EPSRC logo

Details of Grant 

EPSRC Reference: EP/T02612X/1
Title: Probabilistic Tomography of Wireless Networks
Principal Investigator: Coon, Professor J
Other Investigators:
Researcher Co-Investigators:
Dr M Badiu
Project Partners:
His Majesty's Government Communications Moogsoft Toshiba
Department: Engineering Science
Organisation: University of Oxford
Scheme: Standard Research
Starts: 01 July 2020 Ends: 31 December 2023 Value (£): 415,416
EPSRC Research Topic Classifications:
Networks & Distributed Systems RF & Microwave Technology
EPSRC Industrial Sector Classifications:
Communications Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
03 Mar 2020 EPSRC ICT Prioritisation Panel March 2020 Deferred
20 May 2020 EPSRC ICT Prioritisation Panel May 2020 Announced
Summary on Grant Application Form
Large-scale wireless networks are expected to become prevalent in various Internet-of-Things (IoT) applications involving environment sensing and monitoring, communications, and computing. It is a fundamental task of many networks to deduce the network topology, both during the establishment of the network and periodically as the network state evolves. The availability of network topology and performance information is crucial for the operation and management of large wireless systems comprising low-power devices that are required to provide low-latency, high-reliability services. For example, state-of-the-art smart meter networks require this information to carry out routing and resource scheduling tasks, and the estimation of the number of devices in a network is useful for finding out how many sensors are still active or for detecting failures of some subnetworks. Inferring topology information even possess great importance in matters of national security in which one may have to learn the structure of a target network passively from external observables, such as the spectral activity of devices, without having access to the network devices and protocols.

Many network characteristics can be inferred by observing end-to-end data, which often takes the form of packet probes. The general field of study concentrating on such techniques is known as "network tomography". Over the past twenty years, this field has been developed to include the inference of link loss statistics (loss tomography), internal queuing delays (delay tomography), and structural characteristics (topology tomography). Much of the work to date has focused on the formulation of optimal and efficient estimation methods that are primarily geared toward computer networks that exhibit certain constraints on their topologies.

Some more recent studies of network tomography have considered wireless systems. However, investigations have largely been limited by the lack of available statistical models that incorporate spatial and physical characteristics inherent to wireless networks. For example, spatial (wireless) networks exhibit distinctive features (e.g., transitivity, clustering), which have not been fully exploited in topology inference tasks.

This project is concerned with developing improved active methods (topology discovery) and passive techniques (topology inference) of obtaining the topology of a wireless communications network or a portion thereof. The underlying hypothesis is that probabilistic knowledge of structural properties of wireless networks can be used as prior information to improve network inference tasks, particularly topology tomography, in practical systems. The project will begin with fundamental research into the correct modelling and statistical characterisation of wireless networks designed for particular applications, such as smart meter infrastructure and tactical systems. The results of this research will be exploited to develop new topology tomography algorithms that are optimised for use in the chosen applications. The technical contributions of the project will be accompanied and supported by a number of activities aimed at delivering impact through dissemination and technology transfer. The project is supported by three hands-on partners (Toshiba, Moogsoft, and HMGCC), each of which is at the leading edge of its respective field.
Key Findings
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Potential use in non-academic contexts
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Description This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Date Materialised
Sectors submitted by the Researcher
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Project URL:  
Further Information:  
Organisation Website: http://www.ox.ac.uk