An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs

Intelligent Transportation system are becoming an interesting research area, after Internet of Things (IoT)-based sensors have been effectively incorporated in vehicular ad hoc networks (VANETs). The optimal route discovery in a VANET plays a vital role in establishing reliable communication in uplink and downlink direction. Thus, efficient optimal path discovery without a loop-free route makes network communication more efficient. Therefore, this challenge is addressed by nature-inspired optimization algorithms because of their simplicity and flexibility for solving different kinds of optimization problems. NIOAs are copied from natural phenomena and fall under the category of metaheuristic search algorithms. Optimization problems in route discovery are intriguing because the primary objective is to find an optimal arrangement, ordering, or selection process. Therefore, many researchers have proposed different kinds of optimization algorithm to maintain the balance between intensification and diversification. To tackle this problem, we proposed a novel Java macaque algorithm based on the genetic and social behavior of Java macaque monkeys. The behavior model mimicked from the Java macaque monkey maintains well-balanced exploration and exploitation in the search process. The experimentation outcome depicts the efficiency of the proposed Java macaque algorithm compared to existing algorithms such as discrete cuckoo search optimization (DCSO) algorithm, grey wolf optimizer (GWO), particle swarm optimization (PSO), and genetic algorithm (GA).

A Multi-Agent Reinforcement Learning-Based Optimized Routing for QoS in IoT

The Routing Protocol for Low power and lossy networks (RPL) is used as a routing protocol in IoT applications. In an endeavor to bring out an optimized approach for providing Quality of Service (QoS) routing for heavy volume IoT data transmissions this paper proposes a machine learning-based routing algorithm with a multi-agent environment. The overall routing process is divided into two phases: route discovery phase and route maintenance phase. The route discovery or path finding phase is performed using rank calculation and Q-routing. Q-routing is performed with Q-Learning reinforcement machine learning approach, for selecting the next hop node. The proposed routing protocol first creates a Destination Oriented Directed Acyclic Graph (DODAG) using Q-Learning. The second phase is route maintenance. In this paper, we also propose an approach for route maintenance that considerably reduces control overheads as shown by the simulation and has shown less delay in routing convergence.

Improving Quality of Service Provisioning of Optimised Cuckoo Search Ad Hoc on-demand Distance Vector Routing Scheme for Cognitive Radio Ad Hoc Networks.

Spectrum mobility, cloud computing and the Internet of Things (IoTs) create large data sets, while the demand for more spectrum is increasing. Unfortunately, the spectrum is a scarce resource which is being underutilized by licensed users. The cognitive radio network, also known as intelligent radio, is a network that can adjust to environment changes and, detect available channels. It has emerged as a promising solution for the underutilization of the licensed spectrum and overcrowded free spectrum. Furthermore, given spectrum mobility, frequent link breakages impact negatively on the delivery of packets and the performance of the network. Hence there is need to address the routing problem. We therefore investigated which control methods can be utilized to improve the QoS provisioning in CRAHNs to minimize the signal overhead and to increase the achievable throughput.The study integrated the QoS requirements with optimized cuckoo search (OCS) algorithm to enhance the ad hoc on-demand distance vector (AODV) algorithm to establish a scheme we refer to as OCS-AODV. NS 2 simulation were run on Linux operating system. The comparative results show that the proposed scheme performed well in terms of end-to-end delay and throughput. However, the scheme does not backup alternative paths which can be used in the event of link breakages. The route discovery has to be re-initiated again. Though the route discovery process is faster because of the capability of the CS technique, it still degrades the performance of the scheme.

Improvise Route Discovery and Radio Duty Cycle in WSN using SDN.

