A Game of Life on a Pythagorean Tessellation

Annadurai, Soorya

doi:10.1109/rteict52294.2021.9573855

Cited by 1 publication

(1 citation statement)

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“…For instance, a cell is allowed to survive if it has one to two "alive" neighbours, but if it has at least three "alive" neighbours, it will die because the cell population is considered to be too dense. A dead cell, on the other hand, will become alive if it is surrounded by at least three more "dead" cells [8]. Figure 1 also shows how GoL could dynamically regulate population growth on the abstraction level of OMN nodes.…”

Section: Related Workmentioning

confidence: 99%

Replication control strategy based on a simple game of life in opportunistic networks

Ayu,

Soelistijanto,

Larasati

2024

E3S Web Conf.

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Although flooding-based message dissemination in opportunistic mobile networks yields high delivery performance, the nodes' resources, such as energy and buffer, are rapidly depleted due to the enormous quantity of message replicas dispersed over the networks. This study aims to reduce the number of message replicas in the networks while maintaining an acceptable delivery rate. Inspired by Conway's Game of Life, which uses cellular automaton-based neighbor conditions to regulate the total population, we propose RiGoL, a replication control-based technique for determining how many neighbors hold the same messages. We utilize a counter to keep track of the number of neighbors and two thresholds, namely forward and drop thresholds. Our strategy works as follows: when a pair of nodes meet, the node checks to see if the peer has the same message. The counter is increased by one if the peer does not hold the same message. On the other hand, if the peer already has the message, the counter is decremented by one. Only when the counter exceeds the forward threshold, then the message forwarded to another node. In contrast, the message is deleted from the node's buffer if the counter value is less than the drop threshold. We conduct extensive simulations using ONE Simulator to evaluate our proposed strategy. The simulation results show that although RiGoL yields higher latency than Epidemic, RiGoL successfully reduces the message replication in the network and prolongs the hub node's lifetime.

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Section: Related Workmentioning

confidence: 99%