Autonomous Sensor Nodes Research Articles

Enhancing precision in table tennis officiating through an optimized autonomous sensor node system

PurposeThe purpose of the study is to address concerns regarding the subjectivity and imprecision of decision-making in table tennis refereeing by developing and enhancing a sensor node system. This system is designed to accurately detect the points on the table tennis table where balls collide. The study introduces the twined-reinforcement chimp optimization (TRCO) framework, which combines two novel approaches to optimize the distribution of sensor nodes. The main goal is to reduce the number of sensor units required while maintaining high accuracy in determining the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through complex optimization procedures, the study aims to improve the efficiency and reliability of decision-making in table tennis refereeing by leveraging sensor technology.Design/methodology/approachThe study employs a design methodology focused on developing a sensor array system to enhance decision-making in table tennis refereeing. It introduces the twined-reinforcement chimp optimization (TRCO) framework, combining dual adaptive weighting strategies and a stochastic approach for optimization. By meticulously engineering the sensor array and utilizing complex optimization procedures, the study aims to improve the accuracy of detecting ball collisions on the table tennis table. The methodology aims to reduce the number of sensor units required while maintaining high precision, ultimately enhancing the reliability of decision-making in the sport.FindingsThe optimization research study yielded promising outcomes, showcasing a substantial reduction in the number of sensor units required from the initial count of 60 to a more practical 49. The sensor array system demonstrated excellent accuracy in identifying the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through the implementation of the twined-reinforcement chimp optimization (TRCO) framework, which integrates dual adaptive weighting strategies and a stochastic approach, the study achieved its goal of enhancing the efficiency and reliability of decision-making in table tennis refereeing.Originality/valueThis study introduces novel contributions to the field of table tennis refereeing by pioneering the development and optimization of a sensor array system. The innovative twined-reinforcement chimp optimization (TRCO) framework, integrating dual adaptive weighting strategies and a stochastic approach, sets a new standard for sensor node distribution in sports technology. By substantially reducing the number of sensor units required while maintaining high accuracy in detecting ball collisions, this research offers practical solutions to address the inherent subjectivity and imprecision in decision-making processes. The study’s originality lies in its meticulous design methodology and complex optimization procedures, offering significant value to the field of sports technology and officiating.

Energy-aware bio-inspired spiking reinforcement learning system architecture for real-time autonomous edge applications.

Mobile, low-cost, and energy-aware operation of Artificial Intelligence (AI) computations in smart circuits and autonomous robots will play an important role in the next industrial leap in intelligent automation and assistive devices. Neuromorphic hardware with spiking neural network (SNN) architecture utilizes insights from biological phenomena to offer encouraging solutions. Previous studies have proposed reinforcement learning (RL) models for SNN responses in the rat hippocampus to an environment where rewards depend on the context. The scale of these models matches the scope and capacity of small embedded systems in the framework of Internet-of-Bodies (IoB), autonomous sensor nodes, and other edge applications. Addressing energy-efficient artificial learning problems in such systems enables smart micro-systems with edge intelligence. A novel bio-inspired RL system architecture is presented in this work, leading to significant energy consumption benefits without foregoing real-time autonomous processing and accuracy requirements of the context-dependent task. The hardware architecture successfully models features analogous to synaptic tagging, changes in the exploration schemes, synapse saturation, and spatially localized task-based activation observed in the brain. The design has been synthesized, simulated, and tested on Intel MAX10 Field-Programmable Gate Array (FPGA). The problem-based bio-inspired approach to SNN edge architectural design results in 25X reduction in average power compared to the state-of-the-art for a test with real-time context learning and 30 trials. Furthermore, 940x lower energy consumption is achieved due to improvement in the execution time.

