Novel strategies for path stability estimation under topology change using Hello messaging in MANETs

Abstract

Path stability estimation due to connectivity failures is one of the key challenges for effective communication under random mobility of network nodes in mobile ad hoc networks (MANETs). Under random movement of network nodes and topology triggered reactive path distribution statistics among the neighboring nodes, there must be a unified model to determine an adequate path stability estimation strategy in MANETs. We present novel link connectivity metrics (LCM) and path distribution analysis (PDA) strategies for path stability estimation under uniform speed and random trajectory of mobile nodes. We also design an adaptive routing model that guarantees effective communication among neighboring nodes inside a cluster. We find that LCM strategy among neighboring nodes is affected by the link expiration time, relative velocity, link connectivity, and link stability metrics in terms of remaining probability for a connected link, link recovery probability, and stability of a path at different time steps of a Markov process. We also find that PDA strategy among neighboring nodes is affected by the link excess life, distribution of path durations, cluster stability, and path duration statistics in terms of throughput and overhead during Hello message(s) communication at different time steps of a Markov process. Analytical and simulation results indicate efficacy of the proposed LCM and PDA strategies by efficiently estimating path stability under topology change, thereby, increasing the global network connectivity.

Introduction

A mobile ad hoc network (MANET) is a collection of wireless mobile nodes that do not rely on centralized administration or established infrastructure and, thus, form a temporary network [1]. Link stability estimation is an important metric in MANETs for efficient link connectivity and stable route selection process [2], [3], [4]. Several factors can influence the link stability in MANETs such as communication overhead, residual energy of neighboring nodes constituting the path, network throughput and latency, and channel utilization [2].

A volume of literature is available, which addresses path stability estimation among the randomly deployed network nodes for effective communication in MANETs [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]. However, there are a number of shortcomings in the existing literature, in terms of link stability as a function of path stability estimation, distribution of path durations as function of throughput, communication overhead, and cluster stability. In addition, the existing literature do not provide a solution for link stability metrics as function of remaining probability for a connected link, link recovery probability, and stability of a link constituting a path. Therefore, to address these shortcomings, there is an eminent need for an adaptive routing strategy that estimates path stability for effective communication among the neighboring nodes as a continuous function of time.

In this paper, we present two novel strategies for tackling the issue of path stability estimation in the case of proposed adaptive routing model for MANETs. First, we model link connectivity metrics (LCM) for estimating link stability among pairs of neighboring node inside clusters. The proposed LCM is helpful in estimating link connectivity as a function of random movement of network nodes. Secondly, we present path distribution analysis (PDA) for tackling the issue of path distribution under random deployment of nodes inside clusters. The proposed PDA is helpful in achieving path distribution as a function of topology triggered reactive path distribution statistics among neighboring nodes. For designing an adaptive routing model to estimate link stability among neighboring nodes, a movement pattern of nodes, due to a node’s spatial distribution, has a tremendous impact on many network aspects, such as link expiration time, link excess life, relative velocity, link connectivity, and distribution of path durations. In addition, topology triggered reactive path duration statistics has a great impact on the Hello messages throughput, overhead involved in transmitting Hello messages, and cluster stability in terms of transmission rate of Hello messages, capacity of a link, and inter-arrival time between transmission of the consecutive Hello messages, thereby affecting the estimation process for path stability among the neighboring nodes.

The main contributions of this paper are summarized as follows.

• We present a novel LCM strategy under random movement of network nodes and a novel PDA strategy under topology triggered path duration statistics using Hello messaging for link stability estimation in MANETs.

• We propose an adaptive routing model, under random movement of nodes and topology triggered path duration statistics that guarantees effective communication among the neighboring nodes inside a cluster. For this model, we derive expressions for probability of a connected link, average minimum power (AMP), and probability of successful reception of Hello messages, as presented in Section 4 of the paper.

• We also derive expression for the link expiration time between neighboring nodes inside a cluster, as presented in Section 5.1. Through link expiration time, we obtain link longevity, better link connectivity, and high Hello message(s) delivery ratio, as presented in Section 7.1.

