Abstract
Wireless Sensor Networks (WSNs) can be deployed using available hardware and software. The Contiki is an operative system compatible with a wide range of WSN hardware. A Contiki development environment named InstantContiki is also available and includes the Cooja simulator, useful to test WSN simulation scenarios prior to their deployment. Cooja can provide realistic results since it uses the full Contiki's source code and some motes can be emulated at the hardware level. However this implies extending the simulation runtime, which is heightened since the Cooja is single threaded, i.e, it makes use of a single core per instant of time, not taking advantage of the current multi-core processors. This chapter presents a framework to automate the configuration and execution of Cooja simulations. When a multi-core processor is available, this framework runs multiple simultaneous Cooja instances to reduce simulations runtime in exchange of higher CPU load and RAM usage.

Abstract
WSN can be deployed using widely available hardware and software solutions. The Contiki is an open source operative system compatible with a wide range of WSN hardware. A Contiki development environment named InstantContiki is also available and includes the Cooja simulation tool, useful for the simulation of WSN scenarios, prior to their deployment. This simulation tool can provide realistic results since it uses the full Contiki's source code and some motes can be emulated at the hardware level. However, the Cooja simulator uses one process per simulation, not taking advantage of multiple core processors. In this paper we propose a framework to automate the execution of simulations of multiple scenarios and configurations in Cooja. When a multiple cores processor is available, this framework can run multiple simultaneous Cooja instances, taking advantage of processing resources and contributing to reduce the total simulation runtime.

Abstract
Real-time monitoring applications may be used in a wireless sensor network (WSN) and may generate packet flows with strict quality of service requirements in terms of delay, jitter, or packet loss. When strict delays are imposed from source to destination, the packets must be delivered at the destination within an end-to-end delay (EED) hard limit in order to be considered useful. Since the WSN nodes are scarce both in processing and energy resources, it is desirable that they only transport useful data, as this contributes to enhance the overall network performance and to improve energy efficiency. In this paper, we propose a novel cross-layer admission control (CLAC) mechanism to enhance the network performance and increase energy efficiency of a WSN, by avoiding the transmission of potentially useless packets. The CLAC mechanism uses an estimation technique to preview packets EED, and decides to forward a packet only if it is expected to meet the EED deadline defined by the application, dropping it otherwise. The results obtained show that CLAC enhances the network performance by increasing the useful packet delivery ratio in high network loads and improves the energy efficiency in every network load.

Abstract
The Internet of Things can be seen has a growing number of things that inter-operate using an Internet-based infrastructure and that has evolved during the last years with little concern for the privacy of its users, especially regarding how the collected data is stored. Technological measures ensuring users privacy must be established. In this paper we will present a technological framework for the secure storage of data. Things can then interact with the framework's API much in the same way they now interact with its current servers, after which, the framework will performthe required operations in order to secure the data before storing it. Themethods adopted for the secure storage will maintain the sharing ability, conveniently allowing authorized access to other users, the initial user's terms (e.g. data anonymity) and the ability to revoke assigned privileges at all times.

Abstract
Social media has become one of the most prolific felds for interchange of multidisciplinary expertise. In this paper, computer science, communication and management are brought together for the development of a sound strategic content analysis, in the Higher Education Sector. The authors present a study comprised of two stages: analysis of SM content and corresponding audience engagement according to a weighted scale, and a classification of content strategies, which builds on different noticeable articulations of editorial areas among organizations. Their approach is based on an automatic classification of content according to a predefned editorial model. The proposed methodology and research results offer academic and practical fndings for organizations striving on social media.

Abstract
The adoption of social media networks by organizations has been increasing, mainly by using more social networks but also by constantly increasing on the number of messages and received comments posted on these channels. Interestingly, this process apparently has not been accompanied by a carefully planned and strategically design process to provide the essential alignment with organizational goals. This study is framed in the tertiary sector, the Higher Education Sector (HES), which despite its peculiarities, is no exception to the above limitations, and is facing an increased competitive environment. In this paper we present a sector benchmarking process, and the respective analysis, to provide insights on the sector's tendency, as well as a threefold classification of the sector's social media strategies being pursued. The analysis builds upon a regulatory communication framework and respective editorial model. We describe the results of our automatic text-mining and categorization information system, specifically developed to address and analyze the seven categories of HES' social media messages. Our results show that social media strategies have been focusing essentially on mediatization and building/maintaining the organizational image/reputation as well as on advertising educational services, but completely neglecting the dialogical dimension intrinsically linked to social media environments. (C) 2015 The Authors. Published by Elsevier B.V.