Energy‐efficient optimal sink placement using extended pelican optimization‐based clustering with Voronoi‐based node deployment

SummaryA wireless sensor network (WSN) is a network of spatially distributed autonomous sensor nodes that collaborate to monitor physical or environmental conditions, collect data, and transmit it to a sink node. WSNs have a wide range of applications across various domains due to their ability to provide real‐time data collection, remote monitoring, and data analysis. Still, in a WSN with a fixed sink, sensor nodes closer to the sink tend to have higher traffic loads because they forward data to nodes further away. This can lead to hotspots and uneven energy consumption. Introducing a mobile sink can distribute the traffic more evenly across the network, reducing congestion and balancing the energy consumption among nodes. Hence, this research proposes a novel WSN environment with a focus on energy‐efficient routing. The network is deployed using Voronoi‐based criteria to address network coverage issues. The clustering of nodes is employed using the proposed extended pelican optimization (ExPo) algorithm to improve network lifetime and energy efficiency, critical concerns in WSNs due to limited sensor node battery capacity. Cluster heads (CHs) aggregate and process data locally, reducing the energy needed for long‐range communication. Then, an energy‐efficient optimal sink placement (EEOSP) approach is used to optimize the placement of the mobile sink. The proposed system model is evaluated based on various metrics, including average residual energy, delay, network lifetime, packet delivery ratio, and throughput and acquired the values of 0.99 J, 3.68 ms, 99.55%, 99.55%, and 81 Mbps, respectively.

Self-powered wireless sensor node enabled by ultra-high-output swinging hybrid generator toward real-time and in-situ marine meteorological observations

With the intensification of global ocean exploration, there is an increasing need for extensive and dispersed wireless sensor networks (WSNs) to meet the requirements of real-time, in-situ, and high-spatial-temporal-density marine meteorological observations. Nevertheless, providing a reliable power source and ensuring the ongoing maintenance of these WSNs pose significant challenges. Here, an innovative self-powered wireless sensor node is developed to address these challenges, featuring an ultra-high-output triboelectric-electromagnetic hybrid generator (T-EHG) with a swinging mechanism. The T-EHG, which combines triboelectric (TENG) and electromagnetic (EMG) generation, assisted by a universal power management system (UPMS), efficiently converts wave energy into a consistent voltage output between 1.5 V to 3.3 V. Additionally, the self-powered WSNs integrated with T-EHG, UPMS circuits, meteorological sensors, and microcontroller units (MCUs) with LoRa wireless transmission functionality are capable of executing data collection and transmission operations every 10 s under simulated wave conditions. These systems can achieve a maximum wireless transmission range of 1.5 km. Notably, a marine meteorological monitoring system utilizes these autonomous sensor nodes, along with base stations, cloud servers, and terminal devices to effectively implement real-time, on-site measurements of temperature, humidity, and air pressure within the monitored zone. This research underscores the substantial practical utility and extensive application prospects of these advanced self-powered WSNs for marine meteorological surveillance.

Efficient Integration of Ultra-low Power Techniques and Energy Harvesting in Self-Sufficient Devices: A Comprehensive Overview of Current Progress and Future Directions.

Compact, energy-efficient, and autonomous wireless sensor nodes offer incredible versatility for various applications across different environments. Although these devices transmit and receive real-time data, efficient energy storage (ES) is crucial for their operation, especially in remote or hard-to-reach locations. Rechargeable batteries are commonly used, although they often have limited storage capacity. To address this, ultra-low-power design techniques (ULPDT) can be implemented to reduce energy consumption and prolong battery life. The Energy Harvesting Technique (EHT) enables perpetual operation in an eco-friendly manner, but may not fully replace batteries due to its intermittent nature and limited power generation. To ensure uninterrupted power supply, devices such as ES and power management unit (PMU) are needed. This review focuses on the importance of minimizing power consumption and maximizing energy efficiency to improve the autonomy and longevity of these sensor nodes. It examines current advancements, challenges, and future direction in ULPDT, ES, PMU, wireless communication protocols, and EHT to develop and implement robust and eco-friendly technology solutions for practical and long-lasting use in real-world scenarios.

