无源节点能量管理关键技术

能量管理是确保无源节点在不稳定供能环境下稳健运行的必要条件,但也带来了额外的计算负担,致使能量效率下降。为解决这一问题,研究了无源节点的能量理论,并针对性地提出无须本地计算辅助的“边端一体化”能量管理方案。首先,将高开销的细粒度能量规划从节点(端)解耦至网关(边),以降低节点上能量管理的复杂度。同时,创新性地提出了无采样感知和免计算决策电路的设计,解决了节点上无计算单元辅助下能量探测和决策执行所面临的两大关键技术挑战。至此,节点无须额外分配计算资源用于能量管理,从而在原理上避免了传统方式因将能量管理作为“附加任务”执行所导致的高开销问题。此外,在网关侧建立了远程开销预算模型,为多样化任务的供能需求实现了远程动态匹配。最后,实现了设计原型,并通过原型测试验证了设计的可行性。结果表明,与四种参考方式对比,该设计可降低92%的能量管理开销,并能对多变的供能环境作出灵活响应。     

物联网节点动态信任度评估方法

针对现有物联网（IoT）信任度评估方法未考虑信任的时效性、非入侵因素对直接信任度评估的影响以及缺乏对推荐节点可靠度的评估,造成信任评估准确度低且不能有效应对节点恶意行为的不足,提出一种IoT节点动态信任度评估方法（IDTEM）。首先,设计节点服务质量持续因子评估节点行为,并采用动态信任衰减因子表达信任的时效性,改进基于贝叶斯的直接信任度评估方法;其次,从推荐节点价值、评价离散度与节点自身的信任度值三个方面评估推荐节点可靠度,并据此优化推荐信任度权重计算方法;同时,设计推荐信任反馈机制,通过服务提供节点完成服务后的实际信任度与推荐信任度的反馈误差实现对协同恶意推荐节点的惩罚;最后,基于熵计算节点自适应权重,得到节点综合信任度值。实验结果表明,同基于贝叶斯理论的面向无线传感器网络的信誉信任评估框架（RFSN）模型及基于节点行为的物联网信任度评估方法（BITEM）相比,IDTEM可较好地识别恶意服务和抑制恶意推荐行为,且具有较低的传输能耗。     

Analytical models for availability evaluation of edge and fog computing nodes

The Journal of Supercomputing - Although cloud computing environments increase availability, reliability, and performance, many emerging technologies demand latency-aware networks for real-time...     

Content-centric data and computation offloading in AI-supported fog networks for next generation IoT

Fog Computing (FC) based IoT applications are encountering a bottleneck in the data management and resource optimization due to the dynamic IoT topologies, resource-limited devices, resource diversity, mismatching service quality, and complicated service offering environments. Existing problems and emerging demands of FC based IoT applications are hard to be met by traditional IP-based Internet model. Therefore, in this paper, we focus on the Content-Centric Network (CCN) model to provide more efficient, flexible, and reliable data and resource management for fog-based IoT systems. We first propose a Deep Reinforcement Learning (DRL) algorithm that jointly considers the content type and status of fog servers for content-centric data and computation offloading. Then, we introduce a novel virtual layer called FogOrch that orchestrates the management and performance requirements of fog layer resources in an efficient manner via the proposed DRL agent. To show the feasibility of FogOrch, we develop a content-centric data offloading scheme (DRLOS) based on the DRL algorithm running on FogOrch. Through extensive simulations, we evaluate the performance of DRLOS in terms of total reward, computational workload, computation cost, and delay. The results show that the proposed DRLOS is superior to existing benchmark offloading schemes.     

One round cipher algorithm for multimedia IoT devices

Multimedia Tools and Applications - With the exponential growth in Internet-of-Things (IoT) devices, security and privacy issues have emerged as critical challenges that can potentially compromise...     

Autonomous power management in mobile devices using dynamic frequency scaling and reinforcement learning for energy minimization

Embedded systems execute applications that execute hardware differently depending on the computation task, generating time-varying workloads. Energy minimization can be reached by using the low-power central processing unit (CPU) frequency for each workload. We propose an autonomous and online approach, capable of reducing energy consumption from adaptation to workload variations even in an unknown environment. In this approach, we improved the AEWMA algorithm into a new algorithm called AEWMA-MSE, adding new functionality to detect workload changes and demonstrating why it is better to use statistical analysis for real user cases in a mobile environment. Also, a new power model for mobile devices based on k-NN algorithm for regression was proposed and validated proving to have a better trade-off between execution time and precision than neural networks and linear regression-based models. AEWMA-MSE and the proposed power model are integrated into a novel algorithm for energy management based on reinforcement learning that suitably selects the appropriate CPU frequency based on workload predictions to minimize energy consumption. The proposed approach is validated through simulation by using real smartphone data from an ARM Cortex A7 processor used in a commercial smartphone. Our proposal proved to have an improvement in the Q-learning cost function and can effectively minimize the average energy consumption by 21% and up to 29% when compared to the already existing approaches.     

An efficient energy harvesting circuit for batteryless IoT devices

A new energy harvesting circuit for battery-less IoT beacon tags is developed herein to maximize power conversion efficiency as well as high throughput power with a wide input–output range. This design energy harvest (EH) circuit incorporates a charge pump (CP) with shoot-through current suppression, a body selector circuit, a maximum power point tracking circuit (MPPT), a timing control circuit, a hysteresis control circuit and a low dropout regulator. Also in this MPPT circuit is a gated clock tuned in a self-adaptive fashion to match the input impedance of the EH circuit to the output impedance of the photovoltaic (PV) panel, thus achieving successfully maximum power point. The circuit is implemented in an integrated chip in an area of 1.2 mm² via the TSMC 0.18 process. Experiments on the chip are conducted and the results show that the input voltage range is allowed from 0.55 to 1.7 V to effectively harvest the solar power from a flexible dye-sensitized solar cell. The achieved peak power conversion efficiency (PCE) is 77% at the input power of 52 μW. For a wide range of lighting luminance (300–1300 lx,) the achieved average PCE is more than 70%. The achieved wide input–output range and the maximum throughput power of 200 μW is much larger than others reported, while the 77% of PCE is close to that best power conversion efficiency reported.

