Abstract-Driven by the advent of sophisticated and ubiquitous applications, and the ever-growing need for information, wireless networks are without a doubt steadily evolving into profoundly more complex and dynamic systems. The user demands are progressively rampant, while application requirements continue to expand in both range and diversity. Future wireless networks, therefore, must be equipped with the ability to handle numerous, albeit challenging requirements. Network reconfiguration, considered as a prominent network paradigm, is envisioned to play a key role in leveraging future network performance and considerably advancing current user experiences. This paper presents a comprehensive overview of reconfigurable wireless networks and an in-depth analysis of reconfiguration at all layers of the protocol stack. Such networks characteristically possess the ability to reconfigure and adapt their hardware and software components and architectures, thus enabling flexible delivery of broad services, as well as sustaining robust operation under highly dynamic conditions. The paper offers a unifying framework for research in reconfigurable wireless networks. This should provide the reader with a holistic view of concepts, methods, and strategies in reconfigurable wireless networks. Focus is given to reconfigurable systems in relatively new and emerging research areas such as cognitive radio networks, cross-layer reconfiguration and software-defined networks. In addition, modern networks have to be intelligent and capable of self-organization. Thus, this paper discusses the concept of network intelligence as a means to enable reconfiguration in highly complex and dynamic networks. Key processes in network intelligence, such as reasoning, learning, and contextawareness are presented to illustrate how these methods can take reconfiguration to a new level. Finally, the paper is supported with several examples and case studies showing the tremendous impact of reconfiguration on wireless networks.
Figure 1: Thermal images of graphical passwords entered on a smartphone's touchscreen (1 and 2) and a laptop's touchpad (3 and 4) were visually inspected by participants, who recovered 60.65% of touch gestures (2 and 4), and 23.61% of touch taps (1 and 3). Attacks against touchscreens are more accurate (87.04% vs 56.02%). The red circles/arrows illustrate the user's input.
We investigate the effectiveness of thermal attacks against input of text with different characteristics; we study text entry on a smartphone touchscreen and a laptop keyboard. First, we ran a study (N=25) to collect a dataset of thermal images of short words, websites, complex strings (special characters, numbers, letters), passphrases and words with duplicate characters. Afterwards, 20 different participants visually inspected the thermal images to attempt to identify the text input. We found that long and complex strings are less vulnerable to thermal attacks, that visual inspection of thermal images reveals different parts of the entered text (36% on average and up to 82%) even if the attack is not fully successful, and that entering text on laptops is more vulnerable to thermal attacks than on smartphones. We conclude with three learned lessons and recommendations to resist thermal attacks.
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