Battery health prognostics are critical for ensuring safety, efficiency and sustainability in modern energy systems. However, it has been challenging to achieve accurate and robust prognostics due to complex battery degradation behaviors with nonlinearity, noises, capacity regeneration, etc. Existing data-driven models capture temporal degradation features but often lack knowledge guidance, which leads to unreliable long-term health prognostics. To overcome these limitations, we propose KARMA, a knowledge-aware model with frequency-adaptive learning for battery capacity estimation and remaining useful life prediction. The model first performs signal decomposition to derive battery signals in different frequency bands. A dual-stream deep learning architecture is developed, where one stream captures long-term low-frequency degradation trends and the other models high-frequency short-term dynamics. KARMA regulates the prognostics with knowledge, where battery degradation is modeled as a double exponential function based on empirical studies. Our dual-stream model is used to optimize the parameters of the knowledge with particle filters to ensure physically consistent and reliable prognostics and uncertainty quantification. Experimental study demonstrates KARMA’s superior performance, achieving average error reductions of 50.6% and 33.3% over state-of-the-art algorithms for battery health prediction on two mainstream datasets, respectively. These results highlight KARMA’s robustness, generalizability and potential for safer and reliable battery management across diverse applications.

The energy optimization and demand side management (DSM) of Internet of Things (IoT)-enabled microgrids are being transformed by generative artificial intelligence, such as large language models (LLMs). This paper explores an integration of LLMs into energy management, and emphasizes their roles in automating the optimization of DSM strategies with Internet of Electric Vehicles (IoEV) as a representative example of the Internet of Vehicles (IoV). We investigate challenges and solutions associated with DSM and explore new opportunities presented by leveraging LLMs. Then, we propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization. The results demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles, and highlight our solution’s significant advancements in energy efficiency and user adaptability. This work shows LLMs’ potential in energy optimization of the IoT-enabled microgrids and promotes intelligent DSM solutions.

Battery capacity estimation is one of the core functions of battery management systems to ensure optimal battery usage. However, existing works often assume controlled environments and overlook realistic driving behavior, which is highly correlated with battery power usage and capacity. As a result, existing estimation methods become inaccurate and unstable in real-world conditions. In this paper, we propose DrivBat, a driving behavior-aware Transformer model for battery capacity estimation. DrivBat is a lightweight encoder-only Transformer that integrates driving behavior features to complement battery dynamics features to enhance SoC estimation performance. Experimental results on a real-world dataset demonstrate that DrivBat achieves a mean absolute error of just 1.5% in the same season and remains robust, with only a moderate increase to 2.1% in different seasons. These findings highlight the importance of incorporating driving behavior into customized machine learning models for accurate and reliable battery capacity estimation in practical settings.

Sleep staging is critical for assessing sleep quality and tracking health. Polysomnography (PSG) provides comprehensive multimodal sleep-related information, but its complexity and impracticality limit its practical use in daily and ubiquitous monitoring. Conversely, unimodal devices offer more convenience but less accuracy. Existing multimodal learning paradigms typically assume that the data types remain consistent between the training and testing phases. This makes it challenging to leverage information from other modalities in ubiquitous scenarios (e.g., at home) where only one modality is available. To address this issue, we introduce a novel framework for ubiquitous Sleep staging via Supervised Multimodal Coordination, called SleepSMC. To capture category-related consistency and complementarity across modality-level instances, we propose supervised modality-level instance contrastive coordination. Specifically, modality-level instances within the same category are considered positive pairs, while those from different categories are considered negative pairs. To explore the varying reliability of auxiliary modalities, we calculate uncertainty estimates based on the variance in confidence scores for correct predictions during multiple rounds of random masks. These uncertainty estimates are employed to assign adaptive weights to multiple auxiliary modalities during contrastive learning, ensuring that the primary modality learns from high-quality, category-related features. Experimental results on four public datasets, ISRUC-S3, MASS-SS3, Sleep-EDF-78, and ISRUC-S1, show that SleepSMC achieves state-of-the-art cross-subject performance. SleepSMC significantly improves performance when only one modality is present during testing, making it suitable for ubiquitous sleep monitoring.

