Internet of Things (IoT) has becoming a central theme in current technology trend whereby objects, people or even animals and plants can exchange information over the Internet. IoT can be referred as a network of interconnected devices such as wearables, sensors and implantables, that has the ability to sense, interact and make collective decisions autonomously. In short, IoT enables a full spectrum of machine-to-machine communications equipped with distributed data collection capabilities and connected through the cloud to facilitate centralized data analysis. Despite its great potential, the reliability of IoT devices is impeded with limited energy supply if these devices were to deploy particularly in energy-scarced locations or where no human intervention is possible. The best possible deployment of IoT technology is directed to cater for unattended situations like structural or environmental health monitoring. This opens up a new research area in IoT energy efficiency domain. A possible alternative to address such energy constraint is to look into re-generating power of IoT devices or more precisely known as energy harvesting or energy scavenging. This chapter presents the review of various energy harvesting mechanisms, current application of energy harvesting in IoT domain and its future design challenges.

Internet of Things (IoT) has becoming a central theme in current technology trend whereby objects, people or even animals and plants can exchange information over the Internet. IoT can be referred as a network of interconnected devices such as wearables, sensors and implantables, that has the ability to sense, interact and make collective decisions autonomously. In short, IoT enables a full spectrum of machine-to-machine communications equipped with distributed data collection capabilities and connected through the cloud to facilitate centralized data analysis. Despite its great potential, the reliability of IoT devices is impeded with limited energy supply if these devices were to deploy particularly in energy-scarced locations or where no human intervention is possible. The best possible deployment of IoT technology is directed to cater for unattended situations like structural or environmental health monitoring. This opens up a new research area in IoT energy efficiency domain. A possible alternative to address such energy constraint is to look into re-generating power of IoT devices or more precisely known as energy harvesting or energy scavenging. This chapter presents the review of various energy harvesting mechanisms, current application of energy harvesting in IoT domain and its future design challenges.

This article proposes a system equipped with the enhanced Bayesian classification techniques to automatically assign folders to store electronic text documents. Despite computer technology advancements in the information age where electronic text files are so pervasive in information exchange, almost every single document created or downloaded from the Internet requires manual classification by the users before being deposited into a folder in a computer. Not only does such a tedious task cause inconvenience to users, the time taken to repeatedly classify and allocate a folder for each text document impedes productivity, especially when dealing with a huge number of files and deep layers of folders. In order to overcome this, a prototype system is built to evaluate the performance of the enhanced Bayesian text classifier for automatic folder allocation, by categorizing text documents based on the existing types of text documents and folders present in user's hard drive. In this article, the authors deploy a High Relevance Keyword Extraction (HRKE) technique and an Automatic Computed Document Dependent (ACDD) Weighting Factor technique to a Bayesian classifier in order to obtain better classification accuracy, while maintaining the low training cost and simple classifying processes using the conventional Bayesian approach.

The meteoric rise of smart devices in dominating worldwide consumer electronics market complemented with data-hungry mobile applications and widely accessible heterogeneous networks e.g. 3G, 4G LTE and Wi-Fi, have elevated Mobile Internet from a ‘nice-to-have' to a mandatory feature on every mobile computing device. This has spurred serious data traffic congestion on mobile networks as a consequence. The nature of mobile network traffic today is more like little Data Tsunami, unpredictable in terms of time and location while pounding the access networks with waves of data streams. This chapter explains how Big Data analytics can be applied to understand the Device-Network-Application (DNA) dimensions in annotating mobile connectivity routine and how Simplify, a seamless network discovery solution developed at Nextwave Technology, can be extended to leverage crowd intelligence in predicting and collaboratively shaping mobile data traffic towards achieving real-time network congestion control. The chapter also presents the Big Data architecture hosted on Google Cloud Platform powering the backbone behind Simplify in realizing its intelligent traffic steering solution.

The meteoric rise of smart devices in dominating worldwide consumer electronics market complemented with data-hungry mobile applications and widely accessible heterogeneous networks e.g. 3G, 4G LTE and Wi-Fi, have elevated Mobile Internet from a ‘nice-to-have' to a mandatory feature on every mobile computing device. This has spurred serious data traffic congestion on mobile networks as a consequence. The nature of mobile network traffic today is more like little Data Tsunami, unpredictable in terms of time and location while pounding the access networks with waves of data streams. This chapter explains how Big Data analytics can be applied to understand the Device-Network-Application (DNA) dimensions in annotating mobile connectivity routine and how Simplify, a seamless network discovery solution developed at Nextwave Technology, can be extended to leverage crowd intelligence in predicting and collaboratively shaping mobile data traffic towards achieving real-time network congestion control. The chapter also presents the Big Data architecture hosted on Google Cloud Platform powering the backbone behind Simplify in realizing its intelligent traffic steering solution.