Low-cost Sensor Networks Research Articles

Measuring Spatial and Temporal PM2.5 Variations in Sacramento, California, Communities Using a Network of Low-Cost Sensors.

Low-cost sensors can provide insight on the spatio-temporal variability of air pollution, provided that sufficient efforts are made to ensure data quality. Here, 19 AirBeam particulate matter (PM) sensors were deployed from December 2016 to January 2017 to determine the spatial variability of PM2.5 in Sacramento, California. Prior to, and after, the study, the 19 sensors were deployed and collocated at a regulatory air monitoring site. The sensors demonstrated a high degree of precision during all collocated measurement periods (Pearson R2 = 0.98 − 0.99 across all sensors), with little drift. A sensor-specific correction factor was developed such that each sensor reported a comparable value. Sensors had a moderate degree of correlation with regulatory monitors during the study (R2 = 0.60 − 0.68 at two sites). In a multi-linear regression model, the deviation between sensor and reference measurements of PM2.5 had the highest correlation with dew point and relative humidity. Sensor measurements were used to estimate the PM2.5 spatial variability, finding an average pairwise coefficient of divergence of 0.22 and a range of 0.14 to 0.33, indicating mostly homogeneous distributions. No significant difference in the average sensor PM concentrations between environmental justice (EJ) and non-EJ communities (p value = 0.24) was observed.

Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi

Abstract. Wireless low-cost particulate matter sensor networks (WLPMSNs) are transforming air quality monitoring by providing particulate matter (PM) information at finer spatial and temporal resolutions. However, large-scale WLPMSN calibration and maintenance remain a challenge. The manual labor involved in initial calibration by collocation and routine recalibration is intensive. The transferability of the calibration models determined from initial collocation to new deployment sites is questionable, as calibration factors typically vary with the urban heterogeneity of operating conditions and aerosol optical properties. Furthermore, the stability of low-cost sensors can drift or degrade over time. This study presents a simultaneous Gaussian process regression (GPR) and simple linear regression pipeline to calibrate and monitor dense WLPMSNs on the fly by leveraging all available reference monitors across an area without resorting to pre-deployment collocation calibration. We evaluated our method for Delhi, where the PM2.5 measurements of all 22 regulatory reference and 10 low-cost nodes were available for 59 d from 1 January to 31 March 2018 (PM2.5 averaged 138±31 µg m−3 among 22 reference stations), using a leave-one-out cross-validation (CV) over the 22 reference nodes. We showed that our approach can achieve an overall 30 % prediction error (RMSE: 33 µg m−3) at a 24 h scale, and it is robust as it is underscored by the small variability in the GPR model parameters and in the model-produced calibration factors for the low-cost nodes among the 22-fold CV. Of the 22 reference stations, high-quality predictions were observed for those stations whose PM2.5 means were close to the Delhi-wide mean (i.e., 138±31 µg m−3), and relatively poor predictions were observed for those nodes whose means differed substantially from the Delhi-wide mean (particularly on the lower end). We also observed washed-out local variability in PM2.5 across the 10 low-cost sites after calibration using our approach, which stands in marked contrast to the true wide variability across the reference sites. These observations revealed that our proposed technique (and more generally the geostatistical technique) requires high spatial homogeneity in the pollutant concentrations to be fully effective. We further demonstrated that our algorithm performance is insensitive to training window size as the mean prediction error rate and the standard error of the mean (SEM) for the 22 reference stations remained consistent at ∼30 % and ∼3 %–4 %, respectively, when an increment of 2 d of data was included in the model training. The markedly low requirement of our algorithm for training data enables the models to always be nearly the most updated in the field, thus realizing the algorithm's full potential for dynamically surveilling large-scale WLPMSNs by detecting malfunctioning low-cost nodes and tracking the drift with little latency. Our algorithm presented similarly stable 26 %–34 % mean prediction errors and ∼3 %–7 % SEMs over the sampling period when pre-trained on the current week's data and predicting 1 week ahead, and therefore it is suitable for online calibration. Simulations conducted using our algorithm suggest that in addition to dynamic calibration, the algorithm can also be adapted for automated monitoring of large-scale WLPMSNs. In these simulations, the algorithm was able to differentiate malfunctioning low-cost nodes (due to either hardware failure or under the heavy influence of local sources) within a network by identifying aberrant model-generated calibration factors (i.e., slopes close to zero and intercepts close to the Delhi-wide mean of true PM2.5). The algorithm was also able to track the drift of low-cost nodes accurately within 4 % error for all the simulation scenarios. The simulation results showed that ∼20 reference stations are optimum for our solution in Delhi and confirmed that low-cost nodes can extend the spatial precision of a network by decreasing the extent of pure interpolation among only reference stations. Our solution has substantial implications in reducing the amount of manual labor for the calibration and surveillance of extensive WLPMSNs, improving the spatial comprehensiveness of PM evaluation, and enhancing the accuracy of WLPMSNs.

