This vision paper presents the case for MUSIC, a programmable framework for building distributed mobile IoT applications for urban sensing. The Mobile Urban Sensing, Inference and Control (MUSIC) framework is contextualized for scenarios where a distributed collection of static or mobile sensors collectively achieve an urban sensing task. The MUSIC platform is designed for urban-centric sensing applications such as location sensing on mobile phones for road traffic monitoring, air quality sensing and urban quality monitoring using remote cameras. This programmable system, at a high level, consists of several small sensors placed throughout a city on mobile vehicles and a centralized controller that makes decisions on sensing in order to achieve certain well-defined objectives such as improving spatial coverage of sensing and detection of hotspots. The system is programmable in that our framework allows one to create custom smart systems by writing custom control logic for sensing. Our contributions are two-fold -- a backend software stack to enable centralized control of distributed devices and programmability, and algorithms for intelligent control in the presence of practical power and network constraints. We briefly present three different urban sensing applications built on top of the MUSIC stack.
In the field of agriculture, there have been works on making irrigation more smart in order to reduce wastage of water. In @cite_4 and @cite_15 , the authors implement WSNs for remote sprinklers, along with sensing and control for variable rate irrigation. @cite_18 describes a full-fledged system for smart irrigation where the backend server takes real-time moisture data as input to determine sensing decisions. In the context of urban air quality monitoring, given increasing concern over poor air quality in many parts of the world, there has been a recent surge of commercially available low-cost portable sensors by various vendors @cite_0 @cite_19 @cite_12 . The drawback is that each vendor has its own frontend and backend system that is usually compatible only with its own products. This becomes a challenge when conducting large-scale urban air quality monitoring because it restricts freedom in choosing devices based on its specifications. Likewise, there have also been numerous experimental works on mobile urban air quality monitoring @cite_21 @cite_22 @cite_1 @cite_7 @cite_9 . While all of them contain real-time reporting in some form, we have not found evidence for programmability and extensibility for implementing custom policies.
@cite_18 @cite_4 @cite_22 @cite_7 @cite_9 @cite_21 @cite_1 @cite_0 @cite_19 @cite_15 @cite_12
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The development of microelectronics began in 1948 and continued with the miniaturisation of sensors during the last ten years. Today, microsystems which are used in silicon microtechnology are called microelectromechanical systems (MEMS). Microsensors are defined as very small sized devices that convert humidity, moisture, temperature, pressure, pollutants, light intensity, etc. into electrical signal. Integration of MEMS with the radio frequency wireless communication technique put forward low power consuming, cheap and communicating small devices called wireless sensor networks (WSN). WSN have spatially distributed transceivers with microsensors that give the possibility of monitoring and collecting data easily and quickly. Nowadays, these devices are used in many different application areas like home automation, forest fire monitoring, traffic control, noise and pollutant detection in the crowded areas. Classical definition of Geographic Information Systems (GIS), which is known by everybody is a computer based system that collects, stores, edits, analyses and displays spatial data. GIS is a powerful tool to access, analyse and evaluate large amounts of spatial data. The total time to get the results is a strategic part of the study. GIS is very efficient for reaching the correct conclusion in a fraction of time. WSN provides huge amount of data. If it would be possible to integrate the power of WSN with GIS, the data can be obtained in a very short time and the evaluation time can be decreased. In this study, integration of WSN with GIS is used for preparation of the soil moisture distribution map of the studied area. Wireless devices with moisture sensed sensors are located at predetermined locations. Geographic coordinates of these points are obtained with GPS. The sensed data is collected by using WSN and evaluated by using GIS. Finally, moisture distribution map of the soil is prepared. We claim that the irrigation of the soil can be done more efficiently by using this prepared map. Moreover, dry situation can be alarmed with this system and adaquate irrigation can be done just on time. In the following section, we briefly introduce wireless sensor networks. In Section 3, and Section 4 we describe the application of WSN for agricultural studies and the integration of WSN and GIS for agricultural fields, respectively. Finally, in Section 5, we conclude the paper with our recommendations. Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that allows producers to maximize their productivity while saving water. This paper describes details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system. Field conditions were site-specifically monitored by six in-field sensor stations distributed across the field based on a soil property map, and periodically sampled and wirelessly transmitted to a base station. An irrigation machine was converted to be electronically controlled by a programming logic controller that updates georeferenced location of sprinklers from a differential Global Positioning System (GPS) and wirelessly communicates with a computer at the base station. Communication signals from the sensor network and irrigation controller to the base station were successfully interfaced using low-cost Bluetooth wireless radio communication. Graphic user interface-based software developed in this paper offered stable remote access to field conditions and real-time control and monitoring of the variable-rate irrigation controller. A prototype real-time, smart sensor array for measuring soil moisture and soil temperature that uses off-the-shelf components was developed and evaluated for scheduling irrigation in cotton. The array consists of a centrally located receiver connected to a laptop computer and multiple sensor nodes installed in the field. The sensor nodes consist of sensors (up to three Watermark^(R) soil moisture sensors and up to four thermocouples), a specially designed circuit board, and a Radio Frequency IDentification (RFID) tag which transmits data to the receiver. The smart sensor array described here offers real potential for reliably monitoring spatially variable soil water status in crop fields. The relatively low cost of the system ( USD 2400 for a 20-sensor node system) allows for installation of a dense population of soil moisture sensors that can adequately represent the inherent soil variability present in fields. This paper describes the smart sensor array and testing in a cotton crop. Integration of the sensors with precision irrigation technologies will provide a closed loop irrigation system where inputs from the smart sensor array will determine timing and amounts for real-time site-specific irrigation applications. article i nfo The remote detection of water stress in a citrus orchard was investigated using leaf-level measurements of chlorophyll fluorescence and Photochemical Reflectance Index (PRI) data, seasonal time-series of crown tem- perature and PRI, and high-resolution airborne imagery. The work was conducted in an orchard where a reg- ulated deficit irrigation (RDI) experiment generated a gradient in water stress levels. Stomatal conductance (Gs) and water potential (Ψ) were measured over the season on each treatment block. The airborne data consisted on thermal and hyperspectral imagery acquired at the time of maximum stress differences among treatments, prior to the re-watering phase, using a miniaturized thermal camera and a micro-hyperspectral imager on board an unmanned aerial vehicle (UAV). The hyperspectral imagery was acquired at 40 cm resolution and 260 spectral bands in the 400-885 nm spectral range at 6.4 nm full width at half maximum (FWHM) spectral resolution and 1.85 nmsampling interval,enablingthe identificationof pure crownsfor extractingradiance andreflectance hyperspectral spectra from each tree. The FluorMOD model was used to investigate the retrieval of chlorophyll fluorescence by applying the Fraunhofer Line Depth (FLD) principle using three spectral bands (FLD3), which demonstrated that fluorescence retrievalwas feasible with the configuration of the UAV micro-hyperspectral in- strument flown over the orchard. Results demonstrated the link between seasonal PRI and crown temperature acquired from instrumented trees and field measurements of stomatal conductance and water potential. The sensitivity of PRI and Tc-Ta time-series to water stress levels demonstrated a time delay of PRI vs Tc-Ta during the recovery phase after re-watering started. At the time of the maximum stress difference among treatment blocks, the airborneimagery acquired fromthe UAV platform demonstrated that the crown temperature yielded the best coefficient of determination for Gs (r 2