QoI-aware tradeoff between communication and computation in wireless ad-hoc networks

Abstract

Abstract-Data aggregation techniques exploit spatial and temporal correlations among data and aggregate data into a smaller volume as a means to optimize usage of limited network resources including energy. There is a trade-off among the Quality of Information (QoI) requirement and energy consumption for computation and communication. We formulate the energy-efficient data aggregation problem as a non-linear optimization problem to optimize the trade-off and control the degree of information reduction at each node subject to given QoI requirement. Using the theory of duality optimization, we prove that under a set of reasonable cost assumptions, the optimal solution can be obtained despite non-convexity of the problem. Moreover, we propose a distributed, iterative algorithm that will converge to the optimal solution. Extensive numerical results are presented to confirm the validity of the proposed solution approach.

Optimal Energy Tradeoff Among Communication, Computation and Caching with QoI-Guarantee

“…With the growing deployment of smart sensors in modern systems [1], in-network data processing, such as data aggregation, has been widely used as a mean of reducing system energy cost by lowering the data volume for transmission.…”

“…The above energy consumption models for data transmission, compression and caching have been used in literature [1], [5], [20] and are suitable for highlighting the energy consumption in a communication network. However, our formulation can be extended to incorporate various other energy consumption models as well.…”

“…The first constraint is the QoI constraint, i.e., the total data available at the sink node [1]. The second constraint indicates that our decision (caching) variable b k,i is binary.…”

“…However, it has been shown [4] that computational energy cost can be significant and may cause a net-energy increase if data are compressed beyond a certain threshold. Hence, it is necessary to consider both transmission and computation costs, and it is important to characterize the trade-off between them [1].…”

Joint optimization of transmission and propulsion in aerial communication networks

“…Partitioning often arises due to the finite transmission range of the nodes, particularly in dense environments [30]. A predefined hierarchical structure, such as the tree network used in [32] also results in a highly structured network.…”