Efficient Network Management Research Articles

A secure data analytics scheme for multimedia communication in a decentralized smart grid

With the exponential increase in energy demands (commercial as well as residential), the traditional grid infrastructure significantly shifted to intelligent ICT-based Smart Grid (SG) infrastructure. In the SG environment, only efficient energy management may not be sufficient as the SG dynamics have significant impacts on multimedia communications such as video surveillance of the technical/non-technical losses of energy and many more. The inevitable energy losses can be identified by process and analyze the massive amount of heterogeneous data, i.e., Big Data (BD) generated through smart devices such as sensors, Smart Meters (SMs), and others. The key challenges in analyzing multimedia BD are computational complexity, operational integration complexity, data security, and privacy. To overcome the aforementioned issues, this paper proposes a blockchain-based data analytics scheme called ChoIce, which offers secure data collection, analysis, and decision support for the SG systems. It works in two phases; (i) secure data collection over Ethereum and (ii) BD analytics and decision-making using deep learning (DL). The robust and secure data analytics, efficient network management, and high-performance computing for BD are crucial towards the optimization of SG operation. The performance of ChoIce is evaluated considering parameters such as the data storage cost, multimedia communication latency, and prediction accuracy. Thus, the results of ChoIce shows that it outperforms in contrast to other state-of-the-art approaches.

Blockchain and Its Impacts on Agri-Food Supply Chain Network Management

Blockchain is an emerging meta-technology and considered a new institutional technology with the potential to change the governance of vertically integrated food supply chains. This paper investigates the effects on coordination mechanisms in vertically cooperating agri-food networks that result from the implementation of different blockchain technology platform types (BCTPT). The research is based on an extensive literature overview and exploratory use cases of BCTPT implementations in the agri-food industry which are presented to illustrate the applicability of the findings. Our analysis shows that BCTPT predominantly differentiate through the coordination mechanisms exerting of power, information sharing, decision-making, and collective learning benefits. We also reveal that blockchain use cases with high success rates typically operate in a vertical ecosystem where a focal firm assumes the responsibility for coordinating the activities in the supply chain network. These use cases are typically operationalized in tracking and tracing applications as well as in provenance-based information provision, which either operate in vertically coordinated private blockchain or consortium-type blockchain platforms. We conclude that the choice of a specific BCTPT with its respective coordination mechanisms is a key determinant of the economic success of the intended use case, the efficient management of the supply chain network, and eventually for the chosen digital business model. This paper will close a research gap, as the potential impacts of different blockchain technology platform types on digital agri-food business models and its supply chain management have scarcely been researched.

Unified energy efficient distributed network management protocol to forward the data packets and to improve QoS in Wireless Sensor Networks

Background: Enhancing quality of service in forwarding the data packets from source to destination in WSN is a major task in contemporary networks. Everywhere the usage of WSN is increasing in the real world. Data sensing, data capturing and data forwarding is a routine process in modern era. Some of the dynamic problems arise as link failure, routing issues, packet delay etc that lead to the entire network shutdown. Hence, there is a need for development or enhancement of the protocol that finds the finest route to forward data packets in an optimal way. Objectives: The main objective of this study is to propose a unified energy efficient protocol to minimize the delay which will result in successful delivery of data packets from source to destination to improve the quality of service in WSN. Methods: This study proposes Unified Energy Efficient Distributed Network Management Protocol (UEEDNMP) focuses mainly on forwarding the data packets and finite routing to minimize the end to end delay. Parametric search technique along with CAFSM method is utilized to design the proposed protocol, to meet out the objective by forwarding the packets with better path from source to destination. Findings: NS2 Version 35 simulator is used to calculate the performance of the proposed UEEDNMP protocol. The simulation result shows UEEDNMP outperforms the existing protocols EH-WSN and ECO-LEACH in terms of data arrival rate, packet drop ratio, packet delivery ratio, energy consumption, network traffic, delay and network lifetime. Novelty: Comprehensive analyses signify that UEEDNMP has greater performance in forwarding the data packets with the finest routing in distributed networks, minimizing energy consumption, minimize the end to end latency time and maximize the life span of the network. Keywords: Energy efficient protocol; delay; wireless sensor networks; data forwarding

