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Timely data quality assessment has been shown to be crucial for the development of IoT-based applications. Different IoT applications' varying data quality requirements pose a challenge, as each application requires a unique data quality process. This creates scalability issues as the number of applications increases, and it also has financial implications, as it would require a separate data pipeline for each application. To address this challenge, this paper proposes a novel approach integrating fusion methods into end-to-end data quality assessment to cater to different applications within a single data pipeline. By using real-time and historical analytics, the study investigates the effects of each fusion method on the resulting data quality score and how this can be used to support different applications. The study results, based on two real-world datasets, indicate that Kalman fusion had a higher overall mean quality score than Adaptive weighted fusion and Naïve fusion. However, Kalman fusion also had a higher computational burden on the system. The proposed solution offers a flexible and efficient approach to addressing IoT applications' diverse data quality needs within a single data pipeline. [ABSTRACT FROM AUTHOR]

Smart agriculture relies on accurate yield maps as a crucial tool for decision-making. Many yield maps, however, suffer from spatial errors that can compromise the quality of their data, while several approaches have been proposed to address some of these errors, detecting voids or holes in the maps remains challenging. Additionally, the quality of yield datasets is typically evaluated based on root mean squared errors after interpolation. This evaluation method relies on weighbridge data, which can occasionally be inaccurate, impacting the quality of decisions made using the datasets. This paper introduces a novel algorithm designed to identify voids in yield maps. Furthermore, it maps three types of spatial errors (GPS errors, yield surges, and voids) to two standard data quality dimensions (accuracy and completeness). Doing so provides a quality score that can be utilized to assess the quality of yield datasets, eliminating the need for weighbridge data. The paper carries out three types of evaluations: (1) evaluating the algorithm's efficacy by applying it to a dataset containing fields with and without voids; (2) assessing the benefits of integrating void detection and other spatial error identification techniques into the yield data processing chain; and (3) examining the correlation between root mean squared error and the proposed quality score before and after filtering out spatial errors. The results of the evaluations demonstrate that the proposed algorithm achieves a 100% sensitivity, 91% specificity, and 82% accuracy in identifying yield maps with voids. Additionally, there is a decrease in the root mean squared error when various spatial errors, including voids after applying the proposed data pre-processing chain. The inverse correlation observed between the root mean squared error and the proposed quality score (−0.577 and −0.793, before and after filtering spatial errors, respectively) indicates that the quality score can effectively assess the quality of yield datasets. This assessment enables seamless integration into real-time big data quality assessment solutions based on various data quality dimensions. [ABSTRACT FROM AUTHOR]

An understanding of water exchange processes is essential for assessing water quality management issues in coastal bays. This paper evaluates the impact of water exchange processes on pollution persistence in a macro-tidal semi-closed coastal bay through two transport time scales (TTS), namely residence time and exposure time. The numerical model was calibrated against field-measured data for various tidal conditions. Simulated current speeds and directions were shown to agree well with the field data. By considering different release scenarios of a conservative tracer by the refinement of an integrated hydrodynamic and solute transport model (the EFDC), the two TTS were used for interpreting the water exchange processes in a semi-closed system, and for describing the effects of advective and dispersive processes on the transport and fate of pollutants. The results indicate that the magnitudes of river inflows to the bay, tidal ranges, and tracer release times significantly influence the residence and exposure times. Return coefficients were shown to be variable, confirming the different effects of returning water for the different conditions that were studied. For the tested river flow magnitudes and tide conditions, the exposure times were generally higher than the residence times, but particularly so for neap tide conditions. The results, therefore, highlight the risks associated with pollutants leaving a specified domain on an outgoing tide but re-entering on subsequent incoming tides. The spatial distributions of the exposure and residence times across the model domain confirmed that for the case of Dublin Bay, river inputs have a potentially greater impact on water quality on the northern side of the bay. [ABSTRACT FROM AUTHOR]