A self-powered accelerometer with over-range detection for vibration and shock based on triboelectric-electromagnetic mechanism

Accelerometers based on triboelectric nanogenerators (TENGs) have garnered significant interest in recent years. However, many of these devices lack a self-sustained power supply for autonomous wireless operations and a mechanism for over-range detection. Additionally, theoretical investigations on the sensitivity of TENG-based accelerometers are scarce. In this work, a novel self-powered accelerometer for vibration and shock integrating a freestanding triboelectric-layer-based TENG (FT-TENG), two contact-separation TENGs (CS-TENGs) and an electromagnetic generator (EMG) is proposed and then investigated through both numerical simulations and experimental demonstrations. The EMG is used for vibration energy harvesting, exhibiting an impressive power output of 4.11 mW under a vibration acceleration of 12 m/s2. The FT-TENG and the CS-TENGs work for acceleration sensing and over-range detection respectively. Under vibration, the sensitivity and measurement range are related to the frequency of excitation, and the proposed accelerometer has the maximum sensitivity (0.75 V s2/m) at its natural frequency. For shock events, the sensitivity of the accelerometer is dominated by the shock duration in a short-duration range, which is estimated to be 0.12 V s2/m under a shock duration of 0.03 s. This work not only contributes to the understanding of TENGs adopted for sensing but also creates potential applications of triboelectric and electromagnetic energy harvesting and provides a feasible design for autonomous wireless sensor nodes.

Low-cost IoT-based monitoring system for precision agriculture

The increasing impact of climate change on agriculture necessitates advanced monitoring and management of environmental conditions to ensure sustainable agricultural productivity. This paper introduces a cost-effective, Internet of Things (IoT)-based smart monitoring system designed to provide real-time insights into soil moisture levels and weather conditions across various segments of a single agricultural plot. The system comprises autonomous wireless sensor nodes, a comprehensive weather station, and a centralized base station that collectively capture, process, and relay environmental data to a user-friendly mobile application. Our empirical results demonstrate that this system not only facilitates efficient environmental data monitoring and analysis but also empowers farmers with actionable intelligence for timely decision-making. The proposed model showcases a promising avenue for enhancing agricultural resilience and productivity through technology-driven precision farming.

SMEOR: Sink mobility‐based energy‐optimized routing in energy harvesting‐enabled wireless sensor network

SummaryFor accurate environmental monitoring, a wireless sensor network (WSN) uses a collection of autonomous sensor nodes (SNs) that are dispersed at random across a given area. WSNs are used for various applications that require real‐time data. While the SNs in a WSN are limited in their ability to generate energy, the routing process is seen as a vital means of improving the network's ability to conserve resources and prolong its useful life. In this research work, we present sink mobility‐based energy‐optimized routing (SMEOR) in energy harvesting (EH)‐enabled WSN. While following the cluster‐based routing, the election of cluster head (CH) is executed using our proposed extended spotted hyena Lévy flight optimization (ESHLFO) algorithm. Furthermore, to address the energy‐hole problem, four data‐collection energy‐unlimited nodes are used around the periphery of the network. The sink moves from one EH‐enabled node to another to collect data. Further, security in SMEOR is ensured by encrypting the data using a stream cipher and a stochastically generated security key. It is evident from the simulation analysis that SMEOR delivers network longevity and supreme performance.

Chaotic Mapping Lion Optimization Algorithm-Based Node Localization Approach for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%.

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.

Analysis of Influencing Parameters Enhancing the Plucking Efficiency of Piezoelectric Energy Harvesters.

The integration of energy harvesting systems into sensing technologies can result in novel autonomous sensor nodes, characterized by significant simplification and mass reduction. The use of piezoelectric energy harvesters (PEHs), particularly in cantilever form, is considered as one of the most promising approaches aimed at collecting ubiquitous low-level kinetic energy. Due to the random nature of most excitation environments, the narrow PEH operating frequency bandwidth implies, however, the need to introduce frequency up-conversion mechanisms, able to convert random excitation into the oscillation of the cantilever at its eigenfrequency. A first systematic study is performed in this work to investigate the effects of 3D-printed plectrum designs on the specific power outputs obtainable from FUC excited PEHs. Therefore, novel rotating plectra configurations with different design parameters, determined by using a design-of-experiment methodology and manufactured via fused deposition modeling, are used in an innovative experimental setup to pluck a rectangular PEH at different velocities. The obtained voltage outputs are analyzed via advanced numerical methods. A comprehensive insight into the effects of plectrum properties on the responses of the PEHs is attained, representing a new and important step towards the development of efficient harvesters aimed at a wide range of applications, from wearable devices to structural health monitoring systems.

