A WKNN-based approach for NB-IoT sensors localization

With the recent introduction of NarrowBand Internet of Things(NB-IoT)technology in the 4th and 5th generations of mobile radio networks,the mobile communications context opens up significantly to the world of sensors.By means of NB-IoT,the mobile systems within 3GPP standardization introduce the peculiar functions of sensor networks,thus making it possible to satisfy very specific requirements with respect to those which characterize traditional mobile telecommunications.Among the functions of interest for sensor networks,the possibility of locating the positions of the sensors without an increase in costs and energy consumption of the sensor nodes is of utmost interest.The present work describes a procedure for locating the NB-IoT nodes based on the quality of radio signals received by the mobile terminals,which therefore does not require further hardware implementations on board the nodes.This procedure,based on the RF fingerprinting technique and on machine learning processing,has been tested experimentally and has achieved interesting performances.

A Novel MegaBAT Optimized Intelligent Intrusion Detection System in Wireless Sensor Networks

Wireless Sensor Network(WSN),whichfinds as one of the major components of modern electronic and wireless systems.A WSN consists of numerous sensor nodes for the discovery of sensor networks to leverage features like data sensing,data processing,and communication.In thefield of medical health care,these network plays a very vital role in transmitting highly sensitive data from different geographic regions and collecting this information by the respective network.But the fear of different attacks on health care data typically increases day by day.In a very short period,these attacks may cause adversarial effects to the WSN nodes.Furthermore,the existing Intrusion Detection System(IDS)suffers from the drawbacks of limited resources,low detection rate,and high computational overhead and also increases the false alarm rates in detecting the different attacks.Given the above-mentioned problems,this paper proposes the novel MegaBAT optimized Long Short Term Memory(MBOLT)-IDS for WSNs for the effective detection of different attacks.In the proposed framework,hyperpara-meters of deep Long Short-Term Memory(LSTM)were optimized by the meta-heuristic megabat algorithm to obtain a low computational overhead and high performance.The experimentations have been carried out using(Wireless Sensor NetworkDetection System)WSN-DS datasets and performance metrics such as accuracy,recall,precision,specificity,and F1-score are calculated and compared with the other existing intelligent IDS.The proposed framework provides outstanding results in detecting the black hole,gray hole,scheduling,flooding attacks and significantly reduces the time complexity,which makes this system suitable for resource-constraint WSNs.

Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network

Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment.In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment,we proposed a more effective and robust target detection framework based on deep learning,which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection.Firstly,the weighted box fusion method is adopted to generate a fusion box by weighted fusion of prediction boxes with high confidence,so as to obtain accurate acoustic shadow boxes.Further,the acoustic shadow box is cut down to get the feature map containing the acoustic shadow information,and then the acoustic shadow feature map and the target information feature map are adaptively fused to make full use of the acoustic shadow feature information.In addition,we introduce a threshold processing module to improve the attention of the model to important feature information.Through the underwater sonar dataset provided by Pengcheng Laboratory,the proposed method improved the average accuracy by 3.14%at the IoU threshold of 0.7,which is better than the current traditional target detection model.

Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

Irrigation plays a significant role in various agricultural cropping methods deployed in semiarid and arid regions where valuable water applications and managing are considered crucial concerns.Multiple factors such as weather,soil,water,and crop data need to be considered for irrigation maintenance in an efficient besides uniform manner from multifaceted and different information-based systems.A Multi-Agent System(MAS)has been proposed recently based on diverse agent subsystems with definite objectives for attaining global MAS objective and is deployed on Cloud Computing paradigm capable of gathering information from Wireless Sensor Networks(WSNs)positioned in rice,cotton,cassava crops for knowledge discovery and decision making.The radial basis function network has been used for irrigation prediction.However,in recent work,the security of data has not focused on where intruder involvement might corrupt the data at the time of data transferring to the cloud,which would affect the accuracy of decision making.To handle the above mentioned issues,an efficient method for irrigation prediction is used in this work.The factors considered for decision making are soil moisture,temperature,plant height,root depth.The above-mentioned data will be gathered from the sensors that are attached to the cropfield.Sensed data will be forwarded to the local server,where data encryption will be performed using Adaptive Elliptic Curve Cryptography(AECC).After the encryption process,the data will be forwarded to the cloud.Then the data stored in the cloud will be decrypted key before being given to the deci-sion-making module.Finally,the uniform distribution-based fuzzy neural network is formulated based on the received data information in the decisionmaking module.Thefinal decision regarding the level of water required for cropfields would be taken.Based on this outcome,the water volve opening duration and the level of fertilizers required will be considered.Experimental results demonstrate the effectiveness of the proposed model for the United States Geological Survey(USGS)database in terms of precision,accuracy,recall,and packet delivery ratio.

