Evolutionary control of an autonomous field

Owen, Mark W.; Klamer, Dale M.; Dean, Barbara

doi:10.1109/ific.2000.862490

Cited by 8 publications

(5 citation statements)

References 3 publications

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“…Seaweb [38] is another UASN conducted by the US Naval Research Bureau and the Air-Sea Battle System Center. It is a battery-charged system and designed for coastal Antisubmarine Warfare (ASW) sensor networks such as DADS (Deployable Autonomous Distributed System) [39], also used to implement offshore-based sensor systems like Kelp and Hydra.…”

Section: Preliminaries and Backgroundmentioning

confidence: 99%

Advances in Software-Defined Technologies for Underwater Acoustic Sensor Networks: A Survey

et al. 2019

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Underwater Acoustic Sensor Networks (UASNs) are an important technical means to explore the ocean realm. However, most UASNs rely on hardware infrastructures with poor flexibility and versatility. The systems typically deploy in a redundant manner, which not only leads to waste but also causes serious signal interference due to multiple noises in designated underwater regions. Software-Defined Networking (SDN) is a novel network paradigm, which provides an innovative approach to improve flexibility and reduce development risks greatly. Although SDN and UASNs are hot topics, there are currently few studies built on both. In this paper, we provide a comprehensive review on the advances in software-defined UASNs. First, we briefly present the background, and then we review the progress of the Software-Defined Radio (SDR), Cognitive Radio (CR), and SDN. Next, we introduce the current issues and potential research areas. Finally, we conclude the paper and present discussions. Based on this work, we hope to inspire more active studies and take a further step on software-defined UASNs with high performances.

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Section: Preliminaries and Backgroundmentioning

confidence: 99%

Advances in Software-Defined Technologies for Underwater Acoustic Sensor Networks: A Survey

et al. 2019

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“…At its simplest level one might envision a pair or small suite of sensors each checking one another's reports and thereby avoiding the masking of detections that is inevitable with a fluctuating target; and indeed, even with colocated and homogeneous sensors, there is some benefit to splitting one's remote sensing resources in such a way [22]. But a more adventurous interpretation of multi-sensor surveillance involves many very cheap and not colocated sensors, perhaps of the DADS (deployable autonomous distributed system) sort discussed in [11], [15]: there are obvious advantages in terms of robustness, and creation of a decentralized "field" of sensors is a nice way to overcome the r ¡4 power-return law.…”

Section: A Measurement Fusionmentioning

confidence: 99%

“…Along with the vision of a smart "web" of sensors come a number of issues however: there is that of deployment and layout [15], control [19], and, of greatest interest here, application to estimation. Although distributed detection has been studied reasonably extensively (e.g., [6,18,20]), in most applications the focus is, and should be, on the acquiring and tracking of threats using a distributed array of sensors.…”

Section: A Measurement Fusionmentioning

confidence: 99%

Practical fusion of quantized measurements via particle filtering

Ruan

Willett

Marrs

et al. 2008

IEEE Trans. Aerosp. Electron. Syst.

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Most treatments of decentralized estimation rely on some form of track fusion, in which local track estimates and their associated covariances are communicated. This implies a great deal of communication; and it was recently proposed that by an intelligent quantization directly of measurements, the communication needs could be considerably cut.However, several issues were not discussed. The first of these is that estimation with quantized measurements requires an update with a non-Gaussian distribution, reflecting the uncertainty within the quantization "bin." In general this would be a difficult task for dynamic estimation, but Markov-chain Monte-Carlo (MCMC, and specifically here particle filtering) techniques appear quite appropriate since the resulting system is, in essence, a nonlinear filter. The second issue is that in a realistic sensor network it is to be expected that measurements should arrive out-of-sequence. Again, a particle filter is appropriate, since the recent literature has reported particle filter modifications that accommodate nonlinear-filter updates based on new past measurements, with the need to refilter obviated. We show results that indicate a compander/particle-filter combination is a natural fit, and specifically that quite good performance is achievable with only 2-3 bits per dimension per observation.The third issue is that intelligent quantization requires that both sensor and fuser share an understanding of the quantization rule. In dynamic estimation this is a problem since both quantizer and fuser are working with only partial information; if measurements arrive out-of-sequence the problem is compounded. We therefore suggest architectures, and comment on their suitabilities for the task. A scheme based on delta-modulation appears to be promising.

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“…A central unit is required to store the network topology. An adaptive threshold control scheme is developed in [24] in which the evolutionary programming technique is used to develop a control law, such that the energy consumption is small while the field-level probability of detection is maintained at a desired level. The detection thresholds are adjusted dynamically, which is computationally heavy and time consuming for simple low cost sensor nodes.…”

Section: Sensor Management In Wsnsmentioning

confidence: 99%

“…24) where d(•) is the Euclidean distance, G(k + 1) is the set of tasking sensors at the (k + 1) th time step, E c1 (•, •) is the communication energy cost when the previous leader at the k th time step broadcasts the estimatesx(k + 1|k) and P (k + 1|k),E c2 (•, •)is the communication energy between two sensors within the tasking group G(k + 1) (for both transmission and receiving), m that sensor s j is selected to perform the measurement at the (k + 1) th time step, while m that sensor s j is selected to be the local fusion center, the leader, at the (k + 1) th time step, while l (k+1) j = 0 means not), and E s is the sensing energy cost for a single sensor.…”

mentioning

confidence: 99%

Energy efficient sensor scheduling and filtering algorithms for target tracking in wireless sensor networks

Lin¹

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Evolutionary control of an autonomous field

Cited by 8 publications

References 3 publications

Advances in Software-Defined Technologies for Underwater Acoustic Sensor Networks: A Survey

Advances in Software-Defined Technologies for Underwater Acoustic Sensor Networks: A Survey

Practical fusion of quantized measurements via particle filtering

Energy efficient sensor scheduling and filtering algorithms for target tracking in wireless sensor networks

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