Battery health estimation is fundamental to ensure battery safety and reduce cost. However, achieving accurate estimation has been challenging due to the batteries' complex nonlinear aging patterns and capacity regeneration phenomena. In this paper, we propose OSL, an optimal signal decomposition-based multi-stage machine learning for battery health estimation. OSL treats battery signals optimally. It uses optimized variational mode decomposition to extract decomposed signals capturing different frequency bands of the original battery signals. It also incorporates a multi-stage learning process to analyze both spatial and temporal battery features effectively. An experimental study is conducted with a public battery aging dataset. OSL demonstrates exceptional performance with a mean error of just 0.26%. It significantly outperforms comparison algorithms, both those without and those with suboptimal signal decomposition and analysis. OSL considers practical battery challenges and can be integrated into real-world battery management systems, offering a good impact on battery monitoring and optimization.

The surging demand for batteries requires advanced battery management systems, where battery capacity modelling is a key functionality. In this paper, we aim to achieve accurate battery capacity prediction by learning from historical measurements of battery dynamics. We propose GiNet, a gated recurrent units enhanced Informer network, for predicting battery's capacity. The novelty and competitiveness of GiNet lies in its capability of capturing sequential and contextual information from raw battery data and reflecting the battery's complex behaviors with both temporal dynamics and long-term dependencies. We conducted an experimental study based on a publicly available dataset to showcase GiNet's strength of gaining a holistic understanding of battery behavior and predicting battery capacity accurately. GiNet achieves 0.11 mean absolute error for predicting the battery capacity in a sequence of future time slots without knowing the historical battery capacity. It also outperforms the latest algorithms significantly with 27% error reduction on average compared to Informer. The promising results highlight the importance of customized and optimized integration of algorithm and battery knowledge and shed light on other industry applications as well.

With the advancements of generative artificial intelligence (GenAI) models, their capabilities are expanding significantly beyond content generation and the models are increasingly being used across diverse applications. Particularly, GenAI shows great potential in addressing challenges in the electric vehicle (EV) ecosystem ranging from charging management to cyber-attack prevention. In this article, we specifically consider Internet of Electric Vehicles (IoEV) and we categorize GenAI for IoEV into four different layers, namely, EV’s battery layer, individual EV layer, smart grid layer, and security layer. We introduce various GenAI techniques used in each layer of IoEV applications. Subsequently, public datasets available for training the GenAI models are summarized. Finally, we provide recommendations for future directions. This survey not only categorizes the applications of GenAI in IoEV across different layers but also serves as a valuable resource for researchers and practitioners by highlighting the design and implementation challenges within each layer. Furthermore, it provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.

Nutrition estimation is crucial for effective dietary management and overall health and well-being. Existing methods often struggle with sub-optimal accuracy and can be time-consuming. In this paper, we propose NuNet, a transformer-basednetwork designed fornutrition estimation that utilizes both RGB and depth information from food images. We have designed and implemented a multi-scale encoder and decoder, along with two types of feature fusion modules, specialized for estimating five nutritional factors. These modules effectively balance the efficiency and effectiveness of feature extraction with flexible usage of our customized attention mechanisms and fusion strategies. Our experimental study shows that NuNet significantly outperforms its variants and existing solutions for nutrition estimation. It achieves an error rate of 15.65%, the lowest known to us, largely due to our multi-scale architecture and fusion modules. This research holds practical values for dietary management with huge potential for transnational research and deployment and could inspire other applications involving multiple data types with varying degrees of importance.

