A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> E. MCS DATA STORAGE AND CLASSIFICATION APPLICATIONS <s> Signal classification is based on the extraction of several features that will be used as inputs of a classifier. The selection of these features is one of the most crucial parts, because they will design the search space, and, therefore, will determine the difficulty of the classification. Usually, these features are selected by using some prior knowledge about the signals, but there is no method that can determine that they are the most appropriate to solve the problem. This paper proposes a new technique for signal classification in which a Genetic Algorithm is used in order to automatically select the best feature set for signal classification, in combination with a kNN as classifier system. This method was used in a well known problem and its results improve those already published in other works. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> E. MCS DATA STORAGE AND CLASSIFICATION APPLICATIONS <s> An efficient DFA (data fusion algorithm) plays an important role in tracking for moving objects over WSS (wireless sensor system) deployments in order to track the objects accurately. Accuracy in object tracking is mainly dominated by the prediction for those moving targets by filtering and refining the results from wireless mobile sensors deployed in WSS environment. A DFA based on CHHN (competitive Hopfield neural network) technique for obtaining the relationship between measurements results from wireless mobile sensors and estimation of existing tracks over WSS (wireless sensor system) is proposed in this paper. Embedded within the CHNN is also a competitive learning mechanism which creatively removes the dilemma of occasional irrational solutions in traditional HNN (Hopfield neural networks). In this research, except the proposed approach is established with CHNN, the methodology of data fusion over WSS is guaranteed to converge into a stable state when performing a data association. In words, the CHNN-based DFA is combined with wireless mobile sensors in a WSS environment to demonstrate the target tracking capabilities. Computer simulation results illustrate that the new methodology of data fusion based on CHNN is not only successfully able to solve the data association problems addressed over WSS environments, but the specified simulated targets can also be tracked without large scale missing. <s> BIB002 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> E. MCS DATA STORAGE AND CLASSIFICATION APPLICATIONS <s> Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different physiological brain states. In this paper, we propose an efficient framework for evaluating the power-accuracy trade-off for EEG-based compressive sensing and classification techniques in the context of epileptic seizure detection in wireless tele-monitoring. The framework incorporates compressive sensing-based energy-efficient compression, and noisy wireless communication channel to study the effect on the application accuracy. Discrete cosine transform (DCT) and compressive sensing are used for EEG signals acquisition and compression. To obtain low-complexity energy-efficient, the best data accuracy with higher compression ratio is sought. A reconstructed algorithm derived from DCT of daubechie's wavelet 6 is used to decompose the EEG signal at different levels. DCT is combined with the best basis function neural networks for EEG signals classification. Extensive experimental work is conducted, utilizing four classification models. The obtained results show an improvement in classification accuracies and an optimal classification rate of about 95% is achieved when using NN classifier at 85% of CR in the case of no SNR value. The satisfying results demonstrate the effect of efficient compression on maximizing the sensor lifetime without affecting the application's accuracy. <s> BIB003 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> E. MCS DATA STORAGE AND CLASSIFICATION APPLICATIONS <s> Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems. Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes. In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI. The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands. In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers. Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets. The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders. <s> BIB004 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> E. MCS DATA STORAGE AND CLASSIFICATION APPLICATIONS <s> This survey aims to encourage the multidisciplinary communities to join forces for innovation in the mobile health monitoring area. Specifically, multidisciplinary innovations in medical emergency scenarios can have a significant impact on the effectiveness and quality of the procedures and practices in the delivery of medical care. Wireless body sensor networks (WBSNs) are a promising technology capable of improving the existing practices in condition assessment and care delivery for a patient in a medical emergency. This technology can also facilitate the early interventions of a specialist physician during the pre-hospital period. WBSNs make possible these early interventions by establishing remote communication links with video/audio support and by providing medical information such as vital signs, electrocardiograms, etc. in real time. This survey focuses on relevant issues needed to understand how to setup a WBSN for medical emergencies. These issues are: monitoring vital signs and video transmission, energy efficient protocols, scheduling, optimization and energy consumption on a WBSN. <s> BIB005 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> E. MCS DATA STORAGE AND CLASSIFICATION APPLICATIONS <s> Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities. This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals. This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance. The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity. The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments. The accuracy for the proposed method is 80% when dB, 84% when dB, and 88% when dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned. <s> BIB006 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> E. MCS DATA STORAGE AND CLASSIFICATION APPLICATIONS <s> Abstract Epilepsy is the second common brain disorder affecting 70 million people worldwide. Electroencephalogram (EEG) has been widely used for the diagnosis of epileptic seizures. However, most of the existing EEG-based seizure detection methods cannot maintain robust performance in real life conditions. This is where the EEG data are corrupted with different sources of noise and artifacts. This study presents a robust seizure detection system that works efficiently under the real life conditions as well as the ideal conditions. A feature learning method based on L1-penalized robust regression is developed and applied to the EEG spectra to recognize the most prominent features pertinent to epileptic seizures. The extracted features are then fed into the random forest classifier for seizure detection. Results on a public benchmark dataset show that the performance of this seizure detection system is superior to prior work. It first achieves seizure detection rates of 100.00% sensitivity, 100.00% specificity, and 100.00% classification accuracy under the ideal conditions. The proposed method is also proven to be robust in the presence of white noise and EEG artifacts mainly those arising from muscle activities and eyes-blinking. It is found to achieve seizure detection accuracies in the range of 90.00–100.00% when applied to noisy EEG data corrupted with high noise levels. To the best of our knowledge, there exists no work in the literature that detects seizures under these conditions. <s> BIB007
