Autonomous Management Research Articles

Autonomic Security Management for IoT Smart Spaces

Embedded sensors and smart devices have turned the environments around us into smart spaces that could automatically evolve, depending on the needs of users, and adapt to the new conditions. While smart spaces are beneficial and desired in many aspects, they could be compromised and expose privacy, security, or render the whole environment a hostile space in which regular tasks cannot be accomplished anymore. In fact, ensuring the security of smart spaces is a very challenging task due to the heterogeneity of devices, vast attack surface, and device resource limitations. The key objective of this study is to minimize the manual work in enforcing the security of smart spaces by leveraging the autonomic computing paradigm in the management of IoT environments. More specifically, we strive to build an autonomic manager that can monitor the smart space continuously, analyze the context, plan and execute countermeasures to maintain the desired level of security, and reduce liability and risks of security breaches. We follow the microservice architecture pattern and propose a generic ontology named Secure Smart Space Ontology (SSSO) for describing dynamic contextual information in security-enhanced smart spaces. Based on SSSO, we build an autonomic security manager with four layers that continuously monitors the managed spaces, analyzes contextual information and events, and automatically plans and implements adaptive security policies. As the evaluation, focusing on a current BlackBerry customer problem, we deployed the proposed autonomic security manager to maintain the security of a smart conference room with 32 devices and 66 services. The high performance of the proposed solution was also evaluated on a large-scale deployment with over 1.8 million triples.

Challenges and perspective with using a group of small combat unmanned aerial vehicles

The article discusses the features of small unmanned aerial vehicles, perspectives, directions for the development of unmanned aerial vehicles with integrated combat units. The development trends of unmanned aerial vehicles indicate the direction of the use of the unmanned aerial vehicle in the group of small unmanned aerial vehicles operating on the principle of autonomous management. The main problems are related to solving management and groupwork tasks in the group, which leads to the use of group intelligence methods. The possibilities of group use of unmanned aerial vehicles are analyzed. One of the main goals of small drone technology is to create energy sources with high energy capacity and power. The exchange of information within a group of unmanned aerial vehicles requires the use of group-based intelligent methods based on self-organized decentralized management. Such methods are based on the self-organization of movement and communication in a group of insects, fish or birds. The analysis shows that the development trends of small-scale drones are primarily driven by advances in micro and nano technology, new approaches to aerodynamics and management, the creation of high-capacity power supplies and engines, creating autonomous micro and nano systems for navigation and communication using agreed integrated concepts. The direction of new technologies for use in the group of small unmanned aerial vehicles based on the principle of autonomous control is revealed. An algorithm for organizing interaction in a group of unmanned aerial vehicles is presented and the relevant tasks are discussed.

Мінімізація залишкової швидкісної похибки гірокомпасу з автономним управлінням чутливого елементу

Мінімізація залишкової швидкісної похибки гірокомпасу з автономним управлінням чутливого елементу

Human Autonomy in Future Drone Traffic: Joint Human-AI Control in Temporal Cognitive Work.

The roles of human operators are changing due to increased intelligence and autonomy of computer systems. Humans will interact with systems at a more overarching level or only in specific situations. This involves learning new practices and changing habitual ways of thinking and acting, including reconsidering human autonomy in relation to autonomous systems. This paper describes a design case of a future autonomous management system for drone traffic in cities in a key scenario we call The Computer in Brussels. Our approach to designing for human collaboration with autonomous systems builds on scenario-based design and cognitive work analysis facilitated by computer simulations. We use a temporal method, called the Joint Control Framework to describe human and automated work in an abstraction hierarchy labeled Levels of Autonomy in Cognitive Control. We use the Score notation to analyze patterns of temporal developments that span levels of the abstraction hierarchy and discuss implications for human-automation communication in traffic management. We discuss how autonomy at a lower level can prevent autonomy on higher levels, and vice versa. We also discuss the temporal nature of autonomy in minute-to-minute operative work. Our conclusion is that human autonomy in relation to autonomous systems is based on fundamental trade-offs between technological opportunities to automate and values of what human actors find meaningful.

Performance of agricultural tractor with and without automatic transmission and engine rotation management

HIGHLIGHTS Electronic gear selection and autonomous engine rotation management bring benefits to tractor driving. The electronic management responded positively to the increase in the traction load. The use of electronic management software should be more widespread among farmers and researchers.