Deep learning significantly diminishes reliance on human experts for sleep staging, paving the way for fully automated sleep quality assessment in clinical and daily environments. However, existing methods mostly focus on the integration of novel networks through "black boxes" way to boost classification accuracy, often neglecting model interpretability and the identification of critical sleep features. To address these issues, we develop an innovative architecture utilizing Attention-based ensemble Mechanism (namely AeM) for effective sleep staging and further finding out discriminative feature combinations. AeM consists of three main parts, i.e., modality division and combination (MDC) module, individual feature extraction (IFE) module and attention-based weight and ensemble classification (AWEC) module. MDC module divides the raw input data by modality and recombines them along the channel dimension into new inputs. The IFE module employs multiple naive convolutional neural networks to extract features efficiently from each modality combination. Finally, the AWEC module integrates these features, effectively calculates the weight of each modal combination using the attention mechanism, and outputs the classification result. We evaluated AeM on three public-available datasets: ISRUC-S1, ISRUC-S3, and Sleep-EDF78. Experimental results demonstrate that AeM outperforms all comparative methods and can identify effective digital biomarkers in sleep staging tasks.

Batteries play a key role in today's power grid. In this paper, we investigate the impact of battery degradation on the distribution network. We formulate a multi-objective framework for optimizing battery scheduling with the goals of minimizing monetary costs and improving network performance. Our frame-work incorporates energy purchase and battery degradation into the costs and measures the network performance through energy losses and voltage deviation. We propose Bach for battery degradation-aware scheduling based on s-constraint and fuzzy logic methods. Bach is implemented for the IEEE 33-bus network for an experimental study. The results show the effectiveness of Bach in optimizing costs and performance simultaneously with battery degradation awareness and demonstrate the flexibility of further customization.

Battery health monitoring and prediction are critically important in the era of electric mobility with a huge impact on safety, sustainability, and economic aspects. Existing research often focuses on prediction accuracy but tends to neglect practical factors that may hinder the technology’s deployment in real-world applications. In this paper, we address these practical considerations and develop models based on the Bayesian neural network for predicting battery end-of-life. Our models use sensor data related to battery health and apply distributions, rather than single-point, for each parameter of the models. This allows the models to capture the inherent randomness and uncertainty of battery health, which leads to not only accurate predictions but also quantifiable uncertainty. We conducted an experimental study and demonstrated the effectiveness of our proposed models, with a prediction error rate averaging 13.9%, and as low as 2.9% for certain tested batteries. Additionally, all predictions include quantifiable certainty, which improved by 66% from the initial to the mid-life stage of the battery. This research has practical values for battery technologies and contributes to accelerating the technology adoption in the industry.

Road traffic accidents cause injuries, claim lives, and disrupt economic activities. It is among the key problems for intelligent transportation and smart cities. Cities, especially the mega ones, must strive for reducing accidents for public safety and sustainable growth, and the first task is to understand accidents. In this paper, we build up such understanding with the emerging explainable machine learning (ML) technique. We prepare a huge dataset with over two million accident records and use it to deliver ML models for accident modelling. Given ML models of high fidelity for mapping accident features and conditions to the accident severity, we apply several explainable ML techniques to explain the models and understand accidents. We first consider coarse granularity to capture the overall feature importance. Then we consider fine granularity methods including partial dependence plot and Shapley additive explanations. The former shows that feature impact varies at different feature values and uses a plot to reflect the impact changes. The latter puts attention to individual accident and quantifies the feature impact for the specific accident. Our core observation is that traffic accidents are often about weather, followed by location and road type. Extensive experimental study is performed to support our discussion and justify our conclusion. The deliverable of this paper offers an advanced way of understanding traffic accidents accurately in a quantitative manner and has great potential to be used for intelligent transportation and smart city applications.

Accurate and automatic power line anomaly detection is critical to smart grid. However, effective solutions are yet available due to the insufficiency of anomaly data. In this paper, we first collect a dataset from various sources consisting both normal and abnormal power line images. With this dataset, anomaly detection becomes feasible though with limited accuracy due to the limited size of the dataset. As such, we propose TransLine, an approach based on transfer learning to apply the existing knowledge extracted from large-scale datasets to complement the data insufficiency of power line anomaly detection. TransLine customizes and optimizes the knowledge to automate the power line anomaly detection with high accuracy. The experiment results show that TransLine can achieve superb accuracy of 96.1% on average and up to 98.1% given only a hundred abnormal images for model training. TransLine can be a key enabler of smart grid for great stability and efficiency and can inspire the other industrial applications facing data insufficiency issues.

