A community‐based social P2P network for sharing human life digital memories

Social peer‐to‐peer (P2P) networks are usually designed by reflecting a user's interest/behavior for structuring the underlying network. Human interest is affected by various factors such as age, locality, and so on which changes after some time. The behavior when reflected in a network, results in peers moving within the network in order to connect the peer with peers of the same behavior/interest. Especially in community‐based schemes when a peer leaves a community the data that a peer was sharing will not be accessible in the same community anymore. It has an effect on the performance of the network due to the inaccessibility of data and the unavailability of connections, which affect network robustness. We address this issue by considering entities in data in the form of digital memories of a user and structuring network according to entity‐based communities. The simulation results for the proposed entity‐based community are demonstrated, which shows the effect on network performance during varying network size and traffic. In this article, a new problem has been identified in community‐based social P2P networks, which are formed by reflecting social interest and forming communities. Peers in such communities move from one community to another due to change in human interest which disconnects a peer from that community and hence the data is inaccessible. The problem has been addressed by proposing a novel Entity‐based Social P2P Network (ESPN) where communities are formed according to Entities in data which is in the form of digital memories of an individual. As a result, an improved and efficient network is formed. The research has been conducted as part of one of the UK grand challenges which aims to develop a Memory for Life (M4L) system that can capture, store, analyze, annotate, share and generate meaningful information from digital memories of a person.