101 research outputs found
Finite Horizon Throughput Maximization for a Wirelessly Powered Device over a Time Varying Channel
In this work, we consider an energy harvesting device (EHD) served by an
access point with a single antenna that is used for both wireless power
transfer (WPT) and data transfer. The objective is to maximize the expected
throughput of the EHD over a finite horizon when the channel state information
is only available causally. The EHD is energized by WPT for a certain duration,
which is subject to optimization, and then, EHD transmits its information bits
to the AP until the end of the time horizon by employing optimal dynamic power
allocation. The joint optimization problem is modeled as a dynamic programming
problem. Based on the characteristic of the problem, we prove that a time
dependent threshold type structure exists for the optimal WPT duration, and we
obtain closed form solution to the dynamic power allocation in the uplink
period.Comment: arXiv admin note: substantial text overlap with arXiv:1804.0183
MDP-Based Scheduling Design for Mobile-Edge Computing Systems with Random User Arrival
In this paper, we investigate the scheduling design of a mobile-edge
computing (MEC) system, where the random arrival of mobile devices with
computation tasks in both spatial and temporal domains is considered. The
binary computation offloading model is adopted. Every task is indivisible and
can be computed at either the mobile device or the MEC server. We formulate the
optimization of task offloading decision, uplink transmission device selection
and power allocation in all the frames as an infinite-horizon Markov decision
process (MDP). Due to the uncertainty in device number and location,
conventional approximate MDP approaches to addressing the curse of
dimensionality cannot be applied. A novel low-complexity sub-optimal solution
framework is then proposed. We first introduce a baseline scheduling policy,
whose value function can be derived analytically. Then, one-step policy
iteration is adopted to obtain a sub-optimal scheduling policy whose
performance can be bounded analytically. Simulation results show that the gain
of the sub-optimal policy over various benchmarks is significant.Comment: 6 pages, 3 figures; accepted by Globecom 2019; title changed to
better describe the work, introduction condensed, typos correcte
Biometric Identification and Authentication Providence using Fingerprint for Cloud Data Access
The raise in the recent security incidents of cloud computing and its challenges is to secure the data. To solve this problem, the integration of mobile with cloud computing, Mobile biometric authentication in cloud computing is presented in this paper. To enhance the security, the biometric authentication is being used, since the Mobile cloud computing is popular among the mobile user. This paper examines how the mobile cloud computing (MCC) is used in security issue with finger biometric authentication model. Through this fingerprint biometric, the secret code is generated by entropy value. This enables the person to request for accessing the data in the desk computer. When the person requests the access to the authorized user through Bluetooth in mobile, the Authorized user sends the permit access through fingerprint secret code. Finally this fingerprint is verified with the database in the Desk computer. If it is matched, then the computer can be accessed by the requested person
Mobility-Aware Computation Offloading for Swarm Robotics using Deep Reinforcement Learning
Swarm robotics is envisioned to automate a large number of dirty, dangerous,
and dull tasks. Robots have limited energy, computation capability, and
communication resources. Therefore, current swarm robotics have a small number
of robots, which can only provide limited spatio-temporal information. In this
paper, we propose to leverage the mobile edge computing to alleviate the
computation burden. We develop an effective solution based on a mobility-aware
deep reinforcement learning model at the edge server side for computing
scheduling and resource. Our results show that the proposed approach can meet
delay requirements and guarantee computation precision by using minimum robot
energy
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Reliability-oriented optimization of computation offloading for cooperative vehicle-infrastructure systems
Computation offloading is critical for mobile applications that are sensitive to computational power, while dynamic and random nature of vehicular networks makes it challenging to guarantee the reliability of vehicular computation offloading. In this letter, we propose a reliability-oriented stochastic optimization model based on dynamic programming for computation offloading in the presence of the deadline constraint on application execution. Specifically, a theoretical lower bound of the expected reliability of computation offloading is derived, and then an optimal data transmission scheduling mechanism is proposed to maximize the lower bound with consideration of randomness in vehicle-to-infrastructure (V2I) communications. Experimental results demonstrate that our mechanism can outperform the conventional scheme and benefits vehicular computation offloading in terms of reliability performance in stochastic situations
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