Sensor Network Platform Research Articles

Application of IoT Android voice assistant based on sensor networks in higher education network mode

In order to make full use of the potential of wireless sensor networks in higher education, this study aims to provide convenient intelligent learning and management functions by constructing an iot Android voice assistant based on wireless sensor networks. The system structure of Android voice assistant based on wireless sensor network is researched, designed and implemented. Through the integration of wireless sensor network and school network platform, the data collection, transmission and analysis functions are realized. The results show that the Android voice assistant based on wireless sensor network effectively provides intelligent learning and management functions in the network mode of higher education. Students and teachers can realize course inquiry, resource acquisition, teaching evaluation and other operations through voice commands to improve learning and teaching efficiency. By combining the data acquisition capabilities of sensors and wireless transmission capabilities, the system provides students, teachers and school administrators with intelligent learning and management functions, which is expected to promote the further development of higher education network environment.

Research Progress of SHM System for Super High-Rise Buildings Based on Wireless Sensor Network and Cloud Platform

In recent years, the number of super high-rise buildings is increasing due to the rapid development of economy and construction technology. It is important to evaluate the health condition of super high-rise buildings to make them operate safely. However, conventional structural health monitoring (SHM) system requires a great number of wires to connect the sensors, power sources, and the data acquisition equipment, which is an extremely difficult process to plan the layout of all wires. Hence, one of the usually used compromising approaches is to limit the number of sensors to reduce the usage of wires. Recently, wireless sensor networks and cloud platform have been widely used in SHM system for super high-rise buildings because of their convenient installation, low maintenance cost, and flexible deployment. This paper presents a comprehensive review of the existing SHM system for super high-rise buildings based on wireless sensor network and cloud platform, which usually consists of sensing network subsystem, data acquisition subsystem, data transmission subsystem, and condition evaluation subsystem. This paper also reviews the crucial techniques and typical examples of SHM system used for famous super high-rise buildings. In addition, the existing difficulties in wireless sensor network and cloud platform based SHM system for super high-rise buildings and the future research directions are discussed and summarized.

A Deep-DrpXML and IAG-GWO based CHST fostered blockchain technology for secured dynamic optimal routing for wireless sensor networks

In wireless sensor network (WSN), routing is one of the substantial maneuvers for distributing data packets to the base station. But malevolent node outbreaks will happen during routing process, which exaggerate the wireless sensor network operations. Therefore, a secure routing protocol is required, which safeguards the routing fortification and the wireless sensor network effectiveness. The existing routing protocol is dynamically volatile during real time instances, and it is very hard to recognize the unsecured routing node performances. In this manuscript, a Deep Dropout extreme Machine learning optimized Improved Alpha-Guided Grey Wolf based Crypto Hash Signature Token fostered Blockchain Technology is proposed for secure dynamic optimal routing in Wireless Sensor Networks (SDOR-DEML-IAgGWO-CHS-BWSN). In this, Crypto Hash signature (CHS) token are generated for flow accesses with a secret key owned by each routing sensor node and it also offers an optimal path for data transmission. Then the secured dynamic optimal routing information is delivered through the proposed Blockchain based wireless sensor network platform with the help of Deep Dropout Extreme Machine learning optimized Improved Alpha-Guided Grey Wolf routing algorithm. Then the proposed method is simulated using the NS-2 (Network Simulator) tool. The simulation performance of the proposed SDOR-DEML-IAgGWO-CHS-BWSN method provide 76.26%, 65.57%, 60.85%, 48.99% and 42.9% lower delay during 30% malicious routing environment, 73.06%, 63.82%, 59.25%, 44.79% and 38.84% lower delay during 60% malicious routing environment is compared with the existing methods.

Bi-level energy management model for the smart grid considering customer behavior in the wireless sensor network platform

Today, smart grids (SG) and its relationship to wireless sensor network (WSN) provides a suitable platform for establishing the two-way communication of the energy management system and network users. This issue can accurately plan the smart grid in real time with minimum cost. This paper proposes a bi-level energy management model for smart grids in uncertainty and demand-side management (DSM) in the Internet of things (IoT) platform based on WSN. In the upper level, for accurate planning in real-time and relationship with energy management system, the customers are modelled in wireless sensor network platform and clustered by fuzzy C-Means algorithm. Then, the energy consumption of the network sensors in the IoT platform is optimized by the genetic algorithm (GA). Moreover, the customer behavior optimization was performed utilizing the price-based demand response (PBDR) model and transferring demand load from the peak to the valley load times. The objective functions in the lower layer minimized the emission pollution, operation costs and energy not supplied (ENS) index using the multi-objective Moth–Flame optimization (MOMFO) algorithm. The Monte Carlo technique is used for modeling renewable energy sources (RES)'s uncertain output. Finally, the suggested approach is confirmed through the analysis of a case study. The proposed model's capability in planning a uniform smart grid operation is demonstrated by simulation study and comparison with multi-objective particle swarm optimization (MOPSO), epsilon-constraint and benders techniques.

