Context-aware SAR image ship detection and recognition network

With the development of deep learning, synthetic aperture radar (SAR) ship detection and recognition based on deep learning have gained widespread application and advancement. However, there are still challenging issues, manifesting in two primary facets: firstly, the imaging mechanism of SAR results in significant noise interference, making it difficult to separate background noise from ship target features in complex backgrounds such as ports and urban areas; secondly, the heterogeneous scales of ship target features result in the susceptibility of smaller targets to information loss, rendering them elusive to detection. In this article, we propose a context-aware one-stage ship detection network that exhibits heightened sensitivity to scale variations and robust resistance to noise interference. Then we introduce a Local feature refinement module (LFRM), which utilizes multiple receptive fields of different sizes to extract local multi-scale information, followed by a two-branch channel-wise attention approach to obtain local cross-channel interactions. To minimize the effect of a complex background on the target, we design the global context aggregation module (GCAM) to enhance the feature representation of the target and suppress the interference of noise by acquiring long-range dependencies. Finally, we validate the effectiveness of our method on three publicly available SAR ship detection datasets, SAR-Ship-Dataset, high-resolution SAR images dataset (HRSID), and SAR ship detection dataset (SSDD). The experimental results show that our method is more competitive, with AP50s of 96.3, 93.3, and 96.2% on the three publicly available datasets, respectively.

An Improved Dual-Channel Deep Q-Network Model for Tourism Recommendation

Tourism recommendation results are affected by many factors. Traditional recommendation methods have problems such as low recommendation accuracy and lack of personalization due to sparse data. This article uses implicit features such as contextual information, time-series travel trajectories, and comment data to address these issues. First, the Long Short-Term Memory (LSTM) network is introduced as the model basis, and deals with the input data of the model such as contextual information, scenic spot information, and tourist comments and so on for feature extraction. Then, the online behavior and long-term interest preference of users are analyzed, using positive feedback and negative feedback mechanism, the Deep Q-Network (DQN) value function of dual-channel mechanism is constructed. Finally, we propose a recommendation strategy, in which, a value evaluation network and a target network are proposed for each agent to learn the optimal strategy. The model is trained on the Yelp, DP, and Tourism datasets covering multiple scenarios to provide users with tourism recommendation services. Compared with baseline models such as Ultra Simplification of Graph Convolutional Networks, DQN, Actor-Critic, and Latent Factor Model, this model has an average increase of 76.61% in accuracy compared with the comparison model, and an average increase of 43.48% in the normalized discounted cumulative gain compared with the baseline model.

Context-aware lightweight remote-sensing image super-resolution network

In recent years, remote-sensing image super-resolution (RSISR) methods based on convolutional neural networks (CNNs) have achieved significant progress. However, the limited receptive field of the convolutional kernel in CNNs hinders the network's ability to effectively capture long-range features in images, thus limiting further improvements in model performance. Additionally, the deployment of existing RSISR models to terminal devices is challenging due to their high computational complexity and large number of parameters. To address these issues, we propose a Context-Aware Lightweight Super-Resolution Network (CALSRN) for remote-sensing images. The proposed network primarily consists of Context-Aware Transformer Blocks (CATBs), which incorporate a Local Context Extraction Branch (LCEB) and a Global Context Extraction Branch (GCEB) to explore both local and global image features. Furthermore, a Dynamic Weight Generation Branch (DWGB) is designed to generate aggregation weights for global and local features, enabling dynamic adjustment of the aggregation process. Specifically, the GCEB employs a Swin Transformer-based structure to obtain global information, while the LCEB utilizes a CNN-based cross-attention mechanism to extract local information. Ultimately, global and local features are aggregated using the weights acquired from the DWGB, capturing the global and local dependencies of the image and enhancing the quality of super-resolution reconstruction. The experimental results demonstrate that the proposed method is capable of reconstructing high-quality images with fewer parameters and less computational complexity compared with existing methods.