Achieving high performance in many multi-server systems (e.g., web hosting center, cloud) requires finding a good assignment of worker threads to servers and also effectively allocating each server’s resources to its assigned threads. The assignment and allocation components of this problem have been studied extensively but largely separately in the literature. In this paper, we introduce theassign and allocate (AA)problem, which seeks to simultaneously find an assignment and allocation that maximizes the total utility of the threads. Assigning and allocating the threads together can result in substantially better overall utility than performing the steps separately, as is traditionally done. We model each thread by a utility function giving its performance as a function of its assigned resources. We first prove that the AA problem is NP-hard. We then present a$2 (\sqrt {2}-1) > 0.828$factor approximation algorithm for concave utility functions, which runs in$O(mn^{2} + n (\log mC)^{2})$time for$n$threads and$m$servers with$C$amount of resources each. We also give a faster algorithm with the same approximation ratio and$O(n (\log mC)^{2})$time complexity. We then extend the problem to two more general settings. First, we consider threads with nonconcave utility functions, and give a 1/2 factor approximation algorithm. Next, we give an algorithm for threads using multiple types of resources, and show the algorithm achieves good empirical performance. We conduct extensive experiments to test the performance of our algorithms on threads with both synthetic and realistic utility functions, and find that they achieve over 92% of the optimal utility on average. We also compare our algorithms with a number of practical heuristics, and find that our algorithms achieve up to 9 times higher total utility.

Coolingas a Service (CaaS) is an emerging business that provides air conditioning services for buildings. With the rapid development of the business and the continuous increase of energy load, CaaS providers need cost-effective energy procurement to meet the service requirements. In this paper, we propose a data-driven approach for energy procurement for CaaS providers. First, we focus on two dominant variables of cooling energy cost, including outdoor temperature and electricity price. We predict their trends in the next day and accordingly, we estimate the energy usage and purchase the energy in the day-ahead energy market, one day before the actual usage. During the real-time operation, we can obtain the actual temperature and price, and we use this information to adjust the quality of service without violating the service standards. The adjustment serves as the demand response to the real-time energy market and can be cost beneficial. We conducted experimental studies to verify the performance of the proposed solution. The results show that our solution provides high-quality cooling services with minimal energy expenditure and helps improve the stability of the power grid.

Energy usage and thermal comfort are the pillars of smart buildings. Many research works have been proposed to save energy while maintaining a comfortable thermal condition. However, most of them either make the oversimplified assumption on thermal comfort with unsatisfied comfort performance or deal with the single-zone thermal control only with limited practical impact. A few preliminary pieces of research on multizone control are available, but they fail to keep pace with the latest advancements in the deep-learning-based control techniques. In this article, we investigate the multizone thermal control with optimized energy usage and canonical thermal comfort modeling. We adopt the emerging multiagent deep reinforcement learning techniques and propose to model each zone as an agent. A multiagent framework is established to support the information exchange among the agents and enable intelligent thermal control in the heterogeneous zones. Accordingly, we mathematically formulate a problem to optimize both energy and comfort. A multizone thermal control algorithm (MOCA) is proposed to solve the problem by deriving optimal control policies. We validate the performance of MOCA through simulation in professional TRNSYS, configured based on our real-world laboratory. The results are promising with up to 15.4% energy saving as well as satisfied thermal comfort in different zones.

Thermal comfort is a key consideration in smart buildings and a number of comfort models are available nowadays to evaluate the comfort level of occupants. However, the models are often complex and hardly interpretable for the developers and operators. Indeed, the model interpretations are beneficial in multifold such as for system inspection and optimization. In this article, we propose an interpretable thermal comfort system to introduce interpretability to any black-box comfort models. First, we focus on the relationship between a model's input features and output comfort level. The feature impact on comfort is investigated and the impact patterns are shown to be diverse for different features. Second, we unveil the model mechanisms about the data processing inside the model by building the model surrogates based on the interpretable machine learning algorithms. The surrogates offer outstanding fidelity for simulating the actual model mechanisms and the interpretations based on the surrogates are intuitive and informative. Our interpretable comfort system can be integrated with the existing building management systems. Accordingly, we can ease building owner's concerns about adopting new black-box technologies and enable various smart building applications like smart energy management.