Deployment of Wireless Sensor Network and IoT Platform to Implement an Intelligent Animal Monitoring System

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Hybrid optimized convolutional neural network for efficient classification of ECG signals in healthcare monitoring

For the prediction of cardiovascular diseases, a wireless sensor network platform that employs a wireless sensor network-enabled electrocardiogram telemetry system will involve the following steps: the electrocardiogram signal’s acquisition, the electrocardiogram signal’s processing, and alerting the physician in case of any emergencies. This system will aid the physician in early as well as an accurate analysis of heart diseases. Currently, a wireless sensor network enabled electrocardiogram monitoring system is being developed for analyzing the electrocardiogram signal. In the past, the convolutional neural networks have been employed for resolving diverse artificial intelligence problems with good outcomes. Even so, designing the convolutional neural networks architecture continues to be a cumbersome as well as meticulous procedure that needs the field experts’ involvement. This work has explored the neuro-evolution application to the automatic design of convolutional neural networks topologies and also has devised a novel solution which is based on the Artificial Bee Colony as well as the Grey Wolf Optimizer. Moreover, this work has given the proposal of the hybrid Grey Wolf Optimizer-Artificial Bee Colony algorithm, in which the wolves will adopt the bees’ strategy of sharing information for promotion of their exploration capability whilst continuing with their original strategy of hunting strategy for maintenance of their exploitation capability. The experimental outcomes demonstrate the proposed algorithm’s superior performance over other existing algorithms.

A Real-Time Wearable Physiological Monitoring System for Home-Based Healthcare Applications

The acquisition of physiological data are essential to efficiently predict and treat cardiac patients before a heart attack occurs and effectively expedite motor recovery after a stroke. This goal can be achieved by using wearable wireless sensor network platforms for real-time healthcare monitoring. In this paper, we present a wireless physiological signal acquisition device and a smartphone-based software platform for real-time data processing and monitor and cloud server access for everyday ECG/EMG signal monitoring. The device is implemented in a compact size (diameter: 30 mm, thickness: 4.5 mm) where the biopotential is measured and wirelessly transmitted to a smartphone or a laptop for real-time monitoring, data recording and analysis. Adaptive digital filtering is applied to eliminate any interference noise that can occur during a regular at-home environment, while minimizing the data process time. The accuracy of ECG and EMG signal coverage is assessed using Bland–Altman analysis by comparing with a reference physiological signal acquisition instrument (RHS2116 Stim/Recording System, Intan). Signal coverage of R-R peak intervals showed almost identical outcome between this proposed work and the RHS2116, showing a mean difference in heart rate of 0.15 ± 4.65 bpm and a Wilcoxon’s p value of 0.133. A 24 h continuous recording session of ECG and EMG is conducted to demonstrate the robustness and stability of the device based on extended time wearability on a daily routine.

Effective Resource Aware Health Care Monitoring in Body Sensor Network Platform Using Modified Particle Swarm Optimization

Real-time physiological data may be gathered using wearable medical sensors based on a network of body sensors. We do not however have an effective, trustworthy and secure body sensor network platform (BSN) that can satisfy growing e-health requirements. Many of these applications require BSN to provide the dependable and energy efficient data transfer of many data speeds. Cloud computing is giving assets to patient dependent on application request at SLA (service level agreement) rules. The service providers are focusing on giving the necessity based asset to satisfy the QoS (quality of service) prerequisites. Therefore, it has become an assessment to adapt service-oriented assets because of vulnerability and active interest for cloud services. The task scheduling is an option in contrast to appropriating asset by evaluating the inconsistent outstanding task at hand. the allocation of tasks given by the microprocessor Subsequently, a productive asset scheduling method needs to disseminate proper VMs (Virtual Machines). The swarm intelligence is appropriate to deal with such vulnerability issues carefully. In this paper, an effective resource scheduling strategy Utilizing Modified Particle Swarm Optimization approach (MPSO) is presented, with a target to limit execution cost that gives an approach for the microprocessor to deal with the multiple number of tasks gives to the controllers in order to perform the multiple tasks that gets logged in the cloud via Internet of things technology (Iot), energy consumed, bandwidth consumption, speed and execution cost. The near investigation of results has been exhibited that the presented scheduling scheme performed better when contrasted with existing evaluation. In this manner, the presented resource scheduling approach might be utilized to enhance the viability of cloud resources.

