The rapid proliferation of Unmanned Aerial Vehicles (UAVs) and UAV swarm technologies has raised critical concerns about security and safety in low-altitude airspace. In response, we propose a vision-based system for detecting and tracking UAV swarms, which combines a novel UAV detection mechanism with a swarm tracking strategy. Our UAV detector incorporates parallel receptive field blocks alongside an attention mechanism to enhance detection performance. This design effectively captures multiscale features of UAVs while prioritizing salient features, ensuring robust detection under diverse conditions. For swarm tracking, we leverage the inherent formation constraints typically maintained by UAV swarms. These constraints allow us to improve tracking accuracy, particularly in scenarios involving occluded UAVs or those with weak appearance features. By integrating the enhanced UAV detector with the formation-aware swarm tracking framework, our approach achieves notable advancements in both detection and tracking performance.

The significant advancement in Internet of Things (IoT) adoption has enabled Multi-access Edge Computing (MEC) to mitigate IoT sensors' limited computational, transmission power constraints, and data distribution overhead. However, integrating MEC with the IoT ecosystem poses several challenges, resulting in integrity issues with the MECs, impacting their capacity to effectively serve users seeking data generated by IoT sensors. To address this, we propose SmartSLA, a blockchainbased solution to ensure Quality of Service (QoS) from thirdparty IoT devices. SmartSLA leverages the decentralized and immutable nature of blockchain to combat the shortcomings of MECs. Using smart contracts, we develop a blockchain solution to address a multi-objective optimization problem centered around QoS, user satisfaction, and maximizing the lifespan of IoT devices. Leveraging blockchain, we developed an edge-enhanced IoT network and assessed the efficacy of proposed novel dynamic reassignment strategies to enhance user satisfaction with data delivery. Our architecture demonstrate the high performance over time in regards to user satisfaction and SLA adherence, as well as the scalability compared to existing work.

The substantial volume of unstructured social media data generated during disasters often conceals critical information. Developing efficient methods to extract actionable insights from this data can significantly enhance emergency response and resource allocation. However, existing methods, primarily reliant on supervised learning, encounter challenges such as dependence on labeled data, limited adaptability, and scalability. To overcome these limitations, we present KeyMinES, an unsupervised model that extracts minimal keyphrases—bigrams and tokens—from social media data to identify and classify critical sub-events. Our approach integrates semantic and grammar-based reconstruction to ensure that the extracted keyphrases are both grammatically correct and contextually meaningful. Through clustering, we group these reconstructed sub-events, enabling the identification of patterns and offering actionable insights for decision-makers. Our experimental results, attained through quantitative and qualitative evaluations, demonstrate that KeyMinES outperforms baseline methods, achieving higher F1 scores and providing a scalable and cost-effective solution. Our ablation study reveals that combining bigram+token enhances sub-event detection compared to using only bigram or token, capturing both contextual relationships and granular details, thereby leading to more accurate identification of critical sub-events. This model holds significant potential for various stakeholders, including emergency responders and humanitarian organizations, by improving the extraction of actionable insights during disasters.

Machine learning models are increasingly being utilized across various fields and tasks due to their outstanding performance and strong generalization capabilities. Nonetheless, their success hinges on the availability of large volumes of annotated data, the creation of which is often labor-intensive, time-consuming, and expensive. Many active learning (AL) approaches have been proposed to address these challenges, but they often fail to fully leverage the information from the core phases of AL, such as training on the labeled set and querying new unlabeled samples. To bridge this gap, we propose a novel AL approach, Loss Prediction Loss with Gradient Norm (LPLgrad), designed to quantify model uncertainty effectively and improve the accuracy of image classification tasks. LPLgrad operates in two distinct phases: (i) Training Phase aims to predict the loss for input features by jointly training a main model and an auxiliary model. Both models are trained on the labeled data to maximize the efficiency of the learning process—an aspect often overlooked in previous AL methods. This dual-model approach enhances the ability to extract complex input features and learn intrinsic patterns from the data effectively; (ii) Querying Phase that quantifies the uncertainty of the main model to guide sample selection. This is achieved by calculating the gradient norm of the entropy values for samples in the unlabeled dataset. Samples with the highest gradient norms are prioritized for labeling and subsequently added to the labeled set, improving the model’s performance with minimal labeling effort. Extensive evaluations on real-world datasets demonstrate that the LPLgrad approach outperforms state-of-the-art methods by order of magnitude in terms of accuracy on a small number of labeled images, yet achieving comparable training and querying times in multiple image classification tasks. Our code is available at Github.

Deep learning (DL) models are popular across various domains due to their remarkable performance and efficiency. However, their effectiveness relies heavily on large amounts of labeled data, which are often time-consuming and labor-intensive to generate manually. To overcome this challenge, it is essential to develop strategies that reduce reliance on extensive labeled data while preserving model performance. In this paper, we propose FisherMask, a Fisher information-based active learning (AL) approach that identifies key network parameters by masking them based on their Fisher information values. FisherMask enhances batch AL by using Fisher information to select the most critical parameters, allowing the identification of the most impactful samples during AL training. Moreover, Fisher information possesses favorable statistical properties, offering valuable insights into model behavior and providing a better understanding of the performance characteristics within the AL pipeline. Our extensive experiments demonstrate that FisherMask significantly outperforms state-of-the-art methods on diverse datasets, including CIFAR-10 and FashionMNIST, especially under imbalanced settings. These improvements lead to substantial gains in labeling efficiency. Hence serving as an effective tool to measure the sensitivity of model parameters to data samples. Our code is available at https://github.com/sgchr273/FisherMask.

Detecting disasters in underground mining, such as explosions and structural damage, has been a persistent challenge over the years. This problem is compounded for first responders, who often have no clear information about the extent or nature of the damage within the mine. The poor light or even total darkness inside the mines makes rescue efforts incredibly difficult, leading to a tragic loss of life. In this paper, we propose a novel instance segmentation method called DIS-Mine, specifically designed to identify disaster-affected areas within underground mines under low-light or poor visibility conditions, aiding first responders in rescue efforts. DIS-Mine is capable of detecting objects in images, even in complete darkness, by addressing challenges such as high noise, color distortions, and reduced contrast. The key innovations of DIS-Mine are built upon four core components: i) Image brightness improvement, ii) Instance segmentation with segment anything model (SAM) integration, iii) Mask R-CNN-based segmentation, and iv) Mask alignment with feature matching. On top of that, we have collected real-world images from an experimental underground mine, introducing a new dataset named ImageMine, specifically gathered in low-visibility conditions. This dataset serves to validate the performance of DIS-Mine in realistic, challenging environments. Our comprehensive experiments on the ImageMine dataset, as well as on various other datasets demonstrate that DIS-Mine achieves a superior F1 score of 86% and mIoU of 72%, outperforming state-of-the-art instance segmentation methods, with at least 15x improvement and up to 80% higher precision in object detection.We have made our dataset publicly accessible through ImageMine Dataset.

