On the feasibility of attribute-based encryption on Internet of Things devices

Attribute-based encryption (ABE) could be an effective cryptographic tool for the secure management of Internet of Things (IoT) devices, but its feasibility in the IoT has been under-investigated thus far. This article explores such feasibility for well-known IoT platforms, namely, Intel Galileo Gen 2, Intel Edison, Raspberry pi 1 model B, and Raspberry pi zero, and concludes that adopting ABE in the IoT is indeed feasible

HiCH: Hierarchical Fog-Assisted Computing Architecture for Healthcare IoT

The Internet of Things (IoT) paradigm holds significant promises for remote health monitoring systems. Due to their life-or mission-critical nature, these systems need to provide a high level of availability and accuracy. On the one hand, centralized cloud-based IoT systems lack reliability, punctuality and availability (e.g., in case of slow or unreliable Internet connection), and on the other hand, fully outsourcing data analytics to the edge of the network can result in diminished level of accuracy and adaptability due to the limited computational capacity in edge nodes. In this paper, we tackle these issues by proposing a hierarchical computing architecture, HiCH, for IoT-based health monitoring systems. The core components of the proposed system are 1) a novel computing architecture suitable for hierarchical partitioning and execution of machine learning based data analytics, 2) a closed-loop management technique capable of autonomous system adjustments with respect to patient's condition. HiCH benefits from the features offered by both fog and cloud computing and introduces a tailored management methodology for healthcare IoT systems. We demonstrate the efficacy of HiCH via a comprehensive performance assessment and evaluation on a continuous remote health monitoring case study focusing on arrhythmia detection for patients suffering from CardioVascular Diseases (CVDs)

Laboratory study of the behaviour of grouted cable bolts under static and dynamic axial loading

As a typical roof-supporting system of tunnels, Cable bolts are subjected to static and dynamic loading conditions caused by ground movements and mining activities. As mining goes deeper, the chance of undetermined ground stress concentrations increases and subsequently, the probability of sudden unexpected failures such as rock bursts increases. Many studies have been carried out to simulate the axial loading condition of the tendons, especially cable bolts, under static and dynamic loading modes; however, it barely can be seen that both behaviours have been studied and compared simultaneously. From other points of view, although research has been conducted on Barrel and Wedge and load-bearing plates, these components have hardly been considered together in axial loading studies. In this laboratory study, a new pullout testing mechanism has been introduced, which is capable of implementing axial loading of cable bolts in both static and dynamic loading conditions. In addition, in a parametric study, the role of bulbs of the cable bolts as well as barrel and wedges on the load-bearing capacity of the anchors have been examined. The bond between cable bolts and grout material in the bulbed cables behaves almost 30% stronger in static and 40% stronger in dynamic tests in comparison with plain cables. In general, the required pullout energy in dynamic tests was 25-50% less than in static tests

Laboratory study of the behaviour of grouted cable bolts under static and dynamic axial loading

As a typical roof-supporting system of tunnels, Cable bolts are subjected to static and dynamic loading conditions caused by ground movements and mining activities. As mining goes deeper, the chance of undetermined ground stress concentrations increases and subsequently, the probability of sudden unexpected failures such as rock bursts increases. Many studies have been carried out to simulate the axial loading condition of the tendons, especially cable bolts, under static and dynamic loading modes; however, it barely can be seen that both behaviours have been studied and compared simultaneously. From other points of view, although research has been conducted on Barrel and Wedge and load-bearing plates, these components have hardly been considered together in axial loading studies. In this laboratory study, a new pullout testing mechanism has been introduced, which is capable of implementing axial loading of cable bolts in both static and dynamic loading conditions. In addition, in a parametric study, the role of bulbs of the cable bolts as well as barrel and wedges on the load-bearing capacity of the anchors have been examined. The bond between cable bolts and grout material in the bulbed cables behaves almost 30% stronger in static and 40% stronger in dynamic tests in comparison with plain cables. In general, the required pullout energy in dynamic tests was 25-50% less than in static tests

COINS '19 Proceedings of the International Conference on Omni-Layer Intelligent Systems

Smartphones and wearable devices, such as smart watches, can act as mobile gateways and sensor nodes in IoT applications, respectively. In conventional IoT systems, wearable devices gather and transmit data to mobile gateways where most of computations are performed. However, the improvement of wearable devices, in recent years, has decreased the gap in terms of computation capability with mobile gateways. For this reason, some recent works present offloading schemes to utilize wearable devices and hence reducing the burden of mobile gateways for specific applications. However, a comprehensive study of offloading methods on wearable devices has not been conducted. In this paper, nine applications from the LOCUS's benchmark have been utilized and tested on different boards having hardware specification close to wearable devices and mobile gateways. The execution time and energy consumption results of running the benchmark on the boards are measured. The results are then used for providing insights for system designers when designing and choosing a suitable computation method for IoT systems to achieve a high quality of service (QoS). The results show that depending on the application, offloading methods can be used for achieving certain improvements in energy efficiency. In addition, the paper compares energy consumption of a mobile gateway when running the applications in both serial and multithreading fashions.</p

Optimizing energy efficiency of wearable sensors using fog-assisted control

Recent advances in Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient’s vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted for a long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors’ limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life. Furthermore, the patient’s contextual information - including health and activity status - can be exploited to guide energy optimization algorithms more effectively. By incorporating the patient’s contextual information, a desired quality of experience can be achieved by creating a dynamic balance between energy-efficiency and measurement accuracy. We present a runtime distributed control-based solution to find the most energy-efficient system state for a given context while keeping the accuracy of the decision-making process over a certain threshold. Our optimization algorithm resides in the fog layer to avoid imposing computational overheads to the sensor layer. Our solution can be extended to reduce the probability of errors in the data collection process to ensure the accuracy of the results. The implementation of our fog-assisted control solution on a remote monitoring system shows a significant improvement in energy efficiency with a bounded loss in accuracy

Optimizing energy efficiency of wearable sensors using fog-assisted control

Recent advances in Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient’s vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted for a long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors’ limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life. Furthermore, the patient’s contextual information - including health and activity status - can be exploited to guide energy optimization algorithms more effectively. By incorporating the patient’s contextual information, a desired quality of experience can be achieved by creating a dynamic balance between energy-efficiency and measurement accuracy. We present a runtime distributed control-based solution to find the most energy-efficient system state for a given context while keeping the accuracy of the decision-making process over a certain threshold. Our optimization algorithm resides in the fog layer to avoid imposing computational overheads to the sensor layer. Our solution can be extended to reduce the probability of errors in the data collection process to ensure the accuracy of the results. The implementation of our fog-assisted control solution on a remote monitoring system shows a significant improvement in energy efficiency with a bounded loss in accuracy