We present a new vision for smart objects and the Internet of Things wherein mobile robots interact with wirelessly-powered, long-range, ultra-high frequency radio frequency identification (UHF RFID) tags outfitted with sensing capabilities. We explore the technology innovations driving this vision by examining recently-commercialized sensor tags that could be affixed-to or embedded-in objects or the environment to yield true embodied intelligence. Using a pair of autonomous mobile robots outfitted with UHF RFID readers, we explore several potential applications where mobile robots interact with sensor tags to perform tasks such as: soil moisture sensing, remote crop monitoring, infrastructure monitoring, water quality monitoring, and remote sensor deployment.
In the context of agricultural sensing, remote crop monitoring (usually via camera imaging) has already proved useful @cite_19 , and it's even feasible to have UAVs obtain direct measurements for applications such as water quality monitoring @cite_16 . Inexpensive sensorized UHF RFID tags have the potential to augment or supplement existing remote-sensing systems to provide precise, direct measurements (eg. for calibration), and could even be used by existing farm equipment to opportunistically obtain relevant agricultural data. Direct sensing can make agricultural systems safer, more efficient, more accurate, and more productive @cite_17 @cite_21 . However, agricultural applications frequently involve large, expansive areas where wiring for communication and power is undesirable or impracticable. Wireless sensor networks have been used collect agriculture data by distributing sensors throughout a field, and transmitting information back to a base station @cite_12 , sometimes using more-advanced mesh networking techniques @cite_9 . These sensor networks have been shown to provide actionable data that can improve growing conditions and irrigation schedules @cite_13 , but they still suffer from battery and cost constraints. UHF RFID sensor tags are a compelling approach to provide direct measurements that enhance other remote sensing techniques.
@cite_9 @cite_21 @cite_19 @cite_16 @cite_13 @cite_12 @cite_17
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This paper presents an overview on recent development of wireless sensor technologies and standards for wireless communications as applied to wireless sensors. Examples of wireless sensors and sensor networks applied in agriculture and food production for environmental monitoring, precision agriculture, M2M-based machine and process control, building and facility automation and RFID-based traceability systems are given. The paper also discusses advantages of wireless sensors and obstacles that prevent their fast adoption. Finally, based on an analysis of market growth, the paper discusses future trend of wireless sensor technology development in agriculture and food industry. 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. Remote sensing by Unmanned Aerial Vehicles (UAVs) is changing the way agriculture operates by increasing the spatial-temporal resolution of data collection. Micro-UAVs have the potential to further improve and enrich the data collected by operating close to the crops, enabling the collection of higher spatio-temporal resolution data. In this paper, we present a UAV-mounted measurement system that utilizes a laser scanner to compute crop heights, a critical indicator of crop health. The system filters, transforms, and analyzes the cluttered range data in real-time to determine the distance to the ground and to the top of the crops. We assess the system in an indoor testbed and in a corn field. Our findings indicate that despite the dense canopy and highly variable sensor readings, we can precisely fly over crops and measure its height to within 5cm of measurements gathered using current measurement technology. The aim of the present paper is to review the technical and scientific state of the art of wireless sensor technologies and standards for wireless communications in the Agri-Food sector. These technologies are very promising in several fields such as environmental monitoring, precision agriculture, cold chain control or traceability. The paper focuses on WSN (Wireless Sensor Networks) and RFID (Radio Frequency Identification), presenting the different systems available, recent developments and examples of applications, including ZigBee based WSN and passive, semi-passive and active RFID. Future trends of wireless communications in agriculture and food industry are also discussed. Abstract Precision agriculture (PA) utilizes tools and technologies to identify in-field soil and crop variability for improving farming practices and optimizing agronomic inputs. Traditionally, optical remote sensing (RS) that utilizes visible light and infrared regions of the electromagnetic spectrum has been used as an integral part of PA for crop and soil monitoring. Optical RS, however, is slow in differentiating stress levels in crops until visual symptoms become noticeable. Surface temperature is considered to be a rapid response variable that can indicate crop stresses prior to their visual symptoms. By measuring estimates of surface temperature, thermal RS