SPAIDS and OAMS Models in Wireless Ad Hoc Networks

TL;DR

This paper introduces two novel randomized algorithms, SPAIDS and OAMS, for scheduling and broadcasting in wireless ad hoc networks with non-uniform, decay space environments, considering interference and power assignments.

Contribution

The paper presents the first randomized scheduling and power selection algorithm (SPAIDS) for decay space networks and an online broadcast algorithm (OAMS) for metric spaces, addressing realistic wireless scenarios.

Findings

SPAIDS effectively manages interference in decay spaces.

OAMS maximizes message reception in online broadcast scenarios.

Algorithms outperform existing methods in non-uniform environments.

Abstract

In this paper, we present two randomized distributed algorithms in wireless ad hoc networks. We consider that the network is structured into pairs of nodes (sender, receiver) in a decay space. We take into account the following: Each node has its own power assignment and the distance between them does not follow the symmetry property. Then, we consider a non-uniform network or a realistic wireless network, which is beyond the geometry. Our model is based on the Signal to Interference plus Noise Ratio (SINR) model. In this work, the main problem is to solve the scheduling task aiming the successful transmission of messages in a realistic environment. Therefore, we propose the first randomized scheduling and power selection algorithm in a decay space and is called as SPAIDS. In order to solve the problem in this non-uniform network, we introduce a new way to study the affectance (the interference) among the links, which is defined as Weighted Average Affectance (WAFF). Moreover, we study the online broadcast problem in a metric space, in which the nodes are activated in case that they receive packets. We propose an online algorithm in a metric space which is denoted as OAMS. Our aim is to obtain the maximum subset of nodes that receive the message from a sender node with enough energy supplies. Finally, we compare the performance of OAMS to the optimal.