Joint Radio Resource Allocation for Decoupled Control and Data Planes in Densely Deployed Coordinated WLANs

While Wireless Local Area Network (WLAN) gains growing popularity during the last two decades, it faces several new challenges in meeting the requirements of emerging applications due to spectrum shortage, complicated network management and inefficient radio resource allocation (RRA) in densely deployed scenarios. The operating band has evolved from microwave band (a.k.a. sub-6 GHz) to millimeter-wave band (mmWave for short) or even multi-band to provide higher data rates. However, the existing distributed WLAN architecture works in different bands separately and doesn't support the coordination among access points (APs), making it difficult for efficient network management and improved quality of service (QoS) provisioning. In overcoming these challenges, a centralized control architecture for WLAN with decoupled control and data planes can be used to orchestrate the RRA within the whole network while achieving improved communication performance. Therefore, a new control/data plane decoupled WLAN architecture is designed in this paper to realize efficient network management and optimized RRA by separating the control plane and data plane into sub-6 GHz and mmWave respectively. Then, we propose the sub-6 GHz assisted mmWave beamforming protocol, which can significantly reduce the overhead of beamforming training (BFT). Last, we investigate joint RRA for the control/data plane decoupled WLAN since the control plane affects the RRA in the data plane. Performance analysis and simulation show that the joint RRA mechanism in the control/data plane decoupled network can significantly improve the number of successfully scheduled users and the sum-rate compared with the traditional RRA.

OPF solution for a real Czech urban meshed distribution network using a genetic algorithm

Electrical distribution networks are facing an energy transition which entails an increase of decentralised renewable energy sources and electric vehicles. The resulting temporal and spatial uncertainty in the generation/load patterns challenges the operations of an infrastructure not designed for such a transition. In this situation, Optimal Power Flow methods can play a key role in identifying system weak points and supporting efficient management of the electrical networks, including the distribution level. In this work, to support distribution system operators’ decision-making process, we aim at attaining a quasi-optimal solution in the shortest time possible in an electrical network experiencing a large growth of distributed energy sources. We propose an optimisation method based on a modified version of a genetic algorithm and the Python pandapower package. The method is tested on a model of a real urban meshed network of a large Czech city. The optimisation method minimises the total operating costs of the distribution network by controlling selected network components and parameters, namely the transformer tap changers and the active power demand at consumption nodes. The results of our method are compared with the exact solution showing that a close-to-optimal solution of the observed problem can be reached in a relatively short time.

Fast Content Delivery Using a Testbed-Based Information-Centric Network

Key requirements for a better performance of multimedia applications typically include lower latency, improved security, faster content retrieval, and adjustability to the traffic load. However, the current Internet often fails to meet the requirements due to the drawbacks of the host-oriented communication architecture. Those drawbacks can be overcome by a well-recognized networking paradigm, called Information-Centric Networking (ICN), that offers name-based and information-centric communication rather than host-oriented communication. This paradigm uses an in-network caching policy and may provide enhanced security along with user mobility. Therefore, the ICN architecture can outperform the current Internet in many aspects, including, but not limited to, content transfer time, traffic load control, mobility support, and efficient network management. Most of the existing research validating the ICN paradigm’s superior performance compared to the current Internet is based on simulation. In this paper, we propose a novel ICN-based testbed, where we used several modified functions for naming, routing, and caching and developed two new functions that provide mobility support and auto-playing of the retrieved content so that the performance of the ICN network is enhanced even more in terms of content delivery time or response time. The proposed testbed architecture is based on Content-Centric Networking (CCN), a prominent branch of the ICN concept. We present the testbed development procedures and the functions in detail and demonstrate that the testbed-based architecture outperforms the current Internet architecture and the basic CCN-based environment.

