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With the rapid advancement of mobile devices, people have become more attached to them than ever. This rapid growth combined with millions of applications (apps) make smartphones a favorite means of communication among users. In general, the available contents on smartphones, apps, and web, come in two versions: 1) free content that is monetized via advertisements (ads) and 2) paid content that is monetized by user subscription fees. However, the resources, namely, energy, bandwidth, and processing power, on-board are limited, and the existence of ads in websites and free apps can significantly increase the usage of these resources. These issues necessitate a good understanding of the mobile advertising eco-system and how such limited resources can be efficiently used. In this paper, we present the results of a novel web browsing technique that adapts the webpages delivered to smartphone, based on the smartphone's current battery level and the network type. Webpages are adapted by controlling the amount of ads to be displayed. Validation tests confirm that the system can extend smartphone battery life by up to ∼30% and save wireless bandwidth up to ∼44%.INDEX TERMS Smarphones, energy efficiency, efficient web browsing, web adaptation.

With the rapid advancements in mobile devices, users have become more attached to them than ever. This rapid growth, combined with millions of applications (apps), makse smartphones a favourite means of communication among users. In general, the available contents on smartphones, apps and the web, come in two versions: (i) free contents that are monetized via advertisements (ads); and (ii) paid contents that are monetized by user subscription fees. However, the resources, namely, energy, bandwidth, and processing power, on-board are limited, and the existence of ads in websites and free apps can significantly increase the usage of these resources. Therefore, in this paper, we describe an approach that enables the separation of web contents in a number of websites. Having done so, the energy cost due to downloading, rendering, and displaying web ads over Wi-Fi and 3G networks is evaluated. That is, how much energy web ads contribute to the total consumed energy when a user accesses the web. Furthermore, the bandwidth consumed by web ads in a number of well-known websites is also evaluated. The high cost of ads on smartphones must be considered by the designers and vendors of apps.

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Detecting anomalously behaving devices in security-and-safety-critical applications is an important challenge. This paper presents an off-device methodology for detecting the anomalous behavior of devices considering their power consumption data. The methodology takes advantage of the fact that every action on-board a device will be reflected in its power trace. This argument makes it inevitable for anomalously behaving device to go undetected. We transform the device’s 1-D instantaneous power consumption signals to 2-D time-frequency images using Constant Q Transformation (CQT). The CQT images capture valuable information about the tasks performed on-board a device. By applying Histograms of Oriented Gradients (HOG) on the CQT images, we extract robust features that preserve the edges of time-frequency structures and capture the directionality of the edge information. Consequently, we transform the anomaly detection problem into an image classification problem. We train a Convolutional Neural Network on the HOG images to classify the power signals to detect anomaly. We validated the methodology using a wide spectrum of emulated malware scenarios, five real malware applications from the well-known Drebin dataset, DDOS attacks, cryptomining malware, and faulty CPU cores. Across 18 datasets, our methodology demonstrated detection performance of ∼ 88% accuracy and 85% F-Score, resulting in improvements of 9% - 17% over other methods using power signals.

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