Retraction Note: Design and simulation analysis of offshore tidal energy generating set system based on sensor network

Tidal energy refers to the potential energy and kinetic energy of tidal changes. It is a kind of energy that hides a huge amount of energy and can be regenerated endlessly. Tidal energy as an energy source is purely renewable energy. The tide rises and falls every day, and the energy is constant, so it will be endless. Power generation through tidal energy cannot only alleviate the energy shortage but also become the main additional energy source for people to carry out economic construction and development in areas near the sea. Because tidal power plants are usually built-in offshore areas with relatively small populations, they will not cause complicated problems such as flooding of farmland and immigration. In view of the shortage of energy and fuel in the world and the increasingly serious environmental and pollution problems, tidal energy has received widespread attention and influence all over the world due to its infinite reserves, lack of environmental pollution, and high predictability. Based on the submarine acoustic wireless sensor network platform, we have developed an experimental submarine acoustic ray sensor network platform, which provides an effective platform for testing and evaluating the submarine acoustic ray sensor network. This paper determines the capacity of TCF, the layout design of TCF in TCT, and the research on the collection system. Based on the underwater acoustic wireless sensor network platform, the underwater acoustic network experimental test system platform is being developed, and a feasible research plan is proposed for the detection and evaluation of the underwater acoustic wireless sensor network.

Design and experiments of a thermoelectric-powered wireless sensor network platform for smart building envelope

A new thermoelectric-powered wireless sensor network platform is presented for the low-cost environmental sensing in building envelopes through thermoelectric energy harvesting and ultra-low power management. It is designed and prototyped entirely inside a window frame without compromising architectural aesthetics. This self-powered sensing platform is achieved by maximizing the harvested energy from building envelopes and optimizing the wireless sensing unit’s energy consumption. It harvests milli-watt level thermoelectric power from the temperature gradient across building envelopes through thermoelectric generators with an optimized thermal connector. The harvested energy is voltage-boosted and regulated through two integrated circuits that are tailored for ultra-low-power input. A low power system-on-chip is used to supervise the environmental sensing and wireless data communication. The energy consumption is tailored by adjusting the system sleep time to match the harvested energy. The proposed platform is prototyped in a window frame and thoroughly tested, where 1.5 mW of power is harvested from thermoelectric generators under 6 °C of temperature difference, and a 33.4% efficiency to the battery. In the meantime, 0.42 mW power is consumed by the wireless sensing unit under a sampling period of 2 h, which reaches the energy equilibrium state. The energy equilibrium algorithm can project the battery energy based on historical weather conditions, so as to achieve the self-powered condition given a geographic location. This smart building envelope systems include the unique innovations in self-powered system architecture, thermally optimized internal structure, and milli-watt level power management.

RETRACTED ARTICLE: Design and simulation analysis of offshore tidal energy generating set system based on sensor network

Tidal energy refers to the potential energy and kinetic energy of tidal changes. It is a kind of energy that hides a huge amount of energy and can be regenerated endlessly. Tidal energy as an energy source is purely renewable energy. The tide rises and falls every day, and the energy is constant, so it will be endless. Power generation through tidal energy cannot only alleviate the energy shortage but also become the main additional energy source for people to carry out economic construction and development in areas near the sea. Because tidal power plants are usually built-in offshore areas with relatively small populations, they will not cause complicated problems such as flooding of farmland and immigration. In view of the shortage of energy and fuel in the world and the increasingly serious environmental and pollution problems, tidal energy has received widespread attention and influence all over the world due to its infinite reserves, lack of environmental pollution, and high predictability. Based on the submarine acoustic wireless sensor network platform, we have developed an experimental submarine acoustic ray sensor network platform, which provides an effective platform for testing and evaluating the submarine acoustic ray sensor network. This paper determines the capacity of TCF, the layout design of TCF in TCT, and the research on the collection system. Based on the underwater acoustic wireless sensor network platform, the underwater acoustic network experimental test system platform is being developed, and a feasible research plan is proposed for the detection and evaluation of the underwater acoustic wireless sensor network.