Low Cost Sensor Networks: How Do We Know the Data Are Reliable?

The plausibility of data from networks of low-cost measurement devices is a growing and important contentious issue. Informal networks of low-cost devices have particularly come to prominence for air quality monitoring. The contentious point is the believability of data without regular on-site calibration, since this is a specialist task and the costs very quickly become much larger than the cost of installation in the first place. This Sensor Issues suggests that approaches to the problem that involve appropriate use of independent information have the potential to resolve the contention. Ideas are illustrated particularly with reference to low-cost sensor networks for air quality measurement.

Particulate Matter Measurement Indoors: A Review of Metrics, Sensors, Needs, and Applications.

Many populations spend ∼90% of their time indoors, with household particulate matter being linked to millions of premature deaths worldwide. Particulate matter is currently measured using particle mass, particle number, and particle size distribution metrics, with other metrics, such as particle surface area, likely to be of increasing importance in the future. Particulate mass is measured using gravimetric methods, tapered element oscillating microbalances, and beta attenuation instruments and is best suited to use in compliance monitoring, trend analysis, and high spatial resolution measurements. Particle number concentration is measured by condensation particle counters, optical particle counters, and diffusion chargers. Particle number measurements are best suited to source characterization, trend analysis and ultrafine particle investigations. Particle size distributions are measured by gravimetric impactors, scanning mobility particle sizers, aerodynamic particle sizers, and fast mobility particle sizers. Particle size distribution measurements are most useful in source characterization and particulate matter property investigations, but most measurement options remain expensive and intrusive. However, we are on the cusp of a revolution in indoor air quality monitoring and management. Low-cost sensors have potential to facilitate personalized information about indoor air quality (IAQ), allowing citizens to reduce exposures to PM indoors and to resolve potential dichotomies between promoting healthy IAQ and energy efficient buildings. Indeed, the low cost will put this simple technology in the hands of citizens who wish to monitor their own IAQ in the home or workplace, to inform lifestyle decisions. Low-cost sensor networks also look promising as the solution to measuring spatial distributions of PM indoors, however, there are important sensor/data quality, technological, and ethical barriers to address with this technology. An improved understanding of epidemiology is essential to identify which metrics correlate most with health effects, allowing indoor specific PM standards to be developed and to inform the future of experimental applications.

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

The use of low-cost sensor networks (LCSNs) is becoming increasingly popular in the environmental sciences and the unprecedented monitoring data generated enable research across a wide spectrum of disciplines and applications. However, in particular, non-technical challenges still hinder the broader development and application of LCSNs. This paper reviews the development of LCSNs over the last 15 years, highlighting trends and future opportunities for a diverse range of environmental applications. We found air quality, meteorological and water-related networks were particularly well represented with few studies focusing on sensor networks for ecological systems. Furthermore, we identified bias towards studies that have direct links to human health, safety and livelihoods. These studies were more likely to involve downstream data analytics, visualisations, and multi-stakeholder participation through citizen science initiatives. However, there was a paucity of studies that considered sustainability factors for the development and implementation of LCSNs. Existing LCSNs are largely focussed on detecting and mitigating events which have a direct impact on humans such as flooding, air pollution or geo-hazards, while these applications are important there is a need for future development of LCSNs for monitoring ecosystem structure and function. Our findings highlight three distinct opportunities for future research to unleash the full potential of LCSNs: (1) improvement of links between data collection and downstream activities; (2) the potential to broaden the scope of application systems and fields; and (3) to better integrate stakeholder engagement and sustainable operation to enable longer and greater societal impacts.