A Context-Aware Smartphone-Based 3D Indoor Positioning Using Pedestrian Dead Reckoning

The rise in location-based service (LBS) applications has increased the need for indoor positioning. Various methods are available for indoor positioning, among which pedestrian dead reckoning (PDR) requires no infrastructure. However, with this method, cumulative error increases over time. Moreover, the robustness of the PDR positioning depends on different pedestrian activities, walking speeds and pedestrian characteristics. This paper proposes the adaptive PDR method to overcome these problems by recognizing various phone-carrying modes, including texting, calling and swinging, as well as different pedestrian activities, including ascending and descending stairs and walking. Different walking speeds are also distinguished. By detecting changes in speed during walking, PDR positioning remains accurate and robust despite speed variations. Each motion state is also studied separately based on gender. Using the proposed classification approach consisting of SVM and DTree algorithms, different motion states and walking speeds are identified with an overall accuracy of 97.03% for women and 97.67% for men. The step detection and step length estimation model parameters are also adjusted based on each walking speed, gender and motion state. The relative error values of distance estimation of the proposed method for texting, calling and swinging are 0.87%, 0.66% and 0.92% for women and 1.14%, 0.92% and 0.76% for men, respectively. Accelerometer, gyroscope and magnetometer data are integrated with a GDA filter for heading estimation. Furthermore, pressure sensor measurements are used to detect surface transmission between different floors of a building. Finally, for three phone-carrying modes, including texting, calling and swinging, the mean absolute positioning errors of the proposed method on a trajectory of 159.2 m in a multi-story building are, respectively, 1.28 m, 0.98 m and 1.29 m for women and 1.26 m, 1.17 m and 1.25 m for men.

EXPERT: transfer learning-enabled context-aware microbial community classification

Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in diverse contexts, such as classification of microbial communities for multiple diseases with limited number of samples, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.

A Fuzzy-Based Context-Aware Misbehavior Detecting Scheme for Detecting Rogue Nodes in Vehicular Ad Hoc Network

A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs' applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at a high rate due to high vehicles mobility. However, rogue nodes, which exploit the co-operativeness feature and share false messages, can disrupt the fundamental operations of any potential application and cause the loss of people's lives and properties. Unfortunately, most of the current solutions cannot effectively detect rogue nodes due to the continuous context change and the inconsideration of dynamic data uncertainty during the identification. Although there are few context-aware solutions proposed for VANET, most of these solutions are data-centric. A vehicle is considered malicious if it shares false or inaccurate messages. Such a rule is fuzzy and not consistently accurate due to the dynamic uncertainty of the vehicular context, which leads to a poor detection rate. To this end, this study proposed a fuzzy-based context-aware detection model to improve the overall detection performance. A fuzzy inference system is constructed to evaluate the vehicles based on their generated information. The output of the proposed fuzzy inference system is used to build a dynamic context reference based on the proposed fuzzy inference system. Vehicles are classified into either honest or rogue nodes based on the deviation of their evaluation scores calculated using the proposed fuzzy inference system from the context reference. Extensive experiments were carried out to evaluate the proposed model. Results show that the proposed model outperforms the state-of-the-art models. It achieves a 7.88% improvement in the overall performance, while a 16.46% improvement is attained for detection rate compared to the state-of-the-art model. The proposed model can be used to evict the rogue nodes, and thus improve the safety and traffic efficiency of crewed or uncrewed vehicles designed for different environments, land, naval, or air.

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the 'Automatic and Intelligent Data Collector and Classifier' framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The 'Custom-Net' model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the 'Custom-Net'. Furthermore, the impact of transfer learning on the 'Custom-Net' and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the 'Custom-Net' extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the 'Custom-Net' model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of 'Custom-Net' is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.

Dynamic Hand Gesture Recognition Using 3DCNN and LSTM with FSM Context-Aware Model

With the recent growth of Smart TV technology, the demand for unique and beneficial applications motivates the study of a unique gesture-based system for a smart TV-like environment. Combining movie recommendation, social media platform, call a friend application, weather updates, chatting app, and tourism platform into a single system regulated by natural-like gesture controller is proposed to allow the ease of use and natural interaction. Gesture recognition problem solving was designed through 24 gestures of 13 static and 11 dynamic gestures that suit to the environment. Dataset of a sequence of RGB and depth images were collected, preprocessed, and trained in the proposed deep learning architecture. Combination of three-dimensional Convolutional Neural Network (3DCNN) followed by Long Short-Term Memory (LSTM) model was used to extract the spatio-temporal features. At the end of the classification, Finite State Machine (FSM) communicates the model to control the class decision results based on application context. The result suggested the combination data of depth and RGB to hold 97.8% of accuracy rate on eight selected gestures, while the FSM has improved the recognition rate from 89% to 91% in a real-time performance.

