Traditional wireless sensor networks(WSNs)are not suitable for rough terrains that are difficult or impossible to access by humans.Smart dust is a technology that works with the combination of many tiny sensors which ...Traditional wireless sensor networks(WSNs)are not suitable for rough terrains that are difficult or impossible to access by humans.Smart dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough terrains.The tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraftwithout manually setting their locations.Although it is clear that a number of remote sensing applications can benefit from this technology,but the small size of smart dust fundamentally restricts the integration of advanced hardware on tiny sensors.This raises many challenges including how to estimate the location of events sensed by the smart dusts.Existing solutions on estimating the location of events sensed by the smart dusts are not suitable for monitoring rough terrains as these solutions depend on relay sensors and laser patterns which have their own limitations in terms of power constraint and uneven surfaces.The study proposes a novel machine learning based localization algorithm for estimating the location of events.The approach utilizes timestamps(time of arrival)of sensed events received at base stations by assembling them into a multidimensional vector and input to a machine learning classifier for estimating the location.Due to the unavailability of real smart dusts,we built a simulator for analysing the accuracy of the proposed approach formonitoring forest fire.The experiments on the simulator show reasonable accuracy of the approach.展开更多
In-situ measurements are necessary for a long-term analysis of the spatial structure of the geomagnetic tail.This type of mission requires the use of a propellantless propulsion system,such as a classical solar sail,t...In-situ measurements are necessary for a long-term analysis of the spatial structure of the geomagnetic tail.This type of mission requires the use of a propellantless propulsion system,such as a classical solar sail,to continuously rotate the design orbit apse line such that it remains parallel to the Sun-Earth direction.To reduce the mission costs,this paper suggests the employment of Sun-pointing smart dusts,which are here investigated in terms of propulsive acceleration level necessary to guarantee a mission’s feasibility.A Sun-pointing smart dust can be thought of as a millimeter-scale solar sail,whose geometric configuration allows it to passively maintain an alignment with the Sun-spacecraft line.The smart dust external surface is coated with an electrochromic reflective film in such a way that it may change,within some limits,its propulsive acceleration magnitude.A suitable control law is necessary for the smart dust to enable an artificial precession of its Earth-centred orbit,similar to what happens in the GeoSail mission.This paper analyzes the required control law using an optimal approach.In particular,the proposed mathematical model provides a set of approximate equations that allow a simple and effective tradeoff analysis between the propulsive requirements,in terms of the smart dust acceleration,and the characteristics of the design orbit.展开更多
Smart dust,which refers to miniaturized,multifunctional sensor motes,would open up data acquisition opportunities for Internet of Things(IoT)and Environmental protection applications.However,critical obstacles remain ...Smart dust,which refers to miniaturized,multifunctional sensor motes,would open up data acquisition opportunities for Internet of Things(IoT)and Environmental protection applications.However,critical obstacles remain challenging in the integration of high-density sensors,further miniaturization of device platforms,and reduction of cost.Here,we demonstrate the concept of smart digital dust to address these problems,the results of which combine the benefit of(i)maturity of complementary metal-oxide semiconductor(CMOS)processing approaches and(ii)unique form factors of emerging flex-ible electronics.As a prototype for smart digital dust,we present a millimeter-scale multifunctional optoelectronic sensor platform con-sisting of high-performance optoelectronic sensor cores and commer-cially available integrated-circuit components.The smart material-assisted optoelectronic sensing mechanism enables real-time,high-sensitivity hydrogen,temperature,and relative humidity(RH)sens-ing based on a single chip with ultralow power consumption.Such a microsystem presented here introduces a viable solution to the multi-functional sensing need of IoT and could serve as a building block for the rapidly evolving future framework of smart dust.展开更多
基金This research is supported by Universiti Brunei Darussalam(UBD)under FIC allied research grant program.
