Research

My current research is focused on designing and implementing innovative applications using an M2M model that utilize Wireless Sensor Networks (WSNs) and Internet of Things (IoTs) technologies.

Intelligent Traffic Monitoring and Control System using WSN and IoT Technologies

I am currently working on the EU FP7 ICSI project aimed at defining new architecture to enable cooperative sensing in intelligent transportation systems and to develop a reference end-to-end implementation. The project results will enable advanced traffic and travel management strategies, based on reliable and real-time input data. The effectiveness of such new strategies, together with the proposed system, will be assessed in two field trials.

My group is directly involved in the following activities:

- Design of the data distribution platform
- M2M protocols design in compliance with ETSI specifications
- WSN middleware design and implementation
- Security mechanisms in Vehicular communication
- Dissemination and standardization

Predictive Analysis and Light-weight Algorithm for WSN

In collaboration with the Micro Electronics Lab (Micrel) of the University of Bologna, Italy under the EU 3ENCULT - Efficient ENergy for EU Cultural Heritage project, I designed light-weight models using regression and machine learning models to come up with elegant solutions. The overall objective is to achieve long-term autonomy of WSN deployments in EU cultural heritage buildings. The following are my contributions to the project:

- I investigated and designed wireless sensor network test-bed coupled with using an open source software platform viz., Castalia simulator and NXP Jennic for WISPES 24TH nodes.
- I analyzed large data pool from the communication logs of the WSN deployment. I designed and implement predictive and machine learning models on the WISPES 24TH nodes.
- SWIFTNET: A data acquisition protocol that combines a mix of compressed sensing, prediction and adaptive sampling strategies designed for reactive monitoring and event-detection application such as a wildfire monitoring. SWIFTNET is able to reconstruct the signal without significant loss in accuracy and it achieves a prolonged network lifetime of WSN .

For data analysis and processing, I used R and MATLAB . Scientific papers were published and indexed by IEEEXplore.

SWIFTNET in a WSN Star topology

WISPES 24TH Node prototype

Network Layer Clustering Protocol for Wireless Sensor Networks

During my PhD candidacy, I investigated and designed energy-efficient clustering protocols for wireless sensor networks. The overall idea is to design network protocol that is both homogeneous and heterogeneous-aware and that can significantly extend the network lifetime of WSN deployments when compared with the state-of-the art methods.

My contribution is the design and implementation of two protocols namely:

1. Enhanced Stable Election Protocol (SEP-E). Click link for MATLAB source code [Link]
2. A Deterministic Energy-efficient Clustering protocol (DEC). Click link for MATLAB source code [Link]

In addition, spatial uniformity of energy consumption and coverage issues were analyzed for a few clustering protocols such as LEACH, SEP [Link] , SEP-E and DEC.

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