Energy efficient wireless sensor network protocols for monitoring and prognostics of large scale systems

In this work, energy-efficient protocols for wireless sensor networks (WSN) with applications to prognostics are investigated. Both analytical methods and verification are shown for the proposed methods via either hardware experiments or simulation. This work is presented in five papers. Energy-efficiency methods for WSN include distributed algorithms for i) optimal routing, ii) adaptive scheduling, iii) adaptive transmission power and data-rate control --Abstract, page iv

Health Monitoring of a Truss Bridge Using Adaptive Identification

Integration of structural analysis, system identification, and sensor networks provide health monitoring capabilities that benefit many aspects of infrastructure management. This work presents an adaptive identification method based on Lyapunov methods for a truss structure. Lyapunov methods in the identification provide guaranteed convergence for the parameter estimation. Identification is carried out on a simulation model based on FEA methods. The FEA model offers realistic results from a truss structure and allows verification of the identification method for a rolling load case. A rolling load scenario provides use of realistic loading of a structure so that identification methods may be extended for field use. Estimation results show that convergence of health parameters is suitable though the use of the adaptive estimation. Also, results of simulations show that the adaptive estimation methods are able to track changes over time to provide monitoring of a degrading structure

Structural Identification Using A Low-cost Search Method

An easily implementable and trainable damage detection method is proposed and implemented for a simple truss structure. The approach uses the iterative search identification method and is compatible with low-cost and low-power microcontroller hardware. This method employs pattern matching for a data set from a strain sensor array and predicts location (truss member) and severity (member cross sectional area) of damage. As a health monitoring approach, the method is not as robust or rigorous as more complex methods. However, it has modest processing requirements and can handle noisy signals. The work presents an algorithm applied to a truss structure, the simulation performance from a finite-element-analysis, and a discussion of capabilities. The simulation demonstrates differing damage locations, damage severity, and signal noise. Its suitability for low-cost and low-power field processors is discussed. © 2009 IEEE

Dynamic Simulation of a MEMS Cantilever Switch

The dynamic behavior of a micro-electro-mechanicalsystem (MEMS) cantilever switch is investigated. Overactuation of the switch can degrade bounce characteristics and reduce the lifetime of the contacts. This work concerns the development of a control system that limits the number of switch bounces and reduces the impact force on the beam tip. A limited mass-spring analysis of the tip-position is given and an associated control approach is applied. Input limiting, state-feedback, and adaptive control methods are compared. All results demonstrate improved switch bounce characteristics for the simplified beam model with the adaptive showing the best performance improvement. A comprehensive finite element analysis is shown that predicts the dynamic beam behavior along the entire length. This approach produces a realistic model of the beam during switching, especially the tip displacement. A versatile control system is proposed that uses finite-element-analysis simulation and adaptive control. The feasibility of this dynamic control system is also discussed

Joint Adaptive Distributed Rate and Power Control for Wireless Networks

A novel adaptive distributed rate and power control (ADRPC) protocol is introduced for wireless networks. The proposed controller contrasts from others by providing nonlinear compensation to the problem of transmission power and bit-rate adaptation. The protocol provides control of both signal-to-interference ratio (SIR) and quality-of-service (QoS) support to bit-rate adaptation. Bit-rate adaptation is performed by local estimation of congestion levels, rendering little packet overhead, using Lyapunov based adaptive control methods. Performance of the proposed control scheme is shown through analytical proof and simulation examples

Robust Neural Network RISE Observer Based Fault Diagnostics And Prediction

A novel fault diagnostics and prediction scheme in continuous time is introduced for a class of nonlinear systems. The proposed method uses a novel neural network (NN) based robust integral sign of the error (RISE) observer, or estimator, allowing for semi-global asymptotic stability in the presence of NN approximation errors, disturbances and unmodeled dynamics. This is in comparison to typical results presented in the literature that show only boundedness in the presence of uncertainties. The output of the observer/estimator is compared with that of the nonlinear system and a residual is used for declaring the presence of a fault when the residual exceeds a user defined threshold. The NN weights are tuned online with no offline tuning phase. The output of the RISE observer is utilized for diagnostics. Additionally, a method for time-to-failure (TTF) prediction, a first step in prognostics, is developed by projecting the developed parameter-update law under the assumption that the nonlinear system satisfies a linear-in-the-parameters (LIP) assumption. The TTF method uses known critical values of a system to predict when an estimated parameter will reach a known failure threshold. The performance of the NN/RISE observer system is evaluated on a nonlinear system and a simply supported beam finite element analysis (FEA) simulation based on laboratory experiments. Results show that the proposed method provides as much as 25% increased accuracy while the TTF scheme renders a more accurate prediction. © 2010 IEEE

