Abstract
Predicting the inherent traffic behaviour of a network is an essential task, which can be used for various purposes, such as monitoring and managing the network's infrastructure. However, the recent surge of dynamic environments, such as Internet of Things and Cloud Computing have hampered this task. This means that the traffic on these networks is even more complex, displaying a nonlinear behaviour with specific aperiodic characteristics during daily operation. Traditional network traffic predictors are usually based on large historical data bases which are used to train algorithms. This may not be suitable for these highly volatile environments, where the strength of the force exerted in the interaction between past and current values may change quickly with time. In light of this, a taxonomy for network traffic prediction models, including the review of state of the art, is presented here. In addition, an analysis mechanism, focused on providing a standardized approach for evaluating the best candidate predictor models for these environments, is proposed. These contributions favour the analysis of the efficacy and efficiency of network traffic prediction among several prediction models in terms of accuracy, historical dependency, running time and computational overhead. An evaluation of several prediction mechanisms is performed by assessing the Normalized Mean Square Error and Mean Absolute Percent Error of the values predicted by using traces taken from two real case studies in cloud computing.

Abstract
The number of everyday interconnected devices continues to increase and constitute the Internet of Things (IoT). Things are small computers equipped with sensors and wireless communications capabilities that are driven by energy constraints, since they use batteries and may be required to operate over long periods of time. The majority of these devices perform data collection. The collected data is stored on-line using web-services that, sometimes, operate without any special considerations regarding security and privacy. The current work proposes a modified hash-chain authentication mechanism that, with the help of a smartphone, can authenticate each interaction of the devices with a REST web-service using One Time Passwords (OTP) while using open wireless networks. Moreover, the proposed authentication mechanism adheres to the stateless, HTTP-like behavior expected of REST web-services, even allowing the caching of server authentication replies within a predefined time window. No other known web-service authentication mechanism operates in such manner

Abstract
The number of everyday interconnected devices continues to increase and constitute the Internet of Things (IoT). Things are small computers equipped with sensors and wireless communications capabilities that are driven by energy constraints, since they use batteries and may be required to operate over long periods of time. The majority of these devices perform data collection. The collected data is stored on-line using web-services that, sometimes, operate without any special considerations regarding security and privacy. The current work proposes a modified hash-chain authentication mechanism that, with the help of a smart-phone, can authenticate each interaction of the devices with a REST web-service using One Time Passwords (OTP). Moreover, the proposed authentication mechanism adheres to the stateless, HTTP-like behavior expected of REST web-services, even allowing the caching of server authentication replies within a predefined time window. No other known web-service authentication mechanism operates in such manner.

Abstract
Smart Cities are a key application domain for the Internet of Things (IoT), and it is coming nearer everyday through pilot trials and deployments in various cities around the world. In Porto, Portugal, a city-wide IoT Living Lab emerged after we deployed several testbeds, e.g. harbour and a city-scale vehicular networks, and carried out various experiments with the SenseMyCity crowdsensor. In this paper, we discuss how a standard Machine-to-Machine (M2M) middleware is a key enabler of our e-health platform and SenseMyCity crowdsensor, powered by the use of smartphones as M2M gateways. M2M standards provided by ETSI/oneM2M are essential for a paradigm shift, aiming at making the IoT truly interoperable without the need for human intervention. In this work, we map two applications that rely on the role of a smartphone as a gateway, which acts as a proxy to connect legacy devices to the IoT using a standard middleware. We illustrate the advantages of using M2M, and, as a proof-of-concept, we measure and quantify the energy savings obtained, showing improvements of smartphones' battery life.

Abstract
In this paper we analyze the information propagated through three social networks. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors. In this paper we focus on the search for automatic methods for assessing the relevance of a given set of posts. We first retrieved from social networks, posts related to trending topics. Then, we categorize them as being news or as being conversational messages, and assessed their credibility. From the gained insights we used features to automatically assess whether a post is news or chat, and to level its credibility. Based on these two experiments we built an automatic classifier. The results from assessing our classifier, which categorizes posts as being relevant or not, lead to a high balanced accuracy, with the potential to be further enhanced.

Abstract
Organisations have been striving to account for the resources they've been allocating to Social Media integration and management, essentially because this integration has been occurring without a previously designed content strategy, which will foster the desired fan engagement. In order to establish a comparison of social media strategies between HEIs, we developed a seven category model, encompassing the fundamental communication areas of focus for higher education service providers. Then, we performed a classification of these HEI posts in Facebook, according to our model. For this step, we used six of the most promising, and prominent, classifiers to obtain a predicted category for each post. Combining all posts from each HEI according to the model we get the HEI's editorial strategy. By clustering the overall social media strategies and corresponding response rate we discover the sector's monitoring HEI and, through a benchmarking process, we retrieve useful inputs for the design of social media strategies for HEI.