Sensor Network Design Problem Research Articles

Sensor Network Design based on the Observability and Precision degree

The Unscented Kalman Filter is a state estimation method used in nonlinear dynamic systems to estimate the mean and covariance of a random variable undergoing a nonlinear transformation, knowing the process model and the measurements. Therefore, an adequate choice of the measured variables improves the performance of the filter technique. In this context, the sensor network design problem allows selecting a set of variables that minimizes the global estimation error when the instrumentation budget is limited. This is solved using a level traversal tree search algorithm, whose computation time is reduced by evaluating the design criteria sequentially. In this work, it is proposed to address the effect of the circumstantial loss of measurements on the system observability and the estimates precision. The success of the sensor network design methodology is demonstrated for the copolymerization process of Methyl Methacrylate and Vinyl Acetate, widely studied in the literature.

Tuning a hybrid SA based algorithm applied to Optimal Sensor Network Design

Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We are researching on the adjustments of its control parameters to obtain the best HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.

Value-Optimal Sensor Network Design for Steady-State and Closed-Loop Systems Using the Generalized Benders Decomposition

The problem of value-optimal sensor network design for linear systems has been shown to be of the nonconvex mixed integer programming class. While the branch and bound search procedure can be used to obtain a global solution, such a method is limited to fairly small systems. The bottleneck is that during each iteration of the branch and bound search, a fairly slow semi-definite programming (SDP) problem must be solved to its global optimum. In this paper, it is demonstrated that an equivalent reformulation of the nonconvex mixed integer programming problem and subsequent application of the generalized benders decomposition (GBD) algorithm will result in massive reductions in computational effort. While the proposed algorithm has to solve multiple mixed integer linear programs, this increase in computational effort is significantly outweighed by a reduction in the number of SDP problems that must be solved.

Reallocation index based sensor network design

Recently, the concept of reallocation index has been proposed in literature for sensor network design. In this work, we extend this concept to enable base case design of sensor networks for various criteria. Reallocation index indicates the possibilities of future reallocation of sensors while retrofitting an existing design. Higher value of reallocation index indicates more possibilities of reallocation of sensors that can be more cost effective than adding (upgrade) new sensors. Reallocation index is maximized as an additional objective while performing base case design of a sensor network. The resulting design is then optimal not only for the base case, but also ensures that any future upgrade and reallocation can be carried out effectively with less cost. The proposed sensor network design approach is illustrated on three types of sensor network design problems, namely selecting variables to be measured to ensure: (i) robust design for reliable fault diagnosis, (ii) observability of process variables after a change in the process layout, and (iii) estimation of important process variables with low uncertainty. For each case, simulation based experiments are performed to demonstrate the utility of base case designs obtained after maximization of reallocation index.

A Conditional Multi-copy Strategy with Multiple Central Nodes for More Secure Wireless Sensor Networks

We consider the Wireless Sensor Network(WSN) Design Problem under a conditional multi-copy and multi-path routing strategy so as to improve network reliability and security. Our strategy is an improvement over the simple restricted multi-copy strategies where every sensor duplicates its data since we provide comparable l evels of network reliability and yet extend network lifetime significantly. We provide mixed integer programming model for the problem and present several valid inequalities so as to improve our solution times. We provide some preliminary test results to support our assertions.

Dynamic model-based sensor network design algorithm for system efficiency maximization

A dynamic model-based sensor network design (DMSND) algorithm has been developed for maximizing system efficiency for an estimator-based control system. The algorithm synthesizes the optimal sensor network in the face of disturbances or set point changes. Computational expense of the large-scale combinatorial optimization problem is significantly reduced by parallel computing and by using combination of three novel strategies: multi-rate sampling frequency, model order reduction, and use of an incumbent solution that enables early termination of evaluation of infeasible sensor sets. The developed algorithm is applied to an acid gas removal unit as part of an integrated gasification combined cycle power plant with carbon capture. Even though there are more than thousand process states and more than hundred candidate sensor locations, the optimal sensor network design problem for maximizing process efficiency could be solved within couple of hours for a given budget.