The research of cognitive methods of infocommunication management under conditions of uncertainty

The methods of management of the fifth generation infocommunication networks under conditions of uncertainty are shown. The research of super-complex systems into which modern infocommunications with social networks superimposed on them turn is based on nonlinear cognitive methods, which currently claim the role of the basic paradigm in the management of intercommunications of the future. The scientific research is to ensure efficient and sustainable management of infocommunication networks under conditions of higher network capacity, multiservices, meeting the appropriate requirements for control systems for new heterogeneous superdense networks. The probabilistic-temporal characteristics of infocommunications management are used to carry out the research. This article offers a multi-agent management model that consists of a large number of agents, which own limited intellectuality and submit requests for services; this model is described by the Chapman–Kolmogorov equation. The analytical relations of the total number of intelligent agents in the system on receipt and service intensity in telecommunication networks are received. The relations that enable the telecommunication service management with different target functions: minimization of the number of downtime of intelligent agents or, visa versa, maximization of their number when there is the need to reserve resources for the expected burst of requests are described. It should be noted that control can be performed by specifying the probability value of transactions of intelligent agents, and the multi-agent system will perform the operational management independently.

Belief Propagation for Energy Efficiency Maximization in Wireless Heterogeneous Networks

In this article, we study an energy efficient management of two-tier heterogeneous cellular networks (HetNets) which consist of one macro base station (BS) and multiple micro base stations. This article presents a distributed user association algorithm that maximizes the network-wide energy efficiency (EE) in HetNets. A subset of BSs that support only a small number of users can be turned off to save the energy consumption. By turning off BSs in the HetNet and offloading serving users to adjacent active BSs, the network-wide energy consumption is minimized, while the sum throughput is maximized. To solve the problem efficiently, we introduce a new approach based on a message-passing framework and derive a distributed load balancing algorithm. The proposed method provides a very efficient solution with reduced computational complexity compared to existing schemes. Simulation results verify that the proposed algorithm outperforms other conventional load balancing strategies.

User-Centred Scalable Big Data Visualizer for Power Consumption Data in the Electrical Secondary Distribution Network

Establishment of Smart Grids for electrical power has been practised worldwide for the purpose of bringing reliability, security, and efficient management of electrical power networks for enhancing quality service to the society. Apart from the potential aim, smart grid has been a challenge to developing countries, including Tanzania from cost and technology point of view. Due to the use of many smart equipment involved in smart grids like Advanced Metering Infrastructure (AMI) equipped with smart meters and sensors, handling and managing big data has been a challenge. Among the challenges is the issue of visualizing the Big Data due to big volume generated with high velocity. This paper is developing a user-centered scalable big data visualizer for the electrical secondary distribution network by making use of design process model by Akanmu et al. (2017) and design activity framework by McKenna et al. (2014). The approach involves three phases: pre- development, development and post-development phase. The paper reviews several approaches in visualization and demonstrates effective big data visualization. The paper managed to visualize households’ units purchased against power consumed as well as balancing visualization of transformer phases.

Assessment of Irrigation Potentiality over an Area through the Combined Application of Remote Sensing (RS) and Geographic Information System (GIS)

There is a great influence of irrigated land of an area to the acreage and productions of agricultural crops and thus maintain the food security. Recent awareness about climate change and its impacts on global environmental challenges has drawn the great attention on rational and sustainable handling of irrigation resources and its networks. As one of the cutting-edge technologies remote sensing data and geographical information system (GIS) are very much useful for efficient management of irrigation networks and optimum utilization of irrigation schemes for the sustainable agricultural development. Irrigation potentiality is the total area which can be irrigated from a project on its full utilization. This implies that an area where water is available for irrigation in each season during a complete irrigation year. In the present study an attempt has been made to investigate the irrigation potentiality of an area using remote sensing data as primary source and field data and as well as ground water level data from secondary source. Landsat 8 OLI (Operational Land Imager) data of 2016 and 2017 have been used for this purpose. Existing irrigation system has also been identified through the investigation of natural and artificial sources of irrigation water of the study area. Seasonal irrigated area was also monitored during the crop growing season. Ground water level fluctuation was also studied using ancillary data.
 Journal of Engineering Science 11(2), 2020, 141-148

