Maximizing Aggregated Information in Sensor Networks Under Deadline Constraints

Maximizing Quality of Aggregation in Delay-Constrained Wireless Sensor Networks

“…We consider two different scenarios to mainly show how SDMAX outperforms the algorithm of [2] in terms of quality of data aggregation by considering spatial dispersion. At the first scenario, we consider two random treebased WSNs (with the same network density) comprising 20 and 100 nodes, uniformly at random scattered in square fields of 100 × 100 m 2 and 500 × 500 m 2 and sink locations of (50, 100) and (250, 500), respectively.…”

“…We assume that the system is time-slotted and synchronized and that a transmission takes exactly one time slot. We consider a one-hop node-exclusive interference model [2], where simultaneous transmissions over links having a node in common cause interference. We stress that the structure as well as our assumptions is fairly typical in almost all studies on interference-free aggregation scheduling in WSNs.…”

“…A widely accepted model characterizes data correlation between nodes i and j as ρ ij = e −αd β ij , where d ij is the Euclidean distance between i and j, and α ∈ [0, 1] and β ∈ [1,2] are the correlation parameters [4]. Here, we define the joint participation score of two nodes i and j in aggregation as TiTj ρij .…”

“…OPTIMIZATION PROBLEM Inspired by the problem formulation presented in [2], our goal is to find V sel-src ( n) so as to simultaneously maximize the two metrics given aggregation constraints. We cast this as the following bi-objective optimization problem 4 :…”

“…This leads to the extension of the framework of [2] to a bi-objective combinatorial optimization problem. We prove that solving the formulated problem, i.e., finding a Pareto-optimal solution, is at least as hard as the Maximum Edge Weighted Clique Problem (MEWCP), which is known to be NP-hard [3].…”