Data In Wireless Sensor Networks Research Articles

A novel optimization method for WSN based on mixed matrix decomposition of NMF and 2-SVD-QR

WSN(Wireless Sensor Network), as the front-end of data acquisition of IoT(Internet of Things), its data always has the properties of massive, dynamic, correlated, and polymorphic, causing big troubles for the subsequent series of signal processing. Dense sensor nodes are randomly deployed in the monitoring field, so their perceived information cannot be independent so that there must be data redundancy. Therefore, considering the properties of complicated correlation in data acquisition, we propose a mixed matrix decomposition approach based on NMF(Non-negative Matrix Factorization) and 2-SVD-QR(Double-Singular-Value-QR) to optimize WSN: 1. Turn off redundant sensor nodes and retain a few nodes collectively to approximate the raw data output of WSN; 2. Explicit this method to largely eliminate the collected redundant data by the sensor in a coherent time. This approach not only reduces the amount of data that needs to be collected, but also significantly saves energy consumption and prolongs network lifetime. Experimental results verify that this method can effectively eliminate the correlation between the raw collected sensor data and highly improve the data (CR)compression ratio under the premise of ensuring the data reconstruction accuracy, and it is also better than existed WSN data compression approaches in terms of CR and reconstruction accuracy, which demonstrates the effectiveness of this mixed matrix decomposition.

Chain‐routing scheme with compressive sensing‐based data acquisition for Internet of Things‐based wireless sensor networks

The emerging Internet of Things (IoT)-based systems that integrate diverse types of sensors, mobiles and other technologies to physical world is becoming increasingly popular for use in wide varieties of applications. Compressive sensing (CS)-based information acquisition and in-network compression provide an effective method for accurate data recovery at the base station (BS) with reduced cost of communication. In this study, how CS can be combined with routing protocols for gathering data in IoT-based Wireless Sensor Networks (WSNs) in an energy-efficient manner are examined. A novel chain-routing scheme with CS based data acquisition is introduced that includes the following new algorithms: (1) Seed Estimation Algorithm (SEA) to find the best measurement matrix by selecting the best-estimated seed, (2) Chain Construction Algorithm (CCA) to organise the network nodes during transmitting and receiving process, (3) Compression approach with reduced consumption of energy that improves the lifetime of the network by minimising the local data traffic, and (4) Reconstruction Algorithm (RA) that reconstructs the original data with minimum reconstruction error. Here, extensive simulation and analysis results prove the performance of our proposed method in improving the network lifetime 35% better than the ECST algorithm and 93% better than the PEGASIS algorithm. In addition, the proposed reconstruction algorithm exceeds the other reconstruction algorithms performance.

Multilayer validation system for the automation of data in a WSN network with IOT devices

Introduction: Article resulting from the research "Multilevel validation system with an emphasis on data transmission in a Gateway for the IoT," supported by the Research Line in Internet Applications of the University of Cauca during 2020.
 
 Problem: Bridges between wireless sensor networks (WSN) and the Internet are necessary for the for the operation of IoT devices. However, these networks are made up of weak links that can generate packet loss.
 
 Objective: present an alternative to improve the data transmission validation process given a frame implemented in a previously developed WSN-IoT system.
 
 Methodology: Scientometric review in the WoS and Scopus databases on concepts related to Industry 4.0 (data from 2015 to 2019), IoT and WSN (data from 1990 to 2019), and algorithm generation based on Scrum and test-based development (TDD).
 
 Results: A validation application that captures, analyzes frames, and compares the values of the monitored variables in each layer of the implemented model, and ultimately reports the errors detected in the data transmission.
 
 Conclusion: with the completion of this investigation, the data transmission validation process of the WSN-IoT system was improved, focused on logistics and monitoring, automating the process.
 
 Originality: An alternative to validate the correct transmission of data in a WSN-IoT system is achieved through this research and shown for the first time on trending topics such as industry 4.0, the industrial Internet of things, the supply chain, logistics, and traceability.
 
 Limitations: little research in the area of validation, few and delicate hardware devices, in addition to a high dependence on networks to monitor the performance of the designed application.

