Forward Algorithm Research Articles

Single segment approach and landing guidance and control for an unpowered reusable launch vehicle

Various guidance schemes have been implemented in the past to guide an unpowered reusable launch vehicle safely to the runway satisfying the constraints on touchdown. All these schemes rely on a minimum of two or more segments to completely determine the landing reference geometry and generally involves several iterations with numeric integration for multiple segments to achieve continuity between the segments. In contrast, this paper proposes a path planning algorithm during approach and landing making use of a single segment reference path connecting the approach and landing interface to the desired touchdown point on the runway. The approach used in this paper is a novel attempt to use a single segment altitude profile for approach and landing based on a quintic polynomial that relates altitude with downrange. The main advantage is that a solution satisfying the initial as well as final boundary constraints on the vehicle states are attained at a lesser computational complexity. The nominal states obtained are compared with an existing three segment scheme designed based on an on-board trajectory planning algorithm. The three-segment scheme attains the desired touchdown specifications making use of steep glide slope computation and dynamic pressure matching using backward trajectory propagation while the proposed scheme achieves the objective using a forward propagation algorithm for the single segment. Closed loop control is implemented using both PID as well as Fuzzy Logic Control and the numerical results are compared. Simulations are carried out with wind, energy as well as drag variations to establish the robustness of the proposed single segment algorithm.

Detection of Bundle Branch Block using Higher Order Statistics and Temporal Features

Bundle Branch Block (BBB) beats are the most common Electrocardiogram (ECG) arrhythmias and can be indicators of significant heart disease. This study aimed to provide an effective machine-learning method for the detection of BBB beats. To this purpose, statistical and temporal features were calculated and the more valuable ones searched using feature selection algorithms. Forward search, backward elimination and genetic algorithms were used for feature selection. Three different classifiers, K-Nearest Neighbors (KNN), neural networks, and support vector machines, were used comparatively in this study. Accuracy, specificity, and sensitivity performance metrics were calculated in order to compare the results. Normal sinus rhythm (N), Right Bundle Branch Block (RBBB), and Left Bundle Branch Block (LBBB) ECG beat types were used in the study. All beats containing these three beat types in the MIT-BIH arrhythmia database were used in the experiments. All of the feature sets were obtained at a promising classification accuracy for BBB classification. The KNN classifier using backward elimination-selected features achieved the highest classification accuracy results in the study with 99.82%. The results showed the proposed approach to be successful in the detection of BBB beats.

Forward dynamics analysis of origami-folded deployable spacecraft structures

A fast dynamics model that captures the deployment dynamics of self-actuated, origami-inspired, folded planar spacecraft structures is desired for design and verification applications. In this paper, a general simulation framework for numerically generating the equations of motion of any structure that complies with a set of pattern assumptions is presented. The framework is built through application of the articulated body forward dynamics algorithm and the tree-augmented approach for closed-chain forward dynamics. These are multi-body dynamics approaches developed in the literature for complex robotic manipulator systems. Unique adaptations are required to address the highly constrained nature of a folding structure. This solution is desirable due to the computational efficiency of the base algorithms and the ability to analyze multiple systems without reformulation of the core dynamics algorithm. Baumgarte stabilization techniques are applied to address constraint violations, but are found to be a continuing challenge for folding structures with several closed-chains.

Risk and resilience-based optimal post-disruption restoration for critical infrastructures under uncertainty

Post-disruption restoration of critical infrastructures (CIs) often faces uncertainties associated with the required repair tasks and the related transportation network. However, such challenges are often overlooked in most studies on the improvement of CI resilience. In this paper, two-stage risk-averse and risk-neutral stochastic optimization models are proposed to schedule repair activities for a disrupted CI network with the objective of maximizing system resilience. Both models are developed based on a scenario-based optimization technique that accounts for the uncertainties of the repair time and the travel time spent on the underlying transportation network. Given the large number of uncertainty realizations associated with post-disruption restoration tasks, an improved fast forward algorithm based on a wait-and-see solution methodology is provided to reduce the number of chosen scenarios, which results in the desired probabilistic performance metrics. To assess the risks associated with post-disruption scheduling plans, a conditional value-at-risk (CVaR) metric is incorporated into the optimization models through a scenario reduction algorithm. The proposed restoration framework is applied to the French RTE electric power network with a DC power flow procedure, and the results demonstrate the added value of using the stochastic optimization models incorporating the travel times related to repair activities. It is essential that risk-averse decision-making under uncertainty largely impacts the optimum schedule and the expected resilience, especially in the worst-case scenarios.