Energy Aware Trajectory Optimization of Solar Powered AUVs for Optical Underwater Sensor Networks

Visible light communication (VLC) provides an alternative underwater wireless connectivity solution with its low latency and high data rates albeit at relatively shorter distances in the order of tens of meters. In the context of underwater sensor networks (USNs), VLC is particularly suitable to establish connectivity between “data mule” autonomous underwater vehicles (AUVs) and sensor nodes since communications is enabled only when the sensor node and mule AUV are in close proximity. In this paper, we consider a USN scenario where a solar-powered AUV gathers data from the sensor nodes using VLC signaling. We formulate a three-dimensional trajectory optimization for solar-powered AUVs with the goal of maximizing the harvested energy under constraints imposed by the data transmission. The optimization constraints include the minimum required data transfer rate, therefore a corresponding transmission distance, between the sensors and the AUV. We formulate the problem as a bilevel optimization problem. The lower-level objective function is in the form of traveling salesman problem which determines the optimum sequence order of the sensor nodes to be visited while the upper-level objective function is the optimization of the trajectory between each pair of adjacent nodes for the given order of node visits. Our numerical results demonstrate that the proposed trajectory significantly prolongs the mission time and autonomous operation of the AUV without the need to return to home base. Furthermore, since the proposed trajectory optimization is reactive to ocean currents, it brings reductions in the energy consumption of the AUVs.

Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review.

In this paper, a detailed review of microcontroller unit (MCU)-based wireless sensor node platforms from recently published research articles is presented. Despite numerous research efforts in the fast-growing field of wireless sensor devices, energy consumption remains a challenge that limits the lifetime of wireless sensor networks (WSNs). The Internet-of-Things (IoT) technology utilizes WSNs for providing an efficient sensing and communication infrastructure. Thus, a comparison of the existing wireless sensor nodes is crucial. Of particular interest are the advances in the recent MCU-based wireless sensor node platforms, which have become diverse and fairly advanced in relation to the currently available commercial WSN platforms. The recent wireless sensor nodes are compared with commercially available motes. The commercially available motes are selected based on a number of criteria including popularity, published results, interesting characteristics and features. Of particular interest is to understand the trajectory of development of these devices and the technologies so as to inform the research and application directions. The comparison is mainly based on processing and memory specifications, communication capabilities, power supply and consumption, sensor support, potential applications, node programming and hardware security. This paper attempts to provide a clear picture of the progress being made towards the design of autonomous wireless sensor nodes to avoid redundancy in research by industry and academia. This paper is expected to assist developers of wireless sensor nodes to produce improved designs that outperform the existing motes. Besides, this paper will guide researchers and potential users to easily make the best choice of a mote that best suits their specific application scenarios. A discussion on the wireless sensor node platforms is provided, and challenges and future research directions are also outlined.

Smart Wireless Particulate Matter Sensor Node for IoT-Based Strategic Monitoring Tool of Indoor COVID-19 Infection Risk via Airborne Transmission

Indoor and outdoor air pollution are associated with particulate matter concentration of minute size that deeply penetrates the human body and leads to significant problems. These particles led to serious health problems and an increased spread of infection through airborne transmission, especially during the COVID-19 pandemic. Considering the role of particulate matter during the spread of COVID-19, this paper presents a smart wireless sensor node for measuring and monitoring particulate matter concentrations indoors. Data for these concentrations were obtained and used as a risk indicator for airborne COVID-19 transmission. The sensor node was designed to consider air quality monitoring device requirements for indoor applications, such as real-time, continuous, reliable, remote, compact-sized, low-cost, low-power, and accessible. Total energy consumption of the node during measurement and monitoring of particulate matter concentration was minimized using a low-power algorithm and a cloud storage system embedded during software development. Therefore, the sensor node consumed low energy for one cycle of the particulate matter measurement process. This low-power strategy was implemented as a preliminary design for the autonomous sensor node that enables it to integrate with an energy harvester element to harvest energy from ambient (light, heat, airflow) and store energy in the supercapacitor, which extends the sensor node life. Furthermore, the measurement data can be accessed using the Internet of Things and visualized graphically and numerically on a graphical user interface. The test and measurement results showed that the developed sensor node had very small measurement error, which was promising and appropriate for indoor particulate matter concentration measurement and monitoring, while data results were utilized as strategic tools to minimize the risk of airborne COVID-19 transmission.

