## 1 Introduction
This research differs from ours as it is focused on automating home processes rather than responding to user queries. Regarding energy-saving techniques, the focus is on optimizing the operational algorithms of devices and the routing they use for traffic transmission. In this regard, Wireless Multimedia Networks (WMN) have been developed due to the growing availability of low-cost hardware and the wide range of wireless device applications. Energy resources are limited in battery-powered wireless devices, leading to significant threats to the Quality of Service (QoS) for WMN. For these reasons, energy-efficient routing techniques are necessary to manage WMN's dynamic topology, which relies on a vital resource like energy [19] or the incorporation of media access control protocols as proposed in [20]. While minimizing and reducing hardware components contributes to energy savings, additional solutions can be implemented, especially for systems where changes to the equipment are not possible but can be made at the software level, as proposed in this study. Interesting contributions regarding minimizing energy consumption in smart spaces are made in studies such as [21; 22; 23], although these are not directly aimed at VAs. [PERSON] and [PERSON] propose a content caching method based on file value and corresponding algorithms. This method uses Markov Chains to predict a mobile user's trajectory during the next storage time interval and cache replacement. The algorithms are designed to minimize content access latency [24]. This proposal aligns with the present research; however, our project focuses on optimizing consumption by minimizing connection times. In [25], \"Neurosurgeon\" was developed, a scheduler that automatically divides Deep Neural Network (DNN) computation between mobile devices and cloud data centers by characterizing the type of traffic to define where processing is more efficient (device or cloud). This scheduler adapts to various DNN architectures, intelligently dividing the computation to achieve the best latency or mobile energy usage. It demonstrated an average improvement in latency of 3.1\(\times\), a reduction in mobile energy consumption of 59.5%, and a 1.5\(\times\) improvement in data center performance. In [26], a new static analysis approach, \"Energy-Aware Timing Analysis of Intermittent Programs (ETAP)\", is presented, estimating the behavior of intermittently used programs affected by factors such as environmental energy, the energy consumption of target hardware, capacitor size, input space, and program structure. Considering the effects of power outages, ETAP symbolically executes a given program to generate time probability distributions for each function's execution, estimating consumption but not proposing solutions for energy savings. In [27], an Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring (ENSML) is presented for smart energy consumption measurement of devices in a household. With 99.9% accuracy, this technique would be very suitable for determining actual consumption values associated with VAs, allowing comparisons, and determining the amount of energy saved using the cache memory proposed in this work. Regarding cache management, ref. [28] presents a decentralized service migration algorithm that integrates the analysis and selection of service placement decisions and edge base stations to minimize service migration overhead. The need for dynamic content updates and database size is considered in a framework for implementing caching in the front-end and back-end. Due to high resource consumption, this would not be the best solution for VAs with limited memory and processing resources. In [29; 30], proposals by [PERSON] are highlighted with his CHA caching framework, which offers faster action execution in homes with IoT systems and improves resource efficiency, and the memory reduction solution presented by [PERSON], which uses a Large Language Model (LLM). However, it neither caches user queries nor provides an energy consumption metric.