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In arid areas, biodegradable film has recently had the potential to replace polyethylene (PE) film to address plastic pollution. However, the positive effect of biodegradable film on soil moisture and salt control is weaker than that of PE film. Magnetized irrigation water technology is expected to compensate for this limitation. This study comprised a field experiment in 20212023 to study how two types of biodegradable film (M1, black; M2, transparent) and six magnetization intensity on irrigation water (T0, 0 Gs; T1, 1000 Gs; T2, 2000 Gs; T3, 3000 Gs; T4, 4000 Gs; T5, 5000 Gs) affect the degradation rate of biodegradable film, soil watersalt distribution, growth, and quality of processing tomatoes. The traditional PE film mulching and non-magnetized irrigation were used as the control group (MPET0). The results demonstrated that magnetized water irrigation slowed down the degradation rate of biodegradable film. In addition, the magnetized irrigation water can redistributed the soil water-salt patterns under the biodegradable film, improving the soil water content and salt leaching efficiency, with better results in M1 than in M2. Moreover, magnetized water irrigation promoted the growth of tomato leaf area under the biodegradable film, enhancing photochemical efficiency and potential activity of PSII, thereby improving fruit yield, quality, and water use efficiency of tomato. Principal component analysis showed that the comprehensive score of M1T3 treatment was the highest throughout the three years. Furthermore, M1T3 treatment has the highest processing tomato economic benefits during 2021–2023 (24634986 CNY hm2 more than MPET0). Therefore, the use of 3000 Gs magnetized irrigation water combined with black biodegradable film is conducive to improving soil water and salt conditions, reducing residual film pollution, and improving the yield and quality of processing tomatoes, thus ensuring the sustainable development of oasis agriculture.

The biodegradable film, as an ideal substitute for plastic film, has broad application prospects. However, it is uncertain in maize actual evapotranspiration (ETac) components, yield, and water use efficiency (WUE) of biodegradable and plastic films during the different rainfall seasons. Therefore, a 4-year field trial with three mulching patterns (FNM: flat planting with non-mulching, RPM: ridge-furrow with plastic film mulching, and RBM: ridge-furrow with biodegradable film mulching) and two N-fertilization levels (0 and 180 kg N ha–1) was conducted. The results showed that the machine-learning models could accurately estimate maize ETac and its partitioning, and the random forest and artificial neural networks models had the highest accuracy and the least input variables after optimization. Compared to FNM, RBM and RPM increased Et by 10.8 mm, 14.0 mm in the dry season, 9.1 mm, 11.2 mm in the normal season, and 4.0 mm, 7.5 mm in the wet season, respectively, but decreased Es by 75.8 mm, 82.7 mm in the dry season, 48.6 mm, 56.7 mm in the normal season, 67.1 mm, and 74.9 mm in the wet season, respectively. Therefore, RBM and RPM decreased ETac by 65.0 mm, 68.8 mm in the dry season, 39.5 mm, 45.6 mm in the normal season, and 53.1 mm, 67.5 mm in the wet season, respectively, compared to FNM. Nitrogen application had a similar effect on Es and Et but only increased ETac by 13.3 mm in the dry season, 2 mm in the normal season, and 4.3 mm in the wet season, respectively, compared to N0. Furthermore, RBM and RPM under different nitrogen-fertilizations increased maize yield by 4.0 %, 3.6 % in the dry season, 3.0 %, 3.3 % in the normal season, and 5.3 %, 5.9 % in the wet season, respectively, also increased maize WUE by 23.3 %, 24.1 % in the dry season, 12.9 %, 15.0 % in the normal season, and 21.1 %, 23.4 % in the wet season, respectively, compared to FNM. This study proved that RPM could be replaced by RBM under 180 kg N ha–1 in the different rainfall seasons in terms of reducing ETac, increasing maize yield, and improving WUE. The optimized machine learning models in this study also provided a low-cost method for computing regional maize ETac.

