57,243 research outputs found
RF-Powered Cognitive Radio Networks: Technical Challenges and Limitations
The increasing demand for spectral and energy efficient communication
networks has spurred a great interest in energy harvesting (EH) cognitive radio
networks (CRNs). Such a revolutionary technology represents a paradigm shift in
the development of wireless networks, as it can simultaneously enable the
efficient use of the available spectrum and the exploitation of radio frequency
(RF) energy in order to reduce the reliance on traditional energy sources. This
is mainly triggered by the recent advancements in microelectronics that puts
forward RF energy harvesting as a plausible technique in the near future. On
the other hand, it is suggested that the operation of a network relying on
harvested energy needs to be redesigned to allow the network to reliably
function in the long term. To this end, the aim of this survey paper is to
provide a comprehensive overview of the recent development and the challenges
regarding the operation of CRNs powered by RF energy. In addition, the
potential open issues that might be considered for the future research are also
discussed in this paper.Comment: 8 pages, 2 figures, 1 table, Accepted in IEEE Communications Magazin
Optimal Energy Management Policies for Energy Harvesting Sensor Nodes
We study a sensor node with an energy harvesting source. The generated energy
can be stored in a buffer. The sensor node periodically senses a random field
and generates a packet. These packets are stored in a queue and transmitted
using the energy available at that time. We obtain energy management policies
that are throughput optimal, i.e., the data queue stays stable for the largest
possible data rate. Next we obtain energy management policies which minimize
the mean delay in the queue.We also compare performance of several easily
implementable sub-optimal energy management policies. A greedy policy is
identified which, in low SNR regime, is throughput optimal and also minimizes
mean delay.Comment: Submitted to the IEEE Transactions on Wireless Communications; 22
pages with 10 figure
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
Distributed Optimization in Energy Harvesting Sensor Networks with Dynamic In-network Data Processing
Energy Harvesting Wireless Sensor Networks (EH- WSNs) have been attracting increasing interest in recent years. Most current EH-WSN approaches focus on sensing and net- working algorithm design, and therefore only consider the energy consumed by sensors and wireless transceivers for sensing and data transmissions respectively. In this paper, we incorporate CPU-intensive edge operations that constitute in-network data processing (e.g. data aggregation/fusion/compression) with sens- ing and networking; to jointly optimize their performance, while ensuring sustainable network operation (i.e. no sensor node runs out of energy). Based on realistic energy and network models, we formulate a stochastic optimization problem, and propose a lightweight on-line algorithm, namely Recycling Wasted Energy (RWE), to solve it. Through rigorous theoretical analysis, we prove that RWE achieves asymptotical optimality, bounded data queue size, and sustainable network operation. We implement RWE on a popular IoT operating system, Contiki OS, and eval- uate its performance using both real-world experiments based on the FIT IoT-LAB testbed, and extensive trace-driven simulations using Cooja. The evaluation results verify our theoretical analysis, and demonstrate that RWE can recycle more than 90% wasted energy caused by battery overflow, and achieve around 300% network utility gain in practical EH-WSNs
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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