23 research outputs found
A Novel Approach for Efficient Management of Data Lifespan of IoT Devices
No full textWireless sensor networks (WSNs) are mainly data-driven networks adopted to improve the Internet of Things (IoT) in terms of data throughput, energy efficiency, and self-management. Improving the data lifespan of WSN impacts the performance of the IoT. Achieving data reliability in applications of WSNs deployed in harsh environments is challenging due to the extreme constraints in resources of sensor nodes (SNs). Motivated by the inexpensive infrastructure of WSNs, a number of distributed storage systems have been proposed focusing on achieving data survivability rather than network reliability. In this article, we focus on data storage at the things layer (wireless sensors). We evaluate the performance of a number of distributed data storage systems (DDSSs) over WSN running over the ZigBee MAC protocol. Based on our findings, we introduce a new efficient-energy data dissemination scheme called data survivability with energy efficiency (DSwEE) that outperforms the existing schemes. We compare DSwEE against two prominent protocols in data storage, namely, decentralized erasure code for data survivability (DEC-DS) and decentralized erasure code encode-and-disseminate (DEC-EaD). Results show that DSwEE achieves better performance than both DEC-DS and DEC-EaD in terms of the energy consumption and data recoverability for localized failures, which improves the lifespan of the network.This work was supported in part by the Department of Computer Engineering, King Fahd University of Petroleum and Minerals; in part by Najran University; and in part by the Special Research Fund (BOF) of Hasselt University, Belgium.Sheltami, T (corresponding author), King Fahd Univ Petr & Minerals, Dept Comp Engn, Dhahran 31261, Saudi Arabia.
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Mécanismes de réduction de la consommation d'énergie dans les réseaux mobiles
No full textLa consommation en énergie des réseaux de télécommunications a suscité un intérêt grandissant au cours des dernières années et les opérateurs mobiles cherchent des solutions innovantes pour optimiser l’efficacité énergétique. Dans cette thèse, nous nous focaliserons sur les schémas d’optimisation de la consommation d’énergie des réseaux d’accès mobiles. Nous commençons par étudier la stratégie de partage de charge qui minimise la consommation d’énergie dans la cellule tout en limitant l’impact sur la Qualité de Service. Le gain obtenu par ces algorithmes de gestion des ressources radio reste cependant limité; ceci est dû au fait que la consommation d’énergie ne dépend pas que de la charge, mais comporte une importante partie constante. Pour obtenir un gain plus important, nous proposons la mise en veille de certaines ressources du réseaux aux heures creuses où le trafic est faible, ce qui mène à des gains substantiels. Nous proposons ensuite un contrôleur de gestion de la mise en veille qui choisit l’action optimale en fonction de l’état du réseau. En effet, l’activation d’une nouvelle ressource n’est pas instantanée et un effet ping-pong peut apparaitre suite aux commandes simultanées d’activation/désactivation des ressources. Nous adaptons nos contrôleurs afin de prendre en compte ces imperfections, et nous montrons comment déduire la politique optimale. Nous étudions ensuite le cas de déploiement de petites cellules dans des réseaux hétérogènes et montrons que leur efficacité énergétique est améliorée par rapport au réseau purement macro-cellulaire, pourvu que la consommation d’énergie de petites cellules reste faible. Nous proposons ensuite un contrôleur optimal qui active/désactive les petites cellules en se basant sur des informations de trafic et de localisation des usagers. Nous considérons différents cas de figure avec une information complète, partielle ou retardée et montrons que ces schémas de mise en veille permettent d’atteindre d’importants gains de consommation d’énergieIn the recent years, more importance has been given to the energy consumption issue in telecommunication networks and mobile operators are rethinking their network design for optimizing its energy efficiency. In this thesis, we propose schemes for optimizing the energy consumption of mobile access networks. We begin by proposing energy-aware Radio Resource Management (RRM) schemes and show that a load balancing between available resources gives some energy savings. However, these gains remain small as a large part of the energy consumption is load-independent. We thus propose sleep mode schemes of resources in the network (cells or carriers) and show that they give a large gain when traffic is low. We then propose optimal sleep mode controllers that give, for each traffic scenario, the best actions to take in each state of the network. We make two observations: the first is that activating a new resource is not instantaneous, leading to QoS degradation if a conservative policy is not considered, and the second is that a ping-pong effect may appear at the frontier between two capacity regions. We adapt our controllers to take into account these imperfections, and show how to derive the optimal policy using Markov decision theory. We then extend our works to the case of small cell deployment in heterogeneous networks, composed of macro and small cells base stations. We study the capacity and power consumption of these networks and show that the energy efficiency is increased for some deployment strategies when the power consumption of small cells is low. We then propose sleep mode for small cells and develop optimal sleep/wakeup schemes based on the information on traffic load and user localization in the cell, in the cases where this information is complete, partial or delayed. We show that these sleep mode schemes achieve large energy consumption gain
Optimal control for base station sleep mode in energy efficient radio access networks
No full textInternational audienceIn this paper, we investigate network sleep mode for reducing energy consumption of radio access networks. We propose an offline-optimized controller that associates to each traffic an activation/deactivation policy that maximizes a multiple objective function of the Quality of Service (QoS) and the energy consumption. We focus on practical implementation issues that may affect the QoS and the stability of the system. We namely consider the activation time issue that results in the degradation of the throughput and the ping-pong effect that results in unnecessary ON/OFF oscillations. We illustrate our results numerically in beyond 3G network
