Sensor deployment optimization based on optimal recovery interpolation

Automated and Optimized Sensor Deployment using Building Models and Electromagnetic Simulation

The rapid development of Wireless Sensor Network (WSN) has increased its application in the military, industry, medical, and environment. Coverage improvement is one of the main issue in WSN deployment. Maximum coverage ensures a better utility of the WSN. This paper considers the use of Discrete Particle Swarm Optimization (Discrete PSO) algorithm to deal with optimal deployment problem in non-convex region. Simulation results show that the Discrete PSO algorithm leads to performance improvement compared with random sensor deployment.

We study the heterogeneous wireless sensor networks (WSNs) and propose the necessary condition of the optimal sensor deployment. Similar to that in homogeneous WSNs, the necessary condition implies that every sensor node location should coincide with the centroid of its own optimal sensing region. Moreover, we discuss the dynamic sensor deployment in both homogeneous and heterogeneous WSNs with limited communication range for the sensor nodes. The purpose of sensor deployment is to improve sensing performance, reflected by distortion and coverage. We model the sensor deployment problem as a source coding problem with distortion reflecting sensing accuracy. Traditionally, coverage is the area covered by the sensor nodes. However, when the communication range is limited, a WSN may be divided into several disconnected sub-graphs. Under such a scenario, neither the conventional distortion nor the coverage represents the sensing performance as the collected data in disconnected sub-graphs cannot be communicated with the access point. By defining an appropriate distortion measure, we propose a Restrained Lloyd (RL) algorithm and a Deterministic Annealing (DA) algorithm to optimize sensor deployment in both homogeneous and heterogeneous WSNs. Our simulation results show that both DA and RL algorithms outperform the existing Lloyd algorithm when communication range is limited.

We study the heterogeneous wireless sensor networks (WSNs) and propose the necessary condition of the optimal sensor deployment. Similar to that in homogeneous WSNs, the necessary condition implies that every sensor node location should coincide with the centroid of its own optimal sensing region. Moreover, we discuss the dynamic sensor deployment in both the homogeneous and the heterogeneous WSNs with limited communication range for the sensor nodes. The purpose of sensor deployment is to improve sensing performance, reflected by distortion and coverage. We model the sensor deployment problem as a source coding problem with distortion reflecting sensing accuracy. However, when the communication range is limited, a WSN is divided into several disconnected sub-graphs under certain conditions as we will discuss in this paper. In such a scenario, neither the conventional distortion nor the coverage represents the sensing performance as the collected data in disconnected sub-graphs cannot be communicated with the access point. By defining an appropriate sensing performance measure, we propose a Restrained Lloyd (RL) algorithm and a deterministic annealing (DA) algorithm to optimize sensor deployment in both the homogeneous and heterogeneous WSNs. Our simulation results show that both the DA and the RL algorithms outperform the existing algorithms when communication range is limited.

The optimum deployment of sensors to provide the highest rate of environmental coverage in wireless sensor networks is one of the most important challenges in these types of networks. Apart from the analysis of data, the impact of an intrusion detection system on the performance of system is in a direct relation with the amount of collected data by sensors. Thus, proper deployment of the data collection sensors in intrusion detection systems is quite an important issue. In this article, we consider the subject of optimizing the coverage for wireless sensor networks without infrastructures by using wireless intrusion detection sensors. Due to the efficiency of wireless networks without infrastructures in inner and outer environments, both of these environments are considered in the simulations. Moreover, some of effective parameters in installation the sensors, such as existence of connecting chains between the sensors for transferring collected data, preferred areas for coverage, along with negative effects in distribution of wireless waves like environmental obstacles, the signal strength after weakening and the minimum receiving potentials in the original receiver are considered in simulations. In such situations, a real-world environment and actual conditions are provided in covering and deployment of the sensors. Simulations are done by MATLAB and the covering rates are given both numerically and graphically as the outcomes of each experiment. The results indicate the proper deployment of the sensors and obtaining the most rates in environmental covering by the sensors with regards to the applied limitations.

