Related papers: BS-assisted Task Offloading for D2D Networks with …
Caching in wireless device-to-device (D2D) networks can be utilized to offload data traffic during peak times. However, the design of incentive mechanisms is challenging due to the heterogeneous preference and selfish nature of user…
This paper studies a novel user cooperation model in a wireless powered mobile edge computing system where two wireless users harvest wireless power transferred by one energy node and can offload part of their computation tasks to an edge…
Mobile edge computing has become an effective and fundamental paradigm for futuristic autonomous vehicles to offload computing tasks. However, due to the high mobility of vehicles, the dynamics of the wireless conditions, and the…
In recent years, edge computing, as an important pillar for future networks, has been developed rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run…
The rise of delay-sensitive yet computing-intensive Internet of Things (IoT) applications poses challenges due to the limited processing power of IoT devices. Mobile Edge Computing (MEC) offers a promising solution to address these…
With the mass deployment of computing-intensive applications and delay-sensitive applications on end devices, only adequate computing resources can meet differentiated services' delay requirements. By offloading tasks to cloud servers or…
The rapid increase in data traffic demand has overloaded existing cellular networks. Planned upgrades in the communication architecture (e.g. LTE), while helpful, are not expected to suffice to keep up with demand. As a result, extensive…
In this paper, task offloading from vehicles with random velocities is optimized via a novel dynamic programming framework. Particularly, in a vehicular network with multiple vehicles and base stations (BSs), computing tasks of vehicles are…
In this article we propose a novel Device-to-Device (D2D) Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage the network-assisted D2D collaboration for computation and communication…
To overcome devices' limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation. However, current MEC system design is based on…
In this letter, we investigate an intelligent reflecting surface (IRS) aided device-to-device (D2D) offloading system, where an IRS is employed to assist in computation offloading from a group of users with intensive tasks to another group…
Computing at the edge is increasingly important since a massive amount of data is generated. This poses challenges in transporting all that data to the remote data centers and cloud, where they can be processed and analyzed. On the other…
In this paper, the imbalance edge cloud based computing offloading for multiple mobile users (MUs) with multiple tasks per MU is studied. In which, several edge cloud servers (ECSs) are shared and accessed by multiple wireless access points…
As the demand of mobile devices (MDs) for data services is explosively increasing, traditional offloading in the cellular networks is facing the contradiction of energy efficiency and quality of service. Device-to-device (D2D) communication…
This paper addresses the challenge of efficiently offloading heavy computing tasks from ground mobile devices to the satellite-based mist computing environment. With ground-based edge and cloud servers often being inaccessible, the…
Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several…
Multicast services, whereby a common valuable message needs to reach a whole population of user equipments (UEs), are gaining attention on account of new applications such as vehicular networks. As it proves challenging to guarantee…
While mobile edge computing (MEC) alleviates the computation and power limitations of mobile devices, additional latency is incurred when offloading tasks to remote MEC servers. In this work, the power-delay tradeoff in the context of task…
It has been shown that deploying device-to-device (D2D) networks in urban environments requires equipping a considerable proportion of crossroads with relays. This represents a necessary economic investment for an operator. In this work, we…
Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in training shared machine learning models by exploiting their local data samples and communication and computation resources. To deal with…