A Multi-Parameter Based Vertical Handover Decision Scheme for M2M Communications in HetMANET

Abstract

The Machine-to-Machine (M2M) communication has the potential to connect millions of devices in the near future. Since they agree on this potential, several standard organizations need to focus on improved general architecture for M2M communications. Currently, there is a lack of consensus to improve the general feasibility of M2M communication. Heterogeneous Mobile Ad hoc Networks (HetMANETs) can normally be considered appropriate for M2M challenges. When a mobile node (MN) moves inside a HetMANET, various challenges including a selection of the target network and energy efficient scanning take place, which need to be addressed for efficient handover. To cope with these issues, we propose a handover management scheme that efficiently initiates a handover process and selects an optimal network. Our proposed scheme is composed of two phases, i.e., i) the MN performs handover triggering based on the optimization of the Receive Signal Strength (RSS) from an access point/base station (AP/BS), and, ii) the network selection process is carried out byconsidering different parameters such as delay, jitter, velocity, network load, and energy consumption by the network interface. Moreover, if there are more networks available, then the MN selects the one that can provide the highest quality-of-service (QoS) using the Elimination and Choice Expressing Reality (ELECTRE) decision model. The performance of the proposed scheme is compared in the context of the number of handovers, average stay-time of an MN in the network, and energy consumption against periodic and adaptive scanning. Similarly, a two-state Markov model is defined that efficiently distribute the number nodes on the available access points and base stations. The proposed scheme efficiently optimizes the handoff related parameters and outperforms existing schemes.

$P$ -Persistent Energy-Aware Handover Decisions Employing RF Fingerprint for Adaptive-Sized Heterogeneous Cellular Networks

“…Similar studies were performed in [7], [17], with the objective of selecting the optimal networks to maximize the QoE from dynamic network state information (NSI). The cost-function-based algorithms that integrated a wide range of HO decision parameters was mentioned in [18], [19] with the selection of the best network to provide better QoS. Since a number of parameters are considered, latency and cost complexity arguments can be severe, especially in a high-density scenario.…”