Detecting disasters in underground mining, such as explosions and structural damage, has been a persistent challenge over the years. This problem is compounded for first responders, who often have no clear information about the extent or nature of the damage within the mine. The poor light or even total darkness inside the mines makes rescue efforts incredibly difficult, leading to a tragic loss of life. In this paper, we propose a novel instance segmentation method called DIS-Mine, specifically designed to identify disaster-affected areas within underground mines under low-light or poor visibility conditions, aiding first responders in rescue efforts. DIS-Mine is capable of detecting objects in images, even in complete darkness, by addressing challenges such as high noise, color distortions, and reduced contrast. The key innovations of DIS-Mine are built upon four core components: i) Image brightness improvement, ii) Instance segmentation with segment anything model (SAM) integration, iii) Mask R-CNN-based segmentation, and iv) Mask alignment with feature matching. On top of that, we have collected real-world images from an experimental underground mine, introducing a new dataset named ImageMine, specifically gathered in low-visibility conditions. This dataset serves to validate the performance of DIS-Mine in realistic, challenging environments. Our comprehensive experiments on the ImageMine dataset, as well as on various other datasets demonstrate that DIS-Mine achieves a superior F1 score of 86% and mIoU of 72%, outperforming state-of-the-art instance segmentation methods, with at least 15x improvement and up to 80% higher precision in object detection.We have made our dataset publicly accessible through ImageMine Dataset.

Location-based distributed communication in underground mines has been a hard problem to solve due to unreliable centralized architecture such as leaky feeder systems, high attenuation, and the unavailability of GPS signals. Delay Tolerant Networks (DTN) enable decentralized message routing using the store-carry-forward method that can help in creating situational awareness needed to handle emergency and disaster scenarios. The ability to predict where the DTN nodes (miner) might have been at/are headed to (with respect to the mine regions and pillars) at different times, combined with contact-based routing and intelligent handling of buffer, can be used for better delivery of messages. To this end, we propose a hybrid approach, called MinerRouter, that uses Random Forest (RF) and Graph Autoencoder (GAE) - Long Short Term Memory (LSTM) model to exploit the short- and long-mobility patterns of miners, respectively for faster message/content dissemination. Our simulations show that MinerRouter outperforms Opportunistic RF (RF), Opportunistic Contact Graph Routing (O-CGR), MaxProp, SemiBlind, and Blind routing protocols in terms of the delivery ratio of messages received, message latency, buffer occupancy Rate, communication overhead costs, and hop count.

Unmanned Aerial Vehicles (UAVs) are advertised as great tool that benefits society and humanity. However, UAVs also pose significant security threats ranging from privacy invasions, to interfering with commercial aircraft landing and takeoff, to accidently crashing into vehicles or people, to military or terrorist attacks. Consequently, there is a pressing need to detect and identify UAVs to mitigate such potential risks. While image-based methods are crucial for UAV detection, radio frequency (RF) emissions offer additional valuable insights. Analyzing RF signals, such as those used in UAV-ground station communications, can provide information about UAV types based on distinct frequency usage or communication patterns. This work introduces a deep-learning-based approach for recognizing and identifying UAVs using their RF emissions. Captured RF signals are transformed into spectrograms, which are subsequently analyzed using deep neural networks. Existing methods achieve low identification accuracy, for instance the ResNet-50V2 model achieves an accuracy of 85.39% even in controlled, laboratory, noise-free conditions. Moreover, in outdoor environments at distances of 50m and 100m, the accuracy drops to 68.90% and 56.88%, respectively. To improve classification accuracy in outdoors, a CNN model was developed, yielding an accuracy of 78.12%. Leveraging the ResNet 50 V2 architecture, remarkable accuracy of 95.08% was attained in binary classification tasks involving a dataset comprising 195 mixed UAV images and 290 non-mix UAV images.

During emergencies where time is of the essence, efficient management of disasters depends on swiftly recognizing relevant and urgent information from online platforms like X (Twitter), which is imperative for augmenting established response frameworks, such as the 911 emergency system. This paper introduces CURD, a Context-aware Relevance and Urgency Determination system designed to enhance the efficiency of disaster response. The system addresses two critical challenges: filtering out irrelevant data and assessing the urgency of relevant information. Our approach includes a multi-level annotation process for event type, relevancy, and an urgency annotation algorithm that significantly improves information extraction accuracy and efficiency. CURDdl, our classifier, uses a deep learning pipeline architecture with a combination of transformer models, a convolution layer, and custom attention mechanisms to classify disaster-related tweets into multiclass-event type, binary-relevance-and-urgency categories, and rank urgent ones based on significance. Experimental results show that our best baseline classifiers for all three tasks achieved ≥ 88% F1 and accuracy, and ≥ 94%. AUC. Our models also outperformed models from related works in all metrics, validating the effectiveness of CURD in prioritizing response messages that will facilitate decision-making and resource allocation in disaster scenarios. CURD annotated dataset and code are available on GitHub 1.

Sports have been extensively studied for their impact on people’s emotional well-being, with research revealing that they have the ability to reduce anxiety and unhappiness while boosting positive emotions1. Among all sports, soccer stands out as the most popular and controversial2, eliciting a wide range of emotional reactions from fans, players, officials, and spectators, particularly on social media. While sentiment classifications such as positive, negative, and neutral have been extensively studied, low-level emotions, which refer to more specific and granular emotional states beyond the three basic categories, have yet to be given much attention. This study scraped over 300,000 tweets during the 2022 FIFA World Cup in Qatar to gain a more in-depth understanding of human emotions. We use the dataset to finetune transformer models such as pre-trained BERT and RoBERTa to detect high and low-level emotions in the collected tweets. Pre-trained $\mathrm{RoBERTa}_{\mathrm{Sent3}}$ was initially used to predict three high-level classes. Three models were then trained using one model with 28 and two models with 27 GoEmotions labels from Reddit comments to break the high-level sentiment into low-level emotions. Impressively, these models outperformed GoEmotions paper classification research in terms of precision and F1-score for BER$\mathrm{T}_{\mathrm{Emot}28}$, and they predicted less neutral labels compared to $\mathrm{RoBERTa}_{\mathrm{Sent3}}$. To further improve the classification accuracy, BER$\mathrm{T}_{\mathrm{Emot}27}$ and RoBERT$\mathrm{a}_{\mathrm{Emot}27}$ were used to reclassify BER$\mathrm{T}_{\mathrm{Emot}28}$, and these models achieved better performance in terms of precision, recall, and F1-score compared to other models. BER$\mathrm{T}_{\mathrm{Emot}27}$ was particularly effective in eliminating false neutral classifications, reclassifying the predicted neutral label class from BER$\mathrm{T}_{\mathrm{Emot}28}$ into other classes and achieving retrieval counts of 50%, 51%, 53%, and 53% for weeks 1, 2, 3, and 4 respectively. This study stresses the importance of using a nuanced approach to emotion classification and provides valuable insights into the emotional landscape of a major sporting event. It demonstrates that tweets initially perceived as neutral contained other emotional content, highlighting the need for further research.1Mental Health Benefits of Sports2The Most Popular Sports In The World

