Taylor, Martin · Defense Technical Information Center (DTIC) · 2002
Why do users who do not want to be flooded by data want ever bigger screens on which to display more data? It seems somewhat inconsistent for a commander to complain about the dataflood, while at the same time asking for screens that fill whole room walls. or for 3-D worktables that can present ever more data. Yet this is exactly what many commanders do. This presentation discusses the underlying factors that resolve the seeming inconsistency.
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2015
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2006
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Kevin R. Ford (419535), Ailene K. Ettinger (419536), Jessica D. Lundquist (419537) et al. · Figshare · 2015
<p>Sensor deployment in (A) forest and (B) subalpine/alpine biomes. At each elevation in the forest biome (A), sensors were placed in gaps in the forest canopy (top left) and non-gaps with intact forest canopy (top right). Within each of these canopy types, sensors were located in plots where understory vegetation was removed (bottom left) and control plots where it was left undisturbed (bottom right). In the subalpine/alpine biomes (B), temperature sensors were located along transects running from depressions in the landscape to ridges.</p>
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Patire, Anthony, PhD, Dion, Francois, PhD · RePEc: Research Papers in Economics · 2023
The California Department of Transportation (Caltrans) collects megabytes of data every day using a dedicated traffic sensing infrastructure. The collected data provide support for traffic management and system performance monitoring activities that are crucial for supporting the agency’s mission, vision, and strategic goals to strengthen stewardship and drive efficiency. Operating this vast detection system requires extensive resources in the form of engineering and maintenance support, along with millions in capital funds to keep the system running. Within the above context, alternate hybrid data collection models utilizing purchased or third-party data to augment existing data collection system capabilities may enable a reduction in the number of physical detection stations required while maintaining suitable accuracy for Caltrans’ purposes. In addition to the potential for cost savings, the reliance on fewer physical sensors also offers the potential to reduce the exposure of Caltrans employees to the occupational hazard of maintaining roadside detection stations, in alignment with the agency’s “safety first” strategic goal.
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Yi Zou, Krishnendu Chakrabarty · Chapman & Hall/CRC computer and information science series · 2012
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Guiling Wang, Guohong Cao, Thomas La Porta · 2005
Sensor deployment is an important issue in designing sensor networks. We design and evaluate distributed self-deployment protocols for mobile sensors. After discovering a coverage hole, the proposed protocols calculate the target positions of the sensors where they should move. We use Voronoi diagrams to discover the coverage holes and design three movement-assisted sensor deployment protocols, VEC (vector-based), VOR (Voronoi-based), and minimax based on the principle of moving sensors from densely deployed areas to sparsely deployed areas. Simulation results show that our protocols can provide high coverage within a short deploying time and limited movement.
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Yipeng Qu · 2013
Wireless Sensor Networks (WSNs) are widely used for various civilian and military applications, and thus have attracted significant interest in recent years. This work investigates the important problem of optimal deployment of WSNs in terms of coverage and energy consumption. Five deployment algorithms are developed for maximal sensing range and minimal energy consumption in order to provide optimal sensing coverage and maximum lifetime. Also, all developed algorithms include self-healing capabilities in order to restore the operation of WSNs after a number of nodes have become inoperative. Two centralized optimization algorithms are developed, one based on Genetic Algorithms (GAs) and one based on Particle Swarm Optimization (PSO). Both optimization algorithms use powerful central nodes to calculate and obtain the global optimum outcomes. The GA is used to determine the optimal tradeoff between network coverage and overall distance travelled by fixed range sensors. The PSO algorithm is used to ensure 100% network coverage and minimize the energy consumed by mobile and range-adjustable sensors. Up to 30% - 90% energy savings can be provided in different scenarios by using the developed optimization algorithms thereby extending the lifetime of the sensor by 1.4 to 10 times. Three distributed optimization algorithms are also developed to relocate the sensors and optimize the coverage of networks with more stringent design and cost constraints. Each algorithm is cooperatively executed by all sensors to achieve better coverage. Two of our algorithms use the relative positions between sensors to optimize the coverage and energy savings. They provide 20% to 25% more energy savings than existing solutions. Our third algorithm is developed for networks without self-localization capabilities and supports the optimal deployment of such networks without requiring the use of expensive geolocation hardware or energy consuming localization algorithms. This is important for indoor monitoring applications since current localization algorithms cannot provide good accuracy for sensor relocation algorithms in such indoor environments. Also, no sensor redeployment algorithms, which can operate without self-localization systems, developed before our work.
