Design Of Sensor Networks Research Articles

Sensors Allocation and Observer Design for Discrete Bilateral Teleoperation Systems with Multi-Rate Sampling.

This study addresses sensor allocation by analyzing exponential stability for discrete-time teleoperation systems. Previous studies mostly concentrate on the continuous-time teleoperation systems and neglect the management of significant practical phenomena, such as data-swap, the effect of sampling rates of samplers, and refresh rates of actuators on the system’s stability. A multi-rate sampling approach is proposed in this study, given the isolation of the master and slave robots in teleoperation systems which may have different hardware restrictions. This architecture collects data through numerous sensors with various sampling rates, assuming that a continuous-time controller stabilizes a linear teleoperation system. The aim is to assign each position and velocity signals to sensors with different sampling rates and divide the state vector between sensors to guarantee the stability of the resulting multi-rate sampled-data teleoperation system. Sufficient Krasovskii-based conditions will be provided to preserve the exponential stability of the system. This problem will be transformed into a mixed-integer program with LMIs (linear matrix inequalities). These conditions are also used to design the observers for the multi-rate teleoperation systems whose estimation errors converge exponentially to the origin. The results are validated by numerical simulations which are useful in designing sensor networks for teleoperation systems.

Optimal Wireless Distributed Sensor Network Design and Ad-Hoc Deployment in a Chemical Emergency Situation.

Industrial activities involve the manipulation of harmful chemicals. As there is no way to guarantee fail-safe operation, the means and response methods must be planned in advance to cope with a chemical disaster. In these situations, first responders assess the situation from the atmospheric conditions, but they have scant data on the source of the contamination, which curtails their response toolbox. Hence, a sensor deployment strategy needs to be formulated in real-time based on the meteorological conditions, sensor attributes, and resources. This work examined the tradeoff between sensor locations and their attributes. The findings show that if the sensor locations are optimal, the number is more important than quality, in that the sensors’ dynamic range is a significant factor when quantifying leaks but is less important if the goal is solely to locate the leak source/s. This methodology can be used for sensor location-allocation under real-life conditions and technological constraints.

Exact Algorithms for Maximum Lifetime Data-Gathering Tree in Wireless Sensor Networks

We tackle an optimization problem arising in the design of sensor networks: given a set of sensors, only one being connected to a backbone, to establish connection routes from each of them to the sink. Under a shortest path routing protocol, the set of connections form a spanning tree. Energy is required to transmit and receive data, and sensors have limited battery capacity: as soon as one sensor runs out of battery, a portion of the network is disconnected. We, therefore, search for the spanning tree maximizing the time elapsed before such a disconnection occurs, and therefore, maintenance is required. We propose new mathematical formulations for the problem, proving and exploiting theoretical results on its combinatorial structure. On that basis, we design algorithms offering a priori guarantees of global optimality. We undertake an extensive experimental campaign, showing our algorithms to outperform previous ones from the literature by orders of magnitude. We also identify which instance features have higher impact on network lifetime.

Coverage Optimization of Wireless Sensor Networks Using Combinations of PSO and Chaos Optimization

The coverage rate is the most crucial index in wireless sensor networks (WSNs) design; it involves making the sensors with a reasonable distribution, which closely relates to the quality of service (QoS) and survival period of the entire network. This article proposes to use particle swarm optimization (PSO) and chaos optimization in conjunction for the coverage optimization. All sensor locations are encoded together as a particle position. PSO was used first to make sensors move close to their optimal positions; furthermore, a variable domain chaos optimization algorithm (VDCOA) was employed to reach a higher coverage rate, along with improved evenness and average moving distance. Six versions of VDCOA, taking circle, logistic, Gaussian, Chebyshev, sinusoidal and cubic maps, respectively, were investigated. The simulation experiment tested three cases: square, rectangular and circular regions using nine algorithms: six versions of PSO plus VDCOA, PSO and other two PSO variants. All six versions showed better performance than PSO and CPSO, with coverage all exceeding 90% for the first two cases. Moreover, one version, PSO plus circle map (PSO-Circle), increased the coverage rate by 3.17%, 2.41% and 12.94% compared with PSO in three cases, respectively, and outperformed the other eight algorithms.