     

A survey on computation offloading and service placement in fog computing-based IoT

The Journal of Supercomputing - In recent years, fog computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited...     

A reinforcement learning algorithm for rescheduling preempted tasks in fog nodes

Journal of Scheduling - The fog server in a fog computing paradigm extends cloud services to latency-sensitive tasks by employing fog nodes (FNs) near user devices. The resource-constrained FNs...     

A novel protocol for efficient authentication in cloud-based IoT devices

The Internet of Things (IoT) has emerged as one of the most revolutionary technological innovations with the proliferation of applications within almost all fields of the human race. A cloud environment is the main component of IoT infrastructure to make IoT devices efficient, safe, reliable, usable, and autonomous. Reduction in infrastructure cost and demand accessibility of shared resources are essential parts of cloud-based IoT (CIoT) infrastructure. Information leakage in cloud-assisted IoT devices may invite dangerous activities and phenomena. Various cloud-based systems store IoT sensor data and later on access it accordingly. Some of them are public, and some of them are private. Private cloud services must be secured from external as well as internal adversaries. Hence, there must be a robust mechanism to prevent unauthorized access to devices. This paper proposes a novel and efficient protocol based on the Elliptic Curve property known as Elliptic Curve Discrete Logarithm Problem (ECDLP) with hash and XOR functions for the authentication in cloud-based IoT devices. In comparison to the existing protocols, the proposed protocol is resistant to attacks and other security vulnerabilities. The one-way hash function and XOR function effectively ensure a reduction in computation cost. AVISPA and BAN logic have been used for formal analysis of the proposed protocol. As per the performance analysis results, it is clear that the proposed protocol is efficiently suitable for cloud-assisted IoT devices.

     

A decentralized honeypot for IoT Protocols based on Android devices

International Journal of Information Security - The exponential growth of internet connected devices in this past year has led to a significant increase in IoT targeted attacks. The lack of proper...     

IoT-F2N: An energy-efficient architectural model for IoT using Femtolet-based fog network

The Journal of Supercomputing - Energy- and latency-optimized Internet of Things (IoT) is an emerging research domain within fifth-generation (5G) wireless network paradigm. In traditional...     

A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments

Multimedia Tools and Applications - In this paper, we propose a hybrid fog and cloud-aware heuristic for the dynamic scheduling of multiple real-time Internet of Things (IoT) workflows in a...     

Towards collaborative intelligent IoT eHealth: From device to fog,and cloud

The relationship between technology and healthcare due to the rise of intelligent Internet of Things (IoT), Artificial Intelligence (AI), and the rapid public embracement of medical-grade wearables has been dramatically transformed in the past few years. AI-powered IoT enabled disruptive changes and unique opportunities to the healthcare industry through personalized services, tailored content, improved availability and accessibility, and cost-effective delivery. Despite these exciting advancements in the transition from clinic-centric to patient-centric healthcare, many challenges still need to be tackled. The key to successfully unlock and enable this horizon shift is adopting hierarchical and collaborative architectures to provide a high level of quality in key attributes such as latency, availability, and real-time analytics. In this paper, we propose a holistic AI-driven IoT eHealth architecture based on the concept of Collaborative Machine Learning approach in which the intelligence is distributed across Device layer, Edge/Fog layer, and Cloud layer. This solution enables healthcare professionals to continuously monitor health-related data of subjects anywhere at any time and provide real-time actionable insights which ultimately improves the decision-making power. The feasibility of such architecture is investigated using a comprehensive ECG-based arrhythmia detection case study. This illustrative example discusses and addresses all important aspects of the proposed architecture from design implications such as corresponding overheads, energy consumption, latency, and performance, to mapping and deploying advanced machine learning techniques (e.g., Convolutional Neural Network) to such architecture.     

Ultra-thin films and multigate devices architectures for future CMOS scaling

The nanoelectronics industry is facing historical challenges to scale down CMOS devices to meet demands for low voltage, low power, high performance and increased functionality. Using new materials and devices architectures is necessary. HiK gate dielectrics and metal gates have been introduced and have shown their ability to reduce power consumption. Fully depleted ultra-thin SOI devices are a good alternative to bulk for low power applications. Multigate devices are the current goal in device architecture...     

Efficient pairing-free PRE schemes for multimedia data sharing in IoT

Multimedia Tools and Applications - Nowadays, Internet of things (IoT) become more and more popular. At the same time, the requirements of security mechanism for multimedia in IoT received a huge...     

面向物联网移动终端设备的用户轨迹异常检测研究

为了有效地从物联网移动设备的数字信息中挖掘出用户在日常行为中的轨迹异常,针对现有用户异常轨迹检测算法效率低的问题,提出了一种双层聚类的用户轨迹异常检测方法。考虑到移动终端设备中的轨迹信息数据量大、分布不均匀等特点,该方法在特定的空间距离与时间间隔下提取出停留点集合,并对这些点进行层次聚类,根据结果划分出停留区域,进而发现其中的异常停留区域;最后,对停留区域之间发生的运动轨迹段进行二次层次聚类,发现异常轨迹段。实验结果表明,该方法在发现异常轨迹时,相较于传统算法,既全面地检测出异常轨迹,又加快了异常检测的速度。