The technology of Autonomous Vehicles (AVs) and Unmanned Aerial Vehicles (UAVs) has evolved and contributed to improving road safety and traffic efficiency in Intelligent Transportation Systems (ITS). ITS applications are dependent on a communication network formed between AVs and UAVs suffer from security and privacy issues owing to vulnerable wireless communication channels. Considering these issues, SP2AP, a Physically Unclonable Function (PUF) and Elliptic Curve Cryptography (ECC)-based efficient and reliable privacy-preserving authentication protocol for secure UAV-UAV communications in IoT systems is presented in this paper. The proposed protocol not only offers anonymity and untraceability features but also mitigates UAV capture attacks. A robust informal security analysis confirms that the SP2AP protocol safeguards against various known security attacks, such as masquerade, Man-In- The-Middle (MITM), Ephemeral Secret Leakage (ESL), replay, node tampering, cloning, etc. The proposed protocol offers other security features, including mutual authentication, secure key agreement, no clock synchronization, user anonymity, untraceability, (for/back)ward secrecy, property, and drone-to-drone direct authentication. The formal security proof is performed using the Real-OR-Random (ROR) model, and the Scyther tool also demonstrates that the SP2AP protocol is more robust in terms of security and privacy. Performance analysis evaluates computation and communication costs, which show better superiority compared to similar existing protocols.

With the rapid advancements in wireless communication technologies, Unmanned Aerial Vehicles (UAVs), also known as Small Unmanned Aerial Vehicles (SUAVs) or drones, have been increasingly used in various applications, including the civilian sector. As a result, the security of SUAVs has garnered significant attention from the research community. Furthermore, drones are resource-constrained in nature and can be vulnerable to various known cybersecurity attacks over wireless communication. In light of these considerations, we propose aProvablySecure andReliable Privacy-Preserving AuthenticationScheme forDrone-to-Drone Communications in Internet of Autonomous Things (PSRS-D2D). The proposed scheme employs a secure one-way cryptographic hash and Elliptic Curve Cryptography (ECC) to accomplish a certain level of security. We provide security and privacy analysis, comparing it with competing UAV authentication schemes. This ensures that the PSRS-D2D scheme can withstand various prominent security properties, including mutual authentication and strong anonymity, and is secure against several attacks, such as replay, impersonation, and Man-In-The-Middle (MITM) attacks. We evaluated the performance of the proposed scheme in terms of computational and communicational costs. Furthermore, we conducted a formal security analysis using the Real-Or-Random (ROR) model and the Scyther simulation tools, which demonstrate that our scheme offers significant advantages in terms of security and performance.

The rapid advancement of the Internet of Things (IoT) has enabled the adoption of autonomous vehicles (AVs) and drones in intelligent transportation systems (ITS), improving traffic efficiency and safety. However, security and privacy in interconnected ITS environments is a major concern. It is imperative to safeguard sensitive information from various known attacks while enabling secure communication. Keeping in view the security and privacy of autonomous vehicles in IoT systems, this paper puts forward Provable Secure Lightweight Privacy-Preserving Authentication Protocol (PSLAP). The proposed PSLAP protocol achieves high-level security by utilizing cryptographic primitives such as exclusive-OR, secure one-way hash, elliptic curve cryptography (ECC), and concatenation operators . The proposed PSLAP protocol is demonstrated to be resistant to numerous known security assaults through an informal security and privacy assessment. This study presents a formal security analysis using a widely accepted real-or-random (ROR) model which demonstrates the security hardness of the proposed scheme. Additionally, the performance analysis shows that the proposed protocol has minimal computation and communication costs compared to other existing protocols.