Feature selection using self-information and entropy-based uncertainty measure for fuzzy neighborhood rough set

Feature selection based on the fuzzy neighborhood rough set model (FNRS) is highly popular in data mining. However, the dependent function of FNRS only considers the information present in the lower approximation of the decision while ignoring the information present in the upper approximation of the decision. This construction method may lead to the loss of some information. To solve this problem, this paper proposes a fuzzy neighborhood joint entropy model based on fuzzy neighborhood self-information measure (FNSIJE) and applies it to feature selection. First, to construct four uncertain fuzzy neighborhood self-information measures of decision variables, the concept of self-information is introduced into the upper and lower approximations of FNRS from the algebra view. The relationships between these measures and their properties are discussed in detail. It is found that the fourth measure, named tolerance fuzzy neighborhood self-information, has better classification performance. Second, an uncertainty measure based on the fuzzy neighborhood joint entropy has been proposed from the information view. Inspired by both algebra and information views, the FNSIJE is proposed. Third, the K–S test is used to delete features with weak distinguishing performance, which reduces the dimensionality of high-dimensional gene datasets, thereby reducing the complexity of high-dimensional gene datasets, and then, a forward feature selection algorithm is provided. Experimental results show that compared with related methods, the presented model can select less important features and have a higher classification accuracy.

Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation

The more easily available system performance data and advances in data analytics have provided us with opportunities to optimize maintenance programs for engineered systems, for example nuclear power plants. One key task in maintenance optimization is to obtain an accurate model for system degradation. In this research, we propose a Bayesian method to address this problem. Noting that systems usually exhibit multiple states and that the actual state of a system usually is not directly observable, in the method we first model the system degradation process and the observation process based on a hidden Markov model. Then we develop a sequential Bayesian inference algorithm based on importance sampling and the forward algorithm to infer the posterior distributions of the transition rates in the hidden Markov model based on available observations. The proposed Bayesian method allows us to take advantage of evidence from multiple sources, and also allows us to perform Bayesian inference sequentially, without the need to use the entire history of observations every time new observations are collected. We demonstrate the proposed method using both synthetic data for a nuclear power plant feedwater pump and realistic data for a nuclear power plant chemistry analytical device.

Efficient k-dominant skyline query over incomplete data using MapReduce

Skyline queries are extensively incorporated in various real-life applications by filtering uninteresting data objects. Sometimes, a skyline query may return so many results because it cannot control the retrieval conditions especially for high-dimensional datasets. As an extension of skyline query, the k-dominant skyline query reduces the control of the dimension by controlling the value of the parameter k to achieve the purpose of reducing the retrieval objects. In addition, with the continuous promotion of Bigdata applications, the data we acquired may not have the entire content that people wanted for some practically reasons of delivery failure, no power of battery, accidental loss, so that the data might be incomplete with missing values in some attributes. Obviously, the k-dominant skyline query algorithms of incomplete data depend on the user definition in some degree and the results cannot be shared. Meanwhile, the existing algorithms are unsuitable for directly used to the incomplete big data. Based on the above situations, this paper mainly studies k-dominant skyline query problem over incomplete dataset and combines this problem with the distributed structure like MapReduce environment. First, we propose an index structure over incomplete data, named incomplete data index based on dominate hierarchical tree (ID-DHT). Applying the bucket strategy, the incomplete data is divided into different buckets according to the dimensions of missing attributes. Second, we also put forward query algorithm for incomplete data in MapReduce environment, named MapReduce incomplete data based on dominant hierarchical tree algorithm (MR-ID-DHTA). The data in the bucket is allocated to the subspace according to the dominant condition by Map function. Reduce function controls the data according to the key value and returns the k-dominant skyline query result. The effective experiments demonstrate the validity and usability of our index structure and the algorithm.

