Direct Sensor Research Articles

NSUN2 is a glucose sensor suppressing cGAS/STING to maintain tumorigenesis and immunotherapy resistance

Glucose metabolism is known to orchestrate oncogenesis. Whether glucose serves as a signaling molecule directly regulating oncoprotein activity for tumorigenesis remains elusive. Here, we report that glucose is a cofactor binding to methyltransferase NSUN2 at amino acid 1-28 to promote NSUN2 oligomerization and activation. NSUN2 activation maintains global m5C RNA methylation, including TREX2, and stabilizes TREX2 to restrict cytosolic dsDNA accumulation and cGAS/STING activation for promoting tumorigenesis and anti-PD-L1 immunotherapy resistance. An NSUN2 mutant defective in glucose binding or disrupting glucose/NSUN2 interaction abolishes NSUN2 activity and TREX2 induction leading to cGAS/STING activation for oncogenic suppression. Strikingly, genetic deletion of the glucose/NSUN2/TREX2 axis suppresses tumorigenesis and overcomes anti-PD-L1 immunotherapy resistance in those cold tumors through cGAS/STING activation to facilitate apoptosis and CD8+ Tcell infiltration. Our study identifies NSUN2 as a direct glucose sensor whose activation by glucose drives tumorigenesis and immunotherapy resistance by maintaining TREX2 expression for cGAS/STING inactivation.

Ultra-low bending and temperature crosstalk directional torsion sensor by few-mode fiber based Mach-Zehnder interferometer

In this paper, a few-mode-fiber based in-line Mach-Zehnder interferometer is proposed and completed for directional torsion sensing by core-offset splicing technique. The optimal offset and fiber length for fabrication are gained and the comprehensive tests are conducted in terms of torsion, bending and temperature. The experimental results show that our sensor has obvious intensity variation with the applied shear strain. The torsion sensitivity reaches ∼1.1 dB/(rad/m) in the range from −13.08 rad/m to 13.08 rad/m, but with the wavelength shift of 0.102 nm/(rad/m). In addition, thanks for the tiny bending and temperature responses of −0.055 dB/m−1 and 0.014 dB/℃, the ultra-low cross-sensitivities of 4.7 × 10−2 (rad/m)/m−1 and 1.2 × 10−2 (rad/m)/℃ are gained simultaneously, which dramatically improves the practicality of fiber-optic torsion sensor.

Foot-core stability in non-hemorrhagic stroke: A case report

Introduction: Stroke is a neurological deficit or apoplexy that occurs suddenly due to vascular disturbances. Exercise delivery, the function of the foot-core system (foot-core stability), and the potentially beneficial effects of training have received increasing attention in the clinical and athletic fields because of the critical role of the intrinsic leg muscles as local stabilizers and direct sensors of foot deformation. 
 Case description: The research was a case study of an elderly patient aged 67 years diagnosed with hemiparesis non-hemorrhagic stroke. This case was taken at the Magetan Physiotherapy Clinic from December 7 to December 27, 2022. Physiotherapy problems in this case included pain, muscle weakness, limited joint motion, and impaired standing balance.
 Results: The results obtained after administering physiotherapy interventions and exercise therapy can improve the functional abilities of the body experiencing weakness, increase proprioception in patients and provide foot-core stability that can help with problems.
 Conclusions: Physiotherapy interventions with exercise therapy could increase proprioception and provide foot-core stability for stroke patient’s with severe disability.

Growth time dependency on the formation of gold nanobipyramids for efficient detection towards chlorpyrifos-based LSPR sensor

The structure of gold nanobipyramids (GNBPs) is very promising for specific Localized Surface Plasmon Resonance (LSPR) properties, and thus potentially to be used in various applications, especially in sensing. This work reported growth time dependency on the formation of GNBPs for application in LSPR sensors for chlorpyrifos detection. The study on the growth time found that GNBPs formation at the initial stage is dominated by elongation direction and its optimum growth time is 2 h. The optimum GNBPs with 106.32 ± 2.50 in length, 42.17 ± 0.82 in width and an aspect ratio of 2.55 ± 0.08 were implemented as a sensing material for direct and rapid localized surface plasmon resonance (LSPR) sensors to detect chlorpyrifos. The mechanism is based on the change in spectrum intensity and wavelength shift between GNBPs when tested in deionized water and chlorpyrifos solution. The change arises from molecular interaction between the targeted analyte and the sensing material and effectively changes the dielectric features of the medium. The presence of chlorpyrifos in the 1 – 100 mg/mL concentration range has been successfully detected. The proposed sensor offers better sensitivity as high as 1.22 times for transverse LSPR mode and 2.04 for longitudinal LSPR mode than the LSPR sensor based on gold nanosphericals. Moreover, the sensor stability and repeatability response showed a reliable performance for determining chlorpyrifos. Furthermore, this sensor also shows high selectivity towards chlorpyrifos compared to other organophosphate and organochlorine pesticides such as malathion, chlorothalonil, and triclopyr-butotyl.