Simulation analysis method of expandable and flexible sensor networks based on the flexible printed circuit process

Aircraft structural health monitoring (SHM) technology is a revolutionary technology based on sensor networks, which has the advantages of ensuring flight safety, extending structural life, and reducing maintenance costs. Due to the large size of aircraft structures and large deformation of morphing wings, the sensor networks integrated into the aircraft structures should be large-area, expandable, and flexible. Island-interconnect structure design in flexible electronics is expected to realize such sensor networks, with the help of large-area and low-cost flexible printed circuit (FPC) process. Mechanical analysis can guide the design of island-interconnect sensor networks. However, few studies have been reported to analyze the mechanical properties of whole island-interconnect sensor networks. This paper proposes a simulation analysis method based on the finite element method for the mechanical analysis of expandable and flexible sensor networks. This method mainly includes two parts: extracting the buckling modes through linear buckling analysis and obtaining the deformation states and mechanical properties through postbuckling behavior analysis. It is applied on the mechanical analysis of island-interconnect sensor networks with different geometry designs, including serpentine, fractal, and irregular island-interconnect sensor networks. Tensile experiments of island-interconnect sensor networks manufactured by the FPC process show that the deformation of experimental results is basically consistent with that of simulation results. In conclusion, the proposed simulation analysis method is appliable for the mechanical analysis of serpentine, fractal, and irregular island-interconnect structure networks, guiding the structural design of expandable and flexible sensor networks for large-area SHM of aircraft.

Joint Optimization on Power Allocation and Splitting for WSNs With SWIPT-Based Relay

How to prolong network lifetime has become an important issue in the design of large scale wireless sensor networks (WSNs). In this paper, a novel power saving scheme for conventional WSNs via simultaneous wireless information and power transfer (SWIPT) based relays is proposed. Unlike conventional relays based communications need extra energy supply for data forwarding, the relay applied in this paper works in an energy-free manner with the support of SWIPT. The power saving model with both power splitting (PS) and time switching (TS) based SWIPT are proposed. Then, we formulate the joint power allocation and splitting problems of SWIPT relay assisted WSNs as non-convex constrained optimization problems. Since the formulated problems are non-convex, semi-positive definite programming (SDP) algorithms are proposed to find the joint optimization on power allocation and splitting with low complexity. The simulation results show that the PS model has more advantages than the traditional direct communication model in long distance transmission. Under the same information receiving strategy, the PS model outperforms the TS model.

Design of Fiber Bragg Grating Sensor Networks

This study explores the effective use of a spectral area defined by a radiation source for multipoint measurements with fiber Bragg grating (FBG) sensors. The capacity of the sensor network based on a wavelength multiplex is limited by the spectral work area of the used radiation source and by several other parameters, such as the spectral parameters of individual sensors, type of the measured quantity and measurement range, sensitivity coefficients, production tolerances, and protection zones among the measuring channels. This is why it is necessary to use this limited area in an “economical” manner. The initial part of this article explores modeling of sensor networks using Bragg gratings, an output of which is an analysis of the impact of individual parameters on the capacity of a sensor network. These models are then applied to the mathematical definition of the given network proposal formed by real sensors. At the end, the stated principles are verified by real measurements with a sensor network formed by five Bragg sensors. The study points to the necessity of paying close attention to the proposals of sensor networks with FBG for the purpose of the effective use of the given spectral work area, increasing capacity, and reducing the possibility of crosstalk when assessing the Bragg sensors.

Lightweight Key Management Scheme Using Fuzzy Extractor for Wireless Mobile Sensor Network

The mature design of wireless mobile sensor network makes it to be used in vast verities of applications including from home used to the security surveillance. All such types of applications based on wireless mobile sensor network are generally using real time data, most of them are interested in real time communication directly from cluster head of cluster instead of a base station in cluster network. This would be possible if an external user allows to directly access real time data from the cluster head in cluster wireless mobile sensor network instead of accessing data from base station. But this leads to a serious security breach and degrades the performance of any security protocol available in this domain. Most existing schemes for authentication and cluster key management for external users, exchange a number of messages between cluster head and base station to allow external to access real time data from the base station instead of cluster head. This increase communication cost and delay in such real time access information. To handle this critical issue in cluster wireless mobile sensor network, we propose a lightweight authentication and key management scheme using a fuzzy extractor. In this scheme, any external user can access data directly from the cluster head of any cluster without the involvement of the base station. The proposed scheme only uses the one-way hash functions and bitwise XOR operations, apart from the fuzzy extractor method for the user biometric verification at the login phase. The presented scheme supports scalability for an increasing number of nodes using polynomials. The proposed scheme increases the life-time of the network by decreasing the key pool size.

