Reliable Sensor Networks Research Articles

Method for Distributed Node Metric Measurement in Wireless Sensor Networks Based on Multiple Attributes

Traditional sensors require large power inputs and accuracy cannot be guaranteed. In this study, we introduce a revolutionary technique to enhance the accuracy of node measurements in Wi-Fi sensor networks (WSNs) while minimizing power consumption. Our approach focuses on a mixture of key attributes, including overall strength performance, landmark strength, and node geolocation. We have set quantitative measures for these attributes, which is the foundation of our sizing method. The basic framework for evaluating node allocation indicators is superior, mainly based on the multifaceted attributes of individual nodes and adjacent nodes, promoting selection in data transmission and processing. This framework has a powerful statistical collection mechanism that can accumulate every intrinsic attribute, including external attributes such as overall power performance, signal energy, and geographic region. Using these collected records, our techniques include selecting methods for metric calculations. It is worth noting that we have merged a set of adaptive strategies that can dynamically adjust measurement parameters based on discovered community conditions. The adaptability of this strategy ensures normal operational performance in various community states, achieving a balance between measurement accuracy and energy conservation. This method ensures a significant improvement in the operational performance of wireless sensor networks and has broad applicability in the field of reliable high-intensity sensor networks.

Distance-Based Queuing for Scalable and Reliable Linear Wireless Sensor Networks in Smart Cities.

The reliability and scalability of Linear Wireless Sensor Networks (LWSNs) are limited by the high packet loss probabilities (PLP) experienced by the packets generated at nodes far from the sink node. This is an important limitation in Smart City applications, where timely data collection is critical for decision making. Unfortunately, previous works have not addressed this problem and have only focused on improving the network's overall performance. In this work, we propose a Distance-Based Queuing (DBQ) scheme that can be incorporated into MAC protocols for LWSNs to improve reliability and scalability without requiring extra local processing or additional signaling at the nodes. The DBQ scheme prioritizes the transmission of relay packets based on their hop distance to the sink node, ensuring that all packets experience the same PLP. To evaluate the effectiveness of our proposal, we developed an analytical model and conducted extensive discrete-event simulations. Our numerical results demonstrate that the DBQ scheme significantly improves the reliability and scalability of the network by achieving the same average PLP and throughput for all nodes, regardless of traffic intensities and network sizes.

A virtual sensor network for pressure distribution inside a multi-zone building based on spatial adjacency relationships and multivariate adaptive regression spline

Airflows within multi-zone buildings cause variations in environmental parameters both vertically and horizontally. Although it is difficult to monitor the airflows through existing sensors, pressure monitoring allows the identification of airflows, since airflows across zones are induced by pressure differences. This study proposes a data-driven method for establishing a virtual pressure sensor network within a multi-zone building. Three scenarios with various sample sizes are applied to evaluate their impacts on the analyzed results, and scenario 3 is identified as the most suitable database for creating a virtual sensor network. The single-sided or multi-sided spatial adjacency relationships are determined for each zone, and the MARS model is then applied to develop the relationships among the variables, with key physical sensors designated by the frequency analysis of independent variables. The final sensor network comprises four key physical sensors, thirteen direct virtual sensors, and five indirect virtual sensors. All virtual sensors can achieve excellent predicted results with high R2 values and low MAE and RMSE, with mean values of 0.997 Pa and 1.269 Pa, respectively. Notably, the difference in accuracy between direct and indirect virtual sensors is influenced by spatial adjacency relationships. This study demonstrates a reliable virtual sensor network for predicting pressure variations and provides a reference for the identification of airflows and the efficient adjustment of HVAC systems.

ACC-LDPC and ACC-RS/LDPC Schemes for Reliable and Energy-Efficient Multi-hop Wireless Sensor Network

ACC-LDPC and ACC-RS/LDPC Schemes for Reliable and Energy-Efficient Multi-hop Wireless Sensor Network

Ensuring Data Freshness in Wireless Monitoring Networks: Age-of-Information-Sensitive Coverage and Energy Efficiency Perspectives

