Edge Network Routing Protocol Base on Target Tracking Scenario

Edge computing perfectly integrates cloud computing centers and edge-end devices together, but there are not many related researches on how the edge-end node devices work to form an edge network and what the protocols used to implement the communication among nodes in the edge network. Aiming at the problem of coordinated communication among edge nodes in the current edge computing network architecture, this paper proposes an edge network routing and forwarding protocol based on target tracking scenarios. This protocol can meet the dynamic changes of node locations, and the elastic expansion of node scale. Individual node failures will not affect the overall network, and the network ensures efficient real-time with less communication overhead. The experimental results display that the protocol can effectively reduce the communications volume of the edge network, improve the overall efficiency of the network, and set the optimal sampling period, so as to ensure that the network delay is minimized.

Load Balancing in Cloud Computing Empowered with Dynamic Divisible Load Scheduling Method

The need to process and dealing with a vast amount of data is increasing with the developing technology. One of the leading promising technology is Cloud Computing, enabling one to accomplish desired goals, leading to performance enhancement. Cloud Computing comes into play with the debate on the growing requirements of data capabilities and storage capacities. Not every organization has the financial resources, infrastructure & human capital, but Cloud Computing offers an affordable infrastructure based on availability, scalability, and cost-efficiency. The Cloud can provide services to clients on-demand, making it the most adapted system for virtual storage, but still, it has some issues not adequately addressed and resolved. One of those issues is that load balancing is a primary challenge, and it is required to balance the traffic on every peer adequately rather than overloading an individual node. This paper provides an intelligent workload management algorithm, which systematically balances traffic and homogeneously allocates the load on every node & prevents overloading, and increases the response time for maximum performance enhancement.

The stoichiometry of supply chains

We propose a plausible conjecture about what goes on inside the black box of the Leontief-Liebig production function. The approach is based on an idea that the conversion of resources into a final product in an industrial supply chain occurs in much the same manner as does the conversion of substrates into different molecules in an enzyme-catalyzed biochemical reaction. A bottleneck principle is derived according to which at any moment the steady-state output of a supply chain is completely controlled by a single factor of production. The reduction of complexity is made possible owing to the sigmoid inputresponse of an individual node in the chain.

Research Progress on Oviposition-Related Genes in Insects

Oviposition-related genes have remained a consistent focus of insect molecular biology. Previous research has gradually clarified our mechanistic understanding of oviposition-related genes, including those related to oviposition-gland-related genes, oogenesis-related genes, oviposition-site-selection-related genes, and genes related to ovulation and hatching. Moreover, some of this research has revealed how the expression of single oviposition-related genes affects the expression of related genes, and more importantly, how individual node genes function to link the expression of upstream and downstream genes. However, the research to date is not sufficient to completely explain the overall interactions among the genes of the insect oviposition system. Through a literature review of a large number of studies, this review provides references for future research on oviposition-related genes in insects and the use of RNAi or CRISPR/Cas9 technology to verify the functions of oviposition-related genes and to prevent and control harmful insects.

A node-based informed modularity strategy to identify organizational modules in anatomical networks

ABSTRACTThe study of morphological modularity using anatomical networks is growing in recent years. A common strategy to find the best network partition uses community detection algorithms that optimize the modularity Q function. Because anatomical networks and their modules tend to be small, this strategy often produces two problems. One is that some algorithms find inexplicable different modules when one inputs slightly different networks. The other is that algorithms find asymmetric modules in otherwise symmetric networks. These problems have discouraged researchers to use anatomical network analysis and boost criticisms to this methodology. Here, I propose a node-based informed modularity strategy (NIMS) to identify modules in anatomical networks that bypass resolution and sensitivity limitations by using a bottom-up approach. Starting with the local modularity around every individual node, NIMS returns the modular organization of the network by merging non-redundant modules and assessing their intersection statistically using combinatorial theory. Instead of acting as a black box, NIMS allows researchers to make informed decisions about whether to merge non-redundant modules. NIMS returns network modules that are robust to minor variation and does not require optimization of a global modularity function. NIMS may prove useful to identify modules also in small ecological and social networks.

Conventional Combining Scheme in Cooperative Spectrum Sensing

Spectrum Sensing (SS) is a key constituent of software defined radio (SDR) or Cognitive radio (CR). Spectrum sensing (SS) investigate the white hole in allotted spectrum to the primary user. Cooperative spectrum sensing (CSS) has work in a best manner than any other spectrum sensing (SS) technique to detect white space or spectrum hole in the licensed spectrum. In this paper we compare various combining scheme that are to be perform at the Fusion centre (FC). Fusion centre (FC) is the central part of Cooperative spectrum sensing (CSS) that combines individual node decision. Simulation has performed for hard and soft combining scheme. According to the simulation the soft combining scheme performed better then hard combining scheme but the complexity and bandwidth (BW) requirement in the soft combining is more than hard combining scheme. In the proposed paper we also explore detection error that is to be present in various combining scheme.