Research on Design Scheme of Multi-campus College Student Management System Based on Cloud Computing

With the continuous expansion of the enrollment scale of colleges and universities, the number of students in school has doubled, and the amount of student information has increased dramatically; In addition, due to the development and merger of colleges and universities, there are many campuses in many colleges and universities, which are accompanied by the dispersion of students and administrators. To facilitate unified management, a multi-campus college student management system based on cloud computing is designed and developed, which integrates information management, statistical analysis, online information publishing and collection. This paper expounds the system analysis, functional module design, system design and key technologies involved in the process of system implementation. Through the interaction among various models, a complete and dynamic system is formed, which is committed to the management of multi-campus students, so as to improve the autonomous management, self-positioning and self-improvement.

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

The Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

Autonomic energy management with Fog Computing

We introduce an Autonomic System to perform management of energy consumption in Internet of Things (IoT) devices and Fog Computing, including an advanced orchestration mechanisms to manage dynamic duty cycles for extra energy savings. The solution works by adjusting Home (H) and Away (A) cycles based on contextual information, like environmental conditions, user behavior, behavior variation, regulations on energy and network resources utilization, among others. Performance analysis through a proof-of-concept implementation presents average energy savings of up to 61.51% when augmenting with a scheduling system and variable long sleep cycles (LS), and potential for 75.9% savings in specific conditions. We also concluded that there is no linear relation between increasing LS time and additional savings. The significance of this research is to promote autonomic management as a solution to develop more energy efficient buildings and smarter cities, towards sustainable goals.

A Model-Driven and Business Approach to Autonomic Network Management

As corporate networks continue to expand, the technologies that underpin these enterprises must be capable of meeting the operational goals of the operators that own and manage them. Automation has enabled the impressive scaling of networks from the days of Strowger. The challenge now is not only to keep pace with the continuing huge expansion of capacity but at the same time to manage a huge increase in complexity – driven by the range of customer solutions and technologies.
 Recent advances in automation, programmable network interfaces, and model-driven networking will provide the possibility of closed-loop, self-optimizing, and self-healing networks. Collectively these support the goals of a truly automated network, commonly understood as “autonomic networking” even though this is a prospect yet to be achieved.
 This paper outlines the progress made towards autonomic networking and the framework and procedures developed during the UK Next Generation Converged Digital Infrastructure (NG-CDI) project. It outlines the operator-driven requirements and capabilities that have been identified, and proposes an autonomic management framework, and summarizes current art and the challenges that remain.

Intent-driven Closed Loops for Autonomous Networks

Closed loops are key enablers for automation that have been successfully used in many industries for long, and more recently for computing and networking applications. The Zero-touch network and service management (ZSM) framework introduced standardized components that allow the creation, execution, and governance of multiple closed loops, enabling zero-touch management of end-to-end services across different management domains. However, the coordinated and optimal instantiation and operation of multiple closed loops is an open question that is left for implementation by the ZSM specifications. In this paper, we propose a methodology that uses intents as a way of communicating requirements to be considered by autonomous management domains to coordinate hierarchies of closed loops. The intent-driven methodology facilitates hierarchical and peer interactions for delegation and escalation of intents. Furthermore, it extends the existing management capabilities of the ZSM framework and facilitates conflict-free integration of closed loops by setting optimal (and non-conflicting) goals that each closed loop in the hierarchy needs to account for. We show an example of the application of the proposed methodology in a network slicing assurance use case. The new capabilities introduced in this paper can be considered as an extension of the ZSM framework to be used in scenarios where multiple intent-driven closed loops exist.

Design and implementation of GNC system of lander and ascender module of Chang’e-5 spacecraft

<p indent="0mm">The lander and ascender module of Chang’e-5 spacecraft achieved China’s first lunar landing and ascent mission. The guidance, navigation, and control (GNC) system is one of the most important subsystems. To meet the weight, safety, and intelligence requirement of the mission, light-weight integration designs are implemented in the GNC system, so that a single control system could meet the mission requirement of landing, ascent, and rendezvous. Moreover, a high-rate robust optical obstacle avoidance system is applied with a hierarchical controller, and landing security is enhanced while fuel consumption is minimized. Furthermore, autonomous attitude determination on the lunar surface and orbit insertion are achieved during the lunar ascent phase, and momentum wheels are relied on to obtain good attitude stability for the spacecraft during the rendezvous phase. Finally, the GNC system has fully functional redundancy and autonomous management, which significantly increases the fault detection and isolation capabilities. Here, we present key design specifications and implementations in the GNC system of the lander and ascender module.

