A Proactive Scalable Approach for Reliable Cluster Formationin Wireless Networks with D2D Offloading

TL;DR

This paper proposes a proactive, scalable method for reliable device-to-device cluster formation in wireless networks, improving network robustness by considering device reliability metrics in decision-making.

Contribution

It introduces a novel proactive approach that incorporates reliability metrics into cluster formation, addressing device mobility and battery depletion for enhanced network reliability.

Findings

Enhanced network reliability over standard techniques

Effective dynamic operation in dense networks

Validated through extensive simulations and test bed experiments

Abstract

With the current exponential growth in traffic and service demands, device-to-device (D2D) cooperation is identified as a major mechanism to enable 5G networks to effectively and efficiently augment network resources. The effectiveness of D2D cooperation depends on a wide range of decision making processes that include cluster formation, resource allocation, in addition to connection and mobility management. Irrespective of the D2D cooperation scenario whether in sensor, ad hoc, or cellular networks, the literature normally assumes that devices selected as relays or data sources are reliable; this means that they will maintain the connection till the communication session ends. Yet, this assumption is challenged in practice since devices' batteries can be depleted (e.g., sensors in an IoT network) and devices can move leading to connection termination (e.g., mobile users in a WiFi network or cars in a vehicular ad hoc network). To this end, we address the problem of reliable D2D cooperation in wireless networks by proposing a novel approach that is proactive by utilizing reliability metrics in the decision making process, and scalable by having low implementation complexity suitable for dense networks. These differentiating factors are shown to enhance the overall network reliability compared to standard techniques and to facilitate dynamic operation which is essential for practical implementation. Performance is evaluated using extensive simulations in addition to test bed experimental demonstration in order to quantify gains and extract insights on a range of existing design tradeoffs.