Sensor Network Nodes Research Articles

IoT-Based Intrusion Detection System Using New Hybrid Deep Learning Algorithm

The most significant threat that networks established in IoT may encounter is cyber attacks. The most commonly encountered attacks among these threats are DDoS attacks. After attacks, the communication traffic of the network can be disrupted, and the energy of sensor nodes can quickly deplete. Therefore, the detection of occurring attacks is of great importance. Considering numerous sensor nodes in the established network, analyzing the network traffic data through traditional methods can become impossible. Analyzing this network traffic in a big data environment is necessary. This study aims to analyze the obtained network traffic dataset in a big data environment and detect attacks in the network using a deep learning algorithm. This study is conducted using PySpark with Apache Spark in the Google Colaboratory (Colab) environment. Keras and Scikit-Learn libraries are utilized in the study. ‘CICIoT2023’ and ‘TON_IoT’ datasets are used for training and testing the model. The features in the datasets are reduced using the correlation method, ensuring the inclusion of significant features in the tests. A hybrid deep learning algorithm is designed using one-dimensional CNN and LSTM. The developed method was compared with ten machine learning and deep learning algorithms. The model’s performance was evaluated using accuracy, precision, recall, and F1 parameters. Following the study, an accuracy rate of 99.995% for binary classification and 99.96% for multiclassification is achieved in the ‘CICIoT2023’ dataset. In the ‘TON_IoT’ dataset, a binary classification success rate of 98.75% is reached.

A Novel Coverage Optimization Scheme Based on Enhanced Marine Predator Algorithm for Urban Sensing Systems

Ensuring the efficient operation of urban sensing systems under emergency conditions represents a current and significant challenge. To address the typical problem of low network coverage due to random deployment of wireless sensor network nodes in urban sensing systems, a novel coverage optimization scheme with an enhanced Marine Predator Algorithm (EMPA) is proposed. For a given target network monitoring area, a joint node sensing model based on Boolean sensing is constructed with the environmental information of the monitoring area as input, and then the model is optimized using EMPA for several iterations to obtain the optimized maximum network coverage and its corresponding optimal node location deployment scheme. Based on the standard MPA, firstly, the Tent chaotic map is introduced to initialize the population to maintain the population diversity of the algorithm; secondly, the adaptive jump step factor is introduced in the jump phase to improve the ability of the algorithm to jump out of the local optimum; finally, the double-subgroup location update strategy is introduced before the end of each iteration of the algorithm, which can better balance the local and global search and further enhance the algorithm’s optimal search capability. The algorithm is compared with four unimodal and multimodal benchmark test functions and applied to three network coverage optimization scenarios in urban sensing systems. The results show that the EMPA algorithm can efficiently optimize the network nodes’ uniform distribution, enhance average network coverage, and save a certain level of node deployment costs for urban sensing systems.

Auto-localization algorithm for mobile sensor nodes in wireless sensor networks

Auto-localization algorithm for mobile sensor nodes in wireless sensor networks

A fault diagnosis method for wireless sensor network nodes based on a belief rule base with adaptive attribute weights

Due to the harsh operating environment and ultralong operating hours of wireless sensor networks (WSNs), node failures are inevitable. Ensuring the reliability of the data collected by the WSN necessitates the utmost importance of diagnosing faults in nodes within the WSN. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. A solitary data feature is often assigned a high weight, resulting in the failure to effectively distinguish between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating parameters initialized by expert knowledge with the extracted data features. Third, to optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The results of the case study indicate that compared with the traditional BRB method, the accuracy of the proposed model in WSN node fault diagnosis is significantly improved.