Recently, the utilization of Radio Frequency (RF) devices has increased exponentially over numerous vertical platforms. This rise has led to an abundance of Radio Frequency Interference (RFI) continues to plague RF systems today. The continued crowding of the RF spectrum makes RFI efficient and lightweight mitigation critical. Detecting and localizing the interfering signals is the foremost step for mitigating RFI concerns. Addressing these challenges, we propose a novel and lightweight approach, namely RaFID, to detect and locate the RFI by incorporating deep neural networks (DNNs) and statistical analysis via batch-wise mean aggregation and standard deviation (SD) calculations. RaFID investigates the generation of an expected signal using DNNs within the time domain. We performed the statistical analysis to compare our generated expected signal with the received signal to detect the existence of interference and determine interference frequency. Experimental results show that signal estimation is accurate, with a mean squared error of 0.012 and an average run-time of 0.5 seconds.

Recent reports by the Mine Safety and Health Administration suggest that several injuries and fatalities could be attributed to the inability to accurately locate miners in case of disasters. Since underground mines have a complicated geometrical landscape and technological constraints such as no GPS information available, it is difficult to predict the location of a miner and hence may cause delays and inefficiencies in rescue operations during a disaster. A significant amount of research has been done to capture complex spatio-temporal relationships of movement of the nodes/people/things with time, spatial and temporal features to separately extract these relationships for location prediction. Although Markov Chains (MC) and Recurrent Neural Network (RNN) based methods have been used to predict locations, not all of them specifically mention the spatial locations, their connections and the aggregation techniques which would allow for the actual representations of the trajectory of miners. Addressing these concerns, we develop a first-of-its-kind end-to-end system entitled MinerFinder to predict the future location of the miners by incorporating Long Short Term Memory (LSTM) for trajectory information with Graph Autoencoder (GAE) for spatial environmental information representing the node connectivity. In addition, our approach will combine the miners' previous trajectories and daily repetitive patterns enhancing the prediction robustness. We evaluated MinerFinder over synthetic dataset to analyze the structure and location topology of an underground mine compared with foreground locations. Our model outperforms state of the art models and achieves an AP score ranging from (0.62 - 0.68) and Receiver Operating Characteristics (ROC) ranging from (0.63--0.68) with increasing percentage of prominent locations (most visited) to 50%.

In a battlefield, multiple groups operate with different missions, but their missions and groups can dynamically change based on the evolving situation. Due to the unavailability of network infrastructure after deployment, group members form a Delay Tolerant Network (DTN) which is prone to security attacks. Hence, based on the mission attributes, group memberships, nodes' interests, and data tags determination, targeted contents need to be distributed in a secure fashion to different users. Though existing Attributes Based Encryption (ABE) can provide security of information, revoking a member from a group is always an issue in DTN as the Attribute Authority (AA) is unavailable to the DTN nodes. Therefore, we adopt the ReVOABE algorithm for a battlefield DTN application for targeted data forwarding based on mission attributes and content interests.

The vehicular ad-hoc networks (VANETs) are considered a key mechanism for the collection and dissemination of basic safety messages (BSM) in the modern transportation system. However, the presence of compromised or malicious vehicles within the network can disrupt the security of the information and the safety of the passengers. The emergence of a blockchain-based distributed framework in VANETs ensures transparency and security within the network without the assist of a trusted centralized entity. Nonetheless, the presence of the majority of malicious vehicles within the region of interest (ROI) can still bypass the security provided by the state-of-the-art blockchain-based frameworks. In this paper, we propose a Blockchain-assisted Misbehavior Detection and Event Validation (BLAME) framework that can effectively detect the valid traffic events and the malicious vehicles from the ROI by leveraging the neighbor information and the event recorded by the individual vehicles even if they are in majority. The efficacy of BLAME has been validated through simulations in VENTOS simulators and a simulated blockchain environment by extensively addressing different use case scenarios.

Real-time information sharing and propagation using social media such as Twitter has proven itself as a potential resource to improve situational awareness in a timely manner for disaster management. Traditional disaster management systems work well for analyzing static and historical information. However, they cannot process dynamic streams of data that are being generated in real-time. This paper presents STIMULATE - a System for Real-time Information Acquisition and Learning for Disaster Management that can (1) fetch and process tweets in real-time, (2) classify those tweets into FEMA defined categories for rescue priorities using pre-trained deep learning models and generate useful insights, (3) find FEMA defined stranded people for rescue missions of varying priorities, and (4) provide an interactive web interface for rescue management given the available resources. The STIMULATE prototype is primarily built using the Python Flask framework for web interaction. Additionally, it is deployed in the cloud environment using Hadoop and MongoDB for scalable storage, and on-demand computing for processing extensive social media data. The deep learning models in the STIMULATE prototype use Python Keras and the TensorFlow library. We use Bi-directional Long Short-Term Memory (BLSTM) and Convolutional Neural Network (CNN) for developing the tweet classifier. Further, we use the Python PyWSGI WebSocket server for rescue scheduling operations. We present a deep learning system trained on hurricane Harvey and Irma datasets only. The tweet classifier is evaluated using 15 different disaster datasets. Finally, we present the results of multiple simulations using synthetic data with different sizes to measure the performance and effectiveness of the tweets processor and rescue scheduling algorithm.

Vehicular Ad-hoc Networks (VANETs) allow vehicles to share traffic-related events such as congestion to improve the driver's safety and comfort. However, due to the untrusted vehicular network environment, determining the credibility of broadcast messages becomes crucial and challenging. In this paper, we propose an incentive-based distributed trust management system with a secure event detection model employing the Byzantine fault-tolerant Paxos algorithm and game theory. The novelty of the proposed model lies in its ability to validate the accuracy of the broadcast information when the malicious vehicles form the majority compared to non-malicious vehicles within the ROI, unlike the state-of-the-art models. The proposed system's feasibility and effectiveness have been validated using the VENTOS, SUMO, and Omnet++ simulators by comprehensively addressing all possible use-case scenarios, and under the influence of at least one non-malicious vehicle at each RSU.

