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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. [ABSTRACT FROM AUTHOR]

Land registration system is a very critical component of a revenue department as it holds the record of lands and their owners. In many countries, land records are stored in paper form at centralized locations, making them vulnerable to forgery. Moreover, the centralized system has performance bottleneck, non-transparency, double-spending problems, and a single point of failure. Such issues lead to corruption and conflicts. Recently, Blockchain has been conceived as a technology having the potential to implement and enforce critical security requirements in land revenue systems. With Blockchain, the common issues in revenue systems, such as forgery, falsification, misrepresentation, and human-induced anomalies in records at various levels/stages, can be prevented. The focus of this research is to identify the relationship between Blockchain technology and the land administration system. We discuss issues with current paper-based centralized systems. The potential benefits of Blockchain technology in land management systems are also examined. We present the comparisons of state-of-the-art solutions along with their problems in detail. Finally, research challenges and future directions in Blockchain-enabled land management systems are explored. [ABSTRACT FROM AUTHOR]

The method of charging mobile devices with wireless power transfer (WPT) from the base station (BS) integrated with mobile edge computing (MEC) increases the potential of MEC. The increasing demand for intelligent computation offloading requires effective decisions among local or remote computation specifically in wireless fading channels of the dynamic environment. Our main aim is to generate an effective offloading decision between local and remote computation in a real-time environment for each wireless channel while preserving optimal computation rate. In this article, we consider a wireless powered MEC system that governs a binary offloading decision to execute the task locally at the edge devices or the remote server. We propose a reinforcement learning based intelligent online offloading (RLIO) framework that adopts an optimal offloading action based on reinforcement methods. This framework acquires a worthy decision among local or remote computation for the time varying wireless channel conditions in dense networks. Numerical results show that the proposed framework can achieve optimal performance while preserving the computation time compared with existing optimization methods. Second, the average execution cost of RLIO is less than 0.4 ms per channel, which enables real-time and optimal offloading in dynamic and large-scale networks. [ABSTRACT FROM AUTHOR]

The developments in smartphones, high data rates, and substantial video data traffic have increased the burden on cellular networks. Consequently, this burden significantly affects the Quality of Experience of the cellular users leading to an increased network delay for the diverse video content requests. To accommodate the requests from different users with varying requirements, one of the promising solutions is to cache videos in the near vicinity of users and transcode them online. The online transcoding is performed at the edge level of the cellular network to minimize the network delay and use the bandwidth efficiently. However, the feasibility of online transcoding is significantly affected by various factors, such as the codecs, configurations of virtual machines, the cost incurred, and estimated time to complete the transcoding task, among other parameters. Although online transcoding is discussed in the literature adequately, few studies discuss the feasibility of online transcoding while considering all the aforementioned critical parameters. This study examined the effects of a diverse range of critical parameters on the feasibility of online transcoding. We performed extensive simulations on the local machine environment to study various possible factors affecting online transcoding in detail. We then transcoded the same videos on Amazon Elastic Cloud Computing (EC2) Virtual Machines (VMs) to further study realistic cloud settings with fine-tuned configurations. Our experiments show the superior performance of some codecs and the effects of machine configurations on transcoding tasks duration. We aim to provide a benchmark for practitioners and researchers considering online transcoding in real-time multimedia applications. [ABSTRACT FROM AUTHOR]

Innovative performance has become a key aspect of the organizational success and survival. The accomplishment of the organizational success and survival is led by the leader. Accordingly, the study aimed to examine the role of the transformational leadership towards the innovative performance of the organizations. Additionally, the study aimed to test the moderating role of leader's empathic concern on the relationship between the TL and the innovative performance. 340 questionnaires were distributed among the respondents working in the software houses, Lahore by using the simple random sampling. Finally, 203 questionnaires were used for the data analysis. The study findings revealed that TL found to positively influence the innovative performance. Additionally, the study revealed that due to the empathetic concern the relationship between TL and innovative performance tend to decrease. [ABSTRACT FROM AUTHOR]