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Hyungil Ahn, John J. Deyst, Hyungil Ahn · 2004
Recent experiences indicate there are limitations to the surveillance capability of high-value long-distance standoff ISR (Intelligence, Surveillance, Reconnaissance) platforms such as the latest observation satellites and unmanned aircraft. The high-tech systems in the platforms are often influenced by weather conditions or defeated by low-tech adversaries such as foliage, caves and concealment. These limitations indicate that close-in, on-the-ground sensor networks are essential parts for powerful ISR capability. However, it is hard to place ground sensors at planned specific locations. For example, certain sensor nodes may be required to be located on rooftops or tree canopies. Therefore, it is critical to deliver the sensors with high precision and reliability. The current UAV (Unmanned Aerial Vehicle) technologies may give a key solution to
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Volkan Isler, Sampath Kannan, Kostas Daniilidis · 2004
In this paper, we consider the problem of placing networked sensors in a way that guarantees coverage and connectivity. We focus on sampling based deployment and present algorithms that guarantee coverage and connectivity with a small number of sensors. We consider two different scenarios based on the flexibility of deployment. If deployment has to be accomplished in one step, like airborne deployment, then the main question becomes how many sensors are needed. If deployment can be implemented in multiple steps, then awareness of coverage and connectivity can be updated. For this case, we present incremental deployment algorithms which consider the current placement to adjust the sampling domain. The algorithms are simple, easy to implement, and require a small number of sensors. We believe the concepts and algorithms presented in this paper will provide a unifying framework for existing and future deployment algorithms which consider many practical issues not considered in the present work.
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Jérôme Le Ny, George J. Pappas · 2010
In robot deployment problems, the fundamental issue is to optimize a steady state performance measure that depends on the spatial configuration of a group of robots. For such problems, a classical way of designing high-level feedback motion planners is to implement a gradient descent scheme on a suitably chosen objective function. This can lead to computationally expensive algorithms that may not be adaptive to uncertain dynamic environments. We address these challenges by showing that algorithms for a variety of deployment scenarios in uncertain stochastic environments and with noisy sensor measurements can be designed as stochastic gradient descent algorithms, and their convergence properties analyzed via the theory of stochastic approximations. This approach yields often surprisingly simple algorithms that can accommodate complicated objective functions, and work without a detailed model of the environment. To illustrate the richness of the framework, we discuss two applications, namely source seeking with realistic stochastic wireless connectivity constraints, and coverage with heterogeneous sensors.
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V. Ister, S. Kannan, Kostas Daniilidis · 2005
In this paper, we consider the problem of placing networked sensors in a way that guarantees coverage and connectivity. We focus on sampling based deployment and present algorithms that guarantee coverage and connectivity with a small number of sensors. We consider two different scenarios based on the flexibility of deployment. If deployment has to be accomplished in one step, like airborne deployment, then the main question becomes how many sensors are needed. If deployment can be implemented in multiple steps, then awareness of coverage and connectivity can be updated. For this case, we present incremental deployment algorithms, which consider the current placement to adjust the sampling domain. The algorithms are simple, easy to implement, and require a small number of sensors. We believe the concepts and algorithms presented in this paper provide a unifying framework for existing and future deployment algorithms, which consider many practical issues not considered in the present work.
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A. K. CHATTOPADHYAY, Chandan Kr. Bhattacharyya, Sohini Bhattacharya · 2012
Most important issues of sensor deployment within a given terrain are - complete sensing coverage within the terrain, a low cost deployment algorithm, no deployment within obstacles and minimizing the number of sensors to be deployed. A good routing algorithm required for fast commutations and less traffic between the sensor-nodes, for efficient tracking of objects within the terrain and finally low cost sensor with lowest possible memory requirement and better energy management. The proposed Single Hop Sensor Deployment Algorithm or SHSD Algorithm in short, considers an irregular terrain with obstacles for deployment. In the proposed algorithm two types of sensor nodes are considered, client sensor-nodes which are homogeneous in type with same sensing and communication coverage and server-nodes which are heterogeneous in type with greater communication coverage than sensing coverage. Each server with four clients forms a WSN, where any sensing of object by any of the four client nodes will be communicated to the server-node. Each server-node can communicate directly with its neighboring server-nodes. A moving object tracked by a WSN, can be reported to its neighbor WSNs without involving the client nodes, which definitely decrease traffic and increase the speed of communication. The gain in deployment algorithm is, instead finding the location of individual nodes for deployment; locations need to be searched for WSNs (for 5-sensors together).
