37,566 research outputs found
Self-Learning Power Control in Wireless Sensor Networks
Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay
Maximizing Throughput of Decentralized Wireless Sensor Network Using Reinforcement Learning
A reinforcement learning algorithm with the aim to increase the throughput of a Wireless Sensor Network (WSN) and decrease latency in a decentralized manner. WSNs are collections of sensor nodes that gather environmental data, where the main challenges are the limited power supply of nodes and the need for decentralized control. A distributed resource allocation algorithm for cellular MIMO networks by adopting a Reinforcement Learning (RL) approach. We use RL methods which employ Growing Self Organizing Maps to deal with the huge and continuous problem space. The goal of the algorithm is to maximize the network throughput in a fair manner. Indeed, the algorithm maximizes the throughput until fairness violation does not exceed an adjustable threshold
Network Management, Optimization and Security with Machine Learning Applications in Wireless Networks
Wireless communication networks are emerging fast with a lot of challenges and ambitions. Requirements that are expected to be delivered by modern wireless networks are complex, multi-dimensional, and sometimes contradicting. In this thesis, we investigate several types of emerging wireless networks and tackle some challenges of these various networks. We focus on three main challenges. Those are Resource Optimization, Network Management, and Cyber Security. We present multiple views of these three aspects and propose solutions to probable scenarios. The first challenge (Resource Optimization) is studied in Wireless Powered Communication Networks (WPCNs). WPCNs are considered a very promising approach towards sustainable, self-sufficient wireless sensor networks. We consider a WPCN with Non-Orthogonal Multiple Access (NOMA) and study two decoding schemes aiming for optimizing the performance with and without interference cancellation. This leads to solving convex and non-convex optimization problems. The second challenge (Network Management) is studied for cellular networks and handled using Machine Learning (ML). Two scenarios are considered. First, we target energy conservation. We propose an ML-based approach to turn Multiple Input Multiple Output (MIMO) technology on/off depending on certain criteria. Turning off MIMO can save considerable energy of the total site consumption. To control enabling and disabling MIMO, a Neural Network (NN) based approach is used. It learns some network features and decides whether the site can achieve satisfactory performance with MIMO off or not. In the second scenario, we take a deeper look into the cellular network aiming for more control over the network features. We propose a Reinforcement Learning-based approach to control three features of the network (relative CIOs, transmission power, and MIMO feature). The proposed approach delivers a stable state of the cellular network and enables the network to self-heal after any change or disturbance in the surroundings. In the third challenge (Cyber Security), we propose an NN-based approach with the target of detecting False Data Injection (FDI) in industrial data. FDI attacks corrupt sensor measurements to deceive the industrial platform. The proposed approach uses an Autoencoder (AE) for FDI detection. In addition, a Denoising AE (DAE) is used to clean the corrupted data for further processing
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Using artificial intelligence in routing schemes for wireless networks
For the latest 10 years, many authors have focused their investigations in wireless sensor networks. Different researching issues have
been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing
protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has
been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks
such as path discovery. In this paper, we explore the performance of two very well-known routing paradigms, directed diffusion and
Energy-Aware Routing, and our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks
in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study
the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This
paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes
- …