Low-Wind-Speed Galloping Wind Energy Harvester Based on a W-Shaped Bluff Body

Galloping-based piezoelectric energy harvesting systems are being used to supply renewable electricity for low-power wireless sensor network nodes. In this paper, a W-shaped bluff body is proposed as the core component of a piezoelectric wind energy harvester. Experiments and simulations have shown that the W-shaped bluff body can improve harvesting efficiency at low wind speeds. For the W-shaped structure, the finite element simulation results indicate that the structure can help improve the aerodynamic performance to obtain high aerodynamic force. The experimental results demonstrate that compared with the traditional bluff bodies, the piezoelectric wind energy harvester with the W-shaped bluff body (WEHW) can generate higher output voltages and has a lower cut-in speed. When the length L is 30 mm and the rear groove angle β is 30°, the W-shaped structure can induce the best harvesting performance. When an external load resistance of 820 KΩ is connected and the wind speed is 5 m/s, the WEHW generates an average output power of 0.28 mW.

Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.

Wireless Sensor Networks Node Localization Algorithm Based on Range Optimization and Graph Optimization

<p>To address the problem of low localization accuracy in the node localization algorithms of wireless sensor networks (WSN) based on received signal strength indication (RSSI) ranging, a WSN node localization algorithm based on ranging optimization and graph optimization is proposed. In terms of RSSI ranging, the outliers are removed using the Grubbs method, and the data are processed using a moving average smoothing-Gaussian hybrid filter to establish a Bessel function ranging model to reduce the ranging error; in terms of node localization, the signal strength data are employed to construct a distance cost term, a cost function model is built based on these cost terms, and graph optimization is adopted to minimize this function. Then, the node position is estimated to minimize the overall observation error. Simulation results indicate that the proposed algorithm has higher ranging and localization accuracy than existing ranging and localization algorithms, and it can meet the requirements of node localization in large-scale WSN.</p> <p> </p>

A Simultaneous Wireless Information and Power Transfer-Based Multi-Hop Uneven Clustering Routing Protocol for EH-Cognitive Radio Sensor Networks

Clustering protocols and simultaneous wireless information and power transfer (SWIPT) technology can solve the issue of imbalanced energy consumption among nodes in energy harvesting-cognitive radio sensor networks (EH-CRSNs). However, dynamic energy changes caused by EH/SWIPT and dynamic spectrum availability prevent existing clustering routing protocols from fully leveraging the advantages of EH and SWIPT. Therefore, a multi-hop uneven clustering routing protocol is proposed for EH-CRSNs utilizing SWIPT technology in this paper. Specifically, an EH-based energy state function is proposed to accurately track the dynamic energy variations in nodes. Utilizing this function, dynamic spectrum availability, neighbor count, and other information are integrated to design the criteria for selecting high-quality cluster heads (CHs) and relays, thereby facilitating effective data transfer to the sink. Intra-cluster and inter-cluster SWIPT mechanisms are incorporated to allow for the immediate energy replenishment for CHs or relays with insufficient energy while transmitting data, thereby preventing data transmission failures due to energy depletion. An energy status control mechanism is introduced to avoid the energy waste caused by excessive activation of the SWIPT mechanism. Simulation results indicate that the proposed protocol markedly improves the balance of energy consumption among nodes and enhances network surveillance capabilities when compared to existing clustering routing protocols.

A wide-angle incidence, broadband rectified metasurface for radar frequency band EM energy harvesting and wireless power transfer

Traditional power transmission networks with complex physical wiring have struggled to meet the power needs of emerging portable and decentralized devices. Recently, rectified metasurfaces for electromagnetic (EM) energy harvesting have attracted great attention from researchers due to their customizability and application flexibility. However, many previous structures mainly focused on harvesting efficiency and ignored harvesting bandwidth. Here, we propose a broadband rectified metasurface structure for radar frequency band EM energy harvesting and wireless power transfer. The cell structure consists of a centrosymmetric hyperbolic ring (CHR) structure that can exhibit an EM wave absorptivity of greater than 80 % at wide incidence angles. The AC energy of each cell is exported through metal vias, and the energy is pooled through the feed networks. Finally, the rectification circuit is designed to complete the AC-to-DC conversion and the DC energy harvesting. Broadband EM energy harvester can be achieved when the incidence angle is 0° to 60°. The half-power bandwidth (HPBW) can be up to 55 % with a normal incidence. To verify the rectified metasurface function, a prototype with 6 × 6 metasurface array is fabricated and tested. When the rectified metasurface operates at 6.1 GHz, RF-to-DC energy harvesting efficiency of greater than 35 % can be achieved in the incidence angle range of 0° to 60° and maximum efficiency is 57 %. It makes it possible to self-power sensor network nodes working in the vicinity of communication base stations or radar base stations.

