Sensor Network Environment Research Articles

Kalman Filter Algorithm for Security of Network Clock Synchronization in Wireless Sensor Networks

Wireless sensor networks are a significant subfield of distributed systems, and a large number of their applications require time synchronization. Sensor nodes are powered by batteries. In order to reduce the interference of the peripheral environment on the node as much as possible, the size of the node is relatively small and the circuit design, storage, and calculation are relatively simple. Therefore, wireless sensor network nodes have limited energy, low computing power, and low communication capacity. In addition, the clock of the wireless sensor network node is usually realized through the local crystal oscillator and CPU interrupt mechanism, which is obviously affected by manufacturing errors, temperature changes, and CPU processing delays. Therefore, this research reviews and analyzes the development and current situation of time synchronization and puts forward the challenges faced by time synchronization-related research institutes. From the perspective of dynamic cybernetics, the feasibility of optimization based on Kalman filtering is analyzed. Finally, the article describes the system modeling process, including the clock model and the state equation and observation equation of Kalman filter and uses Matlab platform to carry out experimental simulation and analysis on the precise time synchronization protocol based on Kalman filter in the wireless sensor network environment.

A Sensor-aware Offloading Model for IoT Edge Computing

In IoT sensor network environment, offloading is an important factor that affects all design objectives. Since massive amounts of data are collected every second to the gateway and so immediate processing is difficult, offloading is critical to quickly eliminate worthless data in advance. Similar sensor data are continuously generated except in abnormal situations such as sudden changes and failure events. Therefore, the amount of data processing and frequency of data transmission can be greatly reduced by classifying, filtering, and compressing the data. In addition, more meaningful IoT context can be analyzed by combining multiple sensor data, since the sensor values generated by each sensor has its own characteristics. The previous offloading techniques mainly focused on minimizing latency without using data context and data resizing. Therefore, a new filtering technique is required to enhance the offloading efficiency through precision control using sensor context patterns. This paper proposes a new sensor-aware context offloading model called SCOM to support efficient data filtering services for the edge-based IoT environment. The architecture of SCOM consists of three layers of sensor context, pattern context and transmission context. SCOM exploits context-aware stream pattern matching using general string matching based on slide window for sensor stream offloading. Experiments show that the performance gain of SCOM reaches to 14.8% with respect to the operation throughput. Since proposed data layering and pattern-based offloading scheme can improve the sensor data filtering performance in edge gateways, it can be used for IoT sensor monitoring applications.

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

Wireless Visual Sensor Networks (WVSN) play an essential role in tracking moving objects. WVSN's key drawbacks are storage, power, and bandwidth. Background subtraction is used in the early stages of target tracking to extract moving targets from video images. Many standard methods of subtracting backgrounds are no longer suitable for embedded devices because they use complex statistical models to manage small changes in lighting. This paper introduces a system based on the Partial Discrete Cosine Transform (PDCT), reducing the vast dimensions of processed data while retaining most of the important information, thereby reducing processing and transmission energy. It also uses a dual-mode single Gaussian model (SGM) for accurate detection of moving objects. The proposed system's performance is to be assessed using the standard CDnet 2014 benchmark dataset in terms of detection accuracy and time complexity. Furthermore, the suggested method is compared to previous WVSN background subtraction methods. Simulation results show that the proposed method consistently has 15% better accuracy and is up to 3 times faster than the state-of-the-art object detection methods for WVSN. Finally, we showed the practicality of the suggested method by simulating it in a sensor network environment using the Contiki OS Cooja Simulator and implementing it in a real testbed using Cortex M3 open nodes of IOT-LAB.

Data compression algorithms for sensor networks with periodic transmission schemes

The operating state of switch cabinet is significant for the reliability of the whole power system, collecting and monitoring its data through the wireless sensor network is an effective method to avoid accidents. This paper proposes a data compression method based on periodic transmission model under the condition of limited energy consumption and memory space resources in the complex environment of switch cabinet sensor networks. Then, the proposed method is rigorously and intuitively shown by theoretical derivation and algorithm flow chart. Finally, numerical simulations are carried out and compared with the original data. The comparisons of compression ratio and error results indicate that the improved algorithm has a better effect on the periodic sensing data with interference and can make sure the change trend of data by making certain timing sequence.

