Wireless Sensor Network Environment Research Articles

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 new algorithm inspired by Impala Mexican Wave with variable stride for relay node placement as a nested reverse p-median problem in disjoint wireless sensor networks

In the wireless sensor network (WSN) environment, large-scale failure due to the explosion, energy depletion, harsh or hostile environments, can cause disjoint partitions and disconnect the network. In this case, relay nodes (RNs) can deploy to restore connectivity. This paper has proposed a new algorithm inspired by Impala Mexican Wave with variable stride (IMWVS) for relay placement. This method solves the nested reverse p-median problem. The purpose of this method is to reduce the number of deployed RNs and increase the robustness of the network. Also, the coverage and reliability of the method can be satisfied. In terms of the number of deployed RNs, IMWVS has 50.49 and 68.18% improvement averagely in comparison with CRP and RRLC-GBP methods for large-scale multiple partitions resulted from multiple failures, respectively.

An enhanced energy proficient clustering (EEPC) algorithm for relay selection in heterogeneous WSNs

The recent advancements in Wireless Sensor Networks (WSNs) have brought attention to the field of sensor tracking events. Habitat monitoring is considered as one of the most important applications of WSNs, which ensures wildlife conservation. Researchers have proposed various solutions to select the optimal path in the field of sensor tracking. However, energy dissipation of sensor nodes and fault tolerance during data transformation is still one of the major challenges of the WSN environment in dynamic scenarios. In this paper, an Enhanced Energy Proficient Clustering (EEPC) is proposed to reduce the energy consumption of the entire sensor nodes in the field of tracking events. The network is created with both fixed and mobile nodes. Initially, fixed nodes broadcast information, and mobile nodes select the cluster head from fixed nodes. The mobile nodes select their cluster head (CH) based on their associated placement and energy level. Mobile sensor nodes (SNs) transmit data to the CH. It introduces the concept of finding relay nodes, which are fixed nodes. The EEPC algorithm selects the relay nodes based on their velocity and location by calculating particle fitness value. The selected intermediate relay nodes transmit the collected information to the Base Station (BS) using the sensor data fusion technique. The link fault of nodes could be predicted based on the deviation value. The simulation results show that the proposed approach points out progress with reference to various performance metrics. The proposed work minimizes the energy depletion and enhances the network lifetime compared to other existing protocols.

Novel MIMO-UWB-Based AG-UG-AG Routing With Microbit Sensors for WUSN

In this letter, empirical with analytical methodology to decode-and-forward packets over aboveground-to-underground-to-aboveground systems in the wireless underground sensor network environment is corroborated. Multiple-input-multiple-output ultrawideband (MIMO-UWB) radio frequency links were empirically characterized and exploited using microbit moisture-measuring white light-emitting diodes in different volumetric water content and orientation depths. Relay-routing and selection using the novel maximum available channel capacity technique was accomplished. The simulation performance demonstrates relay-routing and selection impacts on channel conditions, overall outage probability, transmission power, and energy consumption, without depreciated aggregate system throughput.

Network Lifetime Improved Optimal Routing in Wireless Sensor Network Environment

In the field of Wireless Sensor Networks, major problem arises due to the unbalanced consumption of energy. The unbalanced consumption will reduce the lifetime of a network performance. In the existing work, replacement of node is a difficult process and conservation of network parameters is not concentrated. It leads to the failure of network in different situations. These issues are resolved by introducing a density aware optimal clustering which is used to perform clustering to ensure the data transmission in a reliable manner. In the proposed work, Energy Balancing and Optimal Routing Based Secured Data Transmission based on clustering is introduced. It enhances the lifetime uniformly deployed data-gathering sensor networks using a balanced energy consumption technique. Ant Colony Optimization method is used to select the optimum head of the cluster to enhance lifetime of a network. Different techniques have been proposed for optimal edge disjoint routing. The data packets are forwarded in an optimum way by these techniques. Hybrid Genetic Particle Swarm Optimization algorithm is used to select the optimum edge disjoint route paths. The parameters of Quality of Service like reliability, capacity of processing, bandwidth and energy are considered to make an optimum selection. Based on the similarity values, sensor data’s are grouped and these data are aggregated to ensure the consumption energy which makes an optimum data transmission.

