History-based consistency algorithm for the trickle-timer with low-power and lossy networks

Recently, the internet of things (IoT) has become an important concept which has changed the vision of the Internet with the appearance of IPv6 over low power and lossy networks (6LoWPAN). However, these 6LoWPANs have many drawbacks because of the use of many devices with limited resources; therefore, suitable protocols such as the Routing Protocol for low power and lossy networks (RPL) were developed, and one of RPL's main components is the trickle timer algorithm, used to control and maintain the routing traffic frequency caused by a set of control messages. However, the trickle timer suffered from the short-listen problem which was handled by adding the listen-only period mechanism. This addition increased the delay in propagating transmissions and resolving the inconsistency in the network. However, to solve this problem we proposed the history based consistency algorithm (HBC), which eliminates the listen-only period based on the consistency period of the network. The proposed algorithm showed very good results. We measured the performance of HBC trickle in terms of convergence time; which was mainly affected, the power consumption and the packet delivery ratio (PDR). We made a comparison between the original trickle timer, the E-Trickle, the optimized trickle and our HBC trickle algorithm. The PDR and the power consumption showed in some cases better results under the HBC trickle compared to other trickle timers and in other cases the results were very close to the original trickle indicating the efficiency of the proposed trickle in choosing optimal routes when sending messages

On reliable and secure RPL (routing protocol low-power and lossy networks) based monitoring and surveillance in oil and gas fields

Different efforts have been made to specify protocols and algorithms for the successful operation of the Internet of things Networks including, for instance, the Low Power and Lossy Networks (LLNs) and Linear Sensor Networks (LSNs). Into such efforts, IETF, the Internet Engineering Task Force, created a working group named, ROLL, to investigate the requirement of such networks and devising more efficient solutions. The effort of this group has resulted in the specification of the IPv6 Routing Protocol for LLNs (RPL), which was standardized in 2012. However, since the introduction of RPL, several studies have reported that it suffers from various limitations and weaknesses including scalability, slow convergence, unfairness of load distribution, inefficiency of bidirectional communication and security, among many others. For instance, a serious problem is RPL’s under-specification of DAO messages which may result in conflict and inefficient implementations leading to a poor performance and scalability issues. Furthermore, RPL has been found to suffer from several security issues including, for instance, the DAO flooding attack, in which the attacker floods the network with control messages aiming to exhaust network resources. Another fundamental issue is related to the scarcity of the studies that investigate RPL suitability for Linear Sensor Networks (LSN) and devising solution in the lieu of that.Motivated by these observations, the publications within this thesis aim to tackle some of the key gaps of the RPL by introducing more efficient and secure routing solutions in consideration of the specific requirements of LLNs in general and LSNs as a special case. To this end, the first publication proposes an enhanced version of RPL called Enhanced-RPL aimed at mitigating the memory overflow and the under-specification of the of DAOs messages. Enhanced-RPL has shown significant reduction in control messages overhead by up to 64% while maintaining comparable reliability to RPL. The second publication introduces a new technique to address the DAO attack of RPL which has been shown to be effective in mitigating the attack reducing the DAO overhead and latency by up to 205% and 181% respectively as well as increasing the PDR by up to 6% latency. The third and fourth publications focus on analysing the optimal placement of nodes and sink movement pattern (fixed or mobile) that RPL should adopt in LSNs. It was concluded based on the results obtained that RPL should opt for fixed sinks with 10 m distance between deployed nodes