Sensor Network Nodes Research Articles

Enhanced Fuzzy Logic Zone Stable Election Protocol for Cluster Head Election (E-FLZSEPFCH) and Multipath Routing in wireless sensor networks

A Wireless Sensor Network (WSN) is a system of sensor nodes that communicate with one another and with their surroundings. Wireless sensor nodes are now used as the foundation for the higher-level industrial internet of things applications. Any type of real-time Internet of Things (IoT) application, as long as the data is collected utilizing the device's sensors. WSN are severely limited in terms of energy. Routing is one of the most challenging components of WSNs. Unbalanced energy utilization during data packet routing operations is one of the most serious difficulties with WSNs. This issue must be considered because the energy levels of sensor nodes are limited. We used WSNs multipath routing to save energy, improve scalability, and balance load. Many investigations are being undertaken to increase sensor node energy efficiency and network lifetime. More attention must be devoted to the clustering and routing components of communication in order to conserve energy. Clustering, also known as node clustering is a network organizing approach which helps with energy efficiency, workload distribution, data collection, and a connected hierarchy. The network is made up of numerous sensor nodes that must connect with one another in order for data to reach its goal, and when messages are not sent to intermediary nodes, problems arise. With multipath routing, numerous paths between the source and destination can be created, improving the possibility of data reaching the target and providing additional copies via alternate paths. The proposed routing solution uses less energy and extends the life of the network while compared with well-known approaches i.e., LEACH, ECPF, CHEF, UCR, DFLC, ACAWT, and Gupta. According to the simulation results and overall analysis, the proposed technique is much better than all the previous approaches under different number of nodes and simulation time in terms of end-to-end time reduced by 17.09% to 68.13%, energy consumption reduced by 13.23% to 29.12%, packet loss rate declined by 16.33% to 82.38% and drop rate of alive sensor nodes are much better shows high network resilience.

Node Scheduling Scheme for Wireless Sensor Networks with Partial Coverage

Sensor nodes in a wireless sensor network are distributed across an area for data collection. These nodes have basic capabilities in terms of interfaces and components, and they often operate in dynamic, hostile environments. Sensor networks present numerous challenges: they are dispersed, generate constant high rates data streams, function in dynamic and time-changing situations; and may involve a large number of sensors. Sensor nodes have enough power to transmit their readings to a central high-performance computing unit for processing. Sensor networks generate data streams, which are sequences of real-time data records characterized by their high data rates that consume significant network computing resources. However, only a few studies address the issue of collecting highly redundant data, leading to nodes wasting energy by sending redundant information to a central high-performance computing unit. Improved scheduling tactics can help reduce energy consumption in sensor nodes. This research developed a Node Scheduling Scheme for Wireless Sensor Networks with Partial Coverage (NSPC). Partial coverage can be obtained by dividing the area of interest into smaller sub-regions and determining the monitoring intensity for each sub-region by sensor nodes. Various strategies, such as clustering and scheduling, can be employed to accomplish partial coverage. Considering partial coverage when designing a WSN is crucial, as it can enhance network stability and reliability while reducing the cost and energy consumption of each sensor nodes.

BSAPM: BlockChain based secured authentication protocol for large scale WSN with FPGA implementation

According to state-of-the-art, several multiple base station-based authentication and key agreement protocols exist. Still, most of the protocols are computationally inefficient and suffer from several potential attacks. In this research work, we consider WSN architecture, where multiple base stations are located in a distributed way. After that, we proposed a blockchain-based authentication and key agreement protocol for establishing secure communication. This protocol utilizes a smart contract facility during the registration of the sensor node. Our work also stores records of the sensor node in blockchain network. Most importantly, we have verified the proposed protocol using FPGA implementation, which further confirms the correctness of the generated session keys. BSAPM is demonstrated in terms of security threats using the Scyther tool, and we have also shown formal security analysis using the Real-or-Random(ROR) model. The informal security analysis demonstrates the protection against security threats. BSAPM is better in terms of communication, computation overheads, and functionality features when compared to the related other competitive schemes.

Data monitoring for indirect metering terminal of membrane gas measure device based on sensor network

In order to improve the data monitoring and information management capabilities of membrane gas measure device indirect metering terminals, a sensor network based data monitoring method for membrane gas measure device indirect metering terminals was proposed. The distributed and centralized sensor network node deployment method is used to achieve data collection for membrane gas measure device indirect metering terminals, and the collected membrane gas measure device indirect metering terminal data is segmented, fused, and reorganized. The distributed energy storage and constant volume two-level optimal control method is used to analyze the operation status of gas measure device indirect metering terminal data. The ZigBee networking protocol is used to build a network structure model for gas measure device indirect metering terminals, an integrated high-precision temperature and pressure sensor arranged parallel to the inner wall of the valve body is used to detect the temperature and pressure at the outlet of the membrane gas measure device. The temperature information is stored in DSP, and the user's expenses, system operation and maintenance, and peak shaving and valley filling benefits are taken as the parameters of the energy storage and constant volume outer layer model. The output stability of the indirect metering terminal data of the membrane gas measure device is analyzed in the high switching frequency mode. Combined with adaptive parameter adjustment, the indirect metering terminal control and data dynamic collection of the membrane gas measure device are realized. Realize dynamic monitoring of metering terminal data in wireless sensor network networking control mode. The test results show that using this method for indirect metering terminal data monitoring of gas measure devices has good output stability, strong dynamic fusion of sensing information, reduced metering errors, and improved metering accuracy of gas measure devices.