Minimizing energy consumption via sleep mode in green base station
No full textInternational audienceIn this paper, we develop new energy-efficient, radio resource management schemes for green wireless networks. Our goal is to optimize energy consumption at the network scale while preserving the Quality of Service (QoS) perceived by users. We specifically propose two new sleep mechanisms for base station where a number of resources in system can be shut down for some traffic scenarios in one of two ways: a dynamic way where resources are activated/deactivated in real-time as a function of the instantaneous load of the system, and a semi-static one where resources are kept unchanged during longer time intervals, in the order of one hour. We apply the proposed schemes to 2G and HSPA (High Speed Packet Access) systems and show that the dynamic one achieves larger energy reductions while the semistatic one has an acceptable performance with low complexit
Integrating Metaheuristics and Machine Learning for Enhanced Vehicle Routing: A Comparative Study of Hyperheuristic and VAE-Based Approaches
No full textThis study addresses the optimization of the Vehicle Routing Problem (VRP) with prioritized customers by introducing and comparing two advanced solution approaches: a metaheuristic-based hyperheuristic framework and a Variational Autoencoder (VAE)-based hyperheuristic. The VRP with prioritized customers introduces additional complexity by requiring efficient routing while ensuring high-priority customers receive service within strict constraints. To tackle this challenge, the proposed metaheuristic-based hyperheuristic dynamically selects and adapts low-level heuristics using Simulated Annealing (SA) and Ant Colony Optimization (ACO), enhancing search efficiency and solution quality. In contrast, the VAE-based approach leverages deep learning to model historical routing patterns and autonomously generate new heuristics tailored to problem-specific characteristics. Through extensive computational experiments on benchmark VRP instances, our results reveal that both approaches significantly enhance routing efficiency, with the VAE-based method demonstrating superior generalization across varying problem structures. Specifically, the VAE-based approach reduces total travel costs by an average of 8% and improves customer priority satisfaction by 95% compared to traditional hyperheuristic methods. Moreover, a comparative analysis with recent state-of-the-art algorithms highlights the competitive performance of our approaches in balancing computational efficiency and solution quality. These findings underscore the potential of integrating metaheuristics with machine learning in complex routing problems and provide valuable insights for real-world logistics and transportation planning. Future research will explore the generalization of these methodologies to dynamic and large-scale routing scenarios
Schemes for Putting Base Stations in Sleep Mode in Mobile Networks: Presentation and Evaluation
No full textImpact of Picocells on the Capacity and Energy Efficiency of Mobile Networks
Get PDFThe deployment of small cells in mobile networks has aroused a large interest in the last few years. This paper investigates the impact of picocell deployment on the performance and power consumption of mobile networks. Since different network upgrades introduce different performance gains, comparing different configurations exclusively on their overall power consumption can be rather biased. For this reason, a new Key Performance Indicator (KPI), termed ‘energy efficiency’, is introduced and used throughout this study, bringing together network performance and its overall power consumption. In the first section of the study, a theoretical analysis for the Erlang-like capacity of a network, considering a uniform topology and traffic, is performed, using queuing theory analysis, namely Processor Sharing queues. Results show that in all cases the deployment of picocells improve the performance of the network, however the energy efficiency is noted to be dependent on the deployment scenario considered. In the second part of the study, a more realistic scenario with non-uniform topology and traffic is considered, which is carried out through a large-scale system level simulator. Results show that by deploying pico cells in areas experiencing high levels of traffic, the energy efficiency of the network can beconsiderably improved
Energy efficiency and capacity of heterogeneous network deployment in LTE-Advanced
No full textInternational audienceWe study in this paper the impact of small cells on the capacity and energy consumption of an LTE-Advanced network in a heterogeneous setting where macro cells coexist with outdoor pico and femtocells. We analyze the Erlang-like capacity in such an environment and show the gains obtained from offloading traffic from macro cells to small ones. Energy efficiency is used throughout this study, bringing together network performance and its overall power consumption. Our results show that small cells are a good alternative to network densification as they achieve higher network capacities with good energy efficiency. In order to further improve the efficiency of the resulting heterogeneous network, we propose a sleep mode that switches off some picocells when the traffic is low and quantify the resulting increase in efficiency
Realistic Energy Saving Potential of Sleep Mode for Existing and Future Mobile Networks
No full textThis paper presents an extensive overview on an energy saving feature referred to as ‘site sleep mode’, designed for existing and future mobile broadband networks. In addition to providing a detailed understanding of the main concept, the paper also provides various studies and results to highlight potential savings, and emphasize some of the expected limitations. Since site measurements show that the energy consumption of base station sites is largely load-independent, this makes such a feature highly effective for reducing the energy consumption of mobile networks during hours of low traffic. After going through a number of different alternatives of the feature, this is applied to different network topologies, macro-only based networks, and a set of heterogeneous networks that employ the use of small cells in traffic hotspots. Results obtained through detailed case studies show that sleep mode can reduce the average daily energy consumption of a network by around 30%. This can be achieved while maintaining a predefined level of performance, used as a measure of comparing different scenarios