has been found to be a promising tool for PA. Compared to optical RS, applications of thermal RS for PA have been limited. Until recently (i.e., before the advancement of low cost RS platforms such as unmanned aerial systems (UAVs)), the availability of high resolution thermal images was limited due to high acquisition costs. Given recent developments in UAVs, thermal images with high spatial and temporal resolutions have become available at a low cost, which has increased opportunities to understand in-field variability of crop and soil conditions useful for various agronomic decision-making. Before thermal RS is adopted as a routine tool for crop and environmental monitoring, there is a need to understand its current and potential applications as well as issues and concerns. This review focuses on current and potential applications of thermal RS in PA as well as some concerns relating to its application. The application areas of thermal RS in agriculture discussed here include irrigation scheduling, drought monitoring, crop disease detection, and mapping of soil properties, residues and tillage, field tiles, and crop maturity and yield. Some of the issues related to its application include spatial and temporal resolution, atmospheric conditions, and crop growth stages. 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. Addressing the challenges of feeding the burgeoning world population with limited resources requires innovation in sustainable, efficient farming. The practice of precision agriculture offers many benefits towards addressing these challenges, such as improved yield and efficient use of such resources as water, fertilizer and pesticides. We describe the design and development of a light-weight, multi-spectral 3D imaging device that can be used for automated monitoring in precision agriculture. The sensor suite consists of a laser range scanner, multi-spectral cameras, a thermal imaging camera, and navigational sensors. We present techniques to extract four key data products — plant morphology, canopy volume, leaf area index, and fruit counts — using the sensor suite. We demonstrate its use with two systems: multi-rotor micro aerial vehicles and on a human-carried, shoulder-mounted harness. We show results of field experiments conducted in collaboration with growers and agronomists in vineyards, apple orchards and orange groves. The Remote Sensing Technologies Center (RSTC) at Mississippi State University (MSU) is investigating the application of high-resolution imagery in the areas of precision agriculture, forest resource management, and environmental assessments of transportation systems. For precision agriculture, the focus of the research is to develop the expertise to utilize this imagery to facilitate management decisions at the farm level. Decision making at the farm level requires high spatial resolution imagery (on the order of 1 meter or less) in order to develop prescriptions for nutrient supplements, integrated pest management (insects, weeds), irrigation, and to serve as a harvesting aid. This degree of analysis of the plant and soil also requires that higher spectral resolution be available to ascertain the nuances in the target vegetation's reflectance and to be able to relate this to specific agricultural problems. In addition, decisions at the farm level must be made within a few days of onset of a problem. Therefore, revisit frequencies for precision agriculture are on the order of 10-14 days. The requirement for high spatial, spectral, and temporal resolution imagery creates special image processing and analysis challenges. These include-data management, geometric and radiometric corrections, ground control points and georeferencing, and data analysis via such approaches as data fusion. The purpose of this paper is twofold. First, to illustrate the diversity of high spatial, spectral, and temporal resolution imagery required for precision agriculture applications. Second, to offer the opportunity to other data fusion researchers to join the author of this paper in developing new data fusion techniques for high-resolution imagery. Agricultural management increasingly uses crop maps based on classification of remotely sensed data. However, classification errors can translate to errors in model outputs, for instance agricultural production monitoring (yield, water demand) or crop acreage calculation. Hence, knowledge on the spatial variability of the classier performance is important information for the user. But this is not provided by traditional assessments of accuracy, which are based on the confusion matrix. In this study, classification uncertainty was analyzed, based on the support vector machines (SVM) algorithm. SVM was applied to multi-spectral time series data of RapidEye from different agricultural landscapes and years. Entropy was calculated as a measure of classification uncertainty, based on the per-object class membership estimations from the SVM algorithm. Permuting all possible combinations of available images allowed investigating the impact of the image acquisition frequency and timing, respectively, on the classification uncertainty. Results show that multi-temporal datasets decrease classification uncertainty for different