With the rapid proliferation of the Internet of Things (IoT), autonomous vehicles (AVs), or self-driving cars, rely heavily on real-time data sharing and message exchanges over wireless networks. AVs use sensors, artificial intelligence, machine learning, and advanced algorithms to perform various functions, enabling users to operate without human intervention. Owing to the flexibility and high mobility of drones, they could aid in the operations of AVs. However, the security and privacy are the main concerns; specifically, the threat of physical capture and violation of anonymity are the main hurdles for realization of secure communication among the AVs and drones. To address these challenges, we propose an anonymous and provably secure lightweight authentication protocol for unmanned-aerial-vehicle-assisted Internet of Autonomous Vehicles (SLAP-IoAV). The proposed protocol uses cryptographic primitives such as exclusive-OR operations, elliptic-curve cryptography, collision-resistant one-way hashing, and concatenation to ensure robust security. An informal security analysis found that SLAP-IoAV is secure against several known attacks, and a performance analysis established that the protocol has less computational and communication overhead than existing competitive protocols. Additionally, Scyther simulation results confirm that no security vulnerabilities are present. Overall, our protocol delivers superior security and performance, making it well-suited to real-world applications in the AV industry.

Directing research on Alzheimer’s disease toward only early prediction and accuracy cannot be considered a feasible approach toward tackling a ubiquitous degenerative disease today. Applying deep learning (DL), Explainable artificial intelligence, and advancing toward the human-computer interface (HCI) model can be a leap forward in medical research. This research aims to propose a robust explainable HCI model using SHAPley additive explanation, local interpretable model-agnostic explanations, and DL algorithms. The use of DL algorithms—logistic regression (80.87%), support vector machine (85.8%), k -nearest neighbor (87.24%), multilayer perceptron (91.94%), and decision tree (100%)—and explainability can help in exploring untapped avenues for research in medical sciences that can mold the future of HCI models. The presented model’s results show improved prediction accuracy by incorporating a user-friendly computer interface into decision-making, implying a high significance level in the context of biomedical and clinical research.

Computational Linguistics (CL) associated with the Internet of Multimedia Things (IoMT)-enabled multimedia computing applications brings several research challenges, such as real-time speech understanding, deep fake video detection, emotion recognition, home automation, and so on. Due to the emergence of machine translation, CL solutions have increased tremendously for different natural language processing (NLP) applications. Nowadays, NLP-enabled IoMT is essential for its success. Sarcasm detection, a recently emerging artificial intelligence (AI) and NLP task, aims at discovering sarcastic, ironic, and metaphoric information implied in texts that are generated in the IoMT. It has drawn much attention from the AI and IoMT research community. The advance of sarcasm detection and NLP techniques will provide a cost-effective, intelligent way to work together with machine devices and high-level human-to-device interactions. However, existing sarcasm detection approaches neglect the hidden stance behind texts, thus insufficient to exploit the full potential of the task. Indeed, the stance, i.e., whether the author of a text is in favor of, against, or neutral toward the proposition or target talked in the text, largely determines the text’s actual sarcasm orientation. To fill the gap, in this research, we propose a new task: stance-level sarcasm detection (SLSD), where the goal is to uncover the author’s latent stance and based on it to identify the sarcasm polarity expressed in the text. We then propose an integral framework, which consists of Bidirectional Encoder Representations from Transformers (BERT) and a novel stance-centered graph attention networks (SCGAT). Specifically, BERT is used to capture the sentence representation, and SCGAT is designed to capture the stance information on specific target. Extensive experiments are conducted on a Chinese sarcasm sentiment dataset we created and the SemEval-2018 Task 3 English sarcasm dataset. The experimental results prove the effectiveness of the SCGAT framework over state-of-the-art baselines by a large margin.

