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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.

In this paper, we propose a hospital sensor network deployment method for smart healthcare systems. Since sensor nodes in hospitals are always in an environment where power can be supplied, it is essential to have stable network connectivity by achieving optimal gateway deployment, rather than focusing on energy efficiency. The proposed technique leads to an access point (AP) layout that minimizes the overall network operation cost. The operation cost is calculated per unit time, and it includes installation cost and maintenance cost. In addition, group numbers are assigned to sensor nodes for guaranteeing network connectivity, no matter where the mobile sensor devices move. The performance of the proposed methodology has been verified through numerical experiments.

In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method&rsquo, s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.

As the largest archipelago country in the world, Indonesia has great potential to be the world's maritime axis. The maritime axis is a strategic idea which embodied to ensure inter-island connectivity, shipping and fishery industries development, marine transportation improvement and focus on maritime security. So maritime monitoring is crucial in order to prevent sea pollution, fish theft, sovereignty offenses, territorial disputes, and piracy. Nowadays, maritime monitoring has been a very interesting issue over the last decade. With the rapid advancement of technology and the Internet, sensor network technology has become very important in monitoring the aquatic environment. Maritime responsibilities, such as vigilance and patrolling, wildlife monitoring, and aqua-culture inspection, mostly require large operational teams and expensive equipment. In this position paper, we study and propose a scheme based on genetic algorithm to deploy nodes in the target area which composed of potentially hundreds sensor nodes. The goals of this work is to optimizing of wireless sensor network (WSN) deployment for maritime environment monitoring by using a genetic algorithm.

Sensor network deployment is the key for sensors to play an important performance. Based on game theory, first, the authors propose a multi-type sensor target allocation method for the autonomous deployment of sensors, considering exploration cost, target detection value, exploration ability and other factors. Then, aiming at the unfavorable environment, e.g., obstacles and enemy interference, the authors design a method to maintain the connectivity of sensor network, under the conditions of effective detection of the targets. Simulation result shows that the proposed deployment strategy can achieve the dynamic optimization deployment under complex conditions.

Wireless sensor networks have gained attention over the last few years and have significant applications for example remote supervising and target watching. They can communicate with each other though wireless interface and configure a network. Wireless sensor networks are often deployed in an unfriendly location and most of time it works without human management; individual node may possibly be compromised by the adversary due to some constraints. In this manner, the security of a wireless sensor network is critical. This work will focus on evaluation of mining techniques that can be used to find malicious nodes. The detection mechanisms provide the accuracy of the classification using different algorithm to detect the malicious node. Pragmatically the detection accuracy of J48 is 99.17%, Random Forest is 80.83%, NF Tree is 81.67% and BF Tree is 72.33%. J48 have very high detection accuracy as compared with BF Tree, NF Tree Random Forest.

Wireless Sensor Networks (WSN’s) consists of small and tiny devices called sensor nodes. These sensor nodes are deployed in the required landscape to gather information. Improving coverage rate of sensor nodes imposes a bigger challenge in the sensor network deployment task. Our work proposes a solution based on the Ant Lion Optimization (ALO) algorithm to augment the coverage rate of the sensor network. The extensive simulations corroborate the approach usability in WSN. The results signify the improved performance and better convergence rate of the proposed algorithm approach achieving the objective of better coverage rate.

Adequate coverage is an important issue in geosensor networks in order to fulfill the sensing applications in urban areas. GIS as well as Optimization methods are widely used to distribute geosensors in the network to achieve the desired level of coverage. Most of the algorithms applied in urban domain suffer from the lack of considering real environmental information. In this paper, the problem of placing sensors to get optimum coverage is studied by investigating the concept of urban contextual information in sensor network. Then, a local GIS-based context-aware framework of sensor network deployment optimization method is introduced. Obtained results of our algorithm under different working conditions and applications show the effectiveness of our approach.

The commonly used methods in the subsurface monitoring of water, gas, and dissolved chemicals rely on samples extracted from monitoring wells. In applying these methods to existing and emerging problems in the subsurface where the zones to be monitored are large and data have to be collected for long time periods, they become impractical and cost prohibitive. Data collected from subsurface sensors and wireless sensor networks provide an attractive alternative. This chapter presents past and ongoing research on the technology and its testing in laboratory test beds. Fundamental sensor network issues such as sensor network deployment, virtual sensor networks, reliable sensor data collection, and network optimization in supporting environmental applications are discussed. The use of sensors coupled with numerical models of inversion and prediction as applied to subsurface plume monitoring is presented. Two potential applications of the technology for long-term monitoring of subsurface remediation sites and greenhouse gas are discussed.

