ROAM: supporting safety critical applications in MANETs with cross-layer middleware

Provision of instantaneous, mobile and dependable media communications in military and disaster scenarios must overcome certain wireless network issues: lack of reliable existing infrastructure, immutability of safety-certified protocols and layer-2 dynamics with contributing factors including hidden transmitters and fading channels. This extended abstract investigates a cross-layer methodology to provide timely MANET communications through optimal channel selection and dynamic contention reduction, without protocol modification. This is done using ROAM: a new Real-time Optimised Ad hoc Middleware based architecture that has been implemented and validated in the ns2-MIRACLE simulator

Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture

Real-time asset tracking in indoor mass production manufacturing environments can reduce losses associated with pausing a production line to locate an asset. Complemented by monitored contextual information, e.g. machine power usage, it can provide smart information, such as which components have been machined by a worn or damaged tool. Although sensor based Internet of Things (IoT) positioning has been developed, there are still key challenges when benchmarked approaches concentrate on precision, using computationally expensive filtering and iterative statistical or heuristic algorithms, as a trade-off for timeliness and scalability. Precise but high-cost hardware systems and invasive infrastructures of wired devices also pose implementation issues in the Industrial IoT (IIoT). Wireless, selfpowered sensors are integrated in this paper, using a novel, communication-economical RSSI/ToF ranging method in a proposed semantic IIoT architecture. Annotated data collection ensures accessibility, scalable knowledge discovery and flexibility to changes in consumer and business requirements. Deployed at a working indoor industrial facility the system demonstrated comparable RMS ranging accuracy (ToF 6m and RSSI 5.1m with 40m range) to existing systems tested in non-industrial environments and a 12.6-13.8m mean positioning accuracy

Adaptive intelligent middleware architecture for mobile real-time communications

Provision of instantaneous, mobile and dependable communications in military and safety-critical scenarios must overcome certain wireless network issues: lack of reliable existing infrastructure, immutability of mission-critical protocols and detrimental wireless dynamics with contributing factors including hidden transmitters and fading channels. Benchmarked approaches do not fully meet these challenges, due to reliance on addressing Quality of Service (QoS) at a layer-specific level rather than taking a system of systems approach. This paper presents an adaptive middleware methodology to provide timely MANET communications through predictive selection and dynamic contention reduction, without invasive protocol modification. This is done using ROAM, the proposed, novel Real-time Optimised Ad hoc Middleware based architecture. Extensive simulation results demonstrate the adaptability and scalability of the architecture as well as capability to bound maximum delay, jitter and packet loss in complex and dynamic MANETs

Process and tool support for real-time performance analysis of integrated modular systems

This paper describes a real-time system performance analysis methodology and toolset that has been developed at SEIC to be an integral part of a broader BAE Systems Military Air Solutions (MAS) process and toolset for Integrated Modular Systems (IMS). The proposed modelling approach and toolset components provide some key ‘through-life’ real-time system engineering benefits relating to system performance, including : the ability to construct a performance prediction model during the early stages of system design and to independently model the timing behaviour of end-to-end transactions across a distributed system of shared processing and network resources

An intelligent real-time cyber-physical toolset for energy and process prediction and optimisation in the future industrial Internet of Things

Energy waste significantly contributes to increased costs in the automotive manufacturing industry, which is subject to energy usage restrictions and taxation from national and international policy makers and restrictions and charges from national energy providers. For example, the UK Climate Change Levy, charged to businesses at 0.554p/kWh equates to 7.28% of a manufacturing business’s energy bill based on an average total usage rate of 7.61p/kWh. Internet of Things (IoT) energy monitoring systems are being developed, however, there has been limited consideration of services for efficient energy-use and minimisation of production costs in industry. This paper presents the design, development and validation of a novel, adaptive Cyber-Physical Toolset to optimise cumulative plant energy consumption through characterisation and prediction of the active and reactive power of three-phase industrial machine processes. Extensive validation has been conducted in automotive manufacture production lines with industrial three-phase Hurco VM1 computer numerical control (CNC) machines