Historical meteorological datasets are indispensable for forming climatic models and the generation of weather forecasts. Such data are core to the training phase of prediction models and may also be harnessed for hydrological and environmental models. GRIB is the most common data format used in meteorology and represents the de facto standard for storing historical weather data. However, GRIB datasets are complex and do not constitute analysis‐ready data without additional preprocessing. The MÉRA dataset of the Met Eireann, the Irish meteorological service, is archetypical of such datasets, emerging from a high‐resolution climatic reanalysis of Irish weather data between 1981 and 2019. This article describes the MÉRA Data Extractor toolkit. This toolkit enables the intuitive, fast extraction and preprocessing of data from this extensive dataset. The toolkit is available as open source and will be of interest to those researching climate modelling in Europe. [ABSTRACT FROM AUTHOR]

In recent years, renewable energy, especially photovoltaic (PV) generation, has been widely applied in commercial and residential areas, forming the structure of regional microgrids (MG). On the other hand, as large-scale electric vehicles (EVs) are connected to the regional MG, their disordered charging and discharging will enable extremely bad effects. However, most of the existing scheduling strategies focused on a single MG or considered the MG as a single node, ignoring the characteristics of different subareas. To address these limitations, we propose an optimal scheduling strategy considering large-scale access of EVs based on the typical regional MG composed of commercial and residential subareas, combining the power consumption characteristics of the two subareas and the travel characteristics of EVs. This strategy is mainly to dynamically adjust the charging and discharging of EVs in each subarea, combined with the real-time deployment of excess PV energy, so as to realize the overall scheduling of the regional MG. Numerical results show that EVs store electric energy during valley hours and output electric energy during peak hours. Furthermore, compared with no scheduling method, the proposed strategy reduces the whole-day electricity bills of the regional MG. [ABSTRACT FROM AUTHOR]

The heterogeneity of knowledge graphs brings great challenges to entity alignment. In particular, the attributes of network entities in the real world are complex and changeable. The key to solving this problem is to expand the neighborhoods in different ranges and extract the neighborhood information effi- ciently. Based on this idea, we propose Multi-neighborhood Sampling Matching Network (MSM), a new KG alignment network, aiming at the structural heterogeneity challenge. MSM constructs a multi-neighborhood network representation learning method to learn the KG structure embedding. It then adopts a unique sampling and cosine cross-matching method to solve different sizes of neighborhoods and distinct topological structures in two entities. To choose the right neighbors, we apply a down-sampling process to select the most informative entities towards the central target entity from its one-hop and two-hop neighbors. To verify the effectiveness of matching this neighborhood with any neighborhood in the corresponding node, we give a cosine cross-graph neighborhood matching method and conduct detailed research and analysis on three entity matching datasets, which proves the effectiveness of MSM. [ABSTRACT FROM AUTHOR]

Smart vehicles use sophisticated sensors to capture real-time data. Due to the weak communication capabilities of wireless sensors, these data need to upload to the cloud for processing. Sensor clouds can resolve these drawbacks. However, there is a large amount of redundant data in the sensor cloud, occupying a large amount of storage space and network bandwidth. Deduplication can yield cost savings by storing one data copy. Chunking is essential because it can determine the performance of deduplication. Content-Defined Chunking (CDC) can effectively solve the problem of chunk boundaries shifted, but it occupies a lot of computing resources and has become a bottleneck in deduplication technology. This paper proposes a Dynamic Asymmetric Maximum algorithm (DAM), which uses the maximum value as the chunk boundaries and reducing the impact of the low-entropy string. It also uses the perfect hash algorithm to optimize the chunk search. Experiments show that our solution can effectively detect low-entropy strings in redundant data, save storage resources, and improve sensor clouds system throughput. [ABSTRACT FROM AUTHOR]

