Smart Sensor Networks Research Articles

Improve Energy Consumption and Signal Transmission Quality of Routings in Wireless Sensor Networks

Wireless (smart) sensor networks (WSNs) comprise a myriad of embedded wireless smart sensors. They play a cardinal role in the functioning of many applications, such as the Internet of Things, smart grids, smart production systems, and smart homes, which ultimately render them paramount instruments in the modern age. Recent advances in WSNs have resulted in the rapid development of sensors. However, WSNs will only able to achieve better execution efficiencies if their energy consumption - owing to limited battery life and difficulty of recharging - can be better controlled. Moreover, signal transmission quality determines WSN performance. Hence, two main concerns - energy consumption and signal transmission quality - should be addressed to improve the performance of WSNs. Thus, a new bi-objective simplified swarm optimization algorithm (bSSO) is proposed by employing the concepts of simple routing, SSO, and crowd distance. The performance and applicability of the proposed bSSO using eight different parameter settings are demonstrated through an experiment involving ten WSN benchmarks ranging from 100 to 1000 sensors. The proposed algorithm is then compared with NSGA-II, which is an algorithm widely used to solve multi-objective problems. The results show that the proposed bSSO can successfully achieve the aim of this work.

Reconstruction and application of flooding routing algorithm for smart street light over wireless sensor networks

For smart street light wireless sensor networks, there are huge number of redundant packets flooding in the route discovery process, resulting in information explosion and high energy consumption. This paper proposes an improved flooding routing protocol, during the route discovery process, each node could receive signals such as the serial number of packets copies, destination address, node types and so on, the algorithm will judge whether the received signals need to be continue forwarded in a certain forward probability or not. Thus, the total forward times will be reduced and accordingly the information explosion could be prevented and the energy consumption could be reduced without reducing the reliability. Simulation experiments are conducted on MATLAB, and results show that the improved algorithm has much more higher flooding efficiency than the traditional flooding routing algorithm, and a very broad application prospect to balance the efficiency versus the reliability of the whole networks.

The Implementation of a Low Power Environmental Monitoring and Soil Moisture Measurement System Based on UHF RFID.

A smart sensor label based on the integration of ultra high frequency (UHF) radio frequency identification (RFID) technology and sensors is presented. The label is composed of a semi-active system that measures temperature, light, relative humidity and gravimetric water content (GWC) in the soil. The deployed system provides a simple, cost effective solution to monitor and control the growing of plants in modern agriculture and is intended be a part of a smart wireless sensor network (WSN) for agricultural monitoring. This paper is focused on analysis and development of a moisture sensor to measure GWC. It is based on a capacitance measurement solution, the accuracy of which is enhanced using several sensor driving frequencies. Thanks to the cancellation of supply voltage variations, the modeling of the GWC sensor and readout circuit was correct. The results we measured were close to modeled values. The maximum measurement resolution of the capacitive moisture sensor was 0.07 pF. To get the GWC from measured capacitance, a scale was used to weigh the mass of water in the soil. The comparison between capacitance measurement and calculated soil GWC is presented. The RFID measurement system has energy harvesting capabilities and an ultra-low power microcontroller, which uses embedded software to control the measurement properties. The microcontroller has to choose the appropriate model depending on the measured amplitude and chosen frequency to calculate the actual voltage on the sensing capacitor.

Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management

The technological landscape of intelligent transport systems (ITS) has been radically transformed by the emergence of the big data streams generated by the Internet of Things (IoT), smart sensors, surveillance feeds, social media, as well as growing infrastructure needs. It is timely and pertinent that ITS harness the potential of an artificial intelligence (AI) to develop the big data-driven smart traffic management solutions for effective decision-making. The existing AI techniques that function in isolation exhibit clear limitations in developing a comprehensive platform due to the dynamicity of big data streams, high-frequency unlabeled data generation from the heterogeneous data sources, and volatility of traffic conditions. In this paper, we propose an expansive smart traffic management platform (STMP) based on the unsupervised online incremental machine learning, deep learning, and deep reinforcement learning to address these limitations. The STMP integrates the heterogeneous big data streams, such as the IoT, smart sensors, and social media, to detect concept drifts, distinguish between the recurrent and non-recurrent traffic events, and impact propagation, traffic flow forecasting, commuter sentiment analysis, and optimized traffic control decisions. The platform is successfully demonstrated on 190 million records of smart sensor network traffic data generated by 545,851 commuters and corresponding social media data on the arterial road network of Victoria, Australia.