Data Authentication for Wireless Sensor Networks with High Detection Efficiency Based on Reversible Watermarking

Data authentication is an important part of wireless sensor networks (WSNs). Aiming at the problems of high false positive rate and poor robustness in group verification of existing reversible watermarking schemes in WSNs, this paper proposes a scheme using reversible watermarking technology to achieve data integrity authentication with high detection efficiency (DAHDE). The core of DAHDE is dynamic grouping and double verification algorithm. Under the condition of satisfying the requirement of the group length, the synchronization point is used for dynamic grouping, and the double verification ensures that the grouping will not be confused. According to the closely related characteristics of adjacent data in WSNs, a new data item prediction method is designed based on the prediction‐error expansion formula, and a flag check bit is added to the data with embedded watermarking during data transmission to ensure the stability of grouping, by which the fake synchronization point can be accurately identified. Moreover, the embedded data can be recovered accurately through the reversible algorithm of digital watermarking. Analysis and experimental results show that compared with the previously known schemes, the proposed scheme can avoid false positive rate, reduce computation cost, and own stronger grouping robustness.

Various Methods of Link Design for Transferring Data in Wireless Sensor Networks for different Applications

ABSTRACTThis paper offers the different type transmission schemes used in wireless sensor networks. The wireless sensor network used in a number of applications such as Agriculture, Military, Medical, Multimedia etc. According to the applications there are different quality of service parameters and designing requirements for different layers in network design. While designing any ad hoc network or sensor network many designing principals and challenges are taken into consideration. For the designing of protocols layering in ad hoc sensor network basically a five layer model is taken into consideration. These layers are application, transport, network, Medium access control and physical layer. In this paper different types of designing constraints associated with physical and medium access layer has been introduced for different applications in sensor networks. In that manner this paper helps us to select the designing of a link for particular application.

Fault Detection Method and Simulation Based on Abnormal Data Analysis in Wireless Sensor Networks

With the rapid development of Internet of things and information technology, wireless sensor network technology is widely used in industrial monitoring. However, limited by the architecture characteristics, software and hardware characteristics, and complex external environmental factors of wireless sensor networks, there are often serious abnormalities in the monitoring data of wireless sensor networks, which further affect the judgment and response of users. Based on this, this paper optimizes and improves the fault detection algorithm of related abnormal data analysis in wireless sensor networks from two angles and verifies the algorithm at the same time. In the first level, aiming at the problem of insufficient spatial cooperation faced by the network abnormal data detection level, this paper first establishes a stable neighbor screening model based on the wireless network and filters and analyzes the reliability of the network cooperative data nodes and then establishes the detection data stability evaluation model by using the spatiotemporal correlation corresponding to the data nodes. Realize abnormal data detection. On the second level, aiming at the problem of wireless network abnormal event detection, this paper proposes a spatial clustering optimization algorithm, which mainly clusters the detection data flow in the wireless network time window through the clustering algorithm, and analyzes the clustering data, so as to realize the detection of network abnormal events, so as to retain the characteristics of events and further classify the abnormal data events. This paper will verify the realizability and superiority of the improved optimization algorithm through simulation technology. Experiments show that the fault detection rate based on abnormal data analysis is as high as 97%, which is 5% higher than the traditional fault detection rate. At the same time, the corresponding fault false detection rate is low and controlled below 1%. The efficiency of this algorithm is about 10% higher than that of the traditional algorithm.

AoI Minimization Data Collection Scheduling for Battery-Free Wireless Sensor Networks

Age of Information (AoI) is a new metric for measuring the freshness of sensory data in wireless sensor networks. The Battery-Free Wireless Sensor Network (BF-WSN) is proposed to break through the lifetime limitation of battery-powered wireless sensor networks. However, the emerging BF-WSN also brings challenges to the minimization of AoI, on account of its energy characteristics. In this paper, we investigate the AoI minimization data collection scheduling problem for BF-WSNs. The off-the-shelf works for the AoI minimization data collection scheduling problem either focus on simple networks with no more than three nodes or assume that battery-free sensor nodes have specific energy harvesting process, such as Bernoulli process and Poisson process. Different from these works, we first consider the AoI minimization data collection scheduling for one-hop BF-WSNs with multiple battery-free sensor nodes transmitting their sensory data to the sink node, where the energy harvesting processes of battery-free sensor nodes are non-specific. We propose the optimal offline algorithm and the online algorithm for the problem, respectively. The optimality of the offline algorithm and the competitive ratio of the online algorithm are theoretical proved and analyzed. Numerical results are provided to verify the performances of the proposed algorithms.

