Sensor Network Nodes Research Articles

Laser-Induced Graphene Formation on Chitosan Derivatives toward Ecofriendly Electronics

Laser-induced graphene (LIG) has aroused a wide range of research interests ranging from micro-nano energy devices to the Internet of Things (IoT). Nevertheless, the non-degradability of most-used synthetic polymer carbon sources poses a serious threat to the environment. In this work, ecofriendly chitosan-based derivatives, including carboxymethyl chitosan (CMCS), chitosan oligosaccharide, and chitosan hydrochloride, are successfully converted into LIGs for the first time via a convenient one-step CO2 laser engraving at ambient air. The obtained LIGs are characterized by a three-dimensional hierarchical porous structure and exhibit good sheet conductivity. The consecutive carbonization and graphitization mechanism of target precursors induced by laser heat accumulation is also deeply discussed. Besides, based on a mechanically reliable LIG/CMCS composite film and tribo-negative acrylic/polyimide anti-layers, two contact-separation mode triboelectric nanogenerators are built and their power densities range from 1.44 to 2.48 mW cm–2. These devices with long cycle life can be used for low-frequency mechanical energy harvesting and commercial capacitance charging, which could be potentially applied in the wireless sensor network nodes. Such a family of chitosan derivatives paves a new route for LIG synthesis and provides new ideas for ecofriendly LIG electronics.

Guest Editorial: Polymer electrets and ferroelectrets

Guest Editorial: Polymer electrets and ferroelectrets

A Distributed Clustering Scheme for Dense Wireless Sensor Networks

Abstract: A sensor node, also known as an Alfeo (mainly in North America), is a sensor network node capable of processing, collecting sensor data, and communicating with other connected nodes in the network. Alpheus is a node, but a node is not always a point. Each sensor node has a microprocessor and some memory. In addition, each sensor node has one or more sensor devices such as acoustic sensors, microphone arrays, and video cameras, infrared, seismic or magnetic sensors. However, dead batteries in sensor nodes are difficult to replace. A typical sensor node consumes part of its energy during wireless communication. This research paper proposes to develop a well estimated distributed clustering model for dense wireless sensor network fields that provides improved performance over the existing clustering algorithm LEACH. The two robbery concepts behind the proposed system are the hierarchical distributed clustering mechanism and the threshold concept. Energy consumption is significantly reduced, which significantly extends the life of sensor nodes

Mobile chargers scheduling algorithm for maximum data flow in wireless sensor networks

Most nodes in wireless sensor networks (WSNs) are battery powered. However, battery replacement is inconvenient, which severely limits the application field of the networks. In addition, the energy consumption of nodes is not balanced in WSNs, nodes with low energy will seriously affect data transmission capability. To solve these problems, we utilize mobile chargers (MCs) in WSNs, which can move by itself and charge low-energy nodes. Firstly, we construct a mixed integer linear programming model (MILP) to solve maximum flow problem, which is proved to be NP-hard problem. To maximize flow to the sink nodes, the BottleNeck algorithm is used to generate the initial population for the genetic algorithm. This algorithm takes path as the unit and schedules MCs to charge the lowest energy node first. Then, the improved adaptive genetic algorithm (IAGA) is utilized to simulate the natural evolution process and search for the optimal deployment location for MCs. The experiment results show that IAGA can effectively improve the maximum flow of sink node compared with other methods.

Research on node coverage in agricultural wireless sensor networks based on deterministic coverage

The application of wireless sensor network in agriculture has always been a concern. In the research of wireless sensor network extension, coverage problem is one of the hot issues in wireless sensor network. Research on the node coverage of agricultural wireless sensor networks based on deterministic deployment is of great significance to improve the coverage quality of agricultural wireless sensor networks and reduce energy consumption. According to the feature of deterministic coverage of wireless sensor network nodes in the agricultural field, this paper summarizes and analyzes the existing deterministic coverage schemes, and proposes to determine the minimum number of nodes according to the size of the repeated coverage area, which provides ideas for the problem of node coverage with deterministic area characteristics in the agricultural field.