crops compared to single data sets, but there was no “one-image-combination-fits-all” solution. The number and acquisition timing of the images, for which a decrease in uncertainty could be realized, proved to be specific to a given landscape, and for each crop they differed across different landscapes. For some crops, an increase of uncertainty was observed when increasing the quantity of images, even if classification accuracy was improved. Random forest regression was employed to investigate the impact of different explanatory variables on the observed spatial pattern of classification uncertainty. It was strongly influenced by factors related with the agricultural management and training sample density. Lower uncertainties were revealed for fields close to rivers or irrigation canals. This study demonstrates that classification uncertainty estimates by the SVM algorithm provide a valuable addition to traditional accuracy assessments. This allows analyzing spatial variations of the classifier performance in maps and also differences in classification uncertainty within the growing season and between crop types, respectively. We present a unique multi-antenna RFID reader (a sensor) embedded in a robot's manipulator that is designed to operate with ordinary UHF RFID tags in a short-range, near-field electromagnetic regime. Using specially designed near-field antennas enables our sensor to obtain spatial information from tags at ranges of less than 1 meter. In this work, we characterize the near-field sensor's ability to detect tagged objects in the robots manipulator, present robot behaviors to determine the identity of a grasped object, and investigate how additional RF signal properties can be used for “pre-touch” capabilities such as servoing to grasp an object. The future combination of long-range (far-field) and short-range (near-field) UHF RFID sensing has the potential to enable roboticists to jump-start applications by obviating or supplementing false-positive-prone visual object recognition. These techniques may be especially useful in the healthcare and service sectors, where mis-identification of an object (for example, a medication bottle) could have catastrophic consequences. 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. This work introduces the concept of a Wireless Underground Sensor Network (WUSN). WUSNs can be used to monitor a variety of conditions, such as soil properties for agricultural applications and toxic substances for environmental monitoring. Unlike existing methods of monitoring underground conditions, which rely on buried sensors connected via wire to the surface, WUSN devices are deployed completely belowground and do not require any wired connections. Each device contains all necessary sensors, memory, a processor, a radio, an antenna, and a power source. This makes their deployment much simpler than existing underground sensing solutions. Wireless communication within a dense substance such as soil or rock is, however, significantly more challenging than through air. This factor, combined with the necessity to conserve energy due to the difficulty of unearthing and recharging WUSN devices, requires that communication protocols be redesigned to be as efficient as possible. This work provides an extensive overview of applications and design challenges for WUSNs, challenges for the underground communication channel including methods for predicting path losses in an underground link, and challenges at each layer of the communication protocol stack. � 2006 Elsevier B.V. All rights reserved. Data-driven techniques help boost agricultural productivity by increasing yields, reducing losses and cutting down input costs. However, these techniques have seen sparse adoption owing to high costs of manual data collection and limited connectivity solutions. In this paper, we present FarmBeats, an end-to-end IoT platform for agriculture that enables seamless data collection from various sensors, cameras and drones. FarmBeats's system design that explicitly accounts for weather-related power and Internet outages has enabled six month long deployments in two US farms. A critical aspect of applications with wireless sensor networks is network lifetime. Battery-powered sensors are usable as long as they can communicate captured data to a processing node. Sensing and communications consume energy, therefore judicious power management and scheduling can effectively extend operational time. To monitor a set of targets with known locations when ground access in the monitored area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The loss of precise sensor placement would then be compensated by a large sensor population density in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node for processing. In this paper we propose an efficient method to extend the sensor network operational time by organizing the sensors into a maximal number of disjoint set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while nodes from all other sets are in a low-energy sleep mode. In this paper we address the maximum disjoint set covers problem and we design a heuristic that computes the sets. Theoretical analysis and performance evaluation results are presented to verify