Ambient Intelligence (AmI) in Internet of Things (IoT) has empowered healthcare professionals to monitor, diagnose, and treat patients remotely. Besides, the AmI-IoT has improved patient engagement and gratification as doctors’ interactions have become more comfortable and efficient. However, the benefits of the AmI-IoT-based healthcare applications are not availed entirely due to the adversarial threats. IoT networks are prone to cyber attacks due to vulnerable wireless mediums and the absentia of lightweight and robust security protocols. This paper introduces computationally-inexpensive privacy-assuring authentication protocol for AmI-IoT healthcare applications. The use of blockchain & fog computing in the protocol guarantees unforgeability, non-repudiation, transparency, low latency, and efficient bandwidth utilization. The protocol uses physically unclonable functions (PUF), biometrics, and Ethereum powered smart contracts to prevent replay, impersonation, and cloning attacks. Results prove the resource efficiency of the protocol as the smart contract incurs very minimal gas and transaction fees. The Scyther results validate the robustness of the proposed protocol against cyber-attacks. The protocol applies lightweight cryptography primitives (Hash, PUF) instead of conventional public-key cryptography and scalar multiplications. Consequently, the proposed protocol is better than centralized infrastructure-based authentication approaches.

Abstract It has been speculated that by the year 2050, technology will have progressed to the point of complete autonomy. This paper scrolls through patent pathways and intellectual developments throughout the industrial revolutions listing significant products and services that landmarked each revolution up to Industry 4.0. The patent trails and the recent IPR inputs are expected to assist readers in fast‐tracking up to speed on the bleeding edge of the current research pools while having an eagle's eye perspective on the previous developments so far. The research pools of Industry 4.0 are classified and explored. A lack of Human‐machine workforce synergy in Industry 4.0 and the nascent “customized manufacturing” concept is addressed in subsequent sections. The paper classifies two expected phases of Industry 5.0, highlighting the subdomains touted to be its focal areas. Lastly, Industry 5.0's niche research areas are checked and a suitable pathway to achieve the goals set for the sixth revolution is proposed.

Internet of Things (IoT) produces massive heterogeneous data from various applications, including digital health, smart hospitals, automated pathology labs, and so forth. IoT sensor nodes are integrated with the medical equipment to enable the health workers to monitor the patients’ health condition and appliances in real time. However, due to security vulnerabilities, an unauthorized user can access health-related information or control the IoT nodes attached to the patient’s body resulting in unprecedented outcomes. Due to wireless channels as a medium of communication, IoT poses several threats such as a denial of service attack, man-in-the-middle attack, and modification attack to the IoT networks’ security and privacy. The proposed research presents a lightweight and anonymity-preserving user authentication protocol to counter these security threats. The given scheme establishes a secure session for the legitimate user and prohibits unauthorized users from gaining access to the IoT sensor nodes. The proposed protocol uses only lightweight cryptography primitives (hash) to alleviate the node’s tiny processor burden. The proposed protocol is efficient and superior because it has low computational and communication costs than conventional protocols. The proposed scheme uses password protection to let only the legitimate user access the IoT sensor nodes to obtain the patient’s real-time health report.

Edge of Things (EoT) technology enables end-users participation with smart sensors and mobile devices (such as smartphones and wearable devices) to the smart devices across the smart city. Trust management is the main challenge in EoT infrastructure to consider the trusted participants. The Quality of Service (QoS) is highly affected by malicious users with fake or altered data. In this article, a robust trust management (RTM) scheme is designed based on Bayesian learning and collaboration filtering. The proposed RTM model is regularly updated after a specific interval with the significant decay value to the current calculated scores to update the behavior changes quickly. The dynamic characteristics of edge nodes are analyzed with the new probability score mechanism from recent services’ behavior. The performance of the proposed trust management scheme is evaluated in a simulated environment. The percentage of collaboration devices is tuned as 10%, 50%, and 100%. The maximum accuracy of 99.8% is achieved from the proposed RTM scheme. The experimental results demonstrate that the RTM scheme shows better performance than the existing techniques in filtering malicious behavior and accuracy.

The smart power grid is a critical energy infrastructure where real-time electricity usage data is collected to predict future energy requirements. The existing prediction models focus on the centralized frameworks, where the collected data from various home area networks (HANs) are forwarded to a central server. This process leads to cybersecurity threats. This article proposes a federated learning based model with privacy preservation of smart grids data using serverless cloud computing. The model considers the blockchain-enabled dew servers in each HAN for local data storage and local model training. Advanced perturbation and normalization techniques are used to reduce the inverse impact of irregular workload on the training results. The experiment conducted on benchmarks datasets demonstrates that the proposed model minimizes the computation and communication costs, attacking probability, and improves the test accuracy. Overall, the proposed model enables smart grids with robust privacy preservation and high accuracy.