In order to improve reconnaissance and detection capabilities better in the vehicle combat, this paper presents a new evaluation index. And based the index, this paper presents a multi-objective optimization model for sensor network deployment of vehicle units. In this paper uses the NSGA-II to solve this problem and given the algorithm flow. In the end, we prove the effectiveness of this method by simulation.

Sensor networks are increasingly used for tracking, monitoring, and observing spatial dynamic phenomena in the real world (e.g. urban area). In order to ensure an efficient deployment of a sensor network, several optimization algorithms have been proposed in recent years. Most of these algorithms often rely on oversimplified sensor models. In addition, they do not consider information on the terrain topography, city models, and the presence of diverse obstacles in the sensing area (e.g. buildings, trees, poles). Only some of those optimization algorithms attempt to consider the terrain information in the optimization of a sensor network deployment. However, most of these algorithms consider that the spatial models used for this purpose are perfect representations of the reality and are not sensitive to the quality of the information. However, spatial models are simplified representations of a complex reality, and hence are inherently uncertain. In this paper we will investigate the impact of the spatial data quality on the optimization of a sensor network and its spatial coverage in an urban area. For this purpose, we will investigate specific implications of spatial data quality criteria for a 3D city model that will be used in sensor network optimization algorithms. Then, we will analyze the impact of some of those criteria on the estimation of sensor network coverage.!Afterwards, a case study for sensor network deployment in an urban area will be presented. This case study will demonstrate the impact of 3D city models quality on the estimation of coverage using global and local optimization algorithms. Finally, the results obtained from this experimentation will be presented and discussed.

Sensor deployment problem for target location estimation is important for sensor networks which fulfill the monitoring tasks. In this paper, we investigate the deployment schemes which guarantee the location estimation accuracy of an arbitrary point within the region, by connecting the location estimation problem with the perspective of k-coverage problem. Our strategies are deploying sensors to guarantee the location-estimation accuracy of an arbitrary point within the monitored region, and to minimize the number of required sensors. Compared with the random deployment, the deterministic deployment provides a better solution to guarantee the location-estimation accuracy over the monitored region. Regular space deployment patterns in 2-dimension, i.e. equilateral triangle, square and regular hexagon, are discussed. The relationship between deployment density and vacancy ratio are derived by mathematical analysis. The derived required sensors deployment density to an arbitrary vacancy ratio for location-estimation coverage provides useful guidelines for sensor network deployment when location-estimation system is required for any practical application.

We address the problem of dispersing a large number of autonomous mobile robots toward building wireless ad hoc sensor networks performing environmental monitoring and control. For the purpose, we propose the adaptive triangular mesh generation algorithm that enables robots to generate triangular meshes of various sizes adapting to changing environmental conditions. A locally interacting, geometric technique allows robots to generate each triangular mesh with their two neighbor robots. Specifically, we have assumed that robots are not allowed to have the identifier, any pre-determined leaders or common coordinate systems, and any explicit communication. Under such minimal conditions, the positions of the robots were shown to converge to the desired distribution, which was mathematically proven and also verified through extensive simulations. Our preliminary results indicate that the proposed algorithm can be applied to the problem regarding the coverage of an area of interest by a swarm of mobile sensors.

Wireless sensor networks are increasingly used for tracking and monitoring dynamic phenomena in urban and natural areas. Spatial coverage is an important issue in sensor networks in order to fulfill the needs of sensing applications. Optimization methods are widely used to efficiently distribute sensor nodes in the network to achieve a desired level of coverage. Most of the existing algorithms do not consider the characteristics of the real environment in the optimization process. In this paper, we propose the integration of contextual information in optimization algorithms to improve sensor network coverage. First, we investigate the implication of contextual information in sensor networks. Then, a conceptual framework for local context-aware sensor network deployment optimization method is introduced and related algorithms are presented in detail. Finally, several experiments are carried out to evaluate the validity of the proposed method. The results obtained from these experiments show the effectiveness of our approach in different contextual situations.

This paper presents a distanceless networking approach for wireless sensor networks sparsely deployed in large areas. By leveraging rateless codes, we provide distanceless transmission to expand the communication range of sensor motes and fully exploit network diversity. We address a variety of practical challenges to accommodate rateless coding on resource-constrained sensor motes and devise a communication protocol to efficiently coordinate the distanceless link transmissions. We propose a new metric (expected distanceless transmission time) for routing selection and further adapt the distanceless transmissions to low duty-cycled sensor networks. We implement the proposed scheme in TinyOS on the TinyNode platform and deploy the sensor network in a real-world project, in which 12 wind measurement sensors are installed around a large urban reservoir of 2.5 $\,\times\,$ 3.0 km $^2$ to monitor the field wind distribution. Extensive experiments show that our proposed scheme significantly outperforms the state-of-the-art approaches for data collection in sparse sensor networks.

