Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Cloud computing & Fuzzy logic. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Bayesian coalition game for the internet of things: an ambient intelligence-based evaluation

Abstract: With the evolution of the Internet and related technologies, there has been an evolution of new paradigm, which is the Internet of Things, IoT. In the IoT, a large number of objects/devices on the Internet are connected to one another for information sharing, irrespective of their locations. These devices may be interconnected with one another using various network protocols and standards to exchange information between them. The underlying network used for information exchange generally has built-in intelligence, which is called ambient intelligence, so that it can make adaptive decisions for information exchange between these objects in theh IoT. This article provides a performance evaluation of the Bayesian coalition game among these objects in the IoT environment by using the concepts of game theory and LA. In comparison to the existing solutions, LA are assumed to be the players in the game having variable learning rates in the coalition game. Most of the existing solutions have considered constant learning rates of the players in the game, which may lead to the possibility of local optima at some points. Each player decides its actions using competitive learning, having variable learning rates, based on the newly defined utility function, which leads to the achievment of a Nash equilibrium in the game quickly. Each player receives feedback from the environment corresponding to the actions taken in a unit interval of time. The performance of the proposed scheme was evaluated with respect to various performance evaluation metrics. The results obtained show that the proposed scheme is useful in the IoT environment.

Comprehensive survey on haze removal techniques

Abstract: Image haze removal techniques are extensively used in several outdoor applications. Lack of sufficient knowledge that is required to restore hazy images, the existing techniques usually use various attributes and assign constant values to these attributes. Unsuitable assignment to these attributes does not provide desired dehazing results. The primary objective of this review paper is to provide a structured outline of some well-known haze removal techniques. This paper also focuses on the methods which can assign optimal values to image dehazing attributes. The review has revealed that the meta-heuristic techniques can attain the optimistic haze removal parameters and also concurrently develops an optimistic objective function to estimate the depth map efficiently. Finally, this paper describes the various issues and challenges of image dehazing techniques, which are required to be further studied.

Blockchain-Envisioned Softwarized Multi-Swarming UAVs to Tackle COVID-I9 Situations

Abstract: With the spread of novel coronavirus, global health concerns have increased as it has flattened the curve of mortality worldwide. To handle such a containment of disease, multi-swarm Unmanned Aerial Vehicles (UAVs) with 5G can be used to reduce human intervention with major benefits of high bandwidth, ultra-low-latency, and reliability. Multi-swarm UAVs sends a huge amount of data to ground stations with real-time connection density of 107/km2, which is a bottleneck on 5G networks; data security is another issue in sharing sensitive data. Motivated by these issues, in this article, we propose a blockchain-envisioned softwarized multi-swarming UAV communication scheme based on a 6G network with intelligent connectivity, Terahertz (THz) frequency bands, and virtualization of link and physical-level protocols. Softwarization makes the communication infrastructure flexible, agile, and easily configu-rabie, and the potential of blockchain supports data security. Results show that the proposed scheme performs better in terms of processing delay, packet loss reduction, and throughput compared to exiting 4G/5G-based systems.

Decolorization and Detoxification of Extraction-Stage Effluent from Chlorine Bleaching of Kraft Pulp by Rhizopus oryzae

Abstract: Rhizopus oryzae, a zygomycete, was found to decolorize, dechlorinate, and detoxify bleach plant effluent at lower cosubstrate concentrations than the basidiomycetes previously investigated. With glucose at 1 g/liter, this fungus removed 92 to 95% of the color, 50% of the chemical oxygen demand, 72% of the adsorbable organic halide, and 37% of the extractable organic halide in 24 h at temperatures of 25 to 45°C and a pH of 3 to 5. Even without added cosubstrate the fungus removed up to 78% of the color. Monomeric chlorinated aromatic compounds were removed almost completely, and toxicity to zebra fish was eliminated. The fungal mycelium could be immobilized in polyurethane foam and used repeatedly to treat batches of effluent. The residue after treatment was not further improved by exposure to fresh R. oryzae mycelium.

A boolean spider monkey optimization based energy efficient clustering approach for WSNs

Abstract: Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.