Real Time Online Sensing of Illicit Discharge of Wastewater into Lakes

Monitoring the illicit discharge of effluent in water bodies is made easier with the present design. The water bodies are monitored using various sensors to know the illicit discharge of effluents. The water quality parameters that are detected by sensors are fed into NODE MCU micro controller for analysis and to compare the data and check with the predefined conditions. Smart sensor network has been designed in such a way that it predicts the overwhelming water quality parameters, for example, pH, temperature, turbidity and electrical conductivity. The process of monitoring the effluents is carried out by the sensors. All the sensors are connected by sensor fusion technology through which they can compare their pre-set values and give us the accurate result on water quality status. It sends the message to the user about the standards of the effluent added to the water and also alert’s the concern authority. All the process executed is saved in the Google drive of the device as a backup every day. This instrument can be placed in various locations of the lakes. The predefined values are as follows: the pH sensor will be maintained between 6-9, turbidity less than 10NTU. If the illicit discharge value exceeds these predefined parameters messages will be sent to the water maintenance department through message and the buzzer will make a sound. The GPS is enabled for anti-theft purpose.

Nanotechnology Research and Development in Upstream Oil and Gas

Research in nanomaterials has brought many performance enhancements to the materials used in oil and gas well drilling, cementing, production, and enhanced oil recovery (EOR). While the products of these investigations and product development processes vary widely in terms of technological readiness, improvements in performance with nanoparticles are being witnessed in the field at this current time. Step‐change innovation in the industry through advances in nanomaterials is anticipated to find a strong footing in the development of smart self‐sensing cements, production technologies, EOR technologies, nanoparticle sensors, sensor networks, and downhole power and automation. Herein, a brief overview of the applications of nanoparticles as they relate to upstream oil and gas is provided and where the current state of the art is in high‐performance oil well construction, production enhancement, and reservoir management materials is evaluated.

Fuzzy logic–based distributed clustering protocol to improve energy efficiency and stability of wireless smart sensor networks for farmland monitoring systems

SummaryWireless smart sensor networks (WSSNs) are emerging as the physical backbone of the internet of things (IoT) technology. On the basis of the IoT platform, web‐based systems and services are been developing such as e‐surveillance, industrial‐IoT, and precision agriculture. For farmland monitoring systems, WSSNs need to be scalable in terms of coverage area. Sensor nodes are energy‐constrained devices, and hence, many energy‐efficient clustering protocols are developed in the literature. But these methods overload the cluster leaders (CLs) with cluster computation and data communication costs. An improper CL selection may lead to the early death of such nodes and hence does not prolong the network lifetime stability. We propose a fuzzy logic (FL)–based distributed clustering protocol to enhance the energy efficiency of WSSN while maximizing the coverage area. The load of CLs is shared by originators and super‐CLs (SCLs) selected in the network. The wireless link and received signal strength (RSS) are greatly affected by environmental conditions and thus cannot be considered as ideal network parameters. We use FL systems to tackle the uncertainty of such network parameters. The proposed protocol is simulated for different scalable WSSNs. The results indicate that the proposed protocol provides better lifetime stability than the recent conventional protocols. The functionalities of the protocol are proposed considering the recent wireless standards. Hence, the proposed protocol can be suitably implemented for farmland monitoring systems.

Tuning oxygen vacancies and improving UV sensing of ZnO nanowire by micro-plasma powered by a triboelectric nanogenerator

Plasma technology is a common method in surface passivation and improving device performances. However, the requirement of expensive instruments makes it difficult to integrate into a device system and work together with devices. In this paper, a device system has been established by introducing a micro-plasma, which is powered by the high voltage of a triboelectric nanogenerator (TENG). It is demonstrated that the oxygen vacancies on ZnO nanowire are passivated by the micro-plasma step-by-step, as the operation time of TENG is controlled. After the oxygen vacancies are passivated by micro-plasma, the intrinsic resistance of the ZnO nanowire is increased significantly. As for the UV photodetector performances after micro-plasma passivation, the on-off ratio, the gain and bandwidth product, and the recovery speed are improved 254, 111, and 2651 times, respectively. The mechanisms of tuning oxygen vacancies and improving UV sensing properties of ZnO nanowire by micro-plasma have been discussed. Given the advantages of low cost, flexible modes, and simple operation of TENG, the TENG-based micro-plasma technology can be integrated into a device system to passivate surface defects and improve device performances, which has potential applications in developing smart and multi-functional sensors network in the era of internet of things.

Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis.

In this paper, an application for the management, supervision and failure forecast of a ship’s energy storage system is developed through a National Marine Electronics Association (NMEA) 2000 smart sensor network. Here, the NMEA 2000 network sensor devices for the measurement and supervision of the parameters inherent to energy storage and energy supply are reviewed. The importance of energy storage systems in ships, the causes and models of battery aging, types of failures, and predictive diagnosis techniques for valve-regulated lead-acid (VRLA) batteries used for assisted and safe navigation are discussed. In ships, battery banks are installed in chambers that normally do not have temperature regulation and therefore are significantly conditioned by the outside temperature. A specific method based on the analysis of the time-series data of random and seasonal factors is proposed for the comparative trend analyses of both the battery internal temperature and the battery installation chamber temperature. The objective is to apply predictive fault diagnosis to detect any undesirable increase in battery temperature using prior indicators of heat dissipation process failure—to avoid the development of the most frequent and dangerous failure modes of VRLA batteries such as dry out and thermal runaway. It is concluded that these failure modes can be conveniently diagnosed by easily recognized patterns, obtained by performing comparative trend analyses to the variables measured onboard by NMEA sensors.

Memory Leakage-Resilient Dynamic and Verifiable Multi-Keyword Ranked Search on Encrypted Smart Body Sensor Network Data

Outsourcing of encrypted smart body sensor network data to the edge or the cloud is now an entrenched practice within organizations, for example to reduce cost and enhance productivity (to some extent), while ensuring data is not accessible to misbehaving cloud service providers. Although there are a large number of searchable symmetric encryption schemes designed to support searching operations on encrypted data, a fully functional and memory leakage-resilient scheme for smart body sensor network data is lacking. This security property is highly desirable as the resource-sharing environment may be prone to various kinds of memory leakage. In this paper, a memory leakage-resilient dynamic and verifiable multi-keyword ranked search scheme (MLR-DVMRS) on encrypted smart body sensor network data is proposed. As each sensor device has inherent characteristics to identify itself, this property can be used to authenticate the device. The proposed scheme utilizes physically unclonable functions and fuzzy extractors to achieve memory leakage-resilience. Meanwhile the vector space model, TF-IDF measure method, and order-preserving encryption, are used to achieve dynamic and multi-keyword ranked search functionalities. A formal security analysis is given to prove the security of MLR-DVMRS. Besides the comprehensive functionalities of MLR-DVMRS, experimental results demonstrate that the efficiency of MLR-DVMRS is superior to MRSE (a multi-keyword ranked search over encrypted cloud data scheme) for large data collection.

Classification of DoS Attacks in Smart Underwater Wireless Sensor Network

Due to deployment in sensitive military areas and other security applications, wireless networks are becoming a famous research spot in the field of computer science. To ratify the security and reliability in such kinds of application intrusion detection system can play an important role. There is a need of intrusion detection system, which has the capability to detect a large number of possible threats in wireless sensor networks. This article contains a customized dataset for smart underwater wireless sensor network that can be categorized into four types of DoS attacks (Blackhole, Grayhole, Flooding and Scheduling attacks). For the experimental purpose, since it is highly used in WSN, Low Energy Aware Cluster Hierarchy protocol has been used in this research work. Vector based forwarding provide scalable routing protocol to deploy smart underwater wireless sensor network. Using NS2 network simulator model a scheme has been defined to collect network traffic and create the dataset. Artificial Neural Network has been applied to train the dataset to classify it into different DoS attacks. Experimental work performed here gives high classification rate and accuracy for mentioning attacks with the help of proposed dataset. In future, suggested method can be useful for more attacks like Sybil/Wormhole presented in datalink layer as DoS attacks.

Анализ и математическое моделирование системы определения местоположения персонала угольной шахты, построенной с применением сенсорной сети Часть 1. </titles> <contributors> <person_name sequence='first' contributor_role='author'> <given_name>T.V.</given_name> <surname>Nasibullina</surname> </person_name> <person_name sequence='additional' contributor_role='author'> <given_name>M.V.</given_name> <surname>Kostenko</surname> </person_name> </contributors> <publication_date media_type='print'> <month>9</month> <day>20</day> <year>2019</year> </publication_date> <publication_date media_type='online'> <month>9</month> <day>20</day> <year>2019</year> </publication_date> <pages> <first_page>126</first_page> <last_page>132</last_page> </pages> <doi_data> <doi>10.30686/1609-9192-2019-4-126-132</doi> <resource>https://mining-media.ru/en/articleen/15023-analysis-and-mathematical-modelling-of-personnel-location-system-in-coal-mines-based-on-smart-sensor-network-part-1</resource> </doi_data> </journal_article> <!-- ============== --> <journal_article publication_type='full_text'> <titles> <title>TEV

