Air pollution poses significant risk to environment and health. Air quality monitoring stations are often confined to a small number of locations due to the high cost of the monitoring equipment. They provide a low fidelity picture of the air quality in the city; local variations are overlooked. However, recent developments in low cost sensor technology and wireless communication systems like Internet of Things (IoT) provide an opportunity to use arrayed sensor networks to measure air pollution, in real time, at a large number of locations. This paper reports the development of a novel low cost sensor node that utilizes cost-effective electrochemical sensors to measure Carbon Monoxide (CO) and Nitrogen Dioxide (NO2) concentrations and an infrared sensor to measure Particulate Matter (PM) levels. The node can be powered by either solar-recharged battery or mains supply. It is capable of long-range, low power communication over public or private LoRaWAN IoT network and short-range high data rate communication over Wi-Fi. The developed sensor nodes were co-located with an accurate reference CO sensor for field calibration. The low cost sensors’ data, with offset and gain calibration, shows good correlation with the data collected from the reference sensor. Multiple linear regression (MLR) based temperature and humidity correction results in Mean Absolute Percentage Error (MAPE) of 48.71% and R2 of 0.607 relative to the reference sensor’s data. Artificial Neural Network (ANN) based calibration shows the potential for significant further improvement with MAPE of 38.89% and R2 of 0.78 for leave one out cross validation.

Accurate, reliable indoor localization or positioning is the key enabler for location-based services. Indoor localization can be broadly classified into two distinct categories. Active localization entails tracking a tag attached to or carried by the target. Passive localization, on the other hand, involves positioning a device free or untagged target. While passive or device free localization is comparatively more difficult to achieve, it is the preferred option for many applications. Vision-based techniques can accurately localize an un-tagged target. However, privacy is a significant concern and thus have limited usability for many applications, especially in non-commercial and residential settings. Passive localization using RF or wireless sensing is an unobtrusive option and has seen extensive research efforts in recent years leading to a saturated research field but no consensus solution. Researchers have been investigating alternative solutions that can facilitate robust passive localization. The rapid proliferation of Internet of Things (IoT) is bringing ubiquitous networked devices and ambient sensors into modern buildings. The consequential pervasive ambient intelligence and signals of opportunity can enable unobtrusive device free positioning. This article presents a comprehensive review of non-RF solutions covering visible light-, infrared-, physical excitation-and electric field sensing-based techniques. Limitations of the state of the art and potential future research directions are also outlined.

Passive indoor positioning, also known as Device-Free Localization (DFL), has applications such as occupancy sensing, human-computer interaction, fall detection, and many other location-based services in smart buildings. Vision-, infrared-, wireless-based DFL solutions have been widely explored in recent years. They are characterized by respective strengths and weaknesses in terms of the desired accuracy, feasibility in various real-world scenarios, etc. Passive positioning by tracking the footsteps on the floor has been put forward as one of the promising options. This article introduces CapLoc, a floor-based DFL solution that can localize a subject in real-time using capacitive sensing. Experimental results with three individuals walking 39 paths on the CapLoc show that it can detect and localize a single target's footsteps accurately with a median localization error of 0.026 m. The potential for fall detection is also shown with the outlines of various poses of the subject lying upon the floor.

This work investigates the performance of five depth cameras in relation to their potential for grape yield estimation. The technologies used by these cameras include structured light (Kinect V1), active infrared stereoscopy (RealSense D415), time of flight (Kinect V2 and Kinect Azure), and LiDAR (Intel L515). To evaluate their suitability for grape yield estimation, a range of factors were investigated including their performance in and out of direct sunlight, their ability to accurately measure the shape of the grapes, and their potential to facilitate counting and sizing of individual berries. The depth cameras’ performance was benchmarked using high-resolution photogrammetry scans. All the cameras except the Kinect V1 were able to operate in direct sunlight. Indoors, the RealSense D415 camera provided the most accurate depth scans of grape bunches, with a 2 mm average depth error relative to photogrammetric scans. However, its performance was reduced in direct sunlight. The time of flight and LiDAR cameras provided depth scans of grapes that had about an 8 mm depth bias. Furthermore, the individual berries manifested in the scans as pointed shape distortions. This led to an underestimation of berry sizes when applying the RANSAC sphere fitting but may help with the detection of individual berries with more advanced algorithms. Applying an opaque coating to the surface of the grapes reduced the observed distance bias and shape distortion. This indicated that these are likely caused by the cameras’ transmitted light experiencing diffused scattering within the grapes. More work is needed to investigate if this distortion can be used for enhanced measurement of grape properties such as ripeness and berry size.

This letter reports the experimental results of a novel Visible Light Positioning system using calibrated propagation model. The developed positioning system requires only twelve offline measurements to estimate the Received Signal Strength-distance relationship. Two calibration techniques are developed and tested within a real life implementation employing ceiling mounted commercial off-the-shelf Light Emitting Diode luminaires as transmitters and cheap photodiode based receiver tags. Mean localisation error of 2.8cm and ninety-percentile error of 5.2cm can be achieved using lateration. The proposed technique is shown to be as accurate as a fingerprint based method while requiring far less effort for offline calibration.

Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.