Experimental Assessment of IEEE 802.15.4e LLDN Mode Using COTS Wireless Sensor Network Nodes

The use of Wireless Sensor Networks in industrial environments imposes critical requirements such as low latency, high reliability, and robustness. To address these constraints, the IEEE 802.15 Task Group 4e developed the amendment IEEE 802.15.4e with three new MAC operation modes: TSCH, DSME, and LLDN. This paper aims to assess the feasibility of implementing the LLDN operation mode in low-cost commercial nodes and their capacity to meet industrial applications’ timing and reliability requirements. LLDN services were implemented in COTS nodes using C programming language with Atmel provided stacks. In order to validate this implementation, a set of experimental scenarios was conducted and the measurement results were compared to simulation results available in the state of the art.

Solar Driven Self-learning Duty Cycle Control Algorithm for Maintenance-free Environmental Data Acquisition Nodes

Environmental wireless sensor network (EWSN) nodes are ordinarily implemented as embedded devices which use low-power and low-cost microcontrollers whose duty cycle is controlled by a machine learning method. This article updates a previously designed Q-learning (QL) algorithm to reduce the number of failed operational duty cycles in solar-powered EWSN nodes. The innovation is based on modification of the QL learning process and was tested and verified on an EWSN node model using a 5-year dataset of solar irradiance values. By designing a new learning algorithm, the number of failures in operational cycles was significantly reduced, specifically by a factor of ten compared to the reference algorithm.

Use of blockchain in health sensor networks to secure information integrity and accountability

Accountability and integrity are likely to play a key role in emerging health sensor network environments. Since health sensor devices are low capability devices, they are particularly susceptible to modern cyber threats. Blockchain technology is an emerging security technology that was explored by researchers in the past few years. Blockchain technology has been designed to protect information integrity and accountability. In a distributed manner making forgery and destruction of data can be very difficult. In this survey paper, we are specifically focused on how blockchain platforms adopt to the healthcare industry and achieve data integrity and accountability. Also, we will discuss the benefits and challenges of blockchain technology, and future blockchain research directions. Blockchain technology can be used to prevent unauthorized information access and enhance the security of sensor data. The use of blockchain technology in health sensor networks may address many of the integrity and accountability concerns of health sensor networks.

A Provably Secure Three-Factor Authentication Protocol Based on Chebyshev Chaotic Mapping for Wireless Sensor Network

Wireless sensor network has been widely used and plays a vital role in the Internet of Things, smart cities, military, and other fields, and its security has also attracted the attention of many researchers. In view of the security defects in Shin and Kwon’s scheme such as failure to provide three-factor security, lack of anonymity and untraceability, user impersonation attack, desynchronization attack and privileged insider attack, we suggest an improved provably secure three-factor user authentication scheme based on Chebyshev chaotic mapping for wireless sensor network, which employs fuzzy verifier technique to prevent attacker from offline guessing attack on user identity and password when the stolen/lost smartcard is acquired by the attacker. During the authentication phase, a dynamic identity mechanism is used to ensure the anonymity of the user and sensor to prevent desynchronization attack, and the Chebyshev chaotic mapping is introduced to improve security and reduce computation overhead. The rigorous security proof under the random oracle model and the formal verification via ProVerif show that our protocol overcomes the weaknesses in Shin and Kwon’s scheme. In addition, by comparing the performance of our proposed scheme with that of others, we demonstrate that our proposal not only solves the security risks of Shin and Kwon.’s protocol, but also achieves a better tradeoff between security and efficiency, therefore, it is more suitable for user authentication in wireless sensor network environments.

CNN-LSTM Based Approach for Dos Attacks Detection in Wireless Sensor Networks

A denial-of-service (DoS) attack is a coordinated attack by many endpoints, such as computers or networks. These attacks are often performed by a botnet, a network of malware-infected computers controlled by an attacker. The endpoints are instructed to send traffic to a particular target, overwhelming it and preventing legitimate users from accessing its services. In this project, we used a CNN-LSTM network to detect and classify DoS intrusion attacks. Attacks detection is considered a classification problem; the main aim is to clarify the attack as Flooding, Blackhole, Normal, TDMA, or Grayhole. This research study uses a computer- generated wireless sensor network-detection system dataset. The wireless sensor network environment was simulated using network simulator NS-2 based on the LEACH routing protocol to gather data from the network and preprocessed to produce 23 features classifying the state of the respective sensor and simulate five forms of Denial of Service (DoS) attacks. The developed CNN-LSTM model is further evaluated on 25 epochs with accuracy, Precision score, and Recall score of 0.944, 0.959, and 0.922, respectively, all on a scale of 0-1.

