It has been accomplished by increasing the energy balancing in clusters among all nodes to minimize the energy dissipation during network communications. The improved method is based on an optimized selection of cluster head. The development of this approach has indicated progress in terms of network lifetime, energy consumption and number of packets transferred to base station, compared to LEACH or other related extended spaces protocols. Mathematical analysis and simulations results show the effectiveness of the proposed approach. The energy consumption of WSN has been reduced up to about 3.5 %. The network life cycle is prolonged by 44 %. The number of packets sent has been increased by 185 % than LEACH.</span></p>

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.

A New Distributed Fault Detection Method for Wireless Sensor Networks

In this article, a new distributed fault detection method based on the nodes' importance in maintaining connectivity of the network is proposed for wireless sensor networks (WSN). Given that the effect of nodes is different on the connectivity of WSN, the proposed method tries to identify the high local importance nodes and keep these nodes in the network as much as possible. In addition, in the proposed method, the probabilistic and deterministic status of the sensor nodes is determined by analyzing the differences between data of neighboring nodes. Extensive simulations have carried out to validate the effectiveness of the proposed method in various scenarios in terms of the detection accuracy and false alarm rate. Moreover, analysis of time complexity and message complexity indicates that the overhead of the proposed method is lower than the counterpart approaches.

Amended DV‐hop scheme based on N‐gram model and weighed LM algorithm

Distance vector-hop (DV-hop) is a classical node localisation method for wireless sensor networks. Its main advantages are ease of use and excellent scalability, but its shortcomings are also very prominent for the high requirements on the network topology and low localisation accuracy in practical applications. An amended DV-hop strategy is provided to improve the shortcomings of the classical DV-hop including two aspects: one is that an N-gram model is adopted to amend the distances between unknown nodes and beacons and the other is that a weighed Levenberg–Marquardt method is adopted to optimise the unknown nodes’ initial positions. Simulation results show that compared with the original DV-hop method and the other two typical improved methods, our proposed strategy improves the adaptability of the DV-hop class methods to network topology changes, and the localisation accuracy is significantly enhanced.

An Enhanced Mac Protocol Design Prolong Sensor Network Lifetime

The major issues in the Wireless Sensor Networks (WSNs) are energy, efficiency, and lifetime. Prolonging the lifetime in the sensor network plays a vital role in the research. In all the sensor based applications, there is need to monitor the lifetime continuously. There are several methods available in order to maintain duty cycling, to balance the node lifetime, retaining energy in the network. However, day by day, the many innovative techniques arrive to overcome these type of problems. The acquired overhead of the MAC-layer communication is circulated between source and destination in various ways with various MAC protocol. This paper proposes an Enhanced MAC protocol in order to prolong the lifetime, which concentrates on end-to-end delay transmission. The different model is analyzed and Cross-Layer Collaboration among Nodes Module (CLCNM) is implemented in order to enhance the lifetime. The obtained results show the superiority of the proposed protocol compared with existing methods in wireless sensor networks.

Fault tolerant data transmission reduction method for wireless sensor networks

Several theoretical studies have clearly demonstrated that the Dual Prediction Mechanism (DPM) remains the most efficient technique for data reduction in Wireless Sensor Networks (WSNs). In real world, the deployed sensor nodes suffers from packet loss and even failures which renders the DPM unreliable, since it requires flawless synchronization between the source (sensor node) and the destination (Sink). In this paper, we introduce a Fault Tolerant Data Transmission Reduction (FTDTR) technique consisting of three main components: DPM-based transmission reduction, synchronization and packet loss detection, and finally reconstruction of missing data. Our method was evaluated on real-world data sets collected at our laboratory and compared to three recent prediction-based data reduction approaches. The results were promising in quality of the replicated measurements and transmission reduction.

Optimal Energy Resources Allocation Method of Wireless Sensor Networks for Intelligent Railway Systems.

The rapid increase of train speed has brought greater challenges to the safety and reliability of railway systems. Therefore, it is necessary to monitor the operation status of trains, infrastructure, and their operating environment in real time. Because the operation environment of railway systems is complex, the construction cost of wired monitoring systems is high, and it is difficult to achieve full coverage in the operation area of harsh environments, so wireless sensor networks are suitable for the status monitoring of railway systems. Energy resources of nodes are the basis of ensuring the lifecycle of wireless sensor networks, but severely restrict the sustainability of wireless sensor networks. A construction method of special wireless sensor networks for railway status monitoring, and an optimal energy resources allocation method of wireless sensor networks for intelligent railway systems are proposed in this paper. Through cluster head selection and rotating probability model, clustering generation and optimization model, and partial coverage model, the energy consumption of nodes can be minimized and balanced. The result of simulation experiment proved that the lifetime of wireless sensor networks can be maximized by the optimal energy resources allocation method based on clustering optimization and partial coverage model, based on polynomial time algorithm.

