METHODS FOR IMPROVING ENERGY EFFICIENCY IN TDM PONs

Abstract Even though Information and Communications Technologies (ICT) are currently consuming between 2% and 4% of the electricity consumed worldwide, the number of efforts devoted to reduce the communications network energy consumption is increasing. This is mainly due to the foreseen growth of ICT even in substitution of personal travel. Access networks are the network segment that currently consumes the highest percentage of energy. Even though the utilization of optical technologies can potentially reduce the energy consumed by current ADSL modems, the further reduction of the energy consumed by passive optical access networks (PON) is attracting a lot of interests. Previous studies showed that, in PONs, the majority of the energy in consumed by the customer premises equipments, i.e. the Optical Network Units (ONUs), because of the many idle periods used only for synchronization. For this reason the target of our work is to save energy by exploiting cyclic sleep periods in the ONU. In particular the Sleep and Periodic Wake-up (SPW) technique is considered. The SPW mechanism is managed by the OLT and the choice of the sleep period for the ONUs can be based on different parameters. In this work two approaches are considered for deciding the sleep period: interarrival-based and service based. The interarrival-based approach has been previously presented. In this thesis a simulator based on Opnet Modeler is built to verify the validity of the previously presented results. Then a novel service-based sleep time scheme is designed and evaluated. The novelty of our work resides in presenting a service-based saving energy technique with variable sleep period to maximize the energy efficiency guaranteeing the maximum tolerable delay of the applications subscribed by the ONU. The main difference between the two approaches is how the sleep period is set. Following SPW technique, the OLT sets the sleep period according to traffic conditions such as average frame interval and queue length in the interarrival based algorithm, and class of service (CoS) in the service-based algorithm. In the interarrival-based the sleep period is fixed, instead in the servicebased the sleep period changes in function of the delay constraints of subscribed services to guarantee the service performance. The simulation results in the interarrival-based approach are very similar to the published ones. In case of low and high bandwidths, the values of average power are matched, instead the values of average queuing delay differ because of reasonable different assumptions. The increasing trend are the same in both results. The service-based approach resulted in the average frame delay, which exploits the maximum tolerable delay maximizing the energy efficiency. The SPW technique with service-based approach was presented in the Optical Fiber Communication Conference and Exposition (OFC) 2012 in Los Angeles. Riassunto analitico Sebbene le Tecnologie di Informazione e Comunicazione (ICT) consumino ad oggi tra il 2% e il 4% del consumo di elettricità mondiale, il numero di sforzi mirati alla riduzione del consumo energetico delle reti di comunicazione è in aumento. Questo è maggiormente dovuto alla prevista crescita di ICT anche in sostituzione agli spostamenti fisici. Le reti di accesso sono la porzione di rete che attualmente consuma la più alta percentuale di energia. Anche se l'uso di tecnologie ottiche possono potenzialmente ridurre l'energia consumata dai correnti modem ADSL, la conseguente energia consumata dalle reti di accesso passive (PON) attrae molto interesse. Studi passati mostrano che, nelle PON, la maggior energia è consumata dalle apparecchiature di utenza, per esempio, le unità di rete ottica (ONU), a causa dei molti periodi di inattività usati solo per la sincronizzazione. Per questo motivo, l'obiettivo del nostro lavoro è il risparmio energetico sfruttando periodi ciclici di sleep nelle ONU. In particolare la tecnica Sleep and Periodic Wake-up è presa in considerazione. Il meccanismo SPW è gestito dall'OLT e la scelta del periodo di sleep per le ONU si può basare su diversi parametri. In questo lavoro due approcci sono considerati per decidere il periodo di sleep: interarrival-based e service-based. L'approccio interarrival-based è stato presentato in precedenza. In questa tesi un simulatore basato su Opnet Modeler è implementato per verificare la validità dei risultati precedentemente presentati. Successivamente un nuovo schema service-based con periodi di sleep è stato progettato e valutato. L'originalità del nostro lavoro consiste nella presentazione di una tecnica per risparmio energetico service-based con periodi di sleep variabile per massimizzare l'efficienza energetica garantendo il massimo ritardo tollerabile delle applicazioni a cui l'ONU è abbonato. La principale differenza tra i due approcci riguarda come il periodo di sleep è impostato. Seguendo la tecnica SPW, l'OLT imposta il periodo di sleep in base alle condizioni di traffico come il tempo d'interarrivo medio e la lunghezza della coda nell'approccio interarrival-based, e come la classe di servizio (CoS) nell'approccio service-based. Riguardo l'interarrival-based il periodo di sleep è fisso, invece nel service-based il periodo di sleep cambia in funzione del limite di ritardo imposto delle applicazioni per garantire le prestazioni di servizio. I risultati delle simulazioni nell'approccio interarrival-based sono molto simili a quelle pubblicate. Nel caso di basse e alte bande, i valori di potenza media combaciano, mentre i valori di ritardo di accodamento medio differiscono a causa di diverse assunzioni. L'andamento delle curve è lo stesso. L'approccio service-based con risultati riguardo il ritardo medio dei pacchetti, sfrutta il massimo ritardo tollerabile per massimizzare l'efficienza energetica. La tecnica SPW con approccio service-based è stato presentato all'Optical Fiber Communication Conference and Exposition (OFC) 2012 a Los Angeles

