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.

Smart Parking Systems have emerged as a transformative solution to address the growing challenges associated with urbanization and increasing vehicular traffic. Such system integrates sensors, cameras, and other IoT connected devices to monitor parking spaces in real time. However, there are many security vulnerabilities in existing solutions, especially when it comes to car authentication at parking entry points. IoT sensors my be susceptible to Cyber-attacks and fraudulent activities, such as car theft, can exploit these vulnerabilities due to limited built-in security features. The reliability of authentication systems, based on IoT sensors can also be compromised by factors such as extreme weather conditions and physical damage. The cyber-physical solution we propose relies on Physical Unclonable Functions (PUFs) for identification and authentication in IoT devices to mitigate these challenges. The use of PUFs enhances the reliability and security of smart parking systems against unauthorized access and fraud. Furthermore, to ensure the integrity and confidentiality of the data within the smart parking ecosystem and to improve authentication process, we propose the implementation of a tailored blockchain framework. This framework incorporates lightweight local blockchains dedicated to individual parking slots, complemented by a central blockchain that manages data at the city level. The experimental results demonstrate the feasibility of the PUF computation process, showcasing an acceptable runtime for practical implementation. In the experimental results, we evaluated the SRAM used for the PUF implementation process and demonstrated its stability (intra HD equals to 2.25.

This work introduces a blockchain-based secure medical prescription-management system seamlessly integrated with a dynamic Internet of Things (IoT) framework. Notably, this integration constitutes a pivotal challenge in the arena of resource-constrained IoT devices: energy consumption. The choice of a suitable blockchain consensus mechanism emerges as the linchpin in surmounting this hurdle. Thus, this paper conducts a comprehensive comparison of energy consumption between two distinct consensus mechanisms: Proof of Work (PoW) and Quorum-based Byzantine fault tolerance (QBFT). Furthermore, an assessment of the most energy-efficient algorithm is performed across multiple networks and various parameters. This approach ensures the acquisition of reliable and statistically significant data, enabling meaningful conclusions to be drawn about the system’s performance in real-world scenarios. The experimental results show that, compared to the PoW, the QBFT consensus mechanism reduced the energy consumption by an average of 5%. This finding underscores the significant advantage of QBFT in addressing the energy consumption challenges posed by resource-constrained IoT devices. In addition to its inherent benefits of privacy and block time efficiency, the Quorum blockchain emerges as a more sustainable choice for IoT applications due to its lower power consumption.

Supercomputing systems are increasingly reliant on heterogeneous Multiprocessors System on-Chip (MPSoCs), merging multiple processors and hardware accelerators (HWAcc) on the same die to achieve power and performance needs. Due to CPU complication and timing closure challenges of tightly coupled design approach, the state-of-the art of HWAcc design methodology relies on coupling the processors with loosely coupled HWAccs. Loosely-coupled HWAccs can be shared or private accelerators running custom instructions to form a heterogeneous multi-processor system. Some works discussed the determination of the sharing degree of the HWAcc over the processors, however the impact of the integrated communication infrastructure is not discussed. Thus, we propose a high-level design exploration tool to select the accelerators and generate the adequate communication interconnect under performance and area constraints. Different homogeneous and heterogeneous multi-processor configurations are evaluated and compared running different signal processing benchmarks. Experimental results show the efficiency of the proposed exploration tool to rapidly explore and select the adequate architecture.