Linear programming approach for performance-driven data aggregation in networks of embedded sensors

Creating accurate data models describing the dynamics of physical phenomena in time and space is important in optimized control and decision making. Models highlight various trends and patterns. However, producing accurate models is challenging as different errors are introduced by sampling platforms with limited resources, e.g., insufficient sampling rates, data loss due to buffer overwriting, reduced communication bandwidth, and long communication delays. Furthermore, the dynamics of the environment, like mobile energy sources and sinks, might further increase errors as resources must be shared between the sampling and communication activities. This paper presents a procedure to systematically construct robust data models using samples acquired through a grid network of embedded sensing devices with limited resources, like bandwidth and buffer memory. Models are in the form of ordinary differential equations. The procedure constructs local data models by lumping state variables. Local models are then collected centrally to produce global models. The proposed modeling scheme uses a linear programming formulation to compute the lumping level at each node, and the parameters of the networked sensing platform, i.e., best data communication paths and bandwidths. Two algorithms are described to predict the trajectories of mobile energy sources/sinks as predictions can further reduce data loss and delays during communication. The computed parameters and trajectory predictions are used to configure the local decision making routines of the networked sampling nodes. Even though the procedure can be used to model a broader set of phenomena, experiments discuss the effectiveness of the method for thermal modeling of ULTRASPARC Niagara T1 architecture. Experiments show that the presented method reduces the overall error between 58.29% and 76.91% with an average of 68.87%, and communication delay between −11.49% and 57.62% with an average of 21.85%.

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“…Collection errors (E (Coll) ) are described as in (8). Equation (6) describes the total error E (Loss) due to data loss at node x.…”

Section: Computing the Parameters Of Decision Making Policiesmentioning

confidence: 99%

“…Traditional aggregation methods [8], [13], [32] focus on reducing communication traffic to lower power and energy consumption. Feedback-based adaptation addresses the changing traffic conditions and time requirements of data aggregates sent by parent nodes to their children over a tree network [11].…”

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confidence: 99%