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Using distributed MEMS pressure sensors to measure small flow rates in high resistance fluidic channels is fraught with challenges far beyond the performance of the pressure sensing element. In a typical core-flood experiment, which may last several months, flow-induced pressure gradients are generated in porous rock core samples wrapped in a polymer sheath. Measuring these pressure gradients along the flow path requires high resolution pressure measurement while contending with difficult test conditions such as large bias pressures (up to 20 bar) and temperatures (up to 125 °C), as well as the presence of corrosive fluids. This work is directed at a system for using passive wireless inductive-capacitive (LC) pressure sensors that are distributed along the flow path to measure the pressure gradient. The sensors are wirelessly interrogated with readout electronics placed exterior to the polymer sheath for continuous monitoring of experiments. Using microfabricated pressure sensors that are smaller than ø15 × 3.0 mm3, an LC sensor design model for minimizing pressure resolution, accounting for sensor packaging and environmental artifacts is investigated and experimentally validated. A test setup, built to provide fluid-flow pressure differentials to LC sensors with conditions that mimic placement of the sensors within the wall of the sheath, is used to test the system. Experimental results show the microsystem operating over full-scale pressure range of 20,700 mbar and temperatures up to 125 °C, while achieving pressure resolution of <1 mbar, and resolving gradients of 10–30 mL/min, which are typical in core-flood experiments.

Optimizing the operation of photovoltaic (PV) storage systems is crucial for meeting the load demands of parks while minimizing curtailment and enhancing economic efficiency. This paper proposes a multi-scenario collaborative optimization strategy for PV storage systems based on a master–slave game model. Three types of energy storage system (ESS) application scenarios are designed to comprehensively stabilize PV fluctuations, compensate for load transfers, and participate in the frequency regulation (FR) market, thereby optimizing the overall operational strategy of PV storage systems in parks. The upper-level objective is to maximize the park operators’ profit, while the lower-level objective is to minimize the user’s power supply costs. Case studies demonstrate that this strategy can significantly increase the economic benefits for park operators by 25.8%, reduce user electricity expenditures by 5.27%, and lower curtailment through a load response mechanism, thereby promoting the development and construction of PV storage parks.

The wireless sensor network (WSN) is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources. Therefore, energy consumption in wireless sensor networks is one of the most challenging problems in practice. On the other hand, data fusion can effectively decrease data redundancy, reduce the amount of data transmission and energy consumption in the network, extend the network life cycle, improve the utilization of bandwidth, and thus overcome the bottleneck on energy and bandwidth consumption. This paper proposes a new data fusion algorithm based on Hesitant Fuzzy Entropy (DFHFE). The new algorithm aims to reduce the collection of repeated data on sensor nodes from the source, and strives to utilize the information provided by redundant data to improve the data reliability. Hesitant fuzzy entropy is exploited to fuse the original data from sensor nodes in the cluster at the sink node to obtain higher quality data and make local decisions on the events of interest. The sink nodes periodically send local decisions to the base station that aggregates the local decisions and makes the final judgment, in which process the burden for the base station to process all the data is significantly released. According to our experiments, the proposed data fusion algorithm greatly improves the robustness, accuracy, and real-time performance of the entire network. The simulation results demonstrate that the new algorithm is more efficient than the state-of-the-art in terms of both energy consumption and real-time performance.

Different kinds of power quality can be sensed in a smart substation. Power quality sensing and analysis are basic functions of a smart substation for situation awareness. The uncertainty principle, which states that the time uncertainty and frequency uncertainty cannot be minimized simultaneously, is a bottleneck problem that undermines the faithfulness of sensing and confines the accuracy of analysis. This paper studies the influence of the uncertainty principle on the power quality monitoring issue in detail and solves the problem by ideal atomic decomposition (IAD). The new method employs a pair of time and frequency bases where the power quality waveform is sensed. Then, both time uncertainty and frequency uncertainty can be minimized simultaneously. The sensing process is realized by orthogonal matching pursuit (OMP). By simulated and field power quality tests with comparisons of developed methods, the new method can give faithful sensing and accurate analysis for various power qualities, and is validated as an effective power quality monitoring method in smart substations.