A Study and Analysis of a New Hybrid Approach for Localization in Wireless Sensor Networks

Accurate localization of nodes in a wireless sensor network (WSN) is imperative for several important applications. The use of global positioning systems (GPS) for localization is the natural approach in most domains. In WSNs, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of the received signal strength indicator (RSSI) exist in literature, but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a machine learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up and follow it up with a feasibility demonstration through a prototypical implementation in the real world.

A detection method of wormhole attack in power communication sensor networks based on hops

Aiming at the problems of low accuracy of determining attack nodes and large detection error in the wormhole attack detection method of power communication sensor networks, a wormhole attack detection method based on hops is proposed. Firstly, the working process of power communication sensor network nodes is analyzed; Regard the node area as a closed circle, set the distance between nodes and communication radius in the area, and determine the wormhole attack location; Then, introduce the hop number, replace the linear distance between attack nodes with the hop distance of hop number, the centroid position is calculated by polygon calculation, the node hop distance is determined, and the wormhole attack probability is calculated; Finally, the average hop distance between wormhole attack nodes is calculated, and the path hop distance of attack behavior is corrected with the help of objective function and minimum mean square error criterion to complete attack detection. The experimental results show that the detection accuracy of this method is 98%, and the detection error is only 2.6%, so it has application value.

A Data-Driven Factor Graph Model for Anchor-Based Positioning.

This work presents a data-driven factor graph (FG) model designed to perform anchor-based positioning. The system makes use of the FG to compute the target position, given the distance measurements to the anchor node that know its own position.The aim was to design a hybrid structure (that involves data and modeling approaches) to address positioning models from a Bayesian point of view, customizing them for each technology and scenario. The weighted geometric dilution of precision (WGDOP) metric, which measures the effect on the positioning solution of distance error to the corresponding anchor node and network geometry of the anchor nodes, was taken into account. The presented algorithms were tested with simulated data and also with real-life data collected from IEEE 802.15.4-compliant sensor network nodes with a physical layer based on ultra-wide band (UWB) technology, in scenarios with one target node, three and four anchor nodes, and a time-of-arrival-based range technique. The results showed that the presented algorithm based on the FG technique provided better positioning results than the least squares-based algorithms and even UWB-based commercial systems in various scenarios, with different setups in terms of geometries and propagation conditions.

Low-cost, high-resolution and no-manning distributed sensing system for the continuous monitoring of fruit growth in precision farming

Accurate, continuous and reliable data gathering and recording about crop growth and state of health, by means of a network of autonomous sensor nodes that require minimal management by the farmer will be essential in future Precision Agriculture. In this paper, a low-cost multi-channel sensor-node architecture is proposed for the distributed monitoring of fruit growth throughout the entire ripening season. The prototype presented is equipped with five independent sensing elements that can be attached each to a sample fruit at the beginning of the season and are capable of estimating the fruit diameter from the first formation up to the harvest. The sensor-node is provided with a LoRa transceiver for wireless communication with the decision making central, is energetically autonomous thanks to a dedicated energy harvester and an accurate design of power consumption, and each measuring channel provides sub-mm 9.0-ENOB effective resolution with a full-scale range of 12 cm. The accurate calibration procedure of the sensor-node and its elements is described in the paper, which allows for the compensation of temperature dispersion, noise and non-linearities. The prototype was tested on field in real application, in the framework of the research activity for next-generation Precision Farming performed at the experimental farm of the Department of Agricultural and Food Science of the University of Bologna, Cadriano, Italy.

Implementation framework for energy efficient routing in wireless sensor network

In recent years, designing of WSN becomes the leading domain for many researchers. In this paper an automaton system is suggested that dynamically monitors and studies the behaviors of the network environment to detect the malicious activities that occur in the network, to identify the attacked nodes and dead nodes in the wireless sensor network, and to transmit the message in the optimized route. The automaton system prevents the attacked and dead nodes from affecting the wireless sensor network. Computational Intelligence technique such as the PSO algorithm is used along with the automaton system so as to obtain the optimized route from the set of available paths. Once the optimized route is obtained the message is transmitted through this optimized route. The transmission takes place at a high rate since the path does not contain dead and attacked nodes, as well as the energy consumed by the optimal path will be minimal. Thus, the proposed model improves the utilization of energy, improves throughput, reduces end-to-end delay, and enhances the lifetime of the network.