Ridge-furrow film mulching (RFFM) and nitrogen (N) fertilization have been widely used in semi-arid regions, it is particularly important to clarify the mechanism of crop physiological indicators in response to RFFM. A two-year (2018 and 2019) field experiment was conducted on summer maize under RFFM (including biodegradable film mulching (B) and plastic film mulching (P)) at N fertilizer application rate of 0 (N0), 90 (N1), 180 (N2) and 270 (N3) kg N ha–1. The aims of this research were to 1) explore the effects of different film mulching and N application rates under RFFM on photosynthesis, water utilization, and yield; 2) identify key factors and their contribution to yield. The results showed that RFFM and nitrogen application significantly improved net photosynthesis rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), intrinsic water use efficiency (WUEi) (grain-filling stage), the maximum quantum yield of PSII photochemistry (Fv/Fm), energy transformation potential activities of PSII (Fv/Fo), the effective quantum yield of PSII photochemistry (ΦPSII), photochemical quenching of variable chlorophyll fluorescence (qP), non-photochemical chlorophyll fluorescence quenching (NPQ), chlorophyll ab (Chl ab), evapotranspiration (ET) and yield, but decreased chlorophyll a/b (Chl a/b) and soil water storage (SWS). The maximum Pn, Tr, Gs, WUEi, Fv/Fm. Fv/Fo, ΦPSII, qP, and Chl ab (anthesis stage) all appeared in N2. At the same N application rate, the positive effects on photosynthetic and chlorophyll fluorescence (ChlF) of the plastic film were better than biodegradable film, especially in water use efficiency (WUE). Nitrogen deficiency had significantly negative effects on various parameters (Photosynthetic parameters, ChlF, Chl ab, grain yield), while nitrogen application (N > N1) had strong positive effects. Photosynthetic parameters, Fv/Fm, Fv/Fo, NPQ, Chl ab, ET, and WUE had strong positive effects on yield. The grain yield of the plastic film were slightly better (5.03% on average) than biodegradable film, but from the perspective of sustainable development, we recommend that biodegradable film and N2 should be used in the semi-arid regions of China.

Abstract Background Understanding the underlying genetic mechanisms that drive phenotypic variations is essential for enhancing the efficacy of crop improvement. Persian walnut (Juglans regia L.), which is grown extensively worldwide, is an important economic tree fruit due to its horticultural, medicinal, and material value. The quality of the walnut fruit is related to the selection of traits such as thinner shells, larger filling rates, and better taste, which is very important for breeding in China. The complex quantitative fruit-related traits are influenced by a variety of physiological and environmental factors, which can vary widely between walnut genotypes. Results For this study, a set of 101 Persian walnut accessions were re-sequenced, which generated a total of 906.2 Gb of Illumina sequence data with an average read depth of 13.8× for each accession. We performed the genome-wide association study (GWAS) using 10.9 Mb of high-quality single-nucleotide polymorphisms (SNPs) and 10 agronomic traits to explore the underlying genetic basis of the walnut fruit. Several candidate genes are proposed to be involved in walnut characteristics, including JrPXC1, JrWAKL8, JrGAMYB, and JrFRK1. Specifically, the JrPXC1 gene was confirmed to participate in the regulation of secondary wall cellulose thickening in the walnut shell. Conclusion In addition to providing considerable available genetic resources for walnut trees, this study revealed the underlying genetic basis involved in important walnut agronomic traits, particularly shell thickness, as well as providing clues for the improvement of genetic breeding and domestication in other perennial economic crops.

