Integrating Machine Learning in WSNs for Intelligent Decision-Making.

According to IBM, machine learning is a form of artificial intelligence (AI) that enables a system to learn from data rather than through explicit programming. It focuses on using data and algorithms to imitate the way that humans learn, gradually increasing its accuracy. Machine learning is used to solve complex tasks in a way that the humans would solve them. According to Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL, machine learning models are to recognize a visual scene, understand a text written in natural language or even perform an action in the physical world.

Machine learning starts with data (numbers, photos, or text) such as bank transactions, pictures if people, bakery items, sales reports, repair records and time series data from sensors. The data is gathered and prepared to train the model. The more the data that the model is trained with, the better. Having that machine learning enables models to train on data sets before being deployed, some of the models are thus online and continually adapting as new data is being ingested into them, or offline where once the algorithms are deployed, they do not change. Online models lead to an improvement in the types of associations that are made between data elements. With this, they continually refine the models by continuously processing new data in near real time and this trains the system to adapt to the changing patterns and association in the data. The functions of machine learning can be descriptive, where the system uses the data to explain what happened; prescriptive, where the system will use the data to make suggestions about what action to take, and can be predictive, where the system uses the data to predict what will happen.

Decisions that are to be made by the machine learning systems are based on how they are trained and what their goal or function is. There are several categories in machine learning which include supervised learning, unsupervised learning, reinforcement learning and semi-supervised learning. Supervised learning typically begins with an established set of data and a certain understanding of how the data is classified. It is intended to find patterns in data that can be applied to an analytics process. The data has labelled features that define the meaning of the data. Because the attributes and the meaning if the data have been identified, it’s well understood by the users. When the label is continuous, it’s a regression and when the data comes from a finite set of values, it’s a classification. Generally, regression is used for supervised learning in understanding the correlation between the variables. This helps in making predictions by taking into account the known historical patterns and the current conditions.

Unsupervised learning is for when the problem requires a massive amount of data that’s unlabelled. Understanding the meaning of this data requires algorithms that can begin to understand the meaning based on being able to classify the data as per the patterns or clusters. These algorithms first segment the data into groups as per features or examples and create parameter values in the classification. In a massive load of data, it is used to analyse the data and later pass it to a supervised learning process making it quicker in determining a solution or outcome.

Reinforcement learning is a behavioural learning model where the algorithm receives feedback from the analysis of the data so the user id guided to the best outcome. Here, the system isn’t trained with the simple data set but it learns through trial and error, and so, a sequence of successful decisions will result in the process of being reinforced, solving the problem at hand.

Finally, the semi-supervised learning offers a medium between the supervised and non-supervised learning. It uses a smaller labelled dataset to guide classification and feature extraction from a larger, unlabelled dataset. It helps solve the problem of not having enough labelled data for a supervised algorithm, and also helps if it’s too costly to label enough data. 

According to Network Interview, a wireless sensor network (WSN) is a network of small, low power and autonomous devices (nodes), that are deployed in a given environment to measure and monitor various environmental parameters. These nodes which are interconnected communicate with each other either directly or indirectly. The nodes of a WSN are equipped with sensors that measure temperature, humidity, air pressure, air quality, weather and many more. They are also used for controlling various appliances such as lights, fans, and even home security systems. The data collected is then transmitted to a central node, a base station, which collects and processes the data. The data is later transmitted to a remote server which can then be used for further analysis and processing.

Some of the benefits of WSNs are the low power and cost effectiveness, their high scalability meaning they can be easily expanded to accommodate more nodes or sensors and the ease of deploying, as they can be in a variety of environments. Despite that, there are some of the challenges that they have, of which one of them is the power management since the nodes require to be powered in order to operate. 

Another issue is that the data transmission range is limited meaning that the network may need to be expanded to cover larger areas. The other is the issue of vulnerability to security threats since data is transmitted between the nodes and is not encrypted.

Manufacturing companies were the early adopters of the sensor technology so as to monitor how well equipment was operating. Since machinery needs to be managed, maintained and monitored regularly to ensure quality control and effective performance, organizations therefore want the ability to spot potential problems and fix them before they can cause downtime. A downtime is where an equipment is taken offline for maintenance purposes. Typically, companies would monitor the output of sensors and determine if their results were matching with the expected outputs. In order to prevent failure though, it’s important to anticipate and predict failures before they can cause damage.

The analytics on WSNs data involves data sets being generated and created by the sensors in the nodes. The data may contain a specific structure which is ideal for applying machine learning techniques. There is a huge amount of data being produced though may not be complex. With known outages and failures, machine learning algorithms can build models to predict the future problems that may arise. The model would include data about the optimal indicators of a well-run machine and even the data points of the failure preceded. Anomalies will be determined as the model is being trained, so as to predict the potential failures. One of the algorithms that can be used in the predictions is neural network algorithms.

Neural networks are incorporated in deep learning, which is a specific method of machine learning. They are used when trying to learn patterns from unstructured data. This makes them of great use in image recognition, speech and computer vision applications. In WSNs, using machine learning is advantageous since it is possible to analyse packets as they travel between the nodes and the base station enhancing data and information encyption. It is possible to thereby detect suspicious nodes which helps in maintenance. It is possible as well to reduce all forms of congestions that happen, detect errors and even help in the authentication processes through the physical layer. In common instances, WSNs interact with security-sensitive information in an unsupervised manner. Data authentication, freshness, integrity and confidentiality is really important in enhancing the security. For example, according to the book, Early Detection of IoT Malware Network Activity using machine learning techniques, a study was done using machine language algorithms such as k-Nearest Neighbour (k-NN), which showed accurate results according to the outcomes of the performance assessment. According to another book, Preserving Support Vector Machine Training over Blockchain-Based Encrypted IoT Data in Smart Cities, SVM algorithm was used for IoT data requiring only two transactions in one iteration with no need of a reliable third party. This reduced the computational complexity. Anomaly detection has provided excellent results against all types of malicious activities where now packet analysis, tracking and protection against Denial-of-Service (DoS) have been accomplished through machine learning methods and algorithms.

With a lot more being offered by machine learning techniques, strong, intelligent decisions can be made in the field of Internet of Things where WSNs are based. By integrating machine learning into them opens up to greater security and more efficient systems. This enhances the productivity and security in company levels as well as at homes.