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Deep-learning-based methods for building-edge-detection have been widely researched and applied in the field of image processing. However, these methods often emphasis the analysis of deep features, which may result in neglecting crucial shallow information representation. Furthermore, abstract features in the deep layers can potentially interfere with the accuracy of edge extraction. To address these challenges, we propose a densely connected edge-detection enhancement network (DCEFE-Net) for building-edge-detection in high-resolution remote sensing images. Firstly, by introducing spatial land channel attention modules, we effectively captured low-level spatial information and high-level semantic information from the input image. Secondly, the proposed edge-aware feature enhancement (EAFE) module emphasis the representation of informative edge features. By alliteratively generating multiple layers of edge-detection maps, it addresses the issue of edge detail loss and enhances edge-detection accuracy. Finally, the dense connectivity blocks strengthen the connections between the convolutional layers, thereby preventing the loss of edge features. Experimental results on the WHU and the Inria Aerial Image Labeling datasets validate the effectiveness of DCEFE-Net, as it consistently produces clear and reliable building-edge results.

ABSTRACTBuildings are one of the most important habitats for humans, and therefore, accurate identification and extraction of building information in remote sensing images are crucial. Buildings in remote sensing images vary in shape and color due to differences in sensor acquisition methods, geographical location, and other factors. However, they all share a common feature – the presence of shadows. Obtaining accurate data from building shadows can provide a wealth of reliable information for building research. Consequently, it is crucial to review various methods for extracting building shadows, especially deep learning-based methods, to illustrate shadow implementation scenarios in building research: 1) building detection in very high resolution remote sensing images (VHRRSI); 2) building detection in SAR; 3) building change detection; 4) building damage assessment; 5) building height estimation; 6) building shadow removal; 7) other methods (such as building shadow data enhancement, detection of building shadows in ghost images, and conservation of historic buildings). This study discusses the advantages and disadvantages of building shadow detection methods and provides an overview of the datasets and evaluation metrics commonly used in studies of building shadow applications. We hope that this study will serve as a valuable reference for researchers in the field of building shadow studies.

Semantic segmentation of remote sensing images is an extensively employed and demanding task. Although deep convolutional neural networks have significantly increased the accuracy of semantic segmentation, the problems of losing detailed features in segmentation and ignoring rich contextual information of images still exist. To solve these challenges, we propose a multilateral semantic with dual relation network (MSDRNet) for remote sensing images segmentation. The proposed MSDRNet consists of two parallel modules, the detail semantic module and the global semantic module, for extracting image detail and global features, respectively. Subsequently, improved spatial relation block and channel relation block are introduced in two separate parallel modules to further enhance the contextual connection of the images. Finally, a feature refinement module is added to balance the multilateral features between the features extracted from the two branches. We display the robustness and effectiveness of the proposed MSDRNet on the publicly available ISPRS Potsdam and Vaihingen datasets. We further experimented with the Gaofen image dataset, which contains information on larger scale features, to demonstrate the validity of our model. The results of extensive experiments conducted on the aforementioned three datasets show that the proposed approach outperforms several state-of-the-art semantic segmentation methods.

Abstract Background Few studies have investigated how the effectiveness of hand washing in removing hand contaminants is influenced by the performance and duration of each step involved. We conducted an observational study by recruiting participants from a university campus, with the aim to comprehensively evaluate how performance, duration and demographic factors influence hand washing effectiveness. Methods A total of 744 videos were collected from 664 participants in July-October 2022 and independently evaluated by two infection control experts through labelling videos for correct and incorrect performance of each step. The individual hand washing effectiveness was determined by quantifying the percentage of residual fluorescent gel on the dorsum and palm areas of each participant’s hands. A logistic regression analysis was conducted to identify factors that were significantly associated with better hand washing effectiveness. An exposure-response relationship was constructed to identify optimal durations for each step. Approximately 2300 hand images were processed using advanced normalization algorithms and overlaid to visualize the areas with more fluorescence residuals after hand washing. Results Step 3 (rub between fingers) was the most frequently omitted step and step 4 (rub the dorsum of fingers) was the most frequently incorrectly performed step. After adjustment for covariates, sex, performance of step 4 and step 7 (rub wrists), rubbing hands during rinsing, and rinsing time were significantly associated with hand washing effectiveness. The optimal overall hand washing time was 31 s from step 1 to step 7, and 28 s from step 1 to step 6, with each step ideally lasting 4–5 s, except step 3. The palms of both hands had less fluorescence residuals than the dorsums. The areas where residuals most likely appeared were wrists, followed by finger tips, finger webs and thumbs. Conclusions Performance and duration of some hand washing steps, sex and rinsing time were associated with hand washing effectiveness. The optimal duration might be applied to all seven steps to achieve the best decontamination results. Further studies are needed to refine hand hygiene standards and enhance compliance.

