Soumya K. Ghosh

Enhancement of Lifetime using Duty Cycle and Network Coding in Wireless Sensor Networks

A fundamental challenge in the design of Wireless Sensor Network (WSN) is to enhance the network lifetime. The area around the Sink forms a bottleneck zone due to heavy traffic-flow, which limits the network lifetime in WSN. This work attempts to improve the energy efficiency of the bottleneck zone which leads to overall improvement of the network lifetime by considering a duty cycled WSN. An efficient communication paradigm has been adopted in the bottleneck zone by combining duty cycle and network coding. Studies carried out to estimate the upper bounds of the network lifetime by considering (i) duty cycle, (ii) network coding and (iii) combinations of duty cycle and network coding. The sensor nodes in the bottleneck zone are divided into two groups: simple relay sensors and network coder sensors. The relay nodes simply forward the received data, whereas, the network coder nodes transmit using the proposed network coding based algorithm. Energy efficiency of the bottleneck zone increases because more volume of data will be transmitted to the Sink with the same number of transmissions. This in-turn improves the overall lifetime of the network. Performance metrics, namely, packet delivery ratio and packet latency have also been investigated. A detailed theoretical analysis and simulation results have been provided to show the efficacy of the proposed approach.

iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments

Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real‐time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.

Fog computing : principles, architectures, and applications

Abstract The Internet of Everything (IoE) solutions gradually bring every object online, and processing data in a centralized cloud does not scale to requirements of such an environment. This is because there are applications such as health monitoring and emergency response that require low latency, so delay caused by transferring data to the cloud and then back to the application can seriously impact the performance. To this end, Fog computing has emerged, where cloud computing is extended to the edge of the network to decrease the latency and network congestion. Fog computing is a paradigm for managing a highly distributed and possibly virtualized environment that provides compute and network services between sensors and cloud data centers. This chapter provides a background and motivations regarding the emergence of Fog computing, and defines its key characteristics. In addition, a reference architecture for Fog computing is presented, and recent related development and applications are discussed.

An approach for service oriented discovery and retrieval of spatial data

Successful information integration and sharing data across disparate systems and designs are required for fast access to and interpretation of many types of geospatial information. Spatial data are highly heterogeneous - not only they differ from data representation and storage methods, but they also differ in the way of querying the data. Finding and accessing spatial data in an environment like this is a crucial task. Enterprise geographic information system (E-GIS) is an organization-wide approach to GIS implementation, operation, and management. The main focus of the paper is to integrate diverse spatial data repositories for geographic applications using service-based methodology. We have adopted service-oriented architecture (SOA) for the discovery and retrieval of geospatial data. The architecture uses a central ontology as metadata information, which acts as service broker. Ontology-based discovery and retrieval of geographic data solves the problem of semantic heterogeneity, the major bottleneck for spatial interoperability. The implementation is in compliant with the Web Map Service (WMS) and Web Feature Service (WFS), the web service standards proposed by OGC. The need for loosely coupled service-based access of data in the spatial domain has been exploited. A query processing mechanism in distributed environment of spatial data sources has been discussed at the end. The proposed system has been implemented and fully tested.

A planner-based approach to generate and analyze minimal attack graph

In the present scenario, even well administered networks are susceptible to sophisticated cyber attacks. Such attack combines vulnerabilities existing on different systems/services and are potentially more harmful than single point attacks. One of the methods for analyzing such security vulnerabilities in an enterprise network is the use of attack graph. It is a complete graph which gives a succinct representation of different attack scenarios, depicted by attack paths. An attack path is a logical succession of exploits, where each exploit in the series satisfies the preconditions for subsequent exploits and makes a causal relationship among them. Thus analysis of the attack graph may help in assessing network security from hackers’ perspective. One of the intrinsic problems with the generation and analysis of such a complete attack graph is its scalability. In this work, an approach based on Planner, a special purpose search algorithm from artificial intelligence domain, has been proposed for time-efficient, scalable representation of the attack graphs. Further, customized algorithms have been developed for automatic generation of attack paths (using Planner as a low-level module). The analysis shows that generation of attack graph using the customized algorithms can be done in polynomial time. A case study has also been presented to demonstrate the efficacy of the proposed methodology.