Nowadays, artificial intelligence (AI) provides tremendous prospects for driving future healthcare while empowering patients and service providers. The extensive use of digital healthcare produces a massive amount of multimedia healthcare data continuously (e.g., MRI, X-Ray, ultrasound images, etc.). Hence, it needs special data analytics techniques to provide a smart diagnosis to the patients. Recent advancements in artificial intelligence and machine learning techniques, particularly Deep learning (DL) methods, have demonstrated tremendous medical diagnosis progress and achievements. Diabetic Retinopathy (DR), cataract, macular degeneration, and glaucoma are the most common eye problems due to diabetes. Numerous models have been proposed using deep learning models to diagnose diabetic retinopathy, but no model is perfect for detecting DR diseases. This article presents a deep learning model to analyze diabetic retinopathy images to classify DR patients’ severity levels. The model applies a custom-weighted loss function in the model’s training and achieves 92.49% accuracy and a 0.945 Cohen Kappa score on test data. The model’s weighted average precision was 93%, recall 92%, and f1 score 93%. The model is compared with several state-of-the-art pre-trained models. We observe that the proposed model performs better in accuracy results and Cohen Kappa score.

Due to the outbreak of COVID-19, the Internet of Medical Things (IoMT) has enabled the doctors to remotely diagnose the patients, control the medical equipment, and monitor the quarantined patients through their digital devices. Security is a major concern in IoMT because the Internet of Things (IoT) nodes exchange sensitive information between virtual medical facilities over the vulnerable wireless medium. Hence, the virtual facilities must be protected from adversarial threats through secure sessions. This article proposes a lightweight and physically secure mutual authentication and secret key establishment protocol that uses physical unclonable functions (PUFs) to enable the network devices to verify the doctor's legitimacy (user) and sensor node before establishing a session key. PUF also protects the sensor nodes deployed in an unattended and hostile environment from tampering, cloning, and side-channel attacks. The proposed protocol exhibits all the necessary security properties required to protect the IoMT networks, like authentication, confidentiality, integrity, and anonymity. The formal AVISPA and informal security analysis demonstrate its robustness against attacks like impersonation, replay, a man in the middle, etc. The proposed protocol also consumes fewer resources to operate and is safe from physical attacks, making it more suitable for IoT-enabled medical network applications.

In a country, air defense systems are designed to reduce threats efficiently. An air defense system is a fundamental part of any country because it provides national security. This study presents an autonomous air defense system (AADS) development that will automatically detect aerial threats (e.g., drones) and target them without any human intervention. The AADS is implemented using radar, camera, and laser gun. The radar system dynamically emits microwaves and detects moving objects around it. It triggers the camera system if it senses the frequency of any aerial threat. The camera receives the radar’s signal and detects using a neural network algorithm whether it is a threat or not. Neural network algorithms are used for the detection and classification of objects. The laser gun locks its target if the live video feed classifies an object as a more than 75% threat. In the detection stage, an average loss of 0.184961 was achieved using YOLOv3 and 0.155 using the Faster-RCNN. This system will ensure that no human errors are made while detecting threats in a region and improve national safety.

Wireless sensor networks have been deployed in the open and unattended environment where the attacker can capture the sensors and create the replica of captured nodes. As the clone nodes have been considered legitimate nodes, clone nodes can initiate different network attacks. We have designed a three-phase clone node detection method named fuzzy logic clone node detection (FLCND). The first phase of FLCND checks whether any node is missing from the network or not. In the next phase, FLCND finds out whether any missing node has arisen in the network in a stipulated time. If any missing node is alive, there is a possibility the node may be cloned. The information of suspected nodes is entered into the Hot-List, which has been maintained in the network. Phase III uses the suspected list and finds out the possibility of clone node using fuzzy logic. Two different scenarios have been simulated in NS2 to evaluate FLCND. The simulation result shows that the proposed method increases the packet delivery ratio (PDR) and reduces packet loss, end-to-end delay, and energy consumption. The simulation results illustrate that the FLCND method reduces the average power consumption by 27% and increases the detection rate by 46% compared to the existing techniques.

