A bridging model for parallel computation
Communications of the ACM
Introduction to parallel algorithms and architectures: array, trees, hypercubes
Introduction to parallel algorithms and architectures: array, trees, hypercubes
Estimating simple functions on the union of data streams
Proceedings of the thirteenth annual ACM symposium on Parallel algorithms and architectures
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Walkers on the Cycle and the Grid
SIAM Journal on Discrete Mathematics
Sensor Field: A Computational Model
Algorithmic Aspects of Wireless Sensor Networks
A tight upper bound on the cover time for random walks on graphs
Random Structures & Algorithms
Survey: Computational models for networks of tiny artifacts: A survey
Computer Science Review
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In this work we consider the problem of continuously monitoring a collection of data sets produced by sensors placed on mobile or static targets. Our computational model, the dynamic sensor field model, is an extension of the static sensor field model (Alvarez et al. (2009) [3]) allowing for computation in the presence of mobility. The dynamicity comes from both the mobile communication devices and the data sensors. The mobility of devices is modeled by a dynamic communication graph depending on the position of the devices. Data mobility is due to measurements performed by sensing units that are not placed on fixed positions but attached to mobile agents or targets. Accordingly, we introduce two additional performance measures: the total traveled distance in a computational step and the gathering period. We study the Continuous Monitoring problem by providing bounds on the performance of several protocols that differ in the use of mobility and the placement of the devices. Our objective is to analyze formally the computational resources needed to solve the Continuous Monitoring in a dynamic context. For doing so, we consider a particular scenario in which communication devices and data sensors move on top of a squared terrain discretized by a mobility grid. We also consider two scenarios, the static data setting in which sensors are placed at fixed but unknown positions and the dynamic data setting, in which sensors are placed on dynamic targets and follow a passive mobility pattern.