Gossip algorithms are widely used in wireless sensor networks for in-network processing due to their simplicity, robustness, and efficiency. These algorithms do not require specialized routing, have no single point of failure, and are resilient to unreliable wireless conditions. The article provides an overview of recent advancements in gossip algorithms, focusing on convergence rates, wireless link issues, and applications in distributed signal processing tasks such as estimation, source localization, and compression. The convergence rate of gossip algorithms is crucial for understanding the number of transmissions required. Pairwise gossip, where only two nodes exchange information, converges slowly on wireless networks, leading to the development of faster algorithms like geographic gossip and Location-Aided Distributed Averaging (LADA). These algorithms leverage location information to improve convergence rates and reduce the number of messages exchanged. Rate limitations in gossip algorithms are also discussed, considering the impact of finite transmission rates and quantization on convergence. Quantized consensus algorithms use quantizers to approximate state variables, and their performance is analyzed in terms of distortion and convergence guarantees. Recent work has also explored information-theoretic performance bounds for gossip algorithms, characterizing the rate-distortion tradeoff. Finally, the article illustrates the application of gossip algorithms to specific problems in wireless sensor networks, including distributed linear parameter estimation, source localization, and compression. These applications demonstrate the versatility and effectiveness of gossip algorithms in solving complex distributed signal processing tasks.Gossip algorithms are widely used in wireless sensor networks for in-network processing due to their simplicity, robustness, and efficiency. These algorithms do not require specialized routing, have no single point of failure, and are resilient to unreliable wireless conditions. The article provides an overview of recent advancements in gossip algorithms, focusing on convergence rates, wireless link issues, and applications in distributed signal processing tasks such as estimation, source localization, and compression. The convergence rate of gossip algorithms is crucial for understanding the number of transmissions required. Pairwise gossip, where only two nodes exchange information, converges slowly on wireless networks, leading to the development of faster algorithms like geographic gossip and Location-Aided Distributed Averaging (LADA). These algorithms leverage location information to improve convergence rates and reduce the number of messages exchanged. Rate limitations in gossip algorithms are also discussed, considering the impact of finite transmission rates and quantization on convergence. Quantized consensus algorithms use quantizers to approximate state variables, and their performance is analyzed in terms of distortion and convergence guarantees. Recent work has also explored information-theoretic performance bounds for gossip algorithms, characterizing the rate-distortion tradeoff. Finally, the article illustrates the application of gossip algorithms to specific problems in wireless sensor networks, including distributed linear parameter estimation, source localization, and compression. These applications demonstrate the versatility and effectiveness of gossip algorithms in solving complex distributed signal processing tasks.