ABSTRACT Accurate stock market prediction is crucial for investors to formulate correct investment strategies. However, the non‐linearity, high dimensionality, and volatility of financial data pose significant challenges to existing stock market prediction models. To effectively address the complex datasets faced by stock market prediction, this paper proposes a new and more efficient deep learning hybrid model, KAN‐LSTM, based on the LSTM (long short‐term memory) and integrating the KAN (Kolmogorov–Arnold network). The hybrid architecture improves the learning process by replacing the original MLP (multi‐layer perceptron) with the KAN, overcoming the limitations of poor interpretability and fixed activation functions in LSTM. Prediction experiments conducted on multidimensional financial data in the stock market show that the KAN‐LSTM hybrid model outperforms the original LSTM in all evaluation metrics, demonstrating superior performance and more efficient prediction capabilities. Specifically, the MAE (mean absolute error) decreased by 2.43%, the RMSE (root mean squared error) decreased by 1.92%, the MAPE (mean absolute percentage error) decreased by 2.2%, and the increased by 19.08%.

Adapter-based parameter-efficient transfer learning has achieved exciting results in vision-language models. Traditional adapter methods often require training or fine-tuning, facing challenges such as insufficient samples or resource limitations. While some methods overcome the need for training by leveraging image modality cache and retrieval, they overlook the text modality’s importance and cross-modal cues for the efficient adaptation of parameters in visual-language models. This work introduces a cross-modal parameter-efficient approach named XMAdapter. XMAdapter establishes cache models for both text and image modalities. It then leverages retrieval through visual-language bimodal information to gather clues for inference. By dynamically adjusting the affinity ratio, it achieves cross-modal fusion, decoupling different modal similarities to assess their respective contributions. Additionally, it explores hard samples based on differences in cross-modal affinity and enhances model performance through adaptive adjustment of sample learning intensity. Extensive experimental results on benchmark datasets demonstrate that XMAdapter outperforms previous adapter-based methods significantly regarding accuracy, generalization, and efficiency.

Time series anomaly detection is one kind of critical time series analytical tasks, which is widely applied to various real-world applications. Recently, the diffusion models have shown promising performance on time series imputation for anomaly detection. And the high computing requirements of the diffusion models naturally extend their computation paradigm from the centralized computing to the cloud computing, and then further to JointCloud computing which allows the diffusion models to be deployed across multiple clouds. However, the JointCloud computing paradigm faces the challenge of task offloading over multiple nodes across multiple clouds. Unfortunately, most of existing task offloading methods overlook the dynamic nature of network conditions among clouds. To address this issue, we propose a time series anomaly detection approach named AdaDiffAD by adaptively segmenting diffusion models in JointCloud environment with dynamic network conditions. Specifically, we design a task offloading strategy by segmenting the denoising process of the diffusion model onto both the edge clouds and central clouds, and utilizing the edge cloud results directly for anomaly detection when the network condition is not ideal. By conducting comprehensive experiments on seven datasets, the experimental results demonstrate that our proposed AdaDiffAD always achieves lower time consumption while maintaining competitive anomaly detection performance compared to the state-of-the art without adaptive task offloading strategy.

Anomaly detection in multivariate time series has emerged as a critical challenge in the time series research community with significant application potentials in various scenarios, ranging from fault diagnosis to system state estimation in Industrial Control Systems (ICSs). Meanwhile, the demand for high availability and extensibility of ICSs necessitates their deployment in the JointCloud environment. Therefore, the performance of the multivariate time series anomaly detection model is expected to be enhanced in the JointCloud environment when encountering dynamic network conditions among multiple clouds. Impressed by the effectiveness of diffusion models in anomaly detection, we have chosen diffusion models for empowering our anomaly detection model. Specifically, we propose Progressively Distilled Diffusion Anomaly Detection model (ProDiffAD) in the JointCloud environment to seek for the balance between effectiveness and efficiency. Moreover, our proposed model is capable of being adaptive with the dynamic network conditions in the JointCloud environment by modeling the intercloud network conditions. To validate the effectiveness and efficiency of our model, comprehensive experiments are conducted on two real and five synthetic datasets. The experimental results demonstrate that our proposed model achieves more accurate and faster multivariate time series anomaly detection in the JointCloud environment under dynamic network conditions compared to state-of-the-art models.

