Sensor Network Environment Research Articles

An Evolutionary-Based Approach for Low-Complexity Intrusion Detection in Wireless Sensor Networks

As security threats are increasingly diversified, a critical problem in Wireless Sensor Network environments (WSNs) is detecting anomalies. WSNs are affected by several limitations, such as limited energy, insufficient memory, weak computation power, and short communication range. Hence, it is necessary to improve the detection accuracy and convergence speed of intrusion detection in such environments. In this article, we propose an intrusion detection model based on Time-Varying Parameter Improved Particle Swarm Optimization (TVP-IPSO) with Principal Component Analysis (PCA) and Support Vector Machine (SVM). The PCA is applied aimed at the data dimension reduction by compressing the data to reduce energy consumption, and an intrusion detection algorithm based on SVM is considered to ensure high detection accuracy. To optimize the SVM algorithm and identify its optimal parameters, the TVP-IPSO is used to improve the intrusion detection algorithm's detection precision and convergence speed. Experimental results show that the detection accuracy of TVP-IPSO-SVM is higher than GA-SVM and IPSO-SVM, demonstrating that the proposed research has better adaptability, higher detection accuracy, and faster convergence speed when compared to other works presented.

Data delivery performance on MongoDB generated by WSN over high data load

Wireless sensor networks (WSN) Environment is a collection of nodes that sensing and sending data to other nodes in a large number of nodes. One of the WSN concepts is continuous or periodic data collection, then the data obtained can be further processed. From this process, it is possible to produce large data, so the process of sending and storing data becomes a high load. Data obtained from WSN nodes vary greatly, which affects on sending and storing of data. Therefore, a study is needed to analyze WSN's ability to send and store large amounts of data. Message query telemetry protocol (MQTT) is used because it could save resources on communication and MongoDB used because it does not have the concept of tables and rows, this is very suitable for variations in data generated by WSN. In this study, it can be concluded that the performance of MongoDB on high data amount is acceptable, so MongoDB is highly recommended for WSN implementation.

Computer simulation algorithm of gymnastics formation change path based on wireless sensor

Today with the rapid development of the information age, the exchange of science and technology has brought closer the closeness of countries, and our country has also begun to conduct in-depth research on WSN. This research mainly discusses the computer simulation algorithm of gymnastics formation change path based on wireless sensor. In this research, an improved Leader-Follower method is designed. In the research of gymnastics formation transformation of mobile nodes in the wireless sensor network environment, the traditional three types of nodes are divided into four categories according to the different formation responsibilities, namely, coordinator, beacon node, master mobile node (Leader), slave mobile node (Follower). After it accurately locates itself with the help of the information of the beacon node, the information should be sent out in the form of broadcast, and the coordinator sends the information to the host computer through the serial port for tracking display. In order to enable the mobile nodes in the network to keep the current gymnastic formation moving toward the target point after completing the gymnastic formation transformation, this paper uses the l-φ closed-loop control method to modify the gymnastic formation in real time. The method based on the received signal strength is selected to realize the positioning of the beacon node to the mobile node, and combined with the positioning engine in the core processor CC2431 of the mobile node, efficient and low-energy wireless positioning can be realized. Multiple mobile nodes coordinate with each other to control communication between each node in a wireless manner, and sense their own heading angle information through geomagnetic sensors, so as to make judgments and adjustments on the maintenance and transformation of the current gymnastics formation. During the formation change, after analysis, it is concluded that the maximum offset of Follower2 from the ideal path in the process of traveling to the desired position in the triangular queue is + 0.28 m. This research effectively realized the computer simulation of autonomous formation.

Data Driven Insight Into Fish Behaviour and Their Use for Precision Aquaculture

Aquaculture, or the farmed production of fish and shellfish, has grown rapidly, from supplying just 7% of fish for human consumption in 1974 to more than half in 2016. This rapid expansion has led to the growth of Precision Aquaculture concept that aims to exploit data-driven management of fish production, thereby improving the farmer's ability to monitor, control, and document biological processes in farms. Fundamental to this paradigm is monitoring of environmental and animal processes within a cage, and processing those data toward farm insight using models and analytics. This paper presents an analysis of environmental and fish behaviour datasets collected at three salmon farms in Norway, Scotland, and Canada. Information on fish behaviour were collected using hydroacoustic sensors that sampled the vertical distribution of fish in a cage at high spatial and temporal resolution, while a network of environmental sensors characterised local site conditions. We present an analysis of the hydroacoustic datasets using AutoML (or automatic machine learning) tools that enables developers with limited data science expertise to train high-quality models specific to the data at hand. We demonstrate how AutoML pipelines can be readily applied to aquaculture datasets to interrogate the data and quantify the primary features that explains data variance. Results demonstrate that variables such as temperature, wind conditions, and hour-of-day were important drivers of fish motion at all sites. Further, there were distinct differences in factors that influenced in-cage variations driven by local variables such as water depth and ambient environmental conditions (particularly dissolved oxygen). The framework offers a transferable approach to interrogate fish behaviour within farm systems, and quantify differences between sites.

