Description:
Reliability and timeliness are two essential requirements of successful detection of critical events in Wireless Sensor Networks (WSNs). The base station (BS) is particularly interested about reliable and timely collection of data sent by the nodes close to the ongoing event, and at that time, the data sent by other nodes have little importance. In this paper, we propose Congestion and Delay Aware Routing (CODAR) protocol that tries to route data in congestion and delay aware manners. If congestion occurs, it also mitigates congestion by utilizing an accurate data-rate adjustment. Each node collects control information from neighbours and works in a distributed manner. CODAR also puts emphasis on successful collection of these control information which eventually provides desirable performance. Experimental results show that CODAR is capable of avoiding and mitigating congestion effectively, and performs better than similar known techniques in terms of reliable and timely event detection.
Description:
Wireless sensor networks (WSNs), now-a-days, are deployed in environmental data collection as well as in critical event monitoring. Successful data collection requires reliability while reliable event detection necessitates timeliness. Simultaneous data gathering and event monitoring is not well studied in literature. In this paper, we propose a system model that works on homogeneous data gathering WSNs. When an event occurs, an event cluster with a different transmission channel is formed and both data gathering and event monitoring are performed at the same time. The proposed model has a novel routing strategy with a built-in congestion control technique to provide timely delivery of event data. Experimental results show that the proposed method performs better than known similar techniques in terms of reliable data gathering and reliable timely event monitoring. It also enhances the network lifetime significantly compared to other existing methods.
Description:
Spectrum resources are becoming more and more congested as the number of wireless devices are increasing and becoming ubiquitous. Cognitive radios or secondary users (SUs) can provide the solution for better spectrum availability, bandwidth and economic aspects for both the primary service providers and the SUs. We propose a pricing model for spectrum sharing in a single level market where the primary service providers can trade spectrum with the secondary service providers. The proposed pricing model incorporates the reliability of the primary service providers and allowable coverage area, quality of the signal along with the pricing and spectrum bandwidth availability. An iterative distributed algorithm is used to reach the market equilibrium so that both the primary and the secondary service providers are satisfied with the allocated spectrum bandwidth and negotiated price. The performance of the proposed model is demonstrated using extensive numerical results with the stability analysis in reaching the market equilibrium.
Description:
For cost effective deployment and implementation, mobility is introduced in sensor networks to provide dynamic event coverage. A hybrid network of static and mobile nodes, can yield the same desired accuracy and robustness of a static k-coverage detection model with fewer nodes. Since node movement is a costly operation and the movement strategy has to be decided instantly after event occurrence, it is desirable to have a lightweight distributed node selection and movement scheme. In this work, we propose a game theoretic model to provide dynamic event coverage that achieves the desired detection accuracy with significantly fewer number of nodes while balancing the energy consumption due to mobility and keeping the travelling distance minimum. We address and exploit the spatial clustering nature of events to maximize the overall detection performance over the network lifetime.
Description:
Wireless access technology has come a long way in its relatively short but remarkable lifetime, which has so far been led by WiFi technology. WiFi enjoys a high penetration in the market.Most of the electronic gadgets such as laptop, notepad, mobile set, etc., boast the provision ofWiFi. Currently most WiFi hotspots are connected to the Internet via wired connections (e.g., Ethernet), and the deployment cost of wired connection is high. On the other hand, since WiMAX can provide a high coverage area and transmission bandwidth, it is very suitable for the backbone networks of WiFi. WiMAX can also provide the better QoS needed for many 4G applications. WiMAX devices, however, are not as common as WiFi devices and it is also expensive to deploy aWiMAX-only infrastructure. An integrated WiMAX/WiFi architecture (using WiMAX as backhaul connection for WiFi) can support 4G applications with QoS assurance and mobility, and provide high-speed broadband services in rural, regional and urban areas while reducing the backhaul cost. WiMAX and WiFi have different MAC mechanisms to handle QoS. WiMAX MAC architecture is connection-oriented providing the platform for strong QoS control. In contrast,WiFi MAC is not connection-oriented, hence can provide only best effort services. Delivering improved QoS in an integrated WiMAX/WiFi architecture poses a serious technological challenge. The paper depicts a converged architecture of WiMAX and WiFi, and then proposes an adaptive resource distribution model for the access points. The resource distribution model ultimately allocates more time slots to those connections that need more instantaneous resources to meet QoS requirements. A dynamic splitting technique is also presented that divides the total transmission period into downlink and uplink transmission by taking the minimum data rate requirements of the connections into account. This ultimately improves the utilization of the available resources, and the QoS of the connections. Simulation results show that the proposed schemes significantly outperform the other existing resource sharing schemes, in terms of maintaining QoS of different traffic classes in an integratedWiMAX/WiFi architecture
Description:
Optimum selection of sensors in target tracking applications has a great potential to maintain right trade-off between energy consumption and quality of tracking. In this paper, we propose a dynamic sensor selection scheme to achieve energy efficiency while ensuring the required quality of tracking. To this end, relative information utility projection of a target on sensors' observation is used in niche overlap measurements. Niche overlap measures are used to assess the similarity in information utilities where information utility is inversely proportional to error in target's state estimation based on prior distribution. The proposed scheme is a greedy approach in which sensor nodes are selected such that the overall niche overlap of all the selected nodes is maximized until the required level of accuracy is achieved. Our simulation results show significant improvement in tracking accuracy and network's lifetime over the existing methods.