An efficient localization approach to locate sensor nodes in 3D wireless sensor networks using adaptive flower pollination algorithm

Localization in wireless sensor networks (WSNs) is required to examine the coordinates of the sensor nodes deployed in the sensing field. It is the process that determines the location of the target nodes relative to the location of deployed anchor nodes. The anchor nodes positions are known as the nodes that have GPS unit incorporated with them. All sensor nodes are generally not configured with GPS as it is not suitable for indoor environments and/or underwater areas. A network becomes more expensive and utilizes more energy if all nodes are equipped with GPS that is a major drawback of WSNs. Various localization schemes have been proposed in literature, while most research proposals deal with the study of 2D applications. However, in the 3D applications, the area under observation may have a complexity in the sensing environment. An optimized algorithm is required for the determination of node location in 3D environment. In this paper, we propose an adaptive flower pollination algorithm (AFPA) with enhanced exploration and exploitation capabilities of conventional FPA for the localization of sensor nodes in WSN. To test the performance of AFPA, benchmark functions (CEC 2019) are used to compare it with other meta-heuristics. The results show that proposed AFPA outperforms in terms of convergence speed and provides better results for most of the benchmark functions. Also, the proposed AFPA is tested on WSN Localization problem, it provides least localization error in comparison to other techniques applied in 3D WSN environments.

WSN-SLAP: Secure and Lightweight Mutual Authentication Protocol for Wireless Sensor Networks.

Wireless sensor networks (WSN) are widely used to provide users with convenient services such as health-care, and smart home. To provide convenient services, sensor nodes in WSN environments collect and send the sensing data to the gateway. However, it can suffer from serious security issues because susceptible messages are exchanged through an insecure channel. Therefore, secure authentication protocols are necessary to prevent security flaws in WSN. In 2020, Moghadam et al. suggested an efficient authentication and key agreement scheme in WSN. Unfortunately, we discover that Moghadam et al.’s scheme cannot prevent insider and session-specific random number leakage attacks. We also prove that Moghadam et al.’s scheme does not ensure perfect forward secrecy. To prevent security vulnerabilities of Moghadam et al.’s scheme, we propose a secure and lightweight mutual authentication protocol for WSNs (WSN-SLAP). WSN-SLAP has the resistance from various security drawbacks, and provides perfect forward secrecy and mutual authentication. We prove the security of WSN-SLAP by using Burrows-Abadi-Needham (BAN) logic, Real-or-Random (ROR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. In addition, we evaluate the performance of WSN-SLAP compared with existing related protocols. We demonstrate that WSN-SLAP is more secure and suitable than previous protocols for WSN environments.

FAJIT: a fuzzy-based data aggregation technique for energy efficiency in wireless sensor network

Wireless sensor network (WSN) is used to sense the environment, collect the data, and further transmit it to the base station (BS) for analysis. A synchronized tree-based approach is an efficient approach to aggregate data from various sensor nodes in a WSN environment. However, achieving energy efficiency in such a tree formation is challenging. In this research work, an algorithm named fuzzy attribute-based joint integrated scheduling and tree formation (FAJIT) technique for tree formation and parent node selection using fuzzy logic in a heterogeneous network is proposed. FAJIT mainly focuses on addressing the parent node selection problem in the heterogeneous network for aggregating different types of data packets to improve energy efficiency. The selection of parent nodes is performed based on the candidate nodes with the minimum number of dynamic neighbors. Fuzzy logic is applied in the case of an equal number of dynamic neighbors. In the proposed technique, fuzzy logic is first applied to WSN, and then min–max normalization is used to retrieve normalized weights (membership values) for the given edges of the graph. This membership value is used to denote the degree to which an element belongs to a set. Therefore, the node with the minimum sum of all weights is considered as the parent node. The result of FAJIT is compared with the distributed algorithm for Integrated tree Construction and data Aggregation (DICA) on various parameters: average schedule length, energy consumption data interval, the total number of transmission slots, control overhead, and energy consumption in the control phase. The results demonstrate that the proposed algorithm is better in terms of energy efficiency.

