## 1 Introduction  
The Internet of Things (IoT) presents a model that moves towards a world integrating the physical with the virtual through the programming and interconnection of objects to solve various problems [1]. Several technologies, such as fog computing, Wireless Sensor Networks (WSN), data mining, context awareness, real-time analytics, virtual reality, and augmented reality, are integrated into IoT, offering solutions across various sectors in industry and at home. Central to these developments is the concept of Artificial Intelligence of Things (AIoT), which enables the processing of large volumes of data generated by IoT systems, enhancing and optimizing these environments [2]. To implement an IoT solution, a control system that interconnects sensors and actuators is required [3]. These interconnections can be established through various options, ranging from wired solutions to low-power wireless technologies such as Near Field Communications (NFC) [4], Zigbee, Bluetooth, or WiFi. Among the most common devices that serve as sensors and actuators in this ecosystem are Voice Assistants (VAs). These devices, equipped with microphones, speakers, and software capable of receiving audio commands, interpreting them, searching for a response, or executing an action, are integral to user control and interaction systems within IoT. VAs take advantage of the complementarity of all these systems, enabling AI-mediated emotional perception of users [5], making them a focal point for new technological advances and developments in device and network domains [6]. In the past decade, AI integration into mobile devices has begun [7] and, since the end of 2022, there has been a proliferation of new AI-based developments and applications, leading people to rely on information technologies for their daily activities increasingly. One of the main ways to interact with these systems is through VAs. As a result, VAs have become ubiquitous, integrated into mobile devices, computers, and standalone VA devices, making their operation a significant factor in energy resource consumption, whether the VA represents an application using device hardware or functions as an independent hardware device. In either case, the operation of VAs depends on constant Internet connectivity, leading to significant energy consumption due to their passive or active listening states and the processes required to connect to the Internet to process and respond to requests. Adopting new AI-based developments and applications, which have intensified VA usage, makes their energy consumption and response times critical factors. Conventional VAs rely on an Internet connection to function, which can be problematic in situations or geographical areas where communication is affected by low coverage or unstable connections due to mobility or high network traffic congestion. In these cases, immediate access to information would be highly beneficial, which could be achieved using cache memory. Moreover, repetitive queries to VAs about data that do not frequently change, such as user information, locations, values, and prices, often occur. For instance, when a user asks a VA about a country's territorial size, the VA must access the Internet every time the user requests this information, unnecessarily increasing resource consumption. This research analyzes the parameters influencing VAs' energy consumption and response times in this context. A VA prototype incorporating cache memory supported by artificial intelligence is proposed to improve energy savings, response times, user experience (UX) [8], and operability in environments with limited or unstable connectivity. Additionally, this solution could reduce data exposure to numerous security threats in networks [9; 10; 11], by minimizing the constant transmission of information over external networks.