Анализ и математическое моделирование системы определения местоположения персонала угольной шахты, построенной с применением сенсорной сети Часть 1. </titles> <contributors> <person_name sequence='first' contributor_role='author'> <given_name>T.V.</given_name> <surname>Nasibullina</surname> </person_name> <person_name sequence='additional' contributor_role='author'> <given_name>M.V.</given_name> <surname>Kostenko</surname> </person_name> </contributors> <publication_date media_type='print'> <month>9</month> <day>20</day> <year>2019</year> </publication_date> <publication_date media_type='online'> <month>9</month> <day>20</day> <year>2019</year> </publication_date> <pages> <first_page>126</first_page> <last_page>132</last_page> </pages> <doi_data> <doi>10.30686/1609-9192-2019-4-126-132</doi> <resource>https://mining-media.ru/en/articleen/15023-analysis-and-mathematical-modelling-of-personnel-location-system-in-coal-mines-based-on-smart-sensor-network-part-1</resource> </doi_data> </journal_article> <!-- ============== --> <journal_article publication_type='full_text'> <titles> <title>TEV

Анализ и математическое моделирование системы определения местоположения персонала угольной шахты, построенной с применением сенсорной сети Часть 1. Источник: https://mining-media.ru/ru/article/newtech/15024-analiz-i-matematicheskoe-modelirovanie-sistemy-opredeleniya-mestopolozheniya-personala-ugolnoj-shakhty-postroennoj-s-primeneniem-sensornoj-seti-chast-1

Анализ и математическое моделирование системы определения местоположения персонала угольной шахты, построенной с применением сенсорной сети Часть 1. Источник: https://mining-media.ru/ru/article/newtech/15024-analiz-i-matematicheskoe-modelirovanie-sistemy-opredeleniya-mestopolozheniya-personala-ugolnoj-shakhty-postroennoj-s-primeneniem-sensornoj-seti-chast-1

An incentive-based protection and recovery strategy for secure big data in social networks

Big data sources, such as smart vehicles, IoT devices, and sensor networks, differ from traditional data sources in both output volume and variety. Big data is usually stored on various types of network nodes, which is prone to data security and privacy problems, such as virus infection. In particular, the spread of viruses through social networks can cause large-scale destruction and privacy leakage in the network. This paper aims to provide a solution to protect the security of big data. First, the users are divided into five states according to their reactions to data virus: susceptible, contagious, doubt, immune, and recoverable. Then, we propose a novel model for studying the propagation mechanism of data virus. To control the spread of virus and protect data security, an incentive mechanism is introduced. After that, a protection and recovery strategy (PRS) is developed to reduce infected users and increase the immunized. The experimental results on two data sets indicate that our model gives a good description of the data virus propagation process, and PRS is better than both acquaintance immunization and target immunization methods, which validates the privacy preserving strategy for big data in networks.

A smart wireless sensor network node for fire detection

Fires generally occur due to human carelessness and the change in environmental conditions. The uncontrolled fire results in death incidents of humans and animals as well as severe threats to the ecosystem. The preservation of the natural environment is important. The wireless sensor networks, widely used in different monitoring applications, is used in this work. For fire detection, we use flame, smoke, temperature, humidity, and light intensity sensors in our proposed network node which is low-cost, reduced-size, and power-efficient. The experiments are performed in a well-controlled real-time environment. The proposed node transmits the sensed data to the central node. The central node then transfers the data gathered from all the nodes to the control station using an air interface. To decide whether there is an incident of fire or not, and to have an idea on fire intensity, we combine multiple attributes sensed from a single node using Bayesian approach due to its simplicity and resemblance with human reasoning. In the experimental setup, the conditions for fire with different intensity are generated and the results confirm the validity of the proposed approach in terms of accuracy and less false alarms.