Description:
Transmission policy, in addition to topology control, routing, and MAC protocols, can play a vital role in extending network lifetime. Existing transmission policies, however, cause an extremely unbalanced energy usage that contributes to early demise of some sensors reducing overall network's lifetime drastically. Considering cocentric rings around the sink, we decompose the transmission distance of traditional multihop scheme into two parts: ring thickness and hop size, analyze the traffic and energy usage distribution among sensors and determine how energy usage varies and critical ring shifts with hop size. Based on above observations, we propose a transmission scheme and determine the optimal ring thickness and hop size by formulating network lifetime as an optimization problem. Numerical results show substantial improvements in terms of network lifetime and energy usage distribution over existing policies. Two other variations of this policy are also presented by redefining the optimization problem considering: 1) concomitant hop size variation by sensors over lifetime along with optimal duty cycles, and 2) a distinct set of hop sizes for sensors in each ring. Both variations bring increasingly uniform energy usage with lower critical energy and further improves lifetime. A heuristic for distributed implementation of each policy is also presented.
Description:
Transmission policy, in addition to topology control, routing, and MAC protocols, can play a vital role in extending network lifetime. Existing transmission policies, however, cause an extremely unbalanced energy usage that contributes to early demise of some sensors reducing overall network's lifetime drastically. Considering cocentric rings around the sink, we decompose the transmission distance of traditional multihop scheme into two parts: ring thickness and hop size, analyze the traffic and energy usage distribution among sensors and determine how energy usage varies and critical ring shifts with hop size. Based on above observations, we propose a transmission scheme and determine the optimal ring thickness and hop size by formulating network lifetime as an optimization problem. Numerical results show substantial improvements in terms of network lifetime and energy usage distribution over existing policies. Two other variations of this policy are also presented by redefining the optimization problem considering: 1) concomitant hop size variation by sensors over lifetime along with optimal duty cycles, and 2) a distinct set of hop sizes for sensors in each ring. Both variations bring increasingly uniform energy usage with lower critical energy and further improves lifetime. A heuristic for distributed implementation of each policy is also presented.
Description:
Wavelet transform has been extensively used in machine fault diagnosis and prognosis owing to its strength to deal with non-stationary signals. The existing Wavelet transform based schemes for fault diagnosis employ wavelet decomposition of the entire vibration frequency which not only involve huge computational overhead in extracting the features but also increases the dimensionality of the feature vector. This increase in the dimensionality has the tendency to 'over-fit' the training data and could mislead the fault diagnostic model. In this paper a novel technique, envelope wavelet packet transform (EWPT) is proposed in which features are extracted based on wavelet packet transform of the filtered envelope signal rather than the overall vibration signal. It not only reduces the computational overhead in terms of reduced number of wavelet decomposition levels and features but also improves the fault detection accuracy. Analytical expressions are provided for the optimal frequency resolution and decomposition level selection in EWPT. Experimental results with both actual and simulated machine fault data demonstrate significant gain in fault detection ability by EWPT at reduced complexity compared to existing techniques.
Description:
Wavelet transform has been extensively used in machine fault diagnosis and prognosis owing to its strength to deal with non-stationary signals. The existing Wavelet transform based schemes for fault diagnosis employ wavelet decomposition of the entire vibration frequency which not only involve huge computational overhead in extracting the features but also increases the dimensionality of the feature vector. This increase in the dimensionality has the tendency to 'over-fit' the training data and could mislead the fault diagnostic model. In this paper a novel technique, envelope wavelet packet transform (EWPT) is proposed in which features are extracted based on wavelet packet transform of the filtered envelope signal rather than the overall vibration signal. It not only reduces the computational overhead in terms of reduced number of wavelet decomposition levels and features but also improves the fault detection accuracy. Analytical expressions are provided for the optimal frequency resolution and decomposition level selection in EWPT. Experimental results with both actual and simulated machine fault data demonstrate significant gain in fault detection ability by EWPT at reduced complexity compared to existing techniques.