Energy redistribution assisted charging of WRSNS with multiple mobile chargers having multiple base stations

The utilization of mobile chargers (MCs) for charging the nodes in wireless rechargeable sensor networks (WRSNs) has been a significant research subject. However, existing works mainly focused on planning the moving paths of MCs whereas neglecting opportunities to enhance charging efficiency through the exploitation of energy redistribution (ERD) processes among nodes and the broadcast nature of radio signals. To improve charging efficiency by exploiting these opportunities, we investigate the underlying energy redistribution assisted charge scheduling (ERACS) problem. This problem aims to identify a charging schedule that meets the nodes’ energy demands while minimizing energy loss and time span in WRSNs with multiple MCs and multiple base stations. After demonstrating the NP-hardness of the ERACS problem, we propose an energy loss minimization charge scheduling (ELMCS) algorithm to solve the problem. The ELMCS algorithm first constructs and solves a linear programming problem to obtain a time-length list that leads to minimum transmission energy loss. It then constructs the moving path set of MCs using a heuristic MCs moving path planning strategy that we propose in this paper to minimize moving energy loss. Additionally, after generating an MC energy transmission list that describes the MCs’ energy transmission time intervals, the ELMCS algorithm minimizes the time span by capitalizing on concurrent energy transmission opportunities while ensuring optimal energy loss. Finally, we compare our ELMCS algorithm with typical algorithms, and the simulation results demonstrate that our algorithm is markedly superior.

Universal Energy Solution for Triboelectric Sensors Toward the 5G Era and Internet of Things.

The launching of 5G technology provides excellent opportunity for the prosperous development of Internet of Things (IoT) devices and intelligent wireless sensor nodes. However, deploying of tremendous wireless sensor nodes network presents a great challenge to sustainable power supply and self-powered active sensing. Triboelectric nanogenerator (TENG) has shown great capability for powering wireless sensors and work as self-powered sensors since its discovery in 2012. Nevertheless, its inherent property of large internal impedance and pulsed "high-voltage and low-current" output characteristic seriously limit its direct application as stable power supply. Herein, a generic triboelectric sensor module (TSM) is developed toward managing the high output of TENG into signals that can be directly utilized by commercial electronics. Finally, an IoT-based smart switching system is realized by integrating the TSM with a typical vertical contact-separation mode TENG and microcontroller, which is able to monitor the real-time appliance status and location information. Such design of a universal energy solution for triboelectric sensors is applicable for managing and normalizing the wide output range generated from various working modes of TENGs and suitable for facile integration with IoT platform, representing a significant step toward scaling up TENG applications in future smart sensing.

Forest Fire Detection System based on Low-Cost Wireless Sensor Network and Internet of Things

Forest fires are one of the most devastating natural disasters that can have a significant impact on the environment, economy, and human lives. Early detection and prompt response are crucial to minimize the damage caused by forest fires. In recent years, Wireless Sensor Networks (WSN) and Internet of Things (IoT) technologies have emerged as promising solutions for forest fire detection due to their low-cost and efficient monitoring capabilities. This paper proposes a low-cost forest fire detection system based on WSN and IoT. The system uses a network of sensor nodes that are strategically placed in the forest to monitor environmental conditions such as temperature, humidity, and smoke. The sensor data is transmitted to a central server, where advanced algorithms are used to detect and predict the occurrence of forest fires. The system provides real-time alerts to forest authorities and users using a mobile application that shows the fire maps and the current updates. The proposed system has been evaluated using based on experiments, and the results show that it can effectively detect forest fires with high accuracy, low false alarms, and low cost. This system has the potential to provide an efficient and cost-effective solution for forest fire detection and can play a vital role in protecting the environment and saving lives.

Real-time detection of mercury ions based on vertically grown ReS2 film

Mercury (Hg2+), one of the most dangerous toxins in water, is a heavy metal that causes organ damage from both short-term and chronic exposure. Conventional methods for detecting mercury such as atomic absorption spectrometry or Raman spectroscopy require bulky equipment with complicated procedures. In this work, we fabricated a highly sensitive, real-time thin-film sensor based on vertically aligned rhenium disulfide (ReS2). Its outstanding large surface area and the unique electronic appearance of its layered architecture make a ReS2 nanosheet a strong contender for such an application. The sensor exhibited a fast response speed (< 2 s) to Hg2+ and an ultralow detection limit of 4 nM, which is significantly less than that of the U.S. Environmental Protection Agency's (U.S. EPA) allowed utmost contamination limit for Hg2+ in drinking water (10 nM). It also exhibited strong selectivity for Hg2+ against other metal ions such as Na+, Zn2+, Fe3+, Cu2+, Ca2+, Ni2+, Ag+, Cd2+, Fe2+, and Pb2+. Because this nanosheet can be replaced with any secondary substrate and possibly patterned into a microscale size, the sensor can be integrated into multiple platforms such as portable devices or sensor nodes in a grid network.