This section discusses data storage, preprocessing, feature extraction, detection, and classification to measure the data accuracy. The data storage and classification application on the server side is a very important element of MCS, to detect and classify the collected data accuracy. The architecture framework has two parts, the client or mobile sensing node, where the acquiring client application runs, and the server, where the collected raw data are stored and kept for future use. The client application regularly acquires the data, applies simple preprocessing, and transfers it to the server over a secure Internet connection. On the other side, the server immediately places the received data into a queue that is later processed in a separate thread. Local storage and processing modules manage the acquired raw data from the sensor. This allows nodes to support an off-line mode for temporary local storage. When network connectivity is accessible, the locally stored data is transferred to an external server for further processing BIB005 . Processing of raw collected data is very important to reduce the transferred data size, which ultimately increases the sensor battery lifetime. However, the normal readings from sensors may not be proper for direct use by applications, depending on the quality of the raw data and the needs of the application. There is a need for local analysis of data to perform specific processing of raw data on the mobile sensor node. This analysis produces intermediate results, which are transferred over the core network to the storage. There are two motivations for such local sensor analysis. First, this processing eventually will reduce the transmitted data therefore less energy consumption and less bandwidth during the transmission. Second, it decreases the amount of processing that is performed at the server side  . Data processing has been simplified and reorganized in recent years using data centers and high-speed cloud computing capabilities. Data processing is automated to the extent that heavy processing applications such as pattern recognition and image processing are executed in near real-time BIB002 . Before saving data to databases, stream computing technology has evolved to provide real-time analysis of huge size of data to help with timely decision-making. Some continuous streams of data may originate from sensors, cameras, news feeds and a variety of other sources to be classified, filtered, interconnected, and transformed into informed decisions. Feature extraction is a process that produces a set of the most relevant features that represents the information for analysis and classification in an efficient way. Feature extraction plays a significant role in identifying most data. Obtaining useful and discriminant features depends largely on the feature extraction method used BIB002 . The objective of feature extraction is to increase the performance and efficiency of the analysis and classification. This can be achieved through removing redundancy and variability in the data that is of little or no value in the classification or discarding entire data sets if applicable. Another option is restructuring the data in the feature space to optimize the performance of the classifier BIB002 . Finally, it is possible to extract spatial information, which is critical to target similarity or identification. The main advantage of these features is the reduction of the dimensions of the cross-correlation sequences for use as inputs into individual classifiers. The motivations of these processes are to minimize the number of features and maximize pattern discrimination. As the increased number of features is not necessarily good; because they increase the redundancy in the features which might confuse the classifier, so the ideal case is to generate a minimum number of discriminant features. Feature extraction techniques can generally be categorized into time-domain or frequency-domainbased according to the features used. These techniques were used in several research work BIB003 , BIB007 , BIB006 . Time-domain features are easily computed, and their time complexity is usually manageable BIB006 . While, Frequency-domain features are obtained by transforming data from time into their basic frequency components using FFT BIB001 . Detection and Classification play an essential role in timely analyses. There are two types of detection such as event detection and object detection. The event detection is used to detect the occurrence of a certain event and the significance of the occurrence, while an object detection, detects the existence of an object and possibly some of its properties such as size, color... etc. A classification is an ordered group of correlated categories used to organize data according to its similarities. Each class must have easily recognizable features that should be few and not overlapping with those of other classes. The overall accuracy of the classifier represents the degree of closeness of the measured results to the true values BIB004 . Specificity and sensitivity are two factors that affect the classification accuracy, defined as functions of the true and false positives and negatives. A false positive (FP) refers to the condition in which the results are incorrectly perceived as positive, while true positives (TP) are test results that show correctly perceived results BIB003 - BIB006 . Similarly, a true negative (TN) is defined as the correct behavior to detect the normal condition; while a false negative (FN) is the incorrect detection of the normal condition BIB003 . Specificity in classification refers to the ability of an assessor to measure a particular substance BIB006 . Specificity, also known as a class precision, is defined as the percentage of true negative tests within the total number of negative tests. The sensitivity, also known as a class recall, in classification testing represents the smallest amount of a substance in a sample that can be accurately measured by an assessor. Sensitivity is defined as the percentage of true positive tests within the total number of affected (positive) testes. Therefore, the specificity, sensitivity, and overall accuracy of the classifier can be defined as BIB003 , BIB006 , BIB004  and where TP, FP, TN, FN are the true positives, false positives, true negatives, and false negatives, respectively. Both positive and negative terms are denoted as the classifier's prediction or expectation and true and false refer to whether that VOLUME 7, 2019 FIGURE 5. Feature extraction and classification.