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A land administration system (LAS) is a structured framework designed to govern the management of land resources in a specific region or country. However, LAS faces challenges like inefficiencies, a lack of transparency, and susceptibility to fraud. The digitization of land records improved efficiency but failed to address manipulation, centralized databases, and double-spending issues. Traditional lease and mortgage management systems also suffer from complexity, errors, and a lack of real-time validation. At present, a significant influx of land transactions produces substantial data, classifiable as big data due to constant minute-to-minute occurrences like land transfers, acquisitions, document verification, and leasing/mortgaging transactions. In this context, we present a Blockchain-driven system that not only tackles alteration and double-spending issues in traditional systems but also implements distributed data management. Current state-of-the-art solutions do not fully incorporate crucial features of Blockchain, such as transparency, prevention of double-spending, auditability, immutability, and user participation. To tackle this problem, this research introduces a comprehensive Blockchain-powered framework for lease and mortgage management, addressing transparency, user involvement, and double-spending prevention. Unlike existing solutions, our framework integrates key Blockchain characteristics for a holistic approach. Through practical use cases involving property owners, banks, and financial institutions, we establish a secure, distributed, and transparent method for property financing. We verify the system by employing smart contracts and assess the cost and security parameters while validating the blockchain-based mortgage and lease functions.

The oil and gas industries (OGI) are the primary global energy source, with pipelines as vital components for OGI transportation. However, pipeline leaks pose significant risks, including fires, injuries, environmental harm, and property damage. Therefore, maintaining an effective pipeline maintenance system is critical for ensuring a safe and sustainable energy supply. The Internet of Things (IoT) has emerged as a cutting-edge technology for efficient OGI pipeline leak detection. However, deploying IoT in OGI monitoring faces significant challenges due to hazardous environments and limited communication infrastructure. Energy efficiency and fault tolerance, typical IoT concerns, gain heightened importance in the OGI context. In OGI monitoring, IoT devices are linearly deployed with no alternative communication mechanism available along OGI pipelines. Thus, the absence of both communication routes can disrupt crucial data transmission. Therefore, ensuring energy-efficient and fault-tolerant communication for OGI data is paramount. Critical data needs to reach the control center on time for faster actions to avoid loss. Low latency communication for critical data is another challenge of the OGI monitoring environment. Moreover, IoT devices gather a plethora of OGI parameter data including redundant values that hold no relevance for transmission to the control center. Thus, optimizing data transmission is essential to conserve energy in OGI monitoring. This article presents the Priority-Based, Energy-Efficient, and Optimal Data Routing Protocol (PO-IMRP) to tackle these challenges. The energy model and congestion control mechanism optimize data packets for an energy-efficient and congestion-free network. In PO-IMRP, nodes are aware of their energy status and communicate node’s depletion status timely for network robustness. Priority-based routing selects low-latency routes for critical data to avoid OGI losses. Comparative analysis against linear LEACH highlights PO-IMRP’s superior performance in terms of total packet transmission by completing fewer rounds with more packet’s transmissions, attributed to the packet optimization technique implemented at each hop, which helps mitigate network congestion. MATLAB simulations affirm the effectiveness of the protocol in terms of energy efficiency, fault-tolerance, and low latency communication.

Using smartphones, tablets, and other portable/handheld devices, we have become more reliant on the video streaming services for entertainment and remote work. Mobile data traffic has grown eighteen folds over the past five years accounting for the majority of IP traffic. YouTube Live, Facebook Live, Twitch, DouYu and other streaming as well as video conferencing services have increased in popularity so at any given moment they serve thousands of live video streams to millions of users. The Enhanced Multimedia Broadcast Multimedia Service (eMBMS) is the standard multicast protocol for 5G networks. Cellular multicast has gained considerable attention to efficiently utilize the limited spectrum to transmit multimedia content to cellular sites with co-located viewers, lowering the cost, and maximizing the Quality of Experience (QoE). However, popular live video content providers use unicast mode for live video delivery and have limited support in the eMBMS service-oriented network architecture. In this paper, we propose an overlay network architecture to augment eMBMS to address the limitations of the standard eMBMS architecture and enable service-less multicast for crowdsourced live video providers. We propose a Virtual Network Function (VNF) service that identifies potential multicast scenarios based on user requests for a live video within a confined area. The VNF Application Server collects information, validates a potential multicast scenario, and initiates an ad-hoc multicast service on the fly. We use a real-world dataset of Facebook Live videos to evaluate the proposed architecture. The simulation results depict considerable advantages in terms of cost, efficiency, and Quality of Experience (QoE). Our results show that the proposed architecture provides significant benefits in bandwidth saving at the backhaul, transit, and RAN links.

Cloud computing provides flexibility, reliability, and scalability to its consumers. Applications that run on Cloud are now more resource hungry than ever, and the need is constantly growing. The number of Cloud applications with diverse nature is growing, expecting greater performance in low cost. Fulfilling the quality of service (QoS) demands of such diverse applications is a challenging task and requires efficient network architectures, robust scheduling schemes, powerful and adaptable routing algorithms, reliable resource management, effective data exchange policies, and QoS improvement policies. Research community endeavors to provide solutions to various challenges faced by Cloud computing. These solutions must be tested thoroughly before their implementation in a real Cloud. Simulation seems a viable choice for testing a solution in different scenarios. There are a number of simulators available, focusing on certain aspects of Cloud while neglecting others. Their generalized implementation of the Cloud ignores various critical factors, such as networking characteristics, consequently affecting the tests and results of a solution. To tackle these issues, simplify simulation process, and provide the detailed implementation of the Cloud, we present Nutshell: a novel Cloud simulator that makes it easy to model, simulate, and experiment new Cloud solutions. Salient features offered by Nutshell include 1) provision of a platform for modeling and simulating Cloud infrastructure, 2) built-in essential components and helpers to create new architectures, 3) pre-built data center architectures, 4) plug-in-based architecture, 5) communication protocols, 6) pre-built virtual machine scheduler, 7) addressing schemes, 8) user applications, 9) virtual machines, 10) Job splitting, and 11) simulation data collection and exporting.