Recently, wireless sensor networks are more and more used in smart building applications, due to their capability to collect data on all phenomena that happens in the building. The most important problem related to this kind of networks, is the limited lifetime of nodes, caused by their finite embedded source of energy (batteries). In order to reduce the impact of this problem, proper node model must be defined. In this work, we propose a behavior modeling of a sensor node. We evaluate the performance of our model and show the impact of the number of nodes and their placement on the maintenance cost of network. Presented results can provide guidelines for real sensor network deployment.

This paper presents an integrated approach to landslide research based on remote sensing and sensor networks. This approach is composed of three important parts: (i) landslide susceptibility mapping using remote-sensing techniques for susceptible determination of landslide spots; (ii) scaled-down landslide simulation experiments for validation of sensor network for landslide monitoring, and (iii) in situ sensor network deployment for intensified landslide monitoring. The study site is the Taziping landslide located in Hongkou Town (Sichuan, China). The landslide features generated by landslides triggered by the 2008 Wenchuan Earthquake were first extracted by means of object-oriented methods from the remote-sensing images before and after the landslides events. On the basis of correlations derived between spatial distribution of landslides and control factors, the landslide susceptibility mapping was carried out using the Artificial Neural Network (ANN) technique. Then the Taziping landslide, located in the above mentioned study area, was taken as an example to design and implement a scaled-down landslide simulation platform in Tongji University (Shanghai, China). The landslide monitoring sensors were carefully investigated and deployed for rainfall induced landslide simulation experiments. Finally, outcomes from the simulation experiments were adopted and employed to design the future in situ sensor network in Taziping landslide site where the sensor deployment is being implemented.

Journal of sensor and actuator networks 2(1), 156-171 (2013). doi:10.18154/RWTH-CONV-075234, Published by MDPI, Basel

Sensor network deployment directly influence the management of network resources and distribution of sensor network data. this paper will introduce the recent condition and relevant algorithms of the study on sensor network deployment, the international situation and the future trend of development, etc.

Pipeline systems are widely used for transportation of petroleum and natural gas. It is clear that a continuous and automatic monitoring system is very necessary for petroleum and gas pipeline system. This paper describes an algorithm of 3D sensor node deployment based on petroleum and gas pipeline architecture and three-tiered network architecture, which was described with the C language pseudo-code. The network coverage performance and connection performance for petroleum and gas pipeline system were discussed and we give the evaluation methods of 3D sensor node deployment algorithm.

Coverage of wireless sensor networks (WSNs) is an important quality of service (QoS) metric and often the desired coverage is not attainable at the initial deployment, but node mobility can be used to improve the coverage by relocating sensor nodes. Unconstrained node mobility is considered infeasible based on the high locomotion cost that would nullify the advantage likely to be gained with the coverage improvement. Coverage improvement based on node mobility depends on many parameters including number of deployed nodes (static and mobile), proportion of mobile nodes, permissible distance the mobile nodes can move and the total distance nodes moved to attain certain coverage. The contribution of this paper is the investigation of the inter correlation of all these parameters for a grid-mesh architecture based on the grid quorum scheme. Having such information available prior to the practical deployment is a major advantage when designing the network, this can help improve both operation and cost.

The accelerated, on-demand deployment of sensor networks raises the challenge of selecting the providers supplying the most "relevant" sensory information to a user's needs. This paper considers the spatial relevancy of information provided that accounts for the spatial gradations in the quality of the information desired and, specifically, considers: (a) selecting the minimum number of providers that cumulatively maximizes the information relevancy; and (b) considering a cost per provider, selecting the subset of providers that cumulatively maximizes the overall information relevancy subject to a budgetary constraint. The performance and robustness of the proposed solutions are studied both analytically and by simulation for a number of provider topologies.

“The world has just ten years to bring greenhouse gas emissions under control before the damage they cause becomes irreversible.” This is a famous prediction raised by climate scientists and environmentalists recently. It reflects the increasing attention in the past decade from human beings on global climate change and environmental pollution. On the other hand, forest, which is regarded as the earths lung, is a critical component in global carbon cycle. It is able to absorb 10% - 30% of CO2 from industrial emissions. Moreover, it has large capacity of water conservation, preventing water and soil loss, and hence reducing the chance of nature disasters like mud-rock flows and floods. Forestry applications usually require long-term, large-scale, continuous, and synchronized surveillance of huge measurement areas with diverse creatures and complex terrains. The state-of-arts forestry techniques, however, support only small-scale, discontinuous, asynchronous, and coarse-grained measurements, which at the same time incur large amount of cost with respect to human resource and equipments. WSNs have great potential in resolving the challenges in forestry. Under such circumstances, GreenOrbs is launched. The information GreenOrbs offers can be used as evidences, references, and scientific tools for human beings in the battle against global climate changes and environmental pollution.

