1,750 research outputs found
How 5G wireless (and concomitant technologies) will revolutionize healthcare?
The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution
Toward Multi-Service Edge-Intelligence Paradigm: Temporal-Adaptive Prediction for Time-Critical Control over Wireless
Time-critical control applications typically pose stringent connectivity
requirements for communication networks. The imperfections associated with the
wireless medium such as packet losses, synchronization errors, and varying
delays have a detrimental effect on performance of real-time control, often
with safety implications. This paper introduces multi-service edge-intelligence
as a new paradigm for realizing time-critical control over wireless. It
presents the concept of multi-service edge-intelligence which revolves around
tight integration of wireless access, edge-computing and machine learning
techniques, in order to provide stability guarantees under wireless
imperfections. The paper articulates some of the key system design aspects of
multi-service edge-intelligence. It also presents a temporal-adaptive
prediction technique to cope with dynamically changing wireless environments.
It provides performance results in a robotic teleoperation scenario. Finally,
it discusses some open research and design challenges for multi-service
edge-intelligence.Comment: Accepted for publication in the IEEE Internet of Things Magazin
Edge Computing Architectures for Enabling the Realisation of the Next Generation Robotic Systems
Edge Computing is a promising technology to provide new capabilities in
technological fields that require instantaneous data processing. Researchers in
areas such as machine and deep learning use extensively edge and cloud
computing for their applications, mainly due to the significant computational
and storage resources that they provide. Currently, Robotics is seeking to take
advantage of these capabilities as well, and with the development of 5G
networks, some existing limitations in the field can be overcome. In this
context, it is important to know how to utilize the emerging edge
architectures, what types of edge architectures and platforms exist today and
which of them can and should be used based on each robotic application. In
general, Edge platforms can be implemented and used differently, especially
since there are several providers offering more or less the same set of
services with some essential differences. Thus, this study addresses these
discussions for those who work in the development of the next generation
robotic systems and will help to understand the advantages and disadvantages of
each edge computing architecture in order to choose wisely the right one for
each application.Comment: 7 pages, 4 figures, med 202
Dissecting the impact of information and communication technologies on digital twins as a service
Recent advances on Edge computing, Network Function Virtualization (NFV) and 5G are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Digital Twin is a key piece in the industrial digital transformation and its benefits are very well studied in the literature. However, designing and implementing a Digital Twin system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. Therefore, prototyping the system is required to gradually validate and optimize Digital Twin solutions. In this work, an Edge Robotics Digital Twin system is implemented as a prototype that embodies the concept of Digital Twin as a Service (DTaaS). Such system enables real-time applications such as visualization and remote control, requiring low-latency and high reliability. The capability of the system to offer potential savings by means of computation offloading are analyzed in different deployment configurations. Moreover, the impact of different wireless channels (e.g., 5G, 4G and WiFi) to support the data exchange between a physical device and its virtual components are assessed within operational Digital Twins. Results show that potentially 16% of CPU and 34% of MEM savings can be achieved by virtualizing and offloading software components in the Edge. In addition, they show that 5G connectivity enables remote control of 20 ms, appearing as the most promising radio access technology to support the main requirements of Digital Twin systems.This work was supported in part by the H2020 European Union/Taiwan (EU/TW) Joint Action 5G-eDge Intelligence for Vertical Experimentation (DIVE) under Grant 859881, in part by the H2020 5Growth Project under Grant 856709, in part by the Madrid Government (Comunidad de Madrid-Spain) through the Multiannual Agreement with Universidad Carlos III de Madrid (UC3M) in the line of Excellence of University Professors under Grant EPUC3M21, and in part by the context of the V PRICIT (Regional Program of Research and Technological Innovation)
A Resilient Framework for 5G-Edge-Connected UAVs based on Switching Edge-MPC and Onboard-PID Control
In recent years, the need for resources for handling processes with high
computational complexity for mobile robots is becoming increasingly urgent.
More specifically, robots need to autonomously operate in a robust and
continuous manner, while keeping high performance, a need that led to the
utilization of edge computing to offload many computationally demanding and
time-critical robotic procedures. However, safe mechanisms should be
implemented to handle situations when it is not possible to use the offloaded
procedures, such as if the communication is challenged or the edge cluster is
not available. To this end, this article presents a switching strategy for
safety, redundancy, and optimized behavior through an edge computing-based
Model Predictive Controller (MPC) and a low-level onboard-PID controller for
edge-connected Unmanned Aerial Vehicles (UAVs). The switching strategy is based
on the communication Key Performance Indicators (KPIs) over 5G to decide
whether the UAV should be controlled by the edge-based or have a safe fallback
based on the onboard controller.Comment: 8 pages, 9 figures, isie202
Twin Delayed DDPG based Dynamic Power Allocation for Mobility in IoRT
The internet of robotic things (IoRT) is a modern as well as fast-evolving technology employed in abundant socio-economical aspects which connect user equipment (UE) for communication and data transfer among each other. For ensuring the quality of service (QoS) in IoRT applications, radio resources, for example, transmitting power allocation (PA), interference management, throughput maximization etc., should be efficiently employed and allocated among UE. Traditionally, resource allocation has been formulated using optimization problems, which are then solved using mathematical computer techniques. However, those optimization problems are generally nonconvex as well as nondeterministic polynomial-time hardness (NP-hard). In this paper, one of the most crucial challenges in radio resource management is the emitting power of an antenna called PA, considering that the interfering multiple access channel (IMAC) has been considered. In addition, UE has a natural movement behavior that directly impacts the channel condition between remote radio head (RRH) and UE. Additionally, we have considered two well-known UE mobility models i) random walk and ii) modified Gauss-Markov (GM). As a result, the simulation environment is more realistic and complex. A data-driven as well as model-free continuous action based deep reinforcement learning algorithm called twin delayed deep deterministic policy gradient (TD3) has been proposed that is the combination of policy gradient, actor-critics, as well as double deep Q-learning (DDQL). It optimizes the PA for i) stationary UE, ii) the UE movements according to random walk model, and ii) the UE movement based on the modified GM model. Simulation results show that the proposed TD3 method outperforms model-based techniques like weighted MMSE (WMMSE) and fractional programming (FP) as well as model-free algorithms, for example, deep Q network (DQN) and DDPG in terms of average sum-rate performance
- …