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Abstract Medical devices are commonly implanted underneath the skin, but how to real‐time noninvasively monitor their migration, integrity, and biodegradation in human body is still a formidable challenge. Here, the study demonstrates that benzyl violet 4B (BV‐4B), a main component in the FDA‐approved surgical suture, is found to produce fluorescence signal in the first near‐infrared window (NIR‐I, 700–900 nm) in polar solutions, whereas BV‐4B self‐assembles into highly crystalline aggregates upon a formation of ultrasmall nanodots and can emit strong fluorescence in the second near‐infrared window (NIR‐II, 1000–1700 nm) with a dramatic bathochromic shift in the absorption spectrum of ≈200 nm. Intriguingly, BV‐4B‐involved suture knots underneath the skin can be facilely monitored during the whole degradation process in vivo, and the rupture of the customized BV‐4B‐coated silicone catheter is noninvasively diagnosed by NIR‐II imaging. Furthermore, BV‐4B suspended in embolization glue achieves hybrid fluorescence‐guided surgery (hybrid FGS) for arteriovenous malformation. As a proof‐of‐concept study, the solid‐state BV‐4B is successfully used for NIR‐II imaging of surgical sutures in operations of patients. Overall, as a clinically translatable solid‐state dye, BV‐4B can be applied for in vivo monitoring the fate of medical devices by NIR‐II imaging.

The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state, neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically, we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects postspiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime yields realistic subthreshold variability (voltage variance ≃4–9 mV^{2}) only when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that, without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.

IntroductionIntegrin αvβ6, which is upregulated in malignancies and remains absent or weak in normal tissue, is a promising target in molecular imaging therapeutics. In vivo imaging of integrin αvβ6 could therefore be valuable for early tumor detection and intraoperative guidance.MethodsIn this study, integrin αvβ6-targeting probe G2-SFLAP3 was labeled with near-infrared (NIR) dye Cy5.5 or radioisotope 68Ga. The resulting probes were evaluated in integrin αvβ6-positive A549 and αvβ6-negative H1703 xenograft mice models.ResultsThe cellar uptake of G2-SFLAP3-Cy5.5 was consistent with the expression of integrin αvβ6. Both subcutaneous and brain metastatic A549 tumors could be clearly visualized by NIR fluorescent imaging of G2-SFLAP3-Cy5.5. A549 tumors demonstrated the highest G2-SFLAP3-Cy5.5 accumulation at 4h post-injection (p.i.) and remain detectable at 84h p.i. The fluorescent signal of G2-SFLAP3-Cy5.5 was significantly reduced in H1703 and A549-blocking groups. Consistently, small-animal PET imaging showed tumor-specific accumulation of 68Ga-DOTA-G2-SFLAP3.DiscussionG2-SFLAP3 represents a promising agent for noninvasive imaging of non-small cell lung cancer (NSCLC) and brain metastases.

To study the aerodynamic characteristics of the convex structure of a surface-monitoring device on a high-speed train and to evaluate its impact on the aerodynamic performance of the high-speed train, numerical simulation research was conducted on three different layouts of the monitoring device. The computational fluid dynamics (CFD) method was used for the simulation study, and the unsteady compressible NS equation was used as the control equation. Hexagonal grid technology was used to reduce the demand for the grid quantity. The rationality of the grid size and layout was verified through grid independence research. To increase the accuracy of the numerical simulation, the γ-Reθ transition model and improved delayed detached eddy simulation (IDDES) method were coupled for the simulation research. The aerodynamic characteristics of the different operation directions and configurations were compared and analyzed. The research results showed that the windward side of the single pantograph detection device experienced positive pressure, and the sideline and leeward sides experienced negative pressure. Increasing the fillet radius of the sideline could appropriately reduce the aerodynamic resistance. When the speed was about 110 m/s, the drag force coefficient of the detection device was 210~410 N, and the lateral force was small, which means that it had little impact on the overall aerodynamic force of the train. According to the results of the unsteady analysis of the layout with a large space, the resistance during forward travel was greater than that during negative travel. The streamlined upwind surface was conducive to reducing the scope of the leeward separation zone and the amplitude of the pressure fluctuation in the leeward zone, and it thus reduced the resistance. For the running trains, a vortex was formed on their leeward surface. The pressure monitoring results showed that the separated airflow had no dominant frequency or energy peak. The possibility of the following train top and other components experiencing resonance damage is low.

