SenSearch: GPS and Witness Assisted Tracking for Delay Tolerant Sensor Networks

Jiang, Lun; Huang, Junsheng; Kamthe, Ankur; Liu, Tao; Freeman, Ian; Ledbetter, John; Mishra, Shivakant; Han, Richard; Cerpa, Alberto E.

doi:10.1007/978-3-642-04383-3_19

Cited by 15 publications

(18 citation statements)

References 2 publications

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“…Observe that different localization protocols estimate node locations with different accuracies and they run at different costs. For example, a GPS localization scheme usually locates a node with a low localization error (e.g., 1~2 m) but at a high cost of power consumption (attributed to the GPS device), while DV-Hop is a distributed localization scheme that yields less accurate node location estimates (e.g., 4~7 m), but at a much lower cost [20], [27]. Hence, if we use outputs of different localization protocols and conditions under which they work as data attributes (features) to predict the actual location of a sensor node, we end up with the same cost-sensitive prediction problem presented in Section III.…”

Section: A Composable Localization Case Studymentioning

confidence: 99%

“…GPS connectivity (whether a node has a GPS signal connection or not) and line of sight availability (whether a node has the line of sight to the Spotlight device or not) are taken as two binary unordered attributes (e.g., no explicit order between on and off state). The costs (i.e., average power consumption) of GPS and Spotlight localization protocols are computed from averaging the energy consumption of nodes equipped with corresponding devices over the localization period, we use numbers reported in [20], [35]. For DV-Hop and Centroid localization protocols, we implemented them in TOSSIM, and the power consumption is obtained from averaging both the energy of running localization algorithms on nodes and the overhead of communication (i.e., sending/receiving packets and idle listening) over the localization period [27], [21].…”

Section: A Composable Localization Case Studymentioning

confidence: 99%

“…For DV-Hop and Centroid localization protocols, we implemented them in TOSSIM, and the power consumption is obtained from averaging both the energy of running localization algorithms on nodes and the overhead of communication (i.e., sending/receiving packets and idle listening) over the localization period [27], [21]. The cost for GPS connectivity is obtained by averaging the energy of the GPS device working in hot start mode to get coordinated with GPS signals over the localization period [20]. For line of sight availability, we assume such information can be obtained at the deployment time of Spotlight system thus has no further cost after deployment.…”

Section: A Composable Localization Case Studymentioning

confidence: 99%

“…A set of four representative localization protocols, (namely, GPS [20], Spotlight [35], DVHop [27] and Centroid [21]) are available to run on the nodes in the network. Each anchor node is able to run a subset or full set of four localization protocols depending on its hardware configuration, power budget and location.…”

Section: A Composable Localization Case Studymentioning

confidence: 99%

“…For example, the average accuracy and energy cost of a particular localization algorithm in a given deployment environment is not likely to change much over time [20]. Similarly, the relation between estimated fuel consumption of a car and various road, trip, and vehicle parameters is likely to remain the same, governed by laws of physics [14].…”

Section: Introductionmentioning

confidence: 99%

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Optimizing quality-of-information in cost-sensitive sensor data fusion

Wang

Ahmadi

Abdelzaher

et al. 2011

2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS)

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Abstract-This paper investigates maximizing quality of information subject to cost constraints in data fusion systems. We consider data fusion applications that try to estimate or predict some current or future state of a complex physical world. Examples include target tracking, path planning, and sensor node localization. Rather than optimizing generic network-level metrics such as latency or throughput, we achieve more resourceefficient sensor network operation by directly optimizing an application-level notion of quality, namely prediction error. This is done while accommodating cost constraints. Unlike prior costsensitive prediction/regression schemes, our solution considers more complex prediction problems that arise in sensor networks where phenomena behave differently under different conditions, and where both ordered and unordered prediction attributes are used. The scheme is evaluated through real sensor network applications in localization and path planning. Experimental results show that non-trivial cost savings can be achieved by our scheme compared to popular cost-insensitive schemes, and a significantly better prediction error can be achieved compared to the cost-sensitive linear regression schemes.

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Section: A Composable Localization Case Studymentioning

confidence: 99%

Section: A Composable Localization Case Studymentioning

confidence: 99%

Section: A Composable Localization Case Studymentioning

confidence: 99%

Section: A Composable Localization Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimizing quality-of-information in cost-sensitive sensor data fusion

Wang

Ahmadi

Abdelzaher

et al. 2011

2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS)

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Developing a data‐transfer model for a novel Wildlife‐tracking network

Davis

Thokala

Xing

et al. 2012

Wildlife Society Bulletin

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The use of low-cost, advanced global positioning system (GPS) telemetry devices for wildlife tracking is growing in popularity, especially use of systems that can communicate with each other to track contacts or transfer data. We evaluated the communication and data-transfer capabilities of a low-cost, custom-built GPS telemetry system with an on-board wireless sensor network using human subjects and captive bighorn sheep (Ovis canadensis) as an experimental model. We conducted tests in July 2010, at 2 facilities in Colorado, USA. Wireless sensor networks use a collection of nodes to ferry data from one destination to another. We tested data-transfer capabilities at several transmission strengths (À25 decibelmilliwatts (dBm), À15 dBm, À10 dBm, À5 dBm, and 0 dBm) and with several types of bodily obstruction (unobstructed, human, bighorn sheep). Under conditions of high transmission strength and low obstruction, a log-logistic decay curve was the best model for data-transfer success. Contrastingly, under conditions of low transmission strength and high obstruction, data transfer was noisier, and was best represented by a linear model. Obstruction resulting from the placement of the collar in relation to the subject's body adversely affected the communication abilities of our system, with bighorn sheep lowering baseline data-transfer success to <50% at low transmission strengths. Obstruction also affected the distance of data transfer for each transmission strength, but bighorn sheep had less of an impact than did humans. Wireless sensor node manufacturers recommend an optimum communications distance for each possible transmission strength, but we suggest that managers calibrate the communications systems of their in-house-constructed GPS telemetry systems, preferably by using the target animal subject, in order to optimize operation in field conditions. ß 2012 The Wildlife Society.

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