0000000000538275
AUTHOR
Zaiwar Ali
A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing
In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realis…
A Stochastic Routing Algorithm for Distributed IoT with Unreliable Wireless Links
Punctual and reliable transmission of collected information is indispensable for many Internet of Things (IoT) applications. Such applications rely on IoT devices operating over wireless communication links which are intrinsically unreliable. Consequently to improve packet delivery success while reducing delivery delay is a challenging task for data transmission in the IoT. In this paper, we propose an improved distributed stochastic routing algorithm to increase packet delivery ratio and decrease delivery delay in IoT with unreliable communication links. We adopt the concept of absorbing Markov chain to model the network and evaluate the expected delivery ratio and expected delivery delay …
A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing
Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by applicati…
Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach
In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy …