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Author: search.filters.author.Chamola, VinaySubject: search.filters.subject.Energy efficiency

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    Multi-Objective MDP-Based Routing in UAV Networks for Search-Based Operations
    (IEEE, 2024-05) Chalapathi, G.S.S.; Chamola, Vinay
    Unmanned aerial vehicle (UAV) systems have gained widespread recognition due to their versatility and autonomy. Their deployment for disaster mitigation and management operations is seen as one of their most important applications over the past decade. In such UAV networks, routing plays a crucial role in determining network performance parameters such as network lifetime, data transmission latency, and packet delivery ratio. This paper presents a novel routing mechanism - Multi-Objective Markov Decision Based Routing (MOBMDP) for UAV networks carrying out search-based operations. MOBMDP models routing decisions in a Markov Decision Process (MDP) framework and uses Q-learning to take decisions. It compares routing paths using three metrics, viz., Remaining Energy of the Minimum Energy Node (REMEN), Power Distance ratio (PD), and Expected Delay (ED). Amongst these metrics, PD is a novel metric proposed by this work. PD simultaneously optimizes the energy efficiency and energy distribution in the network. Further, MOBMDP uses a novel reinforcement learning inspired method to estimate transmission delay in a given path. Intensive simulation studies compare MOBMDP to four state-of-the-art routing protocols. Results show a significant improvement in network lifetime, packet delivery ratio, energy efficiency, average data transmission delay, and error in delay estimation.
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    Battery lifetime estimation for energy efficient telecommunication networks in smart cities
    (Elsevier, 2021-08) Chamola, Vinay
    There has been a surge in telecommunication network deployments across the globe to facilitate advanced communication infrastructure which is necessary for smart cities. This has in turn increased the power consumption of telecommunication networks, thus motivating the need to adopt green energy solutions like solar energy to power them. Base stations (BSs) are the primary entities contributing to the power consumption in the telecommunication network. To efficiently deploy solar powered base stations, it is imperative to optimally provision them with appropriate Photo Voltaic (PV) panel and battery resources. The ultimate goal of such dimensioning is to provide best possible quality of service (QoS) to the consumers while maintaining an optimal cost of deployment and operation. Both PV panels and the batteries are major contributors while calculating the overall cost of deployment and operation for a solar powered BSs. Therefore an accurate calculation of battery lifetime with respect to different PV panel dimension and battery sizes is an important step in cost optimal resource provisioning for the solar powered BSs. This issue is addressed in this paper by presenting an analytical scheme to estimate the battery lifetime for a particular resource provisioning of PV panels and batteries. This is then used for evaluating the cost-optimal photo-voltaic panel dimensions and battery size for the base station with acceptable limit of outage probability. The proposed methodology would find great relevance in developing energy efficient sustainable telecommunication networks for upcoming smart cities.
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    An Energy and Delay Aware Downlink Power Control Strategy for Solar Powered Base Stations
    (IEEE, 2016-05) Chamola, Vinay
    Using renewable resources like solar energy to power the base stations (BSs) has emerged as a promising solution for greening cellular networks. One of the key challenges in operating a network of such BSs is to intelligently manage the green energy available to the BSs while ensuring reliable quality of service (QoS). This letter presents a methodology for maximizing the QoS, in terms of the network latency, given the constraints on the energy availability at the solar-powered BSs. In contrast to existing approaches based on user association reconfiguration, our methodology uses a combination of intelligent energy allocation and BS downlink power control. Using a real BS deployment scenario from U.K., we show the efficacy of our algorithm and demonstrate its superior performance compared to existing benchmarks.