Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing - 2018


Scavenging the idling computation resources at the large variety of mobile devices, ranging from tiny IoT devices to powerful laptop computers, will offer a robust platform for local mobile Cloud Computing. The vision can be realized by peer-to-peer cooperative computing between edge devices, called co-computing. This Project exploits the non-causal helper's CPU-state info to design energy-efficient co-computing policies for scavenging time-varying spare computation resources at peer mobiles. Specifically, we tend to think about a co-computing system where a user offloads computation of input information to a helper. The helper controls the offloading process for the objective of minimizing the user's energy consumption based mostly on a predicted helper's CPU-idling profile that specifies the quantity of accessible computation resource for co-computing. Take into account the situation that the user has one-shot input-information arrival and also the helper buffers offloaded bits. The matter for energy-efficient co-computing is formulated as 2 sub-problems: the slave problem resembling adaptive offloading and therefore the master one to data partitioning. Given a fixed offloaded information size, the adaptive offloading aims at minimizing the energy consumption for offloading by controlling the offloading rate underneath the deadline and buffer constraints. By deriving the necessary and sufficient conditions for the optimal answer, we tend to characterize the structure of the optimal policies and propose algorithms for computing the policies. Furthermore, we tend to show that the problem of optimal data partitioning for offloading and native computing at the user is convex, admitting a straightforward answer using the sub-gradient method. Finally, the developed style approach for co-computing is extended to the situation of bursty data arrivals at the user accounting for knowledge causality constraints. Simulation results verify the effectiveness of the proposed algorithms.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :Estimation of Broadband Multiuser Millimeter Wave Massive MIMO-OFDM Channels by Exploiting Their Sparse Structure - 2018ABSTRACT:In millimeter wave (mm-wave) huge multiple-input multiple-output (MIMO) systems, acquiring
PROJECT TITLE :MPiLoc: Self-Calibrating Multi-Floor Indoor Localization Exploiting Participatory Sensing - 2018ABSTRACT:Whereas location is one of the most important context info in mobile and pervasive computing, giant-scale
PROJECT TITLE :Automatic Identification of Driver’s Smartphone Exploiting Common Vehicle-Riding Actions - 2018ABSTRACT:Texting or browsing the net on a smartphone while driving, referred to as distracted driving, considerably
PROJECT TITLE :Exploiting Transistor-Level Reconfiguration to Optimize Combinational circuits - 2017ABSTRACT:Silicon nanowire reconfigurable field impact transistors (SiNW RFETs) abolish the physical separation of n-sort and p-type
PROJECT TITLE : Exploiting Caching and Multicast for 5G Wireless Networks - 2016 ABSTRACT: The landscape toward 5G wireless communication is currently unclear, and, despite the efforts of academia and business in evolving traditional

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry