Maximizing Quality of Aggregation in Delay-Constrained Wireless Sensor Networks

Abstract

Abstract-In this letter, both the number of participating nodes and spatial dispersion are incorporated to establish a bi-objective optimization problem for maximizing the quality of aggregation under interference and delay constraints in tree-based wireless sensor networks (WSNs). The formulated problem is proved to be NP-hard with respect to Weighted-sum scalarization and a distributed heuristic aggregation scheduling algorithm, named SDMAX, is proposed. Simulation results show that SDMAX not only gives a close approximation of the Pareto-optimal solution, but also outperforms the best, to our knowledge, existing alternative proposed so far in the literature.

Maximum-Quality Tree Construction for Deadline-Constrained Aggregation in WSNs

“…While the first critical challenge on how to schedule the sensors is tackled in the previous studies [4], [8], the ultimate optimal design, however, cannot be fully achieved without taking into account the second critical challenge on how to construct an optimal underlying data aggregation tree. The goal of this paper is to address the second challenge.…”

“…In [8], the authors considered the same problem of [4] by taking into account the effect of data redundancy and spatial dispersion of the participants in the quality of final aggregation result and proposed an approximate solution for proved NP-hard problem. In a more general case, [16] tackles the utility maximization problem in deadline constrained data aggregation and collection then provides efficient approximation solutions.…”

“…Proof: It is proved in [8] that QoA is bounded to 2 D − 1 regardless of the aggregation tree structure. The bound is touchable when the network graph is dense enough where an obvious case is a complete graph (for more details, refer to Section VI).…”

“…It works recursively on the input parent P to develop a tree. Note that the maximum number of participant nodes with deadline D is 2 D − 1 (see [8] for the proof) that is achievable if the graph is well-structured for deadline D, thereby its structure allows to construct a tree with the maximum feasible QoA of 2 D − 1. We emphasize that if a graph is well-structured, it does not imply that the number of communication links in the graph is equal (or even close) to the number of links in the corresponding complete graph.…”

Energy‐aware quality of information maximisation for wireless sensor networks

“…In [4], with rate weight assignment and flow routing determined, a data rate allocation framework was developed to improve the QoI, instead of the quantity of collected information. Considering the experienced delay of each packet, Alinia and Yousefi [5] devised a heuristic aggregation scheduling algorithm to select parts of SNs for the QoI maximisation. By exploiting the strong spatial–temporal correlations among measurements at SNs, Tang and Yuan [6] developed a set of distributed sensing schedules to maximise the QoI under a fixed duty cycle.…”