Abstract Background Cellular response to oxidative stress plays significant roles in hepatocellular carcinoma (HCC) development, yet the exact mechanism by which HCC cells respond to oxidative stress remains poorly understood. This study aimed to investigate the role and mechanism of super enhancer (SE)-controlled genes in oxidative stress response of HCC cells. Methods The GSE112221 dataset was used to identify SEs by HOMER. Functional enrichment of SE-controlled genes was performed by Metascape. Transcription factors were predicted using HOMER. Prognosis analysis was conducted using the Kaplan-Meier Plotter website. Expression correlation analysis was performed using the Tumor Immune Estimation Resource web server. NRF1 and SPIDR expression in HCC and normal liver tissues was analyzed based on the TCGA-LIHC dataset. ChIP-qPCR was used to detect acetylation of lysine 27 on histone 3 (H3K27ac) levels of SE regions of genes, and the binding of NRF1 to the SE of SPIDR. To mimic oxidative stress, HepG2 and Hep3B cells were stimulated with H2O2. The effects of NRF1 and SPIDR on the oxidative stress response of HCC cells were determined by the functional assays. Results A total of 318 HCC-specific SE-controlled genes were identified. The functions of these genes was significant association with oxidative stress response. SPIDR and RHOB were enriched in the “response to oxidative stress” term and were chosen for validation. SE regions of SPIDR and RHOB exhibited strong H3K27ac modification, which was significantly inhibited by JQ1. JQ1 treatment suppressed the expression of SPIDR and RHOB, and increased reactive oxygen species (ROS) levels in HCC cells. TEAD2, TEAD3, NRF1, HINFP and TCFL5 were identified as potential transcription factors for HCC-specific SE-controlled genes related to oxidative stress response. The five transcription factors were positively correlated with SPIDR expression, with the highest correlation coefficient for NRF1. NRF1 and SPIDR expression was up-regulated in HCC tissues and cells. NRF1 activated SPIDR transcription by binding to its SE. Silencing SPIDR or NRF1 significantly promoted ROS accumulation in HCC cells. Under oxidative stress, silencing SPIDR or NRF1 increased ROS, malondialdehyde (MDA) and γH2AX levels, and decreased superoxide dismutase (SOD) levels and cell proliferation of HCC cells. Furthermore, overexpression of SPIDR partially offset the effects of NRF1 silencing on ROS, MDA, SOD, γH2AX levels and cell proliferation of HCC cells. Conclusion NRF1 driven SPIDR transcription by occupying its SE, protecting HCC cells from oxidative stress-induced damage. NRF1 and SPIDR are promising biomarkers for targeting oxidative stress in the treatment of HCC.

With the advancement of intelligent perception in multirobot systems, cooperative localization in dynamic environments has become a critical component. However, existing multirobot cooperative localization systems still fall short in meeting high-precision localization requirements in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a factor-graph-based resilient cooperative localization (FG-RCL) algorithm for multirobot systems. This algorithm integrates measurements from visual sensors and Ultra-WideBand (UWB) to achieve accurate cooperative state estimation—overcoming the visibility issues of visual sensors within limited fields of view. We utilize the Joint Probabilistic Data Association (JPDA) algorithm to calculate the corresponding probabilities of multiple visual detection measurements between robots and assign them to their respective edges in the factor graph, thereby addressing the data association challenges in visual detection measurements. Finally, simulation results demonstrate that the proposed algorithm significantly reduces the influence of visual detection measurement interference on the performance of cooperative localization. Experimental results indicate that the proposed algorithm outperforms UWB-based and vision-based methods in terms of localization accuracy. The system is implemented using a factor-graph-based optimization approach, and it exhibits scalability and enables plug-and-play for sensors. Furthermore, it demonstrates resilience in abnormal situations.

Abstract Visual inertial odometry (VIO) would have an estimation drift problem in the process of long trajectory for indoor localization, especially in the absence of loop detection or in unknown complex scenes. To solve this problem, a low drift visual inertial odometry with ultra‐wideband (UWB) aided for indoor localization was proposed. Firstly, a single UWB anchor was dropped in an unknown position, and a cost function was formed by the position information output by VIO and the UWB ranging information to obtain the position of the anchor. Then, the single anchor position and the UWB ranging constraints were added to the tightly coupled visual inertial fusion algorithm framework, thereby improving the robustness of motion tracking and reducing the drift of the odometry. Finally, the effectiveness of the proposed method was verified in the actual indoor environment, and the experiment results demonstrated that, compared with state‐of‐the‐art localization methods, the positioning accuracy and robustness were improved significantly.

