Vivek Menon

Multimodal identification and tracking in smart environments

We present a model for unconstrained and unobtrusive identification and tracking of people in smart environments and answering queries about their whereabouts. Our model supports biometric recognition based upon multiple modalities such as face, gait, and voice in a uniform manner. The key technical idea underlying our approach is to abstract a smart environment by a state transition system in which each state records a set of individuals who are present in various zones of the environment. Since biometric recognition is inexact, state information is inherently probabilistic in nature. An event abstracts a biometric recognition step, and the transition function abstracts the reasoning necessary to effect state transitions. In this manner, we are able to integrate different biometric modalities uniformly and also different criteria for state transitions. Fusion of biometric modalities is also supported by our model. We define performance metrics for a smart environment in terms of the concepts of ‘precision’ and ‘recall’. We have developed a prototype implementation of our proposed concepts and provide experimental results in this paper. Our conclusion is that the state transition model is an effective abstraction of a smart environment and serves as a good basis for developing practical systems.

The Three Rs of Cyberphysical Spaces

The ability to identify people and answer questions about their where abouts in a cyberphysical space is critical to many applications. Integrating recognition with spatiotemporal reasoning enhances the overall performance of information retrieval.

Spatio-Temporal Querying in Smart Spaces

Abstract A ‘smart space’ is one that automatically identifies and tracks its occupants using unobtrusive biometric modalities such as face, gait, and voice in an unconstrained fashion. Information retrieval in a smart space is concerned with information about the location of people at various points in time. Towards this end, we abstract a smart space by a probabilistic state transition system in which each state records the probabilities of presence of a set of individuals who are present in various zones of the smart space. We formulate a data model based upon an occupancy relation with a real-valued probability attribute and describe some of the spatio-temporal queries in SQL and CLP(R), focusing on the computation of probabilities, an aspect that is novel to this model. We define concepts of precision and recall to quantify the performance of this model based on its ability to answer various spatio-temporal queries and discuss results from our experimental prototype.

Spatio-Temporal Reasoning in Biometrics Based Smart Environments

Abstract We discuss smart environments that identify and track their occupants using unobtrusive recognition modalities such as face, gait, and voice. In order to alleviate the inherent limitations of recognition, we propose spatio-temporal reasoning techniques based upon an analysis of the occupant tracks. The key technical idea underlying our approach is to determine the identity of a person based upon information from a track of events rather than a single event. We abstract a smart environment by a probabilistic state transition system in which each state records a set of individuals who are present in various zones of the smart environment. An event abstracts a recognition step and the transition function defines the mapping between states upon the occurrence of an event. We define the concepts of ‘precision’ and ‘recall’ to quantify the performance of the smart environment. We provide experimental results to show performance improvements from spatio-temporal reasoning. Our conclusion is that the state transition system is an effective abstraction of a smart environment and the application of spatial-temporal reasoning enhances its overall performance.

Adaptation in Pervasive Computing Environments: A Multi-agent approach

This paper proposes a multi-agent based context-aware adaptive system that can autonomously adapt the levels of performance of applications/services in event of a significant mismatch between demand and supply of resources in a pervasive computing environment. In event of scarcity of resources, the system autonomously chooses a best-fit adaptation strategy relevant to the context based on user preferences. The primary emphasis here is to continue to provide acceptable levels of a service, avoiding as far as possible, a direct involvement of the user.

Disaster management in India — An analysis using COBIT 5 principles

From a limited focus on post-disaster activities of response and recovery, disaster management frameworks have evolved over time to include pre-disaster activities of prevention and preparedness. Disaster risk governance, disaster risk reduction and resilience are core aspects of contemporary disaster management frameworks. India is one of the most disaster prone areas in the world with challenges aplenty in effective disaster management. Aligning with the Sendai Framework for Disaster Risk Reduction 2015–2030, India evolved the National Disaster Management Plan (NDMP) of 2016 as a comprehensive disaster management framework that proposes an integrated approach involving relevant stakeholders for addressing diverse natural and human-induced hazards. The five principles of ISACAs COBIT 5 framework, a widely accepted comprehensive IT governance and management framework for enterprise IT, is used to analyze the NDMP 2016 to identify areas for improvement.

Probabilistic spatio-temporal retrieval in smart spaces

A ‘smart space’ is one that automatically identifies and tracks its occupants using unobtrusive biometric modalities such as face, gait, and voice in an unconstrained fashion. Information retrieval in a smart space is concerned with the location and movement of people over time. Towards this end, we abstract a smart space by a probabilistic state transition system in which each state records the probabilities of presence of individuals in various zones of the smart space. We carry out track-based reasoning on the states in order to determine more accurately the occupants of the smart space. This leads to a data model based upon an occupancy relation in which time is treated discretely, owing to the discrete nature of events, but probability is treated as a real-valued attribute. Using this data model, we show how to formulate a number of spatio-temporal queries, focusing on the computation of probabilities, an aspect that is novel to this model. We present queries both in SQL syntax and also in CLP(R), a constraint logic programming language (with reals) which facilitates succinct formulation of recursive queries. We show that the answers to certain queries are better displayed in a graphical manner, especially the movement tracks of occupants of the smart space. We also define query-dependent precision and recall metrics in order to quantify how well the model is able to answer various spatio-temporal queries. We show that a query-dependent metric gives significantly better results for a class of occupancy-related queries compared with query-independent metrics.