Blockchain-Based Wireless Sensor Networks for Malicious Node Detection: A Survey

Wireless Sensor Networks (WSNs) are broadly applied for various applications in tracking and surveillance due to their ease of use and other distinctive characteristics compelled by real-time cooperation among the sensor nodes. In WSNs, security is becoming a critical issue, as the techniques for malicious node detection adopt a one-time, centralized decision-making approach. With this paradigm, errors are difficult to avoid, and reproducibility and traceability are challenging. Hence, malicious node discovery technologies in conventional WSNs cannot assure traceability and fairness of the detection method. Herein, this paper discusses an in-depth survey of a blockchain-based approach for malicious node detection, an exhaustive examination of the integration of blockchain techniques with WSNs (BWSN), and insights into this novel concept. This survey discusses the architecture, sector-wise applications, and uses of BWSN. Moreover, this survey describes malicious node detection based on BWSN in two parts: 1) the BWSN architecture for detecting the malicious nodes and 2) the smart contract aspects in malicious node detection. Next, this survey explains the contributions of blockchain for WSN data management, which involves online information aggregation and may include auditing, event logs, and storage for information analysis and offline query processing. This survey first presents the conventional WSN solutions then the blockchain-based WSN solutions for data management. Additionally, this survey discusses the contributions of blockchain for WSN security management. It first examines the centralized WSN models for security problems, followed by a discussion of the blockchain-based WSN solutions for security management, such as offering access control, preserving information integrity, guaranteeing privacy, and ensuring WSNs’ node longevity.

Data Reliability Analysis of Wireless Sensor Nodes considering Perturbation

Wireless Sensor Network (WSN) is widely used in precision engineering, which requires strict data accuracy. Therefore, it is of practical value to evaluate the reliability of WSN data. Considering the complexity of the real environment, the sensor is bound to be affected by the disturbance factors. Currently, the research result of WSN data disturbance is not ideal. Because the results of reliability analysis are not necessarily credible under a disturbance environment. Thus, it is necessary to judge the reliability of sensor nodes in the disturbance environment. Therefore, disturbance analysis is introduced. In this paper, the temporal correlation and spatial correlation of measured data of WSN nodes are taken as reliability indicators. Through the disturbance analysis method to simulate the disturbance in the working process of nodes, a data reliability evaluation model of WSN nodes is proposed, which is based on the evidence reasoning (ER) rule in the disturbance environment. Based on the wireless sensor experiment of the Intel Berkeley research laboratory, the rationality of the model is explained in this paper. ER rule considering disturbance provides an effective method to analyze the reliability of WSN data.

WSN Data Collection and Routing Protocol with Time Synchronization in Low-cost IoT Environment

The Internet of Things (IoT) is rapidly growing as a topic of research and conversation. IoT includes monitoring applications such as environment, healthcare, and military, requiring real-time gathering and data delivery. In dealing with time-sensitive applications, time synchronization is essential to collaborative wireless sensor network (WSN) nodes’ data collection and routing information sharing. This paper proposes an ad hoc routing and data collection protocol with a time synchronization function. A hybrid packet-based time synchronization is introduced, wherein reference time is sourced from the sink node (centralized) and sourced from the adjacent nodes (distributed). The time synchronization process is integrated into the routing and data transmissions in the network as it lessens overheads and transmission exchanges, which would be beneficial for large-scale WSN. Finally, we evaluate the protocol’s performance using a realistic low-cost WSN testbed. Our proposed protocol has achieved an averaged time offset of less than 100 milliseconds for a 15-minute test duration in multiple topology scenarios.