文摘Traditional wireless sensor networks(WSNs)are not suitable for rough terrains that are difficult or impossible to access by humans.Smart dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough terrains.The tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraftwithout manually setting their locations.Although it is clear that a number of remote sensing applications can benefit from this technology,but the small size of smart dust fundamentally restricts the integration of advanced hardware on tiny sensors.This raises many challenges including how to estimate the location of events sensed by the smart dusts.Existing solutions on estimating the location of events sensed by the smart dusts are not suitable for monitoring rough terrains as these solutions depend on relay sensors and laser patterns which have their own limitations in terms of power constraint and uneven surfaces.The study proposes a novel machine learning based localization algorithm for estimating the location of events.The approach utilizes timestamps(time of arrival)of sensed events received at base stations by assembling them into a multidimensional vector and input to a machine learning classifier for estimating the location.Due to the unavailability of real smart dusts,we built a simulator for analysing the accuracy of the proposed approach formonitoring forest fire.The experiments on the simulator show reasonable accuracy of the approach.
基金This work is supported by the University of Pisa,Progetti di Ricerca di Ateneo(Grant No.PRA_2018_44).
文摘In-situ measurements are necessary for a long-term analysis of the spatial structure of the geomagnetic tail.This type of mission requires the use of a propellantless propulsion system,such as a classical solar sail,to continuously rotate the design orbit apse line such that it remains parallel to the Sun-Earth direction.To reduce the mission costs,this paper suggests the employment of Sun-pointing smart dusts,which are here investigated in terms of propulsive acceleration level necessary to guarantee a mission’s feasibility.A Sun-pointing smart dust can be thought of as a millimeter-scale solar sail,whose geometric configuration allows it to passively maintain an alignment with the Sun-spacecraft line.The smart dust external surface is coated with an electrochromic reflective film in such a way that it may change,within some limits,its propulsive acceleration magnitude.A suitable control law is necessary for the smart dust to enable an artificial precession of its Earth-centred orbit,similar to what happens in the GeoSail mission.This paper analyzes the required control law using an optimal approach.In particular,the proposed mathematical model provides a set of approximate equations that allow a simple and effective tradeoff analysis between the propulsive requirements,in terms of the smart dust acceleration,and the characteristics of the design orbit.
基金supported by the National Key Technologies R&D Program of China(2021YFE0191800)the National Natural Science Foundation of China(61975035,51961145108)+3 种基金Science and Technology Commission of Shanghai Municipality(21142200200,20501130700)E.S.acknowledged the support by Lingang Laboratory(Grant No.LG-QS-202202-02)the support by Shanghai Municipal Science and Technology Major Project(Grant No.2018SHZDZX01)ZJ Lab,and Shanghai Center for Brain Science and Brain-Inspired Technology,and the support by the young scientist project of MOE innovation platform,Zhangjiang Fudan International Innovation Center,Part of the experimental work was carried out in Fudan Nanofabrication Laboratory.
文摘Smart dust,which refers to miniaturized,multifunctional sensor motes,would open up data acquisition opportunities for Internet of Things(IoT)and Environmental protection applications.However,critical obstacles remain challenging in the integration of high-density sensors,further miniaturization of device platforms,and reduction of cost.Here,we demonstrate the concept of smart digital dust to address these problems,the results of which combine the benefit of(i)maturity of complementary metal-oxide semiconductor(CMOS)processing approaches and(ii)unique form factors of emerging flex-ible electronics.As a prototype for smart digital dust,we present a millimeter-scale multifunctional optoelectronic sensor platform con-sisting of high-performance optoelectronic sensor cores and commer-cially available integrated-circuit components.The smart material-assisted optoelectronic sensing mechanism enables real-time,high-sensitivity hydrogen,temperature,and relative humidity(RH)sens-ing based on a single chip with ultralow power consumption.Such a microsystem presented here introduces a viable solution to the multi-functional sensing need of IoT and could serve as a building block for the rapidly evolving future framework of smart dust.