Ultra-reliable low-latency communication (uRLLC) and enhanced mobile broadband (eMBB) are two influential services of the emerging 5G cellular network. Latency and reliability are major concerns for uRLLC applications, whereas eMBB services claim for the maximum data rates. Owing to the trade-off among latency, reliability and spectral efficiency, sharing of radio resources between eMBB and uRLLC services, heads to a challenging scheduling dilemma. In this paper, we study the co-scheduling problem of eMBB and uRLLC traffic based upon the puncturing technique. Precisely, we formulate an optimization problem aiming to maximize the minimum expected achieved rate (MEAR) of eMBB user equipment (UE) while fulfilling the provisions of the uRLLC traffic. We decompose the original problem into two sub-problems, namely scheduling problem of eMBB UEs and uRLLC UEs while prevailing objective unchanged. Radio resources are scheduled among the eMBB UEs on a time slot basis, whereas it is handled for uRLLC UEs on a mini-slot basis. Moreover, for resolving the scheduling issue of eMBB UEs, we use penalty successive upper bound minimization (PSUM) based algorithm, whereas the optimal transportation model (TM) is adopted for solving the same problem of uRLLC UEs. Furthermore, a heuristic algorithm is also provided to solve the first sub-problem with lower complexity. Finally, the significance of the proposed approach over other baseline approaches is established through numerical analysis in terms of the MEAR and fairness scores of the eMBB UEs.

Volunteer computing based data processing is a new trend in healthcare applications. Researchers are now leveraging volunteer computing power to train deep learning networks consisting of billions of parameters. Breast cancer is the second most common cause of death in women among cancers. The early detection of cancer may diminish the death risk of patients. Since the diagnosis of breast cancer manually takes lengthy time and there is a scarcity of detection systems, development of an automatic diagnosis system is needed for early detection of cancer. Machine learning models are now widely used for cancer detection and prediction research for improving the successive therapy of patients. Considering this need, this study implements pre-trained convolutional neural network based models for detecting breast cancer using ultrasound images. In particular, we tuned the pre-trained models for extracting key features from ultrasound images and included a classifier on the top layer. We measured accuracy of seven popular state-of-the-art pre-trained models using different optimizers and hyper-parameters through fivefold cross validation. Moreover, we consider Grad-CAM and occlusion mapping techniques to examine how well the models extract key features from the ultrasound images to detect cancers. We observe that after fine tuning, DenseNet201 and ResNet50 show 100% accuracy with Adam and RMSprop optimizers. VGG16 shows 100% accuracy using the Stochastic Gradient Descent optimizer. We also develop a custom convolutional neural network model with a smaller number of layers compared to large layers in the pre-trained models. The model also shows 100% accuracy using the Adam optimizer in classifying healthy and breast cancer patients. It is our belief that the model will assist healthcare experts with improved and faster patient screening and pave a way to further breast cancer research.