Machine Learning Techniques in Radio-over-Fiber Systems and Networks

The radio-over-fiber (RoF) technology has been widely studied during the past decades to extend the wireless communication coverage by leveraging the low-loss and broad bandwidth advantages of the optical fiber. With the increasing need for wireless communications, using millimeter-waves (mm-wave) in wireless communications has become the recent trend and many attempts have been made to build high-throughput and robust mm-wave RoF systems during the past a few years. Whilst the RoF technology provides many benefits, it suffers from several fundamental limitations due to the analog optical link, including the fiber chromatic dispersion and nonlinear impairments. Various approaches have been proposed to address these limitations. In particular, machine learning (ML) algorithms have attracted intensive research attention as a promising candidate for handling the complicated physical layer impairments in RoF systems, especially the nonlinearity during signal modulation, transmission and detection. In this paper, we review recent advancements in ML techniques for RoF systems, especially those which utilize ML models as physical layer signal processors to mitigate various types of impairments and to improve the system performance. In addition, ML algorithms have also been widely adopted for highly efficient RoF network management and resource allocation, such as the dynamic bandwidth allocation and network fault detection. In this paper, we also review the recent works in these research domains. Finally, several key open questions that need to be addressed in the future and possible solutions of these questions are also discussed.

Social-aware spectrum sharing for D2D communication by artificial bee colony optimization

Next-generation cellular networks must handle an increasing demand for data transmission resources, which requires the study of new technologies, such as Device-to-Device (D2D) communications. D2D expands the use of resources from a location perspective, and takes advantage of the geographical proximity of users to perform data transmission at high rates and low propagation delays. On the other hand, the widespread use of social networks establishes the degree of social relations among users and facilitates the efficient management of D2D communication networks. The integration of social networks and D2D communications into 5G networks has posed important challenges (e.g., exploitation of the social relationships of mobile users and management of interference and resources towards a better performance of D2D communications). This article proposes a social-aware Radio Resource Allocation (RRA) optimization solution for D2D communications in 5G networks that considers partial Channel State Information (CSI). A selected-NM Maximum Distance Ratio (MDR) q-bit feedback scheme designed reduces feedback overhead, since each D2D receiver sends only q-bit feedback CSI of N cellular User Equipment (CUE) and M D2D pairs with the largest MDR metric. The RRA problem is solved by an Artificial Bee Colony (ABC) algorithm for the obtaining of number of D2D connections in each evolved NodeB (eNB) and maximization of the system throughput.

FACEST: feedback-assisted estimation of end-to-end capacity in IP-based communication networks

The end-to-end capacity, defined as the maximal transmission rate of the weakest link on the entire path between two end hosts, plays an important role in efficient network design and management. Although various capacity estimation tools have been proposed in the literature, there is still uncertainty in their accuracy and reliability when they are used in today's IP-based communication networks. The main reason for this is that all current capacity estimation tools only yield a potential candidate for an acceptable estimate, without being aware of its reliability level. In this study, we propose a new feedback-assisted end-to-end capacity estimation (FACEST) procedure that not only produces a candidate for a potentially acceptable estimate but also improves and categorizes its reliability level. Particularly, FACEST follows an ensemble estimation approach which meaningfully utilizes the correlation among the estimates produced by 3 independent capacity estimation tools; namely pathrate, DietTOPP and PBProbe. Through the correlation of 3 individual estimates, additional information about their reliability level is gained and, if necessary, the experiment is iteratively repeated with different sets of measurement parameter values until the required level of estimation accuracy is achieved, or in the worst case a kernel density estimator is applied on the collected experiment results. The proposed ensemble estimation approach has been implemented in a tool called FACEST, the performance of which has experimentally been evaluated on a three-hop testbed using a variety of tests with several scenarios and degrees of cross-traffic. For comparison purposes, individual experiments with pathrate, DietTOPP and PBProbe as well as with other alternative hybrid estimation tool from literature have also been conducted. The results reveal that FACEST outperforms individual and other hybrid capacity estimation tools and yields up to 18.29% lower estimation errors along with additional consistent information about the reliability level of the produced estimates.

