Learning from Data Streams: Processing Techniques in Sensor Networks

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The huge bibliography offers an excellent starting point for further reading and future research. Data Stream Processing. Data Stream Management Systems and Architectures. Querying of Sensor Data. Aggregation and Summarization in Sensor Networks. Sensory Data Monitoring. The algorithm adopts several techniques, such as difference and hop count thresholds, to model node, and distance-based clustering. Initially, each node treats itself as an active cluster. Then, similar adjacent clusters are merged into larger clusters round by round.

In each round, each cluster will try to combine with its most similar adjacent cluster simultaneously. Two clusters can be merged only if both consider one another as the most similar neighbor. DHCS terminates when no merging happens any more. The final clusters, which cannot be merged any more, are called steady clusters.

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Classification is a task of assigning new object into a class of predefined object categories. Classification model is learned using the set of training data and classifies new data into one of the learned class. Figure 5 shows that classification maps input attribute set X to class label Y. Classification-based approaches have adapted the traditional classification techniques such as decision tree-based, rule-based, nearest neighbor-based, and support vector machines-based techniques based on type of the classification model that they used.

Decision tree is a classifier in the form of tree and classifies the instance by starting at the root of tree and moving through it until a leaf node where class label is assigned. The internal nodes are used to partition data into subsets by applying test condition to separate instances that have different characteristics. Nearest neighbor-based approaches classify dataset based on closet training examples. The training examples are vectors in a multidimensional feature space with corresponding class labels. A nearest neighbor classifier is a lazy learner that does not process patterns during training [91].

To respond, a request to classify a query vector is made to locate the closest training vectors according to the distance metric. The classes of these training vectors are used to assign a class to the query vector. Rule-based classifier groups the dataset in predefined classes by using "if.. SVM support vector machine techniques partition the data belonging to different classes by fitting a hyperplane between them which maximizes the partition.

The data is mapped into a higher-dimensional feature space where it can be easily partitioned by a hyperplane. Furthermore, a kernel function is used to approximate the dot products between the mapped vectors in the feature space to find the hyperplane. Chikhaoui et al.


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They applied the classification model to identify the persons in ubiquitous environment. In order to identify persons, the proposed approach first extracts frequent patterns called episodes from the datasets using the Apriori algorithm [53].

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The next step evaluates the extracted patterns and assigns weights to these episodes to construct frequent episode weight matrix FEWM. DT builds pattern classifier from a labeled training data-set using a divide-and-conquer approach. To build up a DT model, it recursively selects the attribute that is used to partition the training data-set into subsets until each leaf node in the tree has uniform class membership.

The proposed approach is validated by experiment using data collected from the Domus Laboratory [93] and the Testbed smart home [94]. The general performance and classification accuracy of algorithm are evaluated by using the Weka framework version 3. Experiment results show good classification. However, using frequent episodes alone without temporal constraints and deep analysis does not guarantee good identification.

Sharma et al. The training phase simply stores every training example with its label. To make a prediction for a test example, first, its distance to every training example is computed.

Machine Learning At Speed: Operationalizing ML For Real-Time Data Streams

This label is the prediction for this test example. The algorithm is evaluated by building a classifier from the preprocessed training data generated from NS2 [97] and test trajectory data [98] using class labels. Experimental investigation yields a significant output in terms of the correctly classified success rate, Akhlaghinia et al.

The sensor NWs collect the variety of attributes including environmental changes and occupant's interaction with the environment.

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The collected data is then used by the learning approach to construct a classification-based predictive model to predict the ambient intelligence environment occupancy. The occupancy is predicted by using the fuzzy rules which are modeled by using the past value of time series data. In the learning process, input from the sensor is compared with stored rules to take appropriate action. The prediction-based approach improves the energy saving in smart homes and enhances the safety and security of occupants. The result shows the ability of the proposed technique to predict the combined occupancy time series.

However, the model is implemented in single-user environment and unable to predict the complex environmental patterns in multi-user environment over long period. Gaber et al. They used the algorithm output granularity AOG [,] technique to preserve the limited memory size and change the algorithm output rate according to data rate, available memory, algorithm output rate history, and time constraints to fill the available memory with generated knowledge. The algorithm works by searching for the nearest instance stored in main memory when a new element arrives.

All instances are already stored in the main memory according to a prespecified distance threshold. The threshold here represents the similarity measure acceptable by the algorithm to consider two or more elements as one element according to the elements attribute values. If the algorithm finds this element, then it checks the class label.

If the class label is the same, then it increases the weight for this instance by one; otherwise, it decrements the weight by one. If the weight becomes zero, then this element is released from the memory. The algorithm is empirically validated using synthetic streaming data under the resource-constrained environment of a common handheld computer.

McConnell and Skillicorn [] presented a distributed framework for building and deploying predictors in sensor networks. By using the computational power of each sensor, a powerful learning structure on whole network is constructed. A distributed voting approach is proposed in which each sensor is a leaf of tree DT to perform local prediction.

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Instead of sending the raw data, the local predictive models built on sensors transmit the target class to the sink. At sink, the local predication models are combined to construct global prediction model.


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It shows how the local model enables sensors to respond to the change in target by relearning local models. The proposed framework is useful especially for sensor networks with limited energy, computation, and bandwidth resources. It makes efficient the distributed data mining in the presence of moving class boundaries.

Data is also confidentially achieved by transmitting a predictive model instead of original data to the. The distributed prediction model is evaluated using J48 decision tree implemented in WEKA on variety of dataset for both simple and weighted voting schemes. According to results, distributed prediction model has the potential of an increase in accuracy combined with a reduction in model size and runtime as compared with a centralized approach.

Major issues in this framework are the need of an expensive CPU on each sensor node for computing and building local predictive model, and also extra memory is required to store local predictive model. Malhotra et al. A distributed cluster-based algorithm for detection and classification of vehicles has been proposed. Sensors form clusters on-demand for the sake of running a classification taskbased on the produced feature vectors.

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The monitoring area is divided into clusters, and a cluster head is selected for each cluster. All sensors send their feature vector to cluster heads. The cluster head combines all received feature vectors including one from itself , executes the classification task using, for example, KNN or ML classifiers, and makes decision on the class ofthe unknown vehicle.

Two approaches were proposed: the first combines extracted features and the second combines individual decisions. Classification using decision fusion and a maximum likelihood ML classifier led to the best results. ML is also compared with KNN classifier with various settings of data and decision fusion schemes.

The proposed technique produced the best classification accuracy of Flouri et al. SVM is incrementally trained on example set called support vector.