Data In Wireless Sensor Networks Research Articles

Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids

Wireless sensor networks are used to monitor the operating status of the microgrids, which can effectively improve the stability of power supplies. The topology control is a critical issue of wireless sensor networks, which affects monitoring data transmission reliability and lifetime of wireless sensor networks. Meanwhile, the data acquisition accuracy of wireless sensor networks has a great impact on the quality of monitoring. Therefore, this paper focuses on improving wireless sensor networks data acquisition satisfaction and energy efficiency. A joint acquisition time design and sensor association optimization algorithm is proposed to prolong the lifetime of wireless sensor networks and enhance the stability of monitoring, which considers the cluster heads selection, data collection satisfaction and sensor association. First, a multi-constrained mixed-integer programming problem, which combines acquisition time design and sensor association, is formulated to maximize data acquisition satisfaction and minimize energy consumption. To solve this problem, we propose an iterative algorithm based on block coordinate descent technology. In each iteration, the acquisition time is obtained by Lagrangian duality. After that, the sensor association is modeled as a 0–1 knapsack problem, and the three different methods are proposed to solve it. Finally, the simulations are provided to demonstrate the efficiency of the algorithm proposed in this paper.

RETRACTED ARTICLE: Research on data transmission model of agricultural wireless sensor network based on game theory

This article has been retracted from publication in the Taylor & Francis journal, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science. Following publication, concerns were raised by multiple third-parties around the content of the special issue and the decision-making process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that this article included in Special Issue titled “ Envisage Computer Modelling and Statistics for Agriculture”, guest edited by Gunasekaran Manogaran was not peer-reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines.

Data detection technology of wireless sensor network based on non-stationary filtering

By optimizing the data detection performance of distributed wireless sensor networks, the data sensing and collecting ability of wireless sensor networks can be improved. Traditional methods adopt statistical characteristic parameter detection algorithm for distributed wireless sensor network data detection. Distributed wireless sensor network data has strong time-frequency coupling, so it is difficult to realize frequency domain spatial parameter clustering in frequency domain, and the detection performance is not good. A distributed wireless sensor network data detection algorithm based on non-stationary filtering and high-order statistical feature peak retrieval is proposed. The data model of distributed wireless sensor network is constructed under the interference of color noise. The weak vibration signal is subjected to time-frequency analysis and noise separation by non-stationary filtering, and the spectral peak of distributed wireless sensor network data is searched by the fourth-order cumulant slice post-operator to realize the optimal detection of signals. The simulation results show that the algorithm has a high probability of accurate detection, and has a good ability of suppressing noise and noise sidelobe information interference, which improves the probability of accurate detection of distributed wireless sensor network data under low signal-to-noise ratio.

Rolling bearing fault diagnosis based on wireless sensor network data fusion

With the continuous development of intelligent manufacturing, mechanical equipment is developing in the direction of large-scale, integration, precision and intelligence. The coupling between different equipment in the operation of the system also makes the mechanical equipment increasingly become a whole, which puts forward higher requirements for the fault diagnosis of mechanical equipment, especially rolling bearings. With the rapid development of wireless network technology, rolling bearing fault diagnosis has become a reality. In this paper, the wireless sensor network will be innovatively used to collect the data of key parts of mechanical equipment, so as to improve the problem of insufficient and accurate collected data in the traditional convolution neural network fault detection algorithm, convert the corresponding time-domain signal into image signal, and combine the global average pool layer on the basis of the combination of nonlinear convolution layers, So as to optimize the corresponding wireless network structure, and finally realize the adaptive extraction and fault diagnosis of rolling bearing fault features. The experimental results show that the fault detection accuracy of the optimized convolution neural network algorithm is about 99.8%, and has good robustness and practical value.