Data centers have experienced dramatic growth in recent years in order to meet the ever-increasing demand for computing. As a result, minimizing the electrical cost to operate data centers has become a crucial issue. In this paper, we observe that electricity prices change over time, and that we can take advantage of periods with low prices by scaling up processor speeds to perform more work, while scaling down speeds during high price periods to reduce cost. We apply this observation to several settings. First, we consider an offline setting which assumes future electricity prices are given, and propose an efficient algorithm for optimally scaling a processor's speed in order to minimize the total electrical cost for completing a task by a deadline. We then consider a more realistic stochastic setting in which future prices are not known, but vary according to a Markov model. We present another efficient algorithm for minimizing the expected cost to meet a deadline. We performed a number of experiments using real electricity price traces to test the performance of our algorithms. We show that our stochastic algorithm is light-weight and relies only on easily obtainable price data, but that it achieves excellent performance, with only a 1 percent cost difference on average from the optimal offline algorithm. In addition, the stochastic algorithm significantly reduced costs compared to several candidate algorithms.

Datacenters have experienced dramatic growth in recent years, and the cost for powering them has become a significant problem. This paper proposes methods to minimize the energy cost for performing a task on a datacenter server before a deadline. We observe that energy prices fluctuate over time, and schedule the task to execute in periods of relatively low cost, despite not having knowledge of future costs during the execution. This problem is studied in several models, starting with an online setting where electricity prices can change arbitrarily. A$\sqrt{\varphi }$-competitive algorithm is proposed, where$\varphi$is the ratio between the maximum and minimum electricity prices, and this algorithm is also shown to be optimal by proving a matching lower bound. Next, we consider a stochastic setting in which prices vary in a Markovian fashion and propose an optimal algorithm based on dynamic programming. We then study the performance of our algorithms in practice using prices derived from real world data. The results show that the stochastic algorithm is very effective, and achieves cost that is within 3.4 percent of the optimum. Moreover, it performs well compared to several heuristics used in practice.

In this paper, we maximize the quality of experience (QoE) for mobile video streaming. QoE is modeled to capture user's video quality assessment as well as the freezing and bitrate variation during playback. Based on an observation that network bandwidth is correlated with location, we predict the future locations and accordingly the bandwidth along a trip. Then, the predicted information is utilized to dynamically adapt the video version and bitrate with maximized QoE. We show that our proposed solution well approximates the offline optimal performance by almost 98% on average. The proposed solution is also competitive compared to several popular streaming algorithms.

The emerging Internet of Things (IoT) technology enables smart building management and operation to improve building energy efficiency and occupant thermal comfort. In this paper, we perform data analysis using the IoT generated building data to derive accurate thermal comfort model for smart building control. Deep neural network (DNN) is used to model the relationship between the controllable building operations and thermal comfort. As thermal comfort is determined by multiple comfort factors, a fine-grained architecture is proposed, where an exclusive model is trained for each factor and accordingly the corresponding thermal comfort can be evaluated. The experimental results show that the proposed fine-grained DNN outperforms its coarse-grained counterpart by 3.5× and is 1.7×, 2.5×, 2.4×, and 1.9× more accurate compared to four popular machine learning algorithms. Besides, DNN's performance promotes with deeper network topology and more neurons, and a simple topology with the same number of neurons per network hidden layer is sufficient to achieve high modeling accuracy. Finally, the derived thermal comfort model reveals a linear relationship between comfort and air conditioning setpoint. The linear property helps quickly and accurately search for the optimal controllable setpoint with the desired comfort.

Small-size and light-weight, as the modern design concept for the emerging wearable devices, has become a trend. However, such trend puts physical limitations to the battery, and the resulting short battery lifetime becomes the bottleneck for most wearable devices today. In this paper, we aim to optimize the energy usage through data compression and transmission rate control. We propose a novel joint compression-transmission approach, which not only minimizes the energy consumption of both compression and transmission, but also maintains the corresponding data distortion and transmission delay within a certain tolerant level. By adopting the Lyapunov framework, we develop an online algorithm to minimize the one-slot drift-plus-penalty function. We conduct numerical analysis and experimental study for our proposed approach. The results show that the size of queuing buffer has the significant impact on the energy cost. Next, we verify a fundamental tradeoff between the energy expenditure and transmission delay, and derive the theoretical performance bounds. After that, we show that the energy cost is also determined by the wireless channel gain and the data compression ratio. Finally, compared to a strategy without compression, our approach can save up to 92% of energy.