A Novel Algorithm for Improving Malicious Node Detection Effect in Wireless Sensor Networks

As an important medium of information transmitting, Wireless Sensor Networks (WSN) is at risk of a series of malicious nodes. In view of the inefficiency of the existing malicious node detection methods in Wireless Sensor Networks, this paper proposed a malicious node detection model based on reputation with enhanced low energy adaptive clustering hierarchy (Enhanced LEACH) routing protocol (MNDREL). MNDREL is a novel algorithm, which is aimed at identifying malicious nodes in the wireless sensor network (WSN) more efficiently. Cluster-head nodes are first selected based on the enhanced LEACH routing protocol. Other nodes in WSN then form different clusters by selecting corresponding cluster-head nodes and determine the packets delivery paths. Each node then adds its node number and reputation evaluation value to the packet before sending it to the sink node. A list of suspicious nodes is then formed by comparing the node numbers, obtained through parsing with the packets by the sink node, with the source node numbers. To determine the malicious nodes in the network, the ratio of the suspect value to the trusted value of each node is further calculated and compared with a predefined threshold. The algorithm proposed in this paper, with other two state-of-the-art methods, which are the fuzzy logic based multi-attribute trust model (FMATM) and the high-reliability trust evaluation model (HRTM), are performed and analysed in the same scenario. According to simulation experiments, the MNDREL model is more efficient in detecting malicious nodes in WSN with lower false alarm rate.

A weighted centroid correction method for wireless sensor network based on GSO algorithm

A weighted centroid correction method for wireless sensor network based on GSO algorithm

Low-cost localisation considering LOS/NLOS impacts in challenging indoor environments

One popular indoor localisation method in wireless sensor networks (WSN) and wireless local area networks (WLAN) is fingerprinting technique. However, the training stage of fingerprinting and constructing the radio map is time-consuming and labour-intensive. Here, we propose a novel method to decrease the training cost by building a simulated radio map using an improved path-loss model in which the impacts of line of sight (LOS) and non-light-of-sight (NLOS) propagations are considered. Including LOS/NLOS effects also improves distance estimation. Furthermore, the simulated radio map helps assess and improve the fingerprinting area and network parameters prior to the actual positioning. For performance evaluation, three different radio maps are created using simulation and a test field experiment and then compared based on deterministic and probabilistic algorithms. The results indicate that the improved model outperforms the typical path-loss model and the localisation error gets closer to the actual error of the fingerprinting network.

A Fuzzy Based Energy Efficient Clustering Routing Protocol in Underwater Sensor Networks

The provision of proper routing methods in wireless sensor networks is important due to the sensors' limited hardware and software resources. Some important metrics should be achieved with the use of an efficient routing algorithm, such as low packet loss, improved quality of service, and low energy consumption. Clustering-based routing algorithms have a more efficient performance in the case of link breakages, compared to the other table-based methods. Therefore, a new clustering-based routing algorithm is proposed in this paper that takes the sensors' energy limitation into consideration. The proposed method is designed based on a three-step fuzzy logic. The steps are used to determine the cluster head node and to discover and select a suitable route. In the proposed fuzzy system, the best selection is done based on the existing real-time information. Simulation results show that the proposed method results in a 7% reduction in the network energy consumption simultaneous with a higher packet delivery ratio up to 4% in comparison with IDACB, as the basic algorithm.

SLGBM: An Intrusion Detection Mechanism for Wireless Sensor Networks in Smart Environments

Wireless sensor network (WSN), which serves as one of the key components of the cyber physical system, is a wireless network composed of many stationary or moving sensors in a self-organizing and multi-hop manner. It cooperatively senses, collects, processes and transmits the information of the perceived objects in the geographical area covered by the network, and finally sends this information to the owner of the network. There are some typical attacks in WSN, such as Blackhole, Grayhole, Flooding and Scheduling attacks, which may cause damages to the WSN system in a short time. Moreover, intrusion detection methods for WSN suffer from the disadvantages of low detection rate, large calculation overhead, and high false alarm rate due to the limited resources of sensor nodes and a large amount of redundancy as well as high correlation of network data. In view of the above problems, we propose SLGBM, an intrusion detection method for wireless sensor networks. First, the sequence backward selection (SBS) algorithm is applied to reduce the data dimension on the feature space of the original traffic data so as to reduce the computational overhead. A LightGBM algorithm is then utilized to detect different network attacks. The experimental results based on the WSN-DS dataset show that F-measure of our proposed SLGBM are obviously superior to current typical detection methods, which are 99.8%, 99.4%, 99.1%, 96.5%, and 96.1% in Normal, Blackhole, Grayhole, Flooding, and Scheduling (TDMA) attack detections respectively.

