Automatic triangulation positioning system for wide area coverage from a fixed sensors network

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn a wide area that many Transmitters (TRs) operate, systems of Fixed Sensors (FS) might be used in order to detect them and find TRs position. The detection and the accurate location of a new TR entering in the area frequently can be missed if the system fails to triangulate accurately the relative readings and analyze the changes in the received data. Additionally, there are cases that a Triangulation Station Network (TSN) can detect the heading as well as the transmitter’s position wrong. This thesis presents the design of a Sensors Network (FSN) system which is able to interact with a user, and exploit the relative data of the Sensors (SRs) in real time. The system performs localization with triangulation and the SRs are detect only TRs bearing data (range free). System design and algorithms are also explained. Efficient algorithms were elaborated and the outcomes of their implementation were calculated. The system design targets to reduce system errors and increase the accuracy and the speed of detection. Synchronously and through interaction with the user and changes of relative settings and parameters will be able to offer the user accurate results on localization of TRs in the area minimizing false readings and False Triangulations (FTRNs). The system also enables the user to apply optimization techniques in order to increase the system detection rate and performance and keep the surveillance in the Field of Interest (FoI) on a high level. The optimization methodology applied for the system proves that the FSN system is able to operate with a high performance even when saturation phenomena appear. The unique outcome of the research conducted, is that this thesis paves the way to enhance the localization via Triangulation for a network of Fixed Sensors with known position. The value of this thesis is that the FSN system performs bearing only detection (Range free) with a certain accuracy and the Area of Interest (AOI) is covered efficiently

An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

Pipelines are the safest tools for transporting oil and gas. However, the environmental effects and sabotage of hostile people cause corrosion and decay of pipelines, which bring financial and environmental damages. Today, new technologies such as the Internet of Things (IoT) and wireless sensor networks (WSNs) can provide solutions to monitor and timely detect corrosion of oil pipelines. Coverage is a fundamental challenge in pipeline monitoring systems to timely detect and resolve oil leakage and pipeline corrosion. To ensure appropriate coverage on pipeline monitoring systems, one solution is to design a scheduling mechanism for nodes to reduce energy consumption. In this paper, we propose a reinforcement learning-based area coverage technique called CoWSN to intelligently monitor oil and gas pipelines. In CoWSN, the sensing range of each sensor node is converted to a digital matrix to estimate the overlap of this node with other neighboring nodes. Then, a Q-learning-based scheduling mechanism is designed to determine the activity time of sensor nodes based on their overlapping, energy, and distance to the base station. Finally, CoWSN can predict the death time of sensor nodes and replace them at the right time. This work does not allow to be disrupted the data transmission process between sensor nodes and BS. CoWSN is simulated using NS2. Then, our scheme is compared with three area coverage schemes, including the scheme of Rahmani et al., CCM-RL, and CCA according to several parameters, including the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. The simulation results show that CoWSN has a better performance than other methods

Road Maintenance through Machine Learning

This thesis explores the use of machine learning techniques for road infrastructure maintenance. We propose an innovative machine learning-based approach to improve the efficiency and effectiveness of road maintenance strategies. The focal point of this investigation is the development and implementation of a machine learning framework to enhance road quality monitoring. We use Long Short-Term Memory (LSTM) networks to accurately predict future road conditions and identify potential areas requiring maintenance before significant deterioration occurs. This predictive approach is designed to enable a shift from reactive to proactive road maintenance, optimizing the use of limited resources and improving overall road safety. The methodology of the research is structured in three phases: the creation of a prototype system for road condition data collection, the application of LSTM networks for predictive analysis, and the utilization of optimization techniques to guide effective maintenance decisions. By focusing on predictive accuracy and the strategic allocation of maintenance efforts, the study seeks to extend the lifespan of road infrastructure, reduce maintenance costs, and enhance the driving experience. This thesis is a contribution to the field of road infrastructure maintenance by introducing a predictive maintenance model that leverages advanced machine learning techniques. It aims to transform the traditional maintenance approach, providing a scalable and efficient solution to road infrastructure management challenges, with the potential to significantly influence policy and practice in infrastructure maintenance.KEYWORDS: Machine learning; Infrastructure maintenance; Proactive maintenanc