Fecal pollution of surface water may introduce bacteria and bacteriophages harboring antibiotic resistance genes (ARGs) into the aquatic environment. Watercourses discharging into the marine environment, especially close to designated bathing waters, may expose recreational users to fecal pollution and therefore may increase the likelihood that they will be exposed to ARGs. This study compares the bacterial and bacteriophage ARG profiles of two rivers (River Tolka and Liffey) and two small urban streams (Elm Park and Trimleston Streams) that discharge close to two marine bathing waters in Dublin Bay. Despite the potential differences in pollution pressures experienced by these waterways, microbial source tracking analysis showed that the main source of pollution in both rivers and streams in the urban environment is human contamination. All ARGs included in this study, bla TEM , bla SHV , qnrS , and sul1 , were present in all four waterways in both the bacterial and bacteriophage fractions, displaying a similar ARG profile. We show that nearshore marine bathing waters are strongly influenced by urban rivers and streams discharging into these, since they shared a similar ARG profile. In comparison to rivers and streams, the levels of bacterial ARGs were significantly reduced in the marine environment. In contrast, the bacteriophage ARG levels in freshwater and the marine were not significantly different. Nearshore marine bathing waters could therefore be a potential reservoir of bacteriophages carrying ARGs. In addition to being considered potential additional fecal indicators organism, bacteriophages may also be viewed as indicators of the spread of antimicrobial resistance. [ABSTRACT FROM AUTHOR]

Small urban streams discharging in the proximity of bathing waters may significantly contribute to the deterioration of water quality, yet their impact may be overlooked. This study focuses on the Elm Park stream in the city of Dublin that is subject to faecal contamination by unidentified sources. The aim of the study was to identify a minimum number of "sentinel" sampling stations in an urban catchment that would provide the maximum amount of information regarding faecal pollution in the catchment. Thus, high-resolution sampling within the catchment was carried out over the course of 1 year at 11 stations. Faecal indicator bacteria were enumerated and microbial source tracking (MST) was employed to evaluate human pollution. In addition, ammonium, total oxidised nitrogen, and phosphorus levels were monitored to determine if these correlated with faecal indicator and the HF183 MST marker. In addition, the effect of severe weather events on water quality was assessed using automated sampling at one of the identified "sentinel" stations during baseflow and high flow conditions over a 24-h period. Our results show that this urban stream is at times highly contaminated by point source faecal pollution and that human faecal pollution is pervasive in the catchment. Correlations between ammonium concentrations and faecal indicator bacteria (FIB) as well as the human MST marker were observed during the study. Cluster analysis identified four "sentinel" stations that provide sufficient information on faecal pollution in the stream, thus reducing the geographical complexity of the catchment. Furthermore, ammonium levels strongly correlated with FIB and the human HF183 MST marker under high flow conditions at key "sentinel" stations. This work demonstrates the effectiveness of pairing MST, faecal indicators, and ammonium monitoring to identify "sentinel" stations that could be more rapidly assessed using real-time ammonium readouts to assess remediation efforts. [ABSTRACT FROM AUTHOR]

To accommodate the proliferation of heterogeneous network models and protocols, the use of semantic technologies to enable an abstract treatment of networks is proposed. Network adapters are employed to lift network specific data into a semantic representation. Semantic reasoning integrates the disparate network models and protocols into a common data model by making intelligent inferences from low‐level network and device details. Automatic discovery of new devices, monitoring of device state, and invocation of device actions in a generic fashion that is agnostic of network types is enabled. A prototype system called SNoMAC is described that employs the proposed approach operating over UPnP, TR‐069, and heterogeneous sensors. These sensors are integrated by means of a sensor middleware named SIXTH that augments the capabilities of SNoMAC to allow for intelligent management and configuration of a wide variety of sensor devices. A major benefit of this approach is that the addition of new models, protocols, or sensor types merely involves the development of a new network adapter based on an ontology. Additionally, the semantic representation of the network and associated data allows for a variety of client interfaces to facilitate human input to the management and monitoring of the system. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, the scale of the city has been continuously expanding, industrial enterprises have been increasing, the discharge of multiple pollutants has reached the top of the world, and the environmental problems become more and more serious. The air pollution problem is particularly prominent. Air quality has become a daily concern for people. In order to control air pollution, it is necessary to grasp the air quality situation in an all-round way. It is necessary to evaluate air quality. Accurate results of air quality evaluation can help people know more about air quality. In this paper, refers to previous research results and different evaluation methods, combined with artificial neural network, fuzzy theory, genetic algorithm, GA-BP hybrid algorithm based on fuzzy theory is proposed to evaluate air quality. At the same time, for the problem that the two-grade standard of air quality annual evaluation is not suitable for practical application, the four-grade standard for annual air quality evaluation has been proposed, and its practicality has been verified through experiments. By setting contrast experiments and comparing the air quality evaluation model based on standard BP algorithm, it is proved that the fuzzy GA-BP evaluation model is better than the standard BP model, both in efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Garlic prices fluctuate dramatically in recent years and it is very difficult to predict garlic prices. The autoregressive integrated moving average (ARIMA) model is currently the most important method for predicting garlic prices. However, the ARIMA model can only predict the linear part of the garlic prices, and cannot predict its nonlinear part. Therefore, it is urgent to adopt a method to analyze the nonlinear characteristics of garlic prices. After comparing the advantages and disadvantages of several major prediction models which used to forecast nonlinear time series, using support vector machine (SVM) model to predict the nonlinear part of garlic prices and establish ARIMA-SVM hybrid forecast model to predict garlic prices. The monthly average price data of garlic in 2010-2017 was used to test the effect of ARIMA model, SVM model and ARIMA-SVM model. The experimental results show that: (1) Garlic price is affected by many factors but the most is the supply and demand relationship; (2) The SVM model has a good effect in dealing with the nonlinear relationship of garlic prices; (3) The ARIMA-SVM hybrid model is better than the single ARIMA model and SVM model on the accuracy of garlic price prediction, it can be used as an effective method to predict the short-term price of garlic. [ABSTRACT FROM AUTHOR]

