Providing Fault Tolerance via Complex Event Processing and Machine Learning for IoT Systems

Fault-tolerance (FT) support is a key challenge for ensuring dependable Internet of Things (IoT) systems. Many existing FT-support mechanisms in IoT are static, tightly coupled, inflexible implementations that struggle to adapt in dynamic IoT environments. This paper proposes Complex Patterns of Failure (CPoF), an approach to providing reactive and proactive FT using Complex Event Processing (CEP) and Machine Learning (ML). Error-detection strategies are defined as nondeterministic finite automata (NFA) and implemented via CEP systems. Reactive-FT support is monitored and learned from to train ML models that proactively handle imminent future occurrences of known errors. We evaluated CPoF on an indoor agriculture system with experiments that used time and error correlations to preempt battery-depletion failures. We trained predictive models to learn from reactive-FT support and provide preemptive error recovery

Complex Patterns of Failure:Fault Tolerance via Complex Event Processing for IoT Systems

Fault-tolerance (FT) support is a key challenge for ensuring dependable Internet of Things (IoT) systems. Many existing FT-support mechanisms for IoT are static, tightly coupled, and inflexible, and so they struggle to provide effective support for dynamic IoT environments. This paper proposes Complex Patterns of Failure (CPoF), an approach to providing FT support for IoT systems using Complex Event Processing (CEP) that promotes modularity and reusability in FT-support design. System defects are defined using our Vulnerabilities, Faults, and Failures (VFF) framework, and error-detection strategies are defined as nondeterministic finite automata (NFA) implemented via CEP systems. We evaluated CPoF on an automated agriculture system and demonstrated its effectiveness against three types of error-detection checks: reasonableness, timing, and reversal. Using CPoF, we identified unreasonable environmental conditions and performance degradation via sensor data analysis

A TE11 Dual-Mode Monoblock Dielectric Resonator Filter

A novel TE11 monoblock dual-mode dielectric resonator filter is presented in this paper. The proposed filter is made of a single piece of ceramic with silver plated external surfaces and metallic lids for hosting tuning elements. The dominant TE11 dual-mode is supported by H-shape dielectric resonator having r =45. The resonator is ultra-compact in size and offers a maximized space utilization since no metallic housing is required. In addition, the proposed resonator offers a high unloaded quality factor, reasonably wide spurious window and lend itself to implement tunability. One prototype filter operating at 1.96 GHz with 50-MHz bandwidth is designed

SGXIO: Generic Trusted I/O Path for Intel SGX

Application security traditionally strongly relies upon security of the underlying operating system. However, operating systems often fall victim to software attacks, compromising security of applications as well. To overcome this dependency, Intel introduced SGX, which allows to protect application code against a subverted or malicious OS by running it in a hardware-protected enclave. However, SGX lacks support for generic trusted I/O paths to protect user input and output between enclaves and I/O devices. This work presents SGXIO, a generic trusted path architecture for SGX, allowing user applications to run securely on top of an untrusted OS, while at the same time supporting trusted paths to generic I/O devices. To achieve this, SGXIO combines the benefits of SGX's easy programming model with traditional hypervisor-based trusted path architectures. Moreover, SGXIO can tweak insecure debug enclaves to behave like secure production enclaves. SGXIO surpasses traditional use cases in cloud computing and makes SGX technology usable for protecting user-centric, local applications against kernel-level keyloggers and likewise. It is compatible to unmodified operating systems and works on a modern commodity notebook out of the box. Hence, SGXIO is particularly promising for the broad x86 community to which SGX is readily available.Comment: To appear in CODASPY'1

Atomic-SDN: Is Synchronous Flooding the Solution to Software-Defined Networking in IoT?

The adoption of Software Defined Networking (SDN) within traditional networks has provided operators the ability to manage diverse resources and easily reconfigure networks as requirements change. Recent research has extended this concept to IEEE 802.15.4 low-power wireless networks, which form a key component of the Internet of Things (IoT). However, the multiple traffic patterns necessary for SDN control makes it difficult to apply this approach to these highly challenging environments. This paper presents Atomic-SDN, a highly reliable and low-latency solution for SDN in low-power wireless. Atomic-SDN introduces a novel Synchronous Flooding (SF) architecture capable of dynamically configuring SF protocols to satisfy complex SDN control requirements, and draws from the authors' previous experiences in the IEEE EWSN Dependability Competition: where SF solutions have consistently outperformed other entries. Using this approach, Atomic-SDN presents considerable performance gains over other SDN implementations for low-power IoT networks. We evaluate Atomic-SDN through simulation and experimentation, and show how utilizing SF techniques provides latency and reliability guarantees to SDN control operations as the local mesh scales. We compare Atomic-SDN against other SDN implementations based on the IEEE 802.15.4 network stack, and establish that Atomic-SDN improves SDN control by orders-of-magnitude across latency, reliability, and energy-efficiency metrics

Research on Smart Environment Monitoring Systems based on Secure Internet of Things (IoT)

Significant environmental threats include poor air quality, water contamination, and radiation pollution. A healthy society must be maintained for the planet to experience sustained growth. Environmental monitoring has transformed into smart environment monitoring (SEM) systems in recent years due to the growth of an internet of things (IoT). The Internet of Things (IoT) concept has developed into technology for creating smart environments and also has its disadvantage. To collect, evaluate, and recommend specific actions in smart environments for various purposes, a secure IoT-based platform is proposed. The proposed method follows the flow outlined here: data collection, normalization technique is used for data preprocessing, Linear Discriminant Analysis (LDA) is used for feature extraction, then data stored in IoT, Advanced Twofish encryption algorithm is proposed for securing the data, then user decryption, and finally performance is analyzed for smart environment monitoring using secure IoT. The proposed work aims to complete a critical evaluation of significant contributions to SEM that focus on the monitoring of water quality, air quality, radiation contamination, and agricultural systems. Secure IoT is based on the optimal integration and use of data gathered from several sources. This algorithm provides smart environment monitoring and also exhibits optimal integration