Section: New Results

Results of axis 4: other application fields

1. Smart Grid

   Table 12.

   Principal Investigators:	Samir Perlaza
   Student:	Matei Moldoveanu (visitor)
   Partners:	Inaki Esnaola
   Publications:	[40]

   We study the recovery of missing data from multiple smart grid datasets within a matrix completion framework. The datasets contain the electrical magnitudes required for monitoring and control of the electricity distribution system. Each dataset is described by a low rank matrix. Different datasets are correlated as a result of containing measurements of different physical magnitudes generated by the same distribution system. To assess the validity of matrix completion techniques in the recovery of missing data, we characterize the fundamental limits when two correlated datasets are jointly recovered. We then proceed to evaluate the performance of Singular Value Thresholding (SVT) and Bayesian SVT (BSVT) in this setting. We show that BSVT outperforms SVT by simulating the recovery for different correlated datasets. The performance of BSVT displays the tradeoff behaviour described by the fundamental limit, which suggests that BSVT exploits the correlation between the datasets in an efficient manner.

2. Molecular Communications

   Table 13.

   Principal Investigators:	Malcolm Egan
   Postdoc:	Bayram Akdeniz
   Funding:	Inria Projet Recherche Exploratoire (PRE)
   Partners:	Valeria Loscri (FUN Team, Inria)
   	Marco Di Renzo (CNRS), Bao Tang (University of Graz, Austria)
   	Trung Duong (Queen's University Belfast)
   	Ido Nevat (TUMCREATE, Singapore)
   Publications:	[38], [39], [24], [26]

   Some of the most ambitious applications of molecular communications are expected to lie in nanomedicine and advanced manufacturing. In these domains, the molecular communication system is surrounded by a range of biochemical processes, some of which may be sensitive to chemical species used for communication. Under these conditions, the biological system and the molecular communication system impact each other. As such, the problem of coexistence arises, where both the reliability of the molecular communication system and the function of the biological system must be ensured. In this paper, we study this problem with a focus on interactions with biological systems equipped with chemosensing mechanisms, which arises in a large class of biological systems. We motivate the problem by considering chemosensing mechanisms arising in bacteria chemo-taxis, a ubiquitous and well-understood class of biological systems. We then propose strategies for a molecular communication system to minimize disruption of biological system equipped with a chemosensing mechanism. This is achieved by exploiting tools from the theory of chemical reaction networks. To investigate the capabilities of our strategies, we obtain fundamental information theoretic limits by establishing a new connection with the problem of covert communications.

3. Intelligent Transportation

   Table 14.

   Principal Investigators:	Malcolm Egan
   Partners:	Michel Jakob (Czech Technical University in Prague),
   	Nir Oren (University of Aberdeen)
   Publications:	[27]

   Market mechanisms are now playing a key role in allocating and pricing on-demand transportion services. In practice, most such services use posted-price mechanisms, where both passengers and drivers are offered a journey price which they can accept or reject. However, providers such as Liftago and GrabTaxi have begun to adopt a mechanism whereby auctions are used to price drivers. These latter mechanisms are neither posted-price nor classical double auctions, and can instead be considered a hybrid mechanism. In this work, we develop and study the properties of a novel hybrid on-demand transport mechanism. Due to the need for incorporating statistical knowledge and communication of system state information, communication-theoretic methods can play a useful role.

   In particular, as these mechanisms require knowledge of passenger demand, we analyze the data-profit tradeoff as well as how passenger and driver preferences influence mechanism performance. We show that the revenue loss for the provider scales with $\sqrt{nlogn}$ for $n$ passenger requests under a multi-armed bandit learning algorithm with beta distributed preferences. We also investigate the effect of subsidies on both profit and the number of successful journeys allocated by the mechanism, comparing these with a posted-price mechanism, showing improvements in profit with a comparable number of successful requests.