385 research outputs found
An Economic Aspect of Device-to-Device Assisted Offloading in Cellular Networks
Traffic offloading via device-to-device (D2D) communications
has been proposed to alleviate the traffic burden
on base stations (BSs) and to improve the spectral and energy
efficiency of cellular networks. The success of D2D communications
relies on the willingness of users to share contents. In
this paper, we study the economic aspect of traffic offloading via
content sharing among multiple devices and propose an incentive
framework for D2D assisted offloading. In the proposed incentive
framework, the operator improves its overall profit, defined as
the network economic efficiency (ECE), by encouraging users
to act as D2D transmitters (D2D-Txs) which broadcast their
popular contents to nearby users. We analytically characterize
D2D assisted offloading in cellular networks for two operating
modes: 1) underlay mode and 2) overlay mode. We model the
optimization of network ECE as a two-stage Stackelberg game,
considering the densities of cellular users and D2D-Tx’s, the
operator’s incentives and the popularity of contents. The closedform
expressions of network ECE for both underlay and overlay
modes of D2D communications are obtained. Numerical results
show that the achievable network ECE of the proposed incentive
D2D assisted offloading network can be significantly improved
with respect to the conventional cellular networks where the D2D
communications are disabled
An Economic Aspect of Device-to-Device Assisted Offloading in Cellular Networks
Traffic offloading via device-to-device (D2D) communications
has been proposed to alleviate the traffic burden
on base stations (BSs) and to improve the spectral and energy
efficiency of cellular networks. The success of D2D communications
relies on the willingness of users to share contents. In
this paper, we study the economic aspect of traffic offloading via
content sharing among multiple devices and propose an incentive
framework for D2D assisted offloading. In the proposed incentive
framework, the operator improves its overall profit, defined as
the network economic efficiency (ECE), by encouraging users
to act as D2D transmitters (D2D-Txs) which broadcast their
popular contents to nearby users. We analytically characterize
D2D assisted offloading in cellular networks for two operating
modes: 1) underlay mode and 2) overlay mode. We model the
optimization of network ECE as a two-stage Stackelberg game,
considering the densities of cellular users and D2D-Tx’s, the
operator’s incentives and the popularity of contents. The closedform
expressions of network ECE for both underlay and overlay
modes of D2D communications are obtained. Numerical results
show that the achievable network ECE of the proposed incentive
D2D assisted offloading network can be significantly improved
with respect to the conventional cellular networks where the D2D
communications are disabled
Edge Robotics: are we ready? An experimental evaluation of current vision and future directions
Cloud-based robotics systems leverage a wide range of Information Technologies (IT) to offer tangible benefits like cost reduction, powerful computational capabilities, data offloading, etc. However, the centralized nature of cloud computing is not well-suited for a multitude of Operational Technologies (OT) nowadays used in robotics systems that require strict real-time guarantees and security. Edge computing and fog computing are complementary approaches that aim at mitigating some of these challenges by providing computing capabilities closer to the users. The goal of this work is hence threefold: i) to analyze the current edge computing and fog computing landscape in the context of robotics systems, ii) to experimentally evaluate an end-to-end robotics system based on solutions proposed in the literature, and iii) to experimentally identify current benefits and open challenges of edge computing and fog computing. Results show that, in the case of an exemplary delivery application comprising two mobile robots, the robot coordination and range can be improved by consuming real-time radio information available at the edge. However, our evaluation highlights that the existing software, wireless and virtualization technologies still require substantial evolution to fully support edge-based robotics systems.This work has been partially funded by European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 101015956,
and the Spanish Ministry of Economic Affairs and
Digital Transformation and the European Union-
NextGenerationEU through the UNICO 5G I+ D 6G-EDGEDT
and 6G-DATADRIVE
When Latency Matters: Measurements and Lessons Learned
Several emerging classes of interactive applications are demanding for extremely low-latency to be fully unleashed, with edge computing generally regarded as a key enabler thanks to reduced delays. This paper presents the outcome of a large-scale end-to-end measurement campaign focusing on task-offloading scenarios, showing that moving the computation closer to the end-users, alone, may turn out not to be enough. Indeed, the complexity associated with modern networks, both at the access and in the core, the behavior of the protocols at different levels of the stack, as well as the orchestration platforms used in data-centers hide a set of pitfalls potentially reverting the benefits introduced by low propagation delays. In short, we highlight how ensuring good QoS to latency-sensitive applications is definitely a multi-dimensional problem, requiring to cope with a great deal of customization and cooperation to get the best from the underlying network
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