Centennial-scale variability of terrestrial near-surface wind speed over China from reanalysis

AbstractInvestigations of variations and causes of near-surface wind speed (NWS) further understanding of the atmospheric changes and improve the ability of climate analysis and projections. NWS varies on multiple temporal scales; however, the centennial-scale variability in NWS and associated causes over China remains unknown. In this study, we employ the European Centre for Medium-Range Weather Forecasts (ECMWF) twentieth century reanalysis (ERA-20C) to study the centennial-scale changes in NWS from 1900–2010. Meanwhile, a forward stepwise regression algorithm is used to reveal the relationships between NWS and large-scale ocean-atmosphere circulations. The results show three unique periods in annual mean NWS over China from 1900–2010. The annual mean NWS displayed a decreasing trend of -0.87% decade-1 and -11.75% decade-1 from 1900–1925 and 1957–2010, respectively, which were caused by the decreases in the days with strong winds, with trends of -6.64 and -4.66 days decade-1, respectively. The annual mean NWS showed an upward trend of 55.47% decade-1 from 1926–1956, which was caused by increases in the days with moderate (0.43 days decade-1) and strong winds (23.55 days decade-1). The reconstructed wind speeds based on forward stepwise regression algorithm matched well with the original wind speeds; therefore, the decadal changes in NWS over China at centennial-scale were mainly induced by large-scale ocean-atmosphere circulations, with the total explanation power of 66%. The strongest explanation power was found in winter (74%), and the weakest explanation power was found in summer (46%).

Ultrasound-guided percutaneous needle tenotomy using Tenex system for refractory lateral epicondylitis; short and long-term effectiveness and contributing factors.

Ultrasound-guided percutaneous needle tenotomy (USPNT) has been proposed as an alternative treatment to surgical intervention for lateral epicondylitis (LE). The Tenex system (Tenex Health Inc., Lake Forest, CA, USA) for USPNT is an ultrasonic device involving a needle which oscillates at high frequency to debride and aspirate diseased tendon under ultrasound image guidance. This investigation evaluates the efficacy of USPNT using the Tenex system for LE refractory to conservative management. We also seek to evaluate patient-specific factors which may correlate with treatment response. PRTEE (Patient-Rated Tennis Elbow Evaluation) and DASH (Disabilities of the Arm, Shoulder, and Hand) questionnaires were completed before performing the Tenex USPNT for all consecutive patients over a course of 38months (Feb 2015-Mar 2018). Patients were contacted for follow-up evaluations. Paired t test was used to evaluate significant changes in treatment response (p < 0.05). The univariate Tobit regression model was applied followed by multivariate Tobit model with forward selection algorithm. All models were adjusted by preoperative score and follow-up duration. A total of 37 patients (average age 51± 9years, M/F:15/22) with refractory LE were included (mean follow-up, 531days; range, 65-1148days). Tenex USPNT significantly improved PRTEE and DASH scores (p < 0.001). In some patients, this decrease persisted for up to 3years after intervention. A significant association between treatment response and post-procedure physical therapy (PT) was present. Post-procedure PT contributes to 60, 68, 59, and 50% of reduction in PRTEE pain, function, total scores, and DASH score, respectively (p < 0.001). No tendon ruptures, post-procedural infections, or other complications were noted. USPNT with Tenex significantly improves symptoms and function in individuals with LE even with long-term follow-up for 3years. Post-procedure PT is associated with improved treatment response and should be considered after USPNT.

Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images.