Design of Cobweb Fiber Bragg Grating Sensor Network Based on Gated Circulation Unit

Design of Cobweb Fiber Bragg Grating Sensor Network Based on Gated Circulation Unit

Design and Validation of a Wireless Body Sensor Network for Integrated EEG and HD-sEMG Acquisitions.

Sensorimotor integration is the process through which the human brain plans the motor program execution according to external sources. Within this context, corticomuscular and corticokinematic coherence analyses are common methods to investigate the mechanism underlying the central control of muscle activation. This requires the synchronous acquisition of several physiological signals, including EEG and sEMG. Nevertheless, physical constraints of the current, mostly wired, technologies limit their application in dynamic and naturalistic contexts. In fact, although many efforts were made in the development of biomedical instrumentation for EEG and High Density-surface EMG (HD-sEMG) signal acquisition, the need for an integrated wireless system is emerging. We hereby describe the design and validation of a new fully wireless body sensor network for the integrated acquisition of EEG and HD-sEMG signals. This Body Sensor Network is composed of wireless bio-signal acquisition modules, named sensor units, and a set of synchronization modules used as a general-purpose system for time-locked recordings. The system was characterized in terms of accuracy of the synchronization and quality of the collected signals. An in-depth characterization of the entire system and an head-to-head comparison of the wireless EEG sensor unit with a wired benchmark EEG device were performed. The proposed device represents an advancement of the State-of-the-Art technology allowing the integrated acquisition of EEG and HD-sEMG signals for the study of sensorimotor integration.

Optimal sensor placement to detect ruptures in pipeline systems subject to uncertainty using an Adam-mutated genetic algorithm

Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which may include economic loss or even human casualty. Optimal sensor placement is required to detect and assess damage so that the optimal amount of resources is deployed and damage is minimized. This paper presents a novel methodology to determine the optimal location of sensors in a pipeline network for real-time monitoring. First, a lumped model of a small-scale pipeline network is built to simulate the behavior of working fluid. By propagating the inherent variability of hydraulic parameters in the simulation model, uncertainty in the behavior of the working fluid is evaluated. Sensor measurement error is also incorporated. Second, predefined damage scenarios are implemented in the simulation model and estimated through a damage classification algorithm using acquired data from the sensor network. Third, probabilistic detectability is measured as a performance metric of the sensor network. Finally, a detectability-based optimization problem is formulated as a mixed integer non-linear programming problem. An Adam-mutated genetic algorithm (AMGA) is proposed to solve the problem. The Adam-optimizer is incorporated as a mutation operator of the genetic algorithm to increase the capacity of the algorithm to escape from the local minimum. The performance of the AMGA is compared with that of the standard genetic algorithm. A case study using a pipeline system is presented to evaluate the performance of the proposed sensor network design methodology.

Інформаційна технологія забезпечення живучості сенсорних мереж

In the modern technological world, sensors and sensor networks are widely used in the all spheres of human activity. One of the key engineering tasks in the design of sensor networks is to ensure the survivability of the network. The article presents algorithms for ensuring the survivability of sensor networks based on a preliminary assessment of this property. Estimation of survivability depends on the network topology. Increasing the survivability of the sensor network is achieved by including additional sensors in the network. An algorithm for finding the position of additional sensors in the stationary sensor network is proposed. Proposed approach takes into account the radius of coverage of the sensors and the need for the intersection of the coverage areas of the sensors to ensure the exchange of information. An iterative algorithm for ensuring the survivability of the network in the presence and consideration of dynamic sensors is presented. The results of computer simulation experiments presented in the paper confirm the effectiveness of the proposed approaches.