With the development of IoT, the sensor usage has been elevated to a new level, and it becomes more crucial to maintain reliable sensor networks. In this paper, we provide how to efficiently and reliably manage the sensor monitoring system for ensuring data freshness. A sensor transmits its sensing information regularly to the data center (DC), and the sensed information generally has the correlation over the space and the time. Hence, the accuracy of the estimated information from the sensed information decreases as the target location for information estimation is further away from the sensor or the AoI, the elapsed time from the information generation, increases. Therefore, by considering the effect of the AoI and the distance from the sensor on the estimation error, we newly define an error-tolerable sensing (ETS) coverage as the area that the estimated information at the DC is with smaller error than the target value. To analyze the impact of the AoI on the ETS coverage, we derive the η-coverage probability, which is the probability that the ETS coverage is greater than η ratio of the maximum sensor coverage, and the average ETS coverage. We also provide the optimal transmission power of the sensor, which minimizes the average energy consumption while guaranteeing certain level of the η-coverage probability. Numerical results validate the theoretical analysis and show the tendency of the optimal transmission power according to the maximum number of retransmissions. This paper can pave the way to efficient design of the AoI-sensitive sensor networks.

A reliable routing mechanism with energy-efficient node selection for data transmission using a genetic algorithm in wireless sensor network

Energy-efficient and reliable data routing is critical in Wireless Sensor Networks (WSNs) application scenarios. Due to oscillations in wireless links in adverse environmental conditions, sensed data may not be sent to a sink node. As a result of wireless connectivity fluctuations, packet loss may occur. However, retransmission-based approaches are used to improve reliable data delivery. These approaches need a high quantity of data transfers for reliable data collection. Energy usage and packet delivery delays increase as a result of an increase in data transmissions. An energy-efficient data collection approach based on a genetic algorithm has been suggested in this paper to determine the most energy-efficient and reliable data routing in wireless sensor networks. The proposed algorithm reduced the number of data transmissions, energy consumption, and delay in network packet delivery. However, increased network lifetime. Furthermore, simulation results demonstrated the efficacy of the proposed method, considering the parameters energy consumption, network lifetime, number of data transmissions, and average delivery delay.

Single Peak Fiber Bragg Grating Sensors in Tapered Multimode Polymer Optical Fibers

This research demonstrates how low loss commercially available multi-mode (MM), perfluorinated polymer optical fibers (POFs) can be used for robust and reliable fiber Bragg grating (FBG) based sensing. A single peak FBG reflection spectrum is achieved by tapering the fiber to a diameter just small enough to make the fiber effectively single-moded and then inscribing the FBG in the waist section of the taper. Here we use plane-by-plane, direct writing inscription with a femtosecond laser. In particular we used the GigaPOF-50SR MM fiber and tapered it by a factor of 0.24, which was found to be the critical ratio that provided a single grating reflection peak. The GigaPOF-50SR fiber was chosen because it allows operation at 1550 nm and has a suitable small 50 μm core diameter, which minimizes the required taper ratio and thus the insertion loss of the taper. The FBG sensor was found to have a strain and relative humidity sensitivity of 20 nm/%strain and ∼ 6.7 pm/%RH, respectively. The grating proved to be largely insensitive to temperature. This approach to single-mode POF sensor fabrication will enable multi-point strain sensing at 1550 nm over 50 m long fiber. It combines the main advantages of single mode POFs (robust FBG sensing) and MM POFs (low transmission loss) and demonstrates that tapered MM POFs constitute an efficient solution for the development of robust and reliable long distance POF FBG sensor networks.

Reliable self-healing FBG sensor network for improvement of multipoint strain sensing

In this paper, we proposed and designed reliable self-healing fiber Bragg grating (FBG) sensor network for improvement of multipoint strain sensing. We use an optical switch (OSW) to reconfigure the sensor network and improve the self-healing function during fault happen in the sensor network. In this work, to prove and validate the detection performance of our proposed Bragg wavelength detection technique even when the number of overlap spectra of FBGs increases, we conduct three experiments and collect three different cases of strain data (namely case 1, case 2, and case 3) by applying different strain steps to FBGs. In case 1 experiment, strain was applied to only FBG1 sensor, while the other four FBGs keeps fixed. In case 2 experiment, strain was applied to FBG1 and FBG2 sensors at the same time and with different strain steps. In case 3 experiment, strain was applied to FBG1, FBG2 and FBG3 sensors at the same time and with different strain steps. As a result, in all cases of the experiment, three situations of spectra were introduced between FBGs like non-overlapped, partially overlapped and completely overlapped spectra. To solve this overlap problem, we used deep learning technique to accurately identify the Bragg wavelength of FBGs in the condition of the partially or fully overlapped spectra. Therefore, our proposed FBG sensor system can improve the reliability and detection accuracy of the sensor system even the number of overlaps FBGs spectra increases.