A Distance Vector Hop-Based Secure and Robust Localization Algorithm for Wireless Sensor Networks

Location information of sensor nodes in a wireless sensor network is important. The sensor nodes are usually required to ascertain their positions so that the data collected by these nodes can be labeled with this information. On the other hand, certain attacks on wireless sensor networks lead to the incorrect estimation of sensor node positions. In such situations, when the location information is not correct, the data may be labeled with wrong location information that may subvert the desired operation of the wireless sensor network. In this work, we formulate and propose a distance vector hop-based algorithm to provide secure and robust localization in the presence of malicious sensor nodes that result in incorrect position estimation and jeopardize the wireless sensor network operation. The algorithm uses cryptography to ensure secure and robust operation in the presence of adversaries in the sensor network. As a result of the countermeasures, the attacks are neutralized and the sensor nodes are able to estimate their positions as desired. Our secure localization algorithm provides a defense against various types of security attacks, such as selective forwarding, wormhole, Sybil, tampering, and traffic replay, compared with other algorithms which provide security against only one or two types. Simulation experiments are performed to evaluate the performance of the proposed method, and the results indicate that our secure localization algorithm achieves the design objectives successfully. Performance of the proposed method is also compared with the performance of basic distance vector hop algorithm and two secure algorithms based on distance vector hop localization. The results reveal that our proposed secure localization algorithm outperforms the compared algorithms in the presence of multiple attacks by malicious nodes.

A zeroing neurodynamics‐based location method for nodes in underwater acoustic sensor network

AbstractZeroing neurodynamics methodology, which dedicates to finding equilibrium points of equations, has been proven to be a powerful tool in the online solving of problems with considerable complexity. In this paper, a method for underwater acoustic sensor network (UASN) localisation is proposed based on zeroing neurodynamics methodology to preferably locate moving underwater nodes. A zeroing neurodynamics model specifically designed for UASN localisation is constructed with rigorous theoretical analyses of its effectiveness. The proposed zeroing neurodynamics model is compatible with some localisation algorithms, which can be utilised to eliminate error in non‐ideal situations, thus further improving its effectiveness. Finally, the effectiveness and compatibility of the proposed zeroing neurodynamics model are substantiated by examples and computer simulations.

Optimization of Energy-Efficient Cluster Head Selection Algorithm for Internet of Things in Wireless Sensor Networks

The Internet of Things (IoT) now uses the Wireless Sensor Network (WSN) as a platform to sense and communicate data. The increase in the number of embedded and interconnected devices on the Internet has resulted in a need for software solutions to manage them proficiently in an elegant and scalable manner. Also, these devices can generate massive amounts of data, resulting in a classic Big Data problem that must be stored and processed. Large volumes of information have to be produced by using IoT applications, thus raising two major issues in big data analytics. To ensure an efficient form of mining of both spatial and temporal data, a sensed sample has to be collected. So for this work, a new strategy to remove redundancy has been proposed. This classifies all forms of collected data to be either relevant or irrelevant in choosing suitable information even before they are forwarded to the base station or the cluster head. A Low-Energy Adaptive Clustering Hierarchy (LEACH) is a cluster-based routing protocol that uses cluster formation. The LEACH chooses one head from the network sensor nodes, such as the Cluster Head (CH), to rotate the role to a new distributed energy load. The CHs were chosen randomly with the possibility of all CHs being concentrated in one locality. The primary idea behind such dynamic clustering was them resulted in more overheads due to changes in the CH and advertisements. Therefore, the LEACH was not suitable for large networks. Here, Particle Swarm Optimization (PSO) and River Formation Dynamics are used to optimize the CH selection (RFD). The results proved that the proposed method to have performed better compared to other methods.

A cooperative security monitoring mechanism aided by optimal multiple slave cluster heads for UASNs

As each cluster head (CH) sensor node is used to aggregate, fuse, and forward data from different sensor nodes in an underwater acoustic sensor network (UASN), guaranteeing the data security in a CH is very critical. In this paper, a cooperative security monitoring mechanism aided by multiple slave cluster heads (SCHs) is proposed to keep track of the data security of a CH. By designing a low complexity "equilateral triangle algorithm (ETA)", the optimal SCHs (named as ETA-based multiple SCHs) are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve large-scale data security monitoring. In addition, by analyzing the entire monitoring process, the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is deduced. The simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing schemes. In addition, the numerical simulation results are consistent with the theoretical analysis results, thus verifying the effectiveness of the proposed scheme.

