64,389 research outputs found
Transmission Power Scheduling for Energy Harvesting Sensor in Remote State Estimation
We study remote estimation in a wireless sensor network. Instead of using a
conventional battery-powered sensor, a sensor equipped with an energy harvester
which can obtain energy from the external environment is utilized. We formulate
this problem into an infinite time-horizon Markov decision process and provide
the optimal sensor transmission power control strategy. In addition, a
sub-optimal strategy which is easier to implement and requires less computation
is presented. A numerical example is provided to illustrate the implementation
of the sub-optimal policy and evaluation of its estimation performance.Comment: Extended version of article to be published in the Proceedings of the
19th IFAC World Congress, 201
Game-Theoretic Pricing and Selection with Fading Channels
We consider pricing and selection with fading channels in a Stackelberg game
framework. A channel server decides the channel prices and a client chooses
which channel to use based on the remote estimation quality. We prove the
existence of an optimal deterministic and Markovian policy for the client, and
show that the optimal policies of both the server and the client have threshold
structures when the time horizon is finite. Value iteration algorithm is
applied to obtain the optimal solutions for both the server and client, and
numerical simulations and examples are given to demonstrate the developed
result.Comment: 6 pages, 4 figures, accepted by the 2017 Asian Control Conferenc
Design and Implementation of a Measurement-Based Policy-Driven Resource Management Framework For Converged Networks
This paper presents the design and implementation of a measurement-based QoS
and resource management framework, CNQF (Converged Networks QoS Management
Framework). CNQF is designed to provide unified, scalable QoS control and
resource management through the use of a policy-based network management
paradigm. It achieves this via distributed functional entities that are
deployed to co-ordinate the resources of the transport network through
centralized policy-driven decisions supported by measurement-based control
architecture. We present the CNQF architecture, implementation of the prototype
and validation of various inbuilt QoS control mechanisms using real traffic
flows on a Linux-based experimental test bed.Comment: in Ictact Journal On Communication Technology: Special Issue On Next
Generation Wireless Networks And Applications, June 2011, Volume 2, Issue 2,
Issn: 2229-6948(Online
A Novel Framework for Software Defined Wireless Body Area Network
Software Defined Networking (SDN) has gained huge popularity in replacing
traditional network by offering flexible and dynamic network management. It has
drawn significant attention of the researchers from both academia and
industries. Particularly, incorporating SDN in Wireless Body Area Network
(WBAN) applications indicates promising benefits in terms of dealing with
challenges like traffic management, authentication, energy efficiency etc.
while enhancing administrative control. This paper presents a novel framework
for Software Defined WBAN (SDWBAN), which brings the concept of SDN technology
into WBAN applications. By decoupling the control plane from data plane and
having more programmatic control would assist to overcome the current lacking
and challenges of WBAN. Therefore, we provide a conceptual framework for SDWBAN
with packet flow model and a future direction of research pertaining to SDWBAN.Comment: Presented on 8th International Conference on Intelligent Systems,
Modelling and Simulatio
Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems
In many Cyber-Physical Systems, we encounter the problem of remote state
estimation of geographically distributed and remote physical processes. This
paper studies the scheduling of sensor transmissions to estimate the states of
multiple remote, dynamic processes. Information from the different sensors have
to be transmitted to a central gateway over a wireless network for monitoring
purposes, where typically fewer wireless channels are available than there are
processes to be monitored. For effective estimation at the gateway, the sensors
need to be scheduled appropriately, i.e., at each time instant one needs to
decide which sensors have network access and which ones do not. To address this
scheduling problem, we formulate an associated Markov decision process (MDP).
This MDP is then solved using a Deep Q-Network, a recent deep reinforcement
learning algorithm that is at once scalable and model-free. We compare our
scheduling algorithm to popular scheduling algorithms such as round-robin and
reduced-waiting-time, among others. Our algorithm is shown to significantly
outperform these algorithms for many example scenarios
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