Variable Link Performance Due to Weather Effects in a Long-Range, Low-Power LoRa Sensor Network.

When aiming for the wider deployment of low-power sensor networks, the use of sub-GHz frequency bands shows a lot of promise in terms of robustness and minimal power consumption. Yet, when deploying such sensor networks over larger areas, the link quality can be impacted by a host of factors. Therefore, this contribution demonstrates the performance of several links in a real-world, research-oriented sensor network deployed in a (sub)urban environment. Several link characteristics are presented and analysed, exposing frequent signal deterioration and, more rarely, signal strength enhancement along certain long-distance wireless links. A connection is made between received power levels and seasonal weather changes and events. The irregular link performance presented in this paper is found to be genuinely disruptive when pushing sensor-networks to their limits in terms of range and power use. This work aims to give an indication of the severity of these effects in order to enable the design of truly reliable sensor networks.

Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks.

Smart buildings benefit from IEEE 802.15.4e time slotted channel hopping (TSCH) medium access for creating reliable and power aware wireless sensor and actuator networks (WSANs). As in these networks, sensors are supposed to communicate to each other and with actuators, IPv6 multicast forwarding is seen as a valuable means to reduce traffic. A promising approach to multicast, based on the Routing Protocol for Low Power and Lossy Networks (RPL) is Bidirectional Multicast RPL Forwarding (BMRF). This paper aimed to analyze the performance of BMRF over TSCH. The authors investigated how an adequate TSCH scheduler can help to achieve a requested quality of service (QoS). A theoretical model for the delay and energy consumption of BMRF over TSCH is presented. Next, BMRF’s link layer (LL) unicast and LL broadcast forwarding modes were analyzed on restricted and realistic topologies. On topologies with increased interference, BMRF’s LL broadcast on top of TSCH causes high energy consumption, mainly because of the amount of energy needed to run the schedule, but it significantly improves packet delivery ratio and delay compared to ContikiMAC under the same conditions. In most cases, the LL unicast was found to outperform the LL broadcast, but the latter can be beneficial to certain applications, especially those sensitive to delays.

Feasibility of Harvesting Solar Energy for Self-Powered Environmental Wireless Sensor Nodes

Energy harvesting has a vital role in building reliable Environmental Wireless Sensor Networks (EWSNs), without needing to replace a discharged battery. Solar energy is one of the main renewable energy sources that can be used to efficiently charge a battery. This paper introduces two solar energy harvesters and their power measurements at different light conditions in order to charge rechargeable AA batteries powering EWSN nodes. The first harvester is a primitive energy harvesting circuit that is built using elementary off-shelf components, while the second harvester is based on a commercial boost converter chip. To prove the effectiveness of harvesting solar energy, five EWSN nodes were distributed at a nature reserve (the Audubon Society of Western Pennsylvania, USA) and the sunlight at their locations was recorded for more than five months. For each recorded illumination, the corresponding harvested energy has been estimated and compared with the average energy consumption of the EWSN with the most power consumption. The results show that the daily harvested energy effectively compensates for the energy consumption of the EWSN nodes, and the battery charge capacity of 295 mAh can reliably support their daily dynamic energy consumption.

WSN Based Embedded System for Field Parameter Monitoring Inside a Low-Cost Polyhouse

Background & Objective:: This paper presents a wireless sensor network for monitoring field parameters inside a low-cost polyhouse. The micro climate inside a polyhouse differs from that on the outside, which provides a favorable condition for unseasonal crops. Methods: The physical parameters associated with the polyhouse’s microclimate were monitored by a reliable low-cost wireless sensor network, which in turn helps to take decisions for enhancing yield quality and quantity. Sensor network development, signal conditioning, calibration of the soil temperature measurement system and field experience of the installed system are discussed in this paper. The field parameters for the growing period of cucumber (Cucumis sativus) inside the polyhouse are provided in the paper. Results & Conclusion: It showed significant variations in temperature, relative humidity and wind speed inside the polyhouse to that of the outside. It was also observed that soil temperature, soil moisture in mulched soil differed from that of the open condition. Enhancement of the crop yield was found for mulched soil.