With the growth of the Internet, crowdsourcing has become a popular way to perform intelligence tasks that hitherto would be either performed internally within an organization or not undertaken due to prohibitive costs and the lack of an appropriate communications infrastructure. In crowdsourcing systems, whereby multiple agents are not under the direct control of a system designer, it cannot be assumed that agents will act in a manner that is consistent with the objectives of the system designer or principal agent. In situations whereby agents’ goals are to maximize their return in crowdsourcing systems that offer financial or other rewards, strategies will be adopted by agents to game the system if appropriate mitigating measures are not put in place. The motivational and incentivization research space is quite large; it incorporates diverse techniques from a variety of different disciplines including behavioural economics, incentive theory, and game theory. This paper specifically focusses on game theoretic approaches to the problem in the crowdsourcing domain and places it in the context of the wider research landscape. It provides a survey of incentive engineering techniques that enable the creation of apt incentive structures in a range of different scenarios. [ABSTRACT FROM AUTHOR]

Growth stage (GS) is an important crop growth metric commonly used in commercial farms. We focus on wheat and barley GS classification based on in-field proximal images using convolutional neural networks (ConvNets). For comparison purposes, use of a conventional machine learning algorithm was also investigated. The research includes extensive data collection of images of wheat and barley crops over a 3-year period. During data collection, videos were recorded during field walks at two camera views: downward looking and 45 deg angled. The resulting dataset contains 110,000 images of wheat and 106,000 of barley taken over 34 and 33 GS classes, respectively. Three methods were investigated as candidate technologies for the problem of GS classification. These methods were: (I) feature extraction and support vector machine, (II) ConvNet with learning from scratch, and (III) ConvNet with transfer learning. The methods were assessed for classification accuracy using test images taken (a) in fields on days imagined in the training data (i.e., seen field-days GS classification) and (b) in fields on days not imagined in the training data (i.e., unseen field-days principal GS classification). Of the three methods investigated, method III achieved the best accuracy for both classification tasks. The model achieved 97.3% and 97.5% GS classification accuracy for seen field-day test data for wheat and barley, respectively. The model also achieved accuracies of 93.5% and 92.2% for the principal GS classification task for wheat and barley, respectively. We provide a number of key research contributions: the collection curation and exposure of a unique GS labeled proximal image dataset of wheat and barley crops, GS classification, and principal GS classification of cereal crops using three different machine learning methods as well as a comprehensive evaluation and comparison of the obtained results. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are typically composed of thousands of sensors powered by limited energy resources. Clustering techniques were introduced to prolong network longevity offering the promise of green computing. However, most existing work fails to consider the network coverage when evaluating the lifetime of a network. We believe that balancing the energy consumption in per unit area rather than on each single sensor can provide better-balanced power usage throughout the network. Our former work—Balanced Energy-Efficiency (BEE) and its Multihop version BEEM can not only extend the network longevity, but also maintain the network coverage. Following WSNs, Internet of Things (IoT) technology has been proposed with higher degree of diversities in terms of communication abilities and user scenarios, supporting a large range of real world applications. The IoT devices are embedded with multiple communication interfaces, normally referred as Multiple-In and Multiple-Out (MIMO) in 5G networks. The applications running on those devices can generate various types of data. Every interface has its own characteristics, which may be preferred and beneficial in some specific user scenarios. With MIMO becoming more available on the IoT devices, an advanced clustering solution for highly dynamic IoT systems is missing and also pressingly demanded in order to cater for differing user applications. In this paper, we present a smart clustering algorithm (Smart-BEEM) based on our former work BEE(M) to accomplish energy efficient and Quality of user Experience (QoE) supported communication in cluster based IoT networks. It is a user behaviour and context aware approach, aiming to facilitate IoT devices to choose beneficial communication interfaces and cluster headers for data transmission. Experimental results have proved that Smart-BEEM can further improve the performance of BEE and BEEM for coverage sensitive longevity. [ABSTRACT FROM AUTHOR]

