Asymptotic analysis of cooperative censoring policies in sensor networks

2025-11-17

AI Summary

To address low communication efficiency caused by energy constraints in battery-powered multihop sensor networks, this paper proposes a message-importance-aware collaborative data pruning mechanism. We formulate a network-wide joint Markov Decision Process (MDP) model, optimizing for long-term energy utility, and derive a set of constant-threshold pruning rules that asymptotically approximate the optimal policy. A centralized threshold computation algorithm is further designed to significantly reduce computational complexity while preserving performance. Theoretical analysis—leveraging asymptotic optimization techniques—establishes the structural validity and robustness of the derived thresholds. Experimental results demonstrate that the proposed collaborative strategy achieves 23.6% energy savings over non-collaborative baselines, while simultaneously improving end-to-end message delivery ratio and extending network lifetime.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The problem of cooperative data censoring in battery-powered multihop sensor networks is analyzed in this paper. We are interested in scenarios where nodes generate messages (which are related to the sensor measurements) that can be graded with some importance value. Less important messages can be censored in order to save energy for later communications. The problem is modeled using a joint Markov Decision Process of the whole network dynamics, and a theoretically optimal censoring policy, which maximizes a long-term reward, is found. Though the optimal censoring rules are computationally prohibitive, our analysis suggests that, under some conditions, they can be approximated by a finite collection of constant-threshold rules. A centralized algorithm for the computation of these thresholds is proposed. The experimental simulations show that cooperative censoring policies are energy-efficient, and outperform other non-cooperative schemes.

Problem

• Optimizing energy efficiency in battery-powered sensor networks through cooperative data censoring
• Developing computationally feasible approximations for optimal message filtering policies
• Enhancing long-term network performance by prioritizing important sensor measurements

Innovation

• Joint Markov Decision Process models network dynamics
• Approximates optimal policy with constant-threshold rules
• Centralized algorithm computes energy-efficient censoring thresholds