Article

# Understanding and Improving the Performance of Constructive Interference Using Destructive Interference in WSNs

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## Abstract

The constructive interference (CI) phenomenon has been exploited by a number of protocols for providing energy-efficient, low-latency, and reliable data collection and dissemination services in wireless sensor networks. These protocols consider CI to provide highly reliable packet delivery. This has attracted attention to understand the working of CI; however, the existing works present inconsistent views. Furthermore, these works do not study in the real-world settings where the physical conditions of deployment and unreliable wireless channels also impact the performance of CI. Therefore, we study the phenomenon of CI, considering a receiver's viewpoint and analyze the parameters that affect CI. We validate our arguments with results from extensive and rigorous experimentation in real-world settings. This paper presents comprehensive insights into the CI phenomenon. With the understanding, we develop the destructive interference-based power adaptation (DIPA), an energy-efficient and distributed algorithm, that adapts transmission power to improve the performance of CI. Since CI-based protocols cannot have an explicit acknowledgment packet, we make use of destructive interference on a designated byte to provide a feedback. We leverage this feedback to adapt transmission powers. We compared CI with and without DIPA in two real-life testbeds. On one testbed, we achieve around 25% lower packet losses while using only half of its transmission power for 64-B packets. On the other testbed, we achieve 25% lower packet losses while consuming only 47% of its transmission power for 128-B packets. Existing CI-based protocols can easily incorporate DIPA into them to achieve lower packet losses and higher energy efficiencies.

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... The noise vector also accounts for different levels of channel state information (CSI) estimation error, where the estimated channel on the receiver side is contaminated by additive Gaussian noise.2 (·) t denotes transpose, R(·), E{·}, V{·}, ∠(·), (·) † , || · ||, p(·) and P (·) represents real-part, expectation, variance, phase, conjugate transpose, Frobenius norm operators, probability density function and probability mass function respectively; N (µ, Σ) denote normal distribution with mean µ and co-variance matrix Σ.3 The DFC estimates the CSI, where half of the coherence interval of 2T is used for training to estimate the channel and establish the frequency and timing synchronization. ...
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Exploiting constructive interference for scalable flooding in wireless networks
• Y Wang
• Y He
• X Mao
• Y Liu
• X Y Li