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.

Examination of monitoring approaches for ambient air pollution: A case study for India

India faces one of the highest disease burdens from air pollution in the world, with an estimated 100% of the population living in areas with PM2.5 concentrations above the World Health Organization Guideline (10 μg/m3 annual average). With development and population growth, increases in ambient air pollution are anticipated. Combined with an aging population and increasing burden of chronic diseases, ambient air pollution will remain a concern for India well into the current century. Air quality measurements make critical contributions to the identification and prioritization of sources and locations of greatest concern, benchmarking against standards and guidelines, and in the evaluation of effectiveness of actions to reduce emissions. We compare the density of India's monitoring network with that of comparator countries and find large differences. For example, given the ~200 PM2.5 monitoring sites in operation during the 2010–2016 period, we find that India's monitor density of ~0.14 monitors/million persons (1 monitor for every 6.8 million people) is well below that of other highly populated countries such as China (1.2 monitors/million persons), the USA (3.4 monitors/million persons), Japan (0.5 monitors/million persons), Brazil (1.8) and most European countries (2–3 monitors/million persons).To address these gaps between India and monitor densities of comparator countries will require 1600–4000 monitors (1.2–3 monitors/million persons) at an estimated capital (annual operating) cost of US $212–540 ($106–270) million. Even at these densities, only relatively basic information on common air pollutants at high temporal, but limited spatial, resolution would be available. Small-scale variability in air pollution levels within urban areas would not be well-characterized, nor would there be information on chemical constituents useful for evaluating and improving simulations and forecasts, or for characterizing source contributions. As a sufficiently dense traditional network is developed over time, the potential for an integrated monitoring framework to serve as a near-term complement to a traditional network is assessed. In this design, a smaller number of traditional monitoring sites would be linked to a single advanced surface monitoring station in each of ~11 airsheds identified as a minimal number for India. These sites would combine measurement of chemical speciation of particulate matter with measurements of aerosol scatter and aerosol optical depth to link measurements with global and regional satellite-based estimates. In turn, the advanced and traditional sites could serve as calibration nodes for low-cost sensor networks designed to complement periodic mobile monitoring campaigns and/or land use regression models, to provide high spatial and temporal resolution. Such a framework could be established at a substantially reduced cost relative to that of a traditional networks, subject to specific design and complexity considerations. The same general approach may also be applicable to the many other countries with limited or no air quality monitoring and where estimates suggest air quality is a concern.

Energy Efficiency in IoT Networks: Integration of Reconfigurable Antennas in Ultra Low-Power Radio Platforms Based on System-on-Chip

The current state-of-the-art of Internet of Things witnesses the development of low-cost and smart-connected wireless sensor networks (WSNs) placed at the edge of clouds, enabling new services over the Internet. In these interconnected smart sensors, energy consumption is an important issue. This paper deals with decreasing energy consumption in WSN nodes by means of antennas with reconfigurability at radiofrequency. Our research has yielded two important novelties. First, the reconfiguration is accomplished with a platform comprised of a commercial ultra low-power (ULP) micro-controller and system-on-chip radio. Second, we consider explicitly the energy overhead involved in the antenna configuration process and its impact on the global energy efficiency of the system. We discuss two complementary ways of reducing the energy consumption; one by lowering the amount of transmitted power, the other by increasing the transmission rate. We employ an antenna array with a steerable beam, controlled by an algorithm embedded in the firmware of the ULP platform. This is designed for line-of-sight conditions, and the beam steering maximizes the received power. Our results show that with a common IEEE 802.11 b/g system-on-chip module and an antenna gain of 12 dB, a net power saving factor of 28% can be obtained by reducing the transmitted power, at unchanged data rate; additionally, by keeping the transmit power constant, but increasing the data rate, an energy saving from 18% to 96% can be achieved, depending on the link distance and achievable data rate. Our experiments have confirmed both routes of energy saving.

