@Article{cmc.2021.016692, AUTHOR = {Ajay Arunachalam, Vinayakumar Ravi, Moez Krichen, Roobaea Alroobaea, Saeed Rubaiee}, TITLE = {Mathematical Model Validation of Search Protocols in MP2P Networks}, JOURNAL = {Computers, Materials \& Continua}, VOLUME = {68}, YEAR = {2021}, NUMBER = {2}, PAGES = {1807--1829}, URL = {http://www.techscience.com/cmc/v68n2/42186}, ISSN = {1546-2226}, ABSTRACT = {Broadcasting is a basic technique in Mobile ad-hoc network (MANET), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique, floods the network with query messages, while the random walk technique operates by contacting the subsets of every node’s neighbors at each step, thereby restricting the search space. One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource. Many earlier works have mainly focused on the simulation-based analysis of flooding, and its variants under a wired network. Although, there have been some empirical studies in peer-to-peer (P2P) networks, the analytical results are still lacking, especially in the context of P2P systems running over MANET. In this paper, we describe how P2P resource discovery protocols perform badly over MANETs. To address the limitations, we propose a new protocol named ABRW (Address Broadcast Random Walk), which is a lightweight search approach, designed considering the underlay topology aimed to better suit the unstructured architecture. We provide the mathematical model, measuring the performance of our proposed search scheme with different widely popular benchmarked search techniques. Further, we also derive three relevant search performance metrics, i.e., mean no. of steps needed to find a resource, the probability of finding a resource, and the mean no. of message overhead. We validated the analytical expressions through simulations. The simulation results closely matched with our analytical model, justifying our findings. Our proposed search algorithm under such highly dynamic self-evolving networks performed better, as it reduced the search latency, decreased the overall message overhead, and still equally had a good success rate.}, DOI = {10.32604/cmc.2021.016692} }