A Novel Adaptive and Efficient Routing Update Scheme for Low-Power Lossy Networks in IoT

Abstract

In this paper, we introduce Drizzle, a new algorithm for maintaining routing information in the Low-power and Lossy Ne tworks (LLNs). The aim is to address the limitations of the curre ntly standardized routing maintenance (i.e. Trickle algorithm) in such networks. Unlike Trickle, Drizzle has an adaptive suppre ssion mechanism that assigns the nodes different transmission probabilities based on their transmission history so to boost the fairne ss in the network. In addition, Drizzle removes the listen-only pe riod presented in Trickle intervals leading to faster convergence time . Furthermore, a new scheme for setting the redundancy counter has be en introduced with the goal to mitigate the negative side effect of the short-listen problem presented when removing the listen-only pe riod and boost further the fairness in the network . The pe rformance of the proposed algorithm is validated through e xte nsive simulation e xperiments under different scenarios and ope ration conditions. In particular, Drizzle is compared to four routing maintenance algorithms in terms of control-plane overhead, powe r consumption, convergence time and packet delivery ratio (PDR) under uniform and random distributions and with lossless and lossy links. The results indicated that Drizzle reduces the control-plane overhead, power consumption and the convergence time by up to 76%, 20% and 34% respectively while maintaining approximately the same PDR rates.

An RPL Based Optimal Sensors Placement in Pipeline Monitoring WSNs

“…The evaluation has been carried out using a customized version of Omnet++ network simulator that includes an implementation of the RPL routing protocol designed by the authors. The transmission and an interference range are set to be of 100 m. The Expected Transmission Count (ETX) [12][13][14] is used for calculating the node ranks and selecting the preferred parent, so that stability in the network topology is built. Further parameters are provided in Table 1.…”

RIATA: A Reinforcement Learning-Based Intelligent Routing Update Scheme for Future Generation IoT Networks

“…The Drizzle algorithm presented in [7] achieves fairness in DIO transmission among nodes by assigning different DIO transmission probabilities based on their previous history. This algorithm removes the listen-only period and selects a random time t T imer between [s × I n , (s + 1) × I n ], which results in faster propagation of network information to neighbor nodes.…”

“…Figure. 7 shows variation of the adaptive redundancy constant c k value selection under various network densities in the proposed algorithm. When the network initializes, the c k value set equal to the value of k, which is set to 10 in this case.…”

“…One of the recently proposed Drizzle algorithm [7] achieves fairness in DIO transmission among nodes by assigning different DIO transmission probabilities to them depending on their DIO transmission history. The variable s keeps track of the total number of DIO transmissions performed by a node in the past intervals.…”

A Novel Method for Secure RPL for Resource Based attacks in Internet of Things

“…Baraq Ghaleb et.al [8]an algorithm named drizzle algorithm was introduced as a new routing primitive for LLNs. Drizzle reduces the delay problems for mitigating the negative effect on the transmission delay problems and also achieve better results than standard rpl with respect to decreasing the delaying and increments the power of the node and it also shows the better results than as compared with normal RPL regarding packet delivery ratio and less delay and also consumes less energy of nodes.…”