The outbreak of coronavirus disease 2019 (COVID-19) has caused massive infections and large death tolls worldwide. Despite many studies on the clinical characteristics and the treatment plans of COVID-19, they rarely conduct in-depth prognostic research on leveraging consecutive rounds of multimodal clinical examination and laboratory test data to facilitate clinical decision-making for the treatment of COVID-19. To address this issue, we propose a multistage multimodal deep learning (MMDL) model to (1) first assess the patient's current condition (i.e., the mild and severe symptoms), then (2) give early warnings to patients with mild symptoms who are at high risk to develop severe illness. In MMDL, we build a sequential stage-wise learning architecture whose design philosophy embodies the model's predicted outcome and does not only depend on the current situation but also the history. Concretely, we meticulously combine the latest round of multimodal clinical data and the decayed past information to make assessments and predictions. In each round (stage), we design a two-layer multimodal feature extractor to extract the latent feature representation across different modalities of clinical data, including patient demographics, clinical manifestation, and 11 modalities of laboratory test results. We conduct experiments on a clinical dataset consisting of 216 COVID-19 patients that have passed the ethical review of the medical ethics committee. Experimental results validate our assumption that sequential stage-wise learning outperforms single-stage learning, but history long ago has little influence on the learning outcome. Also, comparison tests show the advantage of multimodal learning. MMDL with multimodal inputs can beat any reduced model with single-modal inputs only. In addition, we have deployed the prototype of MMDL in a hospital for clinical comparison tests and to assist doctors in clinical diagnosis.

In modern mineral exploration applications, remote sensing technologies have been widely used and affirmed by engineering and mineral industries due to their unique technical advantages. With the advancement of remote sensing technologies, multiple geological remote sensing-derived prospecting methods have been developed. With the development of more accurate sensors, the detection band has not been segmented, and the spectral resolution of such sensors is constantly being improved. Thus, the accuracy of remote sensing geological prospecting methods has improved, and geological prospecting results have shifted from being qualitative to quantitative in nature. In this article, high-resolution remote sensing images are used to extract the ore controlling factors of deposits. The color, shape, texture and other image shapes produced by high-resolution remote sensing images are fully exploited to comprehensively mine the available data utilizing mathematics, image processing methods and other technologies to systematically identify prospective target areas. Based on an analysis of the metalloorganic geological characteristics detected in the study area, combined with multisource data such as geophysical and geochemical exploration-derived observations, the proposed remote sensing model describing the deposits in the study area is summarized. The research results show that deposit location identification technologies based on high-resolution remote sensing image feature decomposition have the potential to provide a reliable basis for peripheral exploration and deposit positioning in geological and mineral exploration studies.

High resolution remote sensing systems provide cheaper and fast way of acquiring images of power lines. However, such images depicting the details of other complex background objects, noises, and complicated brightness measurements, make separate extraction of the power lines challenging. This paper addresses the problem of automatic extraction of power lines from high resolution remote sensing images obtained from different sources. In order to automatically extract the power lines, we proposed an integrated Multiscale Geometric Analysis (MGA) approach. First, complementary Gabor and matched filters (MF) were employed over an image to suppress unnecessary background and noises, and initial discrimination of the power lines. Then, the filtering output was decomposed in to scale and orientation based subband coefficients using the Fast Discrete Curvelet Transform (FDCT) so as to access and modify different image features separately. By employing selective modification operations, well-established power line structures ready for extraction were derived. Finally the powerlines were extracted with hysteresis thresholding. The approach was successful in extracting power lines from high resolution images captured in any orientation. It is robust even when the source image is cluttered, and degraded due to noise and brightness effects. Power lines represented by weak intensities, crossing bright image regions, changing direction, closer power lines and those crossing each other, disconnected/broken power lines due to noise and occlusions were all inferred and extracted successfully. The approach was validated using real test images and the performance measures showed over 90% average accuracy fitting the ground truth.