Abstract Background The aim of our study was to use the differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNAs (DEmiRNAs) to illustrate the underlying mechanism of hypoxia in liver cancer. Methods In this study, a cell model of hypoxia was established, and autophagy activity was measured with western blotting and transmission electron microscopy. The effect of hypoxia conditions on the invasion of liver cancer cell was evaluated. RNA sequencing was used to identify DEmRNAs and DEmiRNAs to explore the mechanism of hypoxia in liver cancer cells. Results We found that autophagy activation was triggered by hypoxia stress and hypoxia might promote liver cancer cell invasion. In addition, a total of 407 shared DEmRNAs and 57 shared DEmiRNAs were identified in both HCCLM3 hypoxia group and SMMC-7721 hypoxia group compared with control group. Furthermore, 278 DEmRNAs and 24 DEmiRNAs were identified as cancer hypoxia-specific DEmRNAs and DEmiRNAs. Finally, we obtained 19 DEmiRNAs with high degree based on the DEmiRNA-DEmRNA interaction network. Among them, hsa-miR-483-5p, hsa-miR-4739, hsa-miR-214-3p and hsa-miR-296-5p may be potential gene signatures related to liver cancer hypoxia. Conclusions Our study may help to understand the potential molecular mechanism of hypoxia in liver cancer.

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent liver disorder. With the improvement in human living standards, the prevalence of NAFLD has been increasing in recent years. Endocrine-disrupting chemicals (EDCs) are a class of exogenous chemicals that simulate the effects of hormones in the body. There has been growing evidence regarding the potential effects of EDCs on liver health, especially in NAFLD. This paper aims to summarize the major EDCs that contribute to the growing burden of NAFLD and to raise public awareness regarding the hazards posed by EDCs with the objective of reducing the incidence of NAFLD.

Abstract Traditional Global Navigation Satellite Systems (GNSS) experience their limitations in urban canyons. However, it is significant to improve the accuracy of positioning with the rapid development of smart cities. To solve this problem, a UGV‐UAV robust cooperative positioning algorithm with object detection is proposed, which utilises an unmanned aerial vehicle (UAV) to assist an unmanned ground vehicle (UGV) to achieve accurate positioning. When the UAV is in the sky with a good reception of satellite signals, the UGV uses the YOLOv3 object detection method to detect the UAV in images captured by camera, and acquires visual measurements including angles and ranges of the ground camera relative to the UAV through the proposed monocular vision measuring with object detection (ODMVM) model. Then, in order to solve the problem that visual measurement is disturbed by the real world, a robust Kalman filter is introduced that integrates measurements from available GNSS, inertial measurement unit (IMU), monocular camera, and the position broadcast of cooperative UAV to obtain more robust and accurate position estimation. Experimental and simulation results show that the proposed cooperation positioning algorithm can improve the positioning accuracy by 73.63% compared with the traditional cooperation positioning algorithm in urban canyons.

Studies are described which are intended to improve our understanding of the primary measurements made in non-invasive neural imaging. The blood oxygenation level-dependent signal used in functional magnetic resonance imaging (fMRI) reflects changes in deoxygenated haemoglobin. Tissue oxygen concentration, along with blood flow, changes during neural activation. Therefore, measurements of tissue oxygen together with the use of a neural sensor can provide direct estimates of neural-metabolic interactions. We have used this relationship in a series of studies in which a neural microelectrode is combined with an oxygen micro-sensor to make simultaneous co-localized measurements in the central visual pathway. Oxygen responses are typically biphasic with small initial dips followed by large secondary peaks during neural activation. By the use of established visual response characteristics, we have determined that the oxygen initial dip provides a better estimate of local neural function than the positive peak. This contrasts sharply with fMRI for which the initial dip is unreliable. To extend these studies, we have examined the relationship between the primary metabolic agents, glucose and lactate, and associated neural activity. For this work, we also use a Doppler technique to measure cerebral blood flow (CBF) together with neural activity. Results show consistent synchronously timed changes such that increases in neural activity are accompanied by decreases in glucose and simultaneous increases in lactate. Measurements of CBF showclear delays with respect to neural response. This is consistent with a slight delay in blood flow with respect to oxygen delivery during neural activation. [ABSTRACT FROM AUTHOR]

Aiming at the problem of not enough influencing factors were considered in traditional methods,a much more realistic stochastic model was built.In which error corrections were introduced into the geometry-based function model,an improved least squares variance component estimation (LS-VCE) algorithm with space-for-time was used to solve the model,two sets of real GPS data were collected to evaluate the performance of the model,and with which the carrier phase integer ambiguity was solved.The experimental results show that the proposed methods are superior to the traditional methods in terms of model accuracy,model solution complexity and integer ambiguity resolution.