Sensor network design for contaminant detection and identification in water distribution networks

Water distribution networks (WDN) are vulnerable to either intentional or accidental contamination. In order to protect against such intrusions, effective and efficient online monitoring systems are needed. Due to cost and maintenance reasons, it is not possible to locate sensors at each and every potential intrusion point. In this work, we design minimal sensor networks which satisfy the two important properties of observability (ability to detect an intrusion) and identifiability (ability to identify the point of intrusion). Based on the hydraulic analysis of the network, a bipartite graph is constructed between intrusion points and the corresponding nodes that can potentially be affected by the contaminant. The problem of sensor network design is converted to a minimum set cover problem on the bipartite graph, and is solved using a greedy heuristic algorithm. The proposed method is illustrated using a medium scale urban WDN.

Exploiting Sensor Response Times to Design Sensor Networks for Monitoring Water Distribution Networks

Abstract Water Distribution Networks (WDNs) are an integral part of society. Deliberate introduction of chemical or biological agents through accessible sites of a WDN can spread through the entire system and cause widespread damage to public health. In order to protect against such deliberate attacks on a WDN, an effective and efficient online monitoring system through sensors is needed. It is clear that sensors located at different nodes respond at different times depending on which vulnerable node is attacked. In the present study, we design sensor networks for contamination detection and identification which exploit the differences in sensor response times as additional information. A hydraulic analysis of the network is first carried out for a given loading condition to determine the flow directions and flow velocities in different pipes. Directed paths between vulnerable nodes and potential sensor nodes are used to construct a bipartite graph, and the sensor network design problem is formulated as a minimum set cover problem. Algorithms based on greedy heuristics are used to solve the set cover problem and obtain the corresponding sensor network. The proposed method is applied on two WDNs, and the use of sensor response times to obtain a design with reduced number of sensors is demonstrated.

Sensor network design for maximizing process efficiency: An algorithm and its application

Sensor network design (SND) is a constrained optimization problem requiring systematic and effective solution algorithms for determining where best to locate sensors. A SND algorithm is developed for maximizing plant efficiency for an estimator‐based control system while simultaneously satisfying accuracy requirements for the desired process measurements. The SND problem formulation leads to a mixed integer nonlinear programming (MINLP) optimization that is difficult to solve for large‐scale system applications. Therefore, a sequential approach is developed to solve the MINLP problem, where the integer problem for sensor selection is solved using the genetic algorithm while the nonlinear programming problem including convergence of the “tear stream” in the estimator‐based control system is solved using the direct substitution method. The SND algorithm is then successfully applied to a large scale, highly integrated chemical process. © 2014 American Institute of Chemical Engineers AIChE J, 61: 464–476, 2015

Optimal design of linear sensor networks for process plants: A multi-objective ant colony optimization approach

This investigation is performed to study nonredundant linear sensor network design problems that simultaneously optimize three objectives, namely cost, precision and reliability. In this article, a novel ant colony algorithm is proposed to efficiently tackle this multi-objective combinatorial problem. Both heuristic information and phenomenon are described as multiple matrices and a dynamic random weighted strategy is introduced to compute selection probability. A linear independent relationship is presented to guide a feasible solution construction process. An external archive is used to improve the convergence speed. Moreover, the TOPSIS method is adopted to aid in multi-criteria decision making with respect to Pareto-optimal solutions. The proposed method is successfully applied to a multi-objective design problem of a steam metering network of a methanol production plant. The results illustrate that the proposed method could find a set of global Pareto-optimal solutions, which is helpful for understanding the relationship among cost, precision and reliability, and consequently is helpful for final decision support. Therefore, the integrated methodology of a multi-objective ant colony algorithm and a multi-criteria decision making technique provides a promising tool for the optimal design of sensor networks by simultaneously considering multiple conflicting objectives.

Branch and Bound Algorithm for Optimal Sensor Network Design

The sensor network design procedure developed by Nabil and Narasimhan (2012), which relates process economics and data reconciliation, involves the formulation of a mixed integer cone program. The solution to this problem yields the globally optimal sensor network. A branch and bound method can be used to find the global optimum; however, for systems with large numbers of variables, this approach may require a large amount of computational effort to find the solution. In this paper, a specialized branch and bound algorithm is proposed for solving the sensor network design problem, which uses certain heuristics to obtain a solution faster. One involves a low rank factorization to reduce the size of the relaxed problem. The other involves an approximation of the global lower bound for the branch and bound solution. The utility of this algorithm is demonstrated on a simple flow network, a small but realistic evaporator system, and a medium sized steam metering network.