Multi-Timeslots Data Collection With Low Rank and Modified Second-Order Horizontal Total Variation for Wireless Sensor Networks

Decreasing the number of data gathered is the most highly effective way to decrease the power consumption for wireless sensor networks. Compressed Data Gathering, as it known to all, is a data collection method in wireless sensor networks, but it cannot achieve sparse sensing as all data need to be sensed and then transmitted in most practical applications. At the same time, it has been shown the effectiveness of the total variation and low rank constraints in data restoration. In order to enhance the accuracy of data recovery and decrease energy cost in wireless sensor networks, we propose a Multi-Timeslots Data Collection scheme, which includes two aspects: Structure Random Sparse Sampling method and data restoration algorithm with Low Rank and Modified Second-Order Horizontal Total Variation Constraints. By adopting the proposed sampling method, the number of data sensing and transmission is greatly reduced, thereby prolong the network lifetime. We fully exploit temporal stability and low rank characteristics of wireless sensor networks data, and build a temporal-stability based nuclear norm regularization minimization model. Meanwhile, we apply the alternating direction method to solve the problem. The simulation results present that the proposed sampling method has a corresponding enhancement effect on the matrix-completion based data restoration algorithms. In terms of recovery precision, the proposed scheme outperforms the state-of-the-art methods for different types of data in the network. Moreover, with the compression ratio increasing, the proposed scheme can still exactly recover the lost data and the advantages become increasingly obvious.

Intrusion Detection Algorithm and Simulation of Wireless Sensor Network under Internet Environment

As an effective security protection technology, intrusion detection technology has been widely used in traditional wireless sensor network environments. With the rapid development of wireless sensor network technology and wireless sensor network applications, the wireless sensor network data traffic also grows rapidly, and various kinds of viruses and attacks appear. Based on the temporal correlation characteristics of the intrusion detection dataset, we propose a multicorrelation‐based intrusion detection model for long‐ and short‐term memory wireless sensor networks. The model selects the optimal feature subset through the information gain feature selection module, converts the feature subset into a TAM matrix using the multicorrelation analysis algorithm, and inputs the TAM matrix into the long‐ and short‐term memory wireless sensor network module for training and testing. Aiming at the problems of low detection accuracy and high false alarm rate of traditional machine learning‐based wireless sensor network intrusion detection models in the intrusion detection process, a wireless sensor network intrusion detection model combining two‐way long‐ and short‐term memory wireless sensor network and C5.0 classifier is proposed. The model first uses the hidden layer of the bidirectional long‐ and short‐term memory wireless sensor network to extract the features of the intrusion detection data set and finally inputs extracted features into the C5.0 classifier for training and classification. In order to illustrate the applicability of the model, the experiment selects three different data sets as the experimental data sets and conducts simulation performance analysis through simulation experiments. Experimental results show that the model had better classification performance.

Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption

Wireless sensor network (WSN) can achieve real-time data collection and transmission of environment, soil, meteorology, crop physiology and other information in agriculture. The data provided by WSN could be used for decision making and management, which is very important in precision agriculture. Wheat farmland wireless sensor network has the characteristics of wide coverage area, long planting period, inconvenient energy supply, and serious impact of crop environment on wireless signal transmission. Routing protocol is an important method to achieve long-term WSN monitoring by selecting an appropriate path with low energy consumption for data transmission. According to the phenomenon of uneven environment and channel parameters caused by intensive crop growth in farmland, a non-uniform clustering routing protocol based on effective energy consumption (UCEEC) was proposed in this work. The method combined with the characteristics of multi-path fading of farmland environment signals. The idea of image segmentation was introduced. Nodes with high similarity were divided into a cluster area by the dissimilarity between nodes in order to improve the intracluster communication performance. Meanwhile, a multi-hop path selection method between cluster-heads based on the estimation of two-hop effective energy consumption is designed. The energy consumption cost factor is calculated by the effective energy consumption and the average energy consumption within the cluster to achieve the minimum and balance of the overall energy consumption of the network. Simulation results show that, compared with the existing Maximum Residual Energy Based Routing (MREBR) protocol, minimum Energy Consumption Based Routing (MEC) routing protocols, UCEEC improves the energy balance effect between nodes, prolongs the network life cycle, and realizes efficient energy utilization of wireless sensor network data collection in the complex environment of wheat field. Keywords: farmland, wireless sensor networks, multi-path fading channel, routing protocol, energy optimization DOI: 10.25165/j.ijabe.20211403.6540 Citation: Miao Y S, Zhao C J, Wu H R. Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption. Int J Agric & Biol Eng, 2021; 14(3): 163–170.