IPSadas: Identity‐privacy‐aware secure and anonymous data aggregation scheme

Intelligent systems are technologically advanced machines that can sense and respond to the surrounding environment. They have been widely used in medicine, military, transportation, automation, and other fields. However, when these systems deal with their environments, problems such as leakage of identities may occur. The adversary can damage the system communication and attack important nodes. To handle resource-constrained wireless sensor network environments, we propose a secure and anonymous data aggregation scheme. First, based on the bilinear mapping operation and onion routing concepts, we propose a key negotiation and secure information transmission scheme, which conducts confidential transmission and anonymous forwarding of messages in data aggregation. Second, an aggregation routing scheme based on link direction and residual energy is proposed to pledge messages that can arrive the base station without passing through many nodes, which saves network resources to a certain extent. Third, on the basis of the first two contributions, we propose an identity-privacy-aware secure and anonymous data aggregation scheme that protects the identity's privacy. This scheme can conceal the real identity of important nodes and protect the anonymity of messages and link relationships. In addition, an anonymous identity update and synchronization scheme is also proposed to ensure the reliability and security of communication. Meanwhile, our performance evaluations and simulations show that the proposed framework is more effective than several standard schemes with respect to the ability against various attacks, security, and overhead.

Duty-Cycle-Based Pre-Emption Protocol for Emergency Networks.

This paper proposes a new duty-cycle-based protocol for transmitting emergent data with high priority and low latency in a sensor network environment. To reduce power consumption, the duty cycle protocol is divided into a listen section and a sleep section, and data can only be received when the receiving node is in the listen section. In this paper, high-priority transmission preempts low-priority transmission by distinguishing between high-priority preamble and low-priority preamble. However, even when a high priority transmission preempts a low priority transmission such that the high priority transmission is received first, if the sleep period is very long, the delay may be large. To solve this problem, the high priority short preamble and high priority data reduce receiver sensitivity and increase coverage through repeated transmission. If there are several receiving nodes within a wide coverage, the receiving node that wakes up first can receive the transmission, thus reducing the delay. The delay can also be further reduced by alternately reducing the sleep cycle of one node among the receiving nodes that can receive it. This paper shows that emergent data can be transmitted effectively and reliably by reducing the delay of high-priority data to a minimum through the use of preemption, coverage extension, and an asymmetric sleep cycle.

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

Environment-Monitoring IoT Devices Powered by a TEG Which Converts Thermal Flux between Air and Near-Surface Soil into Electrical Energy.

Energy harvesting has an essential role in the development of reliable devices for environmental wireless sensor networks (EWSN) in the Internet of Things (IoT), without considering the need to replace discharged batteries. Thermoelectric energy is a renewable energy source that can be exploited in order to efficiently charge a battery. The paper presents a simulation of an environment monitoring device powered by a thermoelectric generator (TEG) that harvests energy from the temperature difference between air and soil. The simulation represents a mathematical description of an EWSN, which consists of a sensor model powered by a DC/DC boost converter via a TEG and a load, which simulates data transmission, a control algorithm and data collection. The results section provides a detailed description of the harvested energy parameters and properties and their possibilities for use. The harvested energy allows supplying the load with an average power of 129.04 W and maximum power of 752.27 W. The first part of the results section examines the process of temperature differences and the daily amount of harvested energy. The second part of the results section provides a comprehensive analysis of various settings for the EWSN device’s operational period and sleep consumption. The study investigates the device’s number of operational cycles, quantity of energy used, discharge time, failures and overheads.

