[Paper Review] Epcast: Controlled Dissemination in Human-based Wireless Networks by means of Epidemic Spreading Models

Salvatore Scellato, Cecilia Mascolo+2 more|ArXiv.org|Nov 18, 2007

Opportunistic and Delay-Tolerant Networks14 references4 citations

TL;DR

This paper proposes Epcast, a controlled information dissemination protocol for human-based wireless networks using epidemic spreading models based on the SIR (Susceptible-Infected-Recovered) framework. By analytically tuning the infection rate according to the dynamic network structure, Epcast achieves desired delivery ratios with significantly reduced message overhead compared to heuristic epidemic protocols, as validated on synthetic and real human contact traces.

ABSTRACT

Epidemics-inspired techniques have received huge attention in recent years from the distributed systems and networking communities. These algorithms and protocols rely on probabilistic message replication and redundancy to ensure reliable communication. Moreover, they have been successfully exploited to support group communication in distributed systems, broadcasting, multicasting and information dissemination in fixed and mobile networks. However, in most of the existing work, the probability of infection is determined heuristically, without relying on any analytical model. This often leads to unnecessarily high transmission overheads. In this paper we show that models of epidemic spreading in complex networks can be applied to the problem of tuning and controlling the dissemination of information in wireless ad hoc networks composed of devices carried by individuals, i.e., human-based networks. The novelty of our idea resides in the evaluation and exploitation of the structure of the underlying human network for the automatic tuning of the dissemination process in order to improve the protocol performance. We evaluate the results using synthetic mobility models and real human contacts traces.

Motivation & Objective

• To address the lack of analytical control in epidemic-based dissemination protocols for mobile ad hoc networks.
• To reduce unnecessary message replication and transmission overhead in human-based wireless networks.
• To enable precise control over the fraction of nodes that receive information, based on network structure and desired reliability.
• To evaluate the protocol using real human contact traces and synthetic mobility models.
• To extend the approach to heterogeneous networks with variable connectivity patterns.

Proposed method

• The protocol is based on the SIR epidemic model, where nodes transition from Susceptible to Infected and then to Recovered (immune to further infections).
• The infection rate is analytically derived from the average degree of the underlying contact network to achieve a target delivery ratio.
• The middleware interface exposes a primitive to specify the desired fraction of nodes to receive the message, which is used to compute the required infection probability.
• The system dynamically adapts the infection rate based on network connectivity, using the minimum degree in the network as a conservative estimate to guarantee reliability.
• Simulations are conducted using synthetic random mobility models and real contact traces from Dartmouth College and the National University of Singapore.
• The approach is extended to heterogeneous networks by using the minimum node degree as a lower bound for reliable infection spread.

Experimental results

Research questions

• RQ1 Can epidemic spreading models be used to analytically control message dissemination in human-based wireless networks?
• RQ2 How can the infection rate be tuned to achieve a desired delivery ratio with minimal message overhead?
• RQ3 What is the impact of network structure and connectivity dynamics on the performance of controlled epidemic dissemination?
• RQ4 How does the proposed method compare to heuristic epidemic protocols in terms of message efficiency and delivery reliability?
• RQ5 Can the approach be extended to heterogeneous networks with non-uniform connectivity?

Key findings

• On the Dartmouth College trace, Epcast achieved 43% delivery for a desired 50% fraction with only 17,132 messages, significantly lower than the 95,969 messages required by a standard epidemic protocol with β=0.25.
• For a desired 100% delivery ratio, Epcast achieved 90% delivery with 32,475 messages, compared to 155,446 messages using a standard epidemic protocol with β=1.00.
• The heterogeneous version of Epcast (epcast-heterogeneous) required 57,342 messages to achieve 90% delivery on the same dataset, indicating higher overhead in non-homogeneous networks.
• The results show that heuristic infection rates (e.g., β=0.25 to 1.00) often fail to achieve full delivery or require excessive message replication.
• Using k_min (minimum node degree) as a basis for infection rate calculation ensures a guaranteed lower bound on reliability in heterogeneous networks.
• The protocol’s performance is sensitive to network connectivity patterns, with underperformance due to intermittent connectivity and incomplete network coverage in real traces.