Traditional Micro-Aerial Vehicles (MAVs) are usually equipped with a low-cost Inertial Measurement Unit (IMU) and monocular cameras, how to achieve high precision and high reliability navigation under the framework of low computational complexity is the main problem for MAVs. To this end, a novel semi-direct point-line visual inertial odometry (SDPL-VIO) has been proposed for MAVs. In the front-end, point and line features are introduced to enhance image constraints and increase environmental adaptability. At the same time, the semi-direct method combined with IMU pre-integration is used to complete motion estimation. This hybrid strategy combines the accuracy and loop closure detection performance of the feature-based method with the rapidity of the direct method, and tracks keyframes and non-keyframes, respectively. In the back-end, the sliding window mechanism is adopted to limit the computation, while the improved marginalization method is used to decompose the high-dimensional matrix corresponding to the cost function to reduce the computational complexity in the optimization process. The comparison results in the EuRoC datasets demonstrate that SDPL-VIO performs better than the other state-of-the-art visual inertial odometry (VIO) methods, especially in terms of accuracy and real-time performance.

Attempts have been made in various studies to identify and trace changes in function in the aging visual system. Some results are conflicting and we report here a unique approach in an attempt to resolve selected issues. We have estimated neurometabolic coupling in the central visual pathway in young and old cats. Our technique provides high resolution simultaneous measurements of neuronal activity and changes in concentration of tissue oxygen in the thalamus of young and old cats. Following visual stimulation, we find shorter latency and time to peak in tissue oxygen responses in old compared with young animals. Estimates of local activity induced initial negative oxygen response show substantial reductions in older animals. Measurements of neural activity in the form of multiple unit activity are similar in the two age groups. To investigate the mechanisms underlying the changes in tissue oxygen response in older animals, we measured vascular capillary density and found it to be substantially lower in old than that in young animals. Together, these findings suggest that the changes in metabolic responses with age may be largely accounted for by alterations in the cerebral microvasculature rather than by changes in neural activity. [ABSTRACT FROM AUTHOR]

The relationships between neural and metabolic processes in activated brain regions are central to the interpretation of noninvasive imaging. To examine this relationship, we have used a specialized sensor to measure simultaneously tissue oxygen changes and neural activity in colocalized regions of the cat's lateral geniculate nucleus (LGN). Previous work with this sensor has shown that a decrease or increase in tissue oxygen can be elicited by selective control of the location and extent of neural activation in the LGN. In the current study, to evaluate the temporal integration and homogeneity of neurometabolic coupling, we have determined the relationship between multiunit extracellular neural activity and tissue oxygen responses to visual stimuli of various durations and contrasts. Our results show that the negative but not the positive oxygen response changes in an approximately linear manner with stimulus duration. The relationship between the negative oxygen response and neural activity is relatively constant with stimulus duration. Moreover, both negative and positive oxygen responses saturate at high stimulus contrast levels. Coupling between neural activity and negative oxygen responses is well described by a power law function. These results help elucidate differences between the initial negative and subsequent positive metabolic responses and may be directly relevant to questions concerning brain mapping with functional magnetic resonance imaging. [ABSTRACT FROM AUTHOR]

The response of a neuron in striate cortex to an optimally oriented stimulus is suppressed by a superimposed orthogonal stimulus. The neural mechanism underlying this cross-orientation suppression (COS) may arise from intracortical or subcortical processes or from both. Recent studies of the temporal frequency and adaptation properties of COS suggest that depression at thalamo-cortical synapses may be the principal mechanism. To examine the possible role of synaptic depression in relation to COS, we measured the recovery time course of COS. We find it too rapid to be explained by synaptic depression. We also studied potential subcortical processes by measuring single cell contrast response functions for a population of LGN neurons. In general, contrast saturation is a consistent property of LGN neurons. Combined with rectifying nonlinearities in the LGN and spike threshold nonlinearities in visual cortex, contrast saturation in the LGN can account for most of the COS that is observed in the visual cortex. [ABSTRACT FROM AUTHOR]

