Deepak Kumar Sharma

Performance Evaluation of GAER Over Various Crossover Techniques in Opportunistic Networks

Proper message transmission among nodes in a non-topological environment has always been a major concern. As there is no predefined end to end path between sender-destination node pairs, very limited predictable information exists about the network among nodes. Hence this area has been drawing attention and potential research. GAER (genetic algorithm-based energy-efficient routing protocol) (Dhurandher et al. J Supercomput Springer 69(3):1183–1214, 2014) is one of the latest routing protocols to catalyse the ease of message delivery by intelligently predicting delivery locations, using genetic algorithm over nodes’ information available in the network. Genetic algorithm uses crossover mechanism to reproduce new set of chromosomes, which further can be used to predict informative results. In this paper, GAER protocol has been analysed over different crossover techniques with an agenda to increase its efficiency by using optimal technique.

A game theory based secure model against Black hole attacks in Opportunistic Networks

Opportunistic Networks, a subset of Delay Tolerant Networks (DTNs), do not have a predefined source to destination end-to-end path and hence, offer a multitude of challenges with regard to security. In this paper a Potential Threat (PT) based security protocol has been developed which models the characters of the incoming nodes on the basis of swift delivery of messages, their reputation and history. Thus, it secures the network against malicious nodes. The PT based mechanism also trumps previous security based protocols as the basis of its conception is rooted in evolutionary game theory and in evaluating the malicious nodes as a player with certain goals and intentions. A comprehensive comparison with an existing protocol has also been done. The simulations of the protocol are run on the Opportunistic Network Environment (ONE) simulator with Spray and Wait as the base routing protocol. It is observed that the security of the network is much more vigorous with the proposed PT based security protocol. The network also benefits in terms of delivery probability, data packets dropped, network overhead ratio and the average transmission delay.

SEIR: A Stackelberg game based approach for energy-aware and incentivized routing in selfish Opportunistic Networks

Opportunistic Networks (OppNets) are a sub-class of wireless Delay Tolerant Networks (DTNs) that can be utilized in areas of sporadic network connectivity. OppNets consist of a network of mobile devices that cooperate with each other to forward messages from the source to the destination. However, in a practical real-world setting, nodes behave selfishly and do not choose to be cooperative all throughout. This selfish behavior could be exhibited due to a variety of reasons, ranging from lower energy levels of the node to memory or buffer shortages. Thus, it is imperative to develop incentivizing mechanisms that reward nodes that are cooperative and penalize nodes that are selfish. In this paper, we present a novel routing protocol called SEIR that is able to reduce energy consumption of nodes as well as incentivize them to participate in message routing. SEIR is based on the Stackelberg game theoretic model and decides the optimal reward to give relay nodes to eliminate their selfishness and improve chances of successful message delivery. Subsequent simulations performed show that SEIR outperforms existing routing protocols in terms of energy efficiency and message delivery in an OppNet with selfish nodes.

An Analytical Study to Find the Major Factors Behind the Great Smog of Delhi, 2016: Using Fundamental Data Sciences

Concerns over the alarming situation of Smog Pollution have come under the broad and current interests of masses since past few decades. Exposure to augmented levels of pollutants forming Photochemical Smog poses threat to human life, plants, animals and property as well. The effects of Smog on health can be felt instantaneously, ranging from minor pains to deadly pulmonary diseases such as lung cancer. The Great Smog of Delhi, 2016 was a manifestation of such situations. The air quality dipped to hazardous levels posing a health emergency situation. With an aim to know the intricacies of the problem, an Analytical study of the factors that contributed to Smog Pollution in Delhi was carried out using fundamental Data Sciences in R programming language. The study covers the major Pollutant analysis and Meteorological factors from the data of two pollution monitoring stations viz. R.K. Puram and Mandir Marg. Statistical Analysis and simple Linear Regression models were used for the correlation study between pollutants’ concentration levels and meteorological factors. Also, the comparative analysis was executed over the conditions of monitoring stations under study. It was found that the Particulate Matter (PM10) turned up as the major pollutant. Concentrations of pollutants are also affected by the meteorological factors. Less greenery and exposure to more vehicular pollution resulted in R.K. Puram being more polluted than Mandir Marg.

A priority based message forwarding scheme for Opportunistic Networks

Opportunistic Networks (Oppnets) are the recent evolution of Mobile Ad hoc Networks (MANETs), where an end to end path from the source to the destination does not exist, or exists for a very small amount of time. That is, the connections between the nodes are often intermittent, and the power of the nodes may go up or down. This paper aims at improving the forwarding strategy in the Epidemic routing protocol for Oppnets, which currently makes use of First in First Out (FIFO) strategy to forward the data packets. In this work, Priority Based Forwarding for Epidemic Routing (PBFER) is presented that forwards the packets based on the priority of messages. Through simulations the performance of PBFER is evaluated and compared with Epidemic routing protocol. It has been found out that PBFER performs better than the Epidemic protocol in terms of the number of high priority messages received, latency, delivery probability, overhead ratio, average hop count.

Cloud computing based routing protocol for infrastructure-based opportunistic networks

In Opportunistic Networks (Oppnets), a permanent fixed path is never assumed to be available between the source and destination. Nodes are not aware of changing network topology. So, static routing is not possible here and dynamic routing is best suited for such networks. In this paper, a Cloud Computing Based Routing Protocol (CCBRP) has been proposed for infrastructure-based Oppnets. It uses the concept of cloud computing in which data and other utilities can be accessed, configured, and manipulated on demand. In this work, 3-tier architecture has been developed for CCBRP that has three layers namely normal/regular nodes, Infostation infrastructure, and Cloud server architecture. The performance of CCBRP is evaluated and compared against the Horizontal and Vertical Ferry (HVF) strategy. Results obtained indicate that CCBRP outperforms HVF in respect of average latency and messages delivered.