Priority-Based Bandwidth Management in Virtualized Software-Defined Networks

In Industrial Internet of Things (IoT) applications, when the network size increases and different types of flows share the bandwidth, the demand for flexible and efficient management of the communication network is compelling. In these scenarios, under varying workload and flow priorities, the combined use of Software-Defined Networking (SDN) and Network Virtualization (NV) is a promising solution, as such techniques allow to reduce the network management complexity. This work presents the PrioSDN Resource Manager (PrioSDN_RM), a resource management mechanism based on admission control for virtualized SDN-based networks. The proposed combination imposes bounds on the resource utilization for the virtual slices, which therefore share the network links, while maintaining isolation from each other. The presented approach exploits a priority-based runtime bandwidth distribution mechanism to dynamically react to load changes (e.g., due to alarms). The paper describes the design of the approach and provides experimental results obtained on a real testbed.

Enhanced cloud based mobile core network with network function virtualization

The mobile technology is improved by adopt the Internet Protocol (IP) based technology. The IP Multimedia Subsystem or IP Multimedia Core Network Subsystem (IMS) is a technology designed by 3rd Generation Partnership Project (3GPP). This technology used IP to deliver the communication packet and recognized as Next Generation Network (NGN). The emerging IP based technology took a lot of advantages by using IP to deliver a packet. However, some issues i.e. availability, security and reliability still need consideration to meet the telecommunication standard. The cloud based computing become buzzword as a technology for cost reduction and hardware efficiencies. This paper conducted the feasibility to setup mobile core network in cloud based environment and enhanced it with network function virtualization (NFV) feature. The NFV made the network more scalable, efficient and robust network management. The QoS is presented as the results to concluded the feasibilities. The results show that cloud based mobile core network with NFV is feasible since the QoS still full fill the telecommunication standard.

Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

The application of statistical and Machine Learning models plays a critical role in planning and decision support processes for efficient and reliable Water Distribution Network (WDN) management. Failure models can provide valuable information for prioritizing system rehabilitation even in data scarcity scenarios, such as developing countries. Few studies have analyzed the performance of more than two models, and examples of case studies in developing countries are insufficient. This study compares various statistical and Machine Learning models to provide useful information to practitioners for the selection of a suitable pipe failure model according to information availability and network characteristics. Three statistical models (i.e., Linear, Poisson, and Evolutionary Polynomial Regressions) were used for failure prediction in groups of pipes. Machine Learning approaches, particularly Gradient-Boosted Tree (GBT), Bayes, Support Vector Machines and Artificial Neuronal Networks (ANNs), were compared in predicting individual pipe failure rates. The proposed approach was applied to a WDN in Bogotá (Colombia). The statistical models showed an acceptable performance (R2 between 0.695 and 0.927), but the Poisson Regression was the most suitable for predicting failures in pipes with lower failure rates. Regarding Machine Learning models, Bayes and ANNs exhibited low performance in the prediction of pipe failure condition. The GBT approach had the best performing classifier.

Universal Path Tracing for Large-Scale Sensor Networks

Most sensor networks employ dynamic routing protocols so that the routing topology can be dynamically optimized with environmental changes. The routing behaviors can be quite complex with increasing network scale and environmental dynamics. Knowledge on the routing path of each packet is certainly a great help in understanding the complex routing behaviors, allowing effective performance diagnosis and efficient network management. We propose PAT, a universal sensornet path tracing approach. PAT includes an intelligent path encoding scheme that allows efficient decoding at the PC side. To make PAT more scalable, we propose techniques to accurately estimate the degree information by exploiting timing information, allowing more compact path encoding. Moreover, we employ subpath concatenation to infer excessively long paths with a high recovery probability. We propose an analytical model to quantify the benefits of PAT with varying network scale, network density, routing dynamics and packet delivery performance. We evaluate PAT’s performance using testbed experiments, trace-driven study, and extensive simulations. Results show that PAT significantly outperforms existing approaches.

