Traffic Behavior Analysis During the Transition from One Level of the Network Hierarchy to Another

The hierarchical network model is the most preferable in the design of computer networks, as it allows you to create a more stable structure of network, rationally allocate available resources, and also provide a higher degree of data protection. In this work, the study of the behavior of the traffic during the transition from one level of the network hierarchy to another, based on the study of the values of the traffic self-similarity degree during this transition. For the study, a simulation model of a computer network with a hierarchical topology was developed using the NS-3 simulator. Also, a window application was developed in the Visual C# programming language. With the help of this application the degree of self-similarity of the traffic was investigated using the files obtained as a result of processing the trace file. Thus, as a result of the study, it can be stated that any changes in the degree of self-similarity of the network traffic when this traffic moves from one level of the hierarchy to another level depends on such a condition as the direction of traffic movement. The initial degree of selfsimilarity of network traffic also effects on the network traffic self-similarity degree.

Categories of context in realist evaluation

Realist evaluation has become widespread partly because of its sensitivity to the influence of contexts on policy implementation. In many such evaluations, the range of contexts considered relevant nevertheless remains disparate and under-conceptualised. This article uses findings from a realist evaluation of English Patient Safety Collaboratives during 2015–2018 to develop a realist taxonomy of contexts, differentiating contexts according to how they affect the corresponding policy mechanism. By analysing the main context-mechanism-outcome configurations that made up the English Patient Safety Collaboratives, we derive a taxonomy of the contexts that affected implementation and outcomes. The categories of context were structural (network, hierarchy, market and organisational contexts); resource-based (actors, material, financial); motivational (receptivity, outcome headroom), and temporal (continuity, history and convergence). To the categories found in previous studies, this study adds the three temporal contexts.

Performance enhancement of sensor network architecture for monitoring underwater oil pipeline

In this paper, a deployment mechanism is designed to distribute heterogeneous nodes to optimally cover the pipeline where the mechanism helps locate each node on the wall of the oil pipeline where the number of nodes can be increased depending on this mechanism. The six-layer network hierarchy includes basic sensor nodes (BSN), aggregation relay node (ARN) that added to the network hierarchy, data relay nodes (DRN), data dissemination node (DDN), base station (sinks), and network control center (NCC). This network relies on the improved smart redirect or jump algorithm (SRJ) by sending packets depend on the active relay nodes in both directions that are within the transmission range of the ARNs instead of relying on the number of hops adopted by the SRJ algorithm to reduce the network delay, the energy consumed in relay nodes, and the number of times the DRNs increased transmission range. The OMNeT++ and MATLAB programs were used to implement the simulation scenario. The results showed superiority in terms of the average overhead communication, energy consumption, and end to the end delay with network delay in some cases rely on the number of active relay nodes.

A Novel Classified Ledger Framework for Data Flow Protection in AIoT Networks

The edge computing node plays an important role in the evolution of the artificial intelligence-empowered Internet of things (AIoTs) that converge sensing, communication, and computing to enhance wireless ubiquitous connectivity, data acquisition, and analysis capabilities. With full connectivity, the issue of data security in the new cloud-edge-terminal network hierarchy of AIoTs comes to the fore, for which blockchain technology is considered as a potential solution. Nevertheless, existing schemes cannot be applied to the resource-constrained and heterogeneous IoTs. In this paper, we consider the blockchain design for the AIoTs and propose a novel classified ledger framework based on lightweight blockchain (CLF-LB) that separates and stores data rights at the source and enables a thorough data flow protection in the open and heterogeneous network environment of AIoT. In particular, CLF-LB divides the network into five functional layers for optimal adaptation to AIoTs applications, wherein an intelligent collaboration mechanism is also proposed to enhance the across-layer operation. Unlike traditional full-function blockchain models, our framework includes novel technical modules, such as block regenesis, iterative reinforcement of proof-of-work, and efficient chain uploading via the system-on-chip system, which are carefully designed to fit the cloud-edge-terminal hierarchy in AIoTs networks. Comprehensive experimental results are provided to validate the advantages of the proposed CLF-LB, showing its potentials to address the secrecy issues of data storage and sharing in AIoTs networks.

A Predictive-Coding Network That Is Both Discriminative and Generative

Predictive coding (PC) networks are a biologically interesting class of neural networks. Their layered hierarchy mimics the reciprocal connectivity pattern observed in the mammalian cortex, and they can be trained using local learning rules that approximate backpropagation (Bogacz, 2017 ). However, despite having feedback connections that enable information to flow down the network hierarchy, discriminative PC networks are not typically generative. Clamping the output class and running the network to equilibrium yields an input sample that usually does not resemble the training input. This letter studies this phenomenon and proposes a simple solution that promotes the generation of input samples that resemble the training inputs. Simple decay, a technique already in wide use in neural networks, pushes the PC network toward a unique minimum two-norm solution, and that unique solution provably (for linear networks) matches the training inputs. The method also vastly improves the samples generated for nonlinear networks, as we demonstrate on MNIST.

Optimal management of distance-based location registration using embedded Markov chain

In this study, we deal with a Distance-Based Registration with Implicit Registration, which is an enhanced scheme of the Distance-Based Registration in mobile-cellular networks. In comparisons with other Location Registration schemes, various studies on the Distance-Based Registration scheme and performance have been performed. However, a real network hierarchy has not been properly reflected in the performance evaluation of the Distance-Based Registration. To accurately evaluate the registration and paging costs of the Distance-Based Registration, a real network hierarchy should reflect that a mobile network is made up of many Visitor Location Register areas. Furthermore, we use an embedded Markov-Chain model in the Visitor Location Register hierarchy, which can reflect not only the Implicit Registration effect of the outgoing calls of user equipment but also cell staying time of the user equipment that may follow a general distribution. Without consideration of the Visitor Location Register, the paging cost decreases due to a small paging area, but the location registration cost rises because of frequent inter Visitor Location Register. The numerical results according to the various conditions show an accurate evaluation of the Distance-Based Registration performance in a real network hierarchy and the general cell staying time. Generally, the total signaling cost will increase when we consider the Visitor Location Register. However, for more appropriate evaluation of the Distance-Based Registration performance, it is necessary to consider the Visitor Location Register hierarchy.