The promising wireless powered mobile edge computing (MEC) can offer sustainable energy and fast network service response for nearby wireless terminals (WTs) to satisfy real-time and flexible requirements. Online task offloading and wireless power transfer (WPT) are critical for the wireless powered MEC system to realize powerful function. However, existing researches usually schedule the task offloading of WTs serially to prevent mutual signal interference, and suffer from high task offloading time. Hence, to lower the task offloading time, we adopt the successive interference cancellation (SIC) technology and realize task concurrent offloading of multiple WTs to the edge server (ES). Specifically, we study the SIC-enabled online task concurrent offloading problem with the aim of optimizing the total task completion time. We prove this optimization problem to be NP-hard, and decompose this problem to reduce the problem solving difficulty. With the support of the SIC and deep reinforcement learning (DRL) technology, we present an efficient and intelligent algorithm named SIOA. Our SIOA algorithm provides online offloading decision generating strategies for WTs through the idea of task concurrent offloading and a well-devised DRL structure. Moreover, our SIOA algorithm assigns the ES’s WPT time via a feasible area analysis approach. Our SIOA algorithm can offer demonstrable feasibility assurance, and requires lower task completion time than existing baseline algorithms with low program running time, which is verified by experiments on a real dataset.

Enset (Ensete ventricosum (Welw.) Cheesman) is an indigenous Ethiopian crop that sustains the livelihood of more than 20 million people. Enset is used as a crop for human food security, animal feed, and a source of fiber for farmers. Since the past few decades, the production of enset has been severely curbed by bacterial wilt disease. The early detection and prevention of this disease are crucial for enhancing production. Deep learning in plant disease management is becoming an effective way to improve agro-productivity. However, standard convolutional neural network (CNN) models require a large number of parameters and higher computational costs. Efficient CNN models allow users to benefit without having to submit their data to a server for analysis, which is especially useful in parts of the world where internet access is fragile or even inaccessible. In this paper, we proposed a MobileNetV3-Small model which we jointly trained newly added classifier layers with selected final layers of the base model. The proposed model achieved 99.93% of accuracy rate on a test set. The model is trained using a dataset that includes 99 popular enset clones we collected from five different locations in Ethiopia with varying altitudes, climates, and weather conditions.

Multi-agent surrounding is a collaborative task that uses multiple agents to surround a stationary or moving target. Multi-agent surrounding has a wide range of applications, such as area monitoring of unmanned ships, environmental monitoring, and exploration of unknown environments. Existing work pay attention to the case of one-to-one surrounding of targets by agents, but there is a lack of consideration for the case where agents are not one-to-one with the target. In this paper, we propose the concept of virtual target, which is used as a mediator to realize the generic multi-agent surrounding a target. The main idea is to surround the actual target with the virtual target, while the agents surround the virtual target, where the generation of the virtual target is based on any given surrounding graph and random sampling, and the performance of the surrounding is ensured by the virtual target control algorithm and the agent controlling algorithm. The Lyapunov stability analysis and simulation results show that the proposed approach can make the virtual target fit the actual target effectively, and the agents can surround the actual target with the shape of the virtual target effectively.

In recent years, radio frequency identification (RFID) technology has been applied in various fields to efficiently identify objects. Considering that identification is usually performed continuously, the latest RFID approaches use previous identification results for subsequent identifications and can identify two tags per unit time. However, these approaches have not utilized bit-level collision information in ID transmissions, which can facilitate the identification. Moreover, the assumption of reliable communication is not always valid in real-world applications. In this article, we propose a new approach called the multiple resolution bit tracking (MRB) to further improve the identification performance. This approach dynamically computes a tag set that can be unambiguously identified irrespective of any missing subset. These tags are requested to transmit their IDs in the same timeslot. Strategies to handle unreliable communication have also been proposed and optimized for MRB. We performed extensive simulations to validate the performance of our proposed approach. The results show that MRB can achieve 3.7 tags per unit time, which is 1.85 times the number achieved using the existing approaches. Furthermore, MRB handles unreliable communications well and achieves a small given tag missing rate.