The massive upsurge in production and demand of crowdsourced live streaming necessitates feasible and cost effective solutions in future cellular networks. Video multicast is one of the viable options to deliver live video using standardized multicast technologies, such as evolved Multimedia Broadcast Multicast Services (eMBMS) architecture. However, eMBMS being a service-oriented architecture, it does not support crowdsourced live video streaming from popular providers like Twitch, YouTube Live, and Facebook Live, where millions of viewers watch live streams. This article explores the possibilities for the crowdsourced service-less video multicast in fifth generation (5G) Radio Access Network (RAN). We highlight major limitations in the standard eMBMS architecture and propose a viable solution using Network Function Virtualization (NFV) and Mobile Edge Computing (MEC). The main challenges related to the initiation of an eMBMS service are presented, followed by our proposed network architecture, which augments the eMBMS architecture to initiate a service-less multicast video delivery for crowdsourced live videos. We evaluate and validate the proposed architecture using a real-world live video dataset. Our results depict the feasibility of the proposed architecture to multicast crowdsourced live streams and significant savings in cost and network consumption. [ABSTRACT FROM AUTHOR]

Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. However, the training process of CNNs is very time-consuming, where large amounts of training samples and iterative operations are required to obtain high-quality weight parameters. In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments. BPT-CNN consists of two main components: (a) an outer-layer parallel training for multiple CNN subnetworks on separate data subsets, and (b) an inner-layer parallel training for each subnetwork. In the outer-layer parallelism, we address critical issues of distributed and parallel computing, including data communication, synchronization, and workload balance. A heterogeneous-aware Incremental Data Partitioning and Allocation (IDPA) strategy is proposed, where large-scale training datasets are partitioned and allocated to the computing nodes in batches according to their computing power. To minimize the synchronization waiting during the global weight update process, an Asynchronous Global Weight Update (AGWU) strategy is proposed. In the inner-layer parallelism, we further accelerate the training process for each CNN subnetwork on each computer, where computation steps of convolutional layer and the local weight training are parallelized based on task-parallelism. We introduce task decomposition and scheduling strategies with the objectives of thread-level load balancing and minimum waiting time for critical paths. Extensive experimental results indicate that the proposed BPT-CNN effectively improves the training performance of CNNs while maintaining the accuracy. [ABSTRACT FROM AUTHOR]

State-of-the-art cloud simulators in use today are limited in the number of features they provide, lack real network communication models, and do not provide extensive Graphical User Interface (GUI) to support developers and researchers to extend the behavior of the cloud environment. We propose CloudNetSim++, a comprehensive packet level simulator that enables simulation of cloud environments. CloudNetSim++ can be used to evaluate a wide spectrum of cloud components, such as processing elements, storage, networking, Service Level Agreement (SLA), scheduling algorithms, fine grained energy consumption, and VM consolidation algorithms. CloudNetSim++ offers extendibility, which means that the developers and researchers can easily incorporate own algorithms for scheduling, workload consolidation, VM migration, and SLA agreement. The simulation environment of CloudNetSim++ offers a rich GUI that provides a high level view of distributed data centers connected with various network topologies. The package also includes an energy computation module that provides a fine grained analysis of energy consumed by each component. This paper shows the flexibility and effectiveness of CloudNetSim++ through experimental results demonstrated using real-world data center workloads. Moreover, to demonstrate the correctness of CloudNetSim++, we performed formal modeling, analysis, and verification using High-level Petri Nets, Satisfiability Modulo Theories (SMT), and Z3 solver. [ABSTRACT FROM PUBLISHER]

With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability. With the expansion of the scale of the random forest model and the Spark cluster, the advantage of the PRF algorithm is more obvious. [ABSTRACT FROM PUBLISHER]

Data centers (DCs) contribute toward the prevalent application and adoption of the cloud by providing architectural and operational foundation. To perform sustainable computation and storage, a DC is equipped with tens of thousands of servers, if not more. It is worth noting that the operational cost of a DC is being dominated by the cost spent on energy consumption. In this paper, we model a DC as a cyberphysical system (CPS) to capture the thermal properties exhibited by the DC. All software aspects, such as scheduling, load balancing, and all the computations performed by the devices, are considered the “cyber” component. The supported infrastructure, such as servers and switches, are modeled as the “physical” component of the CPS. We perform detailed modeling of the thermal characteristics displayed by the major components of the CPS. Moreover, we propose a thermal-aware control strategy that uses a high-level centralized controller and a low-level centralized controller to manage and control the thermal status of the cyber components at different levels. Our proposed strategy is testified and demonstrated by executing on a real DC workload and comparing it with three existing strategies, i.e., one classical and two thermal-aware strategies. Furthermore, we also perform formal modeling, analysis, and verification of the strategies using high-level Petri nets, the Z language, the Satisfiability Modulo Theories Library (SMT-Lib), and the Z3 solver. [ABSTRACT FROM PUBLISHER]