End-to-End Service Monitoring for Zero-Touch Networks

Autonomous management capability is the main pillar for paving Zero-touch Networks and efficiently deliver and operate use cases under the light of 5G requirements. To this end, Closed Control Loop (CCL), Intent-Based Networking (IBN), and Machine Learning (ML) are regarded as enablers to automatically executed all operational processes, ideally without human intervention. In this context, the ETSI Zero-touch network and Service Management (ZSM) framework specifies an end-to-end network and service management reference architecture for managing the full lifecycle of services. However, the whole process of service monitoring is not yet well-consolidated in ETSI ZSM. In this work, we propose the Monitoring Model Generator (MMG) component to automatically construct templates for service monitoring. MMG implements a novel methodology where service deployment models and standard information models are used as inputs to generate a high-level monitoring template, called Service Monitoring Model (SMM) and built upon an ontology-based schema based on the Resource Description Framework (RDF) vocabulary. We present a proof of concept implementation along with an experimental functional validation of the MMG and using RDF data in turtle syntax and format. The resulting monitoring models are then used to define actual monitoring KPIs and construct management policies in a control loop architecture.

A Real Application of an Autonomous Industrial Mobile Manipulator within Industrial Context

In modern industry there are still a large number of low added-value processes that can be automated or semi-automated with safe cooperation between robot and human operators. The European SHERLOCK project aims to integrate an autonomous industrial mobile manipulator (AIMM) to perform cooperative tasks between a robot and a human. To be able to do this, AIMMs need to have a variety of advanced cognitive skills like autonomous navigation, smart perception and task management. In this paper, we report the project’s tackle in a paradigmatic industrial application combining accurate autonomous navigation with deep learning-based 3D perception for pose estimation to locate and manipulate different industrial objects in an unstructured environment. The proposed method presents a combination of different technologies fused in an AIMM that achieve the proposed objective with a success rate of 83.33% in tests carried out in a real environment.

Optimizing Autonomic Resources for the Management of Large Service-Based Business Processes

Cloud Computing, as a distributed computing paradigm, consists of the provisioning of infrastructure, platform, and software resources as services. This paradigm is being increasingly used for the deployment and execution of service-based business processes. To efficiently manage them according to the autonomic computing paradigm, service-based business processes can be associated with autonomic managers that monitor these processes, analyze monitoring data, plan configuration actions, and execute these actions on these processes. Although, during these last years, autonomic management of cloud services has received increasing attention, the optimization of autonomic managers to be assigned to cloud services remains not well explored. In fact, almost all the existing solutions on autonomic computing have been interested in modeling and implementing autonomic mechanisms without making any effort to optimize the number of used autonomic managers. Moreover, when it comes to large service-based business processes, optimization of management resources becomes a critical issue. To overcome this issue, we present in this paper a novel approach to determine how many autonomic managers to use for the management of large service-based business processes in order to minimize their cost while avoiding management bottlenecks. Experiments conducted on three different types of datasets highlight the effectiveness of our approach.

Context-Aware Multi-User Offloading in Mobile Edge Computing: a Federated Learning-Based Approach

Mobile edge computing (MEC) provides an effective solution to help the Internet of Things (IoT) devices with delay-sensitive and computation-intensive tasks by offering computing capabilities in the proximity of mobile device users. Most of the existing studies ignore context information of the application, requests, sensors, resources, and network. However, in practice, context information has a significant impact on offloading decisions. In this paper, we consider context-aware offloading in MEC with multi-user. The contexts are collected using autonomous management as the MAPE loop in all offloading processes. Also, federated learning (FL)-based offloading is presented. Our learning method in mobile devices (MDs) is deep reinforcement learning (DRL). FL helps us to use distributed capabilities of MEC with updated weights between MDs and edge devices (Eds). The simulation results indicate our method is superior to local computing, offload, and FL without considering context-aware algorithms in terms of energy consumption, execution cost, network usage, delay, and fairness.

A Feature Selection Model to Filter Periodic Variable Stars with Data-sensitive Light-variable Characteristics

At present, autonomous management and operation of spacecraft are the main direction and objective of the development of space technology to lighten the burden of ground measurement and control, reduce the cost of operation and management, and expand the application scope of spacecraft. To select the suitable periodic variable stars with a certain quantity at the given conditions of spacecraft, we study the autonomous navigation method of optical variable spacecraft and propose a feature selection model to filter periodic variable stars with light-variable characteristics. It mainly focuses on the learning processes of the pulsating optical variable light variation star clock model, the high precision pulsating optical variable autonomous navigation algorithm and the optical variable light variation characteristic mechanism with the measurement method. From experiments, the sample of the periodic variable star is selected, forms a database of 132 initial candidate samples and 16 navigation sample stars. So, time measurement can be conducted to take advantage of the nature of periodic variable stars that take days as its cycle, ground-based observation and ground-based application can be conducted with the wide spectrum of periodic variable star observation. It can meet the requirements of spacecraft.