Porosity Modulated High-Performance Piezoelectric Nanogenerator Based on Organic/Inorganic Nanomaterials for Self-Powered Structural Health Monitoring.

In the modern era, structural health monitoring (SHM) is critically important and indispensable in the aerospace industry as an effective measure to enhance the safety and consistency of aircraft structures by deploying a reliable sensor network. The deployment of built-in sensor networks enables uninterrupted structural integrity monitoring of an aircraft, providing crucial information on operation condition, deformation, and potential damage to the structure. Sustainable and durable piezoelectric nanogenerators (PENGs) with good flexibility, high performance, and superior reliability are promising candidates for powering wireless sensor networks, particularly for aerospace SHM applications. This research demonstrates a self-powered wireless sensing system based on a porous polyvinylidene fluoride (PVDF)-based PENG, which is prominently anticipated for developing auto-operated sensor networks. Our reported porous PVDF film is made from a flexible piezoelectric polymer (PVDF) and inorganic zinc oxide (ZnO) nanoparticles. The fabricated porous PVDF-based PENG demonstrates ∼11 times and ∼8 times enhancement of output current and voltage, respectively, compared to a pure PVDF-based PENG. The porous PVDF-based PENG can produce a peak-to-peak short-circuit current of 22 μA, a peak-to-peak open-circuit voltage of 84.5 V, a peak output power of 0.46 mW , and a peak output power density of 41.02 μW/cm2 (P/A). By harnessing energy from minute vibrations, the fabricated porous PVDF-based PENG device (area of A = 11.33 cm2) can generate sufficient electrical energy to power up a customized wireless sensing and communication unit and transfer sensor data every ∼4 min. The PENG can generate sufficient electrical energy from an automobile car vibration, which reflects the scenario of potential real-life SHM systems. We anticipate that this high-performance porous PVDF-based PENG can act as a reliable power source for the sensor networks in aircraft, which minimizes potential safety risks.

A Reliable Sensor Network Infrastructure for Electric Vehicles to Enable Dynamic Wireless Charging Based on Machine Learning Technique

In this paper, a hybrid scheme of Dynamic wireless charging (DWC) for electric vehicles EV(s) is proposed to resolve this issue in a network topological infrastructure. The proposed hybrid scheme uses different parameters to allow DWC in EVs. The network infrastructure was established through an enhanced destination sequential distance vector (Enhanced-DSDV) protocol for participating EVs. The DWC charge between paired EV(s) was enabled by magnetic coupling, where the Charge State Estimator (CSE) was used as an unsupervised machine learning technique to learn the current charging status of each EV. Similarly, the captured data of CSE is shared via embedded wireless nodes in the network following enhanced-DSDV routing protocol. Moreover, the proposed model enables each participating EV to transfer charge to another EV participating in the network in DWC environment. To allow, the drivers to monitor the participating EVs in close proximity with their current charge status, location, and distance information, we have have used a dashboard screen in each EV. In addition, each EV uses a generator to produce a magnetic field for magnetic coupling between paired EV(s) to exchange power in wireless environment. The feasibility of the proposed model was thoroughly examined in the real environment of DWC. The results show that the proposed scheme is reliable in terms of DWC in both static and dynamic. Moreover, the enhanced-DSDV routing protocol performed significantly well than existing schemes particularly in terms of throughput, packet lost ratio and latency.

Fully automated snow depth measurements from time-lapse images applying a convolutional neural network

