Connectivity and routes in large geometric network hypergraphs

Despite the recently exhibited importance of higher-order interactions for various processes, few flexible (null) models are available. In particular, most studies on hypergraphs focus on a small set of theoretical models. Here, we introduce a class of models for random hypergraphs which displays a similar level of flexibility of complex network models and where the main ingredient is the probability that a node belongs to a hyperedge. When this probability is a constant, we obtain a random hypergraph in the same spirit as the Erdos-Renyi graph. This framework also allows us to introduce different ingredients such as the preferential attachment for hypergraphs, or spatial random hypergraphs. In particular, we show that for the Erdos-Renyi case there is a transition threshold scaling as 1/sqrt[EN] where N is the number of nodes and E the number of hyperedges. We also discuss a random geometric hypergraph which displays a percolation transition for a threshold distance scaling as r_{c}^{*}∼1/sqrt[E]. For these various models, we provide results for the most interesting measures, and also introduce new ones in the spatial case for characterizing the geometrical properties of hyperedges. These different models might serve as benchmarks useful for analyzing empirical data.

The evaluation and assessment of the design of large wireless networks is an important problem in numerous applications. Direct simulation is a traditional approach for studying such networks but is severely limited in its utility as the size of the network increases. This necessitates other means for studying large networks, one of which is the modeling of large networks with continuum models. In this paper, we introduce nonlinear partial differential equations whose solutions approximate the expected behavior of large networks governed by probabilistic communication rules. The relative speed at which solutions can be obtained from the continuum models allows for the investigation of routing protocols and communication limitations due to interference—a feat that is not feasible via simulation methods. Specifically, we investigate the effects of a directed diffusion routing protocol and explore communication limitations in an interference-sensitive network. Network design studies using the approximating continuum models are then presented.

Objectives: Public key cryptography helps in achieving information security in wireless networks. Public key will be issued by CA (Certificate Authority). But having CA in wireless network makes it vulnerable to various attacks. Hence distributed cache was proposed where each node in network caches public key of some nodes existing in network in its cache and request to obtain keys not cached will be made via trusted nodes. Due to limited memory only few keys can be cached. There exists an optimal threshold value for caching keys so that equal number of both local nodes and remote nodes public keys can be cached. Methods: Random replacement policy was used for updating cache contents, where if a node’s local/ remote key cached ratio exceeds optimal ratio then a key will be randomly chosen from the cache and will be removed. Here we propose implementation of bloom filter in cache to cache public key of nodes existing in network. Findings: By using bloom filter of large size, more number of public keys can be cached thereby providing space and time advantage and reduces query cost with increased usage of memory. MAC (Media Access Control) id and PSN (Processor Serial Number) are used for uniquely identifying each node in network and are stored in encrypted format. When mismatch is found in encrypted MAC id and PSN, then that node is determined to be malicious node and its id will be notified to all nodes in network thus excluding the node from making any communication to other nodes in the network. Applications: Used for checking an element’s presence in the set thus reducing I/O lookup when large data set is used. Used as an alternative to cache as it uses limited memory more efficiently than cache does.

The complex patterns of electroencephalograms(EEGs) have made it very difficult for physiologists and pathologists to interpret the relationships to brain functions. A possible way to approach this problem is to study the dynamic behaviors of a mathematical model of large scale neuron network based on the fundamental electrical properties of neurons and their interrelationships, as done by J.D. Cowan and his co-workers (Cowan 1974; Ermentrout and Cowan 1979, 1980). They proposed two coupled non-linear integro-differential equations, comprising two types of neurons (excitatory and inhibitory), and analysed them by bifurcation theory. Because of the statistical nature of their treatment, all complexities of neurobiological details, such as the plasticities of neurons and synapses, may be taken into consideration, but their results still are very difficult to compare with real EEG patterns, especially those of conscious human beings, which display highly irregular, quasi-random forms. These kinds of behaviors may also be found in general dynamic systems and are called chaotic. This paper is an investigation of the problems concerning the relations between chaotic behaviors of neural network dynamics and EEG patterns, i.e., brain functions.KeywordsChaotic BehaviorStrange AttractorBifurcation TheoryGlia CellNeural Network DynamicThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Wireless sensor networks are an essential component of the fast-growing Internet of Things. The nodes of such network usually have small energy resources and do not have big computing power. The big challenge is to secure transmissions between nodes of the network and continuous authentication of nodes in data link layer of such network. This paper presents a proposal to solve this kind of problem using TPM in the domain of sensors. A model of wireless sensor network as well as operations associated with authentication in the sensors domain are presented. Additionally, an implementation of selected operations in the sensors domain is described. The test environment including the construction of nodes equipped with the TPM and obtained results related to the transmission delay time and power consumption are presented.

Transcriptomic approaches can give insight into molecular mechanisms underlying chemical toxicity and are increasingly being used as part of toxicological assessments. To aid the interpretation of transcriptomic data, we have developed a systems toxicology method that relies on a computable biological network model. We created the first network model describing cardiotoxicity in zebrafish larvae-a valuable emerging model species in testing cardiotoxicity associated with drugs and chemicals. The network is