| Carl Friedrich Gauß Faculty | Department of Computer Science

RPL Objective Function to optimize the lifetime of Wireless Sensor Networks

Student(anonymous, Login required)
SupervisorRobert Hartung
ProfessorProf. Dr.-Ing. Lars Wolf
IBR GroupCM (Prof. Wolf)
TypeMaster Thesis


Wireless Sensor Networks (WSNs) and the recent idea of the Internet of Things (IoT) have become a largely relevant topic in the today's research on communication networks. Both WSNs and the IoT consist of devices which are called sensor nodes in the WSN. These devices are often battery-powered and have therefore a limited lifetime. Additionally, outdoor-located networks are exposed to environmental conditions. Direct sunlight and chaning temperatures affect both the reliability and lifetime of the network. Therefore key challenges in both of these networks are achieving high robustness and reliability and extending the lifetime of battery-powered sensor nodes. The recent concept of energy harvesting can extend the lifetime and even power nodes completely.

Purpose of applications is often to record sensor data and transfer data to a single sink node. Because those networks often consist of a large number of devices and devices might fail, the routing from a node through the network to the sink might change over time. Routing protocols handle path finding and neighbor handling and are designed for specific purposes.


The Routing Protocol for Low power and Lossy Networks (RPL) was designed for low-power and lossy networks which includes WSNs and the IoT. Routing is based on Objective Functions (OF), which decide which path to use. The most primitive version uses the rank of a parent node to forward packets. The rank simply describes the distance to the sink node. Therefore data should always take a minimal path. Because networks might be split up or some nodes may fail more often, a single node is often a bottle neck in this network and has a shorter lifetime. Additionally, available energy from harvesters has an impact on the lifetime. A more balanced routing approach is therefore beneficial.


Assume that you have a model of robustness/reliability and predicted/available energy.

Extend the RPL implementation of RIOT with an objective function that chooses routing based on reliability and energy. An evaluation should show the improved routing. Your solution(s) should be first simulated using the Cooja simulator and then verified by real-world experiments.


The following skills are helpful for the execution of this thesis (not all of them are required!):
  • C
  • Cooja
  • RIOT
  • INGA
  • RPL


last changed 2019-04-24, 14:30 by Robert Hartung