An alternative node deployment scheme for WSNs

© 2001-2012 IEEE. Sensing coverage is a fundamental design problem in wireless sensor networks (WSNs). This is because there is always a possibility that the sensor nodes may function incorrectly due to a number of reasons, such as failure, power, or noise instability, which negatively influences the coverage of the WSNs. In order to address this problem, we propose a fuzzy-based self-healing coverage scheme for randomly deployed mobile sensor nodes. The proposed scheme determines the uncovered sensing areas and then select the best mobile nodes to be moved to minimize the coverage hole. In addition, it distributes the sensor nodes uniformly considering Euclidean distance and coverage redundancy among the mobile nodes. We have performed an extensive performance analysis of the proposed scheme. The results of the experiment show that the proposed scheme outperforms the existing approaches.

Hybrid controller with the combination of FLC and neural network-based IMC for nonlinear processes

This work presents a hybrid controller based on the combination of fuzzy logic control (FLC) mechanism and internal model-based control (IMC). Neural network-based inverse and forward models are developed for IMC. After designing the FLC and IMC independently, they are combined in parallel to produce a single control signal. Mean averaging mechanism is used to combine the prediction of both controllers. Finally, performance of the proposed hybrid controller is studied for a nonlinear numerical plant model (NNPM). Simulation result shows the proposed hybrid controller outperforms both FLC and IMC.

Energy efficient sleep schedule with service coverage guarantee in wireless sensor networks

Service oriented architecture has been proposed to support collaborations among distributed wireless sensor network (WSN) applications in an open dynamic environment. However, WSNs are resource constraint, and have limited computation abilities, limited communication bandwidth and especially limited energy. Fortunately, sensor nodes in WSNs are usually deployed redundantly, which brings the opportunity to adopt a sleep schedule for balanced energy consumption to extend the network lifetime. Due to miniaturization and energy efficiency, one sensor node can integrate several sense units and support a variety of services. Traditional sleep schedule considers only the constraints from the sensor nodes, can be categorized to a one-layer (i.e., node layer) issue. The service oriented WSNs should resolve the energy optimization issue considering the two-layer constraints, i.e., the sensor nodes layer and service layer. Then, the one-layer energy optimization scheme in previous work is not applicable for service oriented WSNs. Hence, in this paper we propose a sleep schedule with a service coverage guarantee in WSNs. Firstly, by considering the redundancy degree on both the service level and the node level, we can get an accurate redundancy degree of one sensor node. Then, we can adopt fuzzy logic to integrate the redundancy degree, reliability and energy to get a sleep factor. Based on the sleep factor, we furthermore propose the sleep mechanism. The case study and simulation evaluations illustrate the capability of our proposed approach.

An alternative data collection scheduling scheme in wireless sensor networks

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Recently, the problem of energy conservation has been addressed by applying mobile sink as an effective technique that can enhance efficiency of energy consumption in the networks. In this paper, the energy conservation problem is firstly formulated to maximize the lifetime of WSN subject to delay and node energy constraints. Then, to solve the defined energy conservation problem, a data collection scheduling with a mobile sink scheme is proposed. In the proposed approach, the sink movement is governed by a type-2 fuzzy controller to be located at the best location and time to collect sensory data. We conducted extensive experiments to study the effectiveness of the proposed protocol and compared it against the streaming data delivery (SDD) and virtual circle combined straight routing (VCCS) protocols. We observed that the proposed protocol outperforms both SDD and VCCS approaches by reducing energy consumption, minimize delays and enhance data collection quality.

Evaluating the whole-life cost implication of revocability and disruption in office retrofit building projects

Retrofit buildings are becoming popular in the United Kingdom as well as many parts of the advanced economies. Existing whole-life costing models have however, not proven to be robust enough to deal with building retrofit scenarios. Recent research has made a case for the existence of revocability and disruption in building retrofit investments. This paper evaluates the whole-life cost implication of revocability and disruption in office retrofit building projects. The potential implication of revocability and disruption are evaluated based on probability and fuzzy logic principles respectively. Two case study projects are selected to appraise the economic potentials of revocability and disruption. It was found that the average cost of revocability relative to the initial capital cost can be up to 119% over a 60-year life. It was also found that the average cost of disruption relative to the initial capital cost can be up to 12%. Future studies will utilise sensitivity analysis in assessing the relative preference of building retrofit configurations in office building projects. The external validity of this work is moderate, as the intention is to establish analytical generalisation rather than statistical generalisation for office retrofit building projects.

