Optimal Forwarding in Opportunistic Delay Tolerant Networks With Meeting Rate Estimations

Shaghaghian, Shohreh; Coates, Mark

doi:10.1109/tsipn.2015.2452811

Cited by 7 publications

(5 citation statements)

References 40 publications

(50 reference statements)

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“…Based on this opportunistic-based paradigm, some researchers have explored the network behaviour to propose forwarding algorithms. For instance, Shaghaghian and Coates [23] have used some simplifying assumptions about the network behaviour to propose two forwarding algorithms. The main goal is to reduce the expected latencies from any node in the network to a particular destination in some situations.…”

Section: Related Workmentioning

confidence: 99%

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

Souza

Mota

Soares

et al. 2019

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

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

Souza

Mota

Soares

et al. 2019

Sensors

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show abstract

“…In [13,14], nodes only generate one copy of the data packets to reduce the load of network. In methods [15][16][17][18], nodes selectively send the copies of packets to their neighbor nodes. In these replicationbased methods, nodes carefully make the routing decisions according to several rules such as utility-based routing [15] or probability-based routing [17].…”

Section: Routing Protocolsmentioning

confidence: 99%

“…In methods [15][16][17][18], nodes selectively send the copies of packets to their neighbor nodes. In these replicationbased methods, nodes carefully make the routing decisions according to several rules such as utility-based routing [15] or probability-based routing [17]. They assume that movement patterns of node are not purely random and that future contacts depend on the past information.…”

Section: Routing Protocolsmentioning

confidence: 99%

Routing Protocol in VANETs Equipped with Directional Antennas: Topology‐Based Neighbor Discovery and Routing Analysis

2018

Wireless Communications and Mobile Computing

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In Vehicular Ad Hoc Networks (VANETs), directional antenna is a good solution if a longer transmission distance is needed. When vehicles are equipped with directional antennas, however, complete paths from the sources to the destinations there may not exist. Epidemic routing protocol is considered as one of the well-performed routing protocols when the networks are intermittently connected but it can cause a heavy load to the network and great energy consumption to the nodes. First in this paper, we propose a novel neighbor discovery algorithm which makes nodes be able to sense the topology changes around them and arrange their directional antennas accordingly. Secondly, we propose a routing protocol which is based on the conventional epidemic routing protocol, and nodes make their routing decisions according to the information collected during the neighbor discovery process. Experimental results show that the proposed neighbor discovery algorithm has better performance especially in the scenario where the node density is low. Moreover, the matching routing protocol can effectively reduce the load of the network and successfully deliver the packets to its destination in a reasonable short delay.

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“…Based on an opportunistic-based paradigm, some researchers have explored the network behavior to propose forwarding algorithms. For instance, Shaghaghian and Coates [31] have used some simplifying assumptions about the network behavior to propose two forwarding algorithms. The main goal is to minimize the expected latencies from any node in the network to a particular destination in some situations.…”

Section: Related Workmentioning

confidence: 99%

Friendship and Selfishness Forwarding: applying machine learning techniques to Opportunistic Networks data forwarding

Souza,

Mota,

Galvao

et al. 2017

Preprint

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Opportunistic networks could become the solution to provide communication support in both cities where the cellular network could be overloaded, and in scenarios where a fixed infrastructure is not available, like in remote and developing regions. A critical issue that still requires a satisfactory solution is the design of an efficient data delivery solution. Social characteristics are recently being considered as a promising alternative. Most opportunistic network applications rely on the different mobile devices carried by users, and whose behavior affects the use of the device itself.This work presents the "Friendship and Selfishness Forwarding" (FSF) algorithm. FSF analyses two aspects to make message forwarding decisions when a contact opportunity arises: First, it classifies the friendship strength among a pair of nodes by using a machine learning algorithm to quantify the friendship strength among pairs of nodes in the network. Next, FSF assesses the relay node selfishness to consider those cases in which, despite a strong friendship with the destination, the relay node may not accept to receive the message because it is behaving selfishly, or because its device has resource constraints in that moment.By using trace-driven simulations through the ONE simulator, we show that the FSF algorithm outperforms previously proposed schemes in terms of delivery rate, average cost, and efficiency.

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Optimal Forwarding in Opportunistic Delay Tolerant Networks With Meeting Rate Estimations

Cited by 7 publications

References 40 publications

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

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

Routing Protocol in VANETs Equipped with Directional Antennas: Topology‐Based Neighbor Discovery and Routing Analysis

Friendship and Selfishness Forwarding: applying machine learning techniques to Opportunistic Networks data forwarding

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