Suparna Biswas

A Novel Cluster Head Selection Algorithm for Energy-Efficient Routing in Wireless Sensor Network

Wireless sensor networks consist of low power lightweight sensor nodes that are often placed remotely for applications like wildlife monitoring, rainforest monitoring, military surveillance, forest fire detection etc. Here energy is a critical issue as nodes cannot be recharged and/or replaced frequently. In this paper we propose an energy efficient hierarchical routing protocol for Wireless Sensor Networks to increase network lifetime. Here the cluster head is selected based on the residual energy, distance of the node from the base station, how recently the node was selected as cluster-head etc. Along with this, the proposed solution can also detect malicious nodes in the network and can prevent them from becoming cluster heads. The simulation results show that proposed solution provides good performance in terms of extending network lifetime, uniform selection of nodes as cluster head that eliminates the probability of a single node from draining away its power by its repetitive selection as a cluster head.

Implementation of Cloud-Assisted Secure Data Transmission in WBAN for Healthcare Monitoring

This work presents the cloud-assisted secure WBAN for healthcare application. There are various security issues associated with WBAN, which need to be solved to provide a secure real-time health monitoring system. Through this implementation, the patient’s vital signals can be accessed in a secure manner in real time remotely by sensors and networks without visiting doctor’s clinic or hospital. Here, we provide the cost-effective solution for the transmission of the patient’s health data to doctor with proper confidentiality, authenticity, freshness, and security using cloud computing. In this work, the biosignals of patients and doctors are used to provide authenticity and vital signals are encrypted by using Advanced Encryption Standard (AES) for the secure m-health application. We have experimentally analyzed the average end-to-end delay for secure healthcare application is 14.59 and 19.31 ms in off-peak hours and peak hours respectively. This delay is only 5.84% in off-peak hours and 7.72% in peak hours of permissible delay of 250 ms for medical application.

Secure Data Transmission Beyond Tier 1 of Medical Body Sensor Network

Medical body sensor network (MBSN), a three-tier architectural network, has been in wide use on demand for remote health monitoring and support in both urban and rural areas. Primary concern of such system is security of sensitive health data along with low end-to-end delay and energy consumption among others. This paper implements secure patient data transmission between tier 2 and tier 3 by ensuring confidentiality and integrity. Man-in-middle attack and distributed denial of service attack can be detected based on end-to-end delay in data transmission. Hash-based secret key is used for encryption which is generated using extracted biological information of user at coordinator PDA of MBSN. Using shared extracted biological information, secret key is regenerated at cloud-based medical server for decryption of data. Experimental results show using different symmetric key encryption techniques, maximum end-to-end delay is only 11.82% of 250 ms which is the maximum permissible delay limit for healthcare application.

An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones

Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to detailed activity recognition through the use of smartphone sensors. Use of smartphones for activity recognition poses challenges such as device independence and various usage behavior in terms of where the smartphone is kept. Only a few works address one or more of these challenges. Consequently, in this paper, we present a detailed activity recognition framework for identifying both static and dynamic activities addressing the above-mentioned challenges. The framework supports cases where (i) dataset contains data from accelerometer; and the (ii) dataset contains data from both accelerometer and gyroscope sensor of smartphones. The framework forms an ensemble of the condition based classifiers to address the variance due to different hardware configuration and usage behavior in terms of where the smartphone is kept (right pants pocket, shirt pockets or right hand). The framework is implemented and tested on real data set collected from 10 users with five different device configurations. It is observed that, with our proposed approach, 94% recognition accuracy can be achieved.

Improved recovery probability of mobile hosts using energy and mobility-based secure checkpointing – recovery

In this work, we have proposed a mobility-based secure checkpointing and log-based rollback recovery technique to provide fault tolerance to mobile hosts (MHs) in infrastructured wireless/mobile computing system, like, wireless cellular network. This work has been modified accordingly to be applicable in infrastructure less network, i.e., mobile ad hoc network (MANET) in which MHs save checkpoint adaptively based on their energy and mobility. Transient failures in cluster-based mobile ad hoc network (MANET) may be caused by insufficient energy of mobile hosts, among other reasons. To prevent data loss due to transient failure caused by lack of sufficient energy, mobile hosts save checkpoint if remaining energy becomes equal to a predefined threshold. A trust-based recovery technique is used for successful recovery of failed mobile hosts. To make checkpoints secure, only trusted mobile hosts participate in the proposed checkpoint-recovery process.