Viability of Liquefied Natural Gas (LNG) in Pakistan

This paper describes the viability of liquefied natural gas (LNG) for the domestic consumers, although Pakistan has commenced the import of LNG since 2015, but still a gap in supply and demand is constantly increasing. Currently, 1.2 BCF per day of re-gasified LNG is being injected into the gas pipeline network which is basically imported for the power plant sector in Punjab province. Yet the deficit of gas supply and demand is more than 2 BCF per day. The present study of local gas field projections tell that they will lose their strength to 1/3rd by 2025. It can be easily forecasted that by then, other sectors including industrial, commercial and maybe domestic will be forced to consume re-gasified LNG. Survey has been conducted from domestic consumers of Karachi and Hyderabad using a self-developed questionnaire and basic statistical tools are used to achieve the objectives. Findings of the study state that domestic consumers have little trust upon the gas suppliers as well as regulating authority (OGRA) in Pakistan. Domestic consumers have sufficient knowledge of natural gas situation in the country and are satisfied with the government subsidy on the natural gas billing, whereas they are not willing to accept LNG even at billing rate twice the current billing. Keywords: Domestic Consumers, Liquefied Natural Gas, Resources JEL Classifications: L95, O13, O38, P18, P43, Q4

Alzheimer Disease Detection Techniques and Methods: A Review

Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper

IMOC: Optimization Technique for Drone-Assisted VANET (DAV) Based on Moth Flame Optimization

Technology advancement in the field of vehicular ad hoc networks (VANETs) improves smart transportation along with its many other applications. Routing in VANETs is difficult as compared to mobile ad hoc networks (MANETs); topological constraints such as high mobility, node density, and frequent path failure make the VANET routing more challenging. To scale complex routing problems, where static and dynamic routings do not work well, AI-based clustering techniques are introduced. Evolutionary algorithm-based clustering techniques are used to solve such routing problems; moth flame optimization is one of them. In this work, an intelligent moth flame optimization-based clustering (IMOC) for a drone-assisted vehicular network is proposed. This technique is used to provide maximum coverage for the vehicular node with minimum cluster heads (CHs) required for routing. Delivering optimal route by providing end-to-end connectivity with minimum overhead is the core issue addressed in this article. Node density, grid size, and transmission ranges are the performance metrics used for comparative analysis. These parameters were varied during simulations for each algorithm, and the results were recorded. A comparison was done with state-of-the-art clustering algorithms for routing such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and Gray Wolf Optimization (GWO). Experimental outcomes for IMOC consistently outperformed the state-of-the-art techniques for each scenario. A framework is also proposed with the support of a commercial Unmanned Aerial Vehicle (UAV) to improve routing by minimizing path creation overhead in VANETs. UAV support for clustering improved end-to-end connectivity by keeping the routing cost constant for intercluster communication in the same grid

Adaptive Node Clustering for Underwater Sensor Networks

Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communication is challenging. In this paper, we proposed a sensor node clustering technique for UWSNs named as adaptive node clustering technique (ANC-UWSNs). It uses a dragonfly optimization (DFO) algorithm for selecting ideal measure of clusters needed for routing. The DFO algorithm is inspired by the swarming behavior of dragons. The proposed methodology correlates with other algorithms, for example the ant colony optimizer (ACO), comprehensive learning particle swarm optimizer (CLPSO), gray wolf optimizer (GWO) and moth flame optimizer (MFO). Grid size, transmission range and nodes density are used in a performance matrix, which varies during simulation. Results show that DFO outperform the other algorithms. It produces a higher optimized number of clusters as compared to other algorithms and hence optimizes overall routing and increases the life span of a network

Mobile Edge-Based Information-Centric Network for Emergency Messages Dissemination in Internet of Vehicles: A Deep Learning Approach