A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments.

Optimal sensor network deployment in built environments for tracking, surveillance, and monitoring of dynamic phenomena is one of the most challenging issues in sensor network design and applications (e.g., people movement). Most of the current methods for sensor network deployment and optimization are empirical and they often result in important coverage gaps in the monitored areas. To overcome these limitations, several optimization methods have been proposed in the recent years. However, most of these methods oversimplify the environment and do not consider the complexity of 3D architectural nature of the built environments specially for indoor applications (e.g., indoor navigation, evacuation, etc.). In this paper, we propose a novel local optimization algorithm based on a 3D Voronoi diagram, which allows a clear definition of the proximity relations between sensors in 3D indoor environments. This proposed structure is integrated with an IndoorGML model to efficiently manage indoor environment components and their relations as well as the sensors in the network. To evaluate the proposed method, we compared our results with the Genetic Algorithm (GA) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithms. The results show that the proposed method achieved 98.86% coverage which is comparable to GA and CMA-ES algorithms, while also being about six times more efficient.

Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review

The design of Wireless Sensor Networks (WSN) requires the fulfillment of several design requirements. The most important one is optimizing the battery’s lifetime, which is tightly coupled to the sensor lifetime. End-users usually avoid replacing sensors’ batteries, especially in massive deployment scenarios like smart agriculture and smart buildings. To optimize battery lifetime, wireless sensor designers need to delineate and optimize active components at different levels of the sensor’s layered architecture, mainly, (1) the number of data sets being generated and processed at the application layer, (2) the size and the architecture of the operating systems (OS), (3) the networking layers’ protocols, and (4) the architecture of electronic components and duty cycling techniques. This paper reviews the different relevant technologies and investigates how they optimize energy consumption at each layer of the sensor’s architecture, e.g., hardware, operating system, application, and networking layer. This paper aims to make the researcher aware of the various optimization opportunities when designing WSN nodes. To our knowledge, there is no other work in the literature that reviews energy optimization of WSN in the context of Smart Energy-Efficient Buildings (SEEB) and from the formerly four listed perspectives to help in the design and implementation of optimal WSN for SEEB.

Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids

Wireless sensor networks are used to monitor the operating status of the microgrids, which can effectively improve the stability of power supplies. The topology control is a critical issue of wireless sensor networks, which affects monitoring data transmission reliability and lifetime of wireless sensor networks. Meanwhile, the data acquisition accuracy of wireless sensor networks has a great impact on the quality of monitoring. Therefore, this paper focuses on improving wireless sensor networks data acquisition satisfaction and energy efficiency. A joint acquisition time design and sensor association optimization algorithm is proposed to prolong the lifetime of wireless sensor networks and enhance the stability of monitoring, which considers the cluster heads selection, data collection satisfaction and sensor association. First, a multi-constrained mixed-integer programming problem, which combines acquisition time design and sensor association, is formulated to maximize data acquisition satisfaction and minimize energy consumption. To solve this problem, we propose an iterative algorithm based on block coordinate descent technology. In each iteration, the acquisition time is obtained by Lagrangian duality. After that, the sensor association is modeled as a 0–1 knapsack problem, and the three different methods are proposed to solve it. Finally, the simulations are provided to demonstrate the efficiency of the algorithm proposed in this paper.

Reliability Analysis of Wireless Sensor Network for Smart Farming Applications.

Wireless Sensor Networks are subjected to some design constraints (e.g., processing capability, storage memory, energy consumption, fixed deployment, etc.) and to outdoor harsh conditions that deeply affect the network reliability. The aim of this work is to provide a deeper understanding about the way redundancy and node deployment affect the network reliability. In more detail, the paper analyzes the design and implementation of a wireless sensor network for low-power and low-cost applications and calculates its reliability considering the real environmental conditions and the real arrangement of the nodes deployed in the field. The reliability of the system has been evaluated by looking for both hardware failures and communication errors. A reliability prediction based on different handbooks has been carried out to estimate the failure rate of the nodes self-designed and self-developed to be used under harsh environments. Then, using the Fault Tree Analysis the real deployment of the nodes is taken into account considering the Wi-Fi coverage area and the possible communication link between nearby nodes. The findings show how different node arrangements provide significantly different reliability. The positioning is therefore essential in order to obtain maximum performance from a Wireless sensor network.