Video streaming is one of the most widely used mobile applications today, and it also accounts for a large fraction of mobile battery usage. Much of the energy consumption is for wireless data transmission and is highly correlated to network bandwidth conditions. In periods of poor connectivity, up to 90% of mobile energy can be used for wireless data transfer. In this article, we study the problem of energy-efficient mobile video streaming. We make use of the observed correlation between bandwidth and user location , and also observe that a user’s location is predictable in many situations, such as when commuting to a known destination. Based on the user’s predicted locations and bandwidth conditions, we optimize wireless transmission times to achieve high quality video playback while minimizing energy use. We propose an optimal offline algorithm for this problem, which runs in O ( Tk ) time, where T is the duration of the video and k is the size of the video buffer. We also propose LAWS, a Location AWare Streaming algorithm. LAWS learns from historical location-aware bandwidth conditions and predicts future bandwidths along a planned route to make online wireless download decisions. We evaluate LAWS using real bandwidth traces, and show that LAWS closely approximates the performance of the optimal offline algorithm, achieving 90.6% of the optimal performance on average, and 97% in certain cases. LAWS also outperforms three popular strategies used in practice by, on average, 69%, 63%, and 38%, respectively. Lastly, we show that LAWS is able to deal with noisy data and can attain the stated performance after sampling bandwidth conditions only five times.

Achieving high performance in many distributed systems requires finding a good assignment of threads to servers as well as effectively allocating each server's resources to its assigned threads. The assignment and allocation components of this problem have both been studied extensively, but separately in the literature. In this paper, we introduce the assign and allocate (AA) problem, which seeks to simultaneously find an assignment and allocations that maximize the total utility of the threads. Assigning and allocating the threads together can result in substantially better overall utility than performing the steps separately, as is traditionally done. We model each thread by a concave utility function giving its throughput as a function of its assigned resources. We first show that the AA problem is NP-hard, even when there are only two servers. We then present a 2(√2-1) > 0.828 factor approximation algorithm, which runs in O(mn2 + n (log mC)2) time for n threads and m servers with C amount of resources each. We also present a faster algorithm with the same approximation ratio and O(n(log mC)2) running time. We conducted experiments to test the performance of our algorithm on threads with different types of utility functions, and found that it achieves over 99% of the optimal utility on average. We also compared our algorithm against several other assignment and allocation algorithms, and found that it achieves up to 5.7 times better total utility.

Individuals and companies, taking advantage of cloud computing which affords both resource and compute scalability, are willing to outsource their exploding data to save the storage and managing cost, however, users often do not fully trust the cloud and therefore outsource their private data after encryption to protect the data privacy. Here, as the data are both encrypted and outsourced in the cloud, how to securely and efficiently store and process such data becomes a challenging task and a primary concern. Support vector machine (SVM) classification, among different data mining and machine learning algorithms, has been very widely used in practical applications, which however, does not have a corresponding solution for such outsourced and encrypted data. Also, existing secure methods only assume that the data is locally stored by users rather than outsourced. To address this problem, we propose a novel Protocol for Outsourced SVM (POS) in this paper. POS lets cloud and users perform collaborative operations on encrypted and outsourced data without violating the data privacy contributed by each user. We formally verified that POS is correct and secure. We also conducted experimental analysis.