Compressed Data Collection Method for Wireless Sensor Networks Based on Optimized Dictionary Updating Learning

Wireless sensor networks (WSNs) is composed of a large number of tiny sensors. These energy-constrained sensors are deployed in a variety of environments to collect data such as temperature, humidity, and light intensity. Therefore, how to suppress the impact of environmental noise on the collection accuracy and extend the lifetime of WSNs is one of the prominent issues. This article proposes an optimized dictionary updating learning-based compressed data collection algorithm (ODUL-CDC) to suppress the impact of environmental noise on the accuracy of WSNs data collection and extend the life cycle of WSNs. The proposed algorithm uses the dictionary learning method to obtain a sparse dictionary by learning from the training data. The collection error caused by environmental noise is positively correlated with the degree of self-coherence of the sparse dictionary. Therefore, the self-coherence penalty term is introduced during the dictionary updating process, which can reduce the over-fitting of the training data in the dictionary learning process. Moreover, the self-coherence penalty term endows the learned sparse dictionary with a low-self-coherence structure. Experimental and simulation results show that, as compared with discrete cosine transform(DCT), K-SVD and IDL learning-based data collection methods, the proposed algorithm exhibits the highest increase in recovery accuracy of 3.2% in the signal-to-noise ratio (SNR) range of 30-50 dB, the sampling ratio range of 25%-40% and the sparsity range from 3 to 30. Furthermore, the energy consumption is significantly less than that of the compared methods, which helps improve the network lifetime.

A weighted centroid correction method for wireless sensor network based on GSO algorithm

To overcome the ambiguity of the location information of wireless sensor network nodes and lead to the low accuracy of weighted centroid localisation results, a new weighted centroid correction method based on group search optimiser (GSO) algorithm for wireless sensor networks is proposed in this paper. This method randomly drops sensor nodes into the area to be monitored to form a wireless sensor network. The experimental results show that the network coverage is close to 100%, the energy consumption per unit during receiving and transmitting accounts for 0.31% of the total battery. The absolute positioning error of each node is 6-8.5 m, which can achieve the expected goal of this study.

A Distributed Multi-Competitive Clustering Method for Wireless Sensor Networks using 5G LTE

Fifth era remote, or 5G, is the most recent emphasis of cell innovation, designed to extraordinarily build the speed and responsiveness of remote systems. With 5G, information transmitted over remote broadband associations could go at rates as high as 20 Gbps by a few appraisals surpassing wire line organize speeds and in addition offer inactivity of 1 ms or lower for utilizes that require ongoing criticism. 5G will likewise empower a sharp increment in the measure of information transmitted over remote frameworks because of more accessible transfer speed and propelled reception apparatus technology. Wireless sensor systems (WSNs) require basic and successful ways to deal with lessen vitality utilization in view of restricted vitality. Grouping hubs is a compelling way to deal with make WSNs vitality productive. In this paper we propose a dispersed multi-aggressive bunching approach (DMCC) for WSNs. To start with, the hubs with high leftover vitality are chosen to go about as group head hopefuls (CHCs). Second, bunch heads (CHs) are chosen from the CHCs in light of a crossover of rivalry. In the event that the separations to the chose CHs are reasonable, a CHC with more neighbour hubs and littler normal separation to its neighbour hubs will probably turn into a CH. In the event that the quantity of CHs chose from the CHCs is insufficient, more CHs are chosen from non-CHCs constantly as indicated by lingering vitality until the CHs number is reasonable. DMCC makes the CHs number stable and disperse the CHs equally. Recreation tests were performed on to contrast DMCC and some related bunching approaches. The exploratory outcomes recommend that DMCC adjusts the heap among various groups and lessens the vitality utilization, which enhances the system lifetime.

Design of various Image Compression Methods in Wireless Sensor Networks

The processing capacity and power of nodes in a Wireless Sensor Network (WSN) are restricted. The quality of the images is deficient, and the contents of the images may vary after decoding when we apply image compression algorithms in WSN. Various compression algorithms are compared in this paper. An Image Compression method based on Restricted Boltzmann Machine (RBM), Auto encoders and Non-negative Matrix Factorization (NMF), Least Square Non-Negative Matrix Factorization (LSNMF), Projective Non-Negative Matrix Factorization (PNMF) network are proposed in this paper. For the WSN, we have used a Message Queue Telemetry Transport (MQTT) protocol. We have used a three Raspberry Pi’s to build a WSN; Publisher, Broker, Subscriber. A Publisher, where it can trigger the camera and captures the images then compress it and send it to another raspberry pi which is a MQTT broker. The PSNR values for those image compression methods were analyzed and compared against each other for images evaluated from the MNIST dataset. Along with the simulation results, all these compression methods are implemented using hardware implementation. Raspberry Pi, a single-board computer with in-built Wi-Fi capabilities, was used in establishing a WSN. Message Queue Telemetry Transport (MQTT) protocol was used for transmitting the compressed images across the WSN, that offers fast and reliable transmission.