Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems

This paper describes a comprehensive prototype of large-scale fault adaptive embedded software developed for the proposed Fermilab BTeV high energy physics experiment. Lightweight self-optimizing agents embedded within Level 1 of the prototype are responsible for proactive and reactive monitoring and mitigation based on specified layers of competence. The agents are self-protecting, detecting cascading failures using a distributed approach. Adaptive, reconfigurable, and mobile objects for reliablility are designed to be self-configuring to adapt automatically to dynamically changing environments. These objects provide a self-healing layer with the ability to discover, diagnose, and react to discontinuities in real-time processing. A generic modeling environment was developed to facilitate design and implementation of hardware resource specifications, application data flow, and failure mitigation strategies. Level 1 of the planned BTeV trigger system alone will consist of 2500 DSPs, so the number of components and intractable fault scenarios involved make it impossible to design an `expert system' that applies traditional centralized mitigative strategies based on rules capturing every possible system state. Instead, a distributed reactive approach is implemented using the tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS), Washington, DC, April, 200

COGNIBUILD: Cognitive Digital Twin framework for advanced building management and predictive maintenance

According to contemporary challenges of digital evolution in management and maintenance of construction processes, the present study aims at defining valuable strategies for building management optimization. As buildings and infrastructures Digital Twins (DT) are directly connected to physical environment through the Internet of Things (IoT), asset management and control processes can be radically transformed. The proposed DT framework connects Building Information Model (BIM) three-dimensional objects to information about the planned maintenance of components, supplying system’s self-learning capabilities through input data coming from Building Management Systems (BMS), ticketing, as well as maintenance activities data flow both as-needed or unexpected. The concept of real-time acquisition and data processing set the basis for the proposed system architecture, allowing to perform analysis and evaluate alternative scenarios promptly responding to unexpected events with a higher accuracy over time. Moreover, the integration of Artificial Intelligence (AI) allows the development of maintenance predictive capabilities, optimizing decision making processes and implementing strategies based on the performed analysis, configuring a scalable approach useful for different scenarios. The proposed approach is related to the evolution from reactive to proactive strategies based on Cognitive Digital Twins (CDT) for Building and Facility Management, providing actionable solutions through operational, monitoring and maintenance data. Through the integration of BIM data with information systems, BMS, IoT and Machine Learning, the optimization and real-time automation of maintenance activities is performed, radically reducing failures and systems breakdowns. Therefore, integrating different technologies in a virtual environment allows to define data-driven predictive models supporting Building Managers in decision making processes improving efficiency over time and moving from reactive to proactive approaches

Performance Analysis of On-Demand Routing Protocols in Wireless Mesh Networks

Wireless Mesh Networks (WMNs) have recently gained a lot of popularity due to their rapid deployment and instant communication capabilities. WMNs are dynamically self-organizing, self-configuring and self-healing with the nodes in the network automatically establishing an adiej hoc network and preserving the mesh connectivity. Designing a routing protocol for WMNs requires several aspects to consider, such as wireless networks, fixed applications, mobile applications, scalability, better performance metrics, efficient routing within infrastructure, load balancing, throughput enhancement, interference, robustness etc. To support communication, various routing protocols are designed for various networks (e.g. ad hoc, sensor, wired etc.). However, all these protocols are not suitable for WMNs, because of the architectural differences among the networks. In this paper, a detailed simulation based performance study and analysis is performed on the reactive routing protocols to verify the suitability of these protocols over such kind of networks. Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR) and Dynamic MANET On-demand (DYMO) routing protocol are considered as the representative of reactive routing protocols. The performance differentials are investigated using varying traffic load and number of source. Based on the simulation results, how the performance of each protocol can be improved is also recommended.Wireless Mesh Networks (WMNs), IEEE 802.11s, AODV, DSR, DYMO