We live in a world where demand for monitoring natural and artificial phenomena is growing. The practical importance of Sensor Networks is continuously increasing in our society due to their broad applicability to tasks such as traffic and air-pollution monitoring, forest-fire detection, agriculture, and battlefield communication. Furthermore, we have seen the emergence of sensor technology being integrated in everyday objects such as cars, traffic lights, bicycles, phones, and even being attached to living beings such as dolphins, trees, and humans. The consequence of this widespread use of sensors is that new sensor network infrastructures may be built out of static (e.g., traffic lights) and mobile nodes (e.g., mobile phones, cars). The use of smart devices carried by people in sensor network infrastructures creates a new paradigm we refer to as Social Networks of Sensors (SNoS). This kind of opportunistic network may be fruitful and economically advantageous where the connectivity, the performance, of the scalability provided by cellular networks fail to provide an adequate quality of service. This paper delves into the issue of understanding the impact of human mobility patterns to the performance of sensor network infrastructures with respect to four different metrics, namely: detection time, report time, data delivery rate, and network coverage area ratio. Moreover, we evaluate the impact of several other mobility patterns (in addition to human mobility) to the performance of these sensor networks on the four metrics above. Finally, we propose possible improvements to the design of sensor network infrastructures

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<span lang="EN-US">Scientific research is currently considered one of the key factors in the development of our life. It plays a significant role in managing our business, study, and work more conveniently. One of the important aspects when it comes to scientific research is the level of collaboration among researchers/disciplines. The collaboration between two different disciplines contributes to obtaining more reliable solutions for our everyday issues. Therefore, it is needed to understand the collaboration patterns among researchers and come up with convenient strategies for strengthening this kind of collaboration. In this work, we aim at investigating the patterns of scientific collaboration among researchers across disciplines. To this end, we generate a co-authorship network for several disciplines. The generated network reveals many interesting facts regarding the collaboration patterns among researchers who work in the same/different disciplines. We involve several measurements in this study that evaluate different aspects, which is of interest to the research communities since most of the studies in the literature measure specific aspects. Moreover, we propose a novel metric for measuring scientific collaboration in a research community and use it to benchmark the collaboration among disciplines. Finally, we use the obtained results/facts in providing recommendations for scientific communities.</span>