Analysis of Security Attacks & Taxonomy in Underwater Wireless Sensor Networks

Abstract: Underwater Wireless Sensor Networks (UWSN) have gained more attention from researchers in recent years due to their advancement in marine monitoring, deployment of various applications, and ocean surveillance. The UWSN is an attractive field for both researchers and the industrial side. Due to the harsh underwater environment, own capabilities, open acoustic channel, it's also vulnerable to malicious attacks and threats. Attackers can easily take advantage of these characteristics to steal the data between the source and destination. Many review articles are addressed some of the security attacks and Taxonomy of the Underwater Wireless Sensor Networks. In this study, we have briefly addressed the Taxonomy of the UWSNs from the most recent research articles related to the well-known research databases. This paper also discussed the security threats on each layer of the Underwater Wireless sensor networks. This study will help the researcher’s design the routing protocols to cover the known security threats and help industries manufacture the devices to observe these threats and security issues

Methods for Self-Healing based on traces and unsupervised learning in Self-Organizing Networks

With the advent of Long-Term Evolution (LTE) networks and the spread of a highly varied range of services, mobile operators are increasingly aware of the need to strengthen their maintenance and operational tasks in order to ensure a quality and positive user experience. Furthermore, the co- existence of multiple Radio Access Technologies (RAT), the increase in the traffic demand and the need to provide a great variety of services are steering the cellular network toward a new scenario where management tasks are becoming increasingly complex. As a result, mobile operators are focusing their efforts to deal with the maintenance of their networks without increasing either operational expenditures (OPEX) or capital expenditures (CAPEX). In this context, it is becoming necessary to effectively automate the management tasks through the concept of the Self-Organizing Networks (SON). In particular, SON functions cover three different areas: Self-Configuration, Self-Optimization and Self- Healing. Self-Configuration automates the deployment of new network elements and their parameter configuration. Self-Optimization is in charge of modifying the configuration of the parameters in order to enhance user experience. Finally, Self-Healing aims reduce the impact that failures and services degradation have on the end-user. To that end, Self-Healing (SH) systems monitor the network elements through several alarms, measurements and indicators in order to detect outage and degraded cells, then, diagnose the cause of their problem and, finally, execute the compensation or recovery actions. Even though mobile networks are become more prone to failures due to their huge increase in complexity, the automation of the troubleshooting tasks through the SH functionality has not been fully realized. Traditionally, both the research and the development of SON networks have been related to Self-Configuration and Self-Optimization. This has been mainly due to the challenges that need to be faced when SH systems are studied and implemented. This is especially relevant in the case of fault diagnosis. However, mobile operators are paying increasingly more attention to self-healing systems, which entails creating options to face those challenges that allow the development of SH functions. On the one hand, currently, the diagnosis continues to be manually done since it requires considerable hard-earned experience in order to be able to effectively identify the fault cause. In particular, troubleshooting experts thoroughly analyze the performance of the degraded network elements by means of measurements and indicators in order to identify the cause of the detected anomalies and symptoms. Therefore, automating the diagnosis tasks means knowing what specific performance indicators have to be analyzed and how to map the identified symptoms with the associate fault cause. This knowledge is acquired over time and it is characterized by being operator-specific based on their policies and network features. Furthermore, troubleshooting experts typically solve the failures in a network without either documenting the troubleshooting process or recording the analyzed indicators along with the label of the identified fault cause. In addition, because there is no specific regulation on documentation, the few documented faults are neither properly defined nor described in a standard way (e.g. the same fault cause may be appointed with different labels), making it even more difficult to automate the extraction of the expert knowledge. As a result, this a lack of documentation and lack of historical reported faults makes automation of diagnosis process more challenging. On the other hand, when the exact root cause cannot be remotely identified through the statistical information gathered at cell level, drive test are scheduled for further information. These drive tests aim to monitor mobile network performance by using vehicles to personally measure the radio interface quality along a predefined route. In particular, the troubleshooting experts use specialized test equipment in order to manually collect user-level measurements. Consequently, drive test entail a hefty expense for mobile operators, since it involves considerable investment in time and costly resources (such as personal, vehicles and complex test equipment). In this context, the Third Generation Partnership Project (3GPP) has standardized the automatic collection of field measurements (e.g. signaling messages, radio measurements and location information) through the mobile traces features and its extended functionality, the Minimization of Drive Tests (MDT). In particular, those features allow to automatically monitor the network performance in detail, reaching areas that cannot be covered by drive testing (e.g. indoor or private zones). Thus, mobile traces are regarded as an important enabler for SON since they avoid operators to rely on those expensive drive tests while, at the same time, provide greater details than the traditional cell-level indicators. As a result, enhancing the SH functionalities through the mobile traces increases the potential cost savings and the granularity of the analysis. Hence, in this thesis, several solutions are proposed to overcome the limitations that prevent the development of SH with special emphasis on the diagnosis phase. To that end, the lack of historical labeled databases has been addressed in two main ways. First, unsupervised techniques have been used to automatically design diagnosis system from real data without requiring either documentation or historical reports about fault cases. Second, a group of significant faults have been modeled and implemented in a dynamic system level simulator in order to generate an artificial labeled database, which is extremely important in evaluating and comparing the proposed solutions with the state-of- the-art algorithm. Then, the diagnosis of those faults that cannot be identified through the statistical performance indicators gathered at cell level is automated by the analysis of the mobile traces avoiding the costly drive test. In particular, in this thesis, the mobile traces have been used to automatically identify the cause of each unexpected user disconnection, to geo-localize RF problems that affect the cell performance and to identify the impact of a fault depending on the availability of legacy systems (e.g. Third Generation, 3G). Finally, the proposed techniques have been validated using real and simulated LTE data by analyzing its performance and comparing it with reference mechanisms