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

Smart transmission power control for dependable wireless sensor networks

Smart Transmission Power Control for Dependable Wireless Sensor Network

Density and transmission power in intelligent wireless sensor networks

This paper covers the problem of interference generated by sensor nodes in Wireless Sensor Networks (WSNs). The interference affects the link quality of wireless communications, thus the Quality of Service (QoS) of Internet of Things (IoT) applications. The interference is the effect of the transmission of a cluster of nodes, at a certain power which is not always efficiently set, or calibrated. In addition, using unnecessary high power values impacts the waste of the node energy. Therefore, we address the interference problem by means of Transmission Power Control (TPC), for spatial reuse across the networks, which allows simultaneous point-to-point communications. Given the dynamics and unpredictability of the wireless channel, theoretical and empirical solutions are too slow, inefficient and memoryless for the problem we are facing. Our proposed protocol, QL-TPC, integrates reinforcement learning with game theory, within the IEEE 802.15.4 standard, at the MAC layer, to learn the combination of power levels per node, through indirect cooperation. The goal is to define the minimum transmission power, related to the density of the network, while respecting the QoS requirements and saving energy. QL-TPC is implemented in Atmel Zigbit, real world sensor devices, and is tested in a Faraday cage. We show the results, focusing on the aspect of reliability, energy efficiency, convergence and scalability. The nodes that use our protocol are estimated to have longer lifetime in order of months, while keeping same performance, than the homogeneous case

In-node cognitive power control in Wireless Sensor Networks

\u3cp\u3eReliability, interoperability and efficiency are fundamental in Wireless Sensor Network deployment. Herein we look at how transmission power control may be used to reduce interference, which is particularly problematic in high-density conditions. We adopt a distributed approach where every node has the ability to learn which transmission power is most appropriate, given the network conditions and quality of service targets. The status of the network is represented by the combination of three parameters: number of retransmissions, clear channel assessment attempts and the quantized average latency. The target is to maintain packet loss at the lowest possible level, whilst striving for minimum transmission power. The learning phase is managed by an ϵ-greedy strategy, which directs the physical layer of each node to choose between either a random action (exploration) or the best action (exploitation). We demonstrate as our learning sensors automatically discover the best trade off between power and quality.\u3c/p\u3

Power Control in Wireless Sensor Networks with Variable Interference

Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counterproductive effects to network performance. Yet, indiscriminate power boosting may detrimentally affect interference. We are interested in understanding the conditions under which coordinated power reduction may lead to better spectrum efficiency and interference mitigation and, thus, have beneficial effects on network performance. Through simulations, we analyze the performance of sensor nodes in an environment with variable interference. Then we study the relation between transmission power and communication efficiency, particularly in the context of Adaptive and Robust Topology (ART) control, showing how appropriate power reduction can benefit both energy and spectrum efficiency. We also identify critical limitations in ART, discussing the potential of more cooperative power control approaches

Predictive Power Control in Wireless Sensor Networks

Communications in Wireless Sensor Networks (WSNs) are affected by dynamic environments, variable signal fluctuations and interference. Thus, prompt actions are necessary to achieve dependable communications and meet quality of service requirements. To this end, the reactive algorithms used in literature and standards, both centralized and distributed ones, are too slow and prone to cascading failures, instability and sub-optimality. We explore the predictive power of machine learning to better exploit the local information available in the WSN nodes and make sense of global trends. We aim at predicting the configuration values that lead to network stability. In this work, we adopt the Q-learning algorithm to train WSNs to proactively start adapting in face of changing network conditions, acting on the available transmission power levels. Our aim is to prove that smart nodes lead to better network performance with the aid of simple machine learning