Exploiting embedded intelligence in manufacturing decision support

Exploiting embedded intelligence in manufacturing decision suppor

ROAM: Supporting safety critical applications in MANETs with cross-layer middleware

Provision of instantaneous, mobile and dependable media communications in military and disaster scenarios must overcome certain wireless network issues: lack of reliable existing infrastructure, immutability of safety-certified protocols and layer-2 dynamics with contributing factors including hidden transmitters and fading channels. This extended abstract investigates a cross-layer methodology to provide timely MANET communications through optimal channel selection and dynamic contention reduction, without protocol modification. This is done using ROAM: a new Real-time Optimised Ad hoc Middleware based architecture that has been implemented and validated in the ns2-MIRACLE simulator

A design of experiments Cyber–Physical System for energy modelling and optimisation in end-milling machining

Industrial energy consumption accounts for 50% of global use and manufacturers that invest in energy waste reduction strategies can have a significant impact on emission reduction while ensuring they operate within energy usage limits. Exceeding these limits can result in taxation from national and international policy makers and charges from national energy providers. For example, the UK Climate Change Levy, charged to businesses at 0.554 p/kWh can equate to 7.28% of a manufacturing business’s energy bill based on an average total usage rate of 7.61 p/kWh. There has been growing interest in optimising the process energy consumption of machining when machine tools are responsible for 13% of industrial energy consumption, generating 16 million tonnes of emissions in the UK alone but demonstrate less than 30% energy efficiency (Gutowski et al., 2006). This paper presents the design, development and validation of a novel automated Design of Experiments (DoE) toolset that forms part of a larger Cyber–Physical System (CPS). The CPS offers the capability to automate, characterise and predict the power of three-phase industrial machining processes and to select the machining toolpath that optimises energy consumption. Validation of the DoE toolset has been conducted through automation of an industrial three-phase Hurco VM1 computer numerical control (CNC) machine and energy feature extraction with a Hidden Markov Model

Predicting electrical power consumption of end milling using a virtual machining energy toolkit (V_MET)

Understanding electrical energy consumption of machines and processes is of increasing importance to (i) minimise costs and environmental impact of production activities and (ii) provide an additional information stream to inform condition monitoring systems (i.e. digital twins) about a machine's status and health. The research outlined in this paper develops a Virtual Machining Energy Toolkit (V_MET) to predict the electrical power consumption of a Computer Numeric Control (CNC) milling machine cutting a particular part program from preparatory codes (i.e. G code). In this way the evaluation of the energy impact of manufacturing part programs prior to implementation and real-time monitoring of the process can become a routine activity at part of a total manufacturing system optimisation. The novelty of this work lies in the inclusion of a virtual CNC process model to determine cutting geometry (i.e. width and depth of cut) to enable the prediction of relatively complex part program geometry. V_MET consists of three components: (i) the NC interpreter to extract key parameters (e.g. spindle speed, feed rate, tool path) from G-code instructions, (ii) a virtual CNC process model to determine instantaneous cutting geometry (i.e. width and depth of cut) and the material removal from the resulting machining by simulating the motion of the tool path to predict the interaction between the tool tip and workpiece and (iii) an energy model to predict the electrical power consumption for a given set of conditions, developed using regression analysis of data collected under real manufacturing conditions. Validation of V_MET has been conducted by physical machining of different product features to evaluate the validity over a range of different cutting parameters, NC operations (i.e. linear, clockwise interpolations) and repasses over previously cut regions. Overall good accuracy has been observed for the predicted energy requirements as a function of the cutting regimes, with 4.3% error in total energy and Mean Average Percentage Error (MAPE) of 5.6% when compared with measurements taken during physical cutting trials

Part 1 Summary: Embedded Intelligence Discussion

Part 1 Summary: Embedded Intelligence Discussio