When monitoring environments with wireless sensor networks, optimal sensor deployment is a fundamental issue and an effective means to achieve desired performance. Selecting best sensor deployment has a dependence on the deployment environments. Existing works address sensor deployment within three types of environments including one dimensional line, 2-D field and 3-D space. However, in many applications the deployment environments usually have network structures, which cannot be simply classified as the three types. The deployed locations and communications of sensor nodes are limited onto the network edges, which make the deployment problem distinct from that in other types of environments. In this paper, we study sensor deployment in network-structured environments and aim to achieve k-coverage while minimizing the number of sensor nodes. Furthermore, we jointly consider the optimization of sink deployment and routing strategies with the goal to minimize the network communication cost of data collection. To the best of our knowledge, this paper is the first one to tackle sensor/sink deployment under the deployment constraints imposed by the network structure. The hardness of the problems is shown. Polynomial-time algorithms are proposed to determine optimal sensor/sink deployment and routing strategies in tree-topology network structure. Efficient approximation algorithms are proposed for the general graph network structure and their performances are analyzed. Theoretical results and extensive simulations show the efficiency of the proposed algorithms.

PREFACE. CONTRIBUTORS. I SENSOR NETWORK OPERATIONS OVERVIEW. 1 Overview of Mission-Oriented Sensor Networks. 1.1 Introduction. 1.2 Trends in Sensor Development. 1.3 Mission-Oriented Sensor Networks: Dynamic Systems Perspective. References. II SENSOR NETWORK DESIGN AND OPERATIONS. 2 Sensor Deployment, Self-Organization, and Localization. 2.1 Introduction. 2.2 SCARE: A Scalable Self-Configuration and Adaptive Reconfiguration Scheme for Dense Sensor Networks. 2.3 Robust Sensor Positioning in Wireless Ad Hoc Sensor Networks. 2.4 Trigonometric k Clustering (TKC) for Censored Distance Estimation. 2.5 Sensing Coverage and Breach Paths in Surveillance Wireless Sensor Networks. References. 3 Purposeful Mobility and Navigation. 3.1 Introduction. 3.2 Controlled Mobility for Efficient Data Gathering in Sensor Networks with Passively Mobile Nodes. 3.3 Purposeful Mobility in Tactical Sensor Networks. 3.4 Formation and Alignment of Distributed Sensing Agents with Double-Integrator Dynamics and Actuator Saturation. 3.5 Modeling and Enhancing the Data Capacity of Wireless Sensor Networks. References. 4 Lower Layer Issues-MAC, Scheduling, and Transmission. 4.1 Introduction. 4.2 SS-TDMA: A Self-Stabilizing Medium Access Control (MAC) for Sensor Networks. 4.3 Comprehensive Performance Study of IEEE 802.15.4. 4.4 Providing Energy Efficiency for Wireless Sensor Networks Through Link Adaptation Techniques. References. 5 Network Routing. 5.1 Introduction. 5.2 Load-Balanced Query Protocols for Wireless Sensor Networks. 5.3 Energy-Efficient and MAC-Aware Routing for Data Aggregation in Sensor Networks. 5.4 LESS: Low-Energy Security Solution for Large-scale Sensor Networks Based on Tree-Ripple-Zone Routing Scheme. References. 6 Power Management. 6.1 Introduction. 