A hybrid optical-acoustic underwater wireless sensor network (OA-UWSN) was proposed to solve the problem of high-speed transmission of real-time video and images in marine information detection. This paper proposes a novel energy-efficient contention-based media access control (MAC) protocol (OA-CMAC) for the OA-UWSN. Based on optical-acoustic fusion technology, our proposed OA-CMAC combines the postponed access mechanism in carrier sense multiple access with collision avoidance (CSMA/CA) and multiplexing-based spatial division multiple access (SDMA) technology to achieve high-speed and real-time data transmission. The protocol first performs an acoustic handshake to obtain the location information of a transceiver node, ensuring that the channel is idle. Otherwise, it performs postponed access and waits for the next time slot to contend for the channel again. Then, an optical handshake is performed to detect whether the channel condition satisfies the optical transmission, and beam alignment is performed at the same time. Finally, the nodes transmit data using optical communication. If the channel conditions do not meet the requirements for optical communication, a small amount of data with high priority is transmitted through acoustic communication. An evaluation of the proposed MAC protocol was performed with OMNeT++ simulations. The results showed that when the optical handshaking success ratio was greater than 50%, compared to the O-A handshake protocol in the literature, our protocol could result in doubled throughput. Due to the low energy consumption of optical communication, the node’s lifetime is 30% longer than that of pure acoustic communication, greatly reducing the network operation cost. Therefore, it is suitable for large-scale underwater sensor networks with high loads.

The Internet of Things (IoT) represents a bright prospect that a variety of common appliances can connect to one another, as well as with the rest of the Internet, to vastly improve our lives. Unique communication and security challenges have been brought out by the limited hardware, low-complexity, and severe energy constraints of IoT devices. In addition, a severe spectrum scarcity problem has also been stimulated by the use of a large number of IoT devices. In this paper, cognitive IoT (CIoT) is considered where an IoT network works as the secondary system using underlay spectrum sharing. A wireless energy harvesting (EH) node is used as a relay to improve the coverage of an IoT device. However, the relay could be a potential eavesdropper to intercept the IoT device’s messages. This paper considers the problem of secure communication between the IoT device (e.g., sensor) and a destination (e.g., controller) via the wireless EH untrusted relay. Since the destination can be equipped with adequate energy supply, secure schemes based on destination-aided jamming are proposed based on power splitting (PS) and time splitting (TS) policies, called intuitive secure schemes based on PS (Int-PS), precoded secure scheme based on PS (Pre-PS), intuitive secure scheme based on TS (Int-TS) and precoded secure scheme based on TS (Pre-TS), respectively. The secure performances of the proposed schemes are evaluated through the metric of probability of successfully secure transmission ( P S S T ), which represents the probability that the interference constraint of the primary user is satisfied and the secrecy rate is positive. P S S T is analyzed for the proposed secure schemes, and the closed form expressions of P S S T for Pre-PS and Pre-TS are derived and validated through simulation results. Numerical results show that the precoded secure schemes have better P S S T than the intuitive secure schemes under similar power consumption. When the secure schemes based on PS and TS polices have similar P S S T , the average transmit power consumption of the secure scheme based on TS is lower. The influences of power splitting and time slitting ratios are also discussed through simulations.

Environmental conditions and air quality monitoring have become crucial today due to the undeniable changes of the climate and accelerated urbanization. To efficiently monitor environmental parameters such as temperature, humidity, and the levels of pollutants, such as fine particulate matter (PM2.5) and volatile organic compounds (VOCs) in the air, and to collect data covering vast geographical areas, the development of cheap energy-autonomous sensors for large scale deployment and fine-grained data acquisition is required. Rapid advances in electronics and communication technologies along with the emergence of paradigms such as Cyber-Physical Systems (CPSs) and the Internet of Things (IoT) have led to the development of low-cost sensor devices that can operate unattended for long periods of time and communicate using wired or wireless connections through the Internet. We investigate the energy efficiency of an environmental monitoring system based on Bluetooth Low Energy (BLE) beacons that operate in the IoT environment. The beacons developed measure the temperature, the relative humidity, the light intensity, and the CO2 and VOC levels in the air. Based on our analysis we have developed efficient sleep scheduling algorithms that allow the sensor nodes developed to operate autonomously without requiring the replacement of the power supply. The experimental results show that low-power sensors communicating using BLE technology can operate autonomously (from the energy perspective) in applications that monitor the environment or the air quality in indoor or outdoor settings.