8,178 research outputs found
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
Optimization of the overall success probability of the energy harvesting cognitive wireless sensor networks
Wireless energy harvesting can improve the performance of cognitive wireless sensor networks (WSNs). This paper considers radio frequency (RF) energy harvesting from transmissions in the primary spectrum for cognitive WSNs. The overall success probability of the energy harvesting cognitive WSN depends on the transmission success probability and energy success probability. Using the tools from stochastic geometry, we show that the overall success probability can be optimized with respect to: 1) transmit power of the sensors; 2) transmit power of the primary transmitters; and 3) spatial density of the primary transmitters. In this context, an optimization algorithm is proposed to maximize the overall success probability of the WSNs. Simulation results show that the overall success probability and the throughput of the WSN can be significantly improved by optimizing the aforementioned three parameters. As RF energy harvesting can also be performed indoors, hence, our solution can be directly applied to the cognitive WSNs that are installed in smart buildings
EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design
The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application
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
Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors
In this paper, we design an optimal sensor collaboration strategy among
neighboring nodes while tracking a time-varying parameter using wireless sensor
networks in the presence of imperfect communication channels. The sensor
network is assumed to be self-powered, where sensors are equipped with energy
harvesters that replenish energy from the environment. In order to minimize the
mean square estimation error of parameter tracking, we propose an online sensor
collaboration policy subject to real-time energy harvesting constraints. The
proposed energy allocation strategy is computationally light and only relies on
the second-order statistics of the system parameters. For this, we first
consider an offline non-convex optimization problem, which is solved exactly
using semidefinite programming. Based on the offline solution, we design an
online power allocation policy that requires minimal online computation and
satisfies the dynamics of energy flow at each sensor. We prove that the
proposed online policy is asymptotically equivalent to the optimal offline
solution and show its convergence rate and robustness. We empirically show that
the estimation performance of the proposed online scheme is better than that of
the online scheme when channel state information about the dynamical system is
available in the low SNR regime. Numerical results are conducted to demonstrate
the effectiveness of our approach
Optimal Energy Allocation for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgments and Energy Harvesting Constraints
This paper presents a design methodology for optimal transmission energy
allocation at a sensor equipped with energy harvesting technology for remote
state estimation of linear stochastic dynamical systems. In this framework, the
sensor measurements as noisy versions of the system states are sent to the
receiver over a packet dropping communication channel. The packet dropout
probabilities of the channel depend on both the sensor's transmission energies
and time varying wireless fading channel gains. The sensor has access to an
energy harvesting source which is an everlasting but unreliable energy source
compared to conventional batteries with fixed energy storages. The receiver
performs optimal state estimation with random packet dropouts to minimize the
estimation error covariances based on received measurements. The receiver also
sends packet receipt acknowledgments to the sensor via an erroneous feedback
communication channel which is itself packet dropping.
The objective is to design optimal transmission energy allocation at the
energy harvesting sensor to minimize either a finite-time horizon sum or a long
term average (infinite-time horizon) of the trace of the expected estimation
error covariance of the receiver's Kalman filter. These problems are formulated
as Markov decision processes with imperfect state information. The optimal
transmission energy allocation policies are obtained by the use of dynamic
programming techniques. Using the concept of submodularity, the structure of
the optimal transmission energy policies are studied. Suboptimal solutions are
also discussed which are far less computationally intensive than optimal
solutions. Numerical simulation results are presented illustrating the
performance of the energy allocation algorithms.Comment: Submitted to IEEE Transactions on Automatic Control. arXiv admin
note: text overlap with arXiv:1402.663
Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning
With the advent of the Internet of Things (IoT), an increasing number of
energy harvesting methods are being used to supplement or supplant battery
based sensors. Energy harvesting sensors need to be configured according to the
application, hardware, and environmental conditions to maximize their
usefulness. As of today, the configuration of sensors is either manual or
heuristics based, requiring valuable domain expertise. Reinforcement learning
(RL) is a promising approach to automate configuration and efficiently scale
IoT deployments, but it is not yet adopted in practice. We propose solutions to
bridge this gap: reduce the training phase of RL so that nodes are operational
within a short time after deployment and reduce the computational requirements
to scale to large deployments. We focus on configuration of the sampling rate
of indoor solar panel based energy harvesting sensors. We created a simulator
based on 3 months of data collected from 5 sensor nodes subject to different
lighting conditions. Our simulation results show that RL can effectively learn
energy availability patterns and configure the sampling rate of the sensor
nodes to maximize the sensing data while ensuring that energy storage is not
depleted. The nodes can be operational within the first day by using our
methods. We show that it is possible to reduce the number of RL policies by
using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure
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