Synergistic effect of AgBiS2 segregated NaV3O8 nanostructures in direct conversion X-ray sensors

Synergistic nanostructures possess enriched electrical and optical properties compared to conventional nanostructures. 1D-3D synergized nanostructure was implied to elucidate the performance of the X-ray sensor for the first time. Current research in X-ray detection only focuses on implementing high attenuating material based on oxides/ perovskites in developing X-ray sensors. Herein we approached tailoring the performance of an X-ray sensor with a synergistic nanostructure composed of sodium vanadium oxide (NaV3O8) with substantial grain boundary (GB) segregation of silver bismuth sulfide (AgBiS2), enabling the structured arrangement of nanostructured grains at the surface of NaV3O8 nanorods. Then, it was coated as a thick film on top of the interdigitated electrodes by glass rod sliding technique to readout the dose-dependent X-ray sensing capability. From the X-ray impinged photocurrent characteristics, the sensor with 75 wt% AgBiS2 segregated NaV3O8 exhibits superior sensitivity 82.6 nC mGy-1 cm-2 and low-noise equivalent dose rate 1.04 mGy Hz-0.5 for 2.49 mGy s-1 than other compositions. These experimental findings explore the implication of synergistic nanostructures as active layers in direct conversion X-rays sensors for improved charge extraction and enhancement in X-ray impinged photocurrent at low dose rates.

Prototype of simple mini-wave gauge using Microcontroller ESP32 on the laboratory scale

The limited availability of real-time wave data is needed for shipping safety and for planning coastal areas. This wave data monitoring system including the wave height and direction gained to be used as a consideration in making decisions related to shipping safety and coastal management. This study aims to design and create a prototype of a simple mini-wave gauge and test the performance of a mini-wave gauge on the laboratory scale. The sensors used in this mini-wave gauge were ADXL335 as a vibration sensor to detect wave movement and the GY-271 sensor module with the HMC5883L chip which was a direction sensor. In the experiment, the wave data generated by the sensors were then received by the microcontroller ESP32 as a data processing center. The wave data was then received by the computer using a Wi-Fi network as well as processed and displayed on an application that had been developed using Embarcadero Delphi. Experiments with this mini-wave gauge resulted in an average range of wave height ± 6cm with a wave trough of -4.5 cm and a wave crest of 2.2 cm. This mini-wave gauge was then validated with the ultrasonic sensor at laboratory scale and the root mean square error obtained was 1.34 cm. Generally, this device functions well and has high accuracy for recording wave data.

Intrinsic photomixing detector based on amorphous silicon for envelope mixing of optical signals

In this work, a promising device for direct optical envelope mixing, the Intrinsic Photomixing Detector (IPD) based on hydrogenated amorphous silicon, is reported. The IPD directly generates a photocurrent proportional to the nonlinear mixing of two optical modulation envelope functions. Experiments illustrate efficient mixing in the visible range at low light levels down to ϕ1 = 4.36 mW/cm2 (444 nm) and ϕ2 = 1.03 mW/cm2 (636 nm). Modulation frequencies exceeding the MHz range are demonstrated. Electro-optical simulations identify defect-induced electrical field screening within the absorber to cause the nonlinear mixing process, opening-up the opportunity to tailor devices toward application-specific requirements. The IPD functionality paves the way toward very simple but high-performance photodetectors for 3D imaging and ranging for direct optical convolutional sensors or for efficient optical logic gates. Using amorphous silicon provides a photodetector material base, which can easily be integrated on top of silicon electronics, enabling fill factors of up to 100%.