Compressed sensing for electrocardiogram acquisition in wireless body sensor network: A comparative analysis

In the past decades, compressed sensing emerges as a promising technique for signal acquisition in low-cost sensor networks. For prolonging the monitoring duration of biosignals, compressed sensing is also exploited for simultaneous sampling and compression of electrocardiogram signals in the wireless body sensor network. This article presents a comprehensive analysis of compressed sensing for electrocardiogram acquisition. The performances of involved important factors, such as wavelet basis, overcomplete dictionaries, and the reconstruction algorithms, are comparatively illustrated, with the purpose to give data reference for practical applications. Drawn from a bulk of comparative experiments, the potential of compressed sensing in electrocardiogram acquisition is evaluated in different compression levels, while preferred sparsifying basis and reconstruction algorithm are also suggested. Relative perspectives and discussions are also given.

Sources of error and variability in particulate matter sensor network measurements

The quality of mass concentration estimates from increasingly popular networks of low-cost particulate matter sensors depends on accurate conversion of sensor output (e.g., voltage) into gravimetric-equivalent mass concentration, typically using a calibration procedure. This study evaluates two important sources of variability that lead to error in estimating gravimetric-equivalent mass concentration: the temporal changes in sensor calibration and the spatial and temporal variability in gravimetric correction factors. A 40-node sensor network was deployed in a heavy vehicle manufacturing facility for 8 months. At a central location in the facility, particulate matter was continuously measured with three sensors of the network and a traditional, higher-cost photometer, determining the calibration slope and intercept needed to translate sensor output to photometric-equivalent mass concentration. Throughout the facility, during three intensive sampling campaigns, respirable mass concentrations were measured with gravimetric samplers and photometers to determine correction factors needed to adjust photometric-equivalent to gravimetric-equivalent mass concentration. Both field-determined sensor calibration slopes and intercepts were statistically different than those estimated in the laboratory (α = 0.05), emphasizing the importance of aerosol properties when converting voltage to photometric-equivalent mass concentration and the need for field calibration to determine slope. Evidence suggested the sensors’ weekly field calibration slope decreased and intercept increased, indicating the sensors were deteriorating over time. The mean correction factor in the cutting and shot blasting area (2.9) was substantially and statistically lower than that in the machining and welding area (4.6; p = 0.01). Therefore, different correction factors should be determined near different occupational processes to accurately estimate particle mass concentrations.

Development of an integrated modelling-measurement system for near-real-time estimates of harbour activity impact to atmospheric pollution in coastal cities

Several studies focused on harbour impact on air quality of port-cities showing that maritime emissions could be comparable to traffic emissions of a medium-size town having relevant impact on air quality nearby the harbours. In order to plan efficient mitigation strategies, environmental and port authorities have an increasing need of reliable tools for investigating the impact of ships traffic and harbour logistics on atmospheric pollutants. In this work a system based on the integration of measurements collected using a network of low-cost on-line sensors with local scale dispersion modelling was developed. It was implemented, as a pilot action, in the information and management software of the Bari harbour (SE Italy). It can operate in near-real-time, in forecast mode, and on archived data for long-term assessments. It was tested studying the impact of the Bari harbour for the year 2018. The lowest impact was observed for CO, being the pollutant with the lowest share of the contribution of ships (between 8% and 68%) compared with the total harbour contribution. The impacts on PM2.5 and PM10 are larger arriving up to 11.8% of concentrations in the harbour area with an influence of shipping between 44% and 97%. The impact on SO2 is almost solely due to ships and it arrives up to 80% in the harbour area. The impact on NOX ranges from a few percent in the urban area up to 40% in the harbour area and it is the pollutant of major concern regarding compliance with air quality standards.

Distributed estimation over a low-cost sensor network: A Review of state-of-the-art

Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted.