With the push of cloud computing which has both resource and compute scalability, data, which has been exploding in the past years, are often outsourced to a server. To this end, secure and efficient data processing and mining on outsourced private database becomes a primary concern for users. Among different secure data mining and machine learning algorithms, gradient descent method, as a widely used optimization paradigm, aims at approximating a target function to reach a local minimum, which is always deemed as a decision model to be discovered. In existing methods, users are assumed to hold and process their own data, and all users follow a secure protocol to perform gradient descent algorithm. However, such methods are not applicable to a cloud platform since that data is outsourced to a centralized server after encryption. To address this problem, we propose an Encrypted Gradient Descent Protocol (EGDP) in this paper. In EGDP, both users and server perform collaborative operations to learn and approximate the target function without violating data privacy. We formally proved that EGDP is secure and can return correct result.

Rule mining, for discovering valuable relations between items in large databases, has been a popular and well researched method for years. However, such old but important technique faces huge challenges and difficulties in the era of cloud computing although which affords both storage and computing scalability: 1) data are outsourced to a cloud due to data explosion and high storage and management cost, 2) moreover, data are usually encrypted first before being outsourced for privacy's sake. Existing privacy-preserving rule mining methods only assume a distributed model where every data owner holds the self data without encryption and together follow a secure protocol to perform rule mining. To address this limitation, we propose a novel Protocol for Outsourced Rule Mining (PORM) in this paper. PORM performs rule mining in a cloud environment where data are both encrypted and outsourced. We formally proved that PORM is both correct and secure, and we also extended PORM to the multiple-user scenario.

To achieve higher performance, cache sizes have been steadily increasing in computer processors and network systems. But caches are often over-provisioned for peak demand and underutilized in typical non-peak workloads. As caches consume substantial power, this results in significant amounts of wasted energy. To address this, existing works turn off parts of the cache when they do not contribute to higher performance. However, while these methods are effective empirically, they lack provable performance bounds. In addition, existing works focus on processor caches and are not applicable to network caches where data size and cost can vary. In this paper, we study the energy-aware caching (EAC) problem, and seek to minimize the total cost incurred due to cache misses and energy consumption. We propose three algorithms to solve different variants of this problem. The first is an optimal offline algorithm that runs in O(kn log n) time for a size k cache and n cache accesses. Then, we propose a simple online algorithm for uniform data size and cost that is $2 + {{h} \over {h-h+1}}$ competitive compared to an optimal algorithm with a size h ≤ k cache. Lastly, we propose a $2 + {{h-1} \over {h-h+1}}$ competitive online algorithm that allows arbitrary data sizes and costs. We give an efficient implementation of the algorithm that takes O(log k) amortized time per cache access, and also present an adaptive version that reacts to workload patterns to achieve better real-world performance. Using trace driven simulations, we show our algorithm has substantially lower cost than algorithms focused on maximizing cache hit rates or minimizing energy usage alone.

Organizations and individuals nowadays face increasing daily operations closely rely on a huge amount of private data which is outsourced to a centralized server. Secure and efficient data processing and mining on such outsourced private data becomes a primary concern for users, especially with the push of cloud computing which has both resource and compute scalability. Among the building blocks of secure data mining algorithms, secure scalar product is used to calculate the sum of the products of the corresponding values of two vectors. Existing privacy preserving methods assume data is stored at the user side, and users follow a protocol to perform privacy preserving scalar product. However, such methods are not applicable as data now is outsourced to a centralized server in its encrypted form. To solve this problem, in this paper, we design a novel Protocol for Outsourced Scalar Product (POSP) that performs collaborative operations between server and users to produce the scalar product result without violating each user's data privacy. We proved that POSP can return the correct result and is secure. We also analysed that POSP has linear complexity in terms of space, computation, and communication with respect to the vector length.

Secure and efficient data storage and computation for an outsourced database is a primary concern for users, especially with the push for cloud computing that affords both compute and resource scalability. Among the diverse secure building blocks for secure analytical computations on outsourced databases, the encrypted set intersection operation extracts common sensitive information from datasets belonging to different users. In existing methods, each user holds their sensitive data and all users follow a secure protocol to perform set intersection. This approach is not applicable to the cloud platform, where data resides in the cloud platform in encrypted form and not at each user site. To address this limitation, in this paper, we design the Encrypted Set Intersection Protocol (ESIP) that allows server and users to perform collaborative operations to obtain the correct set intersection result without violating privacy of data contributed by each user at the server.