Mitigation of stretch type vampire attack using probabilistic variable fuzzy rough set in wireless sensor networks

AbstractEnergy consumptions of intermediate nodes play an anchor role in achieving maximum cooperation for reliable dissemination of data in wireless sensor network (WSNs). WSN is considered a highly challenging issue due to its ad hoc node's organization in the network. In specific, vampire attack weakens the sensor nodes by draining energy slowly resulting in network performance deterioration ending up in network failure. The vampire attack needs to be detected and isolated for improving the sensor nodes life span to the maximum extent. In this article, mitigation of stretch type vampire attack using probabilistic variable fuzzy rough set (MVA‐PVFRS) is proposed with the merits of fuzzy membership ranks to sustain network lifetime. This MVA‐PVFRS scheme derives the benefits of fuzzy information system by including the method of approximation concept that decides upon the mitigation of vampire nodes through the formulation of lower and upper cooperation thresholds. Furthermore, it is proposed to handle the degree of uncertainty, missingness, and vagueness of data disseminated between the sensor nodes during the monitoring process in the network. The simulation results of the proposed MVA‐PVFRS scheme is attained through ns‐2 simulations with different number of sensor nodes, number of vampire nodes, pause time of simulation, number of packets, and packet sizes. The results of the proposed MVA‐PVFRS scheme with the different number of packets confirmed an excellent performance in packet delivery rate by 15.84% and throughput by 16.94%, compared to the baseline schemes.

Efficient Link Scheduling Based on Estimated Number of Packets in Queue on Industrial Wireless Sensor Networks

The links of low power wireless sensor networks are error prone and the transmission on a wireless link is determined probabilistically by the packet reception rate (PRR) of the link. On the other hand, there is a very strict requirement in the end-to-end reliability and delay of sensor data in industrial wireless sensor networks (IWSNs). The existing approaches to provide the end-to-end reliability in IWSNs is retransmitting the packet when failure occurs. These approaches transmit a packet multiple times in successive time slots to provide the required reliability. These approaches, however, can increase the average delay of packets and the number of packets buffered in a queue. This paper proposes a new scheme to estimate the probabilistic amount of packets, called queue level (QL), in the buffer of each node based on the PRRs of the wireless links. This paper also proposes a QL-based centralized scheduling algorithm to assign time slots efficiently in TDMA-based IWSNs. The proposed scheduling algorithm gives higher priority to the nodes with higher QL. By assigning time slots first to the node with the highest QL, we can reduce the average end-to-end delay of packets and reduce the amount of buffered packets in the queue while satisfying the required end-to-end reliability. The performance of the proposed scheduling algorithm have been evaluated through a simulation using the Cooja simulator and compared with the existing approach. In the simulation on an sample network with the target end-to-end reliability of 99%, all of the flows were shown to guarantee the target reliability in both algorithms: on average, 99.76% in the proposed algorithm and 99.85% in the existing approach. On the other hand, the proposed algorithm showed much better performance than the existing approach in terms of the average end-to-end delay of packets (about 47% less) and the number of maximally buffered packets in the queue of each node (maximally, more than 90% less).

An energy aware scheme for layered chain in underwater wireless sensor networks using genetic algorithm

Extending the network lifetime is a very challenging problem that needs to be taken into account during routing data in wireless sensor networks in general and particularly in underwater wireless sensor networks (UWSN). For this purpose, the present paper proposes a multilayer chain based on genetic algorithm routing (MCGA) for routing data from nodes to the sink. This algorithm consists to create a limited number of local chains constructed by using genetic algorithm in order to obtain the shortest path between nodes; furthermore, a leader node (LN) is elected in each chain followed by constructing a global chain containing LNs. The selection of the LN in the closest chain to the sink is as follows: Initially, the closest node to sink is elected LN in this latter because all nodes have initially the same energy value; then the future selection of the LN is based on the residual energy of the nodes. LNs in the other chains are selected based on the proximity to the previous LNs. Data transmission is performed in two steps: intra-chain transmission and inter-chain transmission. Furthermore, MCGA is simulated for different scenarios of mobility and density of nodes in the networks. The performance evaluation of the proposed technique shows a considerable reduction in terms of energy consumption and network lifespan.

Recovery of Corrupted Data in Wireless Sensor Networks Using Tensor Robust Principal Component Analysis

Due to the hardware and network conditions, data collected in Wireless Sensor Networks usually suffer from loss and corruption. Most existing research works mainly consider the reconstruction of missing data without data corruption. However, the inevitable data corruption poses a great challenge to guarantee the recovery accuracy. To address this problem, this letter proposes a data recovery method based on tensor singular value decomposition. Data collected by the spatial distributed sensor nodes in each time slot is arranged in matrix form instead of vector to further exploit the spatial correlation of the data. Therefore, data collected in consecutive time slots can form a three-way tensor. To avoid the influence of corruption on recovery accuracy, a Tensor Robust Principal Component Analysis model is developed to decompose the raw data tensor into a low-rank normal data tensor and a sparse error tensor. The recovery accuracy is further improved by incorporating total variation constraint. Computer experiments corroborate that the proposed method significantly outperforms the existing method in the recovery accuracy.