Examining spatiotemporal variability of urban particulate matter and application of high-time resolution data from a network of low-cost air pollution sensors

Traditional air monitoring approaches using regulatory monitors have historically been used to assess regional-scale trends in air pollutants across large geographical areas. Recent advances in air pollution sensor technologies could provide additional information about nearby sources, support the siting of regulatory monitoring stations, and improve our knowledge of finer-scale spatiotemporal variation of ambient air pollutants and their associated health effects. Sensors are now being developed that are much smaller and lower cost than traditional ambient air monitoring systems and are capable of being deployed as a network to provide greater coverage of a given area. The CitySpace project conducted by the US EPA and the Shelby County Health Department included the deployment of a network of 17 sensor pods using Alphasense OPC-N2 particulate matter (PM) sensors integrated with meteorological sensors in Memphis, TN for six months. Sensor pods were collocated with a federal equivalent method (FEM) tapered element oscillating microbalance (TEOM) monitor both before and after the primary study period. Six of the sensor pods were found to meet the data quality objective (DQO) of coefficient of determination (R2) greater than 0.5 when collocated with the TEOM. Seven pods were decommissioned before the end of the study due to mechanical failure. The six pods meeting the DQO were used to examine the spatiotemporal variability of fine PM (PM2.5) across the Memphis area. One site was found to have higher relative PM2.5 concentrations when compared to the other sites in the network. The 1-min data from this sensor pod were evaluated to quantify the regional urban background and local-scale contributions to PM2.5 at that monitoring location. This method found that approximately 20% of the PM2.5 was attributed to local sources at this location, compared to 9% at a local regulatory monitoring site. Additionally, the 1-min data were combined with 1-min wind speed and wind direction data to examine potential sources in the area using the nonparametric trajectory analysis (NTA) technique. This method geographically identified local source areas that contributed to the measured concentrations at the high reading sensor location throughout the course of the study.

A Distributed Network of 100 Black Carbon Sensors for 100 Days of Air Quality Monitoring in West Oakland, California.

Ambient particulate matter (PM) pollution is a major environmental health risk in urban areas. Dense networks of low-cost air quality sensors are emerging to characterize the spatially heterogeneous concentrations that are typical of urban settings, but are not adequately captured using traditional regulatory monitors at central sites. In this study, we present the 100×100 BC Network, a 100-day deployment of low-cost black carbon (BC) sensors across 100 locations in West Oakland, California. This 15 km2 community is surrounded by freeways and affected by emissions associated with local port and industrial activities. We assess the reliability of the sensor hardware and data collection systems, and identify modes of failure to both quantify and qualify network performance. We illustrate how dynamic, local emission sources build upon background BC concentrations. BC concentrations varied sharply over short distances (∼100 m) and timespans (∼1 hour), depending on surrounding land use, traffic patterns, and downwind distance from pollution sources. Strong BC concentration fluctuations were periodically observed over the diurnal and weekly cycles, reflecting the impact of localized traffic emissions and industrial facilities in the neighborhood. Overall, the results demonstrate how distributed sensor networks can reveal the complex spatiotemporal dynamics of combustion-related air pollution within urban neighborhoods.

Low-cost sensor network in cognitive buildings for maintenance optimisation

Buildings cannot perform without a proper planning, execution and monitoring of Facility Management services. Moreover, occupancy monitor procedures for cognitive buildings are gaining momentum in performance optimization through dynamic adaption of indoor conditions (e.g. temperature, humidity, shadings). In this article, the application of a similar approach for automated execution of maintenance contracts (e.g. cleaning services) is presented. This approach is allowed by monitoring occupancy, through an ultrasonic sensor network. The methodology has been validated on a case study, concerning an office building in Milan, Italy.

Collision Resolution Protocol for Delay and Energy Efficient LoRa Networks

Future 5G and Internet of Things (IoT) applications will heavily rely on long-range communication technologies such as low-power wide area networks. In particular, LoRaWAN built on LoRa physical layer is gathering increasing interest, both from academia and industries, for enabling low-cost energy efficient IoT wireless sensor networks for, e.g., environmental monitoring over wide areas. While its communication range may go up to 20 km, the achievable bit rates in LoRaWAN are limited to a few kilobits per second. In the event of collisions, the perceived rate is further reduced due to packet loss and retransmissions. First, to alleviate the harmful impacts of collisions, we propose a decoding algorithm that enables to resolve several superposed LoRa signals. Our proposed method exploits the slight desynchronization of superposed signals and specific features of LoRa physical layer. Secondly, we design a full MAC protocol enabling collision resolution. The simulation results demonstrate that the proposed method outperforms conventional LoRaWAN jointly in terms of system throughput, energy efficiency as well as delay. These results show that our scheme is well suited for 5G and IoT systems, as one of their major goals is to provide the best trade-off among these performance objectives.