With the rapid advancement of Internet of Things (IoT) communication technologies, the Internet of Vehicles (IoV) has gained significant attention for providing the real-time exchange of emergency traffic information among vehicles and Road Side Units (RSU) to improve ultimate driving experiences and road safety. Information-Centric Networking (ICN) has emerged as a novel networking architecture that shifts the communication model from Internet protocol (IP) based host-centric to content-centric architecture. ICN provides support to push and pull-based messages for efficient content dissemination and retrieval by aiming at content names rather than IP addresses. The Mobile Edge Computing (MEC) paradigm facilitates proximity-based real-time traffic applications and services, reducing the content retrieval latency from the core network without the excessive broadcast overhead. Deep Learning (DL) techniques have been tremendously successful in detecting the severity of real-time traffic data. The integration of DL based ANN model for edge-based ICN-IoV brings real-time traffic prediction, content caching, and forwarding of push-based messages closer to the target area. Furthermore, the deployment of mobile edge servers at critical network positions enhances the availability and responsiveness of the name-based content in the ICN paradigm. In this paper, we propose Mobile Edge-based Emergency Messages Dissemination Scheme (MEMDS) to deliver push-based messages delivery at the event-reported geographical location. We also propose a hybrid DL-based Artificial Neural Network (ANN) and MEMDS model to detect and predict the severity of the safety application under real traces from different cities based on specific parameters. The simulation results demonstrate that the proposed scheme significantly improves the data delivery ratio, average delay, hop count, content retrieval delay, and network overhead than DCN and flooding techniques. Secondly, the proposed hybrid model successfully detects the severity of the request with the highest accuracy, precision, recall, and f1-scores values of 96% than benchmark models using real-time vehicular datasets

An Optimized and Efficient Routing Protocol Application for IoV

IoV is the latest application of VANET and is the alliance of Internet and IoT. With the rapid progress in technology, people are searching for a traffic environment where they would have maximum collaboration with their surroundings which comprise other vehicles. It has become a necessity to find such a traffic environment where we have less traffic congestion, minimum chances of a vehicular collision, minimum communication delay, fewer communication errors, and a greater message delivery ratio. For this purpose, a vehicular ad hoc network (VANET) was devised where vehicles were communicating with each other in an infrastructureless environment. In VANET, vehicles communicate in an ad hoc manner and communicate with each other to deliver messages, for infotainment purposes or for warning other vehicles about emergency scenarios. Unmanned aerial vehicle- (UAV-) assisted VANET is one of the emerging fields nowadays. For VANET’s routing efficiency, several routing protocols are being used like optimized link state routing (OLSR) protocol, ad hoc on-demand distance vector (AODV) routing protocol, and destination-sequenced distance vector (DSDV) protocol. To meet the need of the upcoming era of artificial intelligence, researchers are working to improve the route optimization problems in VANETs by employing UAVs. The proposed system is based on a model of VANET involving interaction with aerial nodes (UAVs) for efficient data delivery and better performance. Comparisons of traditional routing protocols with UAV-based protocols have been made in the scenario of vehicle-to-vehicle (V2V) communication. Later on, communication of vehicles via aerial nodes has been studied for the same purpose. The results have been generated through various simulations. After performing extensive simulations by varying different parameters over grid sizes of 300 × 1500 m to 300 × 6000 m, it is evident that although the traditional DSDV routing protocol performs 14% better than drone-assisted destination-sequenced distance vector (DA-DSDV) when we have number of sinks equal to 25, the performance of drone-assisted optimized link state routing (DA-OLSR) protocol is 0.5% better than that of traditional OLSR, whereas drone-assisted ad hoc on-demand distance vector (DA-AODV) performs 22% better than traditional AODV. Moreover, if we increase the number of sinks up to 50, it can be clearly seen that the DA-AODV outperforms the rest of the routing protocols by up to 60% (either traditional routing protocol or drone-assisted routing protocol). In addition, for parameters like MAC/PHY overhead and packet delivery ratio, the performance of our proposed drone-assisted variants of protocols is also better than that of the traditional routing protocols. These results show that our proposed strategy performs better than the traditional VANET protocols and plays important role in minimizing the MAC/PHY and enhancing the average throughput along with average packet delivery ratio

Adaptive Node Clustering Technique for Smart Ocean Under Water Sensor Network (SOSNET)

Abstract: Smart ocean is a term broadly used for monitoring the ocean surface, sea habitat monitoring, and mineral exploration to name a few. Development of an efficient routing protocol for smart oceans is a non-trivial task because of various challenges, such as presence of tidal waves, multiple sources of noise, high propagation delay, and low bandwidth. In this paper, we have proposed a routing protocol named adaptive node clustering technique for smart ocean underwater sensor network (SOSNET). SOSNET employs a moth flame optimizer (MFO) based technique for selecting a near optimal number of clusters required for routing. MFO is a bio inspired optimization technique, which takes into account the movement of moths towards light. The SOSNET algorithm is compared with other bio inspired algorithms such as comprehensive learning particle swarm optimization (CLPSO), ant colony optimization (ACO), and gray wolf optimization (GWO). All these algorithms are used for routing optimization. The performance metrics used for this comparison are transmission range of nodes, node density, and grid size. These parameters are varied during the simulation, and the results indicate that SOSNET performed better than other algorithms