The COVID-19 pandemic has affected the world socially and economically changing behaviors towards medical facilities, public gatherings, workplaces, and education. Educational institutes have been shutdown sporadically across the globe forcing teachers and students to adopt distance learning techniques. Due to the closure of educational institutes, work and learn from home methods have burdened the network resources and considerably decreased a viewer’s Quality of Experience (QoE). The situation calls for innovative techniques to handle the surging load of video traffic on cellular networks. In the scenario of distance learning, there is ample opportunity to realize multi-cast delivery instead of a conventional unicast. However, the existing 5G architecture does not support service-less multi-cast. In this article, we advance the case of Virtual Network Function (VNF) based service-less architecture for video multicast. Multicasting a video session for distance learning significantly lowers the burden on core and Radio Access Networks (RAN) as demonstrated by evaluation over a real-world dataset. We debate the role of Edge Intelligence (EI) for enabling multicast and edge caching for distance learning to complement the performance of the proposed VNF architecture. EI offers the determination of users that are part of a multicast session based on location, session, and cell information. Moreover, user preferences and network’s contextual information can differentiate between live and cached access patterns optimizing edge caching decisions. While exploring the opportunities of EI-enabled distance learning, we demonstrate a significant reduction in network operator resource utilization and an increase in user QoE for VNF based multicast transmission.

Cloud computing provides online services to customers using pay as you go model. The Cloud computing enables customers to outsource the large and complex tasks to the cloud data centers for the execution and result generations. Cloud data centers host the incoming tasks by providing resources, such as CPU, RAM, storage, and bandwidth. As the large data centers provide the basic resources to hosted tasks, they also consume a huge amount of energy, which leads to higher operating cost and CO2 traces. Therefore, research community felt the need to provide energy-efficient solutions that reduce the impact of the aforementioned issues. Consequently, researchers proposed many solutions, and majority of them are based upon the concept of consolidation. Consolidation techniques place the incoming tasks on minimum possible servers, thus increasing the resource utilization and decreasing energy consumption. In this paper, we use the same workload consolidation concept and present two techniques that reduce energy consumption while ensuring the negotiated quality-of-service. Moreover, we enhanced two existing techniques by improving the energy efficiency and introducing service level agreement (SLA) awareness to minimize the overall SLA violations. Performance evaluation of the proposed techniques is done based on fluctuating workloads, and results show that our techniques outperform existing techniques in terms of energy efficiency, SLA compliance, and performance assurance at the network level. Moreover, correctness of the proposed techniques is demonstrated by modeling and verifying them with the help of high-level Petri Nets, SMT-Lib, and Z3 solver.

Advances and commoditization of media generation devices enable capturing and sharing of any special event by multiple attendees. We propose a novel system to collect individual video streams (views) captured for the same event by multiple attendees, and combine them into multi-view videos, where viewers can watch the event from various angles, taking crowdsourced media streaming to a new immersive level. The proposed system is called Cloud-based Multi-View Crowd sourced Streaming (CMVCS), and it delivers multiple views of an event to viewers at the best possible video representation based on each viewer's available bandwidth. The CMVCS is a complex system having many research challenges. In this paper, we focus on resource allocation of the CMVCS system. The objective of the study is to maximize the overall viewer satisfaction by allocating available resources to transcode views in an optimal set of representations, subject to computational and bandwidth constraints. We choose the video representation set to maximize QoE using Mixed Integer Programming. Moreover, we propose a Fairness-Based Representation Selection (FBRS) heuristic algorithm to solve the resource allocation problem efficiently. We compare our results with optimal and Top-N strategies. The simulation results demonstrate that FBRS generates near optimal results and outperforms the state-of-the-art Top-N policy, which is used by a large-scale system (Twitch).

Effective patient queue management to minimize patient wait delays and patient overcrowding is one of the major challenges faced by hospitals. Unnecessary and annoying waits for long periods result in substantial human resource and time wastage and increase the frustration endured by patients. For each patient in the queue, the total treatment time of all the patients before him is the time that he must wait. It would be convenient and preferable if the patients could receive the most efficient treatment plan and know the predicted waiting time through a mobile application that updates in real time. Therefore, we propose a Patient Treatment Time Prediction (PTTP) algorithm to predict the waiting time for each treatment task for a patient. We use realistic patient data from various hospitals to obtain a patient treatment time model for each task. Based on this large-scale, realistic dataset, the treatment time for each patient in the current queue of each task is predicted. Based on the predicted waiting time, a Hospital Queuing-Recommendation (HQR) system is developed. HQR calculates and predicts an efficient and convenient treatment plan recommended for the patient. Because of the large-scale, realistic dataset and the requirement for real-time response, the PTTP algorithm and HQR system mandate efficiency and low-latency response. We use an Apache Spark-based cloud implementation at the National Supercomputing Center in Changsha to achieve the aforementioned goals. Extensive experimentation and simulation results demonstrate the effectiveness and applicability of our proposed model to recommend an effective treatment plan for patients to minimize their wait times in hospitals.