In recent years, radio frequency identification (RFID) based information query is widely used in many ap-plications. In order to meet various application requirements, different kinds of RFID protocols are proposed, such as ID collection, category estimation, and missing tag identification. We find that the structure and function used in these RFID protocols are quite similar. For example, empty slot skipping and collision slot reconciling are used in many protocols. An improvement in one protocol may also be applied in another protocol, or a combination of two compatible protocols can fulfill a new application requirement. However, currently there is no approach to measure the compatibility of two RFID protocols. In this study, we theoretically proposed the concept of RFID protocol compatibility and designed an approach to measure it. Analytic hierarchy process approach is revised for this purpose. The important features of an RFID protocol are identified, and then determine their weights according to their importance. The similarity of two protocols are computed by the weighted similarity of lowest level features. We validate this approach by using eight typical RFID protocols, and show useful information for the protocol design. For example, the results show the compatibility between CLS and SFMTI reaches 89.5 %, while the compatibility between CLS and TKQ is only 20.71 %, this conforms the characteristics of these protocols.

In the last decade, a growing number of people use web crawlers to collect the data from the Internet for data analysis. The web crawlers greatly increase the workload of web servers and hence hinder normal accesses of the websites located in the servers. The accesses from web crawlers also affect the effectiveness of web mining, which assumes that the accesses are all from normal users. Moreover, the un-licensed collection of data from websites are often prohibited by laws and regulations of government and commercial organizations. To restrict the data collection from web crawlers, currently anti-crawler technique is applied to the websites. The behaviors of web crawler are recognized and their accesses are denied. This overcome the aforementioned problem, however, become a big obstacle for data exchange, considering that the large volume of data in the Internet could be useful for many data analysis applications. The dilemma of data collection using web crawlers and anti-crawler techniques demand a better solution. In this study, we propose to a friendly data sharing system FDataCollector to allow the data collection and also alleviate the workload of web servers by using blockchain techniques. We first make the data uploaded to the data sharing system by a few trustful users and then sell to public users in a traceable and P2P sharing way. The other accesses of web web crawlers are prohibited. On the user side, this design not only enable a convenient search of data but also improve the download efficiency. On the data holder side, this traceable and benefit way encourages them to share the data. We implement the system to demonstrate our idea. The results show that the system has high efficiency even when many transactions occur at the same time.

In recent decades, radio frequency identification (RFID) has been used in many applications in the world. Tag anti-collision is a fundamental technique for RFID, for solving the collisions when multiple RFID tags transmit their IDs to an RFID reader simultaneously. This technique is well investigated in the stationary environment, however, has some deficiencies when the number of tags in the interrogation region of the reader changes dramatically. This paper proposes a tag anti-collision algorithm called TAD to solve this problem. TAD can effectively and fast estimate the number of arriving and leaving tags, and automatically adapt to different changes of the tags using a hybrid method. The simulation results show that TAD significantly outperforms existing approaches in the situation with many leaving tags.

RFID-based stocktaking uses RFID technology to verify the presence of objects in a region e.g., a warehouse or a library, compared with an inventory list. The existing approaches for this purpose assume that the number of missing tags is small. This is not true in some cases. For example, for a handheld RFID reader, only the objects in a larger region (e.g., the warehouse) rather than in its interrogation region can be known as the inventory list, and hence many tags in the list are regarded as missing. The missing objects significantly increase the time required for stocktaking. In this paper, we propose an algorithm called CLS (Coarse-grained inventory list based stocktaking) to solve this problem. CLS enables multiple missing objects to hash to a single time slot and thus verifies them together. CLS also improves the existing approaches by utilizing more kinds of RFID collisions and reducing approximately one-fourth of the amount of data sent by the reader. Moreover, we observe that the missing rate constantly changes during the identification because some of tags are verified present or absent, which affects time efficiency; accordingly, we propose a hybrid stocktaking algorithm called DLS (Dynamic inventory list based stocktaking) to adapt to such changes for the first time. According to the results of extensive simulations, when the inventory list is 20 times that of actually present tags, the execution time of our approach is 36.3 percent that of the best existing algorithm.