Optimizing of Q-Learning Day/Night Energy Strategy for Solar Harvesting Environmental Wireless Sensor Networks Nodes

This research article presents the application of the Q-learning algorithm in the operational duty cycle control of solar-powered environmental wireless sensor network (EWSN) nodes. Those nodes are commonly implemented as embedded devices using low-power and low-cost microcontrollers. Therefore, there is a significant need for an effective and easy way to implement a machine learning (ML) algorithm in terms of computer performance. This approach uses a Q-learning-based policy implementing a sleep/run switching algorithm driven by the state of charge. The presented algorithm is based on two modes: daylight and nighttime, which is a suitable solution for solar-powered systems. The study includes the complete process of design EWSN node strategy with an optimal reward policy. The presented algorithm was tested and verified on an EWSN node model and a 5-year data set of solar irradiance values was used for the learning process and its validation. As part of the study, we are also presenting the validation in terms of Q-learning parameters, which include the learning rate and discount factor. The result section shows that the overall performance of the presented solution is more suitable for solar-powered EWSN then state-of-the-art studies. Both day/night experiments reached 828 203 measurement/transmission cycles, which is 12.7 % more than in the previous studies using the strategy defined by the state of energy storage.

Survey on Plant Disease Detection and Applied Smart Irrigation System

In today’s era farmers have been facing many problems related to climate change, more production of crops. some times plants can become weak by some diseases, and farmers can not be aware of their plant’s growth. To check on the internet of things, the concept helps us to interconnect physical objects equipped with sensing, actuating, computing power and thus lends them the capability to collaborate on a task in unison remaining connected to the internet termed as the IoT. It is placed on the farm field and detects the disease and gives notification to farmers related to the disease on plant leaves and also suggests how to overcome that problem. Farmer also can know about climate conditions before a weak or 2 or 3 days. Wireless sensor network (WSNs) acquisition device, environment user-interfaces, and low-cost monitoring system for detecting the various disease in agriculture and farming domain use innovative of automatic learning decision support are proposed to manage the disease detection system. thus, in this paper, future the need to use technology in the agriculture field is easy to know about the climate condition before one month and the growth or health of the plant.

Internet of Things Sensors and Data Analytics Based Decision Support System for Smart Greenhouse

A decision support model and system is proposed based on Internet of Things (IoT) sensors and data analytic for smart greenhouse. The smart greenhouse system monitors multiple environmental factors around it, analyzes climatic and soil condition data, and automates the irrigation process. We also create dashboards and applications that can monitor the sensed data and help users make decisions. The proposed system architecture model studies a practical method that can operate in a microprocessor, IoT sensor and wireless network environment to control a greenhouse. The mobile application and service prototype are implemented and evaluated. The test was conducted in a general indoor space. Data measured in real time by various sensors notifies the smartphone or smart watch as needed. Automatic indoor gardening was implemented by operating the fan when indoor air quality was deteriorated or the humidity was above the appropriate value. It was also confirmed that water was supplied through the automatic water pump according to the measured value of the soil sensor. Data monitoring of the system has been developed successfully. This system provides real-time data and status of the output. The sensor will monitor the system and the controller will manage the all the system’s decision and activities automatically. This research was successfully conducted. However, some improvements can be done for future works. First, dissemination of green computing techniques where IoT devices shall consume less amount of energy thus increase their life expectancy and increase the productivity. Second, Interoperability between IoT devices where devices should be capable to communicate with others from different genre so as the overall system.

Scale‐free topology security mechanism of wireless sensor network against cascade failure

SummaryAs the basic architecture of the Internet of things, wireless sensor networks are the most important technical means to detect data on the Internet of things, and their security is essential to the development and implementation of the Internet of things. The wireless sensor network based on the scale‐free theory has strong fault tolerance when random nodes fail and can better adapt to the unsupervised and complex environment of wireless sensor networks. However, in a wireless sensor network with scale‐free function, the unevenness of its degree distribution causes some nodes to be very critical in the network. Once attacked, it will cause major damage to the network. Selective promotion attacks are relatively common. At the network level, the attacker destroys the normal collection of network data by selectively losing information or not forwarding sensitive information, which is extremely harmful to the network using this function. In order to overcome the influence of excessive dependence on a single path on the balance performance of the network, starting from the entire network, a secure multi‐path routing algorithm for wireless sensor networks based on scale‐free topology is proposed. Through the analysis of the network topology, the highest degree network node can be found. Optimize the load factor, use digital watermarking technology to locate the network path packet loss rate, and perform Gaussian modeling on the packet loss rate. Then the routing loading function and the routing packet loss rate are used as the confidence model factors to create the routing confidence model. We must first establish a model, select the transmission path according to the received function, and then obtain the global optimal security sensor routing algorithm. Through the algorithm in this thesis, the security of the wireless sensor network is improved by 17%.