CATSWoTS: Context Aware Trustworthy Social Web of Things System

The inevitable revolution of the Internet of Things (IoT) and its benefits can be witnessed everywhere. Two major issues related to IoT are the interoperability and the identification of trustworthy things. The proposed Context-Aware Trustworthy Social Web of Things System (CATSWoTS) addresses the interoperability issue by incorporating web technologies including Service Oriented Architecture where each thing plays the role of a service provider as well as a role of service consumer. The aspect of social web helps in getting recommendations from social relations. It was identified that the context dependency of trust along with Quality of Service (QoS) criteria, for identifying and recommending trustworthy Web of Things (WoT), require more attention. For this purpose, the parameters of context awareness and the constraints of QoS are considered. The research focuses on the idea of a user-centric system where the profiles of each thing (level of trustworthiness) are being maintained at a centralized level and at a distributed level as well. The CATSWoTS evaluates service providers based on the mentioned parameters and the constraints and then identifies a suitable service provider. For this, a rule-based collaborative filtering approach is used. The efficacy of CATSWoTS is evaluated with a specifically designed environment using a real QoS data set. The results showed that the proposed novel technique fills the gap present in the state of the art. It performed well by dynamically identifying and recommending trustworthy services as per the requirements of a service seeker.

A Smart Context-Aware Hazard Attention System to Help People with Peripheral Vision Loss

Peripheral vision loss results in the inability to detect objects in the peripheral visual field which affects the ability to evaluate and avoid potential hazards. A different number of assistive navigation systems have been developed to help people with vision impairments using wearable and portable devices. Most of these systems are designed to search for obstacles and provide safe navigation paths for visually impaired people without any prioritisation of the degree of danger for each hazard. This paper presents a new context-aware hybrid (indoor/outdoor) hazard classification assistive technology to help people with peripheral vision loss in their navigation using computer-enabled smart glasses equipped with a wide-angle camera. Our proposed system augments users' existing healthy vision with suitable, meaningful and smart notifications to attract the user's attention to possible obstructions or hazards in their peripheral field of view. A deep learning object detector is implemented to recognise static and moving objects in real time. After detecting the objects, a Kalman Filter multi-object tracker is used to track these objects over time to determine the motion model. For each tracked object, its motion model represents its way of moving around the user. Motion features are extracted while the object is still in the user's field of vision. These features are then used to quantify the danger using five predefined hazard classes using a neural network-based classifier. The classification performance is tested on both publicly available and private datasets and the system shows promising results with up to 90% True Positive Rate (TPR) associated with as low as 7% False Positive Rate (FPR), 13% False Negative Rate (FNR) and an average testing Mean Square Error (MSE) of 8.8%. The provided hazard type is then translated into a smart notification to increase the user's cognitive perception using the healthy vision within the visual field. A participant study was conducted with a group of patients with different visual field defects to explore their feedback about the proposed system and the notification generation stage. The real-world outdoor evaluation of human subjects is planned to be performed in our near future work.

Context-Aware Gossip-Based Protocol for Internet of Things Applications

This paper proposes a gossip-based protocol that utilises a multi-factor weighting function (MFWF) that takes several parameters into account: residual energy, Chebyshev distances to neighbouring nodes and the sink node, node density, and message priority. The effects of these parameters were examined to guide the customization of the weight function to effectively disseminate data to three types of IoT applications: critical, bandwidth-intensive, and energy-efficient applications. The performances of the three resulting MFWFs were assessed in comparison with the performances of the traditional gossiping protocol and the Fair Efficient Location-based Gossiping (FELGossiping) protocol in terms of end-to-end delay, network lifetime, rebroadcast nodes, and saved rebroadcasts. The experimental results demonstrated the proposed protocol's ability to achieve a much shorter delay for critical IoT applications. For bandwidth-intensive IoT application, the proposed protocol was able to achieve a smaller percentage of rebroadcast nodes and an increased percentage of saved rebroadcasts, i.e., better bandwidth utilisation. The adapted MFWF for energy-efficient IoT application was able to improve the network lifetime compared to that of gossiping and FELGossiping. These results demonstrate the high level of flexibility of the proposed protocol with respect to network context and message priority.

A Context-Aware Indoor Air Quality System for Sudden Infant Death Syndrome Prevention

Context-aware monitoring systems designed for e-Health solutions and ambient assisted living (AAL) play an important role in today's personalized health-care services. The majority of these systems are intended for the monitoring of patients' vital signs by means of bio-sensors. At present, there are very few systems that monitor environmental conditions and air quality in the homes of users. A home's environmental conditions can have a significant influence on the state of the health of its residents. Monitoring the environment is the key to preventing possible diseases caused by conditions that do not favor health. This paper presents a context-aware system that monitors air quality to prevent a specific health problem at home. The aim of this system is to reduce the incidence of the Sudden Infant Death Syndrome, which is triggered mainly by environmental factors. In the conducted case study, the system monitored the state of the neonate and the quality of air while it was asleep. The designed proposal is characterized by its low cost and non-intrusive nature. The results are promising.