In recent years, radio frequency identification (RFID) technology has been applied in various fields to identify objects efficiently. Anti-collision protocols are important for RFID tag identifications because it can overcome the problem of unsuccessful identification caused by simultaneous transmission of IDs from multiple tags. Considering that identification is usually performed multiple times, the latest anti-collision approach uses the previous identification results for later identifications, and can identify two tags per unit time. Existing anti-collision protocols ignore bit information, causing low performance problems. In this study, we propose a new approach called multiple resolution bit tracking protocol (MRB) to improve this performance further. This approach dynamically computes a tag set that can be unambiguously identified irrespective of any missing sub-set. The tags in the tag set are requested to transmit their IDs in the same time slot and terminate their identifications after proper processing. We perform extensive simulations to validate the performance of our proposed approach. The results show that MRB can achieve 3.7 tags per unit time, which is 1.85 times the number achieved using the existing approaches.

As a typical task of time series mining, Time Series Classification (TSC) has attracted lots of attention from both researchers and domain experts due to its broad applications. To get rid of costly hand-crafting feature engineering process, deep learning techniques are applied for automatic feature extraction, which shows competitive or even better performance compared with state-of-the-art TSC solutions. However, on time series datasets presenting complex patterns, neither 1-Nearest-Neighbour classifier nor deep learning models are capable of achieving satisfactory classification accuracy which motivates us to explore new time series representations to help classifiers further improve the classification accuracy. In this paper, by building the binary distribution tree, an approach to time series classification based on deep learning models using new representations is proposed. By conducting comprehensive experiments over 6 most challenging time series datasets and comparing experimental results of the same classifier using the proposed representation or not, the potential of the proposed approach to enhancing time series classification accuracy is validated with a bunch of helpful findings.

RFID-based stocktaking uses RFID technology to verify the presence of objects in a region e.g., a warehouse or a library. The existing approaches for this purpose assume that an inventory list of objects in the interrogation region of an RFID reader is known. This is not true in some cases. For example, for a handheld RFID reader, only the objects in a larger region (e.g., the warehouse) rather than in its interrogation region can be known. The additional objects significantly increase the time required for stocktaking. In this paper, we propose a time-efficient stocktaking algorithm called CLS (Coarse-grained inventory list based stocktaking) to solve this problem. We transform the problem to a missing tag identification problem with a large missing rate. CLS enables multiple missing objects to hash to a single time slot and thus verifies them together. CLS also improves the existing approaches by utilizing more kinds of RFID collisions and reducing approximately one-fourth of the amount of data sent by the reader. Extensive simulations are performed and the results show CLS outperforms the best existing algorithm.

In an asynchronous distributed system, a number of processes communicate with each other via message passing that has a finite but arbitrary long delay. There is no global clock in that system. Predicates, denoting the states of processes and their relations, are often used to specify the information of interest in such a system. Due to the lack of a global clock, the temporal relations between the states at different processes cannot be uniquely determined, but have multiple possible circumstances. Existing works of predicate detection are based on the definitely modality or the possibly modality, denoting that a predicate holds in all of the possible circumstances or in one of them, respectively. No information is provided about the probability that a predicate will hold, which hinders the taking of countermeasures for different situations. Moreover, the detection is based on single occurrence of a predicate, so the results are heavily affected by environmental noise and detection errors. In this paper, we propose a new approach to predicate detection to address these two issues. We generalize the definitely and possibly modalities to an occurrence probability to provide more detailed information, and further investigate how to detect multiple occurrences of a predicate. We propose a unified algorithm framework for detecting various types of predicates and demonstrate the use of it for three typical types of predicates, including simple predicates, simple sequences, and interval-constrained sequences. Theoretical proofs and simulation results show that our approach is effective and outperforms existing approaches.