High Performance SiC Power Module Based on Repackaging of Discrete SiC Devices

Increased adoption of electric vehicles (EV), photovoltaic (PV), and battery energy storage systems (BESS) is driving the need for high-current SiC power modules. The state-of-the-art multichip module is substantially more expensive than the IGBT module. This paper proposes a cost-effective packaging methodology for high-power SiC intelligent power modules (IPMs) with discrete SiC devices. The proposed IPM integrates the gate drivers, decoupling capacitors, snubbers, discrete SiC devices, direct bond copper (DBC), base plate, temperature sensors, and overcurrent protection circuits, achieving very low loop inductance and thermal resistance, and more than 50% cost reduction. A 1200V/480A/2.15mΩ half-bridge IPM is presented for comparison with commercial counterparts. A 1200V/240A/4.3mΩ six-phase IPM is developed, and its performance is experimentally verified.

Design and mechanism of direct sulfur sensor for molten steel with non-existent sulfide ZrO2(MgO)-(CaO-Al2O3) solid electrolyte

This work proposed a strategy that the non-existent sulfide ZrO2(MgO)-(CaO-Al2O3) solid electrolyte was designed by vacuum ion infiltration method based on (Ca2+, Al3+)-infiltrated ZrO2(MgO) porous backbone, aiming to achieve direct sulfur determination in the molten steel. The experimental results showed that the prepared electrolyte took ZrO2(MgO) backbone as the support and the CaO-Al2O3 phase was homogenously filled on the ZrO2(MgO) backbone, forming well-connected network and rafts. Excellent wettability between the prepared electrolyte and the molten steel promoted the occurrence of the interfacial chemical reaction and achieved dynamic equilibrium to obtain stable electrochemical signal. Finally, the assembled sulfur sensor exhibited fine stability and reproducibility and could accurately determine the sulfur activity in molten steel. The design of the direct sulfur sensor utilizing the non-existent sulfide ZrO2(MgO)-(CaO-Al2O3) solid electrolyte overcame the adverse effects of auxiliary electrode. This achievement holds great significance in realizing the intelligent control of steel-making.

Energy-Efficient Long-Range Sectored Antenna for Directional Sensor Network Applications

The popularity of Directional Sensor Network (DSN) is increasing due to their improved transmission range, spectral reusability, interference mitigation, and energy efficiency. In this paper, the radio module of the DSN is implemented using an eight-sector antenna array. Two types of sectored antennas, namely the Rectangular Patch Sectored Antenna (RPSA) and the Triangular Patch Sectored Antenna (TPSA), are proposed to operate at frequency of 2.4 GHz ISM band. The RPSA has a half-power beamwidth (HPBW) of 45° and a peak gain of 5.2 dBi, while the TPSA has an HPBW of 48° and a peak gain of 4.16 dBi. The design and performance evaluation of RPSA and TPSA in terms of gain, reflection characteristics (|S11|), and HPBW are conducted using Ansys High Frequency Structure Simulator (HFSS) and Vector Network Analyzer (VNA). To demonstrate the concept, the fabricated sectored antennas are connected to MicaZ Wireless Sensor Network (WSN) nodes using an indigenously designed Single Pole 8 Throw (SP8T) Radio Frequency (RF) switchboard. The performance of the DSNs based on RPSA and TPSA is evaluated using the Cooja simulator and a testbed consisting of MicaZ nodes. The results show that RPSA outperforms TPSA and omnidirectional-based WSNs in terms of power consumption, received signal strength, and packet delivery ratio.

Discrete grey wolf optimization algorithm for solving k-coverage problem in directional sensor networks with network lifetime maximization viewpoint

