The wireless sensor network (WSN) is a tool for monitoring the physical world, utilizing self-organizing networks of battery-powered wireless sensors that can sense, process and communicate. It can be deployed rapidly and cheaply, thereby enabling large-scale, on-demand monitoring and tracking over a wide range of applications. Sensor nodes in such a network usually have limited onboard processing and wireless communication capabilities, and are equipped with batteries with limited power and thus need to deploy energy saving techniques in order to prolong the network lifetime. However, if all the sensor nodes are simultaneously operated, redundant sensing data, corresponding wireless communication collision and interference will cause much energy to be wasted. How does one cover all the sensing area with the least active nodes so that no blind-point exists and connectivity kept is significant. Coverage becomes a serious problem in large scale sensor networks where hundreds and thousands of nodes are randomly deployed. The coverage problem is one of the most fundamental issues in wireless sensor networks, which directly affects the capability and efficiency of the sensor network. Generally, it can be a measure of QoS in a sensor network. Current solutions are based on node scheduling; the main idea is to find the optimal number of active nodes while maintaining coverage and connectivity. The problem in finding the maximal coverage in a sensor network as a set of nodes that can completely cover the monitored area, and a centralized solution to this problem is proposed. Several algorithms aim to find a close-to-optimal solution based on local information. In this work, we develop an algorithm that controls energy-consumption, Bandwidth (BW), and signal strength using single fitness function to solve Coverage Area Problems.

Tele-rehabilitation is an alternative to the conventional rehabilitation service that helps patients in remote areas to access a service that is practical in terms of logistics and cost, in a controlled environment. It includes the usage of mobile phones or other wireless devices that are applied to rehabilitation exercises. Such applications or software include exercises in the form of virtual games, treatment monitoring based on the rehabilitation progress and data analysis. However, nowadays, physiotherapists use a default profiling setting for patients carrying out rehabilitation, due to lack of information. Medical Interactive Rehabilitation Assistant (MIRA) is a computer-based (virtual reality) rehabilitation platform. The profile setting includes: a level of difficulty, percentage of tolerance and maximum range. To the best of our knowledge, there is a lack of optimization in the parameter values setting of MIRA exergames that could enhance patients' performance. Generally, non-optimal profile setting leads to reduced effectiveness. Therefore, this study aims to develop a method that optimizes the profile setting of each patient according to the estimated (desired) optimal results. The proposed method is developed using unsupervised and supervised machine learning techniques. We use Self-Organizing Map (SOM) to cluster patient records into several distinct clusters. K-fold cross validation is applied to construct the prediction models. Classification And Regression Tree (CART) is utilized to predict the patient's optimal input setting for playing the MIRA games. The combination of these techniques seems to improve the efficiency of the standard (default) way in predicting the optimal settings for exergames. To evaluate the proposed method, we conduct an experiment with data collected from a rehabilitation center. We use three metrics to quantify the quality of the results: R-squared (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of experimental analysis demonstrate that the proposed method is effective in predicting the adequate parameter setting in MIRA platform. The method has potential to be implemented as an intelligent system for MIRA prediction in healthcare. Moreover, the method could be extended to similar platforms for which data is available to train our method on.