A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks

Progressing towards a new era of Artificial Intelligence (AI) - enabled wireless networks, concerns regarding the environmental impact of AI have been raised both in industry and academia. Federated Learning (FL) has emerged as a key privacy preserving decentralized AI technique. Despite efforts currently being made in FL, its environmental impact is still an open problem. Targeting the minimization of the overall energy consumption of an FL process, we propose the orchestration of computational and communication resources of the involved devices to minimize the total energy required, while guaranteeing a certain performance of the model. To this end, we propose a Soft Actor Critic Deep Reinforcement Learning (DRL) solution, where a penalty function is introduced during training, penalizing the strategies that violate the constraints of the environment, and ensuring a safe RL process. A device level synchronization method, along with a computationally cost effective FL environment are proposed, with the goal of further reducing the energy consumption and communication overhead. Evaluation results show the effectiveness of the proposed scheme compared to four state-of-the-art baseline solutions in both static and dynamic environments, achieving a decrease of up to 94% in the total energy consumption.Comment: 27 Pages Single Column, 6 Figures, Submitted for possible publication in the IEEE Transactions on Green Communications and Networking (TGCN). arXiv admin note: text overlap with arXiv:2306.1423

A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset

Topology control in self-managed wireless networks

The vision for future telecommunication systems is considered as a representative example of a complex adaptive organization, where several elements, with various computational capabilities and network resources, are interconnected. The increased complexity and the continuously changing network environment make more intense the need for automation and for localized network management tasks. Self-management will allow the execution of advanced configuration actions, such as the change of the wireless network topology under various performance criteria. This paper focuses on the description of the principles and the architectural framework for the cognitive management of future communication systems, considering a complex radio access environment. This framework is used in order to present a solution on the autonomic topology control of future communication systems, where multi-hop links are established using the available relays stations, under the energy consumption constraint. © 2010 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

A Framework for Reconfigurable Provisioning of Services in

I

Context-aware composition of mobile services

An intelligent distributed framework based on context aware applications has been developed to enable the dynamic composition of mobile services based on existing services and technologies. The framework is able to enrich mobile services with high-level and upto-date contextual information. The framework makes the service implementation totally platform-unaware, thus increasing the number of potentially available services and shortening application time-to-market. Context is the combination of information relevant to a user's nearest environment, including user location, network, and terminal device. A user preference profile is used to express a particular user's desired service provision features, which can be service-independent or user-specific and these profiles are described using XML to assure interoperability. Contextual information is encoded in the user profile consisting of user preferences, terminal, ambient, network, and service profiles. Context-aware applications assure the provision of customized and personalized services to their subscribers

Context-awareness and user profiling in mobile environments

The evolution of mobile communication systems to 3G and beyond introduces requirements for flexible, customized, and ubiquitous multimedia service provision to mobile users. One must be able to know at any given time the network status, the user location, the profiles of the various entities (users, terminals, network equipment, services) involved and the policies that are employed within the system. Namely, the system must be able to cope with a large amount of context information. The present paper focuses on location and context awareness in service provisioning and proposes a flexible and innovative model for user profiling. The innovation is based on the enrichment of common user profiling architectures to include location and other contextual attributes, so that enhanced adaptability and personalization can be achieved. For each location and context instance an associated User Profile instance is created and hence, service provisioning is adapted to the User Profile instance that better apply to the current context. The generic model, the structure and the content of this location- and context-sensitive User Profile, along with some related implementation issues, are discussed. © 2009 World Scientific Publishing Company

An advanced location information management scheme for supporting flexible service provisioning in reconfigurable mobile networks

The evolution of third-generation mobile communications networks has nominated the requirements for flexible service provisioning, intelligent and customized charging, as well as location-aware service and data management as key enablers for the support of new advanced service offerings to mobile users. Our work is related to the design and implementation of a flexible service provisioning and reconfigurability management middleware for third-generation systems and beyond. The article focuses especially on the location-related features and functionality of our architecture, discussing the interactions required to accomplish location- and mobilityaware user profiling, service deployment and discovery, as well as charging and billing