Data Reconstruction in Heterogeneous Environmental Wireless Sensor Networks Using Robust Tensor Principal Component Analysis

The major challenges during the data acquisition process in an environmental wireless sensor network (EWSN) architecture are the presence of outliers and missing data. Outliers and missing data are ubiquitous in EWSN due to sensor failures, external noise, power dwindling, communication failures etc. Robust tensor principal component analysis (RTPCA) decomposes a noisy data tensor into a low-rank tensor and a sparse tensor, which can be exploited for the data recovery in EWSNs, where the low-rank component represents the intrinsic data tensor and the sparse component represents the gross outlier tensor. In this paper, a novel probabilistic outlier modelling scheme using multivariate Chebyshev's inequality hypothesis is proposed, which maps the sample population and the associated magnitudes of outliers with the spatio-temporal correlations in the acquired data. The inherent spatio-temporal and multi-attribute correlations in the EWSN data are established using singular value methods. A tensor nuclear norm (TNN) which extracts more temporal and multi-attribute correlations in the sensory data through block circulant matricization is used as the minimization function in RTPCA. In the forest surveillance scenario, usage of RTPCA results in a reconstruction accuracy of approximately 90% for a dataset with a high missing ratio of 0.95 and the outliers having largest possible magnitudes considered. For oceanographic data, in which the variance is lower, the simulations show similar performance for different outlier contaminations, although RPTCA performs better than other matrix and tensor data completion methods in terms of reconstruction accuracy.

Adaptive Pronunciation Proofreading of Spoken English in a Wireless Sensor Network Environment

This paper presents an in‐depth study and analysis of adaptive proofreading of spoken English pronunciation in a wireless sensor network environment. This paper addresses the above problem by combining two common methods for controlling the transmission rate of sensing nodes maximizing network utility algorithm and congestion control mechanism. Firstly, the transmission rate of one‐hop nodes at a distance from the aggregation node is dynamically adjusted by the increasing exploration algorithm under the premise of unknown link transmission capacity, while the transmission rate of one‐hop nodes is proportionally allocated to multihop nodes in multihop nodes by the congestion control mechanism based on the average reception success rate of the link. A design framework for a speech recognition system with complementary offline recognition and online recognition based on the C/S model is proposed, and a speech recognition system in swarm intelligence awareness is implemented based on the Sphinx engine. The client side implements the speech recognition of decoder in the offline state, and the server side provides the functions of recognition consistency detection, model adjustment training, monitoring, and recommendation in the online state as well as the interface for external access. The scene adaptation module effectively improves the speech recognition system’s speech recognition correct rate under different scenes, and the discourse topic recognition module verifies the recognition effectiveness of the speech recognition system under different discourse topics, which can meet the requirements of users’ personalized speech input.

Security and application of wireless sensor network

Abstract At present, wireless sensor networks are developing rapidly with the support of the Internet of things. Wireless sensor networks can deliver the information people need at any time, free from the constraints of time and space. Wireless sensor network is widely used, which lays a solid foundation for the development of Internet of things. As the node deployment environment of wireless sensor networks is usually very complex, it is necessary to study the security of wireless sensor networks so as to reduce security threats and network attacks. In this paper, the security and application of the wireless sensor network are emphasized.

Lightweight route adjustment strategy for mobile sink wireless sensor networks

<span>As a result of numerous applications and low installation costs, wireless sensor networks (WSNs) have expanded excessively. The main concern in the WSN environment is to lower energy consumption amidst nodes while preserving an acceptable level of service quality. Using multi-mobile sinks to reduce the nodes' energy consumption have been considered as an efficient strategy. In such networks, the dynamic network topology created by the sinks mobility makes it a challenging task to deliver the data to the sinks. Thus, in order to provide efficient data dissemination, the sensor nodes will have to readjust the routes to the current position of the mobile sinks. The route re-adjustment process could result in a significant maximization in the communication cost, which boosts the total energy depletion. This paper proposes a lightweight routes re-adjustment strategy for mobile sink wireless sensor networks (LRAS-MS) aimed at minimizing communication cost and energy consumption by reducing route re-adjustment in a cluster-based WSN environment. The simulation results show a significant reduction in communication costs and extending the network lifetime while maintaining comparable low data delivery delay. </span>