Robustness Performance Analysis of OEERS Clustering Technique for Heterogeneous Network Models

Objectives: To design an Optimized and Energy Efficient Routing Scheme (OEERS) for heterogeneous wireless sensor networks. Methods: In OEERS algorithm the cluster head selection is done with the probability values based on available energy levels along with optimal cluster counts and node distance from base station. Hence, nodes with higher energy and located near to base station have higher probability to be selected as cluster head (CH). Findings: In this work, analysis for OEERS has been executed to prove the robust network performance for the designed sensor network in a multi-level heterogeneous environment in terms of key network parameters such as Arrival of Dead Nodes in various stages, Number of Packets Transmitted to Base Station, Number of Dead Nodes during Each Round, Residual Energy during Each Round, Dissipated Energy during Each Round, etc. Besides this, comparative analysis has also been performed to showcase the out performance of the OEERS technique over the DL-LEACH scheme. Keywords: Cluster Head; Alive node; Advance and master nodes; Network Lifetime; Heterogeneous Wireless Sensor Network

A methodology for sensor number and placement optimization for vibration-based damage detection of composite structures under model uncertainty

Structural health monitoring techniques for composite structures are dependent on the data acquired from sensors; thus, optimal sensor network design is important to provide adequate and reliable information. This paper presents a novel framework for optimizing the number of sensors and sensor placement under model uncertainty for vibration-based damage detection in composite structures. The number of target vibration modes to be identified is first determined, based on the sum of modal effective mass fractions, to sufficiently capture dynamic responses. Since any point on the structure can be a candidate sensor position (causing a large design space), a modal kinetic-energy-based index is proposed to narrow the design space. Design objectives simultaneously minimize the number of sensors and the mean and standard deviation values for the root mean square error of off-diagonal terms in the modal assurance criterion matrix. The nondominated sorting genetic algorithm II is adopted to solve this problem. Monte Carlo simulation (MCS) is applied to evaluate the latter two objective functions. To reduce computation costs, real performance evaluations in MCS are replaced with Gaussian process regression models. To validate the optimized sensors, an optimization-based delamination detection process is applied. Case studies are presented to demonstrate the developed optimization framework.

Compact and Wideband PIFA Design for Wireless Body Area Sensor Networks

The specific advantage of fractal geometry to realize compact antenna features is exploited in this work for the design of a miniaturized Planar Inverted-F Antenna configuration with a large bandwidth. The conventional quadrilateral radiating element of a Planar Inverted-F Antenna is replaced by a Minkowski pre-fractal-based shape, thus increasing the resonant wavelength without affecting the overall antenna dimensions. Consequently, with the new design, a physically smaller antenna can achieve the same resonant frequency of a larger conventional configuration. Measured as well as simulated reflection coefficient and radiation patterns are presented to validate the assumptions. The impedance bandwidth of the antenna (2.19 to 2.52 GHz) covers the ISM band with a boresight gain of 1.5–2 dB over the entire band. Furthermore, to demonstrate the miniaturization effect, a successful comparison is provided with an identically sized, conventional square Planar Inverted-F Antenna design. The proposed antenna design can be usefully adopted for power-efficient communications in the framework of Wireless Body Area Sensor Networks.

End-to-end sensor and neural network design using differential ray tracing.

In this paper we propose a new method to jointly design a sensor and its neural-network based processing. Using a differential ray tracing (DRT) model, we simulate the sensor point-spread function (PSF) and its partial derivative with respect to any of the sensor lens parameters. The proposed ray tracing model makes no thin lens nor paraxial approximation, and is valid for any field of view and point source position. Using the gradient backpropagation framework for neural network optimization, any of the lens parameter can then be jointly optimized along with the neural network parameters. We validate our method for image restoration applications using three proves of concept of focus setting optimization of a given sensor. We provide here interpretations of the joint optical and processing optimization results obtained with the proposed method in these simple cases. Our method paves the way to end-to-end design of a neural network and lens using the complete set of optical parameters within the full sensor field of view.