Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

ODOB: One Drone One Block-based Lightweight Blockchain Architecture for Internet of Drones

Abstract: The Internet of Drones (IoD) is an environment, which facilitates the autonomous and hybrid (semi-autonomous) operations of drones. Due to this advantage, the drones have paved their way into every segment (almost) across the world. Whatever be the application (warfare, surveillance, photography, rescue, delivery, etc), the transmission of data (to and fro) occur between the drones and the other infrastructure over wireless channels. While on the move, this data (operational or service-related) is vulnerable to several security risks and attacks. Hence, the maintenance of the confidentiality, integrity, and authenticity of the data is the primary goal in the IoD environment. Recently, the blockchain, a distributed ledger-based technology is being popularly adapted to store the data immutably, making it a possible solution to handle the above-raised issues. But, there are several challenges with conventional blockchain architecture, which make it difficult to adapt it in its current form. Firstly, the blockchain works by building trust in the trust-less environment by using a consensus mechanism. Secondly, the blockchain inherently involves a large number of network communication operations to synchronize the peer-to-peer network. Even more, the bigger the blockchain grows, the more is the data flowing across the network and so does the challenges. Therefore, to overcome these problems, ODOB: One Drone One Block-based Lightweight Blockchain Architecture for IoD is proposed. ODOB decouples the data part (or block ledger) from the block header to form a distributed architecture. This architecture couple the drones with an individual amendable block (each drone can access only their own block), thereby making it simple, trustworthy, and lightweight. ODOB is evaluated for several parameters and the results obtained favor the proposed architecture.

A q-rung orthopair fuzzy ARAS method based on entropy and discrimination measures: an application of sustainable recycling partner selection.

Abstract: The necessity and policy of eco-economy stimulate enterprises to attain sustainability by executing supply chain management. Generally, the evaluation process of sustainable recycling partner (SRP) selection is treated as a multi-criteria decision-making problem due to existence of numerous influencing aspects. To tackle the uncertain information during the process of SRP selection, the q-rung orthopair fuzzy sets have a good choice, which can refer to a broader range of uncertain decision-making information. Thus, this study presents a combined framework with the additive ratio assessment (ARAS) approach, notions of q-rung orthopair fuzzy set (q-ROFS) and information measures, and further implements to tackle the multi-criteria SRP selection problem with q-ROFSs setting. In this procedure, the criteria weights are evaluated with the integration of the subjective weights given by decision-experts and the objective weights obtain from the entropy and discrimination measures-based approach. For this, new entropy and discrimination measures are introduced for q-ROFSs and discussed the effectiveness of proposed measures. To elucidate the applicability of the present methodology, a case study related to sustainable recycling partner assessment is presented under q-ROFSs context. Sensitivity analysis is conducted over diverse set of criteria weights to verify the robustness of introduced framework. The results of the sensitivity analysis signify that the recycling partner SRP1 constantly secures the best rank and despites how sub-criteria weights differ. A comparison with extant methods is made to validate of the results of proposed one. The findings of the work verify that the developed framework is more valuable and well consistent with formerly proposed decision-making models.

An energy efficient stable clustering approach using fuzzy extended grey wolf optimization algorithm for WSNs

Abstract: Wireless sensor network (WSN) is a cost-effective networking solution for information updating in the coverage radius or in the sensing region. To record a real-time event, a large number of sensor nodes (SNs) need to be arranged systematically, such that information collection is possible for a longer span of time. But, the hurdle faced by WSN is the limited resources of SNs. Hence, there is a high demand to design and implement an energy-efficient scheme to prolong the performance parameters of WSN. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance, and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering based hierarchical approach, efficient CH selection algorithm, and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy extended grey wolf optimization algorithm based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.

Patient Behavioral Analysis with Smart Healthcare and IoT

Abstract: Patient behavioral analysis is the key factor for providing treatment to patients who may suffer from various difficulties including neurological disease, head trauma, and mental disease. Analyzing the patient’s behavior helps in determining the root cause of the disease. In traditional healthcare, patient behavioral analysis has lots of challenges that were much more difficult. The patient behavior can be easily analyzed with the development of smart healthcare. Information technology plays a key role in understanding the concept of smart healthcare. A new generation of information technologies including IoT and cloud computing is used for changing the traditional healthcare system in all ways. Using Internet of Things in the healthcare institution enhances the effectiveness as well as makes it more personalized and convenient to the patients. The first thing that will be discussed in the article is the technologies that have been used to support the smart class, and further, there will be a discussion on the existing problems with the smart healthcare system and how these problems can be solved. This study can provide essential information about the role of smart healthcare and IoT in maintaining behavior of patent. Various biomarkers are maintained properly with the help of these technologies. This study can provide effective information about importance of smart health system. This smart healthcare is conducted with the involvement of proper architecture. This is treated as effective energy efficiency architecture. Artificial intelligence is used increasingly in healthcare to maintain diagnosis and other important factors of healthcare. This application is also used to maintain patient engagement, which is also included in this study. Major hardware components are also included in this technology such as CO sensor and CO2 sensor.