In this paper, we propose a tiling-based wireless sensor network (WSN) deployment approach based on the polygon model for sensor nodes with directional sensing areas. In the tiling-based deployment approach, a hexagon tile is first generated from the polygon that represents the sensing area of a given directional sensor. Then, a tiling process is applied to place tiles to the deployment area. Both sensing coverage holes surrounding the boundaries and the obstacles are considered under the proposed approach. To evaluate the proposed deployment approach, we compare its performance with the strip-based deployment pattern approach, which is under the sector model, in terms of the sensing coverage rate and the usage of sensor nodes. The simulation results show that the sensing coverage rate of the proposed deployment approach is higher than that of the strip-based deployment pattern approach for different types of sensor nodes on deployment areas with/without obstacles.

We discuss the key issues in the deployment of sparse sensor networks. The network monitors several environment parameters and is deployed in a semi-arid region for the benefit of small and marginal farmers. We begin by discussing the problems of an existing unreliable 1 sq km sparse network deployed in a village. The proposed solutions are implemented in a new cluster. The new cluster is a reliable 5 sq km network. Our contributions are two fold. Firstly, we describe a novel methodology to deploy a sparse reliable data gathering sensor network and evaluate the "safe distance" or "reliable" distance between nodes using propagation models. Secondly, we address the problem of transporting data from rural aggregation servers to urban data centres. This paper tracks our steps in deploying a sensor network in a village in India, trying to provide better diagnosis for better crop management. Keywords - Rural, Agriculture, GPRS, Sparse.

In order to improve coverage of randomly deployed wireless sensor networks, a distributed deployment algorithm based on coverage addition factor (CAF) is proposed. In this algorithm, each node acquires the distribution of overlapped coverage arcs on the sensing circle based on its neighbors? positions and moves in the direction the coverage addition factor is the greatest. With the algorithm iterated, the coverage of every node and the total coverage of network are both increased obviously. As enhancements, a movement-adjustment scheme and three oscillation-control schemes are designed to avoid movement oscillation and reduce the deploying time. Simulation results show that CAF has advantages over other distributed algorithms such as VOR and VEC in terms of coverage, moving efficiency, moving distance and deploying time.

In this paper, we attack the sensor network deployment problem. We define the deployment problem as the problem of deciding how many sensor nodes should be deployed in the sensor field and the number of deployment phases. We model WSN availability and the total cost as functions of the deployment strategy and use our modeling in seeking an optimal deployment strategy that meets user-defined availability requirement with minimum total cost.

We consider the sensor deployment problem in the context of distance uncertainty. It is characterized by differentiated arrangement of specific detection probability thresholds at different locations. The problem is formulated as an integer linear programming (ILP) model firstly, aiming at optimizing the number of sensors and their locations. Based on the robust discrete optimization methodology, the uncertain model is transformed into an equivalent ILP problem considering distance uncertainty. The proposed approach can control the tradeoff between optimality and robustness by varying the parameters named protection levels. Uniform and non-uniform event detection probabiliy distributions are considered in the experiment. The results show that, as the distance uncertainty increases, the constraint violation can be avoided in the robust model and the robust solution can provide a significant improvement at the expense of a small loss in optimality when compared to the optimal solution of a deterministic scenario.

In response to the needs of the UK MOD QinetiQ have designed, developed and trialled an ad-hoc, self organising network of acoustic nodes for in-depth deployment that can detect and track military targets in a range of environments and for all types of weapon locating. Research conducted has shown that disposable technologies are sufficiently mature to provide a useful military capability. Work this year has included a 3 month series of trials to exercise the prototype equipment and has provided an indication of in-service capability across a broad range of environments. This paper will discuss the scientific approach that was applied to the development of the equipment, from early laboratory development through to the prototype sensor network deployment in operationally representative environments. Highlights from the trials have been provided. New findings from the fusion of a low cost thermal imager that can be cued by the acoustic network are also discussed.

In this paper we present the design and theoretical analysis of a novel algorithm (LRV) that efficiently solves the problems of coverage, exploration and sensor network deployment at the same time. The basic premise behind the algorithm is that the robot carries network nodes as a payload, and in the process of moving around, emplaces the nodes into the environment based on certain local criteria. In turn, the nodes emit navigation directions for the robot as it goes by. Nodes recommend directions least recently visited by the robot, hence the name LRV. We formally establish the following two properties: 1. LRV is complete on graphs, and 2. LRV is optimal on trees. We present some experimental conjectures for LRV on regular square lattice graphs and compare its performance empirically to other graph exploration algorithms.

Given the rigid energetic constraints under which a sensor network must operate, efficient means of power management are vital to the success of any sensor network deployment, particularly those in rapidly changing environments. Threshold-based algorithms provide a possible in-network method for adaptive distributed control of energy consumption.