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Power saving is a critical issue in wireless sensor networks (WSNs) since sensor nodes are powered by batteries which cannot be generally changed or recharged. As radio communication is often the main cause of energy consumption, extension of sensor node lifetime is generally achieved by reducing transmissions/receptions of data, for instance through data compression. Exploiting the natural correlation that exists in data typically collected by WSNs and the principles of entropy compression, in this Letter we propose a simple and efficient data compression algorithm particularly suited to be used on available commercial nodes of a WSN, where energy, memory and computational resources are very limited. Some experimental results and comparisons with, to the best of our knowledge, the only lossless compression algorithm previously proposed in the literature to be embedded in sensor nodes and with two well-known compression algorithms are shown and discussed

Social networks have been recently employed as a source of information for event detection, with particular reference to road traffic congestions and car accidents. In this paper we present a real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria, processes tweets, by applying text mining techniques, and finally performs the classification of tweets. The aim is to assign the appropriate class label to each tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the Italian road network, allowing to detect traffic events almost in real-time, often before online traffic news web sites. We employed the Support Vector Machine as classification model and we achieved an accuracy of 95.75% by solving a binary classification problem (traffic vs. non-traffic tweets). We were also able to discriminate if traffic is caused by an external event or not, by solving a multi-class classification problem, and obtaining an accuracy of 88.89%.

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Social media have become a common way for people to express their personal viewpoints, including sentiments about health topics. We present the results of an opinion mining analysis on vaccination performed on Twitter from September 2016 to August 2017 in Italy. Vaccine-related tweets were automatically classified as against, in favor or neutral in respect of the vaccination topic by means of supervised machine-learning techniques. During this period, we found an increasing trend in the number of tweets on this topic. According to the overall analysis by category, 60% of tweets were classified as neutral, 23% against vaccination, and 17% in favor of vaccination. Vaccine-related events appeared able to influence the number and the opinion polarity of tweets. In particular, the approval of the decree introducing mandatory immunization for selected childhood diseases produced a prominent effect in the social discussion in terms of number of tweets. Opinion mining analysis based on Twitter showed to be a potentially useful and timely sentinel system to assess the orientation of public opinion toward vaccination and, in future, it may effectively contribute to the development of appropriate communication and information strategies.

The paper presents an intelligent system to automatically infer trends in the public opinion regarding the stance towards the vaccination topic: it enables the detection of significant opinion shifts, which can be possibly explained with the occurrence of specific social context-related events. The Italian setting has been taken as the reference use case. The source of information exploited by the system is represented by the collection of vaccine-related tweets, fetched from Twitter according to specific criteria; subsequently, tweets undergo a textual elaboration and a final classification to detect the expressed stance towards vaccination (i.e. in favor, not in favor, and neutral). In tuning the system, we tested multiple combinations of different text representations and classification approaches: the best accuracy was achieved by the scheme that adopts the bag-of-words, with stemmed n-grams as tokens, for text representation and the support vector machine model for the classification. By presenting the results of a monitoring campaign lasting 10 months, we show that the system may be used to track and monitor the public opinion about vaccination decision making, in a low-cost, real-time, and quick fashion. Finally, we also verified that the proposed scheme for continuous tweet classification does not seem to suffer particularly from concept drift, considering the time span of the monitoring campaign.

Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy classification. The approaches proposed so far to FDT learning, however, have generally neglected time and space requirements. In this paper, we propose a distributed FDT learning scheme shaped according to the MapReduce programming model for generating both binary and multi-way FDTs from big data. The scheme relies on a novel distributed fuzzy discretizer that generates a strong fuzzy partition for each continuous attribute based on fuzzy information entropy. The fuzzy partitions are therefore used as input to the FDT learning algorithm, which employs fuzzy information gain for selecting the attributes at the decision nodes. We have implemented the FDT learning scheme on the Apache Spark framework. We have used ten real-world publicly available big datasets for evaluating the behavior of the scheme along three dimensions: i) performance in terms of classification accuracy, model complexity and execution time, ii) scalability varying the number of computing units and iii) ability to efficiently accommodate an increasing dataset size. We have demonstrated that the proposed scheme turns out to be suitable for managing big datasets even with modest commodity hardware support. Finally, we have used the distributed decision tree learning algorithm implemented in the MLLib library and the Chi-FRBCS-BigData algorithm, a MapReduce distributed fuzzy rule-based classification system, for comparative analysis.

In this paper, we propose the use of a multiobjective evolutionary approach to generate a set of linguistic fuzzy-rulebased systems with different tradeoffs between accuracy and interpretability in regression problems. Accuracy and interpretability are measured in terms of approximation error and rule base (RB) complexity, respectively. The proposed approach is based on concurrently learning RBs and parameters of the membership functions of the associated linguistic labels. To manage the size of the search space, we have integrated the linguistic twotuple representation model, which allows the symbolic translation of a label by only considering one parameter, with an efficient modification of the well-known (2 + 2) Pareto Archived Evolution Strategy (PAES). We tested our approach on nine real-world datasets of different sizes and with different numbers of variables. Besides the (2 + 2)PAES, we have also used the well-known nondominated sorting genetic algorithm (NSGA-II) and an accuracydriven single-objective evolutionary algorithm (EA). We employed these optimization techniques both to concurrently learn rules and parameters and to learn only rules. We compared the different approaches by applying a nonparametric statistical test for pairwise comparisons, thus taking into consideration three representative points from the obtained Pareto fronts in the case of the multiobjective EAs. Finally, a data-complexity measure, which is typically used in pattern recognition to evaluate the data density in terms of average number of patterns per variable, has been introduced to characterize regression problems. Results confirm the effectiveness of our approach, particularly for (possibly high-dimensional) datasets with high values of the complexity metric.Index Terms-Accuracy-interpretability tradeoff, fuzzy rulebased systems (FRBSs), linguistic two-tuple representation, multiobjective evolutionary algorithms (EAs).