1,091 research outputs found
Wireless body area network mobility-aware task offloading scheme
The increasing amount of user equipment (UE) and the rapid advances in wireless body area networks bring revolutionary changes in healthcare systems. However, due to the strict requirements on size, reliability and battery lifetime of UE devices, it is difficult for them to execute latency sensitive or computation intensive tasks effectively. In this paper, we aim to enhance the UE computation capacity by utilizing small size coordinator-based mobile edge computing (C-MEC) servers. In this way, the system complexity, computation resources, and energy consumption are considerably transferred from the UE to the C-MEC, which is a practical approach since C-MEC is power charged, in contrast to the UE. First, the system architecture and the mobility model are presented. Second, several transmission mechanisms are analyzed along with the proposed mobility-aware cooperative task offloading scheme. Numerous selected performance metrics are investigated regarding the number of executed tasks, the percentage of failed tasks, average service time, and the energy consumption of each MEC. The results validate the advantage of task offloading schemes compared with the traditional relay-based technique regarding the number of executed tasks. Moreover, one can obtain that the proposed scheme archives noteworthy benefits, such as low latency and efficiently balance the energy consumption of C-MECs
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
Relay-enabled task offloading management for wireless body area networks
Inspired by the recent developments of the Internet of Things (IoT) relay and mobile edge computing (MEC), a hospital/home-based medical monitoring framework is proposed, in which the intensive computing tasks from the implanted sensors can be efficiently executed by on-body wearable devices or a coordinator-based MEC (C-MEC). In this paper, we first propose a wireless relay-enabled task offloading mechanism that consists of a network model and a computation model. Moreover, to manage the computation resources among all relays, a task offloading decision model and the best task offloading recipient selection function is given. The performance evaluation considers different computation schemes under the predetermined link quality condition regarding the selected vital quality of service (QoS) metrics. After demonstrating the channel characterization and network topology, the performance evaluation is implemented under different scenarios regarding the network lifetime of all relays, network residual energy status, total number of locally executed packets, path loss (PL), and service delay. The results show that data transmission without the offloading scheme outperforms the offload-based technique regarding network lifetime. Moreover, the high computation capacity scenario achieves better performance regarding PL and the total number of locally executed packets
Metaverse for Wireless Systems: Architecture, Advances, Standardization, and Open Challenges
The growing landscape of emerging wireless applications is a key driver
toward the development of novel wireless system designs. Such a design can be
based on the metaverse that uses a virtual model of the physical world systems
along with other schemes/technologies (e.g., optimization theory, machine
learning, and blockchain). A metaverse using a virtual model performs proactive
intelligent analytics prior to a user request for efficient management of the
wireless system resources. Additionally, a metaverse will enable
self-sustainability to operate wireless systems with the least possible
intervention from network operators. Although the metaverse can offer many
benefits, it faces some challenges as well. Therefore, in this tutorial, we
discuss the role of a metaverse in enabling wireless applications. We present
an overview, key enablers, design aspects (i.e., metaverse for wireless and
wireless for metaverse), and a novel high-level architecture of metaverse-based
wireless systems. We discuss metaverse management, reliability, and security of
the metaverse-based system. Furthermore, we discuss recent advances and
standardization of metaverse-enabled wireless system. Finally, we outline open
challenges and present possible solutions
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