FCERP: A Novel WSNs Fuzzy Clustering and Energy Efficient Routing Protocol

Wireless sensor networks (WSNs) are set of sensor nodes to monitor and detect transmitted data to the sink. WSNs face significant challenges in terms of node energy availability, which may impact network sustainability. As a result, developing protocols and algorithms that make the best use of limited resources, particularly energy resources, is critical issues for designing WSNs. Routing algorithms, for example, are unique algorithms as they have a direct and effective relationship with lifetime of network and energy. The available routing protocols employ single-hop data transmission to the sink and clustering per round. In this paper, a Fuzzy Clustering and Energy Efficient Routing Protocol (FCERP) that lower the WSNs energy consuming and increase the lifetime of network is proposed. FCERP introduces a new cluster-based fuzzy routing protocol capable of utilizing clustering and multiple hop routing features concurrently using a threshold limit. A novel aspect of this research is that it avoids clustering per round while considering using fixed threshold and adapts multi-hop routing by predicting the best intermediary node for clustering and the sink. Some Fuzzy factors such as residual energy, neighbors amount, and distance to sink considered when deciding which intermediary node to use.

Load Balancing Routing Algorithm of Low-Orbit Communication Satellite Network Traffic Based on Machine Learning

Satellite communication has become an important research trend in the field of communication technology. Low-orbit satellites have always been the focus of extensive attention by scholars due to their wide coverage, strong flexibility, and freedom from geographical constraints. This article introduces some technologies about low-orbit satellites and introduces a routing algorithm DDPG based on machine learning for simulation experiments. The performance of this algorithm is compared with the performance of three commonly used low-orbit satellite routing algorithms, and a conclusion is drawn. The routing algorithm based on machine learning has the smallest average delay, and the average value is 126 ms under different weights. Its packet loss rate is the smallest, with an average of 2.9%. Its throughput is the largest, with an average of 201.7 Mbps; its load distribution index is the smallest, with an average of 0.54. In summary, the performance of routing algorithms based on machine learning is better than general algorithms.

Routing Algorithms Simulation for Self-Aware SDN

This paper presents a self-aware network approach with cognitive packets, with a routing engine based on random neural networks. The simulation study, performed using a custom simulator extension of OmNeT++, compares RNN routing with other routing methods. The performance results of RNN-based routing, combined with the distributed nature of its operation inaccessible to other presented methods, demonstrate the advantages of introducing neural networks as a decision-making mechanism in selecting network paths. This work also confirms the usefulness of the simulator for SDN networks with cognitive packets and various routing algorithms, including RNN-based routing engines.

Comparative Performance Analysis of Selected Routing Algorithms by Load Variation of 2-Dimensional Mesh Topology Based Network-On-Chip

Network-on-Chip (NoC) has been proposed as a viable solution to the communication challenges on System-on-Chips (SoCs). As the communication paradigm of SoC, NoCs performance depends mainly on the type of routing algorithm chosen. In this paper different categories of routing algorithms were compared. These include XY routing, OE turn model adaptive routing, DyAD routing and Age-Aware adaptive routing. By varying the load at different Packet Injection Rate (PIR) under random traffic pattern, comparison was conducted using a 4 × 4 mesh topology. The Noxim simulator, a cycle accurate systemC based simulator was employed. The packets were modeled as a Poisson distribution; first-in-first-out (FIFO) input buffer channel with a depth of five (5) flits and a flit size of 32 bits; and a packet size of 3 flits respectively. The simulation time was 10,000 cycles. The findings showed that the XY routing algorithm performed better when the PIR is low. In a similar vein, the DyAD routing and Age-aware algorithms performed better when the load i.e. PIR is high.

A Review of Routing Algorithms Based on Multi-Layer Satellite Networks

Compared with single layer satellite network, satellite distribution of multi-layer satellite networks in the double layer or multilayer orbital plane, combines all the advantages of engaging subjects of the satellite, therefore contains its low vulnerability, good robustness, high stability, high spectrum efficiency and system throughput, channel characteristics such as large capacity, in the satellite network is a very promising direction. Multilayer satellite network also has some inevitable disadvantages, that is, the number of nodes and the number of links is large, so it has the characteristics of frequent changes in network topology, which requires higher routing algorithm. There are many researches on routing algorithms based on multi-layer satellite networks at home and abroad. This paper introduces the general situation of multi-layer satellite network, classifies the existing routing algorithms of multi-layer satellite network according to different standards, analyzes several typical routing algorithms in detail, points out the advantages and disadvantages, and summarizes the future development trend.

Fuzzy and Position Particle Swarm Optimized Routing in VANET

In Intelligent Transport Systems, traffic management and providing stable routing paths between vehicles using vehicular ad hoc networks (VANET's) is critical. Lots of research and several routing techniques providing a long path lifetime have been presented to resolve this issue. However, the routing algorithms suffer excessive overhead or collisions when solving complex optimization problems. In order to improve the routing efficiency and performance in the existing schemes, a Position Particle Swarm Optimization based on Fuzzy Logic (PPSO-FL) method is presented for VANET that provides a high-quality path for communication between nodes. The PPSO-FL has two main steps. The first step is selecting candidate nodes through collectively coordinated metrics using the fuzzy logic technique, improving packet delivery fraction, and minimizing end-to-end delay. The second step is the construction of an optimized routing model. The optimized routing model establishes an optimal route through the candidate nodes using position-based particle swarm optimization. The proposed work is simulated using an NS2 simulator. Simulation results demonstrate that the method outperforms the standard routing algorithms in packet delivery fraction, end-to-end delay and execution time for routing in VANET scenarios.

An Adaptive Routing Algorithm Based on Relation Tree in DTN

It is found that nodes in Delay Tolerant Networks (DTN) exhibit stable social attributes similar to those of people. In this paper, an adaptive routing algorithm based on Relation Tree (AR-RT) for DTN is proposed. Each node constructs its own Relation Tree based on the historical encounter frequency, and will adopt different forwarding strategies based on the Relation Tree in the forwarding phase, so as to achieve more targeted forwarding. To further improve the scalability of the algorithm, the source node dynamically controls the initial maximum number of message copies according to its own cache occupancy, which enables the node to make negative feedback to network environment changes. Simulation results show that the AR-RT algorithm proposed in this paper has significant advantages over existing routing algorithms in terms of average delay, average hop count, and message delivery rate.