Optimized Design of Mechanical Chain Drive Based on a Wireless Sensor Network Data Algorithm

In this paper, we use a wireless sensor network data algorithm to optimize the design of mechanical chain drive by conducting an in‐depth study of the mechanical chain drive optimization. We utilize the crowdsourcing feature of the swarm‐wise sensing network for assisted wireless sensor networking to achieve crowdsourcing‐assisted localization. We consider a framework for crowdsourcing‐assisted GPS localization of wireless sensor networks and propose two recruitment participant optimization objectives, namely, minimum participants and time efficiency, respectively. A model and theoretical basis are provided for the subsequent trusted data‐driven participant selection problem in swarm‐wise sensing networks. The sprocket‐chain engagement frequency has the greatest influence on the horizontal bending‐vertical bending composite in different terrain conditions. The dynamic characteristics under working conditions are most influenced, while the scraping of the scraper and the central groove significantly influenced horizontal bending and vertical bending. Under load conditions, the amplitude of the scraper and central groove scraping increases significantly, which harm the dynamics of the scraper conveyor. By monitoring the speed difference between the head and tail sprockets and the overhang of the scraper, the tensioning status of the scraper conveyor chain can be effectively monitored to avoid chain jamming and chain breakage caused by the loose chain, thus improving the reliability and stability of the scraper conveyor.

A hypergrid based adaptive learning method for detecting data faults in wireless sensor networks

In wireless sensor networks (WSNs), data anomalies/faults often occur due to the limited resources and unreliability of sensor nodes. Many traditional anomaly detection methods are designed in a batch manner, but for the nature of streaming data in WSNs, continuous anomaly detection method is preferred. Existing methods often detect only a single type of faults but cannot detect multiple types of faults that actually are more common in the sensor data. Therefore, this paper provides a Hypergrid based Adaptive Detection of Faults (HADF) method, which adopts hypergrid and statistical analysis to recognize three types of faults in the sensor data, including outliers, stuck-at faults, and noisy faults. HADF is a distributed method running on sensor nodes, which can reduce the influence of concept drift in unstable streaming data through combining both lazy leaning and continuous learning to adaptively update its normal profile. In the experimental study, we have manually inserted different types of faults into two real-world datasets, and the results demonstrate that HADF achieves higher accuracy with reasonable efficiency for detecting the data faults than four counterpart methods.

Gathering Big Data in Wireless Sensor Networks by Drone.

The benefits of using mobile sinks or data mules for data collection in Wireless Sensor Network (WSN) have been studied in several works. However, most of them consider only the WSN limitations and sensor nodes having no more than one data packet to transmit. This paper considers each sensor node having a relatively larger volume of data stored in its memory. That is, they have several data packets to send to sink. We also consider a drone with hovering capability, such as a quad-copter, as a mobile sink to gather this data. Hence, the mobile collector eventually has to hover to guarantee that all data will be received. Drones, however, have a limited power supply that restricts their flying time. Hence, the drone’s energy cost must also be considered to increase the amount of collected data from the WSN. This work investigates the problem of determining the best drone tour for big data gathering in a WSN. We focus on minimizing the overall drone flight time needed to collect all data from the WSN. We propose an algorithm to create a subset of sensor nodes to send data to the drone during its movement and, consequently, reduce its hovering time. The proposed algorithm guarantees that the drone will stay a minimum time inside every sensor node’s radio range. Our experimental results showed that the proposed algorithm surpasses, by up to 30%, the state-of-the-art heuristics’ performance in finding drone tours in this type of scenario.