Unlike the traditional wireless sensor networks (WSNs) which apply omni-directional sensors, the directional sensor networks (DSNs) utilize directional sensor nodes. Each directional sensor in DSN is a self-configured in one direction and one coverage range at a same time. In critical industries, the target coverage problem in which every target should be observed by k different sensors increases the reliability and leads the fault tolerant observation. In this case, the huge amount of energy is consumed in comparison with one coverage problem. Since the business continuity in such industries strongly depends on the network lifetime, the energy management and lifetime maximization of such resource-limited networks are still of the most important challenges. To address the issue, this paper formulates the k-coverage challenge to a discrete optimization problem with the network lifespan expansion viewpoint which is an NP-Hard problem. To solve this combinatorial problem, a discrete grey wolf optimization algorithm (D-GWA) is presented. This D-GWA proposes abundant novel exploration and exploitation procedures which permutes discrete search space efficiently. In this regard, the new fitness function is also defined which steers solutions in the directions to engage sensors uniformly in different rounds. It potentially improves network lifetime because of uniform battery usage. During the course of optimization, it utilizes temperature concept of simulated annealing algorithm to running away from local trap. To verify the proposed algorithm in solving k-coverage problem in DSNs, twelve scenarios are conducted. The simulation results experimentally prove that the totally dominance of the proposed algorithm against state-of-the-arts HTOH, LA-based, GWA, and hybrid PSO-GA approaches respectively are the amount of 31.37%, 20.97%, 14.12%, and 7.93% improvement in term of network lifetime expansion. Furthermore, the behavior of D-GWA shows the high potential of scalability in the larger search space.

Accurate On-Chip Thermal Peak Detection Based on Heuristic Algorithms and Embedded Temperature Sensors

The reliability and lifetime of systems-on-chip (SoCs) are being seriously threatened by thermal issues. In modern SoCs, dynamic thermal management (DTM) uses the thermal data captured by thermal sensors to constantly track the hot spots and thermal peak locations in real time. Estimating peak temperatures and the location of these peaks can play a crucial role for DTM systems, as temperature underestimation can cause SoCs to fail and have shortened lifetime. In this paper, a novel sensor allocation algorithm (called thermal gradient tracker, TGT), based on the recursive elimination of regions that likely do not contain any thermal peaks, is proposed for determining regions that potentially contain thermal peaks. Then, based on an empirical source temperature detection technique called GDS (gradient direction sensor), a hybrid algorithm for detecting the position and temperature of thermal peaks is also proposed to increase the accuracy of temperature sensing while trying to keep the number of thermal sensors to a minimum. The essential parameters, H and R, of the GDS technique are determined using an automated search algorithm based on simulated annealing. The proposed algorithm has been applied in a system-on-chip (SoC) in which four heat sources are present, and for temperatures ranging between 45 °C and 115 °C, in a chip area equal to 25 mm2. The simulation results show that our proposed sensor allocation scheme can detect on-chip peaks with a maximum error of 1.48 °C and an average maximum error of 0.49 °C by using 15 thermal sensors.

High throughput computations of the effective removal of liquified gases by novel perchlorate hybrid material

The utilization of hybrid materials in separation technology, sorbents, direct air capture (DAC) technology, sensors, adsorbents, and chiral material recognition has increased in the past decade due to the recognized impact of atmospheric pollutants and hazardous industrial gases on climate change. A novel hybrid material, perchlorate hybrid (PClH), has been proposed in this study for the effective sensory detection and trapping of atmospheric pollutants and industrial hazardous gases. The study evaluated the structural properties, adsorption mechanism, electronic sensitivity, and topological analysis of PClH using highly accurate computational methods (M062X-D3BJ/def2-ccpVTZ and DSDPBEP86/def2-ccpVTZ). The computational analysis demonstrated that PClH has considerable adsorption energies and favorable interaction with CO2, NO2, SO2, COCl2, and H2S. PClH is more suitable for detecting liquefiable gases such as COCl2, CO2, and SO2, and can be easily recovered under ambient conditions. Developing such materials can contribute to reducing hazardous gases and pollutants in the atmosphere, leading to a cleaner and safer environment.

Activation of the NLRP1 Inflammasome and Its Role in Transmissible Gastroenteritis Coronavirus Infection.