Description:
Existing in-network query processing techniques are categorized as approximation and aggregation based approaches, where the former achieves lower network traffic at the expense of query response accuracy, whereas the later reduces query response inaccuracy by executing queries at the actual sensor nodes which necessitates the overhead of query specific sensor selection mechanism. In this paper, we propose a hybrid query processing framework that combines the advantages of both the approximation and aggregation based techniques and avoids their limitations. In our approach, we construct a hierarchical probabilistic data model representing the overall sensor data characteristics across the network, which is query independent and is later used for selecting sensor nodes to process user queries. Experimental results illustrate the efficacy of the proposed framework compared to contemporary approximation and aggregation based query processing techniques.
Description:
Energy efficiency is a vital aspect of resource constrained wireless sensor networks (WSNs). All protocols designed for WSNs must be energy aware in order to prolong the network lifetime. In this paper, we have designed a novel MAC layer protocol (I-MAC: Intelligent MAC) for WSNs. By exercising intelligent sleep and wake-up schedule, I-MAC saves energy of the resource constrained sensor nodes greatly. At the same time, I-MAC does not compromise its operational performances. Both analytical study and simulation prove that I-MAC is not only highly energy efficient but also its operational performances are better than similar protocols.
Description:
The study is focused on estimating the severity level of the bearing faults which helps in determining the residual life of the equipment and planned maintenance. A novel technique, adaptive severity estimation model (ASEM) is proposed based on adaptive selection of wavelet decomposition nodes and support vector machines. Vibration data from multiple severity levels are used to build the fault estimation model. An adaptive criterion for wavelet decomposition node selection is developed which helps ASEM to achieve robustness in estimating fault severity under varying signal to noise ratio (SNR), a key demand in industrial environment. The simulated data with known severity level is used to parameterize the estimation model. The fault severity estimation performance of ASEM is also validated for the real vibration data and its robustness is gauged under varying SNR conditions.
Description:
Spectrum utilization in cognitive radio ad hoc network is a critical task due to the fluctuation of spectrum availability temporally and spatially. Reusing the same channel spatially can effectively improve the spectrum utilization as well as reduce the delay in switching and coordination in different channels. In this paper, a dynamic spectrum sharing method is proposed which allows multiple secondary users to reside in the same channel and use the channel concurrently to maximize the spectrum utilization exploiting variable transmission power and location information, while guaranteeing acceptable level of interference protection to the primary users. Results show that the proposed method successfully outperforms the existing method in maximizing the number of concurrent transmissions as well as the spectrum utilization.
Description:
Occurrence of a multiple-points fault in machine operations could result in exhibiting complex fault signatures, which could result in lowering fault diagnosis accuracy. In this study, a multiple-points defect model (MPDM) is proposed which can simulate fault signature-s dynamics for n-points bearing faults. Furthermore, this study identifies that in case of multiple-points fault in the rotary machine, the location of the dominant component of defect frequency shifts depending upon the relative location of the fault points which could mislead the fault diagnostic model to inaccurate detections. Analytical and experimental results are presented to characterize and validate the variation in the dominant component of defect frequency. Based on envelop detection analysis, a modification is recommended in the existing fault diagnostic models to consider the multiples of defect frequency rather than only considering the frequency spectrum at the defect frequency in order to incorporate the impact of multiple points fault.
Description:
Occurrence of a multiple-points fault in machine operations could result in exhibiting complex fault signatures, which could result in lowering fault diagnosis accuracy. In this study, a multiple-points defect model (MPDM) is proposed which can simulate fault signature-s dynamics for n-points bearing faults. Furthermore, this study identifies that in case of multiple-points fault in the rotary machine, the location of the dominant component of defect frequency shifts depending upon the relative location of the fault points which could mislead the fault diagnostic model to inaccurate detections. Analytical and experimental results are presented to characterize and validate the variation in the dominant component of defect frequency. Based on envelop detection analysis, a modification is recommended in the existing fault diagnostic models to consider the multiples of defect frequency rather than only considering the frequency spectrum at the defect frequency in order to incorporate the impact of multiple points fault.
Description:
Vibration signals used for abnormality detection in machine health monitoring (MHM) are non-stationary in nature. Wavelet packet transform is extensively used in the literature for comprehensive analysis of non-stationary vibration signal but these techniques work only for a specific application lacking in some generalized methodology for selecting appropriate wavelet decomposition level and nodes for optimal performance. This study proposes a framework for inchoate fault detection by selecting the optimal wavelet decomposition level and nodes, named Optimally Parameterized Wavelet Packet Transform (OPWPT). OPWPT uses support vector machine to build the fault diagnostic model. Results in comparison with the existing schemes validate that OPWPT enhances the fault detection accuracy significantly in case of incipient faults when vibration signatures are very weak and overall signal to noise ratio is very poor.