Parallel Quadri-valent Quantum-Inspired Gravitational Search Algorithm on a heterogeneous platform for wireless sensor networks

Sensor nodes in a wireless sensor Network are assigned for different operational modes to perfume application-specific objectives. The decision to assign operational modes to nodes is a challenging problem in the presence of multiple criteria including energy-efficient, maintaining network connectivity, and fulfilling application goals. Several metaheuristic methods are introduced in the literature to address this NP-hard problem, however, these methods require further improvements in execution-time and finding the optimum solution. In this research, we propose an improved version of a metaheuristic method called Quadri-valent Quantum-Inspired Gravitational Search Algorithm (QQIGSA) to solve Quadri-valent problems by applying a Not Q-Gate and paralleling QQIGSA method on the graphics processing unit. The proposed method employs a heterogeneous platform and justifies its parameters. The experimental results show that the performance enhancement from 1.8 to 2.25 compared to the previous parallel implementations. Moreover, we achieve the speedup of 8 by using the proposed heterogeneous paralleling technique.

Use of augmented reality-enabled prototyping of cyber-physical systems for improving cyber-security education

The use of augmented reality-enabled scenarios in cybersecurity teaching is proposed in the article to respond to new requirements for the rapid adoption of new technologies and profound knowledge of cybersecurity issues by professionals. Implementation of project-type activities based on real cybersecurity issues in application fields of cyber-physical systems is suggested to improve the competence forming. A use-case of agricultural cyber-physical system of systems is discussed as a viable example of augmented reality-enabled prototyping of cybersecurity risk-aware architecture. The necessary steps are analysis of general and business-specific tasks on cybersecurity, creation of a list of competencies, formalized in educational standards and curricula, development of gaming scenarios for the formation of hard and soft skills, development of the scenario management system for AR interfaces. The system using AR tools can be easily adapted to different cybersecurity training activities. Industrial cyber-physical systems may be vulnerable due to insecure wireless connectivity, lack of encryption, inadequate access policy. The project-based learning complex is focused on the implementation of a data acquisition, storage and processing platform for new sensor networks and instruments. Representing all the diverse information on different layers will be greatly improved by use of the developed holographic projection AR tools.

Evaluation and Application of a Customizable Wireless Platform: A Body Sensor Network for Unobtrusive Gait Analysis in Everyday Life.

Body sensor networks (BSNs) represent an important research tool for exploring novel diagnostic or therapeutic approaches. They allow for integrating different measurement techniques into body-worn sensors organized in a network structure. In 2011, the first Integrated Posture and Activity Network by MedIT Aachen (IPANEMA) was introduced. In this work, we present a recently developed platform for a wireless body sensor network with customizable applications based on a proprietary communication interface. In particular, we present a sensor setup for gait analysis during everyday life monitoring. The arrangement consists of three identical inertial measurement sensors attached at the wrist, thigh, and chest. We additionally introduce a force-sensitive resistor integrated insole for measurement of ground reaction forces (GRFs), to enhance the assessment possibilities and generate ground truth data for inertial measurement sensors. Since the is not strongly represented in existing BSN implementations, we validate the proposed system concerning an application in gait analysis and use this as a representative demonstration of realizability. Hence, there are three key aspects of this project. The system is evaluated with respect to (I) accurate timing, (II) received signal quality, and (III) measurement capabilities of the insole pressure nodes. In addition to the demonstration of feasibility, we achieved promising results regarding the extractions of gait parameters (stride detection accuracy: , Root-Mean-Square Deviation (RMSE) of mean stride time: , RMSE of percentage stance time: ). Conclusion: With the satisfactory technical performance in laboratory and application environment and the convincing accuracy of the gait parameter extraction, the presented system offers a solid basis for a gait monitoring system in everyday life.

Pseudo-Spatially-Distributed Modeling of Water Balance Components in the Free State of Saxony

Highly-resolved data on water balance components (such as runoff or storage) are crucial to improve water management, for example, in drought or flood situations. As regional observations of these components cannot be acquired adequately, a feasible solution is to apply water balance models. We developed an innovative approach using the physically-based lumped-parameter water balance model BROOK90 (R version) integrated into a sensor network platform to derive daily water budget components for catchments in the Free State of Saxony. The model is not calibrated, but rather uses available information on soil, land use, and precipitation only. We applied the hydro response units (HRUs) approach for 6175 small and medium-sized catchments. For the evaluation, model output was cross-evaluated in ten selected head catchments in a low mountain range in Saxony. The mean values of Kling–Gupta efficiency (KGE) for the period 2005–2019 to these catchments are 0.63 and 0.75, for daily and monthly discharge simulations, respectively. The simulated evapotranspiration and soil wetness are in good agreement with the SMAP_L4_GPH product in April 2015–2018. The study can be enhanced by using different data platforms as well as available information on study sites.