Radio Frequency Identification (RFID) is a rapidly developing digital identification technology that employs radio to collect identification information from RFID tags. In a typical RFID identification scenario, an RFID reader sends a request to RFID tags, and the RFID tags reply with the information pre-stored in their storages. In recent decades, many applications such as supply chain management, auto-ticking, human and animal tracking, smart hospital, etc. employ more and more RFID readers.

In recent decades, a growing number of large-scale RFID systems are used in various applications. In such a system, it is not uncommon that multiple concurrent radio communications among RFID readers and tags cause serious inference (called collision in the RFID field). One important kind of method to achieve collision-free communication is to activate RFID readers in different time slots. Existing activation approaches for solving this problem are mainly centralized, which is impractical due to the lack of central server, one-point failure risk, and performance bottleneck. Some distributed algorithms are proposed recently, but failed to consider the adaptiveness of the identification, where all of the RFID readers need to participate in the coordination control even if they do not have communication requirements any more. As a result, the optimal identification performance cannot be achieved. In this paper, we propose an adaptive distributed reader activation approach called ADRA for large-scale RFID systems. We build a fine-grained conflict graph for different kinds of collisions. And then a shared permission based distributed approach is adopted to eliminate those collisions. We guarantee that the RFID readers that do not need to communicate any more are suspended and excluded from the execution of coordination eventually. Extensive simulation results show that our approach outperforms existing approaches in terms of execution time and message overhead.

RFID technology has been widely used for object tracking in indoor environment due to their low cost and convenience for deployment. In this paper, we consider RFID reader tracking which refers to continuously locating a mobile object by attaching it with a RFID reader that communicates with passive RFID tags deployed in the environment. One difficulty is that the RFID readings gathered from the environment are often noisy. Existing approaches for tracking with noisy RFID readings are mostly based on using Particle Filter (PF). However, continuous execution of PF has extremely high computational cost, and may be difficult to be done on mostly resource constrained mobile RFID devices. In this paper, we propose a hybrid method which combines PF with Weighted Centroid Localization (WCL) to achieve high accuracy and low computational cost. Our observation is that WCL has the same accuracy with PF with much lower cost if the object's velocity is low. Our method has two critical features. The first feature is adaptive switching between using WCL and PF based on the estimated velocity of the mobile object. The second feature is the further reduction of computational cost by offloading costly PF algorithm onto nearby servers. We evaluate the performance of our method through extensive simulations and experiments in two real world applications, namely, indoor wheelchair navigation and in-station Light Rail Vehicle (LRV) tracking at one of Hong Kong MTR depots. The result shows that our proposed approach has significantly less computational cost than existing PF based methods, while being as accurate as them.

Pervasive computing environments (PvCE) are embedded with interconnected smart devices which provide users with services desired. To meet requirements of users, smart devices with different kinds of functions may need to be associated together to provide the service described in the user requirement, which is called service composition. As the service composition environment may be dynamic and large scale, centralized service composition algorithm is usually inefficient due to message cost. On the other hand, a decentralized approach, which employs pre-determined coordinators to search and compose service, may have high cost as well. In this paper, we discuss a localized approach for service composition based on the Ubiquitous Interacting Object (UIO) model we have proposed earlier. UIO is an abstraction of physical devices in PvCE with ability to find and collaborate with other devices through exposing their capabilities as services. In our localized service composition algorithm (LASEC), UIOs collaborate with each other in a bottom-up, localized manner to compose required service without requiring global knowledge. To solve the problem of blind compositions in LASEC, we propose a novel mechanism called Alien-information-based Acknowledging (A-Ack), in which a UIO decides on collaborating with another UIO only after obtaining some additional information from the collaboration candidate. Specifically, this information refers to ability of a given UIO to compose another part of the service. Proposed LASEC is message-efficient and quality-guaranteed. Extensive simulations of LASEC as well as existing decentralized and pull-based centralized algorithms have been conducted. The results show the relatively low communication cost and composition time of LASEC. Moreover, we demonstrate feasibility of our approach with a prototype implementation.

