Distributed consensus algorithm for events detection in cyber-physical systems

In the harsh environmental conditions of cyber-physical systems (CPSs), the consensus problem seems to be one of the central topics that affect the performance of consensus-based applications, such as events detection, estimation, tracking, blockchain, etc. In this paper, we investigate the events detection based on consensus problem of CPS by means of compressed sensing (CS) for applications such as attack detection, industrial process monitoring, automatic alert system, and prediction for potentially dangerous events in CPS. The edge devices in a CPS are able to calculate a log-likelihood ratio (LLR) from local observation for one or more events via a consensus approach to iteratively optimize the consensus LLRs for the whole CPS system. The information-exchange topologies are considered as a collection of jointly connected networks and an iterative distributed consensus algorithm is proposed to optimize the LLRs to form a global optimal decision. Each active device in the CPS first detects the local region and obtains a local LLR, which then exchanges with its active neighbors. Compressed data collection is enforced by a reliable cluster partitioning scheme, which conserves sensing energy and prolongs network lifetime. Then the LLR estimations are improved iteratively until a global optimum is reached. The proposed distributed consensus algorithm can converge fast and hence improve the reliability with lower transmission burden and computation costs in CPS. Simulation results demonstrated the effectiveness of the proposed approach

A Tutorial and Future Research for Building a Blockchain-Based Secure Communication Scheme for Internet of Intelligent Things

The Internet of Intelligent Things (IoIT) communication environment can be utilized in various types of applications (for example, intelligent battlefields, smart healthcare systems, the industrial internet, home automation, and many more). Communications that happen in such environments can have different types of security and privacy issues, which can be resolved through the utilization of blockchain. In this paper, we propose a tutorial that aims in desiging a generalized blockchain-based secure authentication key management scheme for the IoIT environment. Moreover, some issues with using blockchain for a communication environment are discussed as future research directions. The details of different types of blockchain are also provided. Some of the widely-accepted consensus algorithms are then discussed. Next, we discuss different types of applications in blockchain-based IoIT communication environments. The details of the associated system models are provided, such as, the network and attack models for the blockchain-based IoIT communication environment, which are helpful in designing a security protocol for such an environment. A practical demonstration of the proposed generalized scheme is provided in order to measure the impact of the scheme on the performance of the essential parameters. Finally, some of the future research challenges in the blockchain-based IoIT communication environment are highlighted, which will also be helpful to the researchers

EESMR: Energy Efficient BFT-SMR for the masses

Modern Byzantine Fault-Tolerant State Machine Replication (BFT-SMR) solutions focus on reducing communication complexity, improving throughput, or lowering latency. This work explores the energy efficiency of BFT-SMR protocols. First, we propose a novel SMR protocol that optimizes for the steady state, i.e., when the leader is correct. This is done by reducing the number of required signatures per consensus unit and the communication complexity by order of the number of nodes n compared to the state-of-the-art BFT-SMR solutions. Concretely, we employ the idea that a quorum (collection) of signatures on a proposed value is avoidable during the failure-free runs. Second, we model and analyze the energy efficiency of protocols and argue why the steady-state needs to be optimized. Third, we present an application in the cyber-physical system (CPS) setting, where we consider a partially connected system by optionally leveraging wireless multicasts among neighbors. We analytically determine the parameter ranges for when our proposed protocol offers better energy efficiency than communicating with a baseline protocol utilizing an external trusted node. We present a hypergraph-based network model and generalize previous fault tolerance results to the model. Finally, we demonstrate our approach's practicality by analyzing our protocol's energy efficiency through experiments on a CPS test bed. In particular, we observe as high as 64% energy savings when compared to the state-of-the-art SMR solution for n=10 settings using BLE.Comment: Appearing in Middleware 202

Cloud-Edge Orchestration for the Internet-of-Things: Architecture and AI-Powered Data Processing

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThe Internet-of-Things (IoT) has been deeply penetrated into a wide range of important and critical sectors, including smart city, water, transportation, manufacturing and smart factory. Massive data are being acquired from a fast growing number of IoT devices. Efficient data processing is a necessity to meet diversified and stringent requirements of many emerging IoT applications. Due to the constrained computation and storage resources, IoT devices have resorted to the powerful cloud computing to process their data. However, centralised and remote cloud computing may introduce unacceptable communication delay since its physical location is far away from IoT devices. Edge cloud has been introduced to overcome this issue by moving the cloud in closer proximity to IoT devices. The orchestration and cooperation between the cloud and the edge provides a crucial computing architecture for IoT applications. Artificial intelligence (AI) is a powerful tool to enable the intelligent orchestration in this architecture. This paper first introduces such a kind of computing architecture from the perspective of IoT applications. It then investigates the state-of-the-art proposals on AI-powered cloud-edge orchestration for the IoT. Finally, a list of potential research challenges and open issues is provided and discussed, which can provide useful resources for carrying out future research in this area.Engineering and Physical Sciences Research Council (EPSRC