3,778 research outputs found
Panda: Neighbor Discovery on a Power Harvesting Budget
Object tracking applications are gaining popularity and will soon utilize
Energy Harvesting (EH) low-power nodes that will consume power mostly for
Neighbor Discovery (ND) (i.e., identifying nodes within communication range).
Although ND protocols were developed for sensor networks, the challenges posed
by emerging EH low-power transceivers were not addressed. Therefore, we design
an ND protocol tailored for the characteristics of a representative EH
prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND
accounting for unique prototype characteristics (i.e., energy costs for
transmission/reception, and transceiver state switching times/costs). Then, we
present the Power Aware Neighbor Discovery Asynchronously (Panda) protocol in
which nodes transition between the sleep, receive, and transmit states. We
analyze \name and select its parameters to maximize the ND rate subject to a
homogeneous power budget. We also present Panda-D, designed for non-homogeneous
EH nodes. We perform extensive testbed evaluations using the prototypes and
study various design tradeoffs. We demonstrate a small difference (less then
2%) between experimental and analytical results, thereby confirming the
modeling assumptions. Moreover, we show that Panda improves the ND rate by up
to 3x compared to related protocols. Finally, we show that Panda-D operates
well under non-homogeneous power harvesting
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
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
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
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
- …