1,252 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
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
Proportional fairness in wireless powered CSMA/CA based IoT networks
This paper considers the deployment of a hybrid wireless data/power access
point in an 802.11-based wireless powered IoT network. The proportionally fair
allocation of throughputs across IoT nodes is considered under the constraints
of energy neutrality and CPU capability for each device. The joint optimization
of wireless powering and data communication resources takes the CSMA/CA random
channel access features, e.g. the backoff procedure, collisions, protocol
overhead into account. Numerical results show that the optimized solution can
effectively balance individual throughput across nodes, and meanwhile
proportionally maximize the overall sum throughput under energy constraints.Comment: Accepted by Globecom 201
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
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
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