Description:
Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data.
Description:
Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data.
Description:
Fog computing is an emerging computing paradigm which expands cloud-based computing services near the network edge. With this new computing paradigm, new challenges arise in terms of security and privacy. These concerns are due to the distributed ownership of Fog devices. Because of the large scale distributed nature of devices at the Fog layer, secure authentication for communication among these devices is a major challenge. The traditional authentication methods (password-based, certificate-based and biometric-based) are not directly applicable due to the unique architecture and characteristics of the Fog. Moreover, the traditional authentication methods consume significantly more computation power and incur high latency, and this does not meet the key requirements of the Fog. To fill this gap, this article proposes a secure decentralised location-based device to device (D2D) authentication model in which Fog devices can mutually authenticate each other at the Fog layer by using Blockchain. We considered an Ethereum Blockchain platform for the Fog device registration, authentication, attestation and data storage. We presented the overall system architecture, various participants and their transactions and message interaction between the participants. We validated the proposed model by comparing it with the existing method results showed that the proposed authentication mechanism was efficient and secure. From the performance evaluation, it was found that the proposed method is computationally efficient and secure in a highly distributed Fog network.
Description:
Fog computing is a promising computing paradigm for processing time-sensitive Internet of Things (IoT) applications. It helps process application requests close to the users to deliver faster processing outcomes than the Cloud by minimising overall response time. The Fog computing computation environment is highly dynamic regarding resource availability and communication. Furthermore, most Fog devices are battery-powered hence, the chances of the failure of the application processing is high, leading to delaying the application outcome. If we process the application requests on other available devices after the failure occurs, it might cause delay and may not comply with time-sensitive requirements. To avoid application processing failure due to power unavailability, we can run applications in an energy-aware manner. This is a challenging task due to the dynamic nature of the Fog computing environment. It is required to allocate resources for application requests so that the application processing should not fail due to the unavailability of power. In this paper, we propose a multiple linear regression-based resource allocation mechanism to run applications with energy-awareness in the Fog computing environment. This approach minimises failures due to power constraints of the devices. Prior works lack energy-aware application execution considering the dynamism of the Fog computing environment. Hence, we propose multiple linear regression-based approaches to achieve energy-awareness objectives. We present a sustainable energy-aware framework and algorithm that executes applications in a Fog environment in an energy-aware manner that minimises application execution failures. The trade-off between energy-efficient allocation and application execution time has been investigated and shown to have a minimum negative impact on the system while employing energy-aware allocation. The evaluation of the proposed method is carried out in a controlled simulation environment by extending CloudSim toolkit. We compared our proposed method with existing approaches. Our proposed approach minimises the delay and processing time by 20%, and 17% compared with the existing one. Furthermore, SLA violations decreased by 57% for the proposed energy-aware allocation.