Description:
One of the prevalent issues in condition based maintenance (CBM) is to predict the residual life of the equipment. This paper propos-es a novel framework to predict the remnant life of the equipment, called Residual life prediction based on optimally parameterized Wavelet transform and Mute-step Support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications and decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature-signal energy contents. The prediction model is based on multi-step support vector regression (MSVR) and prediction accuracy is improved in comparison with the techniques based on support vector regression (SVR). Performance of RWMS is evaluated in terms of Root Means Square Error (RMSE), studies show that proposed algorithm predicts the residual life of the equipment accurately.
Description:
One of the prevalent issues in condition based maintenance (CBM) is to predict the residual life of the equipment. This paper propos-es a novel framework to predict the remnant life of the equipment, called Residual life prediction based on optimally parameterized Wavelet transform and Mute-step Support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications and decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature-signal energy contents. The prediction model is based on multi-step support vector regression (MSVR) and prediction accuracy is improved in comparison with the techniques based on support vector regression (SVR). Performance of RWMS is evaluated in terms of Root Means Square Error (RMSE), studies show that proposed algorithm predicts the residual life of the equipment accurately.
Description:
Wireless sensor networks promise to guarantee accurate, fault tolerant and timely detection of events in large scale sensor fields. To achieve this the notion of k-coverage is widely employed in WSNs where significant redundancy is introduced in deployment as an event is expected to be sensed by at least k sensors in the neighborhood. As sensor density increases significantly with k, it is imperative to find the optimal k for the underlying event detection system. In this work, we consider the detection probability, fault tolerance and latency as the Quality of Service (QoS) metrics of an event detection system employing k-coverage and present a probabilistic model to guarantee given QoS support with the minimum degree of coverage taking into account the noise related measurement error, communication interference and sensor fault probability. This work eventually resolves the problem of over or under deployment of sensors, increases scalability and provides a well defined mechanism to tune the degree of coverage according to performance needs.
Description:
Wireless sensor networks promise to guarantee accurate, fault tolerant and timely detection of events in large scale sensor fields. To achieve this the notion of k-coverage is widely employed in WSNs where significant redundancy is introduced in deployment as an event is expected to be sensed by at least k sensors in the neighborhood. As sensor density increases significantly with k, it is imperative to find the optimal k for the underlying event detection system. In this work, we consider the detection probability, fault tolerance and latency as the Quality of Service (QoS) metrics of an event detection system employing k-coverage and present a probabilistic model to guarantee given QoS support with the minimum degree of coverage taking into account the noise related measurement error, communication interference and sensor fault probability. This work eventually resolves the problem of over or under deployment of sensors, increases scalability and provides a well defined mechanism to tune the degree of coverage according to performance needs.
Description:
Existing in-network query processing techniques are categorized as approximation and aggregation based approaches. The former achieves lower query response delay at the expense of accuracy, while the latter reduces query response inaccuracy by executing queries at the actual sensor nodes resulting in longer delay. In this paper, we propose a query processing framework which is delay as well as accuracy aware and capable of dynamic adjustment to meet user/application requirements. When query response is required within specific delay, it provides approximated sensor data meeting the delay requirement. On the other hand, when query response accuracy is vital, it tolerates longer delay in acquiring response with the desired accuracy. To achieve this, we propose a novel method of constructing a delay aware spanning tree (DAST) based on query load and organizing sensor data with varied accuracy. Experimental results illustrate superiority of the proposed framework against competing approaches.
Description:
The vibrations induced by machine faults help in diagnosis and prognosis of the machine. It is crucial for the fault diagnostic system to extract resonant frequency band which carries useful information about the defect frequencies and contains maximum signal to noise ratio. The spectral orientation of the resonant frequency band varies with the variation in machine dynamics. The existing techniques which employ wavelet transformation to exploit the signal energy distribution among different frequency sub-bands, are based on fixed decomposition level and do not optimize the wavelet parameters according to varying machine dynamics. The proposed study develops a novel technique: Adaptive Wavelet Decomposition and Resonance Frequency Estimation (AWRE) which estimates the positioning of the resonant frequency band based on adaptive selection of the wavelet decomposition levels. The results for the simulated as well as actual vibration data demonstrate that the proposed technique estimates the bandwidth of the resonant frequency band quite effectively.
Description:
Vibration signals used for abnormality detection in machine health monitoring (MHM) suffer from significant variation in the patterns with fault severity. This variation results in overlap among the features extracted against different fault types and causes severe degradation in fault detection accuracy. This paper identifies a newfangled problem originated by severity variant features and mitigates this impact by using appropriate feature selection based on Fisher linear discriminant (FLD) and Bhattacharyya distance (BCD) to enhance fault classification accuracy. In order to validate the performance of the proposed scheme, training and testing data are obtained from different severity levels. To capture the non-stationary behavior of vibration signal, robust tools such as wavelet transform (WT) for time-frequency analysis is employed. Simulation studies show that the proposed scheme ensures good fault diagnostic accuracy even if training and testing data belong to different severity levels. [ABSTRACT FROM AUTHOR]