Every activity in disaster management demands accurate and up-to-date information to allow a quick, easy, and cost-effective response to reduce the possible loss of lives and properties. It is a challenging and complex task to acquire information from different regions of a disaster-affected area in a timely fashion. The extensive spread and reach of social media and networks such as Twitter allow people to share information in real-time. However, gathering of valuable information requires a series of operations such as (1) processing each tweet for the text classification, (2) possible location determination of people needing help based on tweets, and (3) priority calculations of rescue tasks based on the classification of tweets. These are three primary challenges in developing an effective rescue scheduling operation using social media data. In this paper, first, we propose a deep learning model combining attention based Bi-directional Long Short-Term Memory (BLSTM) and Convolutional Neural Network (CNN) to classify the tweets. Next, we perform feature engineering to create an auxiliary feature map which dramatically increases the model accuracy. In our experiments using data from Hurricanes Harvey and Irma, it is observed that our proposed approach performs better compared to other classification methods based on Precision, Recall, F1-score, and Accuracy, and is highly effective to determine the priority of a tweet. Furthermore, to evaluate the effectiveness and robustness of the proposed classification model a merged dataset comprises of 4 different datasets from CrisisNLP and another 15 different disasters data from CrisisLex are used. Finally, we develop an adaptive multi-task hybrid scheduling algorithm considering resource constraints to perform an effective rescue scheduling operation considering different rescue priorities.

In a disaster or battlefield zone, the rescue workers, soldiers and other survivors (referred to as nodes) may need to survey the damages and send images to the command and control center (the server) in a hop by hop fashion in the absence of any communication infrastructure. The server considers some area/landmark as the point of interest (POI), and distributes the request to the nodes to collect more information about them. Nodes take photos of POIs and share them among each other using store and forward paradigm, called Delay-tolerant Networks (DTNs) to send them to the server. Due to highly intermittent contact characteristics of nodes in a DTN network, and bandwidth and storage limitations, redundant photos need to be omitted in this forwarding technique whereas photos that cover different angles and views of the targets need to be shared. Another challenge is that, over time, some server-listed POIs may not be of importance whereas some new POIs might be of interest. In this work, we propose a scheme that is able to dynamically update the list of POIs based on the current photo metadata, with reduced consumption of the bandwidth, energy and the storage at nodes by sending only important photos of POIs. We compare our proposed schemes with a related well-known scheme [21] to show the scalability of our approaches which provide the same level of photo coverage, but consumes much less energy and bandwidth.

The shared mobility model of transportation services in cities has gained significant attention due to the proliferation of on-demand ride-sharing applications and the advancement of autonomous driving technologies. In this paper, a new shared mobility model is proposed accommodating the activity attribute of users' trip requests. Our key goal is to determine the minimum fleet size required to satisfy all on-demand requests while minimizing the total travel costs. Since this is an NP-hard problem, the model leverages a set of novel heuristic-based components including the clustering-based formation of ride-sharing groups, carpool-like schedule and ridesharing schedule generation, and clique-based trip integration. All work together to obtain the set of energy-efficient shared route schedules. The proposed model can also be extended for a heterogeneous vehicle fleet configuration (e.g. vehicles of various capacity and functionality) to work for different types of trip activities.

In Delay Tolerant Networks (DTNs), to ensure successful message delivery, contribution of mobile nodes in relaying in an opportunistic fashion is essential. In our proposed data-centric dissemination protocol here, messages (images) are annotated with keywords by the source, and then intermediate nodes are presented with an option of adding keyword-based annotations to create higher content strength messages enroute toward the destination. Therefore, the message contents like images get enriched as the ground situation evolves and learned by these intermediate nodes, such as in a disaster situation, or in a battlefield. Due to limited battery and storage capacity in mobile devices, nodes might turn selfish and do not participate in relaying or improving the quality of messages. Thus, additionally, an incentive mechanism is proposed in this paper which considers factors like message quality, level of interests, battery usage, etc for the calculation of incentives. At the same time, in order to prevent the nodes from turning malicious by adding inappropriate message tags in pursuit of acquiring more incentive, a distributed reputation model (DRM) is developed and integrated with the proposed incentive scheme. DRM takes into account inputs from the intermediate users like ratings of the message quality, relevance of annotations in the message, etc. The proposed scheme thus ensures avoidance of congestion due to uncooperative or selfish nodes in the system. The performance evaluations show that our approach delivers more high priority and quality messages with reduced traffic with a slightly lower message delivery ratio compared to a more recent DTN routing like ChitChat, where a source forwards a message to intermediate nodes, which meet or exceed the matching strength of keyword-based interests.

Traditional model of computing with wireless sensors/devices imposes restrictions on how efficiently these devices can be used due to resource constraints. Newer models for interacting with wireless sensors/devices such as Internet of Things and Sensor Cloud aim to overcome these restrictions. In this seminar, I will discuss sensor cloud architectures, which enable different wireless sensor and IoT networks, spread in a huge geographical area to connect together and be used by multiple users at the same time on demand basis. I will further discuss how virtual sensors assist in creating a multiuser environment on top of resource constrained physical wireless sensors and can help in supporting multiple applications on-demand basis. I will discuss security, privacy and data integrity and other security issues in sensor cloud as well as risk assessment in sensor cloud applications.

The objective of this paper is to determine how ridesharing can help lowering the travel cost of a user who already has a preplanned trip. This problem is formulated as the Ridesharing-Inspired Trip Recommendation Query (RSTR). In the first phase of the proposed method, the trip of the query initializer is matched with other users. In the second phase, a heuristic-based algorithm is employed to generate a new trip recommendation. Experimental results showed that the proposed solution is comparable to the optimal solution and performs much better in run-time efficiency and scalability.

Due to recent developments in sensor technologies, mobile sensor device use has become widespread, and many researchers have been attempting to leverage data collected by these devices. We call such data `mobile sensor data'; and environments where mobile sensor data arrive continuously, `mobile sensor stream' environments. Mobile sensor data are geo-referenced data with environmental attribute values; and they enable us to determine the geographical distribution of hot spots by retrieving data with comparatively extreme environmental attribute values (such as higher air-pollution index values). Top-k search result diversification in geographical space is valid for applications of this sort. By monitoring a diversified set over mobile sensor streams, we can trace changes in the distribution of hot spots. However, the computation costs for maintaining such diversified sets are high when we have to monitor a large amount of mobile sensor data. Thus, in this paper, we propose an efficient diversified set monitoring method for mobile sensor stream environments. Our proposed method can reduce the amount of examined data by exploiting our proposed regular grid-based data structure, and the diversified set can thereby be maintained much more efficiently. Our experimental results confirm that the proposed method involves much shorter computation time in comparison with the baseline method.