The inflammasome pathway is a critical early response mechanism of the host that detects pathogens, initiates the production of inflammatory cytokines, and recruits effector cells to the infection site. Nonetheless, the mechanism of inflammasome activation in coronavirus infection and its biological functions in host defense remain unclear. Transmissible gastroenteritis virus (TGEV), a member of the genus Alphacoronavirus, is a significant pathogen that mainly infects piglets and causes intestinal inflammation and inflammatory cell infiltration. Here, we investigated the mechanism of inflammasome activation in intestinal epithelial cells (IECs) infected with TGEV. We observed a substantial increase in interleukin 1β (IL-1β) and IL-18 levels in both IECs and TGEV-infected porcine intestinal tissues. Furthermore, TGEV infection resulted in increased activation of caspase-1 and the NLRP1 (NOD-like receptor [NLR]-containing pyrin domain [PYD]) inflammasome. Our findings revealed that TGEV infection impeded the interaction between porcine NLRP1 (pNLRP1) and porcine dipeptidyl peptidases 9 (pDPP9), yet it did not reduce the expression of pDPP9. Importantly, the ZU5 domain, not the function-to-find domain (FIIND) reported in human NLRP1, was identified as the minimal domain of pNLRP1 for pDPP9 binding. In addition, the robust type I IFN expression induced by TGEV infection also upregulated pNLRP1 expression and pNLRP1 itself acts as an interferon-stimulated gene to counteract TGEV infection. Our data demonstrate that pNLRP1 has antiviral capabilities against coronavirus infection, which highlights its potential as a novel therapeutic target for coronavirus antiviral therapy. IMPORTANCE Coronavirus primarily targets the epithelial cells of the respiratory and gastrointestinal tracts, leading to damage in both humans and animals. NLRP1 is a direct sensor for RNA virus infection which is highly expressed in epithelial barrier tissues. However, until recently, the precise molecular mechanisms underlying its activation in coronavirus infection and subsequent downstream events remained unclear. In this study, we demonstrate that the alphacoronavirus TGEV induces the production of IL-1β and IL-18 and upregulates the expression of pNLRP1. Furthermore, we found that pNLRP1 can serve as an interferon-stimulated gene (ISG) to inhibit the infection of enterovirus TGEV. Our research highlights the crucial role of NLRP1 as a regulator of innate immunity in TGEV infection and shows that it may serve as a potential therapeutic target for the treatment of coronavirus infection.

Performance of optical sensors for cloud measurements deployed by the ARM Aerial Facility during ACE-ENA.

During the Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA), a variety of in situ optical sensors using shadow imaging, scattering and holography were deployed by the Atmospheric Radiation Measurement (ARM) Aerial Facility to determine cloud properties. Taking advantage of the wide, overlapping range of instrumentation, we compare in situ cloud data from several different measurement methods for droplets up to 100µm. Data processing was tailored to the encountered conditions, leading to good agreement. Improvements include noise reduction for holography and better out-of-focus correction for shadow imaging. Comparison between direct liquid water content measurements and optical sensors showed better agreement at higher droplet number concentrations (>120/c m 3).

RADIANT: Radar-Image Association Network for 3D Object Detection

As a direct depth sensor, radar holds promise as a tool to improve monocular 3D object detection, which suffers from depth errors, due in part to the depth-scale ambiguity. On the other hand, leveraging radar depths is hampered by difficulties in precisely associating radar returns with 3D estimates from monocular methods, effectively erasing its benefits. This paper proposes a fusion network that addresses this radar-camera association challenge. We train our network to predict the 3D offsets between radar returns and object centers, enabling radar depths to enhance the accuracy of 3D monocular detection. By using parallel radar and camera backbones, our network fuses information at both the feature level and detection level, while at the same time leveraging a state-of-the-art monocular detection technique without retraining it. Experimental results show significant improvement in mean average precision and translation error on the nuScenes dataset over monocular counterparts. Our source code is available at https://github.com/longyunf/radiant.

Directional Bending Sensor and Multiparameter Sensor Based on D-Core Multimode Fiber

For the sensor with interference between fiber core mode and cladding mode, the directional bending sensor can be realized by processing asymmetric structure on the cladding or using eccentric fiber, but the temperature and strain crosstalk of the sensor with interference between fiber core mode and cladding mode is large. In this article, a kind of fiber intermode interference sensor with asymmetric structure is proposed, which can realize vector bending sensing and reduce temperature and strain crosstalk. The designed D-core multimode fiber has cladding and air on both sides of the fiber core. When 0° and 180° bending are applied to it, the interference spectrum moves in different directions, thus realizing the recognition of bending direction. For curvature detection, in the curvature range of 0–0.62 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{-{1}}$ </tex-math></inline-formula> , the sensitivity of 0° bending is 11.00 nm/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{-{1}}$ </tex-math></inline-formula> , and that of 180° bending is −11.40 nm/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{-{1}}$ </tex-math></inline-formula> . For liquid level detection, the sensing sensitivity is 234 pm/mm with the range of 0–4 cm, and the linearity is 0.993. In addition, the temperature sensitivity is as low as 10 pm/°C, and the strain sensitivity is as low as −1.28 pm/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \varepsilon $ </tex-math></inline-formula> . The proposed sensor realized the directional bending sensing and liquid level sensing of intermodal interference. Its low sensitivity to temperature and strain can be used to avoid the cross-sensitivity problems. In the field of water flow detection, the simultaneous detection of water level and water flow direction can be realized by the proposed sensor.

