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   https://doi.org/10.1016/j.apenergy.2018.07.089  

   Liu, Yang ; Yu, Nanpeng ; Wang, Wei ; Guan, Xiaohong ; Xu, Zhanbo ; Dong, Bing ; Liu, Ting ( October 2018 , Applied Energy)
2. Asymptotic analysis of a new low complexity encryption approach for the Internet of Things, smart cities and smart grid

   https://doi.org/10.1109/ICSGSC.2017.8038576  

   Samudrala, Ananth Narayan ; Blum, Rick S. ( July 2017 , 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC))

   Abstract (WSN) using encrypted non-binary quantized data is studied. In a WSN, sensors transmit their observations to a fusion center through a wireless medium where the observations are susceptible to unauthorized eavesdropping. Encryption approaches for WSNs with fixed threshold binary quantization were previously explored. However, fixed threshold binary quantization limits parameter estimation to scalar parameters. In this paper, we propose a stochastic encryption approach for WSNs that can operate on non-binary quantized observations and has the capability for vector parameter estimation. We extend a binary stochastic encryption approach proposed previously, to a nonbinary generalized case. Sensor outputs are quantized using a quantizer with R + 1 levels, where R in {1.2. 3 ...}, encrypted by flipping them with certain flipping probabilities, and then transmitted. Optimal estimators using maximum-likelihood estimation are derived for both a legitimate fusion center (LFC) and a third party fusion center (TPFC) perspectives. We assume the TPFC is unaware of the encryption. Asymptotic analysis of the estimators is performed by deriving the Cramer-Rao lower bound for LFC estimation, and the asymptotic bias and variance for TPFC estimation. Numerical results validating the asymptotic analysis are presented.
3. The TV is Smart and Full of Trackers: Measuring Smart TV Advertising and Tracking

   https://doi.org/10.2478/popets-2020-0021  

   Varmarken, Janus ; Le, Hieu ; Shuba, Anastasia ; Markopoulou, Athina ; Shafiq, Zubair ( April 2020 , Proceedings on Privacy Enhancing Technologies)

   Abstract In this paper, we present a large-scale measurement study of the smart TV advertising and tracking ecosystem. First, we illuminate the network behavior of smart TVs as used in the wild by analyzing network traffic collected from residential gateways. We find that smart TVs connect to well-known and platform-specific advertising and tracking services (ATSes). Second, we design and implement software tools that systematically explore and collect traffic from the top-1000 apps on two popular smart TV platforms, Roku and Amazon Fire TV. We discover that a subset of apps communicate with a large number of ATSes, and that some ATS organizations only appear on certain platforms, showing a possible segmentation of the smart TV ATS ecosystem across platforms. Third, we evaluate the (in)effectiveness of DNS-based blocklists in preventing smart TVs from accessing ATSes. We highlight that even smart TV-specific blocklists suffer from missed ads and incur functionality breakage. Finally, we examine our Roku and Fire TV datasets for exposure of personally identifiable information (PII) and find that hundreds of apps exfiltrate PII to third parties and platform domains. We also find evidence that some apps send the advertising ID alongside static PII values, effectively eliminating the user’s ability tomore »opt out of ad personalization.« less
4. Urban Science: Putting the “Smart” in Smart Cities

   https://doi.org/10.3390/urbansci2040094  

   Shutters, Shade ( December 2018 , Urban Science)

   Increased use of sensors and social data collection methods have provided cites with unprecedented amounts of data. Yet, data alone is no guarantee that cities will make smarter decisions and many of what we call smart cities would be more accurately described as data-driven cities. Parallel advances in theory are needed to make sense of those novel data streams and computationally intensive decision support models are needed to guide decision makers through the avalanche of new data. Fortunately, extraordinary increases in computational ability and data availability in the last two decades have led to revolutionary advances in the simulation and modeling of complex systems. Techniques, such as agent-based modeling and systems dynamic modeling, have taken advantage of these advances to make major contributions to diverse disciplines such as personalized medicine, computational chemistry, social dynamics, or behavioral economics. Urban systems, with dynamic webs of interacting human, institutional, environmental, and physical systems, are particularly suited to the application of these advanced modeling and simulation techniques. Contributions to this special issue highlight the use of such techniques and are particularly timely as an emerging science of cities begins to crystallize.
5. Smart Stadia as Testbeds for Smart Cities: Enriching Fan Experiences and Improving Accessibility

   https://doi.org/10.1109/ICCNC.2019.8685580  

   Panchanathan, Sethuraman ; McDaniel, Troy ; Tadayon, Ramin ; Rukkila, Adric ; Venkateswara, Hemanth ( February 2019 , 2019 International Conference on Computing, Networking and Communications (ICNC))