With the proliferation of location-based services enabled by a large number of mobile devices and applications, the quantity of location data, such as trajectories collected by service providers, is gigantic. If these datasets could be published, they will be valuable assets to various service providers to explore business opportunities, to governments to research commuter behavior for better transport management, and could also greatly benefit the general public for day to day commute. However, there are two major concerns that considerably limit the availability and the usage of these trajectory datasets. The first is the threat to individual privacy as users' trajectories may be tracked by an adversary to discover sensitive information, such as home locations, their children's school locations, or social information like habits or relationships. The other concern is the ability to analyze the exabytes of location data in a timely manner. Although there have been trajectory anonymization approaches proposed in the past to mitigate privacy concerns, none of these prior works address the scalability issue since it is a newly occurring problem. In this paper, we conquer these two challenges by designing a novel trajectory anonymization algorithm using the MapReduce programming paradigm to provide scalability, strong privacy protection and high utility rate of the anonymized trajectory datasets. We have conducted extensive experiments using real maps with different topologies, and our results prove both effectiveness and efficiency when compared with the latest centralized approaches.

In applications of Wireless Sensor Networks (WSNs) such as monitoring chemical leaks in case of a disaster, a user may be interested in getting top-k with diversity-m (referred as Top (k, m)), that is, top-k data should come from m different sub-regions (clusters) to simultaneously monitor critical areas. In addition, one may also monitor values for persistency, how long the top-k values remain unchanged. In this paper, we have introduced the new problem of continuous top-k query with diversity-m, i.e., we want to find the k highest values from at least m different clusters over a period of time in a WSN. In this context, we introduce an energy efficient scheme Top (k, m) to utilize the Gaussian's probability function to estimate the probability of a sensor node value being in the final top-k set. Based on the probability, the node decides whether to forward data values to the base station or not. Moreover, we make sure that top-k data items are not only collected from m-clusters, but they are also persistency, which is helpful in real-time monitoring applications. We have shown the improved performance of our scheme with respect to recent schemes EXTOK and Grid in terms of communication, energy usage and network life-time.

Recently, ridesharing applications, such as Uber, have gained popularity by helping riders to save money. However, current real-time ridesharing systems with time-window constraints have not yet considered the impact of real-time road conditions. In this work, we propose transferring algorithms and incentive models to enhance the flexibility of an existing large scale real-time ridesharing system. Transferring algorithms aim to transfer those users who experienced delay to other vehicles by using a pair wise bounding-box pruning technique. The incentive models avoid the use of transferring but instead compensate affected passengers with credits to maintain their satisfaction level in a different way. Extensive experiments were conducted using a real road network. Results show that our proposed algorithms and models are able to boost users' satisfaction level up to 90% under the influence of real-time road conditions.

In this demo, an optimal design method for generating a flight path passing through specified waypoints while satisfying the task points sent by the ground base station is proposed. Each specified waypoint is reached exactly at the specified location and time while intermediate waypoints are generated in such a way that they satisfy the task points with high task priority and high QoS priority. Generated waypoints are points from where imagery data of the task points are collected and taking into consideration factors such as task points' priorities, sensory capability and deviation required from the shortest path to satisfy that waypoint aggregates these task points. In this demo paper, we demonstrate the procedure for 4D flight trajectory generation, by taking the velocities, maneuverability of the aerial vehicle into consideration while ensuring that the vehicle avoids no-fly zones.

A Sensor cloud framework is composed of various heterogeneous wireless sensor networks (WSNs) integrated with the cloud platform. Integration with the cloud platform, in addition to the inherent resource and power constrained nature of the sensor nodes makes these WSNs belonging to a sensor cloud susceptible to security attacks. As such there is a need to formulate effective and efficient security measures for such an environment. But in doing so, requires an understanding of the likelihood and impact of different attacks feasible on the WSNs. In this paper, we propose a risk assessment framework for the WSNs belonging to a sensor cloud. The proposed risk assessment framework addresses the feasible set of attacks on a WSN identifying the relationships between them and thus estimating their likelihood and impact. This kind of assessment will give the security administrator a better perspective of their network and help formulating the required security measures.

Given a set of users with unique locations, and a closed region defined as a set of points, the optimal location algorithm determines the best location to meet between the users and their current trajectories. The algorithm is designed to find the location between the users that can be reached by a certain time taking into account traffic conditions, while making travel times and distances between users as fair as possible. The user's locations are periodically updated to ensure that their current path and velocity are consistent to what is expected while they are on route. Thus, the algorithm can adjust meeting locations based on developing traffic conditions.

In this paper, we present an efficient and secure code dissemination technique aimed at sensor clouds. Previous code dissemination techniques were geared toward traditional wireless sensor networks. They did not take into account, the dynamic nature of a sensor cloud, where the applications running on the motes may not just be updated but changed completely in successive code disseminations. The technique presented in this paper is based upon the observation that a large amount of code is common between applications in wireless sensor networks. Our technique first discovers the code common across various wireless sensor applications. It then distributes this code in the form of functions a priori into the network. During code dissemination, these common functions are picked up by the sensors from the network. Only a part of the code needs to be transmitted from the base station. This reduces the overall transmitted code and hence the energy consumption. Since, security is important in sensor clouds, we further present a security scheme based on proxy reencryption to provide confidentiality and integrity of the code. We have implemented our scheme using two different proxy reencryption algorithms, on Mica2 and TelosB mote platforms to measure its energy consumption. We have also evaluated our scheme in terms of disseminated code size and bandwidth usage to illustrate its efficiency compared to a popular secure code dissemination technique, Seluge.

This is a tutorial paper covering issues associated dynamic management of information as well as content in wireless networks of different types such as Mobile Peer-to-Peer (MP2P), Vehicle-to-Vehicle (V2V) and Delay-Tolerant Networks (DTNs).

We propose the E-VeT system for efficient vehicular traffic management in road networks using economy-based reward/penalty schemes. In E-VeT, base stations collaboratively facilitate dynamic vehicular route assignments for reducing the traffic congestion, average time of arrival and fuel consumption. The main contributions of this work are two-fold. First, it proposes an R2A (Revenue-based Route Allocation) scheme, which rewards vehicles for following system-assigned longer-time paths, and charges a fee for following system-assigned shorter-time paths. Furthermore, it penalizes vehicles for any deviations from the system-assigned paths. Second, it discusses a route allocation algorithm, which gives lesser-time paths as a preference to vehicles that have earned higher revenue based on the R2A scheme. Preliminary performance study shows that E-VeT is indeed effective in managing vehicular traffic in road networks by reducing the average time of arrival and fuel consumption.

Making the sensor data look more meaningful in its representation of an observed entity is the primary goal of sensor data fusion. Due to the energy constraint on sensors, there exists a need for algorithms that minimize the fusion cost while maintaining the validity of the data sent to the base station. Maintaining validity is even more difficult when we have a limited knowledge of the factors that govern an observed sensor entity. To achieve this goal, we modeled the uncertainties in sensor data and fed them into the system, employing recursive data estimation. By doing so, we considered the dynamically changing environmental parameters affecting the network to produce the most accurate representation of the observed system state. We propose here a spatio-temporal, correlation-based estimation procedure to corroborate the detection of an event in a sensor field. The number of in-network communications plays a great role from the networking perspective. This is because the power consumption during communication is several times greater than the power consumption during computation. To achieve this, our algorithm ensures that communication is done only during the time of an event. At all other times, the sensor motes maintain an updated global estimate, without communicating, by using a prediction algorithm. This reduces the need for frequent sensor synchronization. We conducted experiments using our distributed fusion architecture to show our algorithm's effectiveness; by a reduction in the power consumption, in terms of both the computation and the communication.

Wireless sensor networks (WSNs) have wide range of applications in military, health-monitoring, smart-home applications, and in other commercial environments. The computation of data aggregation functions like MIN/MAX is one of the commonly used tasks in many such WSN applications. However, due to privacy issues in some of these applications, the individual sensor readings should be kept secret from others. That is, the base station should be the only entity who should receive the output of MIN/MAX function and the individual sensor readings should not be revealed either to other sensor nodes or to the root node for confidentiality reasons. Existing Secure Data Aggregation (SDA) techniques for computing MIN/MAX are based on either order preserving or privacy homomorphic encryption schemes which are either inefficient or insecure. Along this direction, this paper proposes two novel solutions for securely computing MIN/MAX functions in WSNs using probabilistic encryption scheme. The first solution works for WSNs with no duplicate sensor readings whereas the second solution acts as a generic method and works even for duplicate readings but is less efficient compared to the first method. However, the second solution is much more secure compared to the existing protocols. The security of the proposed protocols is justified based on the well known quadratic residuosity assumption. We empirically analyze the efficiency of our schemes and demonstrate the advantages of the proposed protocols over existing approaches.

Caching is a common technique to improve efficiency of data access in MANETs (Mobile Ad hoc Networks), where users communicate using small portable devices connected by resource constraint wireless networks. In some MANET applications, controlling/reducing the cache locations are desirable due to security issues, restricted shared memory and maintenance cost. However, reducing the number of caches should be done by finding optimized cache locations (at highly connected and centrally located nodes) so that it does not affect the performance efficacy of data access in terms of response time. Existing cooperative caching approaches are deficient in finding such optimized cache locations as they do not focus on reducing the number of copies by finding their optimized locations to be shared among nodes. In this paper, we design and evaluate such a caching scheme using a single broker based MANET architecture to improve data access latency. Our scheme reduces the number of caches by efficiently placing them at locations which brings distant data closer to the source. The performance comparison of our scheme with one such recent caching scheme showcases improvement in data access efficiency by 30% along with reduction in number of cache locations by 72%. We evaluated data access efficiency using average hops and average roundtrip delay.

Secure data aggregation in wireless sensor networks has two contrasting objectives, i) Efficiently collecting and aggregating data and ii) Aggregating the data securely. Many schemes do not take into account the possibility of corrupt aggregators and allow the aggregator to decrypt data in hop by hop algorithms. On the other hand using public key cryptography for providing end to end security is not energy efficient. In this paper we present and analyze the performance of the secure hierarchical data aggregation algorithm which uses an efficient public key cryptosystem (elliptic curve cryptography) to achieve end to end security. Unlike many other secure data aggregation algorithms which require separate phases for secure aggregation and integrity verification, the secure hierarchical data aggregation algorithm does not require an additional phase for verification. This saves energy by avoiding additional transmissions and computational overhead on the sensor nodes. We present and implement the secure data aggregation algorithm on Mica2 and TelosB sensor network platforms and measure the execution time and energy consumption of various cryptographic functions. We have also simulated our algorithms to analyze how an end to end scheme increases the network life time. We experimentally analyze our algorithms based on parameters like throughput, end to end delay and resilience to node failures.

For realizing such crowdsourcing-related M-P2P applications, dynamic data management becomes a necessity to improve data availability, given the mobility and fragile wireless connections that connect resource-constrained mobile devices. Moreover, unlike in the case of traditional environments such as clusters, free-riding is a major issue for M-P2P environments, thereby implying that economic models may play a better role in incentivizing peers to collaborate for data discovery and management. Furthermore, traditional methods of data management in mobile environments generally consider only single-hop client-server communication. On the other hand,in M-P2P networks, the network communication is multihop and mobile devices can collect real-time data. Finally, privacy issues need to be addressed effectively to prevent location-based service providers from misusing users' location information.

Efficient power management is vital for increasing the life of sensor nodes in wireless sensor networks (WSN). Thus, techniques such as data aggregation have been widely used in WSN to preserve nodes' energy. Despite its appealing and powerful features, data aggregation processes require a high level of security. Ensuring accuracy with aggregating data can become an issue even due to single bit errors. Hence, many encryption techniques have been proposed to secure the data aggregation process. However, once a wireless sensor is physically compromised, the keys stored on the sensor are exposed, thus rendering the cryptography useless. Moreover, most cryptographic approaches restrict the level of collaboration with other security measures -- especially trust management, where behavior observation is vital. In this paper, we propose a trust management approach on encrypted packets to deal with the faulty nodes in sensor networks. We designed a collaborative framework which incorporates the Order-Preserved Encryption Scheme [2] and Sigmoid Trust Management approaches. We then discuss how these two can coexist and comprehensively evaluate an integrated OPES-trust management scheme in the sensing environment using both the TOSSim simulator and an actual mote implementation. The results have shown that trust management and OPES cryptography does not consume more energy than radio transmission, yet provides a fairly accurate result.

-Wireless sensor networks possess significant limitations in storage, bandwidth, and power. Additionally, real-time sensor networks cannot tolerate high latency. While some good compression algorithms exist specific to sensor networks, in this paper we present an energy-efficient method with high compression ratio that reduces latency, storage and bandwidth usage further in comparison with some other recently proposed algorithms. Our Huffman style compression scheme exploits temporal locality and delta compression to provide better bandwidth utilization in the network, thus reducing latency for real time applications. Our performance evaluations show comparable compression ratios and energy savings with a significant decrease in latency compared to some other existing approaches.

This paper outline some interesting research issues and solution approaches related to collaborative data sampling in wireless sensor networks for efficient energy and bandwidth utilization.

The patterns of movement used by Mobile Ad-Hoc networks are application specific, in the sense that networks use nodes which travel in different paths. When these nodes are used in experiments involving social patterns, such as wildlife tracking, algorithms which detect and use these patterns can be used to improve routing efficiency. The intent of this paper is to introduce a routing algorithm which forms a series of social groups which accurately indicate a node's regular contact patterns while dynamically shifting to represent changes to the social environment. With the social groups formed, a probabilistic routing schema is used to effectively identify which social groups have consistent contact with the base station, and route accordingly. The algorithm can be implemented dynamically, in the sense that the nodes initially have no awareness of their environment, and works to reduce overhead and message traffic while maintaining high delivery ratio.

Data aggregation is a technique used to conserve battery power in wireless sensor networks (WSN). While providing security in such a scenario it is also important that we minimize the number of security operations as they are computationally expensive, without compromising on the security In this paper we evaluate the performance of such an end to end security algorithm. We provide our results from the implementation of the algorithm on mica2 motes and conclude how it is better than traditional hop by hop security.

Outlier detection is a well studied problem in various fields. The unique challenges of wireless sensor networks make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we present a new communication technique to find outliers in a wireless sensor network. Communication is minimized through controlling sensor when sensors are allowed to communicate. At the same time, minimal assumptions are made about the nature of the data set as to minimize the loss of generality in the architecture.

Most of the routing and replication algorithms assume the network to be large and therefore, the architecture and algorithms are designed to be scalable. These algorithms however may not perform well with limited number of nodes in a network of UAVs. It is better to design and simulate such algorithms to increase the efficiency in a small network as scalability is no longer an issue. For such networks, we present design and simulation of some effective routing and replication algorithms to route packets, disseminate information, and replicate data among nodes.

Wireless sensor networks possess significant limitations in storage, bandwidth, and power. Additionally, real-time sensor networks cannot tolerate high latency. While some good compression algorithms exist specific to sensor networks, there remains a need for methods that do not introduce additional latency. This paper introduces a compression scheme which reduces storage, bandwidth, and power while also minimizing latency. Our Huffman style compression scheme exploits temporal locality and delta compression to provide better bandwidth utilization, thus reducing latency for real time applications.

In this paper, we describe a system incorporating an improved technique that detects the similarity of two XML documents based on content and structure similarity using keys. The technique consists of three major components: a subtree generator and validator, a key generator, and similarity components that compare content and structure of the XML documents. First, an XML document is stored in a relational database and extracted into small subtrees using leaf-node parents. The leaf-node parents are considered as a root of a subtree which is then recursively traversed bottom-up for matching. Second, a possible key(s) is identified in order to match XML subtrees from two documents efficiently. Key matchings help in reducing the number of comparisons dramatically. In addition, the number of subtrees to be processed is reduced in the subtree validation phase using instance statistics and taxonomic analyzer. The subtrees are matched by the key(s) first and the remaining subtrees are matched by finding degrees of similarity in content and structure. To obtain improved similarity comparison results, XML element names are transformed according to their semantic similarity. The results show that the clustering points are selected appropriately and the overall execution time is reduced dramatically.

When e-commerce companies merge there is a need to integrate their local schema into a uniform global source that is easily generated and maintained. In this paper we explore the incremental maintenance of global XML schema against updates to local schemas with the use of three simple operations, Add, Remove, and Change. These operations are designed to work as an extension to the AXIS model [12], which currently does not have a way to maintain the global schema once an underlying source schema is updated.

Incentive models are becoming increasingly popular in mobile peer to peer networks (M-P2P) as these models entice node participation in return for a virtual currency to combat free riding and to effectively manage constraint resources in the network. Many routing protocols proposed are based on best effort data traffic policy, such as the shortest route selection (hop minimization). Using virtual currency to find a cost effective optimal route from the source to the destination, while considering quality of service (QoS) aspects such as bandwidth and service capacity constraints for data delivery, remains a challenging task due to the presence of multiple paths and service providers. Modeling the network as a directed weighted graph and using the cost acquired from the price function as an incentive to pay the intermediate nodes in M-P2P networks to forward data, we develop a game theoretic approach based on stochastic games to find an optimal route considering QoS aspect. The performance of our routing protocol is evaluated and compared with some existing routing protocols and the result shows that our protocol proves to be efficient compared to shortest-path DSR and multiple paths SMR in terms of average response time, energy and bandwidth utilization in the network.

Data management in mobile peer to peer (M-P2P) systems needs dynamic data management due to mobility and fragile wireless connection connecting resource constraint devices. Traditional methods of data management and services in mobile P2P environment generally assume all peers to cooperate. Since peer activities in M-P2P are not generally monitored, users assume that they are free to use the resources anyway they like. Under this feeling of freedom, a subset of users (free riders) begins to consume much more resources available on M-P2P than they wish to contribute. In addition, due to the dynamic nature of moving hosts, topology changes very often and traditional schemes fall short in providing reasonable data availability. This becomes much more important in M-P2P where the network communication is generally multi-hop and intermediate peers have to render relay services other than data providers to improve the connectivity. Economic-based incentive schemes have been proposed which may play a better role in inciting free riders to collaborate. The data and service availability can be increased by associating a price with data items and services. In such schemes, peers can bid for better services, intermediate peers can earn incentives by providing relay services and in fact, outgoing peers can lease data items to others to still earn incentives while disconnected. New peers can become data providers by providing hosting services to earn incentives. This tutorial will explore issues involved in managing resources using economic incentives.

In mobile ad-hoc peer-to-peer (M-P2P) networks, economic models become a necessity for enticing non-cooperative mobile peers to provide service. M-P2P users may issue queries with varying constraints on query response time, data quality of results and trustworthiness of the data source. This work proposes ConQuer, which addresses constraint queries in economy- based M-P2P networks. ConQuer proposes a broker-based incentive M-P2P model for handling user-defined constraint queries. It also provides incentives for MPs to form collaborative peer groups for maximizing data availability and revenues by mutually allocating and deallocating data items using a royalty-based revenue-sharing method. Such reallocations facilitate MPs in providing better data quality, thereby allowing them to further increase their revenues.

