Design and Construction of a Weather Instrument and Its Use in Measurements to Determine the Effects of Some Weather Parameters on GSM Signal Strength

Spatial and temporal changes that transmitted radio signals may go through are attributed to variations in the atmospheric conditions as well as other environmental factors. This work evaluates and establishes some atmospheric and environmental variables that have a dominating impact on temporal signal strength fluctuations that are experienced even on a fixed location. The average refractivity gradient dN/dh computed from hourly measurement taken at a fixed location for seven days was -61.3 N/km and so the average propagation conditions correspond to the normal mode, although super refraction was to be expected at about 10 am and 8 pm. On the overall, the variation in dN/dh does not actually explain the temporal variations in the received signal Pr, since the correlation between the variables is as low as 0.091. Among the environmental factors investigated for their effect on signal strength fluctuations, receiver location has a dominating impact. Virtually all weather phenomena take place in the troposphere which is the portion of the Earth's atmosphere that extends from the surface of the Earth to a height of about 6 km at the Poles and 18 km at the equator. The temperature in this region decreases rapidly with altitude, clouds form, and there may be much turbulence because of variations in temperature, density, and pressure. These fluctuations in the atmospheric parameters like temperature, pressure and humidity in the troposphere are said to cause the refractive index of the air in this layer to vary from one point to another (14). This study evaluates the correlation between instantaneous or temporal signal strength fluctuation and the associated refractivity gradient based on hourly data of the atmospheric parameters obtained from Nigerian Meteorological Centre, Bauchi station and the simultaneous hourly GSM Signal Strength measured at a fixed location.

Effects of some weather variables on the signal strength of Maloney FM radio, Nasarawa State, Nigeria

In this paper, we study the characteristics and variation of RF signal strength and electromagnetic (EM) field pattern in a conveyor-based RFID application, and their impact on readability of passive UHF RFID tags. Read-rate (number of reads per second) and received signal strength indicator (RSSI) are employed as a measure of the signal strength within the EM field. It is shown that EM field patterns significantly change depending on the position of tagged case(s) containing metal products, and the number of cases within the read zone. The presence of metal can create read null points in proximity distance to the reader resulting from multipath reflections from objects in the conveyor system environment. Understanding how such significant factors affect the signal strength and EM field pattern is essential for designing an optimised RFID system to achieve a desired level of performance.

Many wireless sensor networks operating outdoors are exposed to changing weather conditions, which may cause severe degradation in system performance. Therefore, it is essential to explore the factors affecting radio link quality in order to mitigate their impact and to adapt to varying conditions. In this paper, we study the effects of temperature and humidity on radio signal strength in outdoor wireless sensor networks. Experimental measurements were performed using Atmel ZigBit 2.4GHz wireless modules, both in summer and wintertime. We employed all the radio channels specified by IEEE 802.15.4 for 2.4GHz ISM frequency band with two transmit power levels. The results show that changes in weather conditions affect received signal strength. Of the studied weather variables, variation in signal strength can be best explained by the variation in temperature. We also show that frequency diversity can reduce the effects of channel-specific variation, and the difference between the transmit power levels.

Abstract. The lack of observations near the surface is often cited as a limiting factor in the observation and prediction of deep convection. Recently, networks of personal weather stations (PWSs) measuring pressure, temperature and humidity in near-real time have been rapidly developing. Even if they suffer from quality issues, their high temporal resolution and their higher spatial density than standard weather station (SWS) networks have aroused interest in using them to observe deep convection. In this study, the PWS contribution to the observation of deep-convection features near the ground is evaluated. Four cases of deep convection in 2018 over France were considered using data from Netatmo, a PWS manufacturer. A fully automatic PWS processing algorithm, including PWS quality control, was developed. After processing, the mean number of observations available increased by a factor of 134 in mean sea level pressure (MSLP), of 11 in temperature and of 14 in relative humidity over the areas of study. Near-surface SWS analyses and analyses comprising standard and personal weather stations (SPWSs) were built. The usefulness of crowdsourced data was proven both objectively and subjectively for deep-convection observation. Objective validations of SWS and SPWS analyses by leave-one-out cross validation (LOOCV) were performed using SWSs as the validation dataset. Over the four cases, LOOCV root-mean-square errors (RMSEs) decreased for all parameters in SPWS analyses compared to SWS analyses. RMSEs decreased by 73 % to 77 % in MSLP, 12 % to 23 % in temperature and 17 % to 21 % in relative humidity. Subjectively, fine-scale structures showed up in SPWS analyses, while being partly, or not at all, visible in SWS observations only. MSLP jumps accompanying squall lines or individual cells were observed as well as wake lows at the rear of these lines. Temperature drops and humidity rises accompanying most of the storms were observed sooner and at a finer resolution in SPWS analyses than in SWS analyses. The virtual potential temperature was spatialized at an unprecedented spatial resolution. This provided the opportunity for observing cold-pool propagation and secondary convective initiation over areas with high virtual potential temperatures, i.e. favourable locations for near-surface parcel lifting.