The rapid growth of unmanned aerial vehicles (UAVs) has huge potential in the oil and gas industry (OGI). These are especially helpful in situations in which human lives are at risk. OGI pipelines are threatened by natural and man-made disasters, which are harmful to both people and assets. Usually, oil and gas pipelines pass through extreme environments for which standard inspection, maintenance, and repairing approaches such as rope access, scaffolds, telescopic elevation platforms supported by cranes, and manned helicopters are not secure and difficult and expensive to implement. However, technological advancements like device miniaturization have boosted the performance of UAVs, offering cost-effective, efficient, and high mission flexibility. UAVs are capable of carrying sensors and cameras to perform monitoring. As pipelines span thousands of kilometers, multi-UAV systems, commonly referred to as flying ad-hoc networks (FANETs), can collaboratively complete monitoring missions more effectively and economically as compared with single UAV systems. Moreover, many issues must be resolved before the effective use of UAVs can provide stable and reliable context-specific networks. Several OGI-specific issues of UAVs such as architecture design, platform, sensors, networking architectures, and path planning models for different OGI pipeline surveillance scenarios must be resolved to use FANETs effectively for robust and sustainable networks. The prime objective of this research is a state-of-the-art review of UAVs in OGI. We first present OGI midstream challenges and parameters to give a brief overview of the challenges faced by the OGI for pipeline surveillance. Then we discuss OGI-specific scenarios for sensor readings, visual leak detection, and detection of any unusual activity that happens in the pipeline while monitoring through UAVs. We also present different OGI-specific UAV platforms, UAV networking architectures, and path planning models of FANET for efficient communication and collaboration. Finally, the challenges of UAVs and future research prospects in OGI-specific UAVs are highlighted. [ABSTRACT FROM AUTHOR]

Recent growth in the processing and memory resources of mobile devices has fueled research within the field of mobile virtualization. Mobile virtualization enables multiple persona on a single mobile device by hosting heterogeneous operating systems (OSs) concurrently. However, adding a virtualization layer to resourceconstrained mobile devices with real-time requirements can lead to intolerable performance overheads. Hardware virtualization extensions that support efficient virtualization have been incorporated in recent mobile processors. Prior to hardware virtualization extensions, virtualization techniques that are enabled by performance prohibitive and resource consuming software were adopted formobile devices. Moreover,mobile virtualization solutions lack standard procedures for device component sharing and interfacing between multiple OSSs. The objective of this article is to survey software- and hardware-based mobile virtualization techniques in light of the recent advancements fueled by the hardware support for mobile virtualization. Challenges and issues faced in virtualization of CPU, memory, I/O, interrupt, and network interfaces are highlighted. Moreover, various performance parameters are presented in a detailed comparative analysis to quantify the efficiency of mobile virtualization techniques and solutions. [ABSTRACT FROM AUTHOR]

Covert channels are widely considered as a major risk of information leakage in various operating systems, such as desktop, cloud, and mobile systems. The existing works of modeling covert channels have mainly focused on using finite state machines (FSMs) and their transforms to describe the process of covert channel transmission. However, a FSM is rather an abstract model, where information about the shared resource, synchronization, and encoding/decoding cannot be presented in the model, making it difficult for researchers to realize and analyze the covert channels. In this paper, we use the high-level Petri Nets (HLPN) to model the structural and behavioral properties of covert channels. We use the HLPN to model the classic covert channel protocol. Moreover, the results from the analysis of the HLPN model are used to highlight the major shortcomings and interferences in the protocol. Furthermore, we propose two new covert channel models, namely: (a) two channel transmission protocol (TCTP) model and (b) self-adaptive protocol (SAP) model. The TCTP model circumvents the mutual inferences in encoding and synchronization operations; whereas the SAP model uses sleeping time and redundancy check to ensure correct transmission in an environment with strong noise. To demonstrate the correctness and usability of our proposed models in heterogeneous environments, we implement the TCTP and SAP in three different systems: (a) Linux, (b) Xen, and (c) Fiasco.OC. Our implementation also indicates the practicability of the models in heterogeneous, scalable and flexible environments. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as the leading paradigm for information technology businesses. Cloud computing provides a platform to manage and deliver computing services around the world over the Internet. Cloud services have helped businesses utilize computing services on demand with no upfront investments. The cloud computing paradigm has sustained its growth, which has led to increase in size and number of data centers. Data centers with thousands of computing devices are deployed as back end to provide cloud services. Computing devices are deployed redundantly in data centers to ensure 24/7 availability. However, many studies have pointed out that data centers consume large amount of electricity, thus calling for energy-efficiency measures. In this survey, we discuss research issues related to conflicting requirements of maximizing quality of services (QoSs) (availability, reliability, etc.) delivered by the cloud services while minimizing energy consumption of the data center resources. In this paper, we present the concept of inception of data center energy-efficiency controller that can consolidate data center resources with minimal effect on QoS requirements. We discuss software- and hardware-based techniques and architectures for data center resources such as server, memory, and network devices that can be manipulated by the data center controller to achieve energy efficiency. [ABSTRACT FROM PUBLISHER]