Adaptive Distributed Fair Scheduling and Its Implementation in Wireless Sensor Networks

A novel adaptive and distributed fair scheduling (ADFS) scheme for wireless sensor networks is shown through hardware implementation. In contrast to simulation, hardware evaluation provides valuable feedback to protocol and hardware development process. The proposed protocol focuses on quality-of-service (QoS) issues to address flow prioritization. Thus, when nodes access a shared channel, the proposed ADFS allocates the channel bandwidth proportionally to the weight, or priority, of the packet flows. Moreover, ADFS allows for dynamic allocation of network resources with little added overhead. Weights are initially assigned using user specified QoS criteria. These weights are subsequently updated as a function of delay, enqueued packets, flow arrival rate, and the previous packet weight. The back-off interval is also altered using the weight update equation. The weight update and the back-off interval selection ensure that global fairness is attained even with variable service rates. The algorithm is implemented using UMR/SLU motes for an industrial monitoring application. Results the hardware implementation demonstrates improved performance in terms of fairness index, flow rate, and delay

Development and Implementation of Optimized Energy-Delay Sub-Network Routing Protocol for Wireless Sensor Networks

The development and implementation of the optimized energy-delay sub-network routing (OEDSR) protocol for wireless sensor networks (WSN) is presented. This ondemand routing protocol minimizes a novel link cost factor which is defined using available energy, end-to-end (E2E) delay and distance from a node to the base station (BS), along with clustering, to effectively route information to the BS. Initially, the nodes are either in idle or sleep mode, but once an event is detected, the nodes near the event become active and start forming sub-networks. Formation of the inactive network into a sub-network saves energy because only a portion of the network is active in response to an event. Subsequently, the sub-networks organize themselves into clusters and elect cluster heads (CHs). The data from the CHs are sent to the BS via relay nodes (RNs) that are located outside the sub-networks in a multi-hop manner. This routing protocol improves the lifetime of the network and the scalability. This routing protocol is implemented over the medium access control (MAC) layer using UMR nodes. Experimental results illustrate that the protocol performs satisfactorily as expected

Adaptive Distributed Fair Scheduling for Multiple Channels in Wireless Sensor Networks

A novel adaptive and distributed fair scheduling (ADFS) scheme for wireless sensor networks (WSN) in the presence of multiple channels (MC-ADFS) is developed. The proposed MC-ADFS increases available network capacity and focuses on quality-of-service (QoS) issues. when nodes access a shared channel, the proposed MC-ADFS allocates the channel bandwidth proportionally to the packet\u27s weight which indicates the priority of the packet\u27s flow. The packets are dynamically assigned to channels based on the packet weight and current channel utilization. The dynamic assignment of channels is facilitated by use of receiver-based allocation and alternative routes. Moreover, MC-ADFS allows the dynamic allocation of network resources with little added overhead. Packet weights are initially assigned using user specified QoS criteria, and subsequently updated as a function of delay and queued packets. The back-off interval is also altered using the weight adaptation. The weight update and back-off interval selection ensure global fairness is attained even with variable service rates

Missouri S&T Mote-Based Demonstration of Energy Monitoring Solution for Network Enabled Manufacturing using Wireless Sensor Networks (WSN)

In this work, an inexpensive electric utilities monitoring solution using wireless sensor networks is demonstrated that can easily be installed, deployed, maintained and eliminate unnecessary energy costs and effort. The monitoring solution is designed to support network enabled manufacturing (NEM) program using Missouri University of Science and Technology (MST), formerly the University of Missouri-Rolla (UMR), motes