With the advance of RFID technology and pervasive computing, a growing number of RFID devices are deployed in the surrounding environment and form large-scale RFID systems. Many applications run on top of such a system, and perform diverse and possibly conflicting data collection tasks. Existing works about RFID data collection either focus on deducing events of interest from primitive data, or scheduling the activation of readers to mitigate various of interference. The former ones assume that the primitive data have been collected already, and the later ones assume that all the readers belong to a single application whose objective is to read all the tags once. It lacks an effective way to specify the constraints in the process of data collection for multiple applications, and coordinate the readers to meet such requirements. In this paper, we proposed a specification language and a reader coordination algorithm to solve this problem. Our language can be used to specify complex constraints in data collection tasks, based on attribute selection, set relations, and temporal relations. And then a permission based data collection approach is developed for the readers to meet these constraints in a distributed way. Extensive simulation results show that the proposed approach outperforms existing approaches in terms of the execution time.

In wireless sensor networks, the connectivity-based localization protocols are widely studied due to low cost and no requirement for special hardware. Many connectivity-based algorithms rely on distance estimation between nodes according to their hop count, which often yields large errors in anisotropic sensor network. In this paper, we propose a virtual potential field algorithm, in which the estimated positions of unknown nodes are iteratively adjusted by eliminating the inconsistency to the connectivity constraint. Unlike current connectivity-based algorithms, VPF effectively exploits the connectivity constraint information, regardless of distance estimation between nodes, thus achieving high localization accuracy in both isotropic and anisotropic sensor networks. Simulation results show that VPF improves the localization accuracy by an average of 47% compared with MDS in isotropic network, and 42% compared with PDM in anisotropic network. As a refinement procedure, the average improvement factor of VPF is 56% and 50%, based on MDS and PDM respectively.

An important category of mobile RFID systems is the RFID system with mobile RFID tags. The mobility of RFID tags poses new challenges to designing RFID anti-collision protocols. Existing RFID anti-collision protocols cannot support high tag moving speed and high identification rate simultaneously. These protocols do not distinguish the identification deadlines of moving tags. Also, when tags move fast, they cannot determine the number of unidentified tags in the interrogation area of an RFID reader. In this paper, we propose a schedule-based RFID anti-collision protocol which, given a high identification rate, achieves the maximal tag moving speed. The protocol, without the need to estimate the number of unidentified tags, schedules an optimal number of tags to compete for the channel according to their identification deadlines, so as to achieve the optimal identification performance. The simulation and experiment results show that our approach can increase the moving speed of tags significantly compared with existing approaches, while achieving a high identification rate.

With the growing use of RFID-based devices, there are increasing attentions on utilizing RFID technology for localization. In this paper, we consider RFID reader localization which locates an object by attaching it with an RFID reader that communicates with passive RFID tags deployed in the environment. One difficulty in RFID reader localization is that frequent RFID faults can affect localization accuracy. More specifically, in a complex localization environment, metal, water, obstacles, etc., causes some tags to fail to communicate with the reader, and consequently the localization result may deviate from the real location. For permanent faults, existing localization approaches can tolerate only faults that occur at individual tags, by utilizing the redundant information from their neighboring tags. However, these approaches cannot handle permanent faults that occur at a group of neighboring tags in a region, which is referred to as regional permanent fault. They will suffer from serious localization errors if such kind of faults occurs. Moreover, existing work lacks quality measurement of localization results, hence the user may be not aware how serious the localization errors can be. In this paper, we propose an effective fault-tolerant RFID reader localization approach that can handle regional permanent fault, and provide quality measurement of localization results. Our approach is applied to both 2D and 3D localization applications. We further study the network localization problem where some objects know their locations and the other objects determine their locations by measuring the distances to their neighbors. Using the localization results and especially the quality information obtained by our approach, we solve the network localization problem with improved localization accuracy. Evaluation results show that our approach outperforms existing approaches in localization accuracy and can provide additional useful quality information.