Improvement of table tennis technology based on data mining in the environment of wireless sensor networks

With the development of Internet of things technology, the combination of Internet of things technology and sports competition parameter collection technology, so as to carry out rapid and accurate retrieval and positioning of technology and tactics, has innovation in the current research field. In the high-level table tennis competition, the use of technology and tactics is closely related to the gain and loss of points. At present, the traditional table tennis video mining algorithm has some problems such as low efficiency and poor performance of optimization classification. Based on this, this article introduces the big data platform of the wireless sensor networks to construct the table tennis match database, realizing the real-time updating of table tennis match parameters and the call of historical data at any time. Then establishing a data mining model to realize the data and dynamic analysis of table tennis matches. Finally, based on this strategic analysis system, the data collected from two table tennis competitions are simulated, and the tactical recommendation of theoretical analysis is obtained, which provides a theoretical basis for the digitization of table tennis sports.

Development of an extended topology-based lightweight cryptographic scheme for IEEE 802.15.4 wireless sensor networks

Among the classes of wireless personal area networks, a wireless sensor network typically refers to a versatile and densely distributed sensing platform that enables the support of a wide variety of application domains. Among the various technical challenges addressed by more than one decade of research in wireless sensor networks, security across wireless links is by far one of the most critical ones and relates to the need of guaranteeing reliability and trustiness of the collected data. This article deals with the cryptographic aspects involved in securing wireless sensor networks, in terms of confidentiality and authentication. In particular, moving from some results previously achieved in our research activity, this article extends a cryptography scheme in order to better comply with the security requirements that arise from real-world wireless sensor network installations. The proposed scheme, called topology-authenticated key scheme 2, takes advantage of hybrid cryptography to provide security in the presence of resource-constrained sensor nodes using topology-authenticated keys to provide increased robustness to the scheme itself. The proposed extensions provide full practical support to star-topology wireless sensor networks and the article presents also some experimental results obtained by implementing the scheme on two different wireless sensor network platforms available for protocol stacks compliant with the IEEE 802.15.4 standard.

Design and Development of an Open Platform for Software-Defined Heterogeneous Wireless Sensor Networks

Design and Development of an Open Platform for Software-Defined Heterogeneous Wireless Sensor Networks

Active Control Parameters Monitoring for Freight Trains, Using Wireless Sensor Network Platform and Internet of Things

Operating in a dynamic and competitive global market, railway companies have realized many years ago that better management of their logistical operations will enhance their strategic positions on the market. The financial component of daily operations is of utmost importance these days and many companies concluded that maximizing the profit relies on the integration of logistical activities with better income management. This paper presents a system consisting of three components: Ferodata BOX, Ferodata MOBILE, and Ferodata SYS, used to transmit to a web-server the status and operating information of an electric or diesel train. Train information includes data from locomotives, wagons, train driver, route, direction, fuel or electric consumption, speed, etc. All this information is processed in real-time and can be viewed in the web-server application. Additionally, the web-server application could manage and report details that are coming from the wagons, such as valuable information regarding the bogie wear, the identification of the wagons attached to a gasket, and identification the situations in which a wagon or group of wagons comes off the gasket configuration. All information about the status of trains is available on-line and at any moment the person responsible for management can use these data in their work.

An Event-based Local Action Paradigm to Improve Energy Efficiency in Queriable Wireless Sensor Actuator Networks

Wireless sensor networks (WSN) are deployed in a multitude of applications both in industrial and academic fields. In recent years, due to the emerge of Internet of Things (IoT) technologies and Vehicle2X communication scenarios, novel challenges for wireless sensor network platforms - regarding hardware and software - arose. Thus, challenges known from big data processing have reached the WSN scope and consequently approaches and methods have been devised to handle these. One such approach is queriable wireless sensor networks which enable their users the specification of sensing tasks in a declarative way without the need to re-program nodes in case the application requirements change. As many current WSN applications feature active parts with which nodes can directly influence their environment, the term wireless sensor actuator networks (WSAN) has been coined, setting such networks apart from solely passively measuring networks.In this article, we will present a short introduction to big data processing in wireless sensor networks which motivates the usage of queriable networks. We will show that in order to enable a WSAN to carry out actions energy-efficiently and in a timely manner, an event-based action model is favorable. Additionally, we will demonstrate how such an event system can be used to improve sub query performance in WSNs. We conclude with an evaluation regarding the benefit of combining this approach with wake-up receiver technologies based on a qualitative energy efficiency definition for WSN.