Orbit angular momentum (OAM) has been considered a new dimension for improving channel capacity in recent years. In this paper, a millimeter-wave broadband multi-mode waveguide traveling-wave antenna with OAM is proposed by innovatively utilizing the transmitted electromagnetic waves (EMWs) characteristic of substrate-integrated gap waveguides (SIGWs) to introduce phase delay, resulting in coupling to the radiate units with a phase jump. Nine “L”-shaped slot radiate elements are cut in a circular order at a certain angle on the SIGW to generate spin angular momentum (SAM) and OAM. To generate more OAM modes and match the antenna, four “Π”-shaped slot radiate units with a 90° relationship to each other are designed in this circular array. The simulation results show that the antenna operates at 28 GHz, with a −10 dB fractional bandwidth (FBW) = 35.7%, ranging from 25.50 to 35.85 GHz and a VSWR ≤ 1.5 dB from 28.60 to 32.0 GHz and 28.60 to 32.0 GHz. The antenna radiates a linear polarization (LP) mode with a gain of 9.3 dBi at 34.0~37.2 GHz, a l = 2 SAM–OAM (i.e., circular polarization OAM (CP–OAM)) mode with 8.04 dBi at 25.90~28.08 GHz, a l = 1 and l = 2 hybrid OAM mode with 5.7 dBi at 28.08~29.67 GHz, a SAM (i.e., left/right hand circular polarization (L/RHCP) mode with 4.6 dBi at 29.67~30.41 GHz, and a LP mode at 30.41~35.85 GHz. In addition, the waveguide transmits energy with a bandwidth ranging from 26.10 to 38.46 GHz. Within the in-band, only a quasi-TEM mode is transmitted with an energy transmission loss |S21| ≤ 2 dB.

Abstract Polysulfide rubber (PSR) is a versatile sealing material, but suffers from poor mechanical properties, which can be solved by increasing the crosslinking density and introducing fillers. In this study, functionalized molybdenum disulfide (MMoS2) by in situ incorporation with different thiol‐containing compounds is obtained. On the basis of the optimum content of 0.5 wt.%, the performance of MMoS2/PSR is systematically studied. The results show that the interfacial interaction between MMoS2 and the matrix is improved. In particular, PSR reinforced by MoS2 modified with polysulfide oligomer (MoS2‐LPO) behaves the most excellently. Its solvent resistance and crosslink density are also improved by 14.2% and 39.4%, respectively. The storage modulus of MoS2‐LPO/PSR is 394% higher than that of the unmodified one. This is attributed to the MoS2‐LPO playing the role of filler and large‐sized solid crosslinker at the same time. Through broadband dielectric spectroscopy (BDS), it is determined that MoS2‐LPO/PSR has the longest relaxation time due to the stronger interfacial interaction. In order to understand the effect of interfacial interaction between MoS2 and PSR on the properties of the composites more clearly, MoS2 modified with other functional groups are also discussed.

In this paper, a rapid and efficient method for the preparation of scopolamine hydrobromide with high purity was established, named as magnetic field-induced crystallization. Based on the difference in solubility between scopolamine and scopolamine hydrobromide, salifying crystallization was selected and then treated with the synergistic effect of magnetic field to achieve the goal of purifying scopolamine. The influence of crystallization solvents and magnetic field intensity on the crystallization process of scopolamine hydrobromide, as well as the impact of magnetic field on the crystal growth direction, were investigated. The results revealed that treatment under magnetic field led to a shortened induction time (25.64–75.46%), an increased purity of crystals (0.95–2.92%), and an enhanced recovery rate (4.51–10.78%). Furthermore, it was also discovered that magnetic field could destroy hydrogen bonds within the solution, and change the physical properties of the mother liquid, so as to promote the nucleation formation and crystal growth. These findings suggested that external magnetic field could be a promising method for scopolamine preparation.

Walnut (Juglans) species are economically important hardwood trees cultivated worldwide for both edible nuts and high-quality wood. Broad-scale assessments of species diversity, evolutionary history, and domestication are needed to improve walnut breeding. In this study, we sequenced 309 walnut accessions from around the world, including 55 Juglans relatives, 98 wild Persian walnuts (J. regia), 70 J. regia landraces, and 86 J. regia cultivars. The phylogenetic tree indicated that J. regia samples (section Dioscaryon) were monophyletic within Juglans. The core areas of genetic diversity of J. regia germplasm were southwestern China and southern Asia near the Qinghai-Tibet Plateau and the Himalayas, and the uplift of the Himalayas was speculated to be the main factor leading to the current population dynamics of Persian walnut. The pattern of genomic variation in terms of nucleotide diversity, linkage disequilibrium, single nucleotide polymorphisms, and insertions/deletions revealed the domestication and selection footprints in Persian walnut. Selective sweep analysis, GWAS, and expression analysis further identified two transcription factors, JrbHLH and JrMYB6, that influence the thickness of the nut diaphragm as loci under selection during domestication. Our results elucidate the domestication and selection footprints in Persian walnuts and provide a valuable resource for the genomics-assisted breeding of this important crop.

Abstract Background and objectives Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments. Methods The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA. Results A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018). Conclusions The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.

Indoor localization technology plays an important role in many indoor application scenarios. Existing WiFi-based indoor localization methods mainly obtain channel state information (CSI) through the personal computer, or obtain coarse-grained received signal strength (RSS) through the smartphone to finish the localization. Little work has been done on using smartphones to obtain fine-grained channel state information for localization. In this paper, we use the smartphone to collect fine-grained CSI that is more convenient and applicable, and propose a indoor fingerprinting localization. Compared with the CSI collected by the computer, the CSI signal collected by the smartphone fluctuates greatly. Hence, we corrects the CSI data through the signal processing technique and selects optimal subcarriers to obtain more stable and effective signals. In order to cope with the noisy WiFi environment, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is used to remove abnormal sample points to reduce environmental interference. Moreover, the support vector machine multi-classification method is used for training and classification to achieve localization. Finally, we use the Google Nexus 5 smartphone to conduct experiments in two typical indoor environments. The localization accuracy is 91% and 86%, respectively, and both average localization errors are less than 0.5m. Experimental results show that the proposed algorithm has higher localization accuracy compared with the typical algorithms.

Background: Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets. Methods: The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. Autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets (n = 780) to evaluate its prognostic and predictive performance. Findings: A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort (n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364]; p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707; 95% CI: 0.664 – 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610; 95% CI: 0.521 – 0.700), and performed equivalently to the APACHE II score (AUC: 0.681; 95% CI: 0.595 – 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82]; p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09]; p = 0.211). Interpretation: Our study identified two classes of sepsis that showed different mortality rates and responses to hydrocortisone therapy. Class 1 was characterized by immunosuppression with higher mortality rate than class 2. We further developed a 5-gene class model to predict class membership. Funding: The study was funded by the National Natural Science Foundation of China (Grant No. 81,901,929).

The purpose of this study was to investigate the effect of okra polysaccharides with similar molecular weight on intestinal ecology. After extraction, ethanol precipitation, deproteinization and purification, two polysaccharide fractions, named as water-extracted polysaccharides (WEP) and alkaline-extracted polysaccharides (AEP), were obtained. According to the results of high performance liquid chromatography, AEP contained mannose, rhamnose, galacturonic acid and galactose, while WEP was composed of galactose, rhamnose, fructose, arabinose, glucose, galacturonic acid and mannose. The compositions of intestinal microbiota were affected by the change of daily substrates. Meanwhile, the concentration of ammonia and gastrointestinal transit time decreased, the total short chain fatty acid (SCFA) levels, moisture of caecum content increased. These findings suggested that the effect of okra polysaccharides on intestinal microbiota was achieved by the output of detrimental compounds and the supply of nutrients. Besides, AEP supported a more pivotal role in manipulating gut microbiota than WEP.

Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.

Deep learning technology has improved the performance of vision-based action recognition algorithms, but such methods require a large number of labeled training datasets, resulting in weak universality. To address this issue, this paper proposes a novel self-deployable ubiquitous action recognition framework that enables a self-motivated user to bootstrap and deploy action recognition services, called FOLLOWER. Our main idea is to build a “fingerprint” library of actions based on a small number of user-defined sample action data. Then, we use the matching method to complete action recognition. The key step is how to construct a suitable “fingerprint”. Thus, a pose action normalized feature extraction method based on a three-dimensional pose sequence is designed. FOLLOWER is mainly composed of the guide process and follow the process. Guide process extracts pose action normalized feature and selects the inner class central feature to build a “fingerprint” library of actions. Follow process extracts the pose action normalized feature in the target video and uses the motion detection, action filtering, and adaptive weight offset template to identify the action in the video sequence. Finally, we collect an action video dataset with human pose annotation to research self-deployable action recognition and action recognition based on pose estimation. After experimenting on this dataset, the results show that FOLLOWER can effectively recognize the actions in the video sequence with recognition accuracy reaching 96.74%.

In this paper, we propose a novel gesture recognition system based on a smartphone. Due to the limitation of Channel State Information (CSI) extraction equipment, existing WiFi-based gesture recognition is limited to the microcomputer terminal equipped with Intel 5300 or Atheros 9580 network cards. Therefore, accurate gesture recognition can only be performed in an area relatively fixed to the transceiver link. The new gesture recognition system proposed by us breaks this limitation. First, we use nexmon firmware to obtain 256 CSI subcarriers from the bottom layer of the smartphone in IEEE 802.11ac mode on 80 MHz bandwidth to realize the gesture recognition system’s mobility. Second, we adopt the cross-correlation method to integrate the extracted CSI features in the time and frequency domain to reduce the influence of changes in the smartphone location. Third, we use a new improved DTW algorithm to classify and recognize gestures. We implemented vast experiments to verify the system’s recognition accuracy at different distances in different directions and environments. The results show that the system can effectively improve the recognition accuracy.

Wireless sensor networks (WSNs) have been used in many fields due to its wide applicability. In this kind of network, each node is independent of each other and has its own local clock and communicates wirelessly. Time synchronization plays a vital role in WSNs and it can ensure accuracy requirements for coordination and data reliability. However, two key challenges exist in large-scale WSNs that are severe resource constraints overhead and multihop time synchronization errors. To address these issues, this paper proposes a novel unmanned aerial vehicle (UAV)-assisted low-consumption time synchronization algorithm based on cross-technology communication (CTC) for a large-scale WSN. This algorithm uses a UAV to send time synchronization data packets for calibration. Moreover, to ensure coverage and a high success rate for UAV data transmission, we use CTC for time synchronization. Without any relays, a high-power time synchronization packet can be sent by a UAV to achieve the time synchronization of low-power sensors. This algorithm can achieve accurate time synchronization with almost zero energy consumption for the sensor nodes. Finally, we implemented our algorithm with 30 low-power RF-CC2430 ZigBee nodes and a Da Jiang Innovations (DJI) M100 UAV on a 1 km highway and an indoor site. The results show that time synchronization can be achieved accurately with almost zero energy consumption for the sensor nodes, and the time synchronization error is less than 30 μs in 99% of cases.

Clock synchronization is still a vital and challenging task for underground coal wireless internet of things (IoT) due to the uncertainty of underground environment and unreliability of communication links. Instead of considering on-demand driven clock synchronization, this paper proposes a novel Adaptive Robust Synchronization (ARS) scheme with packets loss for mine wireless environment. A clock synchronization framework that is based on Kalman filtering is first proposed, which can adaptively adjust the sampling period of each clock and reduce the communication overhead in single-hop networks. The proposed scheme also solves the problem of outliers in data packets with time-stamps. In addition, this paper extends the ARS algorithm to multi-hop networks. Additionally, the upper and lower bounds of error covariance expectation are analyzed in the case of incomplete measurement. Extensive simulations are conducted in order to evaluate the performance. In the simulation environment, the clock accuracy of ARS algorithm is improved by 7.85% when compared with previous studies for single-hop networks. For multi-hop networks, the proposed scheme improves the accuracy by 12.56%. The results show that the proposed algorithm has high scalability, robustness, and accuracy, and can quickly adapt to different clock accuracy requirements.