Audit of sensor networks for efficient fault diagnosis

The problem of sensor network design is to choose a set of variables that are to be measured in the process for satisfying various objectives related to estimability, data reconciliation, gross error detection, fault diagnosis, etc. Approaches to design base case sensor network as well as modify existing network by reallocating and upgrading sensors are available in the literature. In this work we extend the concept of reallocation and upgrade by developing an optimization based strategy to perform audit of a sensor network. Apart from identifying sensors to be reallocated and upgraded, the audit strategy also identifies sensors whose removal does not lead to a decrease in the performance of the sensor network. Such information can possibly be utilized in deciding what variables (or in what sequence) to be displayed to the process operator/engineer to accomplish a particular objective. In this work, we focus on fault diagnosis related performance criteria to illustrate the sensor network audit idea. In particular, we perform sensor network audit with objectives being: (i) ensuring observability of all faults, (ii) minimizing the unreliability of detection of faults (i.e. minimizing the probability of faults occurring and remaining undetected). The latter problem is in turn solved for two scenarios: when probability data and cause–effect models used for sensor network design are accurately known as well as for situations when there is uncertainty associated with such information. The resulting optimization problems are mixed integer linear programming in nature. The utility of the proposed approaches is demonstrated by applying them to the well known Tennessee Eastman process.

Fault Tolerant Sensor Network Design with Respect to Diagnosability Properties

Interest for more reliable and safety systems is growing as control systems become increasingly complex and encounter various unexpected component failures. Indeed, fault-tolerant control, and fault detection and identification take into account, at the early design stage, unexpected system failures. For this reason, the ability of a system (sensors, actuators, components) to tolerate a fault must be taken from designing. This paper deals with the problem of fault tolerant sensor network design. A new approach based on residual structure analysis is proposed to provide a fault tolerant architecture. The proposed analysis is based on diagnosability properties in case of sensor loss situations; it evaluates the capacity of the system to be fault tolerant. A graph representation is used to study diagnosability, and evaluate fault tolerance capacity in different cases of sensors distribution.

Lagrangian heuristic method for the wireless sensor network design problem in railway structural health monitoring

In this paper, we study a design method for minimizing the total cost of a wireless sensor network (WSN) used for health monitoring of railway structures. First, we present the problem, that is to simultaneously determine the number of relays and their deployment locations, the transmission power level for each sensor and relay, and the routes for transferring sensor data to a gateway using multi-hop wireless communication. Second, we formulate this task as a mathematical programming problem, and to solve this problem, we propose a near optimal algorithm based on the Lagrangian heuristic method. Finally, we verify the effectiveness of our algorithm through computational experiments carried out using data acquired from a real WSN used for railway structural health monitoring.

New efficient breadth-first/level traversal tree search method for the design and upgrade of sensor networks

This work studies the problem of optimally locating sensors for monitoring chemical processes, formally known as the sensor network design and upgrade problem. This problem is an integer programming problem and has been solved to global optimality only using tree search methods using depth-first strategy. In this article, we exploit certain cost properties of the different nodes in the tree to efficiently prune nonoptimal nodes using a breadth-first/level traversal tree search method to obtain the global optimum. We show that this strategy allows cutting considerably the computational time for problems that the depth-first strategy was inefficient. V C 2010 American Institute of Chemical Engineers AIChE J, 57: 1302–1309, 2011

Estimation of Distribution Algorithms: Applications to the Design of Process Sensor Networks

The optimal location of sensors involves the selection of type, number and location of sensorsfrom a set of available instruments with certain values of cost, precision and reliability. Theoptimal design not only satisfies economic criteria, but also some requirements on the qualityof key variable estimates. In this work, a methodology for solving the sensor network designproblem based on Estimation of Distribution Algorithms is presented. These algorithms areincluded in the Evolutionary Computation paradigm and substitute the probability distributionestimation of a population composed by potential solutions and its subsequent sampling forthe use of the classic crossover and mutation operators. The performance of the new strategyis evaluated and compared with that provided by other evolutionary techniques for the case ofa steam metering network of a methanol synthesis plant.