High stretch-ability and high linearity for elastic strain sensor constructed by multiple micro-cracks and complex wrinkles

Flexible sensors have been widely studied in smart wear, human health monitoring, biomedical and other applications because of its high bio-compatibility. However, it is still a challenge to achieve high sensitivity, wide sensing range, and high stability at the same time, which limits the application of flexible sensors in many directions. In order to solve the above problems, we construct a micro-crack composite wrinkle structure enhanced fiber strain sensor using SEBS rubber elastomer as the substrate and carbon nanotube film functional materials. The micro-cracks composite wrinkle structure for carbon nanotubes ([email protected]) ensures that the sensor has an ultra-wide sensing range of 0–600 %, and the impact factor reaches 1.0676 under the tension state. The wrinkle structure ensures the sensor can maintains good sensing performance after >10,000 tests, and has high linearity. The response time is 153 ms, the recovery time is 169 ms, and the hysteresis of the sensor can be ignored. At the same time, it also has the sensing performance of bending strain, which broadens the application of the sensor. Our strain sensor has promising applications in wearable electronic devices for detecting human motion.

An Efficient Anti-Intrusion Detection Algorithm Based on MEP for Directional Sensor Networks

In wireless sensor networks(WSNs), the minimum exposure path (MEP) represents the worst coverage of the whole network, so the exploration of MEP is of great significance to prevent unauthorized objects from passing through the monitoring area. The traditional research on finding MEP only focuses on how to avoid actual obstacles, without considering how to avoid high coverage areas. MEP-based coverage optimization studies the scheme of directly adding nodes, which does not conform to the characteristics that WSNs nodes are difficult to add or replace once deployed. In order to effectively improve the monitoring ability of coverage area, this paper proposes an efficient anti-intrusion detection algorithm based on MEP for directional sensor networks. Firstly, in the stage of finding MEP, the relationship between ant colony path planning algorithm and MEP is constructed by using dynamic threshold strategy. Secondly, in order to find MEP more accurately, the exposure factor is added to the state transition probability formula of the original ant colony algorithm. Finally, in the coverage enhancement stage, this paper innovatively proposes a barrier coverage enhancement strategy, which constructs a barrier by adjusting the position of redundant nodes. The simulation results show that the MEP found in this paper accurately avoids the high coverage area, and the improved ant colony algorithm is better than the MEP found by the original ant colony algorithm. Compared with the baseline algorithm, the barrier coverage enhancement strategy has strong applicability and low cost, which significantly improves the network monitoring ability.

A new hybrid algorithm integrating genetic algorithm with Tabu search to solve imbalanced k‐coverage problem in directional sensor networks

AbstractThe target coverage problem is considered as one of the major issues in directional sensor networks (DSNs), which is caused by the nature of these networks, including their limited angle of view. Due to the fault tolerance characteristic of some coverage applications, the target coverage is required to be performed using multiple sensors. This challenge is discussed in the literature under the title of k‐coverage problem. Under certain conditions, the number of sensors may suffer some changes due to various factors such as power depletion of the sensors, sensors' malfunctioning, and harshness of the environment. This can result in unavailability of adequate sensors for providing k‐coverage for all targets. The network suffering from such problem is referred to as under‐provisioned network. This paper was aimed at studying such networks by adopting the network conditions to the real environments. To solve this problem, the present paper proposes a hybrid model integrating the genetic algorithm (GA) and Tabu search (TS). The proposed algorithm generally aimed to identify a subset of sensors with appropriate working directions in order to provide a balanced coverage for all the targets available in the network. In order to evaluate the performance of the algorithm several experiments were conducted and the results have been compared with greedy and learning automat‐abased algorithms. . The results of the experiments show the superiority of the algorithm.