Tiago Koketsu Rodrigues 교수

Due to physical limitations, mobile devices are restricted in memory, battery, processing, among other characteristics. This results in many applications that cannot be run in such devices. This problem is fixed by Edge Cloud Computing, where the users offload tasks they cannot run to cloudlet servers in the edge of the network. The main requirement of such a system is having a low Service Delay, which would correspond to a high Quality of Service. This paper presents a method for minimizing Ser

Mobile Edge Computing (MEC) is considered an essential future service for the implementation of 5G networks and the Internet of Things, as it is the best method of delivering computation and communication resources to mobile devices. It is based on the connection of the users to servers located on the edge of the network, which is especially relevant for real-time applications that demand minimal latency. In order to guarantee a resource-efficient MEC (which, for example, could mean improved Qua

Mobile devices have several restrictions due to design choices that guarantee their mobility. A way of surpassing such limitations is to utilize cloud servers called cloudlets on the edge of the network through Mobile Edge Computing. However, as the number of clients and devices grows, the service must also increase its scalability in order to guarantee a latency limit and quality threshold. This can be achieved by deploying and activating more cloudlets, but this solution is expensive due to th

Cloud computing is an important technology for bringing a big pool of elastic resources to client devices. Their main drawback has long been the long distance between users and servers, but this has been remedied by Edge Cloud Computing, where the cloud servers are located in the network edge. Edge Cloud Computing is regarded as essential for future networks and consequently, there is plenty of research on how to optimize its operation. However, the vast majority of them ignore the decision of w

Future telecommunication systems in beyond 5G/6G networks, will include a massive amount of devices and a high variation of applications, many of which with steep processing requirements and strict latency limitations. To satisfy such demands, Multi-access Edge Computing will play a key role in the future of cloud systems. Users can offload their applications to edge cloud servers, capable of processing their tasks and responding with an output quickly. However, for this to become a reality, it

For future networks in the 6G, it will be important to maintain a ubiquitous connection, bring processing heavy applications to remote areas, and analyze big amounts of data to efficiently provide services. To achieve such goals, the literature has utilized satellite networks to reach areas far away from the network core, and there has even been research into equipping such satellites with edge cloud servers to provide computation offloading to remote devices. However, analyzing the big data cre

Multiaccess edge computing is an essential technology that academia and industry have recognized as fundamental for the future of the Internet of Things. Current research on the subject utilizes virtual machines as the intermediary between end devices and cloud servers. However, recently a new framework was proposed that utilizes Cybertwins instead of virtual machines for the same function. Such framework comes with a myriad of advantages but, most importantly, in this case, it includes a contro

For the goals of beyond 5G and 6G networks, it is essential to maintain access everywhere and offer low latency with high reliability. To achieve such goals, satellite networks are an instrumental technology that grants network coverage even in remote areas without ground infrastructure and provides an offloading option when ground networks are too crowded with workload. However, for an efficient implementation, it is important to take into account not only the particular characteristics surroun

WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. The high-frequency bands can provide high transmission rates, but their high amplitude makes it so the signal cannot go through any walls or obstacles. The signal also has a strong path loss element caused by the high frequency, significantly limiting the reach of connections because the signal is too weak at moderate distances. Due to these issues, users can easily lose connection with th

Mobile devices are naturally limited due to their portable sizes and will therefore never be equal to their desktop counterparts. To overcome this, Edge Cloud Computing can be utilized to execute tasks on behalf of the devices, allowing them to run applications that would normally be too demanding. In this service model, it is important to maintain a low Service Delay to keep the service transparent to the user. This can be achieved by focusing on lowering the Transmission Delay and Processing D

There are many applications which cannot be executed by mobile devices due to their limitations in memory, processing, battery, among others. One solution to this would be offloading heavy tasks to cloud servers in the edge of the network, in a service model called Edge Cloud Computing. The main Quality of Service requirement of this model is a low Service Delay, which can be achieved by lowering Transmission Delay and Processing Delay. Works in literature focus on either one of those two types

With 6G networks, we can expect more devices to start operating in remote areas, away from the conventional network infrastructure. With an increase in the number of devices, we should also see more data that needs to be processed and analyzed. An adequate response to this scenario is using satellite networks to reach remote devices and transfer the big data generated by them to be analyzed in cloud servers through Machine Learning models. However, while this is a good solution for data analysis

Edge Cloud Computing is a key technology for enhancing mobile functionalities and real-time applications in devices with limited resources. This is done by sharing the resources of edge servers and offloading jobs to the edge cloud. In order to ensure a high-quality service and more efficient usage of resources, it is important not only to correctly configure the edge servers but also to carefully select where to deploy them. However, in Edge Cloud Computing there is a high amount of servers and

High-frequency communications and space/air networks are needed to provide fast transmission rates with ubiquitous connections. The complicating factor, however, is that high frequencies are susceptible to high attenuation caused by adverse weather, such as clouds and rain. Given that weather is highly dynamic but follows predictable patterns, the use of Machine Learning to predict network state and thus proactively control connections is a promising network management method in these scenarios.

WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. They can offer extremely high transmission rates but at the cost of low range and penetration. Due to these issues, WiGig systems are unstable and need to rely on frequent handovers to maintain high-quality connections. However, this solution is problematic as it forces users into bad connections and downtime before they are switched to a better access point. In this work, we use Machine L