Efficient Implementation of AES and CTR_DRBG on 8-Bit AVR-Based Sensor Nodes

Since Rijndael algorithm was selected as the Advanced Encryption Standard (AES) by NIST, optimization research for the AES has been actively conducted on various IoT-based processors. In an 8-bit AVR environment, LIGHT version of Fast AES CTR-mode Encryption (FACE-LIGHT) was proposed at ICISC'2019 conference. However, in a Wireless Sensor Network environment, where sessions are frequently changed, FACE-LIGHT seems not efficient in terms of available memory and generating a pre-computation table. In this article, we present a new column-wise fashion implementation. Unlike previous best AES implementations, our proposed implementation in an 8-bit AVR microcontroller combines SubBytes, ShiftRows, and MixColums operations and optimizes the operation speed through efficient register scheduling. Our constant-time implementation uses a significantly less table than FACE-LIGHT in an 8-bit AVR microcontroller, achieving 2,251, 2,706, and 3,160 clock cycles when encrypting 128-bit data for each of three security levels. In particular, our 256-bit security level AES implementation is the fastest AES implementation as far as we know in 8-bit AVR microcontroller. Finally, we apply our implementation in CounTeR-mode_Deterministic Random Bit Generator (CTR_DRBG), one of the upper algorithms of a symmetric-key algorithm, to prove the generality of our optimization technology in various operating modes of AES.

English Listening Teaching Device and Method Based on Virtual Reality Technology under Wireless Sensor Network Environment

This paper presents an in‐depth study and analysis of the design and implementation of English listening instruction using wireless sensing microprocessors combined with the technology of virtual environments. Inaccurate speech hinders smooth communication and restricts the development of various skills in foreign language learning. To learn English well, mastering phonetics is a crucial first step. Especially for learners in junior and senior high schools, phonics teaching is considered an essential part. In the face of the new standards which put higher demands on phonetic intonation, the traditional methods of teaching phonetics have little effect on improving phonetics and are even somewhat overwhelming. Therefore, increased researchers are trying to find effective phonics teaching methods to improve the current problems in high school phonics teaching. Important definitions of virtual reality, augmented reality, and teaching resources are collected and organized, typical applications of augmented reality in education at the present stage are summarized, important events in the development of augmented reality are reviewed, key technologies of augmented reality are briefly described, hardware and software resources required to realize the effect of augmented reality are introduced, and pedagogical theories underlying the study of augmented reality in English teaching resources are discussed. The results of the student walk‐through quizzes are analyzed using SPSS with independent sample t‐tests, and the distribution of the number of scores is statistically analyzed in the form of graphs, and interviews with students and teachers are used to gain a deeper understanding of the effectiveness of the resource in teaching practice. The value of the application of the augmented reality primary school English teaching resource is explored.

Employ DBSCAN and Neighbor Voting to Screen Selective Forwarding Attack Under Variable Environment in Event-Driven Wireless Sensor Networks

In the event-driven wireless sensor networks (EWSNs), the event of interests occurs irregularly and at random in the network. Then, sensor nodes near the event sense the event and send out data packets of the event. Next, router nodes (RNs) forward those packets to the sink node (SN) by multi-hop communications. Compromised RNs would become malicious and launch selective forwarding attacks by dropping part of or all the packets from other nodes. On the other hand, a harsh environment makes the channel poor, so the routing nodes under a harsh environment have low packet forwarding rates because they sometimes have to give up forwarding the current packets after many tries to forward them due to poor channel. If the malicious nodes' forwarding rates become close to those of nodes under a harsh environment, the schemes based on packet forwarding rates for detecting selective forwarding attack may fail because they cannot differentiate the low data packet forwarding rates resulting from the malicious behaviors or harsh environment. To solve this problem, we provide a combined scheme for detecting selective forwarding attack in wireless sensor networks (WSNs) under harsh environments. This scheme employs a data clustering algorithm (DCA) to screen the malicious nodes out by clustering their cumulative forwarding rates (CFRs) and designs a voting decision method to protect the nodes under a harsh environment from being judged as malicious nodes. The simulation results show that our scheme has a low false detection rate (FDR) of 1% and a low missed detection rate (MDR) of 5% respectively with negligible energy consumption in WSNs under a local variable harsh environment.

