Knowledge driven discovery for opportunistic IoT networking

• Main Author: Pozza, Riccardo
• Other Authors: Moessner, Klaus
• Published: University of Surrey 2015
• Subjects: 621.3
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.665288

So far, the Internet of Things (IoT) has been concerned with the objective of connecting every-thing, or any object to the Internet world. By collaborating towards the creation of new services, the IoT has introduced the opportunity to add smartness to our cities, homes, buildings and healthcare systems, as well as businesses and products. In many scenarios, objects or IoT devices are not always statically deployed, but they may be free to move around being carried by people or vehicles, while still interacting with static IoT infrastructure. The Opportunistic Networking paradigm states that, exploiting opportunistic interactions between static and mobile IoT devices, provides for increased network capacity, additional connectivity, reduced deployment costs, improved reliability and overall network lifetime improvements. IoT scenarios do illustrate the increased need to identify and exploit opportunistic interactions between IoT devices in order to recognize when an opportunity for communication is possible. For example, statically deployed devices (i.e. road side sensors) may need to find mobile devices (this may be sensors or actuators) (i.e. connected cars) for exploiting them for collecting and relaying data towards destinations without relying on a static infrastructure. This means that discovery in IoT scenarios needs to determine the availability of other devices in scenarios in which devices' presence is uncertain or may change over time. This directly leads to a contradicting objective where resource wastage in device discovery is to be kept at a minimum. This thesis presents two contributions that provide solutions to overcome the clash between these contradicting objectives. Firstly, a Context Aware Resource Discovery mechanism is introduced, capable of providing optimized discovery and adapting available resources based on learned mobility patterns. Secondly, an Arrival and Departure Time Prediction and Discovery framework is defined and investigated; this framework aims to predict future arrival and departure times and helps to plan the use of devices' resources in advance based on the foreseen resource demand patterns.