Routing Protocol For Data Transmission Research Articles

Quantum Firefly Secure Routing for Fog Based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) become an integral part of Internet of Things (IoT) and finds their applicability in several domains. As classical WSN faces several issues in service-based IoT applications, fog computing has been introduced in real-time, enabling local data processing and avoid raw data transmission to cloud servers. The Fog-based WSN generally involves advanced nodes, normal nodes, and some Fog Nodes (FN). Though the Fog-based WSN offers several benefits, there is a need to develop an effective trust-based secure routing protocol for data transmission among Cluster Heads (CHs) and FNs. In this view, this paper presents a Quantum Firefly Optimization based Multi-Objective Secure Routing (QFO-MOSR) protocol for Fog-based WSN. The main intention of the QFO-MOSR technique is to derive an optimal selection of routes between CHs and FNs in the network. The QFO-MOSR technique has incorporated the concepts of quantum computing and Firefly (FF) optimization algorithm inspired by the flashing behaviour of FFs. In addition, a multi-objective fitness function is derived by the QFO-MOSR technique using seven objectives: distance, inter-cluster distance, energy, delay, intra-cluster distance, link lifetime, and trust. The proposed routing technique derives a fitness function including trust factor from ensuring security. The design of the QFO-MOSR technique with a multi-objective fitness function shows the novelty of the work. To validate the performance of the QFO-MOSR technique, a series of experiments were carried out, and the results are investigated in terms of different measures. The experimental analysis ensured that the QFO-MOSR technique is superior to other methods in terms of different measures.

Energy-efficient hierarchical routing in wireless sensor networks based on fog computing

Wireless sensor networks (WSNs) have been recognized as one of the most essential technologies of the twenty-first century. The applications of WSNs are rapidly increasing in almost every sector because they can be deployed in areas where cable and power supply are difficult to use. In the literature, different methods have been proposed to minimize the energy consumption of sensor nodes to prolong WSNs utilization. In this article, we propose an efficient routing protocol for data transmission in WSNs; it is called energy-efficient hierarchical routing protocol for wireless sensor networks based on fog computing. Fog computing is integrated into the proposed scheme due to its capability to optimize the limited power source of WSNs and its ability to scale up to the requirements of the Internet of things applications. In addition, we propose an improved ant colony optimization algorithm that can be used to construct an optimal path for efficient data transmission for sensor nodes. The performance of the proposed scheme is evaluated in comparison with P-SEP, EDCF, and RABACO schemes. The results of the simulations show that the proposed approach can minimize sensor nodes’ energy consumption, data packet losses, and extends the network lifetime. We are aware that in WSNs, the certainty of the sensed data collected by a sensor node can vary due to many reasons such as environmental factors, drained energy, and hardware failures.

A Comprehensive Comparative Analysis of Two Novel Underwater Routing Protocols

The most unmanned area of this planet is sheltered with water; that is roughly 71.9% of the total area of this planet. A large quantity of marine life is present in this area. That is the reason underwater research is bounded due to unexplored benefits. Due to the addition of sensors and growing interests in the exploration and monitoring of marine life Underwater Wireless Sensor Network (UWSN) can play an important role. A variety of routing protocols has been deployed in order to get information between deployed nodes. Providing stable data transmission, maximum throughput, minimum consumption of the energy and delay are challenging tasks in the UWSN. These routing protocols can be Layer-by-Layer Angle-Based Flooding (L2-ABF) and Diagonal and Vertical Routing Protocol (DVRP). In order to get stable data transmission, the node density plays our role in shallow and deep water. Several parameters are employed to evaluate the output efficiency of these routing protocols. In this paper, like an end to end delay, loss of data packets during transmission and data delivery ratio within communication are considered the major parameters for evaluation. For this, the network simulator is used with the aqua sim package. The results, we have produced during this study; guides us about the best routing protocol for data transmission. It finally reveals that the L2-ABF performs better then DVRP in a different situation, further the tradeoffs relationship is achieved against multiple situations.

T-Whale

One of the infrastructureless networks built by various independent mobile nodes is mobile ad hoc network (MANET), which is an emerging technology, requiring a secure routing protocol for data transmission. Accordingly, literature presents various secure routing protocols for MANETs by utilizing trust and data encryption. In this article, a whale optimization algorithm (WOA) is utilized for selecting the optimal secured routing path in the MANET. The WOA algorithm utilizes the trust factor and the distance between the nodes for computing the fitness for the routing path. Overall, the steps involved in the proposed routing algorithm are as follows: i) Measuring the trust and the distance-based metrics for every node; ii) Discovering k-disjoint path; and iii) Determining the optimal path based on the trust and the distance-based metrics. The performance of the trust-based WOA (T-Whale) is analyzed using the metrics, energy, throughput, and packet delivery rate. From the simulation results, it is evident that the T-Whale algorithm has the improved energy, throughput, and PDR values of 27.4520, 0.4, and 0.4, at the simulation time of 10 sec over the conventional trust random search algorithm when the node is under attack.

Network Performance Evaluation of M2M With Self Organizing Cluster Head to Sink Mapping

In this paper, a machine-to-machine (M2M) networks are arranged hierarchically to support an energy-efficient routing protocol for data transmission from terminal nodes to a sink node via cluster heads in a wireless sensor network (WSN) at perio network congestion caused by heavy M2M traffic is tackled using the load balancing solutions to maintain high levels of network performance. First, a multilevel clustering multiple sinks with IPv6 protocol over low wireless personal area networks is promoted to prolong network lifetime. Second, the enhanced network performance is achieved through non-linear integer-based optimization. A self-organizing cluster head to sink algorithm (SOCHSA) is proposed, hosting discrete particle swarm optimization (DPSO) and genetic algorithm (GA) as evolutionary algorithms to solve the network performance optimization problem. Network Performance is measured based on key performance indicators for load fairness and average residual network energy. The SOCHSA algorithm is tested by two benchmark problems with two and three sinks. DPSO and GA are compared with the exhaustive search algorithm to analyze their performances for each benchmark problem. Both algorithms achieve optimum network performance evaluation values of 108.059 and 108.1686 in the benchmark problems P1 and P2, respectively. Using three sinks under the same simulation settings, the average residual energy is improved by 2% when compared with two sinks. Computational results prove that DPSO outperforms GA regarding complexity and convergence, thus being best suited for a proactive Internet of Things network. The proposed mechanism satisfies different network performance requirements of M2M traffic by instant identification and dynamic rerouting.