Adaptive Optimization for Optimal Mobile Sink Placement in Wireless Sensor Networks

In recent years, Wireless Sensor Networks (WSN) with mobile sinks has attracted much attention as the mobile sink roams over the sensing field and collects sensing data from sensor nodes. Mobile sinks are mounted on moving objects, such as people, vehicles, robots, and so on. However, optimal placement of the sink for the effective management of the WSN is the major challenge. Hence, an adaptive Fractional Rider Optimization Algorithm (adaptive-FROA) is developed for the optimal placement of mobile sink in WSN environment for effective routing. The adaptive FROA, which is the integration of the adaptive concept in the FROA, operates based on the fitness measure based on distance, delay, and energy measure of the nodes in the network. The main objective of the research work is to compute the energy and distance. The proposed method is analyzed based on the metrics, such as energy, throughput, distance, and lifetime of the network. The simulation results reveal that the proposed method acquired a minimal distance of 24.87m, maximal network energy of 94.54 J, maximal alive nodes of 77, maximal throughput of 94.42 bps, minimum delay of 0.00918s, and maximum Packet delivery ratio (PDR) of 87.98%, when compared with the existing methods.

An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy‐Efficient Optimization in Wireless Sensor Networks

Energy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC‐Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC‐Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy‐efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC‐Gauss). The results show that the proposed DEEC‐Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.

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.

Feasibility of Harvesting Solar Energy for Self-Powered Environmental Wireless Sensor Nodes

Energy harvesting has a vital role in building reliable Environmental Wireless Sensor Networks (EWSNs), without needing to replace a discharged battery. Solar energy is one of the main renewable energy sources that can be used to efficiently charge a battery. This paper introduces two solar energy harvesters and their power measurements at different light conditions in order to charge rechargeable AA batteries powering EWSN nodes. The first harvester is a primitive energy harvesting circuit that is built using elementary off-shelf components, while the second harvester is based on a commercial boost converter chip. To prove the effectiveness of harvesting solar energy, five EWSN nodes were distributed at a nature reserve (the Audubon Society of Western Pennsylvania, USA) and the sunlight at their locations was recorded for more than five months. For each recorded illumination, the corresponding harvested energy has been estimated and compared with the average energy consumption of the EWSN with the most power consumption. The results show that the daily harvested energy effectively compensates for the energy consumption of the EWSN nodes, and the battery charge capacity of 295 mAh can reliably support their daily dynamic energy consumption.

A Distributed Border Surveillance (DBS) System for Rectangular and Circular Region of Interest with Wireless Sensor Networks in Shadowed Environments

Border surveillance is indeed one of the most pertinent applications of wireless sensor networks, primarily used for security purposes such as intrusion detection in border regions or protected areas. In order to detect unauthorized access or penetration through the region of interest, sensor nodes are deployed to form barriers, that acts as the performance metric of wireless sensor networks. In this paper, a Distributed Border Surveillance (DBS) system incorporating shadowing effects is proposed for a wireless sensor network deployed in a rectangular region of interest. The DBS system evaluates the number of required barriers to monitor the given region and conserves energy. Besides, a log-normal shadowing model is considered, which incorporates the asymmetry in sensing range along with the stochastic nature of wireless channels. The performance of the proposed DBS system is analyzed based on the number of barriers obtained. Then, the impact of various network and system parameters such as the number of nodes, sensing range of nodes, height and width of the network region on the number of barriers obtained in a rectangular region are analyzed. The same approach is extended for a circular region of interest in terms of sensing range of nodes. The proposed system is implemented in NS-2.35 simulator, and it is found that the performance of the proposed DBS system is 75% better than the existing binary sensing range model-based DBS system.