Hybrid salp swarm–firefly algorithm‐based routing protocol in wireless multimedia sensor networks

SummaryNowadays, there occurs a great demand for effective transmission of video. It is necessary to design an efficient transmission approach to transmit a huge amount of data in wireless multimedia sensor networks (WMSNs). But the routing based on wireless multimedia sensor networks is not much popular like other wireless sensor networks. Due to its peculiar features, it finds few troubles in directly applying the resolutions into the WMSNs. The work outlined in this paper is aimed at minimizing the delay and expected transmission count (ETX) cost of the QoS requirements. Moreover, this paper also proposes a hybrid salp swarm–firefly (HSSFF) approach which is the integration of two algorithms, namely, salp swarm and firefly optimization algorithms. The HSSFF approach employs in obtaining an optimal routing path with minimum end‐to‐end delay and less ETX value. Here, the fitness function for Simulink evaluation is twofold. We also presented the simulation results for two scenarios, and the comparative analysis is carried out to determine the best optimal routing path. Therefore, the simulation results describe that the HSSFF approach provides better performance on various nine test functions.

Trust-based secure directed diffusion routing protocol in WSN

Deployed in the monitoring environment, Wireless Sensor Network (WSN) is a measurement and control network composed of miniature and low-cost sensors with sensing, computing, and communication capabilities. The design of the Directed Diffusion (DD) routing protocol is one of the key problems in WSN. In order to obtain the confidentiality of sensing data and solve the unreliability of relay nodes in the existing DD routing protocols, this paper designs an Energy Trust Model (ETM) by introducing the remaining energy and trust of a node. We further propose a Trust-based Secure Directed Diffusion Routing protocol (TSDDR) based on the model. The proposed protocol achieves the establishment of a credible communication path and the transmission of confidential data in WSN. Meanwhile, the balance of energy consumption and the privacy of sensing data can be ensured. The performance analysis results show that the TSDDR protocol can effectively defeat against MITM attacks and prevent the malicious nodes’ impersonation. At the same time, the protocol achieves secure end-to-end anonymous communication with acceptable energy overhead and computational complexity.

A novel energy-efficient encryption algorithm for secure data in WSNs

Wireless sensor networks (WSNs) are typically deployed environments, often very hostile and without assistance. A certain level of security must be provided. However, the resource constraint is the most important characteristic of this network. Indeed, WSNs are limited in terms of calculation, CPU, battery, etc. Therefore, a solution that aims to conserve these resources is widely desired. In this paper, we focus on the problem of encryption algorithm complexity. Some of the existing encryption algorithms provide high calculation cost, require large memory and are not applicable for WSNs scenarios. Based on Feistel structure, we propose a novel ultra-lightweight encryption algorithm, named ULEA. Besides diffusion and confusion of data, ULEA assumes minor encryption rounds with simplified transformations and functions to complex the cipher. The security analysis and experimental results suggest that the ULEA algorithm is an appropriate, low storage space, energy-efficient encryption process with high security for WSNs.

WITHDRAWN: Developing a software reliability evaluation model for wireless sensor networks based on analysis of software fault growth

Abstract Due to potential of various application in emergency and security monitoring fields, fault tolerance monitoring in wireless sensor networks (WSNs) software reliability became one of the most fundamental concern in computer field. In WSNs Previous literature had been proposed about how to improve network reliability. However the work done on establishing evaluating models of reliability for WSNs are insufficient yet. In this article, a model for wireless sensor networks (WSNs) is developed for the reliability evaluation. This network state based model can effectively do the evaluation of the probability of successful transmission of data in WSNs with the consideration of the work states of both the sensor node’s different modules. For WSNs we did the evacuation for reliability of in both tasks i.e., single, multi-task modes. At the same time, how different network topologies, affect the reliability of WSNs is also discussed respectively. To evaluate the reliability of WSNs the simulation attempts are shown in results that impacts network state on reliability and the effectiveness in different reliable mechanisms.