Live Virtual Machine (VM) migration among geographically distributed edge clouds is an important strategy for providing low latency and reliable services for mobile end users. VM migration among edge clouds is more challenging than that in cloud computing, because the network bandwidth among edge clouds is more constrained than the cloud data center networks. In this paper, we study the bandwidth allocation among multiple concurrent live VM migrations in edge clouds. This problem is novel in that existing works aim to reduce the migration time for a single live VM migration among the edge clouds, and also ignores the QoS requirement for the service running on the VM in migration. However, our problem considers multiple VM migration tasks, and aims to maximize the average QoS while meeting the migration time constraint for each VM migration task. We formulate the problem as a Non-Linear Programming (NLP) problem which is also shown to be NP-Hard. We develop a new method to solve this problem. In our approach, we first transfer the problem into a Linear Programming (LP) problem by reducing the solution space. Taking the output from the LP solver as an initial solution, we then develop a heuristic to adjust it in order to find a better one to the original NLP problem. Finally, we design a set of evolutionary algorithms to select the optimal initial solution from the LP solver. Extensive simulations show that our proposed method can achieve good QoS and also has a fast convergence speed.

For Internet of Vehicles (IoV) systems with multiple users, network coding can be introduced to provide efficient error control and throughput improvement services. However, if the heterogeneity characteristics and requirements of the end users (vehicles) are neglected, it will be difficult for an IoV system to provide each end user with fair system services, without which the advantages of network coding cannot be fully achieved and the performance of the multi-user diversity system will be degraded. In this paper, we propose a Dynamic Resource Scheduling Optimization (DRSO) algorithm, a dynamic fair scheduling algorithm combined with network coding for system resource allocation in a multi-user IoV system. We construct a general solution framework for service scheduling: first, we estimate the fairness index for each end user (vehicle) with the key information on Quality of Service (QoS). Second, we construct a service scheduling control model based on the service capability of control entities (multi-access edge computing servers), and propose a new utility evaluation function. Third, based on the fairness index, we select end users into multiple network coding sets. Network coding sets are the basic units of service scheduling. The optimization objective of the scheduling service is to maximize the total utility of all the network coding sets (the utility of the control entity). Finally, we establish a coding cache queue in the control entity based on the scheduling decision. To obtain the global optimal solution for active queue control, we combine a Quantum Particle Swarm Optimization (QPSO) algorithm with a Proportional Integral (PI) model. Then, the optimal scheduling decision can be made. Extensive simulation results show that DRSO outperforms related scheduling algorithms in varying traffic loads, demonstrating that DRSO can effectively guide service resource allocation.

Distracted driving causes a large number of traffic accident fatalities and is becoming an increasingly important issue in recent research on traffic safety. Gesture patterns are less distinguishable in vehicles due to in-vehicle physical constraints and body occlusions from the drivers. However, by capitalizing on modern camera technology, convolutional neural network (CNN) can be used for visual analysis. In this paper, we present a hybrid CNN framework (HCF) to detect the behaviors of distracted drivers by using deep learning to process image features. To improve the accuracy of the driving activity detection system, we first apply a cooperative pretrained model that combines ResNet50, Inception V3 and Xception to extract driver behavior features based on transfer learning. Second, because the features extracted by pretrained models are independent, we concatenate the extracted features to obtain comprehensive information. Finally, we train the fully connected layers of the HCF to filter out anomalies and hand movements associated with non-distracted driving. We apply an improved dropout algorithm to prevent the proposed HCF from overfitting to the training data. During the evaluation, we apply the class activation mapping (CAM) technique to highlight the feature area involving ten tested classes of typical distracted driving behaviors. The experimental results show that the proposed HCF achieves the classification accuracy of 96.74% when detecting distracted driving behaviors, demonstrating that it can potentially help drivers maintain safe driving habits.

Ridgelet can theoretically approximate low-level image features, and the ridgelet filter is constructed independently of the training sample, but it usually requires to preset a lot of parameters to achieve an ideal representation of complex scenes. Convolutional neural networks (CNNs) can adaptively exploit the high-level image features, but the training process depends on the selection of training samples. Hence, in this paper, the advantages of both the two aspects are fully considered for image feature extraction and a multi-resolution CNNs framework (MRCNNs) is proposed by fusing high-and-low-level features via ridgelet and CNNs. In the proposed method, multi-resolution low-level features are captured by the constructed ridgelet filters, and multi-resolution high-level features are exploited by the trainable convolutional filters. Then the ridgelet features and convolutional features are fused to reduce the dependence of CNNs on training samples, and consequently, improve the classification ability of CNNs. The proposed MRCNNs method is conducted on three very high-resolution (VHR) remote sensing images and compared with several deep-learning approaches. Experimental results present the effectiveness of the proposed method for VHR images classification.