6.2 Adaptive Sensing and Reporting in Energy-Constrained Sensor Networks. 6.3 Sensor Placement and Lifetime of Wireless Sensor Networks: Theory and Performance Analysis. 6.4 Algorithms for Maximizing Lifetime of Battery-Powered Wireless Sensor Nodes. 6.5 Battery Lifetime Estimation and Optimization for Underwater Sensor Networks. References. 7 Distributed Sensing and Data Gathering. 7.1 Introduction. 7.2 Secure Differential Data Aggregation for Wireless Sensor Networks. 7.3 Energy-Conserving Data Gathering Strategy Based on Trade-off Between Coverage and Data Reporting Latency in Wireless Sensor Networks. 7.4 Quality-Driven Information Processing and Aggregation in Distributed Sensor Networks. 7.5 Progressive Approach to Distributed Multiple-Target Detection in Sensor Networks. References. 8 Network Security. 8.1 Introduction. 8.2 Energy Cost of Embedded Security for Wireless Sensor Networks. 8.3 Increasing Authentication and Communication Confidentiality in Wireless Sensor Networks. 8.4 Efficient Pairwise Authentication Protocols for Sensor and Ad Hoc Networks. 8.5 Fast and Scalable Key Establishment in Sensor Networks. 8.6 Weil Pairing-Based Round, Efficient, and Fault-Tolerant Group Key Agreement Protocol for Sensor Networks. References. III SENSOR NETWORK APPLICATIONS. 9 Pursuer-Evader Tracking in Sensor Networks. 9.1 Introduction. 9.2 The Problem. 9.3 Evader-Centric Program. 9.4 Pursuer-Centric Program. 9.5 Hybrid Pursuer-Evader Program. 9.6 Efficient Version of Hybrid Program. 9.7 Implementation and Simulation Results. 9.8 Discussion and Related Work. References. 10 Embedded Soft Sensing for Anomaly Detection in Mobile Robotic Networks. 10.1 Introduction. 10.2 Mobile Robot Simulation Setup. 10.3 Software Anomalies in Mobile Robotic Networks. 10.4 Soft Sensor. 10.5 Software Anomaly Detection Architecture. 10.6 Anomaly Detection Mechanisms. 10.7 Test Bed for Software Anomaly Detection in Mobile Robot Application. 10.8 Results and Discussion. 10.9 Conclusions and Future Work. Appendix A. Appendix B. References. 11 Multisensor Network-Based Framework for Video Surveillance: Real-Time Superresolution Imaging. 11.1 Introduction. 11.2 Basic Model of Distributed Multisensor Surveillance System. 11.3 Superresolution Imaging. 11.4 Optical Flow Computation. 11.5 Superresolution Image Reconstruction. 11.6 Experimental Results. 11.7 Conclusion. References. 12 Using Information Theory to Design Context-Sensing Wearable Systems. 12.1 Introduction. 12.2 Related Work. 12.3 Theoretical Background. 12.4 Adaptations. 12.5 Design Considerations. 12.6 Case Study. 12.7 Results. 12.8 Conclusion. Appendix. References. 13 Multiple Bit Stream Image Transmission over Wireless Sensor Networks. 13.1 Introduction. 13.2 System Description. 13.3 Experimental Results. 13.4 Summary and Discussion. References. 14 Hybrid Sensor Network Test Bed for Reinforced Target Tracking. 14.1 Introduction. 14.2 Sensor Network Operational Components. 14.3 Sensor Network Challenge Problem. 14.4 Integrated Target Surveillance Experiment. 14.5 Experimental Results and Evaluation. 14.6 Conclusion. References. 15 Noise-Adaptive Sensor Network for Vehicle Tracking in the Desert. 15.1 Introduction. 15.2 Distributed Tracking. 15.3 Algorithms. 15.4 Experimental Methods. 15.5 Results and Discussion. 15.6 Conclusion. References. ACKNOWLEDGMENTS. INDEX. ABOUT THE EDITORS.