Time-lapse cameras in combination with simple measuring rods can form a highly reliable low-cost sensor network monitoring snow depth in a high spatial and temporal resolution. Depending on the number of cameras and the temporal recording resolution, such a network produces large sets of image time series. In order to extract the snow depth time series from these collections of images in acceptable time, automated processing methods have to be applied. Besides classic image processing based on edge detection methods, there are nowadays ready-to-use convolutional neural network frameworks like Mask R-CNN that facilitate instance segmentation and thus allow for fully automated snow depth measurements from images using a detectable measuring rod. This study investigates the applicability of Mask R-CNN embedded in a newly developed work flow for snow depth measurements. The new method is compared to an automated image processing method carried out utilizing functionalities provided by the OpenCV library. The quality of both methods was assessed with the inclusion of manual evaluations of the image series. As a result, the newly introduced work flow outperforms the present classic image processing method in regard to stability, accuracy and portability. By applying the Mask R-CNN framework, the overall RMSE of two considered time series is reduced to approximately 20% of the value produced by means of the classic image processing approach. Moreover, the ratio of values within five centimeter deviation from the reference value was increased from 75% to 88% on average. Since no parameters have to be adjusted, the Mask R-CNN framework is able to detect known shapes reliably in almost any environment, making the presented method highly flexible.

A Short Preamble Cognitive MAC Protocol in Cognitive Radio Sensor Networks

Cognitive radio provides a promising solution to reliable and time-efficient wireless sensor networks. However, cognitive capability brings a quite challenging issue to energy-constrained cognitive radio sensor networks (CRSNs) since large energy consumption is required for spectrum sensing and opportunistic access. Medium access control (MAC) is critical for cognitive sensors due to its influence on energy-consuming transceivers. This work proposes a short preamble cognitive MAC (SPC-MAC) protocol for CRSNs. The major contribution of SPC-MAC is the smart combination of short preamble sampling and opportunistic forwarding. As a result, SPC-MAC could support reliable and fast spectrum access while reducing energy consumption. Furthermore, SPC-MAC is a distributed cognitive MAC protocol without requiring any common control channels. The protocol modeling for SPC-MAC is performed rigorously. The analytical and simulation results validate the superior performance of SPC-MAC.

Design of network coding based reliable sensor networks

Network coding can be very useful in achieving a balance between energy efficiency and end-to-end packet error in sensor networks, particularly if only a subset of the nodes act as encoding nodes. In this article, a mathematical model and a heuristic algorithm are proposed to plan for the best placement of encoding nodes while ensuring reliability. These approaches are also able to address scenarios where sensor networks, using different gateways, are federated. In this case a distributed storage system is required to ensure the recovery of packets when related coded packets arrive to the different gateways. The experimental results show that the adequate number and placement of encoding nodes can be effectively determined, significantly enhancing the performance of constrained sensor networks using network coding.

Routing Attacks Detection Method of Wireless Sensor Network

Security is a critical challenge for creating robust and reliable sensor networks. For the particularity and security threats of wireless sensor networks, we proposed an anomaly detection method based on particle swarm optimization K-means clustering algorithm to detect routing attacks caused by abnormal flows in this paper. K-means clustering algorithm is an effective method has been proved for apply to the intrusion detection system, but it is a part of the optimal solution, rather than the overall optimal solution. Particle swarm optimization (PSO) algorithm which is evolutionary computation technology based on swarm intelligence has good global search ability. With the deficiency of global search ability for K-means clustering algorithm, K-means clustering algorithm based on particle swarm optimization (PSO-KM) is proposed in this paper. This algorithm could overcome falling into local minima and has relatively good overall convergence. Experiments on data sets KDD CUP 99 validate the effectiveness of the proposed method and show that the method has higher detection rate and lower false detection rate.

Connectivity analysis of underground sensors in wireless underground sensor networks

Wireless underground sensor networks consist of sensors that are buried under the ground and communicate through soil medium. Due to channel characteristics, the connectivity analysis of wireless underground sensor networks is more complicated than that in the traditional over-the-air wireless sensor networks. This paper focuses on analyzing the connectivity of underground sensors in wireless underground sensor networks in terms of the probability of node isolation and path probability which captures the effects of the environment parameters such as soil moisture and soil composition, and system parameters such as sensor node density and propagation techniques. Throughout this paper, both qualitative and quantitative comparisons between electromagnetic wave system and ordinary magnetic induction system for underground communications are provided. More specifically, we derive the exact closed-form mathematical expressions for the probability of node isolation of these two communication systems and validate the correctness of analytical models through simulations. We also provide the simulation-based path connectivity of these two communication systems. The results obtained in this paper provide useful guidelines on the design of reliable wireless underground sensor networks.

CACA-UAN: a context-aware communication approach to efficient and reliable underwater acoustic sensor networks

CACA-UAN: a context-aware communication approach to efficient and reliable underwater acoustic sensor networks