FSF: Applying Machine Learning Techniques to Data Forwarding in Socially Selfish Opportunistic Networks

Abstract

Opportunistic networks are becoming a solution to provide communication support in areas with overloaded cellular networks, and in scenarios where a fixed infrastructure is not available, as in remote and developing regions. A critical issue, which still requires a satisfactory solution, is the design of an efficient data delivery solution trading off delivery efficiency, delay, and cost. To tackle this problem, most researchers have used either the network state or node mobility as a forwarding criterion. Solutions based on social behaviour have recently been considered as a promising alternative. Following the philosophy from this new category of protocols, in this work, we present our “FriendShip and Acquaintanceship Forwarding” (FSF) protocol, a routing protocol that makes its routing decisions considering the social ties between the nodes and both the selfishness and the device resources levels of the candidate node for message relaying. When a contact opportunity arises, FSF first classifies the social ties between the message destination and the candidate to relay. Then, by using logistic functions, FSF assesses the relay node selfishness to consider those cases in which the relay node is socially selfish. To consider those cases in which the relay node does not accept receipt of the message because its device has resource constraints at that moment, FSF looks at the resource levels of the relay node. By using the ONE simulator to carry out trace-driven simulation experiments, we find that, when accounting for selfishness on routing decisions, our FSF algorithm outperforms previously proposed schemes, by increasing the delivery ratio up to 20%, with the additional advantage of introducing a lower number of forwarding events. We also find that the chosen buffer management algorithm can become a critical element to improve network performance in scenarios with selfish nodes.

ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning

“…Simulation results show that this strategy worked efficiently. Souza et al [27] introduced the "FriendShip and Acquaintanceship Forwarding" (FSF) protocol that performs data forwarding based on the Social relationship between the communicating nodes. It was implemented on ONE Simulator to inspect its performance on Message Delivery.…”

MSSN: An Attribute-Aware Transmission Algorithm Exploiting Node Similarity for Opportunistic Social Networks

“…Yu et al [31] proposed a combination of multi-faceted task attributes to determine whether it can be used as a corresponding relay node to establish a fuzzy matrix and quantify social relationships through social assessment. Souza et al [32] proposed a new routing protocol called FSF protocol (Applying Machine Learning Techniques to Data Forwarding in Socially Selfish Opportunistic Networks. ), which mainly makes decisions based on the social connection between nodes and the resource level of their own devices.…”

Machine Learning in Delay Tolerant Networks: Algorithms, Strategies, and Applications

“…In [8] "FriendShip and Acquaintanceship Forwarding"(FSF) protocol, the social ties of the nodes in the network are classified into friends, acquaintances, and unknowns using the Naive Bayes Classifier. A database built from the MIT research experiment is used, which contains attributes like the number of meetings, meetings outside the University, In [9], a Bayesian classifier classifies the nodes as "delivered" and "non-delivered", for next hop towards the destination, by analyzing the input attributes, region code, and time slot, based on which the posterior probability will be calculated.…”

“…FSF [8] applies the Naive Bayes classifier to discover the social ties of the nodes by classifying the interaction as friends, acquaintances, and unknown. A node in FSF will forward the packet to the node which can be its friend or acquaintance.…”

“…Bayesian Classifier [8], [9], [10] K-Means Clustering [2] Principal Componenet Analysis [18] Q-Learning [13], [22], [25] [ 14] high trust value towards the destination, then that intermediate node is chosen for routing. If the intermediate nodes have the same trust value, then the routing decision is based on the latest connection time.…”