PREFACE. CONTRIBUTORS. I SENSOR NETWORK OPERATIONS OVERVIEW. 1 Overview of Mission-Oriented Sensor Networks. 1.1 Introduction. 1.2 Trends in Sensor Development. 1.3 Mission-Oriented Sensor Networks: Dynamic Systems Perspective. References. II SENSOR NETWORK DESIGN AND OPERATIONS. 2 Sensor Deployment, Self-Organization, and Localization. 2.1 Introduction. 2.2 SCARE: A Scalable Self-Configuration and Adaptive Reconfiguration Scheme for Dense Sensor Networks. 2.3 Robust Sensor Positioning in Wireless Ad Hoc Sensor Networks. 2.4 Trigonometric k Clustering (TKC) for Censored Distance Estimation. 2.5 Sensing Coverage and Breach Paths in Surveillance Wireless Sensor Networks. References. 3 Purposeful Mobility and Navigation. 3.1 Introduction. 3.2 Controlled Mobility for Efficient Data Gathering in Sensor Networks with Passively Mobile Nodes. 3.3 Purposeful Mobility in Tactical Sensor Networks. 3.4 Formation and Alignment of Distributed Sensing Agents with Double-Integrator Dynamics and Actuator Saturation. 3.5 Modeling and Enhancing the Data Capacity of Wireless Sensor Networks. References. 4 Lower Layer Issues-MAC, Scheduling, and Transmission. 4.1 Introduction. 4.2 SS-TDMA: A Self-Stabilizing Medium Access Control (MAC) for Sensor Networks. 4.3 Comprehensive Performance Study of IEEE 802.15.4. 4.4 Providing Energy Efficiency for Wireless Sensor Networks Through Link Adaptation Techniques. References. 5 Network Routing. 5.1 Introduction. 5.2 Load-Balanced Query Protocols for Wireless Sensor Networks. 5.3 Energy-Efficient and MAC-Aware Routing for Data Aggregation in Sensor Networks. 5.4 LESS: Low-Energy Security Solution for Large-scale Sensor Networks Based on Tree-Ripple-Zone Routing Scheme. References. 6 Power Management. 6.1 Introduction. 6.2 Adaptive Sensing and Reporting in Energy-Constrained Sensor Networks. 6.3 Sensor Placement and Lifetime of Wireless Sensor Networks: Theory and Performance Analysis. 6.4 Algorithms for Maximizing Lifetime of Battery-Powered Wireless Sensor Nodes. 6.5 Battery Lifetime Estimation and Optimization for Underwater Sensor Networks. References. 7 Distributed Sensing and Data Gathering. 7.1 Introduction. 7.2 Secure Differential Data Aggregation for Wireless Sensor Networks. 7.3 Energy-Conserving Data Gathering Strategy Based on Trade-off Between Coverage and Data Reporting Latency in Wireless Sensor Networks. 7.4 Quality-Driven Information Processing and Aggregation in Distributed Sensor Networks. 7.5 Progressive Approach to Distributed Multiple-Target Detection in Sensor Networks. References. 8 Network Security. 8.1 Introduction. 8.2 Energy Cost of Embedded Security for Wireless Sensor Networks. 8.3 Increasing Authentication and Communication Confidentiality in Wireless Sensor Networks. 8.4 Efficient Pairwise Authentication Protocols for Sensor and Ad Hoc Networks. 8.5 Fast and Scalable Key Establishment in Sensor Networks. 8.6 Weil Pairing-Based Round, Efficient, and Fault-Tolerant Group Key Agreement Protocol for Sensor Networks. References. III SENSOR NETWORK APPLICATIONS. 9 Pursuer-Evader Tracking in Sensor Networks. 9.1 Introduction. 9.2 The Problem. 9.3 Evader-Centric Program. 9.4 Pursuer-Centric Program. 9.5 Hybrid Pursuer-Evader Program. 9.6 Efficient Version of Hybrid Program. 9.7 Implementation and Simulation Results. 9.8 Discussion and Related Work. References. 10 Embedded Soft Sensing for Anomaly Detection in Mobile Robotic Networks. 10.1 Introduction. 10.2 Mobile Robot Simulation Setup. 10.3 Software Anomalies in Mobile Robotic Networks. 10.4 Soft Sensor. 10.5 Software Anomaly Detection Architecture. 10.6 Anomaly Detection Mechanisms. 10.7 Test Bed for Software Anomaly Detection in Mobile Robot Application. 10.8 Results and Discussion. 10.9 Conclusions and Future Work. Appendix A. Appendix B. References. 11 Multisensor Network-Based Framework for Video Surveillance: Real-Time Superresolution Imaging. 11.1 Introduction. 11.2 Basic Model of Distributed Multisensor Surveillance System. 11.3 Superresolution Imaging. 11.4 Optical Flow Computation. 11.5 Superresolution Image Reconstruction. 11.6 Experimental Results. 11.7 Conclusion. References. 12 Using Information Theory to Design Context-Sensing Wearable Systems. 12.1 Introduction. 12.2 Related Work. 12.3 Theoretical Background. 12.4 Adaptations. 12.5 Design Considerations. 12.6 Case Study. 12.7 Results. 12.8 Conclusion. Appendix. References. 13 Multiple Bit Stream Image Transmission over Wireless Sensor Networks. 13.1 Introduction. 13.2 System Description. 13.3 Experimental Results. 13.4 Summary and Discussion. References. 14 Hybrid Sensor Network Test Bed for Reinforced Target Tracking. 14.1 Introduction. 14.2 Sensor Network Operational Components. 14.3 Sensor Network Challenge Problem. 14.4 Integrated Target Surveillance Experiment. 14.5 Experimental Results and Evaluation. 14.6 Conclusion. References. 15 Noise-Adaptive Sensor Network for Vehicle Tracking in the Desert. 15.1 Introduction. 15.2 Distributed Tracking. 15.3 Algorithms. 15.4 Experimental Methods. 15.5 Results and Discussion. 15.6 Conclusion. References. ACKNOWLEDGMENTS. INDEX. ABOUT THE EDITORS.