Abstract In the past few years, a large variety of IoT applications has been witnessed by fast proliferation of IoT devices (e.g., environment surveillance devices, wearable devices, city-wide NB-IoT devices). However, launching data collection from these mass IoT devices raises a challenge due to limited computation, storage, bandwidth, and energy support. Existing solutions either rely on traditional data gathering methods by relaying data from each node to the sink, which keep data unaltered but suffering from costly communication, or tackle the spacial data in a proper basis to compress effectively in order to reduce the magnitude of data to be collected, which implicitly assumes the sparsity of the data and inevitably may result in a poor data recovery on account of the risk of sparsity violation. Note that these data collection approaches focus on either the fidelity or the magnitude of data, which can solve either problem well but never both simultaneously. This paper presents a new attempt to tackle both problems at the same time from theoretical design to practical experiments and validate in real environmental datasets. Specifically, we exploit data correlation at both temporal and spatial domains, then provide a cross-domain basis to collect data and a low-rank matrix recovery design to recover the data. To evaluate our method, we conduct extensive experimental study with real datasets. The results indicate that the recovered data generally achieve SNR 10 times (10 db) better than compressive sensing method, while the communication cost is kept the same.

As an increasing number of big data processing platforms like Hadoop, Spark, and Storm appear and normally share the resources in the data center, it has been important and challenging to schedule various jobs from these platforms onto the underlying data center resources such that the overall job completion time is minimized. To solve the problem, the existing work either focus on the task-level scheduling techniques, such as Quincy and delay scheduling, or focus on the network flow scheduling techniques, such as D3 and preemptive distributed quick. These works deal with the scheduling of tasks and network flows separately and cannot achieve optimal performance. The reason is that the task scheduling without regard of the available network bandwidths may generate the task placement that causes serious network congestions and thus leads to long data transmission time. In this paper, we propose the joint scheduling technique by coordinating the task placement and the scheduling of network flows arising from these tasks. We develop a software-defined network (SDN)-based online scheduling framework which selects the task placement based on the available bandwidth on the SDN switches and at meanwhile optimally allocates the bandwidth to each data flow. Comprehensive trace-driven simulations show that the joint scheduling technique can take full use of the network bandwidth and thus reduce the job completion time by 55% on average compared with the benchmark methods.

With the explosive growth of smart terminals, access points (APs) are densely deployed in the buildings of enterprise, campus, hotel, and so on, to provide sufficient coverage and capacity for peak user demands. However, existing studies show that during the off-peak periods, not all the capacity is needed and a large fraction of low-utilization or idle APs cause a great deal of energy waste in these buildings. Although many solutions have been proposed to switch on/off the APs according to the user needs, few works consider the energy cost by state transition. In this paper, we propose a state transition-aware energy-saving mechanism for dense wireless local area networks, which can dynamically switch the APs' states to meet the user needs while controlling the switching frequency and balancing the number of associated users of each AP. First of all, we analyze the most recent user behaviors, which are used to design the energy-saving mechanism. Then, we model the proposed mechanism in order to set relevant parameters reasonably. Finally, evaluation results show that comparing with a typical static strategy, the energy consumption is reduced by 24.3%, and the average available bandwidth is increased by 27.8%. Meanwhile, the switching frequency is reduced by 14.3% as well.

In recent years, there has been a paradigm shift in Internet of Things (IoT) from centralized cloud computing to edge computing (or fog computing). Developments in ICT have resulted in the significant increment of communication and computation capabilities of embedded devices and this will continue to increase in coming years. However, existing paradigms do not utilize low-level devices for any decision-making process. In fact, gateway devices are also utilized mostly for communication interoperability and some low-level processing. In this paper, we have proposed a new computing paradigm, named Edge Mesh, which distributes the decision-making tasks among edge devices within the network instead of sending all the data to a centralized server. All the computation tasks and data are shared using a mesh network of edge devices and routers. Edge Mesh provides many benefits, including distributed processing, low latency, fault tolerance, better scalability, better security, and privacy. These benefits are useful for critical applications, which require higher reliability, real-time processing, mobility support, and context awareness. We first give an overview of existing computing paradigms to establish the motivation behind Edge Mesh. Then, we describe in detail about the Edge Mesh computing paradigm, including the proposed software framework, research challenges, and benefits of Edge Mesh. We have also described the task management framework and done a preliminary study on task allocation problem in Edge Mesh. Different application scenarios, including smart home, intelligent transportation system, and healthcare, are presented to illustrate the significance of Edge Mesh computing paradigm.

