MeshMS: Ad Hoc Data Transfer within Mesh Network

File and data distribution can be easily classified as one of the basic uses of networks. With uses ranging from Short Message Service (SMS) to program updates, from micro-blogging to social networking, every network today must support some type of file and data dissemination method. Infrastructure networks have already implemented these services using well known communication protocols. Ad hoc networks pose a greater challenge due to their sporadic network set-up. At a given time we do not know who is connected to the network, and whether the intended recipient of the data can be reached. In this paper we introduce Serval MeshMS, a protocol for ad hoc file and data distribution, enabling the diffusing of data through an ad hoc mesh network. It is based on a single-hop, store and disseminate opportunistic architecture, and has been shown to work over great distances. Preliminary implementations are encouraging, with surprising results achieved.

Improving Compression of Short Messages

Compression of short text strings, such as the GSM Short Message Service (SMS) and Twitter messages, has received relatively little attention compared to the compression of longer texts. This is not surprising given that for typical cellular and internet-based networks, the cost of compression probably outweighs the cost of delivering uncompressed messages. However, this is not necessarily true in the case where the cost of data transport is high, for example, where satellite back-haul is involved, or on bandwidth-starved mobile mesh networks, such as the mesh networks for disaster relief, rural, remote and developing contexts envisaged by the Serval Project [1-4]. This motivated the development of a state-of-art text compression algorithm that could be used to compress mesh-based short-message traffic, culminating in the development of the stats3 SMS compression scheme described in this paper. Stats3 uses word frequency and 3rd-order letter statistics embodied in a pre-constructed dictionary to affect lossless compression of short text messages. This scheme shows that our scheme compressing text messages typically reduces messages to less than half of their original size, and in so doing substantially outperforms all public SMS compression systems, while also matching or exceeding the marketing claims of the commercial options known to the authors. We also outline approaches for future work that has the potential to further improve the performance and practical utility of stats3.

Distributed Sensor Logging: As Easy as a Mesh of Yoyos

The Mass Gathering Data Acquisition and Analysis (MaGDAA) project involved the development of hardware and software solutions to facilitate the rapid and effective collection of autonomous and survey based data during mass gathering events. The aim of the project was the development and trial of a purpose-built Open Hardware based environment monitoring sensor prototypes using IOIO (pronounced “yoyo”) boards. Data from these sensors, and other devices, was collected using Open Source software running on Android powered mobile phones, tablets and other open hardware based platforms. Data was shared using a Wi-Fi mesh network based on an Open Source project called The Serval Project. Additional data in the form of survey based questionnaires were collected using ODK Collect, one of the applications in the Open Data Kit suite. The MaGDAA project demonstrated that it is possible for researchers (through the use of Open Source software and Open Hardware) to own, visualise, and share data without the difficulties of setting up and maintaining servers. MaGDAA proved to be an effective infrastructure independent sensor logging network that enables a broad range of data collection (demographic, predispositions, motivations, psychosocial and environmental influencers and modifiers of audience behaviour, cultural value) in the field of mass gathering research.