No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks. [ABSTRACT FROM AUTHOR]

Ubiquitous robots are robots integrated with smart environments, where they cooperate with networks of heterogeneous sensors to achieve complex tasks. Their successful application opens important research questions for both their engineering and their interaction with human users, especially in public space scenarios, when they need to interact in a socially acceptable manner with multiple and previously unknown people. This article presents a testbed, named PRIveT, which is purposefully designed to support effective human-robot interaction (HRI) in public spaces. The PRIveT testbed consists of an ubiquitous robotic system that is able to autonomously engage and adapt to its users. This article describes the design rationale of the PRIveT testbed and its technical features, and presents four research studies that have used the testbed extensively. Reflections on the testbed features that were important in these studies and lessons learnt are also discussed. An assessment of the PRIveT testbed in terms of utility of the design for human-robot interaction experiments concludes the paper. [ABSTRACT FROM AUTHOR]

With the rapid development of the micro-grid associated with new and clean energies, the smooth switching between grid-connected and islanded operation modes of the micro-grid is a key issue that needs to be addressed urgently. In traditional solutions, V/f (Voltage/frequency) control is adopted for the master micro sources when the micro-grid works in islanded mode, while PQ (real and reactive power) control is adopted when in grid-connected mode. However, when the two controllers switch when mode transfer occurs, transient oscillations usually occur and thereafter the dynamic response will be degraded. This paper considers an archetypical micro-grid with Wind-PV-ES (Wind, Photovoltaic and Energy Storage) hybrid system, which forms the basis of our case study. The underlying reason for such transient oscillation is analyzed in this paper. Thereafter a modified control strategy for seamless mode transfer is designed and implemented. An improved PQ control method is designed by which the output of the PQ controller always synchronously tracks the output of the V/f controller for micro-grid switches from islanded mode to grid-connected; furthermore, a dq rotating coordinate synchronization based V/f control method is proposed for transition from grid-connected mode to islanded mode. Finally, experiments and analysis are undertaken on some basic and important operating cases; the results in our case study indicate that the modified control strategy is effective in dominating the micro-grid during mode transfer and thus yielding significantly better performances. [ABSTRACT FROM AUTHOR]

Conventional historical data based material and energy balance analyses are static and isolated computations. Such methods cannot embody the cross-coupling effect of energy flow, material flow and information flow in the process industry; furthermore, they cannot easily realize the effective evaluation and comparison of different energy transfer processes by alternating the model module. In this paper, a novel method for material balance and energy conservation analysis of process industry energy transfer system is developed based on model property. Firstly, a reconfigurable energy transfer process model, which is independent of energy types and energy-consuming equipment, is presented from the viewpoint of the cross-coupling effect of energy flow, material flow and information flow. Thereafter the material balance determination is proposed based on both a dynamic incidence matrix and dynamic balance quantity. Moreover, the model-weighted conservation determination theorem is proved, and the energy efficiency analysis method is also discussed. Results confirmed the efficacy of the proposed methods, confirming its potential for use by process industry in energy efficiency analyses. [ABSTRACT FROM AUTHOR]