Contrast-enhanced spectral mammography (CESM) is an advanced instrument for breast care that is still operator dependent. The aim of this paper is the proposal of an automated system able to discriminate benign and malignant breast lesions based on radiomic analysis. We selected a set of 58 regions of interest (ROIs) extracted from 53 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) for the breast cancer screening phase between March 2017 and June 2018. We extracted 464 features of different kinds, such as points and corners of interest, textural and statistical features from both the original ROIs and the ones obtained by a Haar decomposition and a gradient image implementation. The features data had a large dimension that can affect the process and accuracy of cancer classification. Therefore, a classification scheme for dimension reduction was needed. Specifically, a principal component analysis (PCA) dimension reduction technique that includes the calculation of variance proportion for eigenvector selection was used. For the classification method, we trained three different classifiers, that is a random forest, a naïve Bayes and a logistic regression, on each sub-set of principal components (PC) selected by a sequential forward algorithm. Moreover, we focused on the starting features that contributed most to the calculation of the related PCs, which returned the best classification models. The method obtained with the aid of the random forest classifier resulted in the best prediction of benign/malignant ROIs with median values for sensitivity and specificity of 88.37% and 100%, respectively, by using only three PCs. The features that had shown the greatest contribution to the definition of the same were almost all extracted from the LE images. Our system could represent a valid support tool for radiologists for interpreting CESM images.

Monitoring of the power system load margin based on a machine learning technique

The voltage stability margin is an important load margin measure used in power system operating centers to prevent a voltage collapse. However, oscillatory problems that arise with increasing load can also compromise the performance and stability of the power system. Thus, it is essential to determine a load margin that meets the requirements for voltage stability and small-signal stability in dynamic security assessment. This article proposes to use a artificial neural network, a supervised machine learning technique, to predict the load margin range of the power system considering the requirements of voltage stability and small-signal stability and using data of electrical quantities of certain buses that have a phasor measurement unit. A direct method based on a power systems model that determines the load margin meeting the voltage and small-signal stability requirements will be applied to generate the database for the training and testing stages of artificial neural network. The sequential forward selection algorithm was used in this research to select the buses to have a phasor measurement unit. Case studies are presented and discussed to verify the proposed load margin monitoring system based on artificial neural networks.

Cloud Resource Scaling for Time-Bounded and Unbounded Big Data Streaming Applications

Recent advancements in technology have led to a deluge of big data streams that require real-time analysis with strict latency constraints. A major challenge, however, is determining the amount of resources required by applications processing these streams given their high volume, velocity and variety. The majority of research efforts on resource scaling in the cloud are investigated from the cloud provider's perspective with little consideration for multiple resource bottlenecks. We aim at analyzing the resource scaling problem from an application provider's point of view such that efficient scaling decisions can be made. This paper provides two contributions to the study of resource scaling for big data streaming applications in the cloud. First, we present a Layered Multi-dimensional Hidden Markov Model (LMD-HMM) for managing time-bounded streaming applications. Second, to cater to unbounded streaming applications, we propose a framework based on a Layered Multi-dimensional Hidden Semi-Markov Model (LMD-HSMM). The parameters in our models are evaluated using modified Forward and Backward algorithms. Our detailed experimental evaluation results show that LMD-HMM is very effective with respect to cloud resource prediction for bounded streaming applications running for shorter periods while the LMD-HSMM accurately predicts the resource usage for streaming applications running for longer periods.

A Precipitation forecasting in central ecotone region in Brazil using artificial neural networks and public climatic data

Precipitation forecasting may be of great value for farming, helping to reduce crop losses and irrigation costs, besides leveraging crop yield estimates. In Brazil, the state of Tocantins has a great part of its economy based on agriculture, where precipitation forecasting may help improve local production. Besides, rainfall forecasting can also contribute to urban planning through the management of water resources, among other applications Artificial Neural Networks (ANNs) have been used with relative success in precipitation forecasting for different locations and climates. With that, this work presents a method for weekly precipitation forecasting in six locations in the Brazilian state of Tocantins using ANNs and public climatic data. For that, MultiLayer Perceptron (MLP) networks were trained with data from local weather stations and El Niño Southern Oscillation (ENSO) related indices. First, input variables were selected using the forward selection algorithm. After that, ANN hyperparameters and input variables lag were optimized. The average Root Means Square Error (RMSE) of the final models was of 31.35 mm/week for the training dataset and 33.38 mm/week for the test dataset. Respectively, these values represent 9,83% and 10,46% of the maximum weekly precipitation found in the work dataset, which was of 319.1 mm. The results suggest that the created models are capable of reasonably good weekly precipitation forecasts, providing valuable information for farming, water resources management, urban planning and other related activies. Although there is possibly room for model improvement.

