33,187 research outputs found
Distributed Optimal Lexicographic Max-Min Rate Allocation in Solar-Powered Wireless Sensor Networks
Understanding the optimal usage of fluctuating renewable energy in Wireless Sensor Networks (WSNs) is
complex. Lexicographic Max-min (LM) rate allocation is a good solution, but is non-trivial for multi-hop
WSNs, as both fairness and sensing rates have to be optimized through the exploration of all possible
forwarding routes in the network. All current optimal approaches to this problem are centralized and
off-line, suffering from low scalability and large computational complexity; typically solving O(N2
) linear
programming problems for N-node WSNs. This paper presents the first optimal distributed solution to
this problem with much lower complexity. We apply it to Solar Powered WSNs (SP-WSNs) to achieve
both LM optimality and sustainable operation. Based on realistic models of both time-varying solar power
and photovoltaic-battery hardware, we propose an optimization framework that integrates a local power
management algorithm with a global distributed LM rate allocation scheme. The optimality, convergence,
and efficiency of our approaches are formally proven. We also evaluate our algorithms via experiments
on both solar-powered MicaZ motes and extensive simulations using real solar energy data and practical
power parameter settings. The results verify our theoretical analysis and demonstrate how our approach
outperforms both the state-of-the-art centralized optimal and distributed heuristic solutions
Distributed Optimal Lexicographic Max-Min Rate Allocation in Solar-Powered Wireless Sensor Networks
Understanding the optimal usage of fluctuating renewable energy in wireless sensor networks (WSNs) is complex. Lexicographic max-min (LM) rate allocation is a good solution but is nontrivial for multihop WSNs, as both fairness and sensing rates have to be optimized through the exploration of all possible forwarding routes in the network. All current optimal approaches to this problem are centralized and offline, suffering from low scalability and large computational complexity—typically solving O( N 2 ) linear programming problems for N -node WSNs. This article presents the first optimal distributed solution to this problem with much lower complexity. We apply it to solar-powered wireless sensor networks (SP-WSNs) to achieve both LM optimality and sustainable operation. Based on realistic models of both time-varying solar power and photovoltaic-battery hardware, we propose an optimization framework that integrates a local power management algorithm with a global distributed LM rate allocation scheme. The optimality, convergence, and efficiency of our approaches are formally proven. We also evaluate our algorithms via experiments on both solar-powered MICAz motes and extensive simulations using real solar energy data and practical power parameter settings. The results verify our theoretical analysis and demonstrate how our approach outperforms both the state-of-the-art centralized optimal and distributed heuristic solutions. </jats:p
Distributed Optimal Lexicographic Max-Min Rate Allocation in Solar-Powered Wireless Sensor Networks
Understanding the optimal usage of fluctuating renewable energy in Wireless Sensor Networks (WSNs) is
complex. Lexicographic Max-min (LM) rate allocation is a good solution, but is non-trivial for multi-hop
WSNs, as both fairness and sensing rates have to be optimized through the exploration of all possible
forwarding routes in the network. All current optimal approaches to this problem are centralized and
off-line, suffering from low scalability and large computational complexity; typically solving O(N2
) linear
programming problems for N-node WSNs. This paper presents the first optimal distributed solution to
this problem with much lower complexity. We apply it to Solar Powered WSNs (SP-WSNs) to achieve
both LM optimality and sustainable operation. Based on realistic models of both time-varying solar power
and photovoltaic-battery hardware, we propose an optimization framework that integrates a local power
management algorithm with a global distributed LM rate allocation scheme. The optimality, convergence,
and efficiency of our approaches are formally proven. We also evaluate our algorithms via experiments
on both solar-powered MicaZ motes and extensive simulations using real solar energy data and practical
power parameter settings. The results verify our theoretical analysis and demonstrate how our approach
outperforms both the state-of-the-art centralized optimal and distributed heuristic solutions
Optimizing Wirelessly Powered Crowd Sensing: Trading energy for data
To overcome the limited coverage in traditional wireless sensor networks,
\emph{mobile crowd sensing} (MCS) has emerged as a new sensing paradigm. To
achieve longer battery lives of user devices and incentive human involvement,
this paper presents a novel approach that seamlessly integrates MCS with
wireless power transfer, called \emph{wirelessly powered crowd sensing} (WPCS),
for supporting crowd sensing with energy consumption and offering rewards as
incentives. The optimization problem is formulated to simultaneously maximize
the data utility and minimize the energy consumption for service operator, by
jointly controlling wireless-power allocation at the \emph{access point} (AP)
as well as sensing-data size, compression ratio, and sensor-transmission
duration at \emph{mobile sensor} (MS). Given the fixed compression ratios, the
optimal power allocation policy is shown to have a \emph{threshold}-based
structure with respect to a defined \emph{crowd-sensing priority} function for
each MS. Given fixed sensing-data utilities, the compression policy achieves
the optimal compression ratio. Extensive simulations are also presented to
verify the efficiency of the contributed mechanisms.Comment: arXiv admin note: text overlap with arXiv:1711.0206
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