Social activities have great impact for human beings in psychological health and social relationship. The recognition of social activities can unobtrusively recognize and record users' daily social activities, enabling users to better manage their life. Existing work of social activity recognition focus on recognizing a limited set of social activities and are mainly based on the patterns of individual user such as location pattern, vocal pattern, or others. However, social activities inherently exhibit the patterns with respect to multiple users rather individual user. In this paper, we introduce the concept of social circle, to extract social patterns associated with multiple users in a generic set of social activities. A social circle refers to a set of users frequently gathering to conduct certain social activities. Based on social circle, we design a system called CircleSense that supports accurate recognition of a generic set of social activities. We validate the effectiveness of CircleSense through the real trace collected by 10 volunteers. The result shows that CircleSense outperforms existing methods in terms of accuracy of social activity recognition.

With the growing use of RFID-based devices, RFID reader localization attracts increasing attentions recently. In this technology, an object carrying an RFID reader is located by communicating with some passive RFID tags deployed in the environment. One important problem of RFID reader localization is that frequent occurred RFID faults affect localization accuracy. Specifically, complex localization environment (may include metal, water, obstacles, etc.) makes some tags fail to communicate with the reader, which makes the localization result deviate from the real location. Existing approaches can tolerate the faults occurred in individual tags and lasting for a short time period, but suffer serious localization error if the faults exist in a large region and last for a long time period. Moreover, existing approaches do not provide quality measurement of a localization result. In this paper, we propose an effective fault-tolerant RFID reader localization approach suitable for the above-mentioned situations, and illustrate how to measure the quality of a localization result. We have taken extensive simulations and implemented an RFID-based localization system. In both cases, our solution outperforms existing approaches in localization accuracy and can provide additional quality information.

Pervasive computing environments are composed of numerous smart entities (objects and human alike) which are interconnected through contextual links in order to create a Web of physical objects. The contextual links can be based on matching context attribute-values (e.g., co-location) or social connections. We call such a Web of smart physical objects as context map. Context maps can be used for context-aware search and browse of the physical world. However, changes of dynamic context values over time may render a context map inconsistent. So, it is important to update contextual links with changes in specific context values. Given the asynchronous nature of pervasive environments, it is non-trivial to detect events generated by contextual changes in real time. We propose two algorithms for instantaneous and periodic detection of events with concurrent timing relations. Our algorithms have low time complexity and they can address the needs of different types of pervasive computing applications. We have evaluated our proposed algorithms through simulations as well as test bed experiments.

Passive RFID tags have been widely utilized for object tracking in indoor environment due to their low cost and convenience for deployment. The RFID readings gathered from real world are often noisy. Existing approaches for tracking objects with noisy RFID readings are mostly based on using Particle Filter (PF). However, continuous execution of particle filter will suffer from high computational cost on resource constrained RFID-enabled devices. In this paper, we propose a hybrid method for tracking mobile objects with high accuracy and low computational cost. This is achieved by an adaptively switching between using WCL (Weighted Centroid Localization) and PF according to the estimated velocity of the moving object. We have evaluated the performance of our hybrid method through extensive simulations. We have also validated the performance results by implementing the method in two applications, namely, indoor wheelchair navigation and in-station LRV (Light Rail Vehicle) tracking in one of the Hong Kong MTR depots. The result shows that our proposed method outperforms both WCL and PF in either accuracy or computational cost.

Event aggregation in Wireless Sensor Networks (WSNs) is a process of combining several low-level events into a high-level event to eliminate redundant information to be transmitted and thus save energy. Existing works on event aggregation consider either latency constraint or aggregation function, but not both. A solution jointly considering the two issues will be desirable. Moreover, existing works only consider optimal aggregation for single high-level event type, but many applications are composed of multiple types of high-level events. This paper studies the problem of aggregating multiple high-level events in WSNs with different latency constraints and aggregation functions. We first propose an event aggregation algorithm considering the two issues for single high-level event, and then extend it for multiple high-level events. The simulation results show that our algorithm outperforms existing approaches and saves significant amount of energy (up to 35% in our system).