Due to the significant advances of wireless sensor networks (WSNs), researchers are eager to use this technology in the subsea applications. Because of rapid absorption of high radio frequency in the water, acoustic waves are used as communication medium, which pose new challenges, including high propagation delay, high path loss, low bandwidth, and high-energy consumption. Because of these challenges and high movement of nodes by water flow, end-to-end routing methods used in most of existing routing protocols in WSNs are not applicable to underwater environments. Therefore, new routing protocols have been developed for underwater acoustic sensor networks (UWASNs) in which most of the routing protocols take advantage of greedy routing. Due to inapplicability of global positioning system (GPS) in underwater environments, finding location information of nodes is too costly. Therefore, based on a need for location information, we divided the existing greedy routing protocols into two distinctive categories, namely, location-based and location-free protocols. In addition, location-free category is divided into two subcategories based on method of collecting essential information for greedy routing, including beacon-based and pressure-based protocols. Furthermore, a number of famous routing protocols belonging to each category are reviewed, and their advantages and disadvantages are discussed. Finally, these protocols are compared with each other based on their features.

In recent years, there has been considerable interest among people to use short message service (SMS) as one of the essential and straightforward communications services on mobile devices. The increased popularity of this service also increased the number of mobile devices attacks such as SMS spam messages. SMS spam messages constitute a real problem to mobile subscribers; this worries telecommunication service providers as it disturbs their customers and causes them to lose business. Therefore, in this paper, we proposed a novel machine learning method for detection of SMS spam messages. The proposed model contains two main stages: feature extraction and decision making. In the first stage, we have extracted relevant features from the dataset based on the characteristics of spam and legitimate messages to reduce the complexity and improve performance of the model. Then, an averaged neural network model was applied on extracted features to classify messages into either spam or legitimate classes. The method is evaluated in terms of accuracy and F-measure metrics on a realworld SMS dataset with over 5000 messages. Moreover, the achieved results were compared against three recently published works. Our results show that the proposed approach achieved successfully high detection rates in terms of F-measure and classification accuracy, compared with other considered researches.

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Parkinson’s disease (PD) is a neurodegenerative nervous system disorder that mainly affects body movement, and it is one of the most common diseases, particularly in elderly individuals. This paper proposes a new machine learning approach to predict Parkinson’s disease severity using UCI’s Parkinson’s telemonitoring voice dataset. The proposed method analyses the patient's voice data and classifies them into “severe” and “nonsevere” classes. At first, a subset of features was selected, then a novel approach with a combination of Rotation Forest and Random Forest was applied on selected features to determine each patient’s disease severity. Analysis of the experimental results shows that the proposed approach can detect the severity of PD patients in the early stages. Moreover, the proposed model is compared with several algorithms, and the results indicate that the model is highly successful in classifying records and outperformed the other methods concerning classification accuracy and F1-measure rate.

Wireless Sensor Actor Networks (WSANs) have contributed to the development of pervasive computing wherein time consideration to perform the tasks of pervasive applications is necessary. Hence, time constraint is one of the major challenges of WSANs. In this paper, we propose an analytical approach based on queuing theory to minimize the total time taken for completion of tasks, i.e., make-span, in WSANs with hybrid architecture. The best allocation rates of tasks to actor nodes are figured out through solving inequities and qualities resulting from a steady state analysis of the proposed model. Applying the calculated tasks arrival rates at each of the actors, the make-span could be minimized. To assess the accuracy of the tasks assignment rates to each of the actors attained from the suggested analytical approach and to provide a graphical representation of the WSAN a formal model in terms of the generalized stochastic Petri net (GSPN) is presented. The proposed GSPN model is analyzed, tasks distribution weights to the actors are determined, and then tasks allocation rates can be computed. Comparing the results achieved from the analytical approach and the GSPN model demonstrates that allocation rates and hence, the make-span figured out from proposed approach and the formal model are the same. Experimental results in typical scenarios show shorter make-span and longer network lifetime compared to when one of the two popular traditional task allocation algorithms, namely, opportunistic load balancing (OLB), and stochastic allocation (SA) algorithms, is used.

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