DeepQ Based Automated Irrigation Systems Using Deep Belief WSN

Deep learning is the subset of artificial intelligence and it is used for effective decision making.Wireless Sensor based automated irrigation system is proposed to monitor and cultivate crop.Our system consists of Distributed wire-less sensor environment to handle the moisture of the soil and temperature levels.It is automated process and useful for minimizing the usage of resources such as water level,quality of the soil,fertilizer values and controlling the whole system.The mobile app based smart control system is designed using deep belief network.This system has multiple sensors placed in agriculturalfield and collect the data.The collected transmitted to cloud server and deep learning process is applied for making decisions.DeepQ residue analysis method is proposed for analyzing auto-mated and sensor captured data.Here,we used 512×512×3 layers deep belief network and 10000 trained data and 2500 test data are taken for evaluations.It is automated process once data is collected deep belief network is generated.The performance is compared with existing results and our process method has 94%of accuracy factor.Also,our system has low cost and energy consumption also suitable for all kind of agriculturalfields.

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).

A Systematic Overview of Underwater Wireless Sensor Networks:Applications,Challenge and Research Perspectives

Underwater Wireless Sensor Networks(UWSNs)are becoming increasingly popular in marine applications due to advances in wireless and microelectronics technology.However,UWSNs present challenges in processing,energy,and memory storage due to the use of acoustic waves for communication,which results in long delays,significant power consumption,limited bandwidth,and packet loss.This paper provides a comprehensive review of the latest advancements in UWSNs,including essential services,common platforms,critical elements,and components such as localization algorithms,communication,synchronization,security,mobility,and applications.Despite significant progress,reliable and flexible solutions are needed to meet the evolving requirements of UWSNs.The purpose of this paper is to provide a framework for future research in the field of UWSNs by examining recent advancements,establishing a standard platform and service criteria,using a taxonomy to determine critical elements,and emphasizing important unresolved issues.

Home control system based on ZigBee wireless sensor networks

A new scheme of the home control system based on ZigBee wireless sensor networks is presented. The design and development of the software and hardware of the proposed system are given. In addition to the basic data acquisition and processing functions, the gateway supports the Bluetooth-based local interface and the general packet radio service （GPRS）-based remote interface. Users on the client service side can use a pocket PC or notebook PC to achieve real-time data acquisition and control instruction implementation, or remotely control the home control system through a mobile phone by sending a short message. The Labview graphical development environment is adopted to create PDA applications running on pocket PCs and monitoring platform established on notebook PCs. Except for the gateway, other nodes in the system work in sleep mode most of the time on the system, and thus it improves the lifetime of the whole system efficiently.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

Physiological signal acquisition system based on wireless sensor networks

Based on wireless sensor networks, a physiological signal acquisition system is proposed. The system is used in classroom education in order to understand the physiological changes in the students. In the system,the biological electrical signal related to student attention and emotion states can be measured by electrocardiography signals. The bioelectrical signal is digitalized at a 200 Hz sampling rate and is transmitted by the ZigBee protocol. Simultaneously, the Bluetooth technology is also embedded in the nodes so as to meet the high sampling rate and the high-bandwidth transmission. The system can implement the monitoring tasks for 30 students, and the experimental results of using the system in the classroom are proposed. Finally, the applications of wireless sensor networks used in education is also discussed.