Signal strength received by a fixed antenna increased as a transmitter was positioned closer to it and decreased as the transmitter was positioned further away. Depth and transmitter orientation also influenced signal strength, but these were less pronounced than the effect of distance. The variations in signal strength recorded from fixed distances, depths or orientations were low, suggesting that variation in signal strength only occurs if the transmitter is moved. The use of signal strength variation as a measure of fish activity over a 24 h period was compared with observed patterns of point habitat use of a live fish. Close correspondence of assessments of activity using signal strength variation and habitat use was observed. This study demonstrated the potential utility of radiotelemetry in association with point‐in‐time habitat use data to determine home‐range and diel and seasonal patterns of fish activity over 24 h periods.

This paper focuses on the determination of the diurnal variation of signal strength generated by Orient 94.4 FM transmitter along six (6) selected route in Imo State, Nigeria. This was carried out with the aid of a constructed signal strength meter (SSM). Signal strength measurements were collected at different time, on different days, and in different months. The measurement was carried out at a constant distance of 20 Km. Arrangement was made with the management of the base station to ensure that the transmitting parameters were kept constant throughout the period of signal strength measurement. The average results of these measurements were taken. The data obtained from the measurements was plotted in a graph to establish the diurnal variation in signal strength along the different routes of signal strength measurement. It was observed from this research that transmission and reception of signals are dependent on the time of the day. High signal strength was noted between the hours of 8 am and 11 am, while low signal strength was recorded between the hours of 1 pm and 5 pm. Better signal strengths were recorded at night. The result of this study shows that signal strength generated by FM transmitter vary with time of the day and the prevalent weather conditions. Stronger signals are recorded mostly at night along the different routes of the study. The signal strength consequently drops in the afternoon and recovers in the morning hours.

Signal Strength Reduction Effects in OCT Angiography

When thunderstorms develop in the atmosphere, they cause changes in temperature, relative humidity, pressure, wind and precipitation near the surface that can be detected by weather stations. However, standard weather station networks, which are maintained by national meteorological and hydrological services, cannot observe all of these variations, particularly those whose characteristic scale is smaller than the meso-γ-scale (2-20 km). One opportunistic solution for obtaining spatially denser observations comes from citizen science. Advances in wireless communication networks allow an increasing number of objects to be connected to the Internet (IoT for Internet of Things). These objects include personal weather stations, also known as citizen weather stations. In mainland France, the number of active personal weather stations providing real-time observations exceeded that of standard weather stations by a factor of approximately 40 in 2020.Although there are many personal weather stations, the quality of their observations varies greatly due to the variable quality of their physical sensors, their highly heterogeneous installation environments, and their maintenance, among other reasons. This usually limits the use of this data unless it is coupled with strict quality control. Several fully automatic quality control algorithms have been developed in the scientific literature. One such example is the Mandement and Caumont (2020) algorithm, whose general principle is to statistically check surface pressure, temperature, and relative humidity observations against those from neighbouring standard weather stations, in such a way as to retain as much as possible the sudden variations caused by deep convection.This algorithm has been adapted for real-time use, and the quality-controlled observations it produces are used in high-frequency meteorological analyses of temperature, relative humidity, and mean sea level pressure, over France. Case studies in which fine-scale structures appear in these analyses but are either partly or completely absent from analyses that only include observations from standard weather stations, will be presented. These fine-scale structures include temperature drops, humidity rise and mean sea level pressure jumps that accompany squall lines or individual convective cells, as well as oscillations that are associated with gravity waves triggered by deep convection. Initiatives launched at a European level to concentrate and share personal weather station observations will also be presented.

Monitoring the onset of a geo-event such as the intrusion of a chemical plume or a slow progressive mass slide that results in marked changes in the physical properties of the host soil could be potentially accomplished using a distributed network of embedded radio transceivers. This paper introduces a new concept of subsurface geo-event monitoring, which takes advantage of the spatial and temporal variations in signal strength of electromagnetic (EM) waves transmitted within the net of distributed radios within a sensing area. Results of experiments in the laboratory and the field demonstrated that variations in EM signal strength could be used to detect physical changes in the subsurface. Changes in selected physical properties of host soil including water content, density, and formation of discontinuities could be discerned from the changes in the signal strength of the transmitted wave between embedded radio transceivers. Good agreement was observed between a theoretical model and the experimental results for inter-transceiver distances less than 55 cm. These results demonstrated a viable new approach for distributed sensing and monitoring of subsurface hazards for civil infrastructure within a networked domain of radio transceivers.

