Cars as a Diffuse Network of Road-Environment Monitoring Nodes

Allegretti, Marco; Bertoldo, Silvano

doi:10.4236/wsn.2014.69018

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…[ sources and converting them to charge the battery and energy storage. Some potential energy sources are, but not limited to, light [79], [80], kinetics [8], [9], [81], [82], acoustics [83], [84], thermal [12], [85], wireless [86], [87], chemical, wind [39], [81], [84], [88], and hybrid [89]. Light is the recurrent source for environmental EHs, where the solar cells harvest the energy from the sunlight.…”

Section: Keys Issues On Ehmentioning

confidence: 99%

A Review of Energy Harvesting in Localization for Wireless Sensor Node Tracking

et al. 2021

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The localisation and positioning in Wireless Sensor Node (WSN) are prone to tracking loss because of battery depletion resulted from high power consumption. Considering this, Energy Harvesting (EH) is a significant factor to ensure the sustainability of WSN trackers. Therefore, the key objective of the paper is to review the existing EH approaches, specifically for WSN trackers. An overview of WSNs including the underlying wireless communication technologies is initially presented. We compared the communication range, data rates, power consumption and also the cost across wireless technologies used for WSN. This paper further discussed the localisation and positioning techniques using the Global Positioning System (GPS) and other types of sensors exploiting signal parameters like Received Signal Strength Indicator (RSSI) and Angle of Arrival (AoA). Subsequently, we reviewed the energy harvesting approaches in terms of power density, efficiency and also highlighted their advantages and disadvantages. The EH components such as energy storage and energy-combining circuit for active monitoring are presented as well. Finally, this paper outlined the key challenges and future regards EH for WSN.

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Section: Keys Issues On Ehmentioning

confidence: 99%

A Review of Energy Harvesting in Localization for Wireless Sensor Node Tracking

et al. 2021

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“…At the same time, the catalog of available WSN platforms has increased to include many types of radio and processor features in order to be employed in a huge number of fields (e.g. in [1]- [10]).…”

Section: Introductionmentioning

confidence: 99%

Recharging RFID Tags for Environmental Monitoring Using UAVs: A Feasibility Analysis

Allegretti¹,

Bertoldo²

2015

WSN

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RFID tags are used for different purposes. One of the major problems to be addressed, particularly for monitoring purposes, is their limited power autonomy. Tags must perform different tasks with limited power consumption and their batteries capacities are often too low, even if low power consumption techniques are implemented. In these operational situations tags should be kept in operation for long periods of time and the common solution is to go directly where they are installed and recharge them manually or change their batteries; alternatively, when possible, small photovoltaic (PV) panels may be adopted. This paper proposes a feasibility analysis of how it is possible to recharge a multipurpose RFID tag using a UAV (Unmanned Aerial Vehicle), which is programmed to go above the tags and recharge them. This possibility is analyzed from an energetic point of view assuming to recharge a Wireless Sensor Network (WSN) using a common commercial UAV adequately instrumented using the wireless power transfer technique.

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“…Modern cars are equipped with a multitude of sensors and can provide road condition and weather information. Several earlier studies discuss the potential use of cars as sensors (Allegretti and Bertoldo 2014;Petty and Mahoney 2007). A disadvantage of thermal mapping and mobile observations is that they provide only a snapshot of the current situation and do not tell about the temporal behavior of surface temperature .…”

Section: Introductionmentioning

confidence: 99%

Observing and forecasting road surface temperatures

Karsisto¹

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Wintertime weather conditions can be hazardous for road traffic. Icy roads and poor visibility caused by snowfall increase the accident risk. Accurate forecasting of road conditions is important, because reliable and precise forecasts help the road maintenance personnel to plan their operations accordingly. Well timed maintenance operations increase safety and enable economical savings as unnecessary actions can be avoided. Drivers can also adjust their route plan and driving behaviour appropriately when warnings of hazardous conditions are given well beforehand. Road conditions are forecasted in the Finnish Meteorological Institute (FMI) with specialized road weather model. Before executing the actual forecast, the model is first initialized by feeding it with observation data. The quality of this data is essential for forecast accuracy, as the forecast is greatly dependent of the initial model state. Road weather stations have traditionally been one of the main sources of information, but their density is sparse especially in rural areas. Road surface temperature can vary considerably across the road network, so observations should be done in dense enough spatial scale. Nowadays it is possible to gather real time information from vehicles. Mobile sources provide observations with high spatial density and thus facilitate detecting the road stretches most prone to freezing. However, the quality of mobile observations should be assessed before implementing them to the road weather forecasting systems. This dissertation aims to answer to two research questions. Firstly, it has been studied how to best use available surface temperature observations in the road weather model initialization. Secondly , it has been studied how differences in two road weather models' physics affect to the surface temperature forecast accuracy. A method called coupling was implemented to the FMI road weather model. The main idea of the method is to adjust the incoming radiation flux so that the modelled surface temperature fits to the last observed value. The results show that this method improves considerably the short range surface temperature forecasts. Mobile surface temperature observations done with Teconer RTS411 were compared to road weather station measurements to assess the mobile data quality. According to the results, the mobile observations were on average 0.62 ○ C warmer than the road weather station measurements at 0 ○ C and in dry conditions. It was found out that the difference between mobile observations and road weather station measurements was dependent on the road status. A calibration equation for mobile observations was developed using linear mixed models to get mobile observations more in line with road weather station measurements. The effect of the mobile observations to the road surface temperature forecast accuracy was studied. According to the results, using the mobile observations calibrated with the developed equation improved the accuracy of road surface temperature forecasts compared to a theoretical situ...

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Cars as a Diffuse Network of Road-Environment Monitoring Nodes

Cited by 4 publications

References 11 publications

A Review of Energy Harvesting in Localization for Wireless Sensor Node Tracking

A Review of Energy Harvesting in Localization for Wireless Sensor Node Tracking

Recharging RFID Tags for Environmental Monitoring Using UAVs: A Feasibility Analysis

Observing and forecasting road surface temperatures

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