Smart Wireless CO2 Sensor Node for IoT Based Strategic Monitoring Tool of The Risk of The Indoor SARS-CoV-2 Airborne Transmission

A close correlation between CO2 concentration and aerosol enables the wide utilization of CO2 concentration as a good representation of Severe Acute Respiratory Syndrome-Coronavirus-2 infection airborne transmission. On the other side, many indoor air-quality monitoring devices have been developed for indoor monitoring applications. However, most of them are multiparameter air-quality sensor systems and tend to consume relatively high power, are relatively large devices, and are fairly expensive; therefore, they not meet the requirement for indoor monitoring applications. This paper presents a smart wireless sensor node that can measure and monitor CO2 concentration levels. The node was designed to meet the requirements of indoor air-quality monitoring applications by considering several factors, such as compact size, low cost, and low power, as well as providing real-time, continuous, reliable, and remote measurement. Furthermore, the commercial off-the-shelf and low-power consumption components are chosen to fit with the low-cost development and reduce energy consumption. Moreover, a low-power algorithm and cloud-based data logger also were applied to minimize the total power consumption. This power strategy was applied as a preliminary development toward an autonomous sensor node. The node has a compact size and consumes low energy for one cycle of CO2 measurement, accompanied by high accuracy with very low measurement error. The experiment result revealed the node could measure and monitor in real-time continuous, reliable, and remote CO2 concentration levels in indoor and outdoor environments. A user interface visualizes CO2 concentration graphically and numerically using the Adafruit platform for easy accessibility over the Internet of Things. The developed node is very promising and suitable for indoor CO2 monitoring applications with the acquired data that could be utilized as an indicator to minimize the risk of indoor Severe Acute Respiratory Syndrome-Coronavirus-2 airborne transmission.

Power Management Unit for Solar Energy Harvester Assisted Batteryless Wireless Sensor Node

This work describes an energy-efficient monolithic Power Management Unit (PMU) that includes a charge pump adapted to photovoltaic cells with the capability of charging a large supply capacitor and managing the stored energy efficiently to provide the required supply voltage and power to low energy consumption wireless sensor nodes such as RFID sensor tags. The proposed system starts-up self-sufficiently with a light source luminosity equal to or higher than 500 lux using only a 1.42 cm2 solar cell and integrating an energy monitor that gives the ability to supply autonomous sensor nodes with discontinuous operation modes. The system occupies an area of 0.97 mm2 with a standard 180 nm CMOS technology. The half-floating architecture avoids losses of charging the top/button plate of the stray capacitors in each clock cycle. Measurements’ results on a fabricated IC exhibit an efficiency above 60% delivering 13.14 W over 1.8 V. The harvested energy is enough to reach the communication range of a standard UHF RFID sensor tag up to 21 m.

Influence of the operation strategy on the energy consumption of an autonomous sensor node