• Additionally, we derive expression for optimum relative velocity between CH and its neighbor inside a cluster, as presented in Section 5.2. Through optimum relative velocity, we obtain better network lifetime, as presented in Section 7.2.

• Moreover, we derive expression for optimal link connectivity among the neighboring nodes inside the cluster, as presented in Section 5.3. Through optimal link connectivity, we obtain higher network connectivity, higher throughput, and higher energy efficiency of the network in terms of AMP, as presented in Section 7.3.

• We also evaluate expressions for link stability metrics in the proposed LCM strategy, as presented in Section 5.4. By analyzing link stability metrics, we obtain higher link stability in terms of remaining probability for a connected link and faster re-connectivity in terms of probability of link recovery, as presented in Sections 8.8 and 8.9. In addition, we obtain better network stability and higher Hello message(s) delivery rate in terms of normalized stability of a path, as presented in Section 8.10.

• We analyze the normalized cluster stability in terms of dynamic link connectivity among neighboring nodes in terms of link capacity, transmission rate of Hello messages, and expected Hello messages transmission rate, as presented in Section 8.1.

• We also analyze normalized overhead of cluster, in terms of Hello messages transmission to a neighboring node, based on maximum cardinality between CH and its neighbor node, and transmission retries of Hello message(s) for link re-connectivity, as presented in Section 8.2.

• Furthermore, we analyze the link excess life between CH and its neighbor node based on probabilities of link connectivity between them, as presented in Section 6.1.

• Finally, we obtain normalized routing load (NRL), better network efficiency in terms of lower Hello message(s) drop rate, higher channel utilization, controlled network congestion, and lower network latency for the proposed PDA strategy, as presented in 8.3 Normalized throughput, 8.4 Frequency of path breaks, 8.5 Throughput in terms of transmitted Hello messages, 8.6 Total time spent in repairing broken paths, and 8.7, respectivley.

We achieve path stability among the neighboring nodes by presenting multiple parameters of interest for the adaptive routing model and various performance metrics for the proposed LCM and PDA strategies. We determine an adaptive routing model, as presented in Section 4, in terms of position of a cluster head (CH) at different time steps of a Markov process, probability of a connected link, average minimum power, and CH selection criteria for determining various network aspects that can affect path stability under topology change in MANETs. In addition, we determine LCM, as presented in Section 5, in terms of link expiration time, relative velocity, link connectivity between neighboring nodes and link stability metrics for determining link connectivity under random movement of network nodes. Furthermore, we determine PDA, as presented in Section 6, in terms of link excess life, uniform distribution of path durations, path duration statistics, and cluster stability for analyzing random deployment of network nodes under topology triggered reactive path duration statistics. Simulation and analytical results indicate that LCM inside a cluster obtains higher link longevity, better network lifetime, higher Hello message(s) delivery rate, better link connectivity, and higher energy efficiency of the network in terms of average minimum power, thus, providing effective measures for estimating path stability among neighboring nodes under random trajectory of network nodes. For PDA, we also achieve normalized routing load, lower Hello message(s) drop rate, higher channel utilization, lower network latency, and controlled network congestion, thus, providing effective tools for estimating path stability among neighboring nodes after initial deployment of the network nodes. Finally, by analyzing the obtained results, it can be suggested that the proposed strategies significantly enable us to obtain global network connectivity by succssfully achieving effective path stability estimation and optimum path distributions inside cluster, under topology change in MANETs Section 1.1.

The rest of the paper is organized as follows. In Section 2, we present the related work. In Section 3, we present the network scenarios and basic assumptions for the proposed strategies. In Section 4, we design an adaptive routing model by formulating it as a Markov process. We model and analyze the proposed LCM and PDA strategies in Sections 5 and 6, respectivley. In Sections 7 and 8, we present performance evaluation of the proposed LCM and PDA strategies, respectivley. The paper is concluded in Section 9.