ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-time Constraints

Remote health monitoring is becoming indispensable, though, Internet of Things (IoTs)-based solutions have many implementation challenges, including energy consumption at the sensing node, and delay and instability due to cloud computing. Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. However, it is usually associated with computational complexity at the decoding end, increasing the latency of the system. Meanwhile, mobile processors are becoming computationally stronger and more efficient. Heterogeneous multicore platforms (HMPs) offer a local processing solution that can alleviate the limitations of remote signal processing. This paper demonstrates the real-time performance of compressed ECG reconstruction on ARM's big.LITTLE HMP and the advantages they provide as the primary processing unit of the IoT architecture. It also investigates the efficacy of CS in minimizing power consumption of a wearable device under real-time and hardware constraints. Results show that both the orthogonal matching pursuit and subspace pursuit reconstruction algorithms can be executed on the platform in real time and yield optimum performance on a single A15 core at minimum frequency. The CS extends the battery life of wearable medical devices up to 15.4% considering ECGs suitable for wellness applications and up to 6.6% for clinical grade ECGs. Energy consumption at the gateway is largely due to an active internet connection; hence, processing the signals locally both mitigates system's latency and improves gateway's battery life. Many remote health solutions can benefit from an architecture centered around the use of HMPs, a step toward better remote health monitoring systems.Peer reviewedFinal Published versio

Developing an Efficient Secure Query Processing Algorithm on Encrypted Databases using Data Compression

Distributed computing includes putting aside the data utilizing outsider storage and being able to get to this information from a place at any time. Due to the advancement of distributed computing and databases, high critical data are put in databases. However, the information is saved in outsourced services like Database as a Service (DaaS), security issues are raised from both server and client-side. Also, query processing on the database by different clients through the time-consuming methods and shared resources environment may cause inefficient data processing and retrieval. Secure and efficient data regaining can be obtained with the help of an efficient data processing algorithm among different clients. This method proposes a well-organized through an Efficient Secure Query Processing Algorithm (ESQPA) for query processing efficiently by utilizing the concepts of data compression before sending the encrypted results from the server to clients. We have addressed security issues through securing the data at the server-side by an encrypted database using CryptDB. Encryption techniques have recently been proposed to present clients with confidentiality in terms of cloud storage. This method allows the queries to be processed using encrypted data without decryption. To analyze the performance of ESQPA, it is compared with the current query processing algorithm in CryptDB. Results have proven the efficiency of storage space is less and it saves up to 63% of its space.

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Role Based Secure Data Access Control for Cost Optimized Cloud Storage Using Data Fragmentation While Maintaining Data Confidentiality

The paper proposes a role-based secure data access control framework for cost-optimized cloud storage, addressing the challenge of maintaining data security, privacy, integrity, and availability at lower cost. The proposed framework incorporates a secure authenticity scheme to protect data during storage or transfer over the cloud. The framework leverages storage cost optimization by compressing high-resolution images and fragmenting them into multiple encrypted chunks using the owner's private key. The proposed approach offers two layers of security, ensuring that only authorized users can decrypt and reconstruct data into its original format. The implementation results depicts that the proposed scheme outperforms existing systems in various aspects, making it a reliable solution for cloud service providers to enhance data security while reducing storage costs

Sensors data collection framework using mobile identification with secure data sharing model

Sensors are the modules or electronic devices that are used to measure and get environmental events and send the captured data to other devices, usually computer processors allocated on the cloud. One of the most recent challenges is to protect and save the privacy issues of those sensors data on the cloud sharing. In this paper, sensors data collection framework is proposed using mobile identification and proxy re-encryption model for data sharing. The proposed framework includes: identity broker server, sensors managing and monitoring applications, messages queuing sever and data repository server. Finally, the experimental results show that the proposed proxy re-encryption model can work in real time

IoT-Based Secure Healthcare Framework Using Blockchain Technology with A Novel Simplified Swarm-Optimized Bayesian Normalized Neural Networks

The Internet of Things (IoT) is growing in popularity nowadays and has many potential uses, particularly in healthcare. A large amount of sensing data is generated from a variety of sensing devices as a result of the growing needs of the IoT. The use of artificial intelligence (AI) methods is crucial for real-time, scalable, and accurate data processing. However, there are certain obstacles in the way of the layout and implementation of a successful analysis of the big data approach. These include a lack of suitable training data, resource limits, and a centralized architecture for the data. However, emerging blockchain technology provides a distributed system. It is advocated for getting rid of centralized control and solving AI issues, and it allows for safe data and resource exchange across the many nodes of the IoT network. Thus, this research develops a novel simplified swarm-optimized Bayesian normalized neural network (SSO-BNNN) for the secret transmission of medical images to address the aforementioned challenge. The neighborhood indexing sequence (NIS) approach is also used to encrypt the hash value. Several experiments were conducted to verify the outcomes of the suggested approach, and several facets of those results are discussed. Experimental results show that the proposed excellent findings with the best accuracy, sensitivity, and specificity were produced by the model

A Component-Based Approach for Securing Indoor Home Care Applications

eHealth systems have adopted recent advances on sensing technologies together with advances in information and communication technologies (ICT) in order to provide people-centered services that improve the quality of life of an increasingly elderly population. As these eHealth services are founded on the acquisition and processing of sensitive data (e.g., personal details, diagnosis, treatments and medical history), any security threat would damage the public's confidence in them. This paper proposes a solution for the design and runtime management of indoor eHealth applications with security requirements. The proposal allows applications definition customized to patient particularities, including the early detection of health deterioration and suitable reaction (events) as well as security needs. At runtime, security support is twofold. A secured component-based platform supervises applications execution and provides events management, whilst the security of the communications among application components is also guaranteed. Additionally, the proposed event management scheme adopts the fog computing paradigm to enable local event related data storage and processing, thus saving communication bandwidth when communicating with the cloud. As a proof of concept, this proposal has been validated through the monitoring of the health status in diabetic patients at a nursing home.This work was financed under project DPI2015-68602-R (MINECO/FEDER, UE), UPV/EHU under project PPG17/56 and GV/EJ under recognized research group IT914-16

Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective

Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy expertsComment: To appear in the Proceedings on Privacy Enhancing Technologies (PoPETs), Vol. 2019, Issue

Enhanced Cauchy Matrix Reed-Solomon Codes and Role-Based Cryptographic Data Access for Data Recovery and Security in Cloud Environment

In computer systems ensuring proper authorization is a significant challenge, particularly with the rise of open systems and dispersed platforms like the cloud. Role-Based Access Control (RBAC) has been widely adopted in cloud server applications due to its popularity and versatility. When granting authorization access to data stored in the cloud for collecting evidence against offenders, computer forensic investigations play a crucial role. As cloud service providers may not always be reliable, data confidentiality should be ensured within the system. Additionally, a proper revocation procedure is essential for managing users whose credentials have expired.  With the increasing scale and distribution of storage systems, component failures have become more common, making fault tolerance a critical concern. In response to this, a secure data-sharing system has been developed, enabling secure key distribution and data sharing for dynamic groups using role-based access control and AES encryption technology. Data recovery involves storing duplicate data to withstand a certain level of data loss. To secure data across distributed systems, the erasure code method is employed. Erasure coding techniques, such as Reed-Solomon codes, have the potential to significantly reduce data storage costs while maintaining resilience against disk failures. In light of this, there is a growing interest from academia and the corporate world in developing innovative coding techniques for cloud storage systems. The research goal is to create a new coding scheme that enhances the efficiency of Reed-Solomon coding using the sophisticated Cauchy matrix to achieve fault toleranc