Electronic rain meter for mobile sensor node using law of Torricelli

Rain in a city can cause material damage and risk for the population, hence the importance of implementing prevention and mitigation measures. These measures must be taken based on the analysis of the data collected by networks of environmental sensors. The rainfall-meter is one of the instruments used to measure rain, these are designed to operate at a fixed point. Coverage of the entire area of a city requires the installation of several of these elements. This paper shows the development of an electronic rain gauge that can operate in motion applying the principles of fluid dynamics. Two stages are proposed for its elaboration. The first step is the design, construction and testing of the sensor and transducer for the rain gauge. In the second step, the rain gauge communication is implemented. For this, the internet of things technology is incorporated, and the network is designed to provide mobility. The main result is a prototype mobile electronic rain gauge with a measurement error of 8.5%. Besides, mathematical model for the sensor, algorithm for the transducer, and communications architecture are obtained. It can be concluded that, rainfall can be monitoring in a city with few sensitive units in motion.

Routing failure prediction and repairing for AUV-assisted underwater acoustic sensor networks in uncertain ocean environments

Underwater acoustic sensor networks (UASNs) provide temporary links, which is of great significance when it comes to dealing with abnormal situations or emergencies in Internet of underwater things (IoUT). However, UASNs are susceptible to changes and uncertainties in network topology, channel conditions, etc., which can easily lead to frequent link interruptions. In this paper, we introduce a link failure prediction mechanism and an autonomous underwater vehicle (AUV)-assisted routing holes repairing mechanism for routing design of UASNs in uncertain ocean environments, to save system energy consumption and improve network connectivity. The proposed link failure prediction mechanism takes into account residual energy of sensor nodes, node drifting information, and uncertain ocean ambient noise. When the energy of multiple sensor nodes is exhausted, the particle swarm optimization algorithm (PSO) is adopted to calculate the optimal repair location, and an AUV is used for fixed point repairing. The proposed method can effectively reduce the energy consumption of sensor nodes, increase the packet delivery ratio, and extend the life of entire network of UASNs.

Sea Water Quality Estimation Using Machine Learning Algorithms

Oh, H.Y.; Jeong, M.-H.; Jeon, S.B.; Lee, T.Y.; Kim, G., and Youm, M., 2021. Sea water quality estimation using machine learning algorithms. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S.; and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 424–428. Coconut Creek (Florida), ISSN 0749-0208. The water quality index (WQI) determines the quality of drinking water and seawater. Currently, coastal and seawater quality is monitored and managed by classifying it into five grades based on the WQI value in the Republic of Korea. The Korea Marine Environment Management Corporation (KOEM) utilizes automatic environmental sensor networks to monitor the coastal environment. However, these sensor data do not include the essential variables for determining the water quality level. The KOEM manually evaluates the WQI four days per year. This study estimates the water quality level using machine-learning algorithms based on the measurements of the automatic environmental sensor network data. The experiments show that random forest and support vector machine algorithms perform better than other algorithms. The results of this study can be applied to monitor and predict water quality in real time.

Recognizing occupant presence status in residential buildings from environment sensing data by data mining approach

Actual information of occupant presence is an essential input for the simulation of building energy use and home automation system. Previous studies installed motion sensors, cameras, environment sensor networks or smart meters to detect the occupant presence status directly or indirectly in offices. These approaches would raise the household cost and privacy concerns when were applied to residential buildings. Recently, home indoor environment monitors are becoming a part of daily life with the population of smart home products, and massive indoor environment data in the residential buildings could be collected remotely. Therefore, we conducted an experiment in three bedrooms that were already installed with home indoor environment monitors, based on the hypothesis that information of occupant presence could be extracted from indoor environment data by appropriate data mining approach. Two algorithms were designed in the experiment for the data mining, where one recognized the presence status directly from the environment data, and the other indirectly from the usage information of light and AC that were recognized from illuminance, air temperature and relative humidity. Results showed that it was difficult to develop an algorithm that performed well both on the training and testing datasets when we expected to directly mine the occupant presence from indoor environment parameters, but the performance could be greatly improved when we added the bridge information of light and AC operations into the algorithm, and then the recognition accuracy could be improved higher than 92% in all the three rooms.

