Failure Localization Aware Protection in All-Optical Networks

The recent development of optical signal processing and switching makes the all-optical networks a potential candidate for the underlying transmission system in the near future. However, despite its higher transmission data rate and efficiency, the lack of optical-electro-optical (OEO) conversions makes fault management a challenge. A single fiber cut can interrupt several connections, disrupting many services which results in a massive loss of data. With the ever-growing demand for time-sensitive applications, the ability to maintain service continuity in communication networks has only been growing in importance. In order to guarantee network survivability, fast fault localization and fault recovery are essential. Conventional monitoring-trail (m-trail) based schemes can unambiguously localize link failures. However, the deployment of m-trail requires extra transceivers and wavelengths dedicated to monitoring the link state. Non-negligible overhead makes m-trail schemes neither scalable nor practicable. In this thesis, we propose two Failure Localization Aware (FLA) routing schemes to aid failure localization. When a link fails, all traversing lightpaths become dark, and the transceiver at the end node of each interrupted ligthpath issues an alarm signal to report the path failure. By correlating the information of all affected and unaffected paths, it is possible to narrow down the number of possible fault locations to just a few possible locations. However, without the assistance of dedicated supervisory lightpaths, and based solely on the alarm generated by the interrupted lightpaths, ambiguity in failure localization may be unavoidable. Hence, we design a Failure Localization Aware Routing and Wavelength Assignment (FLA-RWA) scheme, the Least Ambiguous Path (LAP) routing scheme, to dynamically allocate connection requests with minimum ambiguity in the localization of a link failure. The performance of the proposed heuristic is evaluated and compared with traditional RWA algorithms via network simulations. The results show that the proposed LAP algorithm achieves the lowest ambiguity among all examined schemes, at the cost of slightly higher wavelength consumption than the alternate shortest path scheme. We also propose a Failure Localization Aware Protection (FLA-P) scheme that is based on the idea of also monitoring the protection paths in a system with path protection for failure localization. The Least Ambiguous Protection Path (LAPP) routing algorithm arranges the protection path routes with the objective of minimizing the ambiguity in failure localization. We evaluate and compare the ambiguity in fault localization when monitoring only the working paths and when monitoring both working and protection paths. We also compare the performance of protection paths with different schemes in regards to fault localization

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance

Artificial Intelligence in Civil Infrastructure Health Monitoring—historical Perspectives, Current Trends, and Future Visions

Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Reliable many-to-many routing in wireless sensor networks using ant colony optimisation

A wireless Sensor Network (WSN) consists of many simple sensor nodes gathering information, such as air temperature or pollution. Nodes have limited energy resources and computational power. Generally, a WSN consists of source nodes that sense data and sink nodes that require data to be delivered to them; nodes communicate wirelessly to deliver data between them. Reliability is a concern as, due to energy constraints and adverse environments, it is expected that nodes will become faulty. Thus, it is essential to create fault-tolerant routing protocols that can recover from faults and deliver sensed data efficiently. Often studied are networks with a single sink. However, as applications become increasingly sophisticated, WSNs with multiple sources and multiple sinks become increasingly prevalent but the problem is much less studied. Unfortunately, current solutions for such networks are heuristics based on specific network properties, such as number of sources and sinks. It is beneficial to develop efficient (fault-tolerant) routing protocols, independent of network architecture. As such, the use of meta heuristics are advocated. Presented is a solution for efficient many-to-many routing using the meta heuristic Ant Colony Optimisation (ACO). The contributions are: (i) a distributed ACObased many-many routing protocol, (ii) using the novel concept of beacon ants, a fault-tolerant ACO-based routing protocol for many-many WSNs and (iii) demonstrations of how the same framework can be used to generate a routing protocol based on minimum Steiner tree. Results show that, generally, few message packets are sent, so nodes deplete energy slower, leading to longer network lifetimes. The protocol is scalable, becoming more efficient with increasing nodes as routes are proportionally shorter compared to network size. The fault-tolerant variant is shown to recover from failures while remaining efficient, and successful at continuously delivering data. The ACO-based framework is used to create Steiner Trees in WSNs, an NP-hard problem with many potential applications. The ACO concept provides the basis for a framework that enables the generation of efficient routing protocols that can solve numerous problems without changing the ACO concept. Results show the protocols are scalable, efficient, and can successfully deliver data in numerous different topologies