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure

Nature-inspired survivability: Prey-inspired survivability countermeasures for cloud computing security challenges

As cloud computing environments become complex, adversaries have become highly sophisticated and unpredictable. Moreover, they can easily increase attack power and persist longer before detection. Uncertain malicious actions, latent risks, Unobserved or Unobservable risks (UUURs) characterise this new threat domain. This thesis proposes prey-inspired survivability to address unpredictable security challenges borne out of UUURs. While survivability is a well-addressed phenomenon in non-extinct prey animals, applying prey survivability to cloud computing directly is challenging due to contradicting end goals. How to manage evolving survivability goals and requirements under contradicting environmental conditions adds to the challenges. To address these challenges, this thesis proposes a holistic taxonomy which integrate multiple and disparate perspectives of cloud security challenges. In addition, it proposes the TRIZ (Teorija Rezbenija Izobretatelskib Zadach) to derive prey-inspired solutions through resolving contradiction. First, it develops a 3-step process to facilitate interdomain transfer of concepts from nature to cloud. Moreover, TRIZ’s generic approach suggests specific solutions for cloud computing survivability. Then, the thesis presents the conceptual prey-inspired cloud computing survivability framework (Pi-CCSF), built upon TRIZ derived solutions. The framework run-time is pushed to the user-space to support evolving survivability design goals. Furthermore, a target-based decision-making technique (TBDM) is proposed to manage survivability decisions. To evaluate the prey-inspired survivability concept, Pi-CCSF simulator is developed and implemented. Evaluation results shows that escalating survivability actions improve the vitality of vulnerable and compromised virtual machines (VMs) by 5% and dramatically improve their overall survivability. Hypothesis testing conclusively supports the hypothesis that the escalation mechanisms can be applied to enhance the survivability of cloud computing systems. Numeric analysis of TBDM shows that by considering survivability preferences and attitudes (these directly impacts survivability actions), the TBDM method brings unpredictable survivability information closer to decision processes. This enables efficient execution of variable escalating survivability actions, which enables the Pi-CCSF’s decision system (DS) to focus upon decisions that achieve survivability outcomes under unpredictability imposed by UUUR