Illegal short-term vehicle stalls are frequent in developing countries, which disturb smooth traffic flow and results in congestion, road blockage, and even lead to accidents. Short-term stalls span a few seconds—e.g., a vehicle stopping to make way for people crossing the road. However, traditional stall detection techniques detect stalls that span 10 s or more. Moreover, existing techniques, such as background subtraction algorithms, are vulnerable to illumination changes and indicate too many false stalls with smaller stopping criteria. Furthermore, for violation indication, post-stall analysis is required to filter out the legitimate stalls where a vehicle stops for some valid reason. We present a two-fold methodology to detect illegally parked vehicles. An object identification-based technique is employed over stationary images to detect stall. Furthermore, the legitimacy of the stall is determined by performing pedestrian detection and its position analysis. We tested our proposed approach using publicly available advanced video and signal based surveillance 2007 PV dataset and ViSOR dataset. The technique successfully detects stalls with a recall of 0.96, after weaving the stopping criterion from the ground truth. [ABSTRACT FROM AUTHOR]

One of the major challenges in the high performance computing (HPC) clusters is intelligent power management to improve energy efficiency. The key contribution of the presented work is the modeling of a Power Aware Job Scheduler (PAJS) for HPC clusters, such that the: (a) threshold voltage is adjusted judiciously to achieve energy efficiency and (b) response time is minimized by scaling the supply voltage. The PAJS considers the symbiotic relationship between power and performance and caters the optimization of the both, simultaneously. The key novelty in our work is utilization of the dynamic threshold-voltage scaling (DTVS) for the reduction of cumulative power utilized by each node in the cluster. Moreover, to enhance the performance of the resource scheduling strategies in this work, independent tasks within a job are scheduled to most suitable computing nodes (CNs). This paper analyzes and compares eight scheduling techniques in terms of energy consumption and makespan. Primarily, the most suitable dynamic voltage scaling (DVS) level adhering to the deadline is identified for each of the CNs by the scheduling heuristics. Afterwards, the DTVS is employed to scale down the static, as well as dynamic power by regulating the supply and bias voltages. Finally, the per node threshold scaling is used attain power saving. Our simulation results affirm that the proposed methodology significantly reduces the energy consumption using the DTVS. [ABSTRACT FROM AUTHOR]

In the large-scale parallel computing environment, resource allocation and energy efficient techniques are required to deliver the quality of services (QoS) and to reduce the operational cost of the system. Because the cost of the energy consumption in the environment is a dominant part of the owner's and user's budget. However, when considering energy efficiency, resource allocation strategies become more difficult, and QoS (i.e., queue time and response time) may violate. This paper therefore is a comparative study on job scheduling in large-scale parallel systems to: (a) minimize the queue time, response time, and energy consumption and (b) maximize the overall system utilization. We compare thirteen job scheduling policies to analyze their behavior. A set of job scheduling policies includes (a) priority-based, (b) first fit, (c) backfilling, and (d) window-based policies. All of the policies are extensively simulated and compared. For the simulation, a real data center workload comprised of 22385 jobs is used. Based on results of their performance, we incorporate energy efficiency in three policies i.e., (1) best result producer, (2) average result producer, and (3) worst result producer. We analyze the (a) queue time, (b) response time, (c) slowdown ratio, and (d) energy consumption to evaluate the policies. Moreover, we present a comprehensive workload characterization for optimizing system's performance and for scheduler design. Major workload characteristics including (a) Narrow, (b) Wide, (c) Short, and (d) Long jobs are characterized for detailed analysis of the schedulers' performance. This study highlights the strengths and weakness of various job scheduling polices and helps to choose an appropriate job scheduling policy in a given scenario. [ABSTRACT FROM AUTHOR]

The information and communication technology (ICT) sector has grown exponentially in the recent years. An essential component of the ICT organizations is constituted by the data centers that are densely populated with redundant servers and communicational links to ensure the provision of 99.99 % availability of services; a fact responsible for the heavy energy consumption by data centers. For energy economy, the redundant elements can be powered off based on the current workload within the data center. We propose a Data Center-wide Energy- Efficient Resource Scheduling framework (DCEERS) that schedules data center resources according to the current workload of the data center. Minimum subset of resources to service the current workload are calculated by solving the Minimum Cost Multi Commodity Flow (MCMCF) using the Benders decomposition algorithm. The Benders decomposition algorithm is scalable: it solves the MCMCF problem in linear time for large data center environments. Our simulated results depict that DCEERS achieves better energy efficiency than the previous data center resource scheduling methodologies. [ABSTRACT FROM AUTHOR]