Various properties of external scenes are integrated during the transmission of information along central visual pathways. One basic property concerns the sensitivity to direction of a moving stimulus. This direction selectivity (DS) is a fundamental response characteristic of neurons in the visual cortex. We have conducted a neurophysiological study of cells in the visual cortex to determine how DS is affected by changes in stimulus contrast. Previous work shows that a neuron integration time is increased at low contrasts, causing temporal changes of response properties. This leads to the prediction that DS should change with stimulus contrast. However, the change could be in a counterintuitive direction, i.e., DS could increase with reduced contrast. This possibility is of intrinsic interest but it is also of potential relevance to recent behavioral work in which human subjects exhibit increased DS as contrast is reduced. Our neurophysiological results are consistent with this finding, i.e., the degree of DS of cortical neurons is inversely related to stimulus contrast. Temporal phase differences of inputs to cortical cells may account for this result. [ABSTRACT FROM AUTHOR]

In the central visual pathway of binocular animals, the property of directional selectivity (DS) is first exhibited in striate cortex. In this study, we sought to determine the neural circuitry underlying the transformation from non-DS neurons to DS cortical cells. In a well established model, DS receptive fields (RFs) are derived from the sum of two non-DS inputs with 90° (quadrature) spatiotemporal phase differences. We explored possible input sources for this model, which include non-DS simple cells and lateral geniculate nucleus (LGN) neurons, by examination of spatiotemporal RFs of single cells and of pairs of cells. We find that distributions of non-DS simple RFs do not match the temporal predictions of the quadrature model because of a lack of long-latency responses. The long-latency inputs could potentially arise from lagged LGN afferents. However, analysis of cell pairs indicates that DS cells receive cortical input from non-DS simple cells for both short- and long-latency components, with temporal phase differences typically <90°. Furthermore, the distribution of minimum phase differences needed to generate DS cells overlaps that exhibited by non-DS simple cells. Considered together, these results are consistent with a linear model whereby DS simple cells are formed from simple-cell inputs, with temporal phase differences often less than quadrature. [ABSTRACT FROM AUTHOR]

In multipath-assisted simultaneous localization and mapping (SLAM), the geometric association of specular multipath components based on radio signals with environmental features is used to simultaneously localize user equipment and map the environment. We must contend with two notable model parameter uncertainties in multipath-assisted SLAM: process noise and clutter intensity. Knowledge of these two parameters is critically important to multipath-assisted SLAM, the uncertainty of which will seriously affect the SLAM accuracy. Conventional multipath-assisted SLAM algorithms generally regard these model parameters as fixed and known, which cannot meet the challenges presented in complicated environments. We address this challenge by improving the belief propagation (BP)-based SLAM algorithm and proposing a robust multipath-assisted SLAM algorithm that can accommodate model mismatch in process noise and clutter intensity. Specifically, we describe the evolution of the process noise variance and clutter intensity via Markov chain models and integrate them into the factor graph representing the Bayesian model of the multipath-assisted SLAM. Then, the BP message passing algorithm is leveraged to calculate the marginal posterior distributions of the user equipment, environmental features and unknown model parameters to achieve the goals of simultaneous localization and mapping, as well as adaptively learning the process noise variance and clutter intensity. Finally, the simulation results demonstrate that the proposed approach is robust against the uncertainty of the process noise and clutter intensity and shows excellent performances in challenging indoor environments.

The problem of fault propagation which exists in the deeply integrated GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) system makes it difficult to identify faults. Once a fault occurs, system performance will be degraded due to the inability to identify and isolate the fault accurately. After analyzing the causes of fault propagation and the difficulty of fault identification, maintaining correct navigation solution is found to be the key to prevent fault propagation from occurring. In order to solve the problem, a novel robust algorithm based on convolutional neural network (CNN) is proposed. The optimal expansion factor of the robust algorithm is obtained adaptively by utilizing CNN, thus the adverse effect of fault on navigation solution can be reduced as much as possible. At last, the fault identification ability is verified by two types of experiments: artificial fault injection and outdoor occlusion. Experiment results show that the proposed robust algorithm which can successfully suppress the fault propagation is an effective solution. The accuracy of fault identification is increased by more than 20% compared with that before improvement, and the robustness of deep GNSS/INS integration is also improved.