Tipping Bucket Rain Gauge Data Processing System: A Review

Abstract: The tipping bucket system consists of funnel which collects the water of the rain in a container which is like a seesaw type module which tips side by side and collects the water. When the level of the water decreases below a preset level, the lever changes its side, causing the collected water to dump in a vessel and electrical signal is sent. By this system the high, medium or heavy rainfall character can be obtained. The rainfall character is calculated by the rainfall in 1 hour and corresponding number of pulses clicking in a period of 10 minutes. Various types of tipping bucket systems are reviewed by using rainfall and snow precipitation, using internet enabling, using rain drop imaging and artificial intelligence and also using wireless sensor network and GSM data transmission. Tipping Bucket is the most useful parameter for measuring the rainfall. In this way the rainfall is measured using the Tipping Bucket Rain Gauge System.

Majority Decision Aggregation with Binarized Data in Wireless Sensor Networks

Wireless sensor networks (WSNs) are the cornerstone of the current Internet of Things era. They have limited resources and features, a smaller packet size than other types of networks, and dynamic multi-hop transmission. WSNs can monitor a particular area of interest and are used in many different applications. For example, during the COVID-19 pandemic, WSNs have been used to measure social distancing/contact tracing among people. However, the major challenge faced by WSN protocols is limited battery energy. Therefore, the whole WSN area is divided into odd clusters using k-means++ clustering to make a majority rule decision to reduce the amount of additional data sent to the base station (or sink) and achieve node energy-saving efficiency. This study proposes an energy-efficient binarized data aggregation (EEBDA) scheme, by which, through a threshold value, the collected sensing data are asserted with binary values. Subsequently, the corresponding cluster head (CH), according to the Hamming weight and the final majority decision, is calculated and sent to the base station (BS). The EEBDA is based on each cluster and divides the entire WSN area into four quadrants. All CHs construct a data-relay transmission link in the same quadrant; the binary value is transferred from the CHs to the sink. The EEBDA adopts a CH rotation scheme to aggregate the data based on the majority results in the cluster. The simulation results demonstrate that the EEBDA can reduce redundant data transmissions, average the energy consumption of nodes in the cluster, and obtain a better network lifetime when compared to the LEACH, LEACH-C, and DEEC algorithms.

Application of Wireless Sensor Networks in the Sensitive Data Security of Intelligent Data Center under the Big Data Environment

In the big data environment, wireless sensor networks (WSN) are constantly improving themselves and are widely used in all walks of life. The WSN collects, processes and transmits the information of the sensed object in the monitoring area. In recent years, WSN have also made good progress in environmental change detection, traffic control, and sensitive data security management. The purpose of this article is to study the application of WSN in the security management of sensitive data in the data center under the big data environment. This article first describes the characteristics of WSN, and discusses the security management requirements of sensitive data in WSN based on big data technology. Secondly, it analyzes the problems in the security management of sensitive data and conducts research from different directions. Finally, in response to these problems, the implementation of the WSN in the security management of sensitive data provides some help to solve the problem. Thereby effectively reducing the energy consumption of network nodes, reducing the amount of data transmitted by the network, and extending the life cycle of the WSN. Experimental research results show that the most demanded level in security management is data confidentiality, which is 47.15%, followed by data freshness and data integrity, which are 28.49% and 24.36%, respectively. In summary, it is necessary to increase efforts to improve the security management of sensitive data.

An Efficient and Secure Session Key Management Scheme in Wireless Sensor Network

Wireless Sensor Network (WSN) is a particular network built from small sensor nodes. These sensor nodes have unique features. That is, it can sense and process data in WSN. WSN has tremendous applications in many fields. Despite the significance of WSN, this kind of network faced several issues. The biggest problems rising in WSN are energy consumption and security. Robust security development is needed to cope with WSN applications. For security purposes in WSN, cryptography techniques are very favorable. However, WSN has resource limitations, which is the main problem in applying any security scheme. Hence, if we are using the cryptography scheme in WSN, we must first guarantee that it must be energy-efficient. Thus, we proposed a secure hybrid session key management scheme for WSN. In this scheme, the major steps of public key cryptography are minimized, and much of the operations are based on symmetric key cryptography. This strategy extensively reduces the energy consumption of WSN and ensures optimum security. The proposed scheme is implemented, and their analysis is performed using different parameters with benchmark schemes. We concluded that the proposed scheme is energy-efficient and outperforms the available benchmark schemes. Furthermore, it provides an effective platform for secure key agreements and management in the WSN environment.