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Karl Aberer, Manfred Hauswirth, Ali Salehi · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2007
The availability of cheap and smart wireless sensing devices provides unprecedented possibilities to monitor the physical world. On the technical side these devices introduce several original research problems, many of them related to the integration of the rampant technology proposals. Global Sensor Networks (GSN) is a platform which provides a scalable infrastructure for integrating heterogeneous sensor network technologies using a small set of powerful abstractions. GSN supports the integration and discovery of sensor networks and sensor data, provides distributed querying, filtering, and combination of sensor data, and offers dynamic adaption of the system configuration during operation through a declarative XML-based language, and enables zero-programming deployment and management.
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Kevin R. Ford (419535), Ailene K. Ettinger (419536), Jessica D. Lundquist (419537) et al. · Figshare · 2015
*<p>These sites include the three sites with sensors deployed in Summer 2009.</p>†<p>Differences in accuracy and logging intervals were due to differences in the specific model of iButton sensor used.</p>
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Nadjib Aitsaadi, Nadjib Achir, Khaled Boussetta et al. · HAL (Le Centre pour la Communication Scientifique Directe) · 2009
International audience
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Karl Aberery, Manfred Hauswirthz, Ali Salehiy · 2015
The availability of cheap and smart wireless sensing de-vices provides unprecedented possibilities to monitor the physical world. On the technical side these devices intro-duce several original research problems, many of them re-lated to the integration of the rampant technology propos-als. Global Sensor Networks (GSN) is a platform which provides a scalable infrastructure for integrating heteroge-neous sensor network technologies using a small set of pow-erful abstractions. GSN supports the integration and discov-ery of sensor networks and sensor data, provides distributed querying, filtering, and combination of sensor data, and of-fers dynamic adaption of the system configuration during operation through a declarative XML-based language, and enables zero-programming deployment and management.
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(:Unkn) Unknown · George Mason University · 2020
Wireless sensor networks (WSN’s) have been receiving a lot of interest in the last two decades due to the variety of applications that can benefit from them. As the interest in WSN’s grows, addressing the physical constraints of these networks has become extremely important to ensure their reliability and efficiency. It was noted in the US Military challenge in 2016 that the wireless networks were already overcrowded and by 2030 the demands on wireless networks will be 250 times greater. Taking this into account, reducing the amount of messages exchanged (communication) between the sensors in the network while maintaining an adequate level of performance has become a critical task, and this will be the focus of this dissertation. A trivial solution which can reduce the amount of communication over a wireless network is by using a periodic communication model and specifying a long enough communication period. In spite of this, this solution may not ensure the level of performance desired. For a WSN to do its job the controller must have sufficient information to be updated. This requires finding a communication strategy for the sensors to exchange messages and find the conditions for the controllers to be updated. This dissertation addresses this problem for different deployment approaches. In this dissertation, we explore various existing and new methods to reduce the amount of communication with sufficient autonomy that allows the sensors to determine when information is needed and what kind of information is needed to perform a given task. The first chapter of this dissertation focuses on the virtual force deployment approach, the second chapter revisits the problem of the Voronoi diagram deployment approach to provide a more practical solution, and the third chapter adapts the tools and concepts from chapter 2 to any type of Voronoi diagram.
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Bilal Al-qudah, Rabie A. Ramadan · 2015
This paper proposes a Billiards algorithm for two sensor deployment problems, random and deterministic deployment. The deployment considers both homogenous and heterogeneous sensors. The algorithm aims to maximize the coverage of a given monitored field with obstacles. The coverage is maximized by avoiding sensors overlapping and minimizing the uncovered areas. By adjusting the expansion ratio, the algorithm was able to find the best sensing ranges that maximize the field coverage. The conducted experiments point out the effect of expansion ratio, iterations between expansions, number of collisions, and mobility, on the overall coverage. At the same time, Billiards algorithm shows significant improvement in the coverage performance from the initial field’s coverage. 1.
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Guy Pujolle, Khaled Boussetta, Nadjib Achir et al. · Wireless networks and mobile communications · 2009
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