The main objective of sensor deployment problem in Wireless Sensor Network (WSN) is to use minimum number of sensor nodes with given sensing range that can cover any target in the coverage area to monitor the environment. The optimal sensor deployment enables accurate sensing information on target behavior with minimum sensing range and number of sensor nodes. The target coverage terrain in a locality need not be a smooth rectangle which makes the deployment problem more complex. The optimal sensor deployment is a problem of maximizing coverage and minimizing number of sensor nodes which has been proved to be NP-hard. Artificial Bee Colony (ABC) algorithm, inspired by the food foraging behavior of honey bees is recently being used for different optimization problems and found to be efficient for a wide range of applications including data clustering. In this paper, the sensor deployment problem is modeled as a data clustering problem and optimal solution to the deployment problem is obtained using ABC algorithm. The results show that ABC algorithm gives robust and good quality of solution.

Studies on the deployment of sensors mostly involve a 2D plane or 3D volume. However, the optimal sensor deployment in field environments is actually the resource distribution on 3D surfaces. Compared with the traditional deployment environments, field environments are more complicated, owing to some interferences on the detection capability of sensors and limitations on the maneuverability of platforms. In this paper, an optimal sensor deployment algorithm in 3D complex environments is discussed. First, considering the characteristics of field environments, the maneuverability matrix of heterogeneous platforms was introduced as a constraint. Then, a non-isomorphic environment value distribution map was constructed to mark the differences among mission areas. Furthermore, the sensor detection range model was improved to better deal with the occlusion issue. Finally, based on the multi-objective particle swarm optimization (MOPSO) algorithm, a sensor deployment strategy was deployed for complex environments. Experiments demonstrated that the proposed algorithm can better deal with the sensor deployment problem in field environments, while improving the detection accuracy of the objects in mission areas.

Efficient sensor deployment has been one of the most challenging and interesting research areas. The importance and effectiveness of real-world sensing applications, such as underwater and atmospheric sensing, military applications, health systems, and alert systems, which target specific events, raise the need for adaptable design of Wireless Sensor Networks (WSNs). The main challenge in the design of such networks is the optimal sensor deployment, which helps extend the operational network lifetime. Indeed, by maintaining coverage and connectivity with the least number of active nodes and least communication cost, the operable time of the network is guaranteed to be prolonged. The study of two-dimensional (2D) WSNs introduced a significant advancement to the wireless sensor computing technology for different types of smart environments. Nevertheless, 2D WSNs were not sufficient concerning certain applications that require three-dimensional (3D) design. Previous work focused on the design and analysis of various approaches to cover a 3D field of interest, and expanded existing design from 2D to 3D space. Hence, the complexity of such approaches is a major stumbling block. To alleviate this problem, more efficient solutions for the design of WSNs for 3D space deployment have been introduced. By tessellation of the 3D space, which is one of the proposed solutions, researchers studied the partitioning of the space based on Voronoi tessellation by generating identical space-filling cells. Using space fillers cells, which are represented by polyhedra, to model the sensing range of the sensor nodes is assumed to be an optimal solution since these polyhedra can fill a 3D space without leaving gaps or overlaps among them. In the existing literature, the coverage problem in 3D space is concerned with finding the polyhedron that can best approximate the spherical sensing range and eliminates gaps without scarifying the network connectivity. Therefore, the latter is directly related to the sensor node placement strategy. This book chapter studies various proposed solutions for the design of 3D WSNs, with a focus on coverage and connectivity. More specifically, it presents several space filling polyhedra, including the cube, truncated octahedron, hexagonal prism, and rhombic dodecahedron. Also, it compares all these space filling polyhedra to cover a 3D space.