A Recursive Robust Set-Membership Estimator for WSN-Assisted Moving Targets Tracking With UBB Anchor Location Uncertainty

In this paper, a recursive robust set-membership fusion estimator (RSMFE) is presented to track the moving targets treated as the mobile nodes in a mobile wireless sensor network (MWSN) system with unknown but bounded (UBB) anchor location uncertainty. Firstly, the accurate ellipsoidal set containing the Lagrange remainder of nonlinear distance measurement, which contains the effect of anchor location uncertainty, is estimated and adopted to improve the stability and accuracy of the moving target tracking. Secondly, the recursive RSMFE-based tracking formulae of reduced computation complexity, consisting of the prediction formulae and the fusion update formulae, are derived to estimate the robust minimized or optimal ellipsoidal set containing the actual moving target state by appropriately fusing the measured distances from the nearby anchors subject to the UBB anchor location uncertainty, where the computation complexity of the fusion update formulae is reduced substantially based on the decoupled techniques and the equivalent transformation. Finally, the numerical simulation and experiment of the WSN-assisted moving target tracking system with various anchor location uncertainty settings are performed to verify the effectiveness and superiority of the proposed recursive RSMFE-based moving target tracking or localization method.

Data Fusion Algorithm For Wireless Sensor Networks Based On Cluster Routing And Deep Learning

In order to make full use of node energy and extend the network’s lifetime, a data fusion algorithm for wireless sensor networks is proposed in this paper. The algorithm improves the traditional clustering routing protocol and introduces a deep learning model to build the data fusion algorithm based on the optimized clustering architecture. When optimizing cluster routing protocol, the cluster head is selected by comprehensively considering such factors as node density, residual energy of nodes and the reasonable cluster size. The deep learning model and classification model are introduced in the data fusion process. The optimal parameters of the network are obtained by training the deep learning model of the sink node, then the sink nodes transfer these parameters to the corresponding sensor nodes. The cluster head extracts, classifies and fuses similar features of the data. The nodes in the cluster use the deep learning model to fit the collected raw data. Simulation results show that the data fusion algorithm proposed in this paper can reduce the redundant data transmitted to the sink node of wireless sensor networks, reduce the network energy consumption and prolong the network’s life. The overall performance of the network is improved.

Toward Sustainable Greenhouses Using Battery-Free LiFi-Enabled Internet of Things

As the world faces a changing climate, agriculture needs to develop more efficient and sustainable food production systems. Traditional farming methods consume considerable amounts of energy and are largely manually controlled, which leads to sub-optimal production. Green-houses, which enable year-round crop growth, can play an important role in efficient food production. Leveraging the need for artificial light in greenhouses when the natural sunlight available is not sufficient, we envision that recent progress in Internet of Things (IoT) technology, together with novel Light-Fidelity (LiFi)-based methods have the potential to significantly reduce energy and resources used in food production. In this article we describe our work towards sustainable and precision greenhouses by using LiFi-enabled IoT. Here we present a battery-free wireless network of IoT sensor nodes that exploit LiFi for both communication and power harvesting, while monitoring environmental conditions for optimal greenhouse operation and plant production. We highlight the research challenges and the way forward to integrate LiFi to monitor and control greenhouses, as well as a proof-of-concept LiFi-enabled IoT system for a real-world greenhouse.

Transfer Learning Approaches for Gait-Based Wireless Person Identification

The human gait is a biometric feature that is unique to any individual. Since the human body affects the propagation of wireless signals, wireless networks can be used for identification of persons walking nearby. This biometric identification approach has been object of research in several studies described in the literature. In this work, we present a gait-based wireless user identification system that outperforms these existing solutions in terms of cost, effort and accuracy. Firstly, using a mesh network of low-cost wireless sensor nodes, the identification error rate could be reduced by more than 80in comparison to the best of prior studies. For a set of six persons to be identified, the identification accuracy could thus be improved to more than 99. Secondly, using the technique of Transfer Learning, the capabilities of the proposed system can be easily transferred to identify unknown persons and locations with only a few minutes of training effort. Training the system on the identification of six unknown persons, it has been found that the training effort can be reduced by about 96without a significant loss of accuracy.