Mechanoluminescence enhancement of ZnS:Cu,Mn with piezotronic effect induced trap-depth reduction originated from PVDF ferroelectric film

Mechanoluminescence is a light emission process that is induced by a mechanical stimulus. The mechanisms that have been proposed to mediate this effect include elastic potential energy conversion and piezoelectric field-induced release of trapped charges for recombination. However, how to enhance this phenomenon is an open question for the application of mechanoluminescent (ML) effect. Herein, an effective ML enhancement approach was suggested through piezotronic effect driven trap-depth reduction of typical ML material (ZnS:Cu,Mn) based on charge release of ferroelectric polyvinylidene fluoride (PVDF) film. The experimental results illustrated that a layer of PVDF film covered on the both sides of ZnS:Cu,Mn ML layer can be nearly double the ML intensity in the particular external pressure, compared to that of the individual ZnS:Cu,Mn films. The films with an enhanced ML property show high potential for use in real-time pressure mapping systems, smart sensor networks, high-level security systems, and artificial intelligence.

Increasing surface charge density by effective charge accumulation layer inclusion for high-performance triboelectric nanogenerators

Abstract

Sensor fault management techniques for wireless smart sensor networks in structural health monitoring

Faulty sensor data often occur in structural health monitoring (SHM) systems using wireless smart sensor networks (WSSN). Serious sensor faults in the raw data may negatively affect SHM analysis and subsequent informed decisions. This issue is more critical for WSSN running decentralized data acquisition, because raw data are not transmitted back, and hence, data quality assessment can be extremely difficult for users. Therefore, developing efficient techniques to autonomously detect, identify, and recover sensor faults is essential. In the vibration data collected from the Jindo Bridge, some data sets are corrupted with sensor faults, which can be categorized as one of three types: drift, spikes, and bias. Accordingly, this paper presents a three-stage strategy to address this problem in WSSN. First, a distributed similarity test is employed to detect sensor faults; this test is based on the similarity of the power spectral density of the data among sensors within a cluster of nodes. Second, an artificial neural network model is trained to identify the types of sensor faults. Third, sensor data are recovered from the identified faults by applying a correction function or replacing faulty data with estimated values. Subsequently, numerical analysis is performed using a set of field measurements collected from the Jindo Bridge. Decentralized system identification is conducted using the original data and data recovered using the proposed strategy. The three-stage strategy is shown to successfully detect, identify, and recover sensor faults, which improves the results of system identification. Finally, the benefits and limitations of this strategy are discussed.

A Low-Cost Smart Sensor Network for Catchment Monitoring.

Understanding hydrological processes in large, open areas, such as catchments, and further modelling these processes are still open research questions. The system proposed in this work provides an automatic end-to-end pipeline from data collection to information extraction that can potentially assist hydrologists to better understand the hydrological processes using a data-driven approach. In this work, the performance of a low-cost off-the-shelf self contained sensor unit, which was originally designed and used to monitor liquid levels, such as AdBlue, fuel, lubricants etc., in a sealed tank environment, is first examined. This process validates that the sensor does provide accurate water level information for open water level monitoring tasks. Utilising the dataset collected from eight sensor units, an end-to-end pipeline of automating the data collection, data processing and information extraction processes is proposed. Within the pipeline, a data-driven anomaly detection method that automatically extracts rapid changes in measurement trends at a catchment scale. The lag-time of the test site (Dodder catchment Dublin, Ireland) is also analyzed. Subsequently, the water level response in the catchment due to storm events during the 27 month deployment period is illustrated. To support reproducible and collaborative research, the collected dataset and the source code of this work will be publicly available for research purposes.

Hybrid Energy Harvesters: Toward Sustainable Energy Harvesting.

Recently, sustainable green energy harvesting systems have been receiving great attention for their potential use in self-powered smart wireless sensor network (WSN) systems. In particular, though the developed WSN systems are able to advance public good, very high and long-term budgets will be required in order to use them to supply electrical energy through temporary batteries or connecting power cables. This report summarizes recent significant progress in the development of hybrid nanogenerators for a sustainable energy harvesting system that use natural and artificial energies such as solar, wind, wave, heat, machine vibration, and automobile noise. It starts with a brief introduction of energy harvesting systems, and then summarizes the different hybrid energy harvesting systems: integration of mechanical and photovoltaic energy harvesters, integration of mechanical and thermal energy harvesters, integration of thermal and photovoltaic energy harvesters, and others. In terms of the reported hybrid nanogenerators, a systematic summary of their structures, working mechanisms, and output performances is provided. Specifically, electromagnetic induction, triboelectric, piezoelectric, photovoltaic, thermoelectric, and pyroelectric effects are reviewed on the basis of the individual and hybrid power performances of hybrid nanogenerators and their practical applications with various device designs. Finally, the perspectives on and challenges in developing high performance and sustainable hybrid nanogenerator systems are presented.