Theoretical and experimental research on a Quasi-Zero-Stiffness-Enabled nonlinear piezoelectric energy harvester

Piezoelectric material provides a convenient and efficient avenue to convert ambient vibration energy into electrical energy to power wireless sensor network nodes of Internet of Things. However, the design of effective ultralow-frequency and low-amplitude harvesters using piezoelectric materials remains a challenge. To address this challenging problem, this paper devises a quasi-zero-stiffness-enabled (QZSE) piezoelectric energy harvester based on a geometrical nonlinear structure. The basic configuration and operation principles of the QZSE piezoelectric energy harvester ware elaborated firstly, then the nonlinear electromechanical coupling equation is derived, whose solution informs on both the dynamic and electrical responses of the harvester. The electromechanical-coupled equations are solved with the aid of the fourth-order Runge-Kutta algorithm. The energy harvesting performance is then investigated with respect to the parameter effects of excitation, damping and proof mass. For validating numerical results and demonstrating the advantages of the proposed design, a prototype is fabricated and experimentally tested. The results confirm the ability of the QZSE piezoelectric energy harvester in producing effective vibration-to-electricity energy conversion in ultralow frequency range. This study provides new impetus to energy harvesting from ultralow frequency ambient vibration using piezoelectric materials.

TUBER: Time-aware UAV-based energy-efficient reconfigurable routing scheme for smart wireless livestock sensor network.

This paper is a follow-up to a recent work by the authors on recoverable UAV-based energy-efficient reconfigurable routing (RUBER) scheme for addressing sensor node and route failure issues in smart wireless livestock sensor networks. Time complexity and processing cost issues connected to the RUBER scheme are consequently treated in this article by proffering a time-aware UAV-based energy-efficient reconfigurable routing (TUBER) scheme. TUBER scheme employs a synchronized clustering-with-backup strategy, a minimum-hop neighborhood recovery mechanism, and a redundancy minimization technique. Comparative network performance of TUBER was investigated and evaluated with respect to RUBER and UAV-based energy-efficient reconfigurable routing (UBER) schemes. The metrics adopted for this comparative performance analysis are Cluster Survival Ratio (CSR), Network Stability (NST), Energy Dissipation Ratio (EDR), Network Coverage (COV), Packet Delivery Ratio (PDR), Fault Tolerance Index (FTI), Load Balancing Ratio (LBR), Routing Overhead (ROH), Average Routing Delay (ARD), Failure Detection Ratio (FDR), and Failure Recovery Ratio (FRR). With reference to best-obtained values, TUBER demonstrated improvements of 36.25%, 24.81%, 34.53%, 15.65%, 38.32%, 61.07%, 31.66%, 63.20%, 68.96%, 66.19%, and 78.63% over RUBER and UBER in terms of CSR, NST, EDR, COV, PDR, FTI, LBR, ROH, ARD, FDR, and FRR, respectively. These experimental results confirmed the relative effectiveness of TUBER against the compared routing schemes.

Energy Efficient Clustering Mechanism for Malicious Sensor Nodes in IOT Based MWSN

A mobile wireless sensor network (MWSN) is a collection of wireless nodes that may be set up anywhere and at any time without the need for a pre-existing network structure. Network performance is challenging factor due to nature of mobility and misbehaving nodes. Data loss and misbehaving in sensor nodes degrades performance of network. Some circumstances some malevolent sensor nodes predisposed to eradicate capability of network. The goal of this study is to use an irregular set approach to distinguish hostile nodes. Broadcasting information in route entry table identifies malicious node. Each sensor node in network preserves entry table and broadcast record regarding nearby nodes. To estimate broadcasting record metrics are premeditated as data deliverance proportion, throughput, delay, packet drop, fault rate. Mobile nodes with diverse velocity are simulated in NS2 environment which contains variable node clusters. Based broadcasting records of mobile nodes with diverse velocity are considered to create information table. Fine and malicious nodes are recognized based on rules derived in table of irregular set tactic. Shortest path without malicious node is elected to broadcast packets in the environment. Proposed tactic consequences reveal irregular set tactic amplifies network capability data deliverance percentage, throughput and dwindled end-to-end delay in mobile sensors.

Supporting Ultra-Low-Power Nodes in 6TiSCH Industrial Wireless Sensor Networks

Supporting Ultra-Low-Power Nodes in 6TiSCH Industrial Wireless Sensor Networks

An Efficient Processing Scheme for Concurrent Applications in the IoT Edge

Due to the large volume of IoT data, conventional sensor network based and the cloud base IoT systems cannot handle latency-sensitive and resource-consuming IoT applications. Sensor networks do not have enough computation resources and also suffer from a limited network lifetime. On the other hand, the cloud based IoT system is far away from the users and the physical world, and cannot satisfy the real-time requirement of IoT applications. We adopt the IoT edge network to address these challenges and process IoT applications in modern IoT systems. The IoT edge network is an emerging computing architecture in the IoT. Compared to the sensor nodes in conventional sensor networks, the edge servers have more computation resources. Compared to the remote cloud, the edge servers are closer to the users and the physical world. However, processing IoT applications in the edge network still remains challenging. First, how to process concurrent IoT applications has not been fully investigated. Second, the inner relationship between the network resource and the application latency has not been deeply analyzed. Third, the function conflict problem in edge servers has not been taken seriously. To solve the above challenges, we propose the Energy and Latency Efficient Processing Plan for Concurrent IoT Applications Problem which aims to construct an application processing plan by jointly considering the concurrency, the energy-latency relationship, and the function conflict problems. We prove that such a problem is NP-Hard, and algorithms are proposed accordingly. Furthermore, we also estimate the performance of the proposed algorithms by numerical results.

Enhancing energy use efficiency of wireless sensor networks using newly proposed fault tolerance multipath routing protocol (MRP-FT)

The present study compared the newly proposed fault tolerance multipath routing protocol (MRP-FT) under the particle swarm optimization-based fault tolerant routing (PSO-FT) technique to the existing low-energy adaptive clustering hierarchy (LEACH) protocol applied in different combinations of inter-node numbers (N ) ranging from 100 to 300 nodes (N100−300) in wireless sensor networks (WSNs) with different area sizes (Xm × Ym) of 100–1000 m2 (WSN100−1000) to enhance energy efficiency and reliability of WSNs. The implementation of MRP-FT protocol significantly decreased the packet overhead by 36.0–69.5%, delay by 40.4–52.9%, and energy consumption by 35.9–52.9%, while increasing reliability by 97.0–104.1%, compared to the existing LEACH protocol. For instance, at N100−200, the LEACH simulated packet overhead increased from 76-89 packets at t = 500 s with an increase in WSN size from 100 to 1000. At N300 + WSN100, the packet overhead showed the highest decrease of ca. 69.5% with MRP-FT over the LEACH protocol. The implementation of the MRP-FT protocol reduced energy consumption by 35–123 J over the LEACH protocol.

An optimization method for wireless sensor networks coverage based on genetic algorithm and reinforced whale algorithm.

In response to the problem of coverage redundancy and coverage holes caused by the random deployment of nodes in wireless sensor networks (WSN), a WSN coverage optimization method called GARWOA is proposed, which combines the genetic algorithm (GA) and reinforced whale optimization algorithm (RWOA) to balance global search and local development performance. First, the population is initialized using sine map and piecewise linear chaotic map (SPM) to distribute it more evenly in the search space. Secondly, a non-linear improvement is made to the linear control factor 'a' in the whale optimization algorithm (WOA) to enhance the efficiency of algorithm exploration and development. Finally, a Levy flight mechanism is introduced to improve the algorithm's tendency to fall into local optima and premature convergence phenomena. Simulation experiments indicate that among the 10 standard test functions, GARWOA outperforms other algorithms with better optimization ability. In three coverage experiments, the coverage ratio of GARWOA is 95.73, 98.15, and 99.34%, which is 3.27, 2.32 and 0.87% higher than mutant grey wolf optimizer (MuGWO), respectively.

Energy-efficient classification strategy for detecting interference and malicious sensor nodes in wireless body area Networks

Wireless Body Area Networks (WBANs) play a vital role in healthcare monitoring, using wireless sensors to track physiological parameters and predict illness onset. This study proposes a novel approach for detecting interference and malicious sensor nodes in WBANs, crucial for maintaining system integrity and performance. The method combines feature-based techniques with classification strategies to accurately identify anomalies. Features are taken from WBAN nodes and used to train Support Vector Machine (SVM) classifiers, which makes interference detection work well. A neurofuzzy inference system (ANFIS) classifier is also used to train the system on trusted and untrusted nodes at the start, which makes classification easier in real-world WBAN situations. Link failures due to rogue sensor nodes can severely impact WBAN performance, emphasizing the need for efficient detection and correction mechanisms. The proposed strategy introduces a weight metric to identify broken links, enhancing system reliability. Evaluation metrics, including LFD latency and packet delivery ratio, are analyzed to assess the efficacy of the approach. By improving interference detection and addressing link failures, this study contributes to enhancing the efficiency and reliability of WBAN networks, critical for advancing healthcare monitoring technologies.

A review of wireless body area networks

<span>The widespread adoption of wireless body area network (WBAN) in healthcare presents opportunities to meet the increasing demand for medical services. WBAN enables continuous real-time (RT) monitoring through biomedical sensor nodes positioned in or around a patient's body, collecting vital physiological data. In addition, WBAN imposes stringent criteria for energy efficiency and reliability throughout data collection and transmission. This review paper places significant emphasis on the fundamental concept and essential characteristics of WBAN technology. First, the WBAN features, including architecture, sensor nodes types and network topology is presented. Then, the study explores a wide variety of communication standards and multiple access (MA) mechanism deployed in this technology. Moreover, it discusses open research and challenging issues such as heterogeneous traffic, quality of services (QoS), energy consumption, reliability, interference management, and human body movements effect. Finally, the paper is concluded, and future directions are identified in this evolving field of human health monitoring technology.</span>

Application of intelligent algorithm in wireless sensor network node deployment optimization

Application of intelligent algorithm in wireless sensor network node deployment optimization

Water droplet energy harvesting

AbstractHarnessing abundant kinetic water energy in diverse forms of river flows, ocean waves, tidal currents, raindrops, and others, is highly attractive to ease the energy crisis and satisfy the demands of scattered sensor network nodes in the Internet of things. Among them, raindrops, widely and ubiquitously distributed in nature and ambient living life, have been extensively explored and regarded as significant renewable energy carriers. Extensive efforts have been made to investigate droplet‐based electricity nanogenerators in fundamental mechanism, performance, and applications for achieving sustainable energy demands of the rapidly developing society over the past decade. In this review, we introduce the remarkable progress in this field and discuss the fundamental mechanisms of droplet energy harvesting technology for achieving high‐power generation. More significantly, a systematic review of droplet energy harvesting in different two‐phase interfaces, including liquid–solid, liquid–liquid, and liquid–gas interfaces, is provided. Finally, this survey reveals that droplet‐based electricity generators present vast potential in the power supply. At the same time, several development challenges and prospective solutions are discussed to spur future technological advancements.