Unsupervised Machine Learning based Energy Efficient Routing for Mobile Ad-Hoc Networks

Mobile Ad-hoc Networks (MANETs) are temporary networks formed by a group of mobile hosts without the need for centralized administration or specific support services. Energy consumption is a critical issue in MANETs due to their reliance on limited battery resources. Reducing energy consumption is crucial for increasing network lifespan and throughput. Existing energy-saving techniques often fall short in their effectiveness. This research proposes a novel approach that combines a proactive MANET routing protocol with an energy-efficient strategy to address these limitations. The proposed solution considers both node mobility and energy levels in the routing process. Traditional AODV routing relies on flooding, which broadcasts RREQ packets to all nodes within the sender's range. This often leads to unnecessary retransmissions of RREQ and RREP packets, resulting in collisions and network congestion. To overcome this issue, we propose an optimized route discovery mechanism for AODV. The key idea is to leverage the K-means clustering algorithm to select the optimal cluster of nodes to forward RREQ packets instead of relying on broadcasting. This approach aims to alleviate network congestion and reduce end-to-end delay by minimizing unnecessary control packet transmissions

MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication

With the rapid development of future wireless networks, device-to-device (D2D) technology is widely used as the communication system in the Internet of Things (IoT) fifth generation (5G) network. The IoT 5G network based on D2D communication technology provides pervasive intelligent applications. However, to realize this reliable technology, several issues need to be critically addressed. Firstly, the device’s energy is constrained during its vital operations due to limited battery power; thereby, the connectivity will suffer from link failures when the device’s energy is exhausted. Similarly, the device’s mobility alters the network topology in an arbitrary manner, which affects the stability of established routes. Meanwhile, traffic congestion occurs in the network due to the backlog packet in the queue of devices. This paper presents a Mobility, Battery, and Queue length Multipath-Aware (MBMQA) routing scheme for the IoT 5G network based on D2D communication to cope with these key challenges. The back-pressure algorithm strategy is employed to divert packet flow and illuminate the device selection’s estimated value. Furthermore, a Multiple-Attributes Route Selection (MARS) metric is applied for the optimal route selection with load balancing in the D2D-based IoT 5G network. Overall, the obtained simulation results demonstrate that the proposed MBMQA routing scheme significantly improves the network performance and quality of service (QoS) as compared with the other existing routing schemes