Over the past several years, the adoption of HealthCare Monitoring Systems (HCS) in health centers and organizations like hospitals or eldery homes growth significantly. The adoption of such systems is revolutionized by a propelling advancements in IoT and Blockchain technologies. Owing to technological advancement in IoT sensors market, innovations in HCS to monitor patients health status have motivated many countries to strength their efforts to support their citizens with such care delivery systems under the directives of a physician who has access to patient’s data. Nevertheless, secure data sharing is a principal patient’s concern to be comfort to use such systems. Current HCS are not able to provide reassuring security policies. For that, one of our focus in this work, is to provide security countermeasures, likewise cost-efficient solution for HCS by integrating storage model based on Blockchain and Interplanetary File Systems (IPFS). Blockchain technology is an emerging solution in pharmaceutical industry and starts to take place for HCS and allows HealthCare providers to track connected devices and control access to shared data, hence protecting patients’ privacy. Furthermore, the addition of Edge and Fog computing has improved HCS to react in real-time and enhance their reliability. A variety of communication protocols can connect sensor devices to edge/Fog layer and the best choice will depend upon connectivity requirements: range, bandwidth, power, interoperability, security, and reliability. Instead, systems efficiency would decline and hurt if communication protocol is inconsistent. LoRa (Long Range) communications technology is emerging as the leader among Low-Power Wide-Area Networks (LPWANs) entering the IoT domain benefiting from many features such as long-range distances and low power consumption. This work proposes LoRaChainCare, an architecture model for HCS which combines the technologies Blockchain, Fog/Edge computing, and the LoRa communication protocol. A real implementation of LoRaChainCare system is presented and evaluated in terms of cost, run time and power consumption.

To better combat the impact of adversarial samples on deep neural networks, a model-agnostic stochastic input transformation (SIT) preprocessing technique is proposed in this article. The inputs are transformed into a new domain to minimize the impact of the adversarial perturbations.

Patient medical data is the key data of an e-health application. Medical data frequently play an important role in disease analysis and are implicated in motivating values for educating diagnostic methods and modifying diagnostic results. Confidentiality of patient health documents is more important, and therefore researchers have considered many security measures, including access control, confidentiality, and integrity of these documents. To solve the security problem, this paper presents an advanced solution based on converting medical data into compact size, in the form of QR code before being deployed on our Blockchain. In this way, performing complex operations will be safely performed off-chain, such as collecting and preprocessing patient data, while submission of this data is done on an hourly basis. Chain. We recommend a data flow architecture that combines the Internet of Things (IoT) with blockchain (especially the Ethereum architecture), using smart contracts to access and store data while keeping Strong against many attacks. Our proposed system is effective in sharing and managing patient electronic health data. The tests involved implementing Ethereum chain configurations on PCs and two embedded IoT platforms, namely Raspberry Pi and PYNQ cards. Performance is evaluated in terms of security, execution time, memory, power and CPU consumption of various patient data scenarios.

In recent decades, falls have posed multiple critical health issues, especially for the older population, with their emerging growth. Recent research has shown that a wrist-based fall detection system offers an accessory-like comfortable solution for Internet of Things (IoT)-based monitoring. Nevertheless, an autonomous device for anywhere-anytime may present an energy consumption concern. Hence, this paper proposes a novel energy-aware IoT-based architecture for Message Queuing Telemetry Transport (MQTT)-based gateway-less monitoring for wearable fall detection. Accordingly, a hybrid double prediction technique based on Supervised Dictionary Learning was implemented to reinforce the detection efficiency of our previous works. A controlled dataset was collected for training (offline), while a real set of measurements of the proposed system was used for validation (online). It achieved a noteworthy offline and online detection performance of 99.8% and 91%, respectively, overpassing most of the related works using only an accelerometer. In the worst case, the system showed a battery consumption optimization by a minimum of 27.32 working hours, significantly higher than other research prototypes. The approach presented here proves to be promising for real applications, which require a reliable and long-term anywhere-anytime solution.