The mine cage has an important role in the production of coal mines. It has many safety problems in the transportation of people, such as overloading of personnel and illegal outreach of human limbs. However, the harsh mine environment makes it very difficult to monitor personnel overload and limb extension. To solve these two problems, we propose a novel safety monitoring algorithm of the mine cage based on visible light. With visible light technology, our algorithm cleverly utilizes the existing underground lighting equipment (i.e., miner’s headlamp and the miner’s lamp deployed on the mine cage) as the transmitter to broadcast the light beacons representing unique identity information through visible light frequency modulation. Next, cheap photodiodes deployed in the mine cage are used as the receiver to perceive the modulated optical signals. Then we use the frequency matching method for personnel counting and the frequency power comparison method for illegal limb extension monitoring. Moreover, a novel method of monitoring the delineated safe area of the mine cage is also proposed to ensure that all the miners are in the delineated safe area. Finally, we conducted extensive experiments with a simulated mine cage model. Results show that our algorithm has superior performance. With the photodiode SD5421-002, the accuracy of personnel overload judgment and safe area monitoring of our algorithm can reach 99%, and the accuracy of limb extension monitoring is more than 96%.

Nowadays, wireless sensor networks are often deployed on the sea surface for ocean scientific monitoring. One of the important challenges is to localize the nodes’ positions. Existing localization schemes can be roughly divided into two types: range-based and range-free. The range-based localization approaches heavily depend on extra hardware capabilities, while range-free ones often suffer from poor accuracy and low scalability, far from the practical ocean monitoring applications. In response to the above limitations, this paper proposes a novel signal similarity-based localization (SSL) technology, which localizes the nodes’ positions by fully utilizing the similarity of received signal strength and the open-air characteristics of the sea surface. In the localization process, we first estimate the relative distance between neighboring nodes through comparing the similarity of received signal strength and then calculate the relative distance for non-neighboring nodes with the shortest path algorithm. After that, the nodes’ relative relation map of the whole network can be obtained. Given at least three anchors, the physical locations of nodes can be finally determined based on the multi-dimensional scaling (MDS) technology. The design is evaluated by two types of ocean experiments: a zonal network and a non-regular network using 28 nodes. Results show that the proposed design improves the localization accuracy compared to typical connectivity-based approaches and also confirm its effectiveness for large-scale ocean sensor networks.

Node localization plays a key and basic role for wireless sensor network. Existing range-free localization approaches suffer from precision limit of positioning, while range-based solutions may obtain good accuracy but pay high costs for ranging hardware. Instead of directly mapping received signal strength values into physical distances, a novel localization scheme with signal-based signature distance estimation is proposed for wireless sensor network. More specifically, we first quantify the near-far relationship between neighbor nodes through received signal strength comparison, and then the distance between non-neighbor nodes is calculated by an improved shortest path algorithm. Based on the obtained node distance, a relative map can be constructed by multidimensional scaling technology. Eventually, the locations of nodes can be acquired by virtue of Procrustes analysis method. For purpose of verifying the effectiveness of our design, several simulations are made in a sensor network with 200 randomly deployed nodes, and two experiments are implemented in real outdoor environments: a linear network and a two-dimensional grid network with 25 nodes. It is remarkable from results that the proposed scheme performs better in positioning and reduces localization errors by as much as 37% compared with state-of-the-art methods.

Due to the combination of constrained power, low duty cycle, and high mobility, neighbor discovery is one of the most challenging problems in wireless sensor networks. Existing discovery designs can be divided into two types: pairwise-based and group-based. The former schemes suffer from high discovery delay, while the latter ones accelerate the discovery process but incur too much energy overhead, far from practical. In this article, we propose a novel efficient group-based discovery method based on relative distance, which makes a delicate trade-off between discovery delay and energy consumption. Instead of directly referring to the wake-up schedules of a whole group of nodes, efficient group-based discovery selectively recommends nodes that are most likely to be neighbors, in which the probability is calculated based on the nodes’ relative distance. Moreover, the sequence of received signal strengths are employed to estimate the relative distance for avoiding the effect of the node distribution. Extensive simulations are conducted to verify the effectiveness of the design. The results indicate that efficient group-based discovery statistically achieves a good trade-off between energy cost and discovery latency. Efficient group-based discovery also shows one order of magnitude reduction in discovery delay with a maximum of 6.5% increase in energy consumption compared with typical discovery methods.