Abstract. Motivated by the application of industry 4.0 and Internet of Things (IoT) technologies, the development of cyber physical systems (CPS) is gaining momentum. As CPS require multiple measurement technologies to drive the intended function, e.g., condition monitoring and in situ measurement, the integration of measurement systems into industrial processes or individual products becomes a critical activity within the development process. Development methods like the V-Model support developers with methodological guidelines, but the related methods and models do not provide sufficient information regarding the energy supply of embedded systems. If the measurement system, consisting of sensor, calculation and communication unit is integrated inside an either sealed or moveable system, e.g., in a gearbox, ensuring a reliable communication and energy supply is a challenging task. This contribution therefore focuses on the energy supply, in particular the electric power consumption of autarchic measurement systems, referred to as sensor nodes. Based on a literature review of existing physical principles determining the energy consumption of semiconductors, a simple estimation model is derived. Estimation models in the current literature mainly focus on the effects of source code or software in general without analyzing a possible impact of operation strategies, such as generic data processing logics in practical applications. The model presented in this contribution is therefore used to identify the energy consumption of sensor nodes influenced by ambient and operating conditions of sensor nodes. Strategies are examined experimentally using an exemplary sensor node, a climatic chamber and a sensor-integrated gearbox as the system to be observed. An analysis of the conducted experiments leads to a more precise model, which is evaluated regarding its significance for predicting the energy consumption and the underlying simplifications. Finally, general relations influencing the energy consumption are presented and necessary research suggested.

Real-Time Data Analytics for Monitoring Electricity Consumption Using IoT Technology

Rising electricity bills as a result of climate variability and new home electrical and electronic appliances are becoming a major source of concern for most end users. Consumers are typically unaware of their household electricity consumption patterns and the costs associated with them, making proper planning and budgeting difficult. Monitoring and controlling energy consumption on appliances can reduce energy costs for end-users. The Internet of Things (IoT) has the potential to provide remote monitoring and control of devices via automated monitoring and control. In this study, we propose an IoT-enabled system for monitoring and controlling energy consumption in homes to save money and make electricity more affordable to low-income people. Autonomous sensor nodes attached to power outlets collect and route power consumption data to the GSM-enabled gateway. Data aggregation is performed by the gateway for data received from all end-nodes within its coverage area. The gateway sends the data to the cloud, where real-time data analytics is performed. We also developed an algorithm that enables users to not only understand their usage patterns but also remotely control their appliances at any time. Mobile phones are used to remotely turn on and off household appliances via a machine-to-machine interface, as well as to provide real-time visualization of household energy consumption patterns.

Energy efficient chaotic sparrow search algorithm based clustering protocol for wireless sensor network communications

Wireless sensor networks (WSN) include a collection of autonomous sensor nodes, which are generally deployed in a harsh or hostile environment to sense the physical parameters involved in the target region. It is highly utilized for target tracking, surveillance, and data gathering applications. Since the sensors in WSN operates on limited inbuilt batteries, it is needed to design energy efficient approaches to preserve energy and extend lifetime of the network. Earlier studies have reported that clustering is an effectual manner used for accomplishing energy efficiency and it is addressed with use of metaheuristic optimization algorithms. In this view, this paper present an energy efficient chaotic sparrow search algorithm based clustering (EE-CSSAC) technique for WSN. The proposed EE-CCSAC technique is dependent upon the integration of chaotic maps into the traditional sparrow search algorithm (SSA). In addition, the EE-CCSAC technique develops a fitness function with four variables to properly select the CHs and then organize clusters, thereby achieving energy efficiency. The experimental validation of the EE-CCSAC system takes place and the outcomes are related to recent clustering approaches. The simulation outcomes exhbited the effective outcome of the EE-CCSAC method on the other ones with respec to distinct estimation parameters.

Detecting Sinkhole Attacks in IoT-Based Wireless Sensor Networks Using Distance From Base Station

Wireless sensor networks (WSNs) are infrastructure-less in nature, which contains numbers of autonomous sensor nodes. These sensor nodes are dedicated to monitoring the physical conditions of the environment and are organizing the collected data at a central location. Application area of WSN, like - heath care, military surveillance, are sensitive with respective to information sensed, that’s why security of WSN needs to be very effective. Providing security to WSN plays a major role as it consists of limited resources. The security system should lie within the boundary of the resource potential as well as should be competent enough to handle attacks. Intrusion detection system (IDS) is one such type of defense system, which can fulfill the measure of limitation of resources. In this paper, a detection technique is proposed against sinkhole attack using the Euclidean distance of each node from base station. The main advantage of the proposed technique is that it doesn't require any extra hardware setup as well as doesn't require extra communication cost.