A Review on Telemedicine-Based WBAN Framework for Patient Monitoring

OBJECTIVE In this article, we describe the important aspects like major characteristics, research issues, and challenges with body area sensor networks in telemedicine systems for patient monitoring in different scenarios. Present and emerging developments in communications integrated with the developments in microelectronics and embedded system technologies will have a dramatic impact on future patient monitoring and health information delivery systems. The important challenges are bandwidth limitations, power consumption, and skin or tissue protection. MATERIALS AND METHODS This article presents a detailed survey on wireless body area networks (WBANs). RESULTS AND CONCLUSIONS We have designed the framework for integrating body area networks on telemedicine systems. Recent trends, overall WBAN-telemedicine framework, and future research scope have also been addressed in this article.

Policy Based Security Analysis in Enterprise Networks: A Formal Approach

In a typical enterprise network, there are several sub-networks or network zones corresponding to different departments or sections of the organization. These zones are interconnected through set of Layer-3 network devices (or routers). The service accesses within the zones and also with the external network (e.g., Internet) are usually governed by a enterprise-wide security policy. This policy is implemented through appropriate set of access control lists (ACL rules) distributed across various network interfaces of the enterprise network. Such networks faces two major security challenges, (i) conflict free representation of the security policy, and (ii) correct implementation of the policy through distributed ACL rules. This work presents a formal verification framework to analyze the security implementations in an enterprise network with respect to the organizational security policy. It generates conflict-free policy model from the enterprise-wide security policy and then formally verifies the distributed ACL implementations with respect to the conflict-free policy model. The complexity in the verification process arises from extensive use of temporal service access rules and presence of hidden service access paths in the networks. The proposed framework incorporates formal modeling of conflict-free policy specification and distributed ACL implementation in the network and finally deploys Boolean satisfiability (SAT) based verification procedure to check the conformation between the policy and implementation models.

Spatial Interpolation to Predict Missing Attributes in GIS Using Semantic Kriging

Prediction of spatial attributes has attracted significant research interest in recent years. It is challenging especially when spatial data contain errors and missing values. Geostatistical estimators are used to predict the missing attribute values from the observed values of known surrounding data points, a general form of which is referred as kriging in the field of geographic information system and remote sensing. The proposed semantic kriging ( SemK) tries to blend the semantics of spatial features (of surrounding data points) with ordinary kriging (OK) method for prediction of the attribute. Experimentation has been carried out with land surface temperature data of four major metropolitan cities in India. It shows that SemK outperforms the OK and most of the existing spatial interpolation methods.

Conditional Random Field Based Named Entity Recognition in Geological Text

The paper describes about the development of a Named Entity Recognition (NER) system for Geological text using Conditional Random Fields (CRFs). The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the various named entity (NE) classes. The NE tagged geological corpus was developed from the collection of scientific reports and articles on the geology of the Indian subcontinent has been used to build up the system. The training set consists of more than 2 lakh words and has been manually annotated with a NE tag set of seventeen tags. The system is able to recognize 17 classes of NEs with 75.8% Fmeasure.

An Approach for Security Assessment of Network Configurations Using Attack Graph

With increasing network security threats, the network vulnerability must consider exploits in the context of multistage, multi-host attack scenarios. The general approach to this problem is to construct an attack graph for a given network configuration. An attack graph consists of a number of attack paths which are essentially series of exploits which an attacker employs to reach the destination. Each attack path depicts an attack scenario. As the number of attack scenarios increases, the overall security of the network reduces. Thus there is need for quantification of security level of a given network. In this paper, two security metrics, namely probabilistic security metric and attack resistance metric, have been employed to evaluate the relative security levels of various network configurations. A case study has been presented to demonstrate the applicability of the proposed approach.