An Energy-Saving and Efficient Deployment Strategy for Heterogeneous Wireless Sensor Networks Based on Improved Seagull Optimization Algorithm.

The Internet of Things technology provides convenience for data acquisition in environmental monitoring and environmental protection and can also avoid invasive damage caused by traditional data acquisition methods. An adaptive cooperative optimization seagull algorithm for optimal coverage of heterogeneous sensor networks is proposed in order to address the issue of coverage blind zone and coverage redundancy in the initial random deployment of heterogeneous sensor network nodes in the sensing layer of the Internet of Things. Calculate the individual fitness value according to the total number of nodes, coverage radius, and area edge length, select the initial population, and aim at the maximum coverage rate to determine the position of the current optimal solution. After continuous updating, when the number of iterations is maximum, the global output is output. The optimal solution is the node's mobile position. A scaling factor is introduced to dynamically adjust the relative displacement between the current seagull individual and the optimal individual, which improves the exploration and development ability of the algorithm. Finally, the optimal seagull individual position is fine-tuned by random opposite learning, leading the whole seagull to move to the correct position in the given search space, improving the ability to jump out of the local optimum, and further increasing the optimization accuracy. The experimental simulation results demonstrate that, compared with the coverage and network energy consumption of the PSO algorithm, the GWO algorithm, and the basic SOA algorithm, the coverage of the PSO-SOA algorithm proposed in this paper is 6.1%, 4.8%, and 1.2% higher than them, respectively, and the energy consumption of the network is reduced by 86.8%, 68.4%, and 52.6%, respectively. The optimal deployment method based on the adaptive cooperative optimization seagull algorithm can improve the network coverage and reduce the network cost, and effectively avoid the coverage blind zone and coverage redundancy in the network.

WLARS: Workload-Aware Recharge Scheduling Mechanism for Improving Surveillance Quality in Wireless Rechargeable Sensor Networks

With radio frequency (RF), the mobile charger (MC) can wirelessly transmit energy to the sensor nodes in the network. The wireless energy transfer enhances the lifetime of sensor nodes in wireless rechargeable sensor networks (WRSNs). Most of the studies improved the efficiency of MC or perpetuated the lifetime of WRSNs. At the same time, none of the studies focused on considering the spatial and temporal surveillance qualities (STSQ) of each sensor recharged. Therefore, this paper proposed an efficient energy recharge scheduling for MC, aiming to maximize the STSQ of the given network. Initially, sensor routing load (RL) is considered to partition the network to distribute the charging load of the MCs evenly. Second, the busy level of the MC is considered to send the recharging requests to the MC. Based on the busy level of MC, the sensors adjust the sensing rate frequency to manage their energy. Finally, cooperation between the neighboring MCs is proposed to reduce the waiting time for recharging requested sensors. The simulation results present that the proposed work yields the literature in terms of STSQ, traveling distance, average waiting time and energy usage efficiency.

A distributed beamforming interference minimization algorithm based on node selection

Distributed beamforming uses nodes in a wireless sensor network to transmit signals in different phases with controllable delay, to obtain coherent output signals with a gain after superposition. However, a wireless sensor network has a large topological area and wide distribution range, and it is difficult for distributed beamforming to obtain a highly directional beam as centralized beamforming does, which will cause interference to the non-target base stations. To solve this problem, a discrete adaptive dual-population cooperative differential evolution (DPCDE) algorithm is proposed, which can effectively reduce the interference by selecting nodes suitable for participating in distributed beamforming in a wireless sensor network. Simulation results show that the proposed algorithm can optimize the node set participating in distributed beamforming to minimize the interference of the wireless sensor network to the non-target base stations, and the effect is better than other classic intelligent optimization algorithms.

IoT-based smart monitoring and management system for fish farming

Fish farming is still controlled and managed in the traditional way where water quality and fish feeding are manually controlled. There is a need to use computer and communication technology in fish farms for remote monitoring and control. This paper deals with the design and implementation of an internet of things (IoT) based system for real-time monitoring, control and management of fish farming. The design of such a system is based on measuring different types of variables and using the information to control fish growth and increase productivity. Each fish pond is a node in a wireless sensor network. The node contains an embedded microcontroller connected to a set of sensors and actuators and a wireless communication module. Two fuzzy controllers are designed to control the water quality in the ponds as well as the environment using five sensors in each pond plus three environmental sensors. Practical results indicate the accuracy of the measurement system compared to the results obtained from commercial devices used on the farm. These results also showed that the proposed approach achieves the best performance of the real-time monitoring and control system in fish ponds.

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.