936 research outputs found
Wireless Power Transfer and Data Collection in Wireless Sensor Networks
In a rechargeable wireless sensor network, the data packets are generated by
sensor nodes at a specific data rate, and transmitted to a base station.
Moreover, the base station transfers power to the nodes by using Wireless Power
Transfer (WPT) to extend their battery life. However, inadequately scheduling
WPT and data collection causes some of the nodes to drain their battery and
have their data buffer overflow, while the other nodes waste their harvested
energy, which is more than they need to transmit their packets. In this paper,
we investigate a novel optimal scheduling strategy, called EHMDP, aiming to
minimize data packet loss from a network of sensor nodes in terms of the nodes'
energy consumption and data queue state information. The scheduling problem is
first formulated by a centralized MDP model, assuming that the complete states
of each node are well known by the base station. This presents the upper bound
of the data that can be collected in a rechargeable wireless sensor network.
Next, we relax the assumption of the availability of full state information so
that the data transmission and WPT can be semi-decentralized. The simulation
results show that, in terms of network throughput and packet loss rate, the
proposed algorithm significantly improves the network performance.Comment: 30 pages, 8 figures, accepted to IEEE Transactions on Vehicular
Technolog
On Spectrum Sharing Between Energy Harvesting Cognitive Radio Users and Primary Users
This paper investigates the maximum secondary throughput for a rechargeable
secondary user (SU) sharing the spectrum with a primary user (PU) plugged to a
reliable power supply. The SU maintains a finite energy queue and harvests
energy from natural resources and primary radio frequency (RF) transmissions.
We propose a power allocation policy at the PU and analyze its effect on the
throughput of both the PU and SU. Furthermore, we study the impact of the
bursty arrivals at the PU on the energy harvested by the SU from RF
transmissions. Moreover, we investigate the impact of the rate of energy
harvesting from natural resources on the SU throughput. We assume fading
channels and compute exact closed-form expressions for the energy harvested by
the SU under fading. Results reveal that the proposed power allocation policy
along with the implemented RF energy harvesting at the SU enhance the
throughput of both primary and secondary links
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Power-Optimal Feedback-Based Random Spectrum Access for an Energy Harvesting Cognitive User
In this paper, we study and analyze cognitive radio networks in which
secondary users (SUs) are equipped with Energy Harvesting (EH) capability. We
design a random spectrum sensing and access protocol for the SU that exploits
the primary link's feedback and requires less average sensing time. Unlike
previous works proposed earlier in literature, we do not assume perfect
feedback. Instead, we take into account the more practical possibilities of
overhearing unreliable feedback signals and accommodate spectrum sensing
errors. Moreover, we assume an interference-based channel model where the
receivers are equipped with multi-packet reception (MPR) capability.
Furthermore, we perform power allocation at the SU with the objective of
maximizing the secondary throughput under constraints that maintain certain
quality-of-service (QoS) measures for the primary user (PU)
Energy Harvesting Wireless Communications: A Review of Recent Advances
This article summarizes recent contributions in the broad area of energy
harvesting wireless communications. In particular, we provide the current state
of the art for wireless networks composed of energy harvesting nodes, starting
from the information-theoretic performance limits to transmission scheduling
policies and resource allocation, medium access and networking issues. The
emerging related area of energy transfer for self-sustaining energy harvesting
wireless networks is considered in detail covering both energy cooperation
aspects and simultaneous energy and information transfer. Various potential
models with energy harvesting nodes at different network scales are reviewed as
well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications
(Special Issue: Wireless Communications Powered by Energy Harvesting and
Wireless Energy Transfer
Capacity of Fading Gaussian Channel with an Energy Harvesting Sensor Node
Network life time maximization is becoming an important design goal in
wireless sensor networks. Energy harvesting has recently become a preferred
choice for achieving this goal as it provides near perpetual operation. We
study such a sensor node with an energy harvesting source and compare various
architectures by which the harvested energy is used. We find its Shannon
capacity when it is transmitting its observations over a fading AWGN channel
with perfect/no channel state information provided at the transmitter. We
obtain an achievable rate when there are inefficiencies in energy storage and
the capacity when energy is spent in activities other than transmission.Comment: 6 Pages, To be presented at IEEE GLOBECOM 201
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