Edge Artificial Intelligence (AI) is the latest trend for next-generation computing for data analytics, particularly in predictive edge analytics for high-risk diseases like Parkinson’s Disease (PD). Deep learning learning techniques facilitate edge AI applications for enhanced, real-time handling of data. Dopamine is the cause of Parkinson’s that happens due to the interference of brain cells that produce the substance to regulate the communication of brain cells. The brain cells responsible for generating the dopamine perform adaptation, control, and movement with fluency. Parkinson’s motor symptoms appear on the loss of 60% to 80% of cells, due to the non-production of appropriate dopamine. Recent research found a close connection between the speech impairment and PD. Many researchers have developed a classification algorithm to identify the PD from speech signals. In this article, Adaptive Crow Search Algorithm (ACSA) and Deep Learning (DL)–based optimal feature selection method are introduced. The proposed model is the combination of CROW Search and Deep learning (CROWD) stack sparse autoencoder neural network. Parkinson’s dataset is taken for the experiment from the Irvine dataset repository at the University of California (UCI). In the first phase, dataset cleaning is performed to handle the missing values in the dataset. After that, the proposed ACSA algorithm is employed to find the scrunched feature vector. Furthermore, stack spare autoencoder with seven hidden layers is employed to generate the compressed feature vector. The performance of the proposed CROWD autoencoder model is compared with three feature selection approaches for six supervised classification techniques. The experiment result demonstrates that the performance of the proposed CROWD autoencoder feature selection model has outperformed the benchmarked feature selection techniques: (i) Maximum Relevance (mRMR) (ii) Recursive Feature Elimination (RFE), and (iii) Correlation-based Feature Selection (CFS), to classify Parkinson’s disease. This research has significance in the healthcare sector for the enhancement of classification accuracy up to 0.96%.

The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short‐term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

Summary ECG and EEG signals are very helpful in the early diagnosis of epileptic seizures. The research focuses on analysis of ECG and EEG signals applying a deep learning technique to study early prediction of epileptic seizure. Signal processing methods like Empirical Mode Decomposition, spectral analysis, and statistical methods were used. The algorithms were implemented in MATLAB, and the EEG and ECG data were collected from Physiobank and EPILEPSIAE databases. In the window‐based analysis of low‐frequency spectral area of EEG signals, 78.5% of the cases displayed a significant change as the windows progressed and the onset of seizure was approached. The spectral area of IMF components indicated a possible seizure prediction in 68.9% of the analyzed cases. Considering signals from individual EEG electrodes, the least percentage of seizure prediction was indicated by signals from T4 and F4 electrodes (52.3% and 40.7%, respectively, for spectral peaks and 23.8% and 29.6%, respectively, for spectral area). The results of regression analysis show that prediction of seizures can be possible around 20‐30 minutes prior to the actual occurrence of seizures.

Malaria is a contagious disease that affects millions of lives every year. Traditional diagnosis of malaria in laboratory requires an experienced person and careful inspection to discriminate healthy and infected red blood cells (RBCs). It is also very time-consuming and may produce inaccurate reports due to human errors. Cognitive computing and deep learning algorithms simulate human intelligence to make better human decisions in applications like sentiment analysis, speech recognition, face detection, disease detection, and prediction. Due to the advancement of cognitive computing and machine learning techniques, they are now widely used to detect and predict early disease symptoms in healthcare field. With the early prediction results, healthcare professionals can provide better decisions for patient diagnosis and treatment. Machine learning algorithms also aid the humans to process huge and complex medical datasets and then analyze them into clinical insights. This paper looks for leveraging deep learning algorithms for detecting a deadly disease, malaria, for mobile healthcare solution of patients building an effective mobile system. The objective of this paper is to show how deep learning architecture such as convolutional neural network (CNN) which can be useful in real-time malaria detection effectively and accurately from input images and to reduce manual labor with a mobile application. To this end, we evaluate the performance of a custom CNN model using a cyclical stochastic gradient descent (SGD) optimizer with an automatic learning rate finder and obtain an accuracy of 97.30% in classifying healthy and infected cell images with a high degree of precision and sensitivity. This outcome of the paper will facilitate microscopy diagnosis of malaria to a mobile application so that reliability of the treatment and lack of medical expertise can be solved.

The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis.

E-Health systems provide a collaborative platform for sharing patients medical data typically stored in distributed autonomous healthcare data sources. Each autonomous source stores its medical and multimedia data without following any global structure. This causes heterogeneity in the underlying sources with respect to the data and storage structure. Therefore, a data interoperability mechanism is required for sharing the data among the heterogeneous sources. A proper metadata structure is also necessary to represent multimedia content in the sources to enable efficient query processing. Considering these needs, we present an interoperability solution for sharing data among heterogeneous data sources. We also propose a metadata management framework for medical multimedia con-tent including X-ray, ECG, MRI, and ultrasound images. The framework identifies features, generates and represents metadata, and produces identifiers for the medical multimedia content to facilitate efficient query processing. The framework has been tested with various user queries and the accuracy of the query results evaluated by means of precision, recall, and user feedback methods. The results confirm the effectiveness of the proposed approach.