Distributed networked localization using neighboring distances only through a computational topology control approach

For large-scale wireless sensor networks, the nonlinear localization problem where only neighboring distances are available to each individual sensor nodes have been attracting great research attention. In general, distributed algorithms for this problem are likely to suffer from the failures that localizations are trapped in local minima. Focusing on this issue, this article considers a fully distributed algorithm by introducing a novel mechanism, where each individual node is allowed to computationally interact with a random subset of its neighbors, for helping localizations escape from local minima. Theoretical analyses reveal that with the proposed algorithm, any local minimum of the localization will be unstable, and the global optimum would finally be achieved with probability 1 after enough time of iterations. Numerical simulations are given as well to demonstrate the effectiveness of the algorithm.

A Novel and Flexible Criterion to Improve Data Transmission in Clustering Protocols in WSNs

In this paper a new criterion called Energy-Cost Function is presented according to which in each round the energy cost for any individual node is calculated. When transmitting their data, the nodes make decisions based on this very cost function. In case the nodes decides that it will cost a lower amount of energy transmitting the data to the sink by itself rather than by the cluster-head to the sink, then the node transmits the data directly. In this way the cluster overload is reduced and both the network lifetime and instability period is boosted. Based on the conditions of the problem, cost function parameters are variable and since all the decisions are made locally, network scalability potential is retained. Simulation results show that the network lifetime or its instability period based on the cost function employed will be improved up to 40% in relation to the LEACH protocol.

The effect of the structural composition on the resilience of pipeline systems to node damage

TheAimof this paper is to study the effect of the structural features of pipeline systems on the development of emergency situations by the mechanism of progressive blocking of transportation nodes. The blocking of an individual point element of a system is considered as the result of simultaneous failure of all the pipelines converging into the node. The process of progressive blocking of a certain set of nodes of a pipeline system in random order is called a progressive blocking. The development of progressive blocking is associated with the disconnection of the consumers from the source of end product and is a dangerous scenario of emergency development. The system’s resilience against progressive blocking is estimated by the resilience indicator F„, the average share of the system’s nodes whose blocking in a random order causes the disconnection of all consumers from the source of the end product.Methods of research.The values of 0 <F„< 1 were identified by means of computer simulation. After each fact of damage associated with a random blocking of an individual node, the connection between the source and consumers of the end product was established. The statistical characteristics of the process of progressive blocking were evaluated according to the results of repeated simulation of the procedure of damage of the analyzed network structure. In general, the structure of a pipeline system is characterized by a graph that describes the connections between point elements. The valence of an individual graph node is the number of edges that converge into it. Similarly, the valence of the respective network node is the number of converging linear elements (pipelines). Furthermore, an important characteristic of an individual node is the composition of the converging linear elements. Thus, the set of a system’s linear elements includes the following varieties that ensure the connection between: the source and the consumer (subset G1), two consumers (subset G2), a consumer and a hub (subset G3), two hubs (subset G4), the source and a hub (subset G5).Results.The author analyzed and examined the effect of the structural characteristics on the ability of pipeline systems to resist the development of emergency situations through the mechanism of progressive blocking of nodes. It was established that with regard to structural optimization the most pronounces positive effect associated with the increase of the values F^ is observed as the valence of the source node grows and additional linear elements of subset G1 are included in the system.Conclusions.The process of progressive blocking of pipeline transportation system nodes is a hazardous development scenario of an emergency situation. The most efficient method of improving pipeline system resilience against progressive blocking consists in increasing the valence of the source node and inclusion of additional linear elements of subset G1 in the system. Structural optimization of pipeline systems should be achieved by defining the values F^ for each of the alternatives with subsequent adoption of a substantiated design solution.

Analyzing time-aggregated networks: the role of bootstrapping, permutation, and simulation

Networks are often used to describe adaptive social systems, where individual (node) behaviour generates network-level structures that influence subsequent individual-level behaviour. To address questions about the dynamics of network structure in these systems, there is a need to analyze networks through time. Various statistical methods exist for estimating the behaviour of networks in time, in terms of both time-ordered and time-aggregated networks. In this paper, we discuss three main analytical steps for the analysis of time-aggregated network data: 1) aggregation choices, 2) null-model comparisons, and 3) constructing, parameterizing, and making inferences from time series models. We then present a custom R package, netTS, which facilitates these steps. Observed grooming data from a group of vervet monkeys, a highly social primate species, is used as an example to highlight three potential analyses: 1) quantifying the stability of network-level social structures through time, 2) identifying keystone nodes driving/maintaining network structures, and 3) quantifying the interdependence between node behaviour through time. In particular, we highlight the role of bootstrapping, permutation, and simulation as critical components in the analysis of time-aggregated networks.