A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting.

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

Adaptive Learning of Drug Quality and Optimization of Patient Recruitment for Clinical Trials with Dropouts

Problem definition: Clinical trials are crucial to new drug development. This study investigates optimal patient enrollment in clinical trials with interim analyses, which are analyses of treatment responses from patients at intermediate points. Our model considers uncertainties in patient enrollment and drug treatment effectiveness. We consider the benefits of completing a trial early and the cost of accelerating a trial by maximizing the net present value of drug cumulative profit. Academic/practical relevance: Clinical trials frequently account for the largest cost in drug development, and patient enrollment is an important problem in trial management. Our study develops a dynamic program, accurately capturing the dynamics of the problem, to optimize patient enrollment while learning the treatment effectiveness of an investigated drug. Methodology: The model explicitly captures both the physical state (enrolled patients) and belief states about the effectiveness of the investigated drug and a standard treatment drug. Using Bayesian updates and dynamic programming, we establish monotonicity of the value function in state variables and characterize an optimal enrollment policy. We also introduce, for the first time, the use of backward approximate dynamic programming (ADP) for this problem class. We illustrate the findings using a clinical trial program from a leading firm. Our study performs sensitivity analyses of the input parameters on the optimal enrollment policy. Results: The value function is monotonic in cumulative patient enrollment and the average responses of treatment for the investigated drug and standard treatment drug. The optimal enrollment policy is nondecreasing in the average response from patients using the investigated drug and is nonincreasing in cumulative patient enrollment in periods between two successive interim analyses. The forward ADP algorithm (or backward ADP algorithm) exploiting the monotonicity of the value function reduced the run time from 1.5 months using the exact method to a day (or 20 minutes) within 4% of the exact method. Through an application to a leading firm’s clinical trial program, the study demonstrates that the firm can have a sizable gain of drug profit following the optimal policy that our model provides. Managerial implications: We developed a new model for improving the management of clinical trials. Our study provides insights of an optimal policy and insights into the sensitivity of value function to the dropout rate and prior probability distribution. A firm can have a sizable gain in the drug’s profit by managing its trials using the optimal policies and the properties of value function. We illustrated that firms can use the ADP algorithms to develop their patient enrollment strategies.

Computation of solutions to linear difference and differential equations with a prescribed asymptotic behavior

Linear differential equations usually arise from mathematical modeling of physical experiments and real-world problems. In most applications these equations are linked to initial or boundary conditions. But sometimes the solution under consideration is characterized by its asymptotic behavior, which leads to the question how to infer from the asymptotic growth of a solution to its initial values. In this paper we show that under some mild conditions the initial values of the desired solution can be computed by means of a continuous-time analogue of a modified matrix continued fraction. For numerical applications we develop forward and backward algorithms which behave well in most situations. The topic is closely related to the theory of special functions and its extension to higher-dimensional problems. Our investigations result in a powerful tool for solving some classical mathematical problems. To demonstrate the efficiency of our method we apply it to Poincaré type and Kneser’s differential equation.

Application of artificial intelligence in predicting the dynamics of bottom hole pressure for under-balanced drilling: Extra tree compared with feed forward neural network model

This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure (BHP) estimation. The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling. For the two case studies, measured field data of the wellbore filled with gasified mud system was utilized, and the wellbores were drilled using rotary jointed drill strings. Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy, BHP from measured field data. For modeling purpose, an extensive data from six fields was used, and the proposed model was further validated with two data from two new fields. The gathered data encompasses a variety of well data, general information/data, depths, hole size, and depths. The developed model was compared with data obtained from two new fields based on its capability, stability and accuracy. The result and model’s performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R2 greater than 0.9. The high values of R2 for the two models suggest the relative reliability of the modelling techniques. The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%–2.14%, for the Extra tree model and 0.40–0.41 and 3.90%–3.99% for Feed Forward model respectively; the least errors were recorded for the Extra Tree model. Also, the mean absolute error of the Extra Tree model for both fields (9.13–10.39 psi) are lower than that of the Feed Forward model (10.98–11 psi), thus showing the higher precision of the Extra Tree model relative to the Feed Forward model. Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability, because it mainly depends on the relationship between the bottomhole pressure and its corresponding critical point. Thus, the application of this study proposed models for predicting bottomhole pressure trends.

