Monideepa Roy

Base Station Controlled Spectrum Allocation Technique to Detect the PUE Attack in CWSN

Primary User Emulation (PUE) attack is a type of Denial-of-Service (DoS) attack which is commonly faced by Cognitive Wireless Sensor Networks (CWSNs). In CWSNs, malicious secondary users try to emulate primary users to maximize their own spectrum usage or to obstruct secondary users from accessing the spectrum. In this paper we have proposed a base station controlled spectrum allocation protocol for the secondary users to deal with PUE attacks in CWSNs. We have used the well-known lightweight hash function SHA-1 for the authentication process of a secondary user to the base station. We demonstrate that our authentication protocol can be implemented efficiently on CWSN nodes. Our experimental results show that the Base Station controlled Primary User Emulation Attack performs well in CWSN scenario.

A lightweight Clone-Node Detection Algorithm for Efficiently Handling SSDF Attacks and Facilitating Secure Spectrum Allocation in CWSNs

Cognitive Wireless Sensor Networks (CWSNs) provide better bandwidth utilization when compared with normal wireless sensor networks. CWSNs use a technique called opportunistic spectrum access for data transfer. While doing so, however, CWSNs are subject to several security threats. The spectrum sensing data falsification attack comes under the DoS attack. In this attack, a malicious node sends a modified spectrum sensing report so that the resulting collaborative spectrum sensing decision becomes wrong and a good cognitive sensor node receives a wrong decision regarding the vacant spectrum band of others network. In the presence of the node cloning attack, the solution of the SSDF attack becomes even more difficult. In the node cloning attack, the malicious node creates many clones of the compromised node in the network. In order to confuse the collaborative spectrum sensing system, the clone nodes can send false spectrum sensing reports in a large number. The maximum-match filtering (MMF) algorithm is used for making a secure spectrum sensing decision in CWSNs. The Cloned-Node Detection (CND) algorithm is proposed here to detect cloned nodes. This study also explains how the CND algorithm assists the MMF algorithm to make better spectrum sensing decisions by avoiding the node cloning attack.

EDOT: Context-aware tracking of similar data patterns of patients for faster diagnoses

Availability of a particular doctor for a patient every time in emergency arises, may not be possible in case of remote monitoring of patients with chronic illnesses or for elderly people. It has been observed often that such patients display or experience situations that have occurred are identical to what has occurred to them in the past. Under such circumstances, the diagnoses and medications are mostly similar. In the case of unreachability of the original doctor, if the new doctor is provided with the past similar pattern with a comparison study between different symptoms parameters at that time, prior identical situations, medications and drug allergies, then it becomes easier and faster for a new doctor to arrive at a more accurate diagnoses for the patient. In this paper we have proposed a framework for remote health monitoring of patients that will continuously store and monitor patient data and mark identical or similar patterns in the readings and establish relationship between the different symptoms parameters, on a remote server, which can be sent to the new treating doctor in case the need arises.

Dynamic service deployment using JSON based decision trees in remote healthcare

Providing quality healthcare in remote and rural areas pose several challenges like high patient-doctor ratio, and difficulty in accessing these locations. In this paper we propose to use JSON - based decision trees to assist doctors in providing medical help to such patients through dynamic service provisioning. A patient enters his preliminary symptoms details through a GUI in a mobile device, which is then sent for matching in a database which contains more detailed questionnaire for different ailments in JSON format. Based on the matches the subsequent decision tree is deployed on the user device for providing a finer - tuned diagnosis.

A Regression Based Spectrum-Sensing Data - Falsification Attack Detection Technique in CWSN

CWSNs provide better bandwidth utilization as compared to a normal wireless sensor network because they use opportunistic spectrum access to transfer data. Opportunistic spectrum access is a very promising and spectrum efficient communication process when there is bur sty traffic in the network. IEEE 802.22 is the first standard based on the concept of cognitive radio. It also helps the network to eliminate collisions and delays in data delivery. While doing so, however, CWSNs are subject to several security threats, attacks on secrecy and authentication, attacks on network availability, stealthy attacks on service integrity etc. The attacks on network availability are known as the Denial of Service (DOS) attacks. The Spectrum Sensing Data Falsification (SSDF) attack is a type of DOS attack. Here the attackers modify the spectrum sensing report in order to compel the cognitive sensor node to take a wrong decision regarding the vacant spectrum band in others networks. In this paper we have proposed a new algorithm for dynamic spectrum sensing and spectrum allocation in Cognitive wireless sensor networks (CWSNs), the Maximum-Match Filtering algorithm (MMF). This algorithm is executed at the base station to counter the above attack.

Designing a WSN Data Format for WLAN and Cellular Network Compatibility in an E-healthcare Framework

The emergence of wireless sensor networks (WSNs) has powerful results on various areas of applications such as medicine and healthcare, air pollution detection, fire detection, water quality monitoring etc. Wireless biomedical sensor networks can supply non-obstructive , long-time, and real-time medical convoy for the chronic patients. The use of wireless medical sensor networks on a large scale can successfully minimize the maximum workloads for the technically growing society and prolonged health service for the chronically ill patients. It also reduces the possibility of errors caused by human. For this there is a need for a variety of types of data to be collected and sent to the sink for processing and diagnosis. In addition there may be heterogeneity in the types of devices which are participating in the network. Here author presents a Zigbee based Ehealthcare framework which will provide seamless support to patients from far off or rural areas. Here also proposed a data format to be captured in WSNs that will be compatible in WLANs as well as in the cellular networks which will collect data from the patients. General Terms Sensor Network, Clustering Technique, E-healthcare System.

A Jini Based Implementation for Best Leader Node Selection in MANETs

MANETs provide a good alternative for handling the constraints of disconnectivity and disruption of static communication infrastructure faced during emergency situations. In such frameworks, it sometimes becomes necessary to elect a leader node for the MANET. Electing a leader node in a MANET poses several challenges due to the inherent properties of mobility, resource constraints etc. of these ad hoc networks. Although there are at present some existing leader node selection algorithms, it is difficult to apply them to MANET based frameworks where multiple selection of leader nodes is allowed, because of various reasons. This paper presents an algorithm for the best leader node selection, along with a network wide searching technique that a client uses to search for a particular service in the network, both of which are especially suitable for these types of frameworks.