Currently, many services benefit greatly from the availability of accurate tracking. Tracking in wireless sensor networks remains a challenging issue. Most tracking methods often require a large number of anchors and do not take advantage of potential localization information, leading to poor accuracy. To solve this problem, this paper proposes a Novel Monte Carlo-based Tracking (NMCT) algorithm with area-based and neighbor-based filtering, which fully extracts the proximity information embedded in the neighborhood sensing. We describe the entire system design in detail and conduct extensive simulations. The results show that the proposed algorithm outperforms the typical schemes under a wide range of conditions.

Node localization is an essential issue in wireless sensor networks (WSNs). Many range-free localization methods have been proposed to satisfy the requirement of low-system cost. However, some range-free methods only depend on network connectivity, and others only utilize the proximity information attached in neighborhood ordering. To employ the strength of the above two aspects, this paper introduces a new metric system called Combined and Weighted Diffusion Distance (CWDD). CWDD is designed to obtain the relative distance among nodes based on both graph diffusion property and neighbor information. We implement our design by embedding CWDD into two well-known localization algorithms and evaluate it by extensive simulations. Results show that our design improves the localization performance in large scale and non-uniform sensor networks, which reduces positioning errors by as much as 26%.

Localization is crucial for the monitoring applications of cities, such as road monitoring, environment surveillance, vehicle tracking, etc. In urban road sensor networks, sensors are often sparely deployed due to the hardware cost. Under this sparse deployment, sensors cannot communicate with each other via ranging hardware or one-hop connectivity, rendering the existing localization solutions ineffective. To address this issue, this paper proposes a novel Traffic Lights Schedule-based localization algorithm (TLS), which is built on the fact that vehicles move through the intersection with a known traffic light schedule. We can first obtain the law by binary vehicle detection time stamps and describe the law as a matrix, called a detection matrix. At the same time, we can also use the known traffic light information to construct the matrices, which can be formed as a collection called a known matrix collection. The detection matrix is then matched in the known matrix collection for identifying where sensors are located on urban roads. We evaluate our algorithm by extensive simulation. The results show that the localization accuracy of intersection sensors can reach more than 90%. In addition, we compare it with a state-of-the-art algorithm and prove that it has a wider operational region.

Ultra-dense wireless cellular networks have been envisioned as a promising technique for handling the explosive increase of wireless traffic volume. With the extensive deployment of small cells in wireless cellular networks, the network spectral efficiency (SE) is improved with the use of limited frequency. However, the mutual inter-tier and intra-tier interference between or among small cells and macro cells becomes serious. On the other hand, more chances for potential cooperation among different cells are introduced. Energy efficiency (EE) has become one of the most important problems for future wireless networks. This paper proposes a cooperative bargaining game-based method for comprehensive EE management in an ultra-dense wireless cellular network, which highlights the complicated interference influence on energy-saving challenges and the power-coordination process among small cells and macro cells. Especially, a unified EE utility with the consideration of the interference mitigation is proposed to jointly address the SE, the deployment efficiency (DE), and the EE. In particular, closed-form power-coordination solutions for the optimal EE are derived to show the convergence property of the algorithm. Moreover, a simplified algorithm is presented to reduce the complexity of the signaling overhead, which is significant for ultra-dense small cells. Finally, numerical simulations are provided to illustrate the efficiency of the proposed cooperative bargaining game-based and simplified schemes.

This paper studies the remote Kalman filtering problem for a distributed system setting with multiple sensors that are located at different physical locations. Each sensor encapsulates its own measurement data into one single packet and transmits the packet to the remote filter via a lossy distinct channel. For each communication channel, a time-homogeneous Markov chain is used to model the normal operating condition of packet delivery and losses. Based on the Markov model, a necessary and sufficient condition is obtained, which can guarantee the stability of the mean estimation error covariance. Especially, the stability condition is explicitly expressed as a simple inequality whose parameters are the spectral radius of the system state matrix and transition probabilities of the Markov chains. In contrast to the existing related results, our method imposes less restrictive conditions on systems. Finally, the results are illustrated by simulation examples.