A novel topology optimization of coverage-oriented strategy for wireless sensor networks

Aiming at the key optimization problems of wireless sensor networks in complex industrial application environments, such as the optimum coverage and the reliability of the network, a novel topology optimization of coverage-oriented strategy for wireless sensor networks based on the wolf pack algorithm is proposed. Combining the characteristics of topology structure of wireless sensor networks and the optimization idea of the wolf pack algorithm redefines the group’s wandering and surprise behavior. A novel head wolf mutation strategy is proposed, which increases the neighborhood search range of the optimal solution, enhances the uniformity of wolf pack distribution and the ergodicity ability of the wolf pack search, and greatly improves the calculation speed and the accuracy of the wolf pack algorithm. With the same probability, the cluster heads are randomly selected periodically, and the overall energy consumption of wireless sensor networks is evenly distributed to the sensor node to realize the balanced distribution of the data of the member nodes in the cluster and complete the design of the topology optimization of wireless sensor networks. Through algorithm simulation and result analysis, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the wolf swarm algorithm shows its advantages in terms of the residual energy of the sensor node, the average transmission delay, the average packet delivery rate, and the coverage of the network. Among them, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the remaining energy of nodes has increased by 9.5% and 15.5% and the average coverage of the network has increased by 10.5% and 5.6%, respectively.

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.

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.

A Binary Adaptive Clone Shuffled Frog Leaping Algorithm for Three‐Dimensional Low‐Energy Target Coverage Optimization in Environmental Monitoring Wireless Sensor Networks

In recent years, more and more researchers have paid attention to the three‐dimensional target coverage of environmental monitoring wireless sensor networks (EMWSNs) under real environmental conditions. However, the target coverage method studied in the traditional two‐dimensional plane is full of loopholes when applied in the real three‐dimensional physical world. Most coverage algorithms usually only optimize for a single problem of target coverage or network energy consumption and cannot reduce network energy consumption while improving coverage. This paper proposes a novel binary adaptive clone shuffled leapfrog algorithm (BACSFLA) suitable for EMWSNs. BACSFLA has an excellent performance in the coverage of three‐dimensional nodes, which can significantly reduce the network energy consumption of ENWSNs in the coverage process, and greatly improve the coverage of nodes. Through simulation experiments, BACSFLA was compared with simulated annealing (SA) and genetic algorithm (GA) in the same conditional parameters. The coverage rate of BACSFLA in EMWSNs is 3.9% higher than that of GA and 5.4% higher than that of SA. The network energy consumption of BACSFLA is 36.0% lower than GA and 35.9% lower than SA. Moreover, BACSFLA can significantly reduce the calculation time and get better results in a shorter time.

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.

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.

Reinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks: A Comparative Survey

Underwater wireless sensor networks (UWSNs) have emerged as a promising networking technology owing to their various underwater applications. Many applications require sensed data to be routed to a centralized location. However, the routing of sensor networks in underwater environments presents several challenges in terms of underwater infrastructure, including high energy consumption, narrow bandwidths, and longer propagation delays than other sensor networks. Efficient routing protocols play a vital role in this regard. Recently, reinforcement learning (RL)-based routing algorithms have been investigated by different researchers seeking to exploit the learning procedure via trial-and-error methods of RL. RL algorithms are capable of operating in underwater environments without prior knowledge of the infrastructure. This paper discusses all routing protocols proposed for RL-based UWSNs. The advantages, disadvantages, and suitable application areas are also mentioned. The protocols are compared in terms of the key ideas, RL designs, optimization criteria, and performance-evaluation techniques. Moreover, research challenges and outstanding research issues are also highlighted, to indicate future research directions.

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.

A Chaotic Elite Niche Evolutionary Algorithm for Low‐Power Clustering in Environment Monitoring Wireless Sensor Networks

In recent years, as people’s demand for environmental quality has increased, it has become inevitable to monitor sensitive parameters such as temperature and oxygen content. Environmental monitoring wireless sensor networks (EMWSNs) have become a research hotspot because of their flexibility and high monitoring accuracy. This paper proposes a chaotic elite niche evolutionary algorithm (CENEA) for low‐power clustering in EMWSNs. To verify the performance of CENEA, simulation experiments are carried out in this paper. Through simulation experiments, CENEA was compared with shuffled frog leaping algorithm (SFLA), differential evolution algorithm (DE), and genetic algorithm (GA) in the same conditional parameters. The results show that CENEA balances node energy and improved node energy usage efficiency. CENEA’s network energy consumption is reduced by 8.3% compared to SFLA, 3.9% lower than DE, and 4.6% lower than GA. Moreover, CENEA improves the precision and minimizes the computation time.

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.