Data Center Networks (DCNs) constitute the communication backbone for the cloud computing paradigm. Recently, network connectivity analysis in terms of reliability has received attention from the network research community. The traditional network features are useful; however, they are insufficient to determine how well-connected or well-designed a DCN is against the node or link removals. In this letter, we present a connectivity analysis of three well-known DCN architectures, namely: (a) ThreeTier, (b) FatTree, and (c) DCell. Our analysis reveals that the classic connectivity measures are inadequate for evaluating DCN connectivity. Therefore, we propose μ-A2TR, a novel metric to characterize network connectivity in the case of node or link failures. Experimental results reveal that the DCNs exhibit a moderate level of connectivity in the case of random node removals. However, connectivity decays abruptly when considering the targeted nodes removal. Moreover, the connectivity analysis depicts significant differences among the considered DCNs. [ABSTRACT FROM PUBLISHER]

The Information and Communication Technology sector is considered to be a major consumer of energy and has become an active participant in Green House Gas emissions. Lots of efforts have been devoted to make network infrastructure and network protocols power-aware and green. Among these efforts, Adaptive Link Rate (ALR) is one of the most widely discussed approaches. This survey highlights the most recent ALR approaches with a brief taxonomy and the state of the art. [ABSTRACT FROM AUTHOR]

SUMMARY Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and ascertaining the performance parameters for the data center. Legacy data center network (DCN) infrastructure lacks the inherent capability to meet the data centers growth trend and aggregate bandwidth demands. Deployment of even the highest-end enterprise network equipment only delivers around 50% of the aggregate bandwidth at the edge of network. The vital challenges faced by the legacy DCN architecture trigger the need for new DCN architectures, to accommodate the growing demands of the 'cloud computing' paradigm. We have implemented and simulated the state of the art DCN models in this paper, namely: (a) legacy DCN architecture, (b) switch-based, and (c) hybrid models, and compared their effectiveness by monitoring the network: (a) throughput and (b) average packet delay. The presented analysis may be perceived as a background benchmarking study for the further research on the simulation and implementation of the DCN-customized topologies and customized addressing protocols in the large-scale data centers. We have performed extensive simulations under various network traffic patterns to ascertain the strengths and inadequacies of the different DCN architectures. Moreover, we provide a firm foundation for further research and enhancement in DCN architectures. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Energy consumption of the Information and Communication Technology (ICT) sector has grown exponentially in recent years. A major component of the today's ICT is constituted by the data centers which have experienced an unprecedented growth in their size and population, recently. The Internet giants like Google, IBM and Microsoft house large data centers for cloud computing and application hosting. Many studies, on energy consumption of data centers, point out to the need to evolve strategies for energy efficiency. Due to large-scale carbon dioxide ( $$\mathrm{CO}_2$$) emissions, in the process of electricity production, the ICT facilities are indirectly responsible for considerable amounts of green house gas emissions. Heat generated by these densely populated data centers needs large cooling units to keep temperatures within the operational range. These cooling units, obviously, escalate the total energy consumption and have their own carbon footprint. In this survey, we discuss various aspects of the energy efficiency in data centers with the added emphasis on its motivation for data centers. In addition, we discuss various research ideas, industry adopted techniques and the issues that need our immediate attention in the context of energy efficiency in data centers. [ABSTRACT FROM AUTHOR]

SUMMARY Wireless mesh networks (WMNs) have gained considerable popularity in recent times thanks to their self-healing, self-organizing, and self-configuring nature. Because of their ability to provide high throughput and minimum packet delay, WMNs are considered to be favorable for broadband applications. For such applications, WMNs employ multiple channels, which give rise to issues like channel assignment, load balancing, and interference avoidance. Most of these issues fall into two broad categories, namely routing and channel assignment. For routing, we propose a novel proactive protocol, the stable-path multi-channel routing protocol (SMRP). Our proposed solution, to address channel assignment, is the extended level-based channel assignment (ELCA) scheme. SMRP is designed to work in combination with ELCA in order to minimize interference and balance the load among the underlying nodes. Simulation results show enhanced throughput and minimal packet delay as compared with the contemporary schemes. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]