Optimal Power Control for Transmitting Correlated Sources With Energy Harvesting Constraints

Dong, Yunquan; Chen, Zhi; Wang, Jian; Shim, Byonghyo

doi:10.1109/twc.2017.2767581

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Yunquan Dong et al investigated the weighted-sum distortion minimization problem in transmitting two correlated Gaussian sources over Gaussian channels using two EH-nodes. They developed an off-line and an online power control policies to optimize the transmission power of two nodes [17]. Alyssa Kody et al considered an EH-node equipped with a piezoelectric vibration energy harvester, which is excited by a series of periodic base acceleration impulses.…”

Section: Transmission Power Controlmentioning

confidence: 99%

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Predictive Power Management for Wind Powered Wireless Sensor Node

Yin

Zhang

2018

Future Internet

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A conventional Wireless Sensor Network (WSN) cannot have an infinite lifetime without a battery recharge or replacement. Energy Harvesting (EH), from environmental energy sources, is a promising technology to provide sustainable powering for a WSN. In this paper, we propose and investigate a novel predictive energy management framework that combines the Maximal Power Transferring Tracking (MPTT) algorithm, a predictive energy allocation strategy, and a high efficiency transmission power control mechanism: First, the MPTT optimal working point guarantees minimum power loss of the EH-WSN system; Then, by exactly predicting the upcoming available energy, the power allocation strategy regulates EH-nodes’ duty cycle accurately to minimize the power failure time; Ultimately, the transmission power control module further improves energy efficiency by dynamically selecting the optimum matching transmission power level with minimum energy consumption. A wind energy powered wireless sensor system has been equipped and tested to validate the effectiveness of the proposed scheme. Results indicate that compared with other predictive energy managers, the proposed mechanism incurs relatively low power failure time while maintaining a high-energy conversion rate.

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Section: Transmission Power Controlmentioning

confidence: 99%

“…We validate the effectiveness of our TPC module by using two comparisons. The existing RSS-based transmission power controller [17] is implemented as a reference, which is referred as a fixed-RSS scheme. The fixed transmission power scheme is also implemented, which is referred to as a fixed-TX scheme.…”

Section: Tpc Executionmentioning

confidence: 99%

Predictive Power Management for Wind Powered Wireless Sensor Node

Yin

Zhang

2018

Future Internet

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“…Similarly, battery-powered sensor nodes are named as EH-WSN (Lu et al , 2018). Energy harvesting is a key technique in rechargeable sensor nodes on WSN; it increases the service life and upgrades the WSN energy (Dong et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

Sailfish optimizer algorithm (SFO) for optimized clustering in wireless sensor network (WSN)

Kumar

Santhi

Narayana

2021

JEDT

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Purpose Inspired optimization algorithms respond to numerous scientific and engineering difficulties based on its flexibility and simplicity. Such algorithms are valid for optimization difficulties devoid of structural alterations. Design/methodology/approach This paper presents a nature-inspired optimization algorithm, named Sailfish optimizer (SFO) stimulated using sailfish group. Monetary custom of energy is a dangerous problem on wireless sensor network (WSN). Findings Network cluster is an effective method of reducing node power consumption and increasing network life. An algorithm for selecting cluster head (CHs) based on enhanced cuckoo search was proposed. But this algorithm uses a novel encoding system and wellness work. It integrates a few problems. To overthrow this method many metaheuristic-based CH selection algorithms are presented. To avoid this problem, this paper proposed the SFO algorithm based energy-efficient CH selection of WSN. Originality/value The proposed SFO algorithm based energy-efficient algorithm is used for discovering the CHs ideal situation. The simulations under delay, delratio, drop, energy, network lifetime, overhead and throughput are carried out.

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“…In IoT networks and sensor neworks, the distortion of the recovered signals are often measured by the mean-squared error (MSE) of the decoded signal or the estimated signal. In [4], for example, the weighted-sum distortion in recovering two correlated Gaussian sources was optimized in the framework of network information theory. In [5], [6], the random source is estimated by combining sensor observations with a best linear unbiased estimator (BLUE), for sensor networks with orthogonal channels and coherent multiple access channels (MAC), respectively.…”

Section: Introductionmentioning

confidence: 99%

Energy Harvesting Powered Sensing in IoT: Timeliness Versus Distortion

Dong

Fan

Letaief

2019

Preprint

Self Cite

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We consider an Internet-of-Things (IoT) system in which an energy harvesting powered sensor node monitors the phenomenon of interest and transmits its observations to a remote monitor over a Gaussian channel. We measure the timeliness of the signals recovered by the monitor using age of information (AoI), which could be reduced by transmitting more observations to the monitor. We evaluate the corresponding distortion with the mean-squared error (MSE) metric, which would be reduced if a larger transmit power and a larger source coding rate were used. Since the energy harvested by the sensor node is random and limited, however, the timeliness and the distortion of the received signals cannot be optimized at the same time. Thus, we shall investigate the timeliness-distortion trade-off of the system by minimizing the average weighted-sum AoI and distortion over all possible transmit powers and transmission intervals. First, we explicitly present the optimal transmit powers for the performance limit achieving save-and-transmit policy and the easy-implementing fixed power transmission policy. Second, we propose a backward water-filling based offline power allocation algorithm and a genetic based offline algorithm to jointly optimize the transmission interval and transmit power. Third, we formulate the online power control as an Markov Decision Process (MDP) and solve the problem with an iterative algorithm, which closely approach the trade-off limit of the system. Also, we show that the optimal transmit power is a monotonic and bi-valued function of current AoI and distortion. Finally, we present our results via numerical simulations and extend results on the save-and-transmit policy to fading sensing systems.

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Optimal Power Control for Transmitting Correlated Sources With Energy Harvesting Constraints

Cited by 7 publications

References 37 publications

Predictive Power Management for Wind Powered Wireless Sensor Node

Predictive Power Management for Wind Powered Wireless Sensor Node

Sailfish optimizer algorithm (SFO) for optimized clustering in wireless sensor network (WSN)

Energy Harvesting Powered Sensing in IoT: Timeliness Versus Distortion

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