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.

An Algorithm with Efficiently Collecting and Aggregating Data for Wireless Sensor Networks

Due to the resource constraint, in wireless sensor network, the node processing ability, wireless bandwidth and battery capacity and other resources are scarcer. For improving the energy efficient and extend the lifetime of the network, this paper proposes a novel algorithm with the distributed and energy-efficient for collecting and aggregating data of wireless sensor network. In the proposed protocol, nodes can autonomously compete for the cluster head based on its own residual energy and the signal strength of its neighbouring nodes. To reduce the energy overhead of cluster head nodes, with a multi-hop way among cluster heads, the collected data from cluster heads is sent to a designated cluster head so as to further send these data to a base station. For improving the performance of the proposed protocol, a new cluster coverage method is proposed to fit the proposed protocol so that when the node density increases, network lifetime can be increased linearly as the number of nodes is increased. Simulations experiments show that network lifetime adopting the proposed protocol is sharply increased. And, the proposed protocol makes all the nodes die (network lifetime is defined as the death of last one node) in the last 40 rounds so that networks adopting the proposed protocol have higher reliability than networks adopting compared protocols.

LDCA: Lightweight Dynamic Clustering Algorithm for IoT-Connected Wide-Area WSN and Mobile Data Sink Using LoRa

Wide-area monitoring applications of the Internet-of-Things (IoT) connected wireless sensor network (WSN) consists of sensor nodes (SNs) with limited hardware and energy sources. The distributed nature of such a network and the difficulty of remote access make it more demanding to design an energy-efficient WSN. Moreover, long-range and low-power wireless connectivity is a challenge in IoT-connected applications. Present WSN topologies deal mainly with fixed SNs, SN distribution, and fixed data sink (DS). The majority of the control layer is implemented in the lower hierarchical layer or in a virtual middle layer, which reduces the network lifetime due to excessive processing and data transmission activities. This article proposes a real-time lightweight dynamic clustering algorithm (LDCA) for a WSN that supports the following two scenarios with limited processing resources: 1) with mobile DSs and static SNs (such as DS and SNs mounted on unmanned aerial vehicles, autonomous vehicles) and 2) with mobile SNs and static DSs (such as livestock monitoring or autonomous robots in smart farming, and urban monitoring). The proposed algorithm is based on the received signal strength indicator and signal-to-noise ratio of a long-range (LoRa) interface and its residual energy. Mathematical models were derived for real-time clustering using LoRa. Memory requirement and clustering efficiency of the constrained SN and DS for various mobility scenario were evaluated. The proposed LDCA reduces the energy requirement to 33% compared to static clustering, by reducing the number of concurrent clusters and hops. In addition, a hardware-based approach was used to validate the LDCA algorithm, and evaluate its performance in terms of energy efficiency, packet delivery rate, and network lifetime.

Investigating the Energy Efficient Routing Protocol for Wireless Sensor Networks

A Wireless Sensor Network(WSN) consists of chain of sensors with capabilities of consuming energy for sensing, computing, and wireless communication to track physical device and relay their data to a base station cooperatively via the network. Efficient path for the processing of data in WSN and also to identify the active devices in the path. The ad-hoc networks which are active in WSN due to protocol, they facilitate versatility that often alters the topology. The Energy Efficient Zone based Routing Protocol (ZBEEP) which is the latest protocols in this direction, and the most common is On Demand Distance Vector Ad-hoc routing protocols. In ABODV and ZBEEP protocols using Network Simulator, I simulated this Black Hole Attack and tried that in the case of ZBEEP protocol the effect of energy due to Black Hole Attack is ineffective and better as compared to ABODV. I used two service quality metrics, such as Packet Transmission Ratio and Through-put, to help my opinions.