Air pollution monitoring network using low-cost sensors, a case study in Hanoi, Vietnam

Air pollution is a serious problem in Vietnam, especially in urban areas with high pressures of population, traffic, construction, and industrial development. Besides high accurate measurements from automatic and continuous monitoring ground stations and high-cost sensor devices, low-cost sensors have recently utilized to extent air pollution monitoring networks although their data quality is still argumentative. In this paper, we present a low-cost device, named FAirKit, which measured 6 basic air pollutants including PM2.5, PM10, CO, O3, NO2, and SO2, and temperature and relative humidity. The sensors are calibrated with standard devices to improve their data quality. FAirKits are installed and transferred data in real-time to servers where an information system based on Sensor Web Enablement (SWE) standard of Open Geospatial Consortium (OGC) has been developed to store, process, and visualize real-time air pollution information. Currently, the low-cost sensors network has been deploying in Hanoi, Vietnam to enhance public awareness and alert local people to air pollution.

Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps.

We present an approach to analyzing fine particulate matter (PM2.5) data from a network of “low cost air quality monitors” (LCAQM) to obtain a finely resolved concentration map. In the approach, based on a dispersion model, we first identify the probable locations of the sources, and then estimate the magnitudes of the emissions from these sources by fitting model estimates of concentrations to corresponding measurements. The emissions are then used to estimate concentrations on a grid covering the domain of interest. The residuals between model estimates at the monitor locations and the measured concentrations are then interpolated to the grid points using Kriging. We illustrate this approach by applying it to a network of 20 LCAQMs located in the Imperial Valley of Southern California. Estimating the underlying mean concentration field with a dispersion model provides a more realistic estimate of the spatial distribution of PM2.5 concentrations than that from the Kriging observations directly.

Performability of Retransmission of Loss Packets in Wireless Sensor Networks

Latest progress in wireless communication technology has enabled the development of low-cost sensor networks with major concern on quality of service (QoS) provisioning. Wireless sensor networks (WSNs) can be adopted in various application domains but each use is likely to pose peculiar technical issues. Basically, we demonstrate that congestion, packet loss and delay have strong influence on the performance of WSNs. In order to implement a realistic sensor network policy to resolve the problem of data delay and avoidance of collisions that lead to packet losses, we develop a system that guarantees QoS in WSNs using Fuzzy Logic Controller (FLC) for sensitivity analysis of the effect of adaptive forward error correction (AFEC). The AFEC approach improves the throughput by dynamically tuning FEC subject to the nature of wireless channel loss thereby optimizing throughput, sensor power utilization, while minimizing traffic retransmission, bit error rate (BER), and energy consumption. Basically, parameters such as packet delivery ratio, packet loss, delay, error rate, and throughput are appraised. The system has a spread procedure which is able to schedule the transmission of the nodes in order to have a data flow that converges from the furthest nodes toward the fusion centre. The key benefit of the scenario showed that, after extensive simulation using realistic field data, the procedure permits a practical approach to obtaining optimal solution to loss packets retransmission problem in WSNs giving a strong improvement on QoS provisioning.

Review of the Techniques Against the Wormhole Attacks on Wireless Sensor Networks

With the advancement of technology, the application of Wireless Sensor Networks (WSNs) has increased in various fields, such as; military, medical and business. WSNs have the ability to control geographic areas and obtaining data and processing it in different environments. Sensor nodes independently sample local information and if necessary send this information to neighboring sensors and eventually send it to the well. These networks are independent and autonomous and humans have no interference in it. In particular, all nodes are identical, and in practice they work together to meet the overall purpose of the network. Due to inherent constraints in the resources and computing power of sensor nodes, security in WSNs has different challenges in comparison with security in traditional computer networks. Low-cost sensor networks allow the expansion of sensor groups in a variety of environments which are capable of operating in military fields and in urban areas. On the other hand, unreliable communication channels in many applications make it more difficult to provide information security in these networks. One of the common attacks in WSNs is a wormhole attack. In this paper, we will review and evaluate paper related to the wormhole attack.