NORA: An Approach for Transforming Network Management Policies into Automated Planning Problems.

Realizing autonomic management control loops is pivotal for achieving self-driving networks. Some studies have recently evidence the feasibility of using Automated Planning (AP) to carry out these loops. However, in practice, the use of AP is complicated since network administrators, who are non-experts in Artificial Intelligence, need to define network management policies as AP-goals and combine them with the network status and network management tasks to obtain AP-problems. AP planners use these problems to build up autonomic solutions formed by primitive tasks that modify the initial network state to achieve management goals. Although recent approaches have investigated transforming network management policies expressed in specific languages into low-level configuration rules, transforming these policies expressed in natural language into AP-goals and, subsequently, build up AP-based autonomic management loops remains unexplored. This paper introduces a novel approach, called NORA, to automatically generate AP-problems by translating Goal Policies expressed in natural language into AP-goals and combining them with both the network status and the network management tasks. NORA uses Natural Language Processing as the translation technique and templates as the combination technique to avoid network administrators to learn policy languages or AP-notations. We used a dataset containing Goal Policies to evaluate the NORA’s prototype. The results show that NORA achieves high precision and spends a short-time on generating AP-problems, which evinces NORA aids to overcome barriers to using AP in autonomic network management scenarios.

AI-Empowered Software-Defined WLANs

The complexity of wireless and mobile networks is growing at an unprecedented pace. This trend is proving current network control and management techniques based on analytical models and simulations to be impractical, especially if combined with the data deluge expected from future applications such as augmented reality. This is particularly true for software-defined wireless local area networks (SO-WLANs). It is our belief that to battle this growing complexity, future SO-WLANs must follow an artificial intelligence (AI) -native approach. In this article, we introduce aiOS, which is an AI-based platform that builds toward the autonomous management of SD-WLANs. Our proposal is aligned with the most recent trends in in-network AI promoted by the ITU Telecommunication Standardization Sector (ITU-T) and with the architecture for disaggregated radio access networks promoted by the Open Radio Access Network Alliance. We validate aiOS in a practical use case, namely frame size optimization in SD-WLANs, and we consider the long-term evolution, challenges, and scenarios for AI-assisted network automation in the wireless and mobile networking domain.

A Big-data Inspired Precision Improvement Algorithm for Autonomous Navigation Based on Period Variable Stars

Periodic variable navigation can realize the integration of positioning, attitude determination, timing and other functions. As a novel autonomous navigation method, autonomous management and autonomous operation of spacecraft are the main direction of great significance to reduce the burden of ground measurement and control, to improve the viability of spacecraft, While, the precision of the navigation is very critical for the use of periodic variable navigation for the long range spacecraft. So, we propose a big-data inspired precision improvement algorithm in this paper. Period variable star phase time measurement is used as observation information, and accomplished by the orbital dynamics equation of spacecraft motion. According to the gathering of the sampling data and the sensor data, a self-learning system is trained with the parameters from the nonlinear filtering methods. Based on the Unscented Kalman Filtering, an autonomous navigation algorithm of period variable star is established to realize the navigation and positioning of spacecraft. Under the measurement conditions of a single sampling interval of 0.01s and the measurement precision of period variable star phase observation time of 10− 5s, It can be find that the navigation position determination precision can reach 400m, the speed determination precision can reach 0.3m/s, and the measurement precision can reach 10− 5s with our proposed algorithm.

Intelligent and Autonomous Management in Cloud-Native Future Networks—A Survey on Related Standards from an Architectural Perspective

Cloud-native network design, which leverages network virtualization and softwarization together with the service-oriented architectural principle, is transforming communication networks to a versatile platform for converged network-cloud/edge service provisioning. Intelligent and autonomous management is one of the most challenging issues in cloud-native future networks, and a wide range of machine learning (ML)-based technologies have been proposed for addressing different aspects of the management challenge. It becomes critical that the various management technologies are applied on the foundation of a consistent architectural framework with a holistic vision. This calls for standardization of new management architecture that supports seamless the integration of diverse ML-based technologies in cloud-native future networks. The goal of this paper is to provide a big picture of the recent developments of architectural frameworks for intelligent and autonomous management for future networks. The paper surveys the latest progress in the standardization of network management architectures including works by 3GPP, ETSI, and ITU-Tand analyzes how cloud-native network design may facilitate the architecture development for addressing management challenges. Open issues related to intelligent and autonomous management in cloud-native future networks are also discussed in this paper to identify some possible directions for future research and development.