Over the last years, the Internet has become the backbone of the information processing environments. The peer to peer concept is ideal for the development of a healthcare data sharing system because it respects the internal autonomy of each of the participating agencies (e.g. physicians, clinics, pharmacies, laboratories, etc). In this paper we introduce interoperability and data sharing settings considering a healthcare data management system where two parties or peers exchange and share data without any middleware data management system. We assume that databases in peers or data sources are created independently and may have semantic inter-dependencies with regards to data. Each peer specifies pair-wise data sharing settings/mappings with acquainted peers for sharing and exchanging related data.

In a peer-to-peer database management system(P2PDBMS) system, peers exchange data in a pair-wise fashion on-the-fly in response to a query without any centralized control. Generally, peers create a temporary session during data exchange. The data might be trapped and disclosed by the intruders while exchanged over an insecure communication network. As there is no centralized control for data exchange among peers, we cannot assume any central third party security infrastructure (e.g. PKI) to protect confidential data of an eHealth P2PDBMS. So far, there is currently no available/existing security protocol for secured data exchange in eHealth P2PDBMS. In this paper we propose a security protocol for data exchange in eHealth P2PDBMSs based on pairing-based cryptography and data exchange policy. The proposed protocol allows the peers to compute their secret session keys dynamically by computing pairing on elliptic curve based on the policies between them during data exchange. Our proposed protocol is robust against the man-in-the middle attack, the masquerade attack and the replay attack.

In an eHealth peer-to-peer database management system(P2PDBMS), peers exchange data in a pair-wise fashion on-the-fly in response to a query without any centralized control. Generally, the communication link between two peers is insecure and peers create a temporary session while exchanging data. When peers exchange highly confidential data in an eHealth network over an insecure communication link, the data might be tampered with or trapped and disclosed by intruders, which is a serious offence for the clients of an eHealth P2PDBMS. As there is no centralized control for data exchange in eHealth P2PDBMS, it is infeasible to assume a centralized third party security infrastructure to protect confidential data. So far, there is currently no available/existing security protocol for secured data exchange in eHealth P2PDBMS. In this paper we propose three models for secure data exchange in eHealth P2PDBMSs and the corresponding security protocols. The proposed protocol allows the peers to compute their secret session keys dynamically during data exchange based on the policies between them. Our proposed protocol is robust against the man-in-the middle attack, the masquerade attack, and the replay attack.

We consider the problem of update processing in a peer-to-peer (P2P) database network where each peer consists of an independently created relational database. We assume that peers store related data, but data has heterogeneity wrt instances and schemas. The differences in schema and data vocabulary are bridged by value correspondences called mapping tables. Peers build an overlay network called acquaintance network, in which each peer may get acquainted with any other peer that stores related data. In this setting, the updates are free to initiate in any peer and are executed over other peers which are acquainted directly or indirectly with the updates initiator. The execution of an update is achieved by translating, through mapping tables, the update into a set of updates that are executed against the acquainted peers. We consider both the soundness and completeness of update translation. When updates are generated and propagated in the network initiated from a peer, a tree is built dynamically called Update Dependency Tree (UDT). The UDT depicts the relationships among the component updates generated from the initial update. We also discuss the issues of the update propagation when a peer is temporarily unavailable or offline. Our propagation mechanism keeps track of a peer when the peer is not available for a certain period of time and once the peer comes back online the system propagates the updates destined to the returning peer to keep it's database synchronized. Moreover, conflict detection and resolution strategies have been proposed for such a dynamic P2P database network. We have implemented and experimentally tested a prototype of our update processing mechanism on a small P2P database network. We show the results of our experiments.