Wireless sensor networks (WSNs) consist of sensor nodes. It is a collection of wild number of low cost device constraint sensor nodes that communicates using wireless medium and they are small in size, low battery power and limited processing capability. This restraint of low electricity power of a sensor node and limited energy capability makes the wireless sensor network failure. A data aggregation is very important techniques in wireless sensor networks and it reduces the energy consumption by eliminating redundancy. In WSNs, Sensor nodes are resources constrained in memory, data sensing, and battery power and communication capability. Data communication is the process of communications between nodes that consumes a large portion of the total amount of energy used up for WSNs. One of the solutions to reduce number of bits transmitted during communication is data aggregation. As wireless sensor networks are usually deployed in remote and hostile environments to trajectory sensitive information or data, sensor nodes are affected by attacks. Thus, security is an important issue to be considered in WSNs.
The Brain Data Analysis and evaluation is a challenging task. Focusing towards the human-thinking-centric studies characteristics, brain informatics (BI) emphasizes a systematic methodology with key issue of systematic brain data analysis. The brain data analytical methods are very rare and thus brain data research and implementation is more challenging. This paper reviews the existing research and methodologies developed so far, in the area of Brain Informatics, Web Intelligence, Learning Schemes, Analytical methods and relevant aspects of these along with their limitations. More data and analytical methods need to be added in the existing BI approaches. Also, there is high need to embed these into a mature computing environment.
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