A Review of the Control Plane Scalability Approaches in Software Defined Networking

Recent advances in information and communications cloud-based services hold the potential to overcome the scalability and complex maintenance limitations of traditional networks. Software Defined Networking (SDN) surfaced as a promising paradigm to mitigate such limitations while offering flexible networks management. Particularly, SDN separates the control plane from the data plane to achieve abstraction of lower-level functionality, hence, allowing more efficient network management and utilization. However, SDN suffers from various performance and scalability problems leading to significant research efforts on maximizing the scalability of the control plane. This paper aims at reviewing different SDN controller scalability, topology-based and mechanism-based approaches, as well as discussing and analyzing how they attempt to solve the scalability challenge. Furthermore, this paper elaborates on the promising research trends and challenges. Our insights are also discussed to stimulate further research efforts addressing the control plane scalability in SDN.

Adaptive Scatter Search to Solve the Minimum Connected Dominating Set Problem for Efficient Management of Wireless Networks

An efficient routing using a virtual backbone (VB) network is one of the most significant improvements in the wireless sensor network (WSN). One promising method for selecting this subset of network nodes is by finding the minimum connected dominating set (MCDS), where the searching space for finding a route is restricted to nodes in this MCDS. Thus, finding MCDS in a WSN provides a flexible low-cost solution for the problem of event monitoring, particularly in places with limited or dangerous access to humans as is the case for most WSN deployments. In this paper, we proposed an adaptive scatter search (ASS-MCDS) algorithm that finds the near-optimal solution to this problem. The proposed method invokes a composite fitness function that aims to maximize the solution coverness and connectivity and minimize its cardinality. Moreover, the ASS-MCDS methods modified the scatter search framework through new local search and solution update procedures that maintain the search objectives. We tested the performance of our proposed algorithm using different benchmark-test-graph sets available in the literature. Experiments results show that our proposed algorithm gave good results in terms of solution quality.

Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting

Short term electric load forecasting plays a crucial role for utility companies, as it allows for the efficient operation and management of power grid networks, optimal balancing between production and demand, as well as reduced production costs. As the volume and variety of energy data provided by building automation systems, smart meters, and other sources are continuously increasing, long short-term memory (LSTM) deep learning models have become an attractive approach for energy load forecasting. These models are characterized by their capabilities of learning long-term dependencies in collected electric data, which lead to accurate prediction results that outperform several alternative statistical and machine learning approaches. Unfortunately, applying LSTM models may not produce acceptable forecasting results, not only because of the noisy electric data but also due to the naive selection of its hyperparameter values. Therefore, an optimal configuration of an LSTM model is necessary to describe the electric consumption patterns and discover the time-series dynamics in the energy domain. Finding such an optimal configuration is, on the one hand, a combinatorial problem where selection is done from a very large space of choices; on the other hand, it is a learning problem where the hyperparameters should reflect the energy consumption domain knowledge, such as the influential time lags, seasonality, periodicity, and other temporal attributes. To handle this problem, we use in this paper metaheuristic-search-based algorithms, known by their ability to alleviate search complexity as well as their capacity to learn from the domain where they are applied, to find optimal or near-optimal values for the set of tunable LSTM hyperparameters in the electrical energy consumption domain. We tailor both a genetic algorithm (GA) and particle swarm optimization (PSO) to learn hyperparameters for load forecasting in the context of energy consumption of big data. The statistical analysis of the obtained result shows that the multi-sequence deep learning model tuned by the metaheuristic search algorithms provides more accurate results than the benchmark machine learning models and the LSTM model whose inputs and hyperparameters were established through limited experience and a discounted number of experimentations.