Lightweight Aggregated Data Encryption for Wireless Sensor Networks (WSNs)

The primary task of wireless sensor networks (WSNs) is to collect various types of data, for example, weather data, traffic data, etc. Collected data in WSNs are different from most data transmitted in digital communication applications. Most collected data in WSNs contain only few bits of information, but most data in data communication applications contain a large number of bits. All existing data encryptions in WSNs use conventional symmetric-key encryptions (e.g., advanced encryption standard (AES)) in which each large-size key is used to encrypt a block of large-size of data (e.g., a fixed block size of 128 bit in AES), and the security is based on a large-size of pairwise shared key (e.g., 128/192/ 256 bits in AES) between the sender node and the receiver node. Obviously, these conventional encryptions are not suitable for WSNs since the size of keys are much larger than the size of data. We propose a novel data encryption, in which it encrypts only few bits of data, and the security is based on multiple pairwise shared keys with short length. The encryption speed is much faster than conventional symmetric encryptions. Data aggregation is an efficient way to transmit data in a network. In the past, most encrypted data aggregations were designed based on public-key based homomorphic encryption. Public-key encryptions need time-consuming computation, so these schemes are not suitable for applications in WSNs. Most data in WSNs are routed through a path from the source node to the receiver node, which involves multiple sensor nodes. Our encrypted data aggregation is also called path-oriented data encryption. When multiple data are collected in WSNs, our scheme allows sensors in the path to aggregate all collected data in a piggyback ride. Our proposed encrypted data aggregation is a lightweight solution, which is suitable for applications in WSNs.

Data transmitted encryption for clustering protocol in heterogeneous wireless sensor networks

In this paper, we propose a novel encryption schema (ECDH – RSA) based on Elliptic Curves Diffie – Hellman algorithm (ECDH) and RSA algorithm to secure transmitted data in Heterogeneous Wireless sensor networks (HWSNs). The proposed encryption is built under Cheesboard Clustering Routing Method (CCRM). The CCRM used to balancing node energy consumption. To achieve good scalability and performance by deploying a small number of powerful high-end sensors in addition to a large number of lowend sensors. ECDH is used to exchange public and private keys due to its ability to provide high security with small key size. The proposed encryption key is 176-bit and is produced by combining it’s key, node identification number, and distance to its cluster head (CH). To reduce energy consumption of CH, RSA encryption is used to allow CH to aggregate the encrypted data without having to decrypt them. We demonstrated that the proposed method is capable to work with different sensing environments that need to capture text data as well as images. The Simulation results demonstrated that our proposed method greatly improve the network performance in terms of energy consumption and highest paths.

Greedy Forwarding Routing Schemes using an Improved K-Means Approach for Wireless Sensor Networks

Reducing energy consumption in wireless sensor networks (WSNs) is one of the main ways to extend network lifetime since, in most cases, sensor nodes are battery-powered and are not rechargeable. One way to achieve this goal is to reduce the amount of redundant data sent to the base station (BS) through an aggregation operation carried out by the coordinating nodes. The clustering approach is considered as one of the most energy-efficient approaches for routing data in WSNs because in this approach the cluster-heads are responsible for the aggregation of the data packets, which reduces the amount of data sent to the BS. In this paper, we propose an approach based on an improved version of the K-Means method. This approach allows to find the appropriate number of clusters and generate clusters based on the radio communication of the nodes (RC). Moreover, to send data to the BS, we propose three greedy forwarding schemes. The first is a pure greedy forwarding (DKM-GF), the second is based on the distance and the residual energy of the relay nodes (DKM-GFE) and the third on the distance, the residual energy of the relay nodes and the energy dissipated to send data from a node to the forwarding node (DKM-GF2E). The proposed routing schemes have been implemented over MATLAB simulator. The results obtained show that our proposals bring significant improvements in terms of energy consumption, network lifetime and the number of packets sent to the base station compared to other protocols.

Мультимедийные сенсорные сети на основе сверхширокополосных хаотических радиоимпульсов

The fast direction in information and communication systems of the wireless sensor network is discussed. Special attention was paid to ultra-wideband wireless sensor networks, which use chaotic radio pulses as information carriers for communication between sensor nodes. The creation of such systems became possible after many years of research on the creation and management of chaos in electronic systems. The requirements for transceivers for transmitting multimedia data in wireless sensor networks and the characteristics of existing systems are considered. The prospects of using ultra-wideband wireless systems based on direct chaotic data transmission in multimedia sensor networks are analyzed.