Adaptive Deep Rider LSTM-enabled Objective Functions for RPL routing in IoT applications

This paper presents a proposed Objective Function (OF) design using various routing metrics for improving the performance of IoT applications. The most important idea of the proposed design is the selection of the routing metrics with respect to the application requirements. The various metrics, such as Energy, Distance, Delay, Link quality, Trust (EDDLT) are used for improving the objective function design of the RPL in various IoT applications. Here, the Adaptive Deep rider LSTM is newly employed for the energy prediction where the Adaptive Deep Rider LSTM is devised by the combination of the adaptive theory with the Rider Adam Algorithm (RAA), and the Deep-Long Short Memory (Deep-LSTM). However, the evaluation of the proposed method is carried out energy dissipation, throughput, and delay by achieving a minimum energy dissipation of 0.549, maximum throughput of 1, and a minimum delay of 0.191, respectively.

Trilateral Location based Maximum Weighted Directive Spanning Tree for Optimal Routing in IoV

Internet of Vehicles (IoV) is one of the developing models in the Vehicular adhoc networks (VANETs) with the vast improvement of communication technologies. In order to improve data transmission among the multiple communities without link breakage, a novel Trilateral Location Identified Maximum Weighted Directive Spanning Tree (TLIMWDST) technique is introduced. The proposed TLIMWDST technique consists of two major phases, namely location identification and optimal path identification to improve the reliability of data transmission from source vehicle to destination vehicle. In the first phase, the location of the neighboring vehicles is identified by applying a trilateration technique. After the location identification, an optimal route path between the source and destination is identified using Maximum Weighted Directive Spanning Tree (MWDST) through the intermediate nodes. The performance of the TLIMWDST technique is assessed through simulation as compared to the previous path selection techniques in terms of different routing metrics such as packet delivery ratio, packet loss rate, end-to-end delay and throughput with respect to the number of data packets.

Energy harvesting effect on prolonging low-power lossy networks lifespan

Low-power lossy networks performance relies heavily on the wireless node battery status. Furthermore, Routing Protocol for Low-Power and Lossy Network routing protocol was not optimally designed with sustainable energy consumption in mind to suit these networks. Prolonging the lifespan of these networks is of utmost priority. This article introduces a solar energy harvesting module to power energy-constrained network devices and quantifies the effect of using harvested energy on prolonging their network lifetime when Routing Protocol for Low-Power and Lossy Network routing protocol is used. Simulation of the new developed module is conducted in three different scenarios using Contiki Cooja simulator sporting Zolertia Z1 motes. Furthermore, the harvested energy used was fed from a Cooja-based Simulation model of actual PV supercapacitor circuit design. All battery levels were set to 1% of their total capacity for all nodes in the network to speed up observing the energy harvesting effect. The performance evaluation results showed that the network with no-energy harvesting operated for time duration of 4:08:04 time units (i.e. hour:minute:second) with a dramatic decrease in connection between nodes in the network. However, the same network, when using the harvested energy to back up the battery operation, lasted for 6:40:01 in time units with improved connectivity, a total extended network lifetime of 2:31:97-time units. Furthermore, for the Routing Protocol for Low-Power and Lossy Network routing metrics, OF0 outperformed ETX in term of throughput, packet delivery ratio, energy consumption, and network connectivity. Results indicate that the developed harvested energy module fits perfectly for any Cooja-based simulation and mimics actual photovoltaic-based supercapacitor battery. It should also help researchers introduce and quantify accurately new energy consumption-based routing metrics for Routing Protocol for Low-Power and Lossy Network.

Trust-Aware Routing Framework for Internet of Things

Establishing security in internet of things (IoT) is a critical challenge, as it is connected to the network's extremely resource-constrained devices. The RPL is a standard routing protocol for IoT. It is well-suited for low power and lossy networks (LLN). The RPL provides little security in the IoT network against various attacks. However, one needs to strengthen the security concern in RPL. So, this paper proposes a trust-aware, energy-based reliable routing (TAER-RPL) for IoT to enhance security among network nodes. The TAER-RPL is taken into account the routing metrics, namely trust, ETX, RER to pick the optimal parent for data transmission. The simulation is conducted in COOJA simulator. TAER-RPL's efficiency is compared with SecTrust-RPL and RPL. The TAER-RPL increases the lifespan of the network by 15%.

FTC-OF: Forwarding Traffic Consciousness Objective Function for RPL Routing Protocol

The diversity of Internet of Things applications require a flexible routing protocol to cope with several constraints. In this context, the RPL protocol was designed to meet the needs of IoT. RPL relies on an objective function based on specific metrics to fulfill its routing strategy. The single routing metric problem leads generally to non-optimized routes selection. As a consequence, two major issues emerge, mainly the node’s congestion due to the high number of forwarded packets, also the greedy energy consumption by those nodes that conduct to fast batteries draining. In that purpose, Forwarding Traffic Consciousness Objective Function has been proposed, which combines three routing metrics, namely hop count, RSSI and a newly designed Forwarded Traffic Metric (FTM). The proposed method, evaluated using COOJA against ETX and Energy based RPL, showed a packet delivery ratio increase respectively with 2% and 11% in low and high traffics, considerably reduces the power consumption with approximately 47% as well as it achieves a good balance of traffic managed by the relay nodes.

Mobility management for RPL protocol in internet of things

<p>The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was proposed for various applications of IPv6 low power wireless networks. While RPL supports various routing metrics and is designed to be suitable for wireless sensor network environments, it does not consider the mobility of nodes. Therefore, there is a need for a method that is energy efficient and that provides stable and reliable data transmission by considering the mobility of nodes in RPL networks. In this paper a new heuristic flabellum algorithm inspired by physical and biological behaviour of flabella in the sea is presented, and bottleneck and swarm problems are resolved through managing the moving nodes by flabellum algorithm. Finally, the proposed algorithm’s performance is evaluated using the Cooja simulator. The proposed algorithm;Flabellum RPL; shows significant improvements with regards to packet delivery, and convergence and lifetime.</p>