Device-free passive localization can be used for detecting physical activity being exhibited by an individual in a typical indoor environment, only by examining the variations in wireless signal strength caused by that activity. This paper uses machine learning classifiers to distinguish between four physical activities performed by an individual in a controlled indoor setting. The activities of interest include two diagonal walking movements in opposite directions, and two similar movements culminating in the individual abruptly stopping to emulate a fall. It has been shown in this paper that an analysis of variations in signal strength can accurately distinguish between the concerned physical activities. This paper is a step towards passively and non-intrusively detecting whether an individual has fallen down in an indoor environment.

Cellphone signal level often drops to a significant amount inside a building resulting in call drops though the signal level is strong outside. This happens due to the penetration loss, experienced by the GSM signals as they pass through the building materials that are not transparent to them. Mainly penetration loss, along with the path loss, degrades the received signal strength level (RSSI) inside a building. In this work, the measurements of the received GSM signal strength level (RSSI) were made, outside and inside a 4-floored multi-storied building and a tin-shed single-floored building, which represent the prevalent types of buildings in all the cities in Bangladesh. An Android phone, with a Teletalk 2G (GSM-900) SIM and Network Signal Info application installed in it, was used as the measurement tool for this purpose. The obtained results show a mean average penetration loss of 19.29 dBm and 21.43 dBm for the multi-storied building and the single-floored building respectively. Besides, this work also consists of study of the penetration loss effect of several prevalent building construction materials on the electromagnetic (EM) signals at 900 MHz. This work will help the network service providers in carrying out efficient site-specific network planning in Bangladesh.

Worldwide the number of old and older people is increasing alongside the increase in average life expectancy. Due to this increase the number of age related impairments within the older society, in addition to the prevalence of chronic disease are also heightened. One of the most widespread chronic diseases is dementia, specifically Alzheimer’s disease (AD). AD is a brain related condition which impairs a person’s memory, thought and judgment. The aim of the current research has been to identify and alleviate a set of problems related to AD using smartphone technology. In order to determine if the level of support for those suffering from AD can be improved, our current work investigates the use of activity/inactivity monitoring using various smartphone services. Inactivity levels are being monitored in order to detect if a smartphone handset has been misplaced unintentionally, and to avoid any impact this may have on smartphone services. Specifically, GSM signal strength, Wi-Fi signal strength and accelerometer data are considered. Three smartphone applications have been developed and tested on a cohort of 8 healthy adult users as part of a pre-study investigation. Results from the pre-study indicate that the optimal approach to detect inactivity on a smartphone handset was via GSM signal strength coupled with accelerometer data.

Leaf wetness duration (LWD) is a key parameter in agrometeorology because it is related to plant disease occurrence. As LWD is seldomly measured in a standard weather station it must be estimated to run warning systems for schedule chemical disease control. The objective of the present study was to estimate LWD over turfgrass considering different models with data from a standard weather station, and to evaluate the correlation between estimated LWD over turfgrass and LWD measured in a 'Niagara Rosada' vineyard, cultivated in a hedgerow training system, in Jundiaí, São Paulo State, Brazil. The wetness sensors inside the vineyard were located at the top of the plants, deployed at an inclination angle of 45º and oriented southwest, with three replications. The methods used to estimate LWD were: number of hours with relative humidity above 90% (NHRH > 90%), dew point depression (DPD), classification and regression tree (CART) and Penman-Monteith (PM). The CART model had the best performance to estimate LWD over turfgrass, with a good precision (R² = 0.82) and a high accuracy (d = 0.94), resulting in a good confidence index (c = 0.85). The results from this model also presented a good correlation with measured LWD inside the vineyard, with a good precision (R² = 0.87) and a high accuracy (d = 0.96), resulting in a high confidence index (c = 0.93), showing that LWD in a 'Niagara Rosada' vineyard can be estimated with data from a standard weather station.

Efficient wireless sensor nodes have significantly motivated the usage of wireless sensor networks for intrusion detection and surveillance. A passive wireless surveillance network has the ability to detect humans by analyzing only the variations of the signal strength with respect to distance and alignment between nodes. When a human passes through an area covered by radio network, his/her body interferes with radio signals resulting in signal strength variations due to absorption, reflection and diffraction. In this paper, we analyze the signal strength variation induced by human presence, as a reliable method for passive surveillance. The proposed method analyzes principal components from a covariance matrix composed of samples that present signal strength variations gathered from wireless nodes. By using smart wireless outlets and inter-outlets communication signals, the original environment is not visually modified, but a certain level of sensorial intelligence is introduced without additional sensors. Principal component analysis enhances the detection accuracy level and improves the overall robustness of the surveillance method. Compared to conventional sensor networks, the use of smart wireless outlets and signal strength analysis preserves the transparency of the surveillance system and supports high level of sensorial intelligence, retaining low installation costs.