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Article Citation - WoS: 14Citation - Scopus: 16Reliability of Linear Wsns: a Complementary Overview and Analysis of Impact of Cascaded Failures on Network Lifetime(Elsevier, 2022) Carsancakli, Muhammed Fatih; Imran, Md Abdullah Al; Yildiz, Huseyin Ugur; Kara, Ali; Tavli, BulentLinear Wireless Sensor Networks (LWSNs) are used in applications where deployment scenarios necessitate sensor nodes to be placed over a line topology. However, such a deployment raises reliability concerns because almost all the nodes in the network are critical with respect to the survivability of the LWSN. It is possible that an LWSN can stay connected even if a subset of the nodes are eliminated, yet, the potential reduction in Network Lifetime (NL) due to such an occurrence can be significant. In this study, after presenting a concise survey of the literature on LWSN reliability, we present an elaborate optimization framework to model the operation of an LWSN, which is built upon a comprehensive system model. Our framework encompasses three transmission power and packet size assignment strategies, which are instrumental in characterizing LWSN behavior. Furthermore, we utilized two-node failure models (i.e., random and coordinated) to assess the vulnerability of LWSNs from multiple perspectives. The results of this study reveal that the impact of coordinated node failures on NL is more severe than the impact of random node failures to such extent that in strongly connected LWSNs, the percentage decrease in NL due to coordinated node failures can be more than a magnitude higher than the NL decrease due to random node failures.Article Citation - WoS: 4Citation - Scopus: 4Privacy Protection Via Joint Real and Reactive Load Shaping in Smart Grids(Elsevier, 2022) Kement, Cihan Emre; Ilic, Marija; Gultekin, Hakan; Cicek, Cihan Tugrul; Tavli, BulentFrequent metering of electricity consumption is crucial for demand-side management in smart grids. However, metered data can be processed fairly easily by employing well-established nonintrusive appliance load monitoring techniques to infer appliance usage, which reveals information about consumers' private lives. Existing load shaping techniques for privacy primarily focus only on altering metered real power, whereas smart meters collect reactive power consumption data as well for various purposes. This study addresses consumer privacy preservation via load shaping in a demand response scheme, considering both real and reactive power. We build a multi-objective optimization framework that enables us to characterize the interplay between privacy maximization, user cost minimization, and user discomfort minimization objectives. Our results reveal that minimizing information leakage due to a single component, e.g., real power, would suffer from overlooking information leakage due to the other component, e.g., reactive power, causing sub-optimal decisions. In fact, joint shaping of real and reactive power components results in the best possible privacy preservation performance, which leads to more than a twofold increase in privacy in terms of mutual information. (c) 2022 Elsevier Ltd. All rights reserved.