Due to the centralized control, network-wide monitoring and flow-level scheduling of software-defined-networking (SDN), it can be utilized to achieve quality of service (QoS) for cloud applications and services, such as voice over IP, video conference, and online games. However, most existing approaches stay at the QoS framework design and test level, while few works focus on studying the basic QoS techniques supported by SDN. In this paper, we enable SDN with QoS guaranteed abilities, which could provide end-to-end QoS routing for each cloud user service. First of all, we implement an application identification technique on SDN controller to determine required QoS levels for each application type. Then, we implement a queue scheduling technique on SDN switch. It queues the application flows into different queues and schedules the flows out of the queues with different priorities. At last, we evaluate the effectiveness of the proposed SDN-based QoS technique through both theoretical and experimental analysis. Theoretical analysis shows that our methods can provide differentiated services for the application flows mapped to different QoS levels. Experiment results show that when the output interface has sufficiently available bandwidth, the delay can be reduced by 28% on average. In addition, for the application flow with the highest priority, our methods can reduce 99.99% delay and increase 90.17% throughput on average when the output interface utilization approaches to the maximum bandwidth limitation.

One of the promises of edu-metaverse is its ability to provide a virtual environment that enables us to engage in learning activities that are similar to or on par with reality. The digital enhancements introduced in a virtual environment contribute to our increased expectations of novel learning experiences. However, despite its promising outcomes, there appears to be limited adoption of the edu-metaverse for practical learning at this time. We believe this can be attributed to the fact that there is a lack of investigation into learners' behavior given a social learning environment. This lack of investigation is critical, as without behavioral insight, it hinders the development of education material and the direction of an edu-metaverse. Upon completing our work with the pilot user studies, we provide the following insights: 1) compared to Zoom, a typical video conferencing and remote collaboration platform, learners in the edu-metaverse demonstrate heightened involvement in learning activities, particularly when drawing mind mapping aided by the embedded knowledge graph, and this copresence significantly boosts learner engagement and collaborative contribution to the learning tasks; and 2) the interaction and learning activity design within the edu-metaverse, especially concerning the use of MM.

With the advance in mobile network-based systems, dynamic system has become one of the hotspots in fundamental study of distributed systems. In this article, we consider the dynamic system with frequent topology changes arising from node mobility or other reasons, which is also referred to as “dynamic network.” With the model of dynamic network, fundamental distributed computing problems, such as information dissemination and election, can be formally studied with rigorous correctness. Our work focuses on the node counting problem in dynamic environments. We first define two new dynamicity models, named ( Q, S )- distance and ( Q, S )*- distance , which describe dynamic changes of information propagation time against topology changes. Based on these two models, we design three different counting algorithms which basically adopt the approach of diffusing computation. These algorithms mainly differ in communication cost due to different information collection procedures. The correctness of all the algorithms is formally proved and their performance is evaluated via both theoretical analysis and experimental simulations.

Seat-level positioning of a smartphone in a vehicle can provide a fine-grained context for many interesting in-vehicle applications, including driver distraction prevention, driving behavior estimation, in-vehicle services customization, etc. However, most of the existing work on in-vehicle positioning relies on special infrastructures, such as the stereo, cigarette lighter adapter or OBD (on-board diagnostic) adapter. In this work, we propose iLoc, an infrastructure-free, in-vehicle, cooperative positioning system via smartphones. iLoc does not require any extra devices and uses only embedded sensors in smartphones to determine the phones’ seat-level locations in a car. In iLoc, in-vehicle smartphones automatically collect data during certain kinds of events and cooperatively determine the relative left/right and front/back locations. In addition, iLoc is tolerant to noisy data and possible sensor errors. We evaluate the performance of iLoc using experiments conducted in real driving scenarios. Results show that the positioning accuracy can reach 90% in the majority of cases and around 70% even in the worst-cases.

The ubiquity of mobile devices brings forth a sensing paradigm, participatory sensing, to collect and interpret sensory information from the environment. Participants join in multifarious sensing tasks and share their data. The sensing result can be obtained in light of shared data. It is not uncommon that some corrupted data is provided by participants, which makes sensing result unreliable accordingly. To address this nontrivial issue, we proposed the accumulated reputation model (ARM) to improve the accuracy of the sensing result. In ARM, participants’ reputation will be computed and accumulated based on their sensing data. The sensing data from reputable participants make higher contributions to the sensing result. ARM performs well on calculating accurate sensing results, even in extreme scenarios, where there are many inexperienced or malicious participants.

In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial−spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial−spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complement the insufficiency of the spectral feature, and then fused the spatial−spectral features with different strategies. Next, the Manhattan distance between the corresponding spatial−spectral feature vectors of the bi-temporal images was employed to measure the change magnitude between the bi-temporal images and generate a change magnitude image (CMI). Second, the use of the Otsu binary threshold algorithm was proposed to divide the CMI into a binary change detection map (BCDM) and a multi-scale segmentation voting decision algorithm to fuse the initial BCDMs as the final change detection map was proposed. Experiments were carried out on three pairs of bi-temporal remote sensing images with VHR remote sensing images. The results were compared with those of the state-of-the-art methods including four popular contextual-based LCCD methods and three post-processing LCCD methods. Experimental comparisons demonstrated that the proposed approach had an advantage over other state-of-the-art techniques in terms of detection accuracies and performance.

This paper discusses how the state-of-the-art techniques in cyber-physical systems facilitate building smart warehouses to achieve the promising vision of industry 4.0. We focus on four significant issues when applying CPS techniques in smart warehouses. First, efficient CPS data collection: when limited communication bandwidth meets numerous CPS devices, we need to make more effort to study efficient wireless communication scheduling strategies. Second, accurate and robust localization: localization is the basis for many fundamental operations in smart warehouses, but still needs to be improved from various aspects like accuracy and robustness. Third, human activity recognition: human activity recognition can be applied in human–computer interaction for remote machine operations. Fourth, multi-robot collaboration: smart robots will take the place of humans to accomplish most tasks particularly in a harsh environment, and smart and fully-distributed robot collaborating algorithms should be investigated. Finally, we point out some challenging issues in the future CPS-based smart warehouse, which could open some new research directions.

The most important issue that must be solved in designing a data gathering algorithm for wireless sensor networks (WSNS) is how to save sensor node energy while meeting the needs of applications/users. In this paper, we propose a novel energy-aware routing protocol (EAP) for a long-lived sensor network. EAP achieves a good performance in terms of lifetime by minimizing energy consumption for in-network communications and balancing the energy load among all the nodes. EAP introduces a new clustering parameter for cluster head election, which can better handle the heterogeneous energy capacities. Furthermore, it also introduces a simple but efficient approach, namely, intra-cluster coverage to cope with the area coverage problem. We use a simple temperature sensing application to evaluate the performance of EAP and results show that our protocol significantly outperforms LEACH and HEED in terms of network lifetime and the amount of data gathered.

Owing to the dynamic characteristics of mobile environments, a mobile application needs to adapt to changing contexts to improve performance and resource utilization. We have developed an adaptive middleware infrastructure that simultaneously satisfies the individual needs of applications while maintaining overall system performance. A Model Reference Adaptive Control mechanism has been implemented in the middleware using control theory and fuzzy-based techniques. With reference to the model reference adaptive control theory, we also present a Self-Adaptive Fuzzy-based Service Adaptation Model (SA-FSAM) by taking historical adaptation information into account, and utilizing a closed-loop control mechanism to fine-tune adaptation decisions. The SAFSAM and a conventional threshold-based linear-control model have been evaluated using a campus assistant mobile application. With the introduction of self-adaptive elements in the control model, the SA-FSAM shows significant improvements in service adaptation decisions.

Minimizing the communication overhead is always a hot topic in wireless sensor networks. In a multirate query system, data sources disseminate the data streams to users at the frequency they request. However, sending data in different frequencies to individual users is very costly. We address this problem by broadcasting a single consolidated data stream, aiming at reducing the amount of transmitted data. Taking into account the data correlation, we can reconstruct the data streams at lower frequencies from the consolidated stream at a higher frequency. In this paper, we propose an energy-efficient framework to process multirate queries and investigate the path-sharing routing tree construction method together with the rate conversion mechanism. We evaluate both the accuracy and energy efficiency by simulation. Simulation results indicate that with a reasonable level of tolerance, the performance gain is significant. As far as we know, this is the first energy-efficient solution for multirate query in wireless sensor networks.