Considering the radio-based indoor positioning system pertaining to signal degradation due to the environmental factors, and rising popularity of IP (Internet Protocol) cameras in cities, a novel fusion of inertial measurement units (IMUs) with external IP cameras to determine the positions of moving users in indoor environments is presented. This approach uses a fine-tuned Faster R-CNN (Region Convolutional Neural Network) to detect users in images captured by cameras, and acquires visual measurements including ranges and angles of users with respect to the cameras based on the proposed monocular vision relatively measuring (MVRM) method. The results are determined by integrating the positions predicted by each user’s inertial measurement unit (IMU) and visual measurements using an EKF (Extended Kalman Filter). The results experimentally show that the ranging accuracy is affected by both the detected bounding box’s by Faster R-CNN height errors and diverse measuring distances, however, the heading accuracy is solely interfered with bounding box’s horizontal biases. The indoor obstacles including stationary obstacles and a pedestrian in our tests more significantly decrease the accuracy of ranging than that of heading, and the effect of a pedestrian on the heading errors is greater than stationary obstacles on that. We implemented a positioning test for a single user and an external camera in five indoor scenarios to evaluate the performance. The robust fused IMU/MVRM solution significantly decreases the positioning errors and shows better performance in dense multipath scenarios compared with the pure MVRM solution and ultra-wideband (UWB) solution.

Robustness and energy efficiency are critical for sensor information system, in which an abundance of wireless sensor nodes collects useful data from the deployed field. The chain-based protocols (like PEGASIS (Lindsey and Raghavendra, 2002)) are elegant solutions where sensor node has high energy efficiency. Unfortunately, if one node in the chain is failed due to some reasons such as energy exhaust, then the information cannot be forwarded to the sink. To improve system robustness and balance the energy consumption, this paper proposes a robust and energy-efficient data gathering (REEDG) approach, which is an improvement over the chain-based and grid-based network structures, in sensor information collecting system. In REEDG, data gathering is executed by a data transmitting chain which is composed by a series of virtual grids. Each grid communicates only with its neighbor grid and takes turns transmitting the information to the base station. Furthermore, an adaptive scheduling scheme is proposed to trade off energy consumption on each node and data forwarding delay. Experimental results show that, when compared with state-of-the-art approaches, REEDG achieves network lifetime extension of at least 13% as measured in terms of 20% dead nodes and improves the data transmission ratio at lowest 24% as 20% nodes fail.

Energy efficiency and data transmission delay are critical issues for mobile target tracking wireless sensor networks, in which abundant sensor nodes are deployed to collect the target information from the sensing field. At present, many existing works have been concentrated on extending network lifetime, while less emphasis was placed on both transmission delay reduction and the adaptive sleep of sensor nodes considering the application constraints. In this paper, we propose a smart power management and delay reduction approach for target tracking based on a grid network structure, where sensor nodes adjust their sleep intervals according to the distance between the node and the moving target. Sensor nodes can distributedly decide their sleeping time using the information from their neighbors. Furthermore, we propose a real-time chain to relay the sensed data for transmission delay reduction. The proposed approach allows sensor nodes that are far away from the target to sleep more and make the target information forward to the sink in time. Experimental results verify that, in contrast to adaptive coordinate and local power management protocols, the proposed approach achieves a significant energy saving while maintaining a short transmission delay.

Energy efficiency is very important for wireless sensor networks (WSNs) since sensor nodes have a limited energy supply from a battery. So far, a lot research has focused on this issue, while less emphasis has been placed on the adaptive sleep time for each node with a consideration for the application constraints. In this paper, we propose a hierarchically coordinated power management (HCPM) approach, which both addresses the energy conservation problem and reduces the packet forwarding delay for target tracking WSNs based on a virtual-grid-based network structure. We extend the network lifetime by adopting an adaptive sleep scheduling scheme that combines the local power management (PM) and the adaptive coordinate PM strategies to schedule the activities of the sensor nodes at the surveillance stage. Furthermore, we propose a hierarchical structure for the tracking stage. Experimental results show that the proposed approach has a greater capability of extending the network lifetime while maintaining a short transmission delay when compared with the protocol which does not consider the application constraints in target tracking sensor networks.