Optimal Sensor Deployment Strategy Research Articles

A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. In this paper, a propagation-basedfaultdetectionanddiscrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the IntegratedSystemFaultAnalysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system’s capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification.

Sensor Deployment for Wireless Sensor Networks: A Conjugate Learning Automata-Based Energy-Efficient Approach

Wireless sensor networks (WSNs) boost the development of the Internet of Things by their ability to monitor environments and flexibility. It is desirable to study an efficient configuration of a WSN that balances its energy consumption and its functionality. In this article, we propose to formulate the sensor deployment task as a combinatorial optimization problem and introduce an effective sensor deployment paradigm in which both the randomness and the dynamics of the environment are captured. Following the activity scheduling mechanism, we adopt a powerful non-associative reinforcement learning method, conjugate learning automata (CLA), to learn the optimal sensor deployment strategy. Compared to conventional methods, the proposed CLAbased sensor deployment method yields good performance by activating only a subset of all sensors and does not lean on prior expertise about environments. Meanwhile, the learning process is efficient, and thus energy is saved in multiple aspects. Comprehensive experiments under different settings demonstrate the effectiveness of the proposed method.

Optimal Heterogeneous Sensor Deployment Strategy for Dynamic Origin–Destination Demand Estimation

A network sensor location problem (NSLP) model is proposed to determine the optimal heterogeneous sensor deployment strategy in terms of the number of links (counting) and node (video or image) sensors and their installation locations for dynamic origin–destination (O-D) demand estimates under a budget constraint. The proposed NSLP model also captures the impact of the time duration for which traffic data measurements are available on the optimal sensor deployment strategy; this topic had not been addressed in previous studies. A sequential sensor location algorithm that avoids matrix inversions was proposed to solve the NSLP model under the assumption of multivariate normal distribution for the prior dynamic O-D demand estimates. A network from a part of Chennai, India, was used to illustrate the performance of the proposed NSLP model and solution algorithm. The results show that the proposed algorithm can identify the sensor locations on the basis of the amount of information provided and can determine the number of each sensor type to maximize the accuracy of O-D demand estimation. The results also illustrate that the proposed NSLP model can capture the time-varying characteristics of the amount of information provided by each sensor location. The proposed method can be used to analyze the impacts of various sensor types, their number, location, and the time duration for which traffic data measurements are available for estimation of dynamic O-D demand under limited resources.

Integrated Determination of Network Origin–Destination Trip Matrix and Heterogeneous Sensor Selection and Location Strategy

This paper proposes a two-stage optimization model to determine the origin–destination (O–D) trip matrix and the heterogeneous sensor deployment strategy in an integrated manner for a vehicular traffic network using sensor information from active (camera-based license plate recognition) and passive (vehicle detector) sensors. The first stage solves the heterogeneous sensor selection and location problem to determine the optimal sensor deployment strategy, in terms of the selection of the numbers of the two sensor types and their installation locations, to maximize the traffic information available for the O–D matrix estimation problem. The traffic information includes the observed link flow, path trajectory, and path coverage information. The second stage leverages this traffic information to determine the network O–D matrix that minimizes the error between the observed and estimated traffic flows (link, O–D, and/or path). Correspondingly, two network O–D matrix estimation models are proposed where the link-based model incorporates the flow conservation rule between O–D and link flows and uses the link-node incidence matrix, and the path-based model assumes a given link-path incidence matrix. An iterative solution procedure is designed to determine the network O–D matrix and link flow estimates. Results from numerical experiments suggest that the path-based model outperforms the link-based model in the estimation of network O–D matrices. The relative contributions of combinations of the two sensor types to the network O–D matrix estimation problem are also analyzed. They suggest that active sensors provide valuable path information to solve the O–D matrix estimation problem, but at the cost of a significantly higher unit price. The study results have key implications for heterogeneous sensor selection and location strategies.

Holes Detection in Wireless Sensor Networks: A Survey

Now a day's, it has been a great idea of research on using Wireless Sensor Networks (WSNs) to assist in the initial deployment of sensor nodes. Hole problem in WSNs is the most fundamental Problem in WSNs. Hole means a communication gap in WSNs. Finding an optimal sensor deployment strategy that would minimize the cost, reduce the node failure and also reduce the communication overhead. Then it provides a maximum degree of area coverage with lower cost of deployment of sensor nodes, best possible communication and maintaining the network connectivity. However, it increases the quality of service in WSNs that is extremely challenging. In this article, we present various types of holes, a comparative study of various types of holes and various types of coverage holes. At the end, we proposed an Algorithm to detect hole. In this paper, we aim to give the solution of hole problems of area coverage in WSNs. Index Terms—Holes, Coverage, Hole detection, Size estimation of hole.

Network design for distributed consensus estimation over heterogeneous sensor networks

This paper considers distributed estimation over heterogeneous sensor networks. We propose a distributed estimation strategy based on PageRank algorithm, where the link weight depends on the edge estimation covariances. We prove that the proposed estimator obtains better estimates than one typical estimator under identical initial conditions. Motivated by the advantage of the sensors with high accuracy locating at important positions, we propose an optimal sensor deployment strategy to arrange locations for sensors in a given physical network. Numerical examples are provided to demonstrate the effectiveness of the proposed estimator, showing a better performance under the optimal sensor deployment.

Distributed sensor networks—a review of recent research

Advances in sensor technology and computer networks have enabled distributed sensor networks (DSNs) to evolve from small clusters of large sensors to large swarms of micro-sensors, from fixed sensor nodes to mobile nodes, from wired communications to wireless communications, from static network topology to dynamically changing topology. However, these technological advances have also brought new challenges to processing large amount of data in a bandwidth-limited, power-constraint, unstable and dynamic environment. This paper reviews recent developments in DSNs from four aspects: network structure, data processing paradigm, sensor fusion algorithm with emphasis on fault-tolerant algorithm design, and optimal sensor deployment strategy.