Potential applications of discrete-event simulation and fuzzy rule-based systems to structural reliability and availability

This chapter discusses and illustrates some potential applications of discrete-event simulation (DES) techniques in structural reliability and availability analysis, emphasizing the convenience of using probabilistic approaches in modern building and civil engineering practices. After reviewing existing literature on the topic, some advantages of probabilistic techniques over analytical ones are highlighted. Then, we introduce a general framework for performing structural reliability and availability analysis through DES. Our methodology proposes the use of statistical distributions and techniques – such as survival analysis – to model component-level reliability. Then, using failure- and repair-time distributions and information about the structural logical topology (which allows determination of the structural state from their components’ state), structural reliability, and availability information can be inferred. Two numerical examples illustrate some potential applications of the proposed methodology to achieving more reliable and structural designs. Finally, an alternative approach to model uncertainty at component level is also introduced as ongoing work. This new approach is based on the use of fuzzy rule-based systems and it allows the introduction of experts’ opinions and evaluations in our methodology.

Sliding mode-like learning control for SISO complex systems with T-S fuzzy models

In this paper, a sliding mode-like learning control scheme is developed for a class of single input single output (SISO) complex systems. First, the Takagi-Sugeno (T-S) fuzzy modelling technique is employed to model the uncertain complex dynamical systems. Second, a sliding mode-like learning control is designed to drive the sliding variable to converge to the sliding surface, and the system states can then asymptotically converge to zero on the sliding surface. The advantages of this scheme are that: 1) the information about the uncertain system dynamics and the system model structure is not required for the design of the learning controller; 2) the closed-loop system behaves with a strong robustness with respect to uncertainties; 3) the control input is chattering-free. The sufficient conditions for the sliding mode-like learning control to stabilise the global fuzzy model are discussed in detail. A simulation example for the control of an inverted pendulum cart is presented to demonstrate the effectiveness of the proposed control scheme.

A robust learning control for SISO Nonlinear systems with T-S Fuzzy Model : C02-robust control

In this paper, a robust learning control is developed for a class of single input single output (SISO) nonlinear systems with T-S fuzzy model. It is seen that the proposed sliding mode learning control with the powerful Lipshitz-like condition can guarantee the stability, convergence and robustness of the closed-loop system without involving any assumptions on uncertain system dynamics. In addition, theconcept that the local system with the maximum membership function dominates the system dynamic behaviours helps to greatly simplify the control system design. It will be further seen that the continuous learning control ensures the advantage of chattering-free that may occur in conventional sliding mode systems. Simulation examples are presented to demonstrate the effectiveness of the proposed learning control through the comparison with the H-infinity control.

A new monotonicity index for fuzzy rule-based systems

A search in the literature reveals that mathematical conditions (usually sufficient conditions) for the Fuzzy Inference System (FIS) models to satisfy the monotonicity property have been developed. A monotonically-ordered fuzzy rule base is important to maintain the monotonicity property of an FIS. However, it may difficult to obtain a monotonically-ordered fuzzy rule base in practice. We have previously introduced the idea of fuzzy rule relabeling to tackle this problem. In this paper, we further propose a monotonicity index for the FIS system, which serves as a metric to indicate the degree of a fuzzy rule base fulfilling the monotonicity property. The index is useful to provide an indication whether a fuzzy rule base should (or should not) be used in practice, even with fuzzy rule relabeling. To illustrate the idea, the zero-order Sugeno FIS model is exemplified. We add noise as errors into the fuzzy rule base to formulate a set of non-monotone fuzzy rules. As such, the metric also acts as a measure of noise in the fuzzy rule base. The results show that the proposed metric is useful to indicate the degree of a fuzzy rule base fulfilling the monotonicity property.