Intelligent emergency service system based on wireless sensor and actuator networks

An intelligent emergency service（ IES） system is designed for indoor environments based on a wireless sensor and actuator network（ WSAN） composed of a gateway, sensor nodes, and a multi-robot system（ MRS）. If the MRS receives accident alarm information, the group of robots will navigate to the accident sites and provide corresponding emergency services.According to the characteristics of the MRS, a distributed consensus formation protocol is designed, which can assure that the multiple robots arrive at the accident site in a specified formation. The prototype emergency service system was designed and implemented, and some relevant simulations and experiments were carried out. The results showthat the MRS can successfully provide emergency lighting and failure node replacement services when accidents happen. The effectiveness of the algorithm and the feasibility of the system are verified.

Distributed H_∞ fusion filter design for INS/WSN integrated positioning system

In order to keep stable navigation accuracy when the blind node （BN） moves between two adjacent clusters, a distributed fusion method for the integration of the inertial navigation system （INS） and the wireless sensor network （WSN） based on H∞ filtering is proposed. Since the process and measurement noise in the integration system are bounded and their statistical characteristics are unknown, the H∞ filter is used to fuse the information measured from local estimators in the proposed method. Meanwhile, the filter can yield the optimal state estimate according to certain information fusion criteria. Simulation results show that compared with the federal Kalman solution, the proposed method can reduce the mean error of position by about 45% and the mean error of velocity by about 85 %.

Monitoring System for Field Soil Water Content Based on the Wireless Sensor Network

[Objective] To water content monitoring study the application of wireless sensor network in field so and to discuss the methods for solving the problems of low sampling rate, high cost and poor real-time in actual monitoring. [Method] The architecture of wireless sensor network, network nodes, hardware design as well as principle for the program structure of software operating system and corresponding parameters were analyzed to illustrate the characteristics of monitoring system for field soil water content based on wireless sensor network, and the advantages in application of this system. [Result] Sensor nodes could correctly collect and transmit soil water content, realize stable data transmission of soil water content, indicating that wireless sensor network is suitable for real-time monitoring of field soil water content. [Conclusion] This study indicates that wireless sensor network possesses a widely application foreground in the development of agriculture.

The Study of Farmland Information Management System based on Wireless Sensor Networks

To satisfy the need of good quality and high yield primary production,the farmland information management system based on wireless Sensor Network has been proposed.We give priority to analyzing the basic function of the system,building the systematic structure of applied system and network system,and implementing the energy control and safety design of system.The system can reduce manpower operation and the error of manual measuration in the course of practical production,reduce the cost of agricultural production,and realize automatization of agricultural production to the largest extent to provide an effective way to realize good quality and high yield primary production,which has an important realistic meaning.

A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their applications.Therefore,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN applications.This paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering packets.The proposed version employs a network-based reputation system to select the best and most secure path to a destination.To achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation system.To minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation tables.Moreover,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast approaches.The proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other protocols.Using the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing path.The paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different zones.Furthermore,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks

A dynamical model is constructed to depict the spatial-temporal evolution of malware in mobile wireless sensor networks(MWSNs). Based on such a model, we design a hybrid control scheme combining parameter perturbation and state feedback to effectively manipulate the spatiotemporal dynamics of malware propagation. The hybrid control can not only suppress the Turing instability caused by diffusion factor but can also adjust the occurrence of Hopf bifurcation induced by time delay. Numerical simulation results show that the hybrid control strategy can efficiently manipulate the transmission dynamics to achieve our expected desired properties, thus reducing the harm of malware propagation to MWSNs.

A blockchain-empowered authentication scheme for worm detection in wireless sensor network

Wireless Sensor Network(WSN)is a distributed sensor network composed a large number of nodes with low cost,low performance and self-management.The special structure of WSN brings both convenience and vulnerability.For example,a malicious participant can launch attacks by capturing a physical device.Therefore,node authentication that can resist malicious attacks is very important to network security.Recently,blockchain technology has shown the potential to enhance the security of the Internet of Things(IoT).In this paper,we propose a Blockchain-empowered Authentication Scheme(BAS)for WSN.In our scheme,all nodes are managed by utilizing the identity information stored on the blockchain.Besides,the simulation experiment about worm detection is executed on BAS,and the security is evaluated from detection and infection rate.The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial operations.Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization.Network infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node distribution.This work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and design.By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation.Through implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network operation.We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world problems.Being the most efficient in the optimization process,Biruni displays the lowest error rate at 0.00032.The two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific problems.Due to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made.Through delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.