A Proposal for IoT Dynamic Routes Selection Based on Contextual Information

The Internet of Things (IoT) is based on interconnection of intelligent and addressable devices, allowing their autonomy and proactive behavior with Internet connectivity. Data dissemination in IoT usually depends on the application and requires context-aware routing protocols that must include auto-configuration features (which adapt the behavior of the network at runtime, based on context information). This paper proposes an approach for IoT route selection using fuzzy logic in order to attain the requirements of specific applications. In this case, fuzzy logic is used to translate in math terms the imprecise information expressed by a set of linguistic rules. For this purpose, four Objective Functions (OFs) are proposed for the Routing Protocol for Low Power and Loss Networks (RPL); such OFs are dynamically selected based on context information. The aforementioned OFs are generated from the fusion of the following metrics: Expected Transmission Count (ETX), Number of Hops (NH) and Energy Consumed (EC). The experiments performed through simulation, associated with the statistical data analysis, conclude that this proposal provides high reliability by successfully delivering nearly 100% of data packets, low delay for data delivery and increase in QoS. In addition, an 30% improvement is attained in the network life time when using one of proposed objective function, keeping the devices alive for longer duration.

Hybrid-Aware Model for Senior Wellness Service in Smart Home

Smart home technology with situation-awareness is important for seniors to improve safety and security. With the development of context-aware computing, wearable sensor technology, and ubiquitous computing, it is easier for seniors to manage their health problem in smart home environment. For monitoring senior activity in smart home, wearable, and motion sensors—such as respiration rate (RR), electrocardiography (ECG), body temperature, and blood pressure (BP)—were used for monitoring movements of seniors. For context-awareness, environmental sensors—such as gas, fire, smoke, dust, temperature, and light sensors—were used for senior location data collection. Based on senior activity, senior health status can be classified into positive and negative. Based on senior location and time, senior safety is classified into safe and emergency. In this paper, we propose a hybrid inspection service middleware for monitoring elderly health risk based on senior activity and location. This hybrid-aware model for the detection of abnormal status of seniors has four steps as follows: (1) data collection from biosensors and environmental sensors; (2) monitoring senior location and time of stay in each location using environmental sensors; (3) monitoring senior activity using biometric data; finally, (4) expectation-maximization based decision-making step recommending proper treatment based on a senior health risk ratio.

Context Sensing System Analysis for Privacy Preservation Based on Game Theory

In a context sensing system in which a sensor-equipped mobile phone runs an unreliable context-aware application, the application can infer the user’s contexts, based on which it provides personalized services. However, the application may sell the user’s contexts to some malicious adversaries to earn extra profits, which will hinder its widespread use. In the real world, the actions of the user, the application and the adversary in the context sensing system affect each other, so that their payoffs are constrained mutually. To figure out under which conditions they behave well (the user releases, the application does not leak and the adversary does not retrieve the context), we take advantage of game theory to analyze the context sensing system. We use the extensive form game and the repeated game, respectively, to analyze two typical scenarios, single interaction and multiple interaction among three players, from which Nash equilibriums and cooperation conditions are obtained. Our results show that the reputation mechanism for the context-sensing system in the former scenario is crucial to privacy preservation, so is the extent to which the participants are concerned about future payoffs in the latter one.

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution.

A Bio-Inspired Model-Based Approach for Context-Aware Post-WIMP Tele-Rehabilitation

Tele-rehabilitation is one of the main domains where Information and Communication Technologies (ICT) have been proven useful to move healthcare from care centers to patients’ home. Moreover, patients, especially those carrying out a physical therapy, cannot use a traditional Window, Icon, Menu, Pointer (WIMP) system, but they need to interact in a natural way, that is, there is a need to move from WIMP systems to Post-WIMP ones. Moreover, tele-rehabilitation systems should be developed following the context-aware approach, so that they are able to adapt to the patients’ context to provide them with usable and effective therapies. In this work a model-based approach is presented to assist stakeholders in the development of context-aware Post-WIMP tele-rehabilitation systems. It entails three different models: (i) a task model for designing the rehabilitation tasks; (ii) a context model to facilitate the adaptation of these tasks to the context; and (iii) a bio-inspired presentation model to specify thoroughly how such tasks should be performed by the patients. Our proposal overcomes one of the limitations of the model-based approach for the development of context-aware systems supporting the specification of non-functional requirements. Finally, a case study is used to illustrate how this proposal can be put into practice to design a real world rehabilitation task.

A Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone

Continuously monitoring the ECG signals over hours combined with activity status is very important for preventing cardiovascular diseases. A traditional ECG holter is often inconvenient to carry because it has many electrodes attached to the chest and because it is heavy. This work proposes a wearable, low power context-aware ECG monitoring system integrated built-in kinetic sensors of the smartphone with a self-designed ECG sensor. The wearable ECG sensor is comprised of a fully integrated analog front-end (AFE), a commercial micro control unit (MCU), a secure digital (SD) card, and a Bluetooth module. The whole sensor is very small with a size of only 58 × 50 × 10 mm for wearable monitoring application due to the AFE design, and the total power dissipation in a full round of ECG acquisition is only 12.5 mW. With the help of built-in kinetic sensors of the smartphone, the proposed system can compute and recognize user’s physical activity, and thus provide context-aware information for the continuous ECG monitoring. The experimental results demonstrated the performance of proposed system in improving diagnosis accuracy for arrhythmias and identifying the most common abnormal ECG patterns in different activities. In conclusion, we provide a wearable, accurate and energy-efficient system for long-term and context-aware ECG monitoring without any extra cost on kinetic sensor design but with the help of the widespread smartphone.

Configuring a context-aware middleware for Wireless Sensor Networks

In the Future Internet, applications based on Wireless Sensor Networks will have to support reconfiguration with minimum human intervention, depending on dynamic context changes in their environment. These situations create a need for building these applications as adaptive software and including techniques that allow the context acquisition and decisions about adaptation. However, contexts use to be made up of complex information acquired from heterogeneous devices and user characteristics, making them difficult to manage. So, instead of building context-aware applications from scratch, we propose to use FamiWare, a family of middleware for Ambient Intelligence specifically designed to be aware of contexts in sensor and smartphone devices. It provides both, several monitoring services to acquire contexts from devices and users, and a context-awareness service to analyze and detect context changes. However, the current version of FamiWare does not allow the automatic incorporation related to the management of new contexts into the FamiWare family. To overcome this shortcoming, in this work, we first present how to model the context using a metamodel to define the contexts that must to be taken into account in an instantiation of FamiWare for a certain Ambient Intelligence system. Then, to configure a new context-aware version of FamiWare and to generate code ready-to-install within heterogeneous devices, we define a mapping that automatically transforms metamodel elements defining contexts into elements of the FamiWare family, and we also use the FamiWare configuration process to customize the new context-aware variant. Finally, we evaluate the benefits of our process, and we analyze both that the new version of the middleware works as expected and that it manages the contexts in an efficient way.

Assessing ambiguity of context data in intelligent environments: towards a more reliable context managing system

Modeling and managing correctly the user context in Smart Environments is important to achieve robust and reliable systems. When modeling reality we must take into account its ambiguous nature. Considering the uncertainty and vagueness in context data information it is possible to attain a more precise picture of the environment, thus leading to a more accurate inference process. To achieve these goals we present an ontology that models the ambiguity in intelligent environments and a data fusion and inference process that takes advantage of that extra information to provide better results. Our system can assess the certainty of the captured measurements, discarding the unreliable ones and combining the rest into a unified vision of the current user context. It also models the vagueness of the system, combining it with the uncertainty to obtain a richer inference process.

Study on the context-aware middleware for ubiquitous greenhouses using wireless sensor networks

Wireless Sensor Network (WSN) technology is one of the important technologies to implement the ubiquitous society, and it could increase productivity of agricultural and livestock products, and secure transparency of distribution channels if such a WSN technology were successfully applied to the agricultural sector. Middleware, which can connect WSN hardware, applications, and enterprise systems, is required to construct ubiquitous agriculture environment combining WSN technology with agricultural sector applications, but there have been insufficient studies in the field of WSN middleware in the agricultural environment, compared to other industries. This paper proposes a context-aware middleware to efficiently process data collected from ubiquitous greenhouses by applying WSN technology and used to implement combined services through organic connectivity of data. The proposed middleware abstracts heterogeneous sensor nodes to integrate different forms of data, and provides intelligent context-aware, event service, and filtering functions to maximize operability and scalability of the middleware. To evaluate the performance of the middleware, an integrated management system for ubiquitous greenhouses was implemented by applying the proposed middleware to an existing greenhouse, and it was tested by measuring the level of load through CPU usage and the response time for users’ requests when the system is working.