Optimizing electric vehicle routing problems with mixed backhauls and recharging strategies in multi-dimensional representation network

Electric vehicles are environmental transportation modes that are widely applied in green logistics systems. To guarantee the energy efficiency, the impacts of customer service modes and recharging strategies need to be integrated into the optimization of electric logistics resource. This paper proposes an electric vehicle routing problem with mixed backhauls, time windows, and recharging strategies (EVRPMBTW-RS), minimizing the total travel cost with sophisticated constraints on the time-dependent pickup and delivery requests, limited recharging station capacity, and battery remaining capacity of electric vehicles. Mixed service sequences of linehaul and backhaul customers is allocated for the routing planning, with the synchronous optimization of recharging strategies including the selection of recharging stations and determination of recharging time. A time-discretized multi-commodity network flow model is constructed based on an extended space–time-state modeling framework, which is formulated as a quadratic 0–1 programming model by using the augmented Lagrangian relaxation technique. After the dualization and linearized transformation, we decompose the model into a sequence of least-cost path subproblems based on the alternating direction multiplier method (ADMM). The subproblems are alternately minimized and solved using the time-dependent forward dynamic programming algorithm. The solution quality can be guaranteed through calculating the optimality gap between the best lower bound and upper bound for each iteration. The proposed solution approach is examined on examples of a simple 7-node network and real-world Yizhuang road network. This paper provides a theoretical foundation for the route optimization method of electric logistics vehicles, and contributes to improve the operational efficiency of electric logistics systems.

Phantom motion intent decoding for transhumeral prosthesis control with fused neuromuscular and brain wave signals

In recent years, the electroencephalography (EEG) brain–computer interface (BCI) has been researched in the area of upper-limb prosthesis control due to the promise of being able to record neurological signals which follow activation patterns in the cortex directly from the brain with non-invasive electrodes. This is seen as a way of bypassing the limitation posed by acquiring neuromuscular signals predominantly with electromyography (EMG) directly from the stump, which possesses residual limb anatomy post-amputation. In this study, the sequential forward selection algorithm to form a 10-optimal-channel representation, alongside an extended signal feature vector was applied, to investigate the motion intent decoding performance of EMG-only, EEG-only, and a fused EMG–EEG sensing configuration for four transhumeral amputees with varying stump lengths. The results showed a considerable improvement for the EMG-only configuration with the advanced feature vector, but only a small increase for the EEG-only, and thus a marginal improvement when information from both signals was fused together. This is likely due to the EEG requiring a greater number of channels spread across the skull to provide a reliable intent decoding. Further work will now involve optimisation studies to find a greater representation of electrode representation and parsimony, to minimise the number of channels while boosting motion intent decoding accuracy.

CRedit-based and reputation retrieval system

The challenge of a mobile ad-hoc network (MANET) or hybrid wireless network (HWN) is that every node must rely on others to forward its messages, but selfish nodes might refuse to forward those messages or move to other subnetworks at any time, thus degrading network speed and retrieval efficiency. This paper presents a system called “cRedit-based and Reputation Retrieval system” that employs a distributed message transmission protocol to avoid censorship problems, forwarding and reliable routing algorithms to ensure reliable message routing, reputation and credit algorithms to encourage greater cooperation incentives, location and refresh algorithms to deal with nodes’ limited view even in high-churn network, and a protection algorithm to address the problem of selfish nodes. A series of experiments confirmed the system’s effectiveness in achieving superior network scalability, high retrieval rates, low transmission costs, and mobility resilience in hybrid wireless networks.