our approach. A critical aspect of applications with wireless sensor networks is network lifetime. Power-constrained wireless sensor networks are usable as long as they can communicate sensed data to a processing node. Sensing and communications consume energy, therefore judicious power management and sensor scheduling can effectively extend network lifetime. To cover a set of targets with known locations when ground access in the remote area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The lack of precise sensor placement is compensated by a large sensor population deployed in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node (e.g. cluster head) for processing. In this paper we propose un efficient method to extend the sensor network life time by organizing the sensors into a maximal number of set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while all other nodes are in a low-energy sleep mode. By allowing sensors to participate in multiple sets, our problem formulation increases the network lifetime compared with related work [M. ], that has the additional requirements of sensor sets being disjoint and operating equal time intervals. In this paper we model the solution as the maximum set covers problem and design two heuristics that efficiently compute the sets, using linear programming and a greedy approach. Simulation results are presented to verify our approaches. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... Coverage path planning is the operation of finding a path that covers all the points of a specific area. Thanks to the recent advances of hardware technology, Unmanned Aerial Vehicles (UAVs) are starting to be used for photogrammetric sensing of large areas in several application domains, such as agriculture, rescuing, and surveillance. However, most of the research focused on finding the optimal path taking only geometrical constraints into account, without considering the peculiar features of the robot, like available energy, weight, maximum speed, sensor resolution, etc. This paper proposes an energy-aware path planning algorithm that minimizes energy consumption while satisfying a set of other requirements, such as coverage and resolution. The algorithm is based on an energy model derived from real measurements. Finally, the proposed approach is validated through a set of experiments. Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR). Sensor-equipped smartphones and wearables are transforming a variety of mobile apps ranging from health monitoring to digital assistants. However, reliably inferring user behavior and context from noisy and complex sensor data collected under mobile device constraints remains an open problem, and a key bottleneck to sensor app development. In recent years, advances in the field of deep learning have resulted in nearly unprecedented gains in related inference tasks such as speech and object recognition. However, although mobile sensing shares many of the same data modeling challenges, we have yet to see deep learning be systematically studied within the sensing domain. If deep learning could lead to significantly more robust and efficient mobile sensor inference it would revolutionize the field by rapidly expanding the number of sensor apps ready for mainstream usage. In this paper, we provide preliminary answers to this potentially game-changing question by prototyping a low-power Deep Neural Network (DNN) inference engine that exploits both the CPU and DSP of a mobile device SoC. We use this engine to study typical mobile sensing tasks (e.g., activity recognition) using DNNs, and compare results to learning techniques in more common usage. Our early findings provide illustrative examples of DNN usage that do not overburden modern mobile hardware, while also indicating how they can improve inference accuracy. Moreover, we show DNNs can gracefully scale to larger numbers of inference classes and can be flexibly partitioned across mobile and remote resources. Collectively, these results highlight the critical need for further exploration as to how the field of mobile sensing can best make use of advances in deep learning towards robust and efficient sensor inference. Advances in artificial intelligence, sensors and big data management have far-reaching societ al impacts. As these systems augment our everyday lives, it becomes increasing-ly important for people to understand them and remain in control. We investigate how HCI researchers can help to develop accountable systems by performing a literature analysis of 289 core papers on explanations and explaina-ble systems, as well as 12,412 citing papers. Using topic modeling, co-occurrence and network analysis, we mapped the research space from diverse domains, such as algorith-mic accountability, interpretable machine learning, context-awareness, cognitive psychology, and software learnability. We reveal fading and burgeoning trends in explainable systems, and identify domains that are closely connected or mostly isolated. The time is ripe for the HCI community to ensure that the powerful new autonomous systems have intelligible interfaces built-in. From our results, we propose several implications and directions for future research to-wards this goal.