In this paper, we describe a query approximation system which uses the Multi-Layered Database (MLDB), a collection of summarized relational data generated using domain-based concept hierarchies. The system generates approximate answers to queries to handle environmental constraints and access control levels, thus preserving the privacy and security of data. Using concept hierarchy (CH), we generalize attributes to transform base relations to different layers of summarized relations corresponding to access control levels. The summary databases thus formed are the compression of the tuples in the main database using the CH constructed using the domain set. The query is rewritten by traversing the MLDB layers according to the user's access control level. We present summarization methods, query rewriting algorithms, implementation and experimental results of the system. In addition, we analyze some of the known inferences in Multi Level Secure (MLS) databases and then proceed to explore their effects on an approximate query processor that uses the MLDB model. The common relationships among inferential queries are found by analyzing them, and are used in possible solutions to detect and prevent inference problems. These patches are added to the query processor in MLDB to form a system that provides approximate results by preserving privacy and at the same time block the possible inferences. We have observed that these extra patches introduce only very small overheads in the MLDB generation and query processing.

This paper proposes CADRE (collaborative allocation and deallocation of replicas with efficiency), a dynamic replication scheme for improving the typically low data availability in mobile ad-hoc peer-to-peer (M-P2P) networks. The main contributions of CADRE are two-fold. First, it collaboratively performs both replica allocation and deallocation in tandem to facilitate optimal replication and to avoid 'thrashing' conditions. Second, it addresses fair replica allocation across the MHs. CADRE deploys a hybrid super-peer architecture in which some of the MHs act as the 'gateway nodes' (GNs) in a given region. GNs facilitate both search and replication. Our performance study indicates that CADRE indeed improves query response times and data availability in M-P2P networks as compared to some recent existing schemes

In this paper, we present a mechanism for detecting and representing changes, given the old and new versions of a set of interlinked Web documents, retrieved in response to a user's query. In particular, we show how to detect and represent Web deltas, i.e., changes in the Web documents that are relevant to a user's query in the context of our Web warehousing system called WHOWEDA (Warehouse of Web Data). In WHOWEDA, Web information is materialized views stored in Web tables in the form of Web tuples. These Web tuples, represented as directed graphs, can be manipulated using a set of Web algebraic operators. In this paper, we present a mechanism to detect relevant Web deltas using Web algebraic operators such as the Web join and the outer Web join. Web join is used to detect identical documents residing in two Web tables, whereas, outer Web join, a derivative of Web join, is used to identify dangling Web tuples. We show how to represent these changes using delta Web tables. We develop formal algorithms for the generation of delta Web tables identifying Web documents which have been added, deleted, or modified since the last query.

In this paper, we present, formalize and prove the correctness of recovery algorithm for our open and safe nested transaction model using I/O automaton model. Our nested transaction model uses the notion of a recovery point subtransaction in the nested transaction tree. It introduces a prewrite operation before each write operation to increase the potential concurrency. Our transaction model is termed as "open and safe" as prewrites allow early reads (before database writes on disk) without cascading aborts. The systems restart and buffer management operations are modelled as nested transactions to exploit possible concurrency during restart. Each non-access transaction, object, and the scheduler is modeled as I/O automaton. Each of these automata is specified with the help of some pre-and post-conditions. These pre-and post-conditions capture the operational semantics and the behavior of each automaton during recovery operations. Our proof technique makes use of assertional reasoning and provide many interesting invariant, thus gives a better understanding of our recovery algorithm.

With the enormous amount of data stored in the World Wide Web, it is increasingly important to design and develop powerful web warehousing tools. The key objective of our web warehousing project, called WHOWEDA (Warehouse of Web Data), is to design and implement a web warehouse that materializes and manages useful information from the web. We introduce the concept of /spl Pi/-web join in the context of WHOWEDA. /spl Pi/-web join operator is a web information manipulation operator to combine relevant web information residing in two web tables. Informally, it is the combination of web join and web project operators which filter out irrelevant information from a joined web table. We show how to construct the /spl Pi/-joined web table and its schema. We also highlight the benefits of the /spl Pi/-web join operator.

Multiversions of data are used in database systems to increase concurrency and to provide efficient recovery. Data versions improve the concurrency by allowing the concurrent execution of non-conflicting read-write lock requests on different versions of data in an arbitrary fashion. A transaction that accesses a data item version which is later diagnosed to lead to an incorrect execution, is aborted. This act is reminiscent of the validation phase in the optimistic concurrency control schemes. Various performance studies suggest that these schemes perform poorly in high data contention environments where the excessive transaction aborts result due to the failed validation. We propose an adaptable constrained two-version two-phase locking (C2V2PL) scheme in which these non-conflicting requests are allowed only in a constrained manner. C2V2PL schemes assume that a lock request failing to satisfy the specific constraints will lead to an incorrect execution and hence, must be either rejected or blocked. This eliminates the need for a separate validation phase. When the contention for data among the concurrent transactions is high, the C2V2PL scheduler in aggressive state rejects such lock requests. The deadlock free nature of C2V2PL scheduler adapts to the low data contention environments by accepting the lock requests that have failed the specific constraints but contrary to the assumption, will not lead to an incorrect execution. Thus improving the performance due to reduced transaction aborts in this conservative state.

Sets and bags are closely related structures and have been studied in relational databases. A bag is different from a set in that it is sensitive to the number of times an element occurs, while a set is not. In this paper, we introduce the concept of a Web bag in the context of a World Wide Web warehouse called WHOWEDA (WareHouse Of WEb DAta) which we are currently building. Informally, a Web bag is a Web table which allows multiple occurrences of identical Web types. A Web bag helps one to discover useful knowledge from a Web table, such as visible documents or Web sites (i.e. documents/sites which can be reached by many paths), luminous documents (i.e. documents with many outgoing links) and luminous paths (i.e. frequently traversed paths). In this paper, we provide a cost-benefit analysis of materializing Web bags as compared to Web tables with distinct Web tuples.

We introduce a prewrite operation before a write operation in a mobile transaction to improve data availability. A prewrite operation does not update the state of a data object but only makes visible the value that the data object will have after the commit of the transaction. Once the transaction has read all the values and declares all the prewrites, it can precommit at a mobile host. The remaining transaction's execution is shifted to the stationary host. Writes on a database consume both time and resources at the stationary host and are therefore delayed. A pre-committed transaction's prewrite values are made visible both at mobile and stationary hosts before the final commit of the transaction. This increases data availability during frequent disconnection common in mobile computing. Since the expensive part of the transaction execution is shifted to the stationary host, it reduces the computing expenses at the mobile host.