Coverage and connectivity in three‐dimensional underwater sensor networks

AbstractUnlike a terrestrial network, an underwater sensor network can have significant height which makes it a three‐dimensional network. There are many important sensor network design problems where the physical dimensionality of the network plays a significant role. One such problem is determining how to deploy minimum number of sensor nodes so that all points inside the network is within the sensing range of at least one sensor and all sensor nodes can communicate with each other, possibly over a multi‐hop path. The solution to this problem depends on the ratio of the communication range and the sensing range of each sensor. Under sphere‐based communication and sensing model, placing a node at the center of each virtual cell created by truncated octahedron‐based tessellation solves this problem when this ratio is greater than 1.7889. However, for smaller values of this ratio, the solution depends on how much communication redundancy the network needs. We provide solutions for both limited and full communication redundancy requirements. Copyright © 2008 John Wiley & Sons, Ltd.

Design of robust, reliable sensor networks using constraint programming

Various criteria have been considered in the literature for selection of optimal sensor networks. Amongst these, maximization of network reliability is an important criterion. While there are several approaches for designing maximum reliability networks, uncertainty in the available sensor reliability data has not been considered in these designs. In this article we present two novel formulations that incorporate robustness to uncertainties in the reliability data. Towards this end the sensor network design problem for maximizing reliability is formulated as explicit-optimization (MINLP) problem using failure rates of sensors which have better scaling properties instead of sensor reliabilities. Constraint programming (CP) has been used for solving the resulting optimization problems. Use of CP also enables easy generation of pareto front characterizing trade-offs between performance, cost and robustness for various uncertainty scenarios. The utility of the proposed approach is demonstrated on a case study taken from the literature.

Modeling Pairwise Key Establishment for Random Key Predistribution in Large-Scale Sensor Networks

Sensor networks are composed of a large number of low power sensor devices. For secure communication among sensors, secret keys are required to be established between them. Considering the storage limitations and the lack of post-deployment configuration information of sensors, random key predistribution schemes have been proposed. Due to limited number of keys, sensors can only share keys with a subset of the neighboring sensors. Sensors then use these neighbors to establish pairwise keys with the remaining neighbors. In order to study the communication overhead incurred due to pairwise key establishment, we derive probability models to design and analyze pairwise key establishment schemes for large-scale sensor networks. Our model applies the binomial distribution and a modified binomial distribution and analyzes the key path length in a hop-by-hop fashion. We also validate our models through a systematic validation procedure. We then show the robustness of our results and illustrate how our models can be used for addressing sensor network design problems.

A duality based framework for integrating reliability and precision for sensor network design

In sensor network design literature, requirements such as maximization of the network reliability [Y. Ali, S. Narasimhan, Sensor network design for maximizing reliability of linear processes, AIChE J. 39 (1993) 820–828; Y. Ali, S. Narasimhan, Redundant sensor network design for linear processes, AIChE J. 41 (1995) 2237–2249] and minimization of cost subject to precision constraints [M. Bagajewicz, Design and retrofit of sensor networks in process plants, AIChE J. 43 (1997) 2300–2306; M. Bagajewicz, E. Cabrera, New MILP formulation for instrumentation network design and upgrade, AIChE J. 48 (2002) 2271–2282] have been proposed as a criteria for optimally locating sensors. In this article, we show that the problems of maximizing reliability and maximizing precision (or minimizing variance) for linear processes are dual of each other. To achieve this duality, we propose transformations which can be used to convert sensor failure probabilities into equivalent sensor variances and vice versa. Thus, the duality enables working in a single framework with specified criteria on reliability as well as precision. As an application of this duality, we propose two formulations for the sensor network design problem viz., maximization of the network reliability subject to precision constraints and minimization of the network variance subject to reliability constraints. We also show the utility of these formulations to determine the pareto-front for the combinatorial sensor network design problem. Hydrodealkylation and steam-metering case studies are used to illustrate the proposed ideas.