Region Sampling: Continuous Adaptive Sampling on Sensor Networks

Lin, Shan; Arai, Benjamin; Gunopulos, Dimitrios; Das, Gautam

doi:10.1109/icde.2008.4497488

Cited by 21 publications

(19 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An optimal sensor placement should be able to maximize the sensor coverage and in the meanwhile to minimize the number of sensors required. For placing K sensors optimally in an arbitrary sensor field, this work is known to be NP hard; thus a couple of efficient nearoptimal algorithms are provided [17][20] [7] [21]. In Andreas' works like [17], a greedy solution is designed by using mutual information when selecting next sensor point; this has better performance than traditional random solution or other basic entropy-based sensor selection.…”

Section: Introductionmentioning

confidence: 99%

“…In [7], the method supports imprecise sensor detection like terrain properties, which can support sensor placement with probabilistic guarantee in a polynomial time. The work of regions sampling in [21] studies local aggregation on sensor network and builds adaptive sampling for each partial region. All of these works are not designed for mobile sensors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

OptiMoS: Optimal Sensing for Mobile Sensors

Yan¹,

Eberle²,

Aberer³

2012

2012 IEEE 13th International Conference on Mobile Data Management

View full text Add to dashboard Cite

Abstract-Both sensor coverage maximization and energy cost minimization are the fundamental requirements in the design of real-life mobile sensing applications, e.g., (1) deploying environmental sensors (like CO2, fine particle measurement) on public transports to monitor air pollution, (2) analyzing smartphone embedded sensors (like GPS, accelerometer) to recognize people daily activities. However sensor coverage and energy cost contradict each other: the higher frequency mobile sensing takes, the more energy is used; and vise versa.In this paper, we design a novel two-step mobile sensing process ("OptiMoS") to achieve optimal mobile sensing that can effectively balance sensor coverage and energy cost. In the first step, OptiMoS divides the continuous mobile sensor readings into several segments, where the readings in one segment are highlycorrelated rather than readings amongst different segments. In the second step, OptiMoS identifies optimal sampling for the sensor readings in each segment, where the selected readings can guarantee reasonably high sensor coverage with limited sampling rate. Various greedy & near-optimal segmentation and sampling methods are designed in OptiMoS, and are evaluated using reallife environmental data from mobile sensors.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

OptiMoS: Optimal Sensing for Mobile Sensors

Yan¹,

Eberle²,

Aberer³

2012

2012 IEEE 13th International Conference on Mobile Data Management

View full text Add to dashboard Cite

show abstract

“…Spatial data, such as sensor network data and object movement logs, have recently become more widely available [4,10,14,15,17,19,21,26,28]. Spatial data usually have two kinds of attributes: optimization attributes and geographic attributes [16,18,23].…”

mentioning

confidence: 99%

Clustering spatial data with a geographic constraint: exploring local search

Liao

Peng

2011

Knowl Inf Syst

View full text Add to dashboard Cite

Spatial data objects that possess attributes in the optimization domain and the geographic domain are now widely available. For example, sensor data are one kind of spatial data objects. The location of a sensor is an attribute in the geographic domain, while its reading is an attribute in the optimization domain. Previous studies discuss dual clustering problems that attempt to partition spatial data objects into several groups, such that objects in the same group have similar values in their optimization attributes and form a compact region in the geographic domain. However, previous studies do not clearly define compact regions. Therefore, this paper formulates a connective dual clustering problem with an explicit connected constraint given. Objects with a geographic distance smaller than or equal to the connected constraint are connected. The goal of the connective dual clustering problem is to derive clusters that contain objects with similar values in the optimization domain and are connected in the geographic domain. This study further proposes an algorithm CLS (Clustering with Local Search) to efficiently derive clusters. This algorithm consists of two phases: the ConGraph (standing for Connective Graph) transformation phase and the clustering phase. In the ConGraph transformation phase, CLS first transforms the data objects into a ConGraph that captures geographic constraints among data objects and selects initial seeds for clustering. Then, the initial seeds selected nearby data objects and formed coarse clusters by exploring local search in the clustering phase. Moreover, coarse clusters are merged and finely turned. Experiments show that CLS algorithm is more efficient and scalable than existing methods.

show abstract

“…q sets of wavelet coefficients, could be greater than that for obtaining any of the q versions alone. Sampling methods can be used to incrementally obtain data from sensor networks [24], as the proposed EAQ scheme does. However, the full set of data usually cannot be recovered from samples with bounded L ∞ -norm error.…”

Section: Related Workmentioning

confidence: 99%

Enabling ε-approximate querying in sensor networks

Liu

Gao

et al. 2009

Proc. VLDB Endow.

View full text Add to dashboard Cite

Data approximation is a popular means to support energy-efficient query processing in sensor networks. Conventional data approximation methods require users to specify fixed error bounds a prior to address the trade-off between result accuracy and energy efficiency of queries. We argue that this can be infeasible and inefficient when, as in many real-world scenarios, users are unable to determine in advance what error bounds can lead to affordable cost in query processing. We envision -approximate querying (EAQ) to bridge the gap. EAQ is a uniform data access scheme underlying various queries in sensor networks. It allows users or query executors to incrementally 'refine' previously obtained approximate data to reach arbitrary accuracy. EAQ not only grants more flexibility to in-network query processing, but also minimizes energy consumption through communicating data upto a just-sufficient level. To enable the EAQ scheme, we propose a novel data shuffling algorithm. The algorithm converts sensed datasets into special representations called multi-version array (MVA). From prefixes of MVA, we can recover approximate versions of the entire dataset, where all individual data items have guaranteed error bounds. The EAQ scheme supports efficient and flexible processing of various queries including spatial window query, value range query, and queries with QoS constraints. The effectiveness and efficiency of the EAQ scheme are evaluated in a real sensor network testbed.

show abstract

Region Sampling: Continuous Adaptive Sampling on Sensor Networks

Cited by 21 publications

References 29 publications

OptiMoS: Optimal Sensing for Mobile Sensors

OptiMoS: Optimal Sensing for Mobile Sensors

Clustering spatial data with a geographic constraint: exploring local search

Enabling ε-approximate querying in sensor networks

Contact Info

Product

Resources

About