Predicting of mutton sheep stress coupled with multi-environment sensing and supervised learning network in the transportation process

Stress response caused by transportation is an important reason that affects the health and welfare of livestock, and the intelligent and precise monitoring of the stress state during transportation is a blank in research. On-site tracking experiments, literature review, and expert consultation indicate that environmental temperature, relative humidity and vibration intensity of vehicle are important stressors in the transportation process, and cortisol (COR) and adrenocorticotropic hormone (ACTH) are the key biomarkers to characterize the stress level. In this paper, stressors causing stress response were collected through environmental sensor network, and key biomarkers representing stress response were identified through biochemical analysis, and their change rules were analyzed and compared. Furthermore, the predictive models coupled with environmental parameters and stress markers were constructed through supervised learning networks. The results showed that: (1) After testing, the data collection performance of the multi-sensor network is reliable (P < 0.05); transportation will cause significant changes in the concentration of COR and ACTH (P < 0.05); the simulation results of stress markers data have no significant difference compared with the sample data (PSpline > PPchip > 0.90). (2) The validation results of models showed that: for COR, the absolute error and relative error based on general regression neural network (GRNN) are 2.59 ± 0.16 ng/mL and 1.30 ± 0.07%, respectively, which is better than the accuracy based on back propagation neural network (BPNN) and Elman neural network (Elman); for ACTH, the absolute error and relative error based on GRNN models were 1.75 ± 0.13 pg/mL and 2.14 ± 0.17%, respectively, which were better than the accuracy based on BPNN and Elman models. (3) For the prediction performance, the prediction accuracy of GRNN model for COR concentration is basically equal to that of Elman model, which is 0.17% higher than that of BPNN model for COR concentration, but the maximum relative error of BPNN model for COR concentration is the smallest. The prediction accuracy of Elman model for ACTH concentration is 0.09% higher than that of GRNN model, and 0.29% higher than that of BPNN model. But the maximum relative error of Elman model for ACTH concentration is the largest, and the running time is significantly longer. Although the performance of the three modeling methods is different, their overall accuracy has reached more than 97%. Therefore, this study can effectively identify the stress state of mutton sheep in the actual transportation process, and provide technical support for the health traceability and control of mutton sheep.

Nutrient Analysis in Arctic Waters Using a Portable Sensing Platform

A portable sensing platform for the detection of nutrients (PO43−, NO2−, NO3−) in natural waters has been realized through the use of rapid prototyping techniques, colorimetric chemistries, electronics, and LED-based optical detection. The sensing platform is modular in design incorporating interchangeable optical detection units, with a component cost per unit of ca. €300, and small form factor (20 cm × 6 cm x 3.5 cm). Laboratory testing and validation of the platform was performed prior to deployment at the CNR Dirigibile Italia Arctic Research Station, Ny-Aselund (79°N, 12°E). Results obtained showed excellent linear response, with a limit of detection of 0.05 μM (NO2−, NO3−), and 0.03 μM (PO43−). On the June 22, 2016 a field campaign took place within Kongsfjorden, Ny-Aselund (78.5–79°N, 11.6–12.6°E), during which 55 water samples were acquired using 10 L Niskin bottles on board the MS Teisten research vessel. 23 hydrological casts were also performed using a Seabird 19plus V2 SeaCAT Profiler CTD probe with turbidity and dissolved oxygen sensors. Water samples were subsequently analyzed for PO43−, NO2−, NO3− at the CNR Dirigibile Italia Arctic Research Station Laboratory using the adaptive sensing platform. Nutrient concentrations were compared to hydrological data to assess the processes that influence the nutrient concentrations within the Fjord. This research highlights the potential use of the adaptive sensing platform in remote locations as a stand-alone platform and/or for the validation of deployable environmental sensor networks.

Multiple-Model Mobile Localization Based on Unscented Kalman Filter in Mixed Indoor

In this paper, we present an Interactive multiple mode-Unscented Kalman filter (IMM-UKF) algorithm to achieve mobile node location under wireless sensor networks environments. In the IMM structure, UKF and Variational Bayesian Adaptive Method based on UKF are adapted in parallel, which can improve positioning accuracy in the process of line-of sight (LOS) and non-line-of-sight (NLOS) signal state switching. The estimated values by filtering are fused according to the weighting factors to get the estimated positions. Moreover, when NLOS measurement noise covariance change, we propose Variational Bayesian Adaptive Method based on UKF to improve robustness. Both Simulation and experiments illustrate that the propose algorithm performs can achieve competitive localization accuracy.