Sensor networks play crucial roles in the environmental monitoring. So far, the large amount of resource consumption in traditional sensor networks has been a huge challenge for environmental monitoring. Compressive sensing (CS) provides us a method to significantly decrease the number of sensors needed and Bayesian compressive sensing (BCS) makes it possible to deploy sensors selectively rather than randomly. By deploying sensors to the most informative places, we expect to reduce the reconstruction errors further compared with random sensor deployment. In this paper we employ multiple sensor deployment algorithms and BCS based signal recovery algorithm to build novel environmental monitoring systems, in which the environmental signals can be recovered accurately with undersampled measurements. Besides, we apply these environmental monitoring models to ozone data experiments to evaluate them and compare their performance. The results show a significant improvement in the recovery accuracy from random sensor deployment to selective sensor deployment. With 100 measurements for 16641 data points, the reconstruction error of one of the sensor deployment approaches was 40 % less than that of random sensor deployment, with 3.52 % and 6.08 % respectively.

Sensor deployment based on fuzzy graph considering heterogeneity and multiple-objectives to diagnose manufacturing system

Networked robots refer to multiple robots operating together in coordination. This work focuses on the problem of path planning of these robots in an unknown environment with obstacles. In the existing techniques, the robots collaboratively find the location of obstacles and available path and exchange the entire map of the environment among themselves. This creates computational and communication overhead in complex environments. We propose the Directed Ant Colony Optimization Algorithm(D-ACO) in which the environment detection task is shared between the robot and the static sensor network. The location of the target and obstacles is stored in the static sensor nodes instead of storing it in the robot's memory, thereby reducing the computational overhead. The information about the neighboring nodes and the obstacles within the communication range is relayed in the form of packets to the robots. Thus the proposed scheme reduces the communication overhead. The D-ACO algorithm is analyzed for both grid and random deployment of sensors. On comparison with the existing ACO techniques, simulation results show that the rate of convergence of the D-ACO algorithm is increased by two times in grid deployment, and by four times in random deployment of static sensors.

Efficient sensor deployment has been one of the most challenging and interesting research areas. The importance and effectiveness of real-world sensing applications, such as underwater and atmospheric sensing, military applications, health systems, and alert systems, which target specific events, raise the need for adaptable design of Wireless Sensor Networks (WSNs). The main challenge in the design of such networks is the optimal sensor deployment, which helps extend the operational network lifetime. Indeed, by maintaining coverage and connectivity with the least number of active nodes and least communication cost, the operable time of the network is guaranteed to be prolonged. The study of two-dimensional (2D) WSNs introduced a significant advancement to the wireless sensor computing technology for different types of smart environments. Nevertheless, 2D WSNs were not sufficient concerning certain applications that require three-dimensional (3D) design. Previous work focused on the design and analysis of various approaches to cover a 3D field of interest, and expanded existing design from 2D to 3D space. Hence, the complexity of such approaches is a major stumbling block. To alleviate this problem, more efficient solutions for the design of WSNs for 3D space deployment have been introduced. By tessellation of the 3D space, which is one of the proposed solutions, researchers studied the partitioning of the space based on Voronoi tessellation by generating identical space-filling cells. Using space fillers cells, which are represented by polyhedra, to model the sensing range of the sensor nodes is assumed to be an optimal solution since these polyhedra can fill a 3D space without leaving gaps or overlaps among them. In the existing literature, the coverage problem in 3D space is concerned with finding the polyhedron that can best approximate the spherical sensing range and eliminates gaps without scarifying the network connectivity. Therefore, the latter is directly related to the sensor node placement strategy. This book chapter studies various proposed solutions for the design of 3D WSNs, with a focus on coverage and connectivity. More specifically, it presents several space filling polyhedra, including the cube, truncated octahedron, hexagonal prism, and rhombic dodecahedron. Also, it compares all these space filling polyhedra to cover a 3D space.