Routing Optimizing Decisions in MANET: The Enhanced Hybrid Routing Protocol (EHRP) with Adaptive Routing based on Network Situation

Mobile ad hoc networks (MANETs) are wireless networks that operate without a fixed infrastructure or base station. In MANETs, each node acts as a data source and a router, establishing connections with its neighboring nodes to facilitate communication. This research has introduced the Enhanced Hybrid Routing Protocol (EHRP), which combines the OLSR, AOMDV, and AODV routing protocols while considering the network situation for improved performance. The EHRP protocol begins by broadcasting a RREP (Route Reply) packet to discover a route. The selection of routing options is based on the current network situation. To determine the distance between the source and destination nodes, the proposed EHRP initiates a RREQ (Route Request) packet. In situations where network mobility exceeds the capabilities of the AODV protocol, the EHRP protocol can utilize the OLSR routing protocol for route selection and data transmission, provided that at least 70% of the network nodes remain stable. Additionally, the EHRP protocol effectively handles network load and congestion control through the utilization of the AOMDV routing protocol. Compared to the hybrid routing protocol, the enhanced hybrid routing protocol (EHRP) demonstrates superior performance. Its incorporation of the OLSR, AOMDV, and AODV protocols, along with its adaptive routing adaptation based on network conditions, allows for efficient network management and improved overall network performance. The analysis of packet delivery ratio for EHRP and ZRP reveals that EHRP achieves a packet delivery ratio of 98.01%, while ZRP achieves a packet delivery ratio of 89.99%. These results indicate that the enhanced hybrid routing protocol (EHRP) outperforms the hybrid routing protocol (ZRP) in terms of packet delivery ratio. EHRP demonstrates a higher level of success in delivering packets to their intended destinations compared to ZRP. The analysis of normal routing load for EHRP and ZRP reveals that EHRP exhibits a normal routing load of 0.13%, while ZRP exhibits a higher normal routing load of 0.50%. Based on these results, it can be concluded that the performance of the Enhanced Hybrid Routing Protocol (EHRP) is significantly better than that of the Hybrid Routing Protocol (ZRP) when considering the normal routing load. EHRP demonstrates a lower level of routing overhead and more efficient resource utilization compared to ZRP in scenarios with normal routing load. When comparing the average end-to-end delay between the Enhanced Hybrid Routing Protocol (EHRP) and ZRP, the analysis reveals that EHRP achieves an average delay of 0.06, while ZRP exhibits a higher average delay of 0.23. These findings indicate that the Enhanced Hybrid Routing Protocol (EHRP) performs better than ZRP in terms of average end-to-end delay. EHRP exhibits lower delay, resulting in faster and more efficient transmission of data packets from source to destination compared to ZRP. After considering the overall parameter matrix, which includes factors such as normal routing load, data send and receive throughput, packet delivery ratio, and average end-to-end delay, it becomes evident that the performance of the Enhanced Hybrid Routing Protocol (EHRP) surpasses that of the current hybrid routing protocol (ZRP). Across these metrics, EHRP consistently outperforms ZRP, demonstrating superior performance and efficiency. The Enhanced Hybrid Routing Protocol (EHRP) exhibits better results in terms of normal routing load, higher throughput for data transmission and reception, improved packet delivery ratio, and lower average end-to-end delay. Overall, EHRP offers enhanced performance and effectiveness compared to the existing hybrid routing protocol (ZRP)

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future