Data Acquisition for Estimating Energy-Efficient Solar-Powered Sensor Node Performance for Usage in Industrial IoT

In the era of rapid technological growth, we are facing increased energy consumption. The question of using renewable energy sources is also essential for the sustainability of wireless sensor networks and the Industrial Internet of Things, especially in scenarios where there is a need to deploy an extensive number of sensor nodes and smart devices in industrial environments. Because of that, this paper targets the problem of monitoring the operations of solar-powered wireless sensor nodes applicable for a variety of Industrial IoT environments, considering their required locations in outdoor scenarios and the efficient solar power harvesting effects. This paper proposes a distributed wireless sensor network system architecture based on open-source hardware and open-source software technologies to achieve that. The proposed architecture is designed for acquiring solar radiation data and other ambient parameters (solar panel and ambient temperature, light intensity, etc.). These data are collected primarily to define estimation techniques using nonlinear regression for predicting solar panel voltage outputs that can be used to achieve energy-efficient operations of solar-powered sensor nodes in outdoor Industrial IoT systems. Additionally, data can be used to analyze and monitor the influence of multiple ambient data on the efficiency of solar panels and, thus, powering sensor nodes. The architecture proposal considers the variety of required data and the transmission and storage of harvested data for further processing. The proposed architecture is implemented in the small-scale variants for evaluation and testing. The platform is further evaluated with the prototype sensor node for collecting solar panel voltage generation data with open-source hardware and low-cost components for designing such data acquisition nodes. The sensor node is evaluated in different scenarios with solar and artificial light conditions for the feasibility of the proposed architecture and justification of its usage. As a result of this research, the platform and the method for implementing estimation techniques for sensor nodes in various sensor and IoT networks, which helps to achieve edge intelligence, is established.

A Survey on the Design Aspects and Opportunities in Age-Aware UAV-Aided Data Collection for Sensor Networks and Internet of Things Applications

Due to the limitations of sensor devices, including short transmission distance and constrained energy, unmanned aerial vehicles (UAVs) have been recently deployed to assist these nodes in transmitting their data. The sensor nodes (SNs) in wireless sensor networks (WSNs) or Internet of Things (IoT) networks periodically transmit their sensed data to UAVs to be relayed to the base station (BS). UAVs have been widely deployed in time-sensitive or real-time applications, such as in disaster areas, due to their ability to transmit data to the destination within a very short time. However, timely delivery of information by UAVs in WSN/IoT networks can be very complex due to various technical challenges, such as flight and trajectory control, as well as considerations of the scheduling of UAVs and SNs. Recently, the Age of Information (AoI), a metric used to measure the degree of freshness of information collected in data-gathering applications, has gained much attention. Numerous studies have proposed solutions to overcome the above-mentioned challenges, including adopting several optimization and machine learning (ML) algorithms for diverse architectural setups to minimize the AoI. In this paper, we conduct a systematic literature review (SLR) to study past literature on age minimization in UAV-assisted data-gathering architecture to determine the most important design components. Three crucial design aspects in AoI minimization were discovered from analyzing the 26 selected articles, which focused on energy management, flight trajectory, and UAV/SN scheduling. We also investigate important issues related to these identified design aspects, for example, factors influencing energy management, including the number of visited sensors, energy levels, UAV cooperation, flight time, velocity control, and charging optimization. Issues related to flight trajectory and sensor node scheduling are also discussed. In addition, future considerations on problems such as traffic prioritization, packet delivery errors, system optimization, UAV-to-sensor node association, and physical impairments are also identified.

Research on Node Scheduling Algorithm of Sensor Network based on Support Vector Machine

In the current Internet era with information explosion, SN (Sensor network) has important scientific research value and social and economic value in many fields because of its micro-intelligence and huge and basic perceptual data. Ensuring the coverage quality of the network cannot be at the expense of network connectivity. The decrease of network connectivity is equivalent to the failure of some nodes in the network, and the resulting data collection hole will affect the judgment of end users. In this paper, the scheduling algorithm of SN nodes based on SVM (Support vector machine) is studied. Considering the excellent performance of EN (ensemble learning), a scheduling algorithm of EN-LS-SVM is proposed by introducing bagging and random subspace method. The research results show that the network lifetime of the proposed algorithm is extended by 180s and 13.28% compared with the traditional algorithm on the premise that the network coverage is not less than 80%. The algorithm proposed in this paper can reduce network coverage vulnerabilities, keep network coverage at a high level and ensure network QoS.