In the conventional Wireless Sensor Network, every mote role is to collect data, discover route and then sends the packet to its neighbor to reach a destination, consequently demanding both computation time and power. Pertaining to same, a promising framework is required to mitigate both power consumption and computational costs of the nodes inside the network. It also required sufficient planning and proper execution of the strategies. In this way, an attempted to gain benefits of a Software Defined Network (SDN) approach in Wireless Sensor Networks (WSN) We have proposed a W-SDN framework for the traditional network using OpenFlow protocol and controller. The goal is to investigate the significant impact of SDN in WSN. Result analysis of the designed framework is proposed using the following parameters Radio Duty cycle, flow request, delay, and latency for QoS

Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks

In home and building automation applications, wireless sensor devices need to be connected via unreliable wireless links within a few hundred milliseconds. Routing protocols in Low-power and Lossy Networks (LLNs) need to support reliable data transmission with an energy-efficient manner and short routing convergence time. IETF standardized the Point-to-Point RPL (P2P-RPL) routing protocol, in which P2P-RPL propagates the route discovery messages over the whole network. This leads to significant routing control packet overhead and a large amount of energy consumption. P2P-RPL uses the trickle algorithm to control the transmission rate of routing control packets. The non-deterministic message suppression nature of the trickle algorithm may generate a sub-optimal routing path. The listen-only period of the trickle algorithm may lead to a long network convergence time. In this paper, we propose Collision Avoidance Geographic P2P-RPL, which achieves energy-efficient P2P data delivery with a fast routing request procedure. The proposed algorithm uses the location information to limit the network search space for the desired route discovery to a smaller location-constrained forwarding zone. The Collision Avoidance Geographic P2P-RPL also dynamically selects the listen-only period of the trickle timer algorithm based on the transmission priority related to geographic position information. The location information of each node is obtained from the Impulse-Response Ultra-WideBand (IR-UWB)-based cooperative multi-hop self localization algorithm. We implement Collision Avoidance Geographic P2P-RPL on Contiki OS, an open-source operating system for LLNs and the Internet of Things. The performance results show that the Collision Avoidance Geographic P2P-RPL reduced the routing control packet overheads, energy consumption, and network convergence time significantly. The cooperative multi-hop self localization algorithm improved the practical implementation characteristics of the P2P-RPL protocol in real world environments. The collision avoidance algorithm using the dynamic trickle timer increased the operation efficiency of the P2P-RPL under various wireless channel conditions with a location-constrained routing space.

Research on Optimization of Routing Protocol for Quantum wireless Ad Hoc Network

This paper addresses the optimization of on-demand routing protocols for quantum wireless Ad Hoc network. This paper proposes an optimal quantum route metric algorithm that integrates quantum route metric and classic route metric to select a better route path for quantum communication. The paper also improves the route discovery protocol by proposing a "reverse synchronization method" which means after the route request is completed, the quantum entanglement channel is established synchronously when the reverse path is established. This method, better than general methods which builds quantum channels after the forward path establishment, reduces the time and the numbers of messages for quantum channel establishment, thus improving the efficiency. Accordingly, this paper elaborates the specific methods, procedures and related upgrading message formats involved in quantum route discovery, quantum channel establishment and quantum information transmission of on-demand routing protocols for quantum wireless Ad Hoc network. The advantage of the "reverse synchronization method" in reducing the time and the numbers of messages for quantum channel establishment is verified through analysis on protocol performance.

Mobility-Aware Routing Algorithm for Mobile Ad Hoc Networks

Mobile ad hoc network (MANET) is a group of wireless mobile nodes that create a temporary network without the help of any central administration or standard support services. Mobility of nodes determines the overall performance of MANET networks. High mobility of nodes causes frequent changes in the network topology, and this leads to link breakage and increases reinitiating of the route discovery process. MANETs commonly use broadcasting techniques for route discovery process. However, it can cause redundant rebroadcasts, packet collisions, and channel contention. The main objective of this paper is to design and develop the mobility-aware routing algorithm (MARA) to enhance the performance of the routing protocol in MANETs. The proposed scheme allows mobile nodes to rebroadcast or discard received broadcasted messages. The decision is based on the combination of node speed, distance between nodes, and residual energy of nodes. These parameters are considered both in route request and route reply process to reduce the chance of link breakage and broadcast storm problems. The proposed algorithm has been evaluated based on the performance metrics: packet delivery ratio, average end-to-end delay, throughput, and routing overhead. We have used network simulator NS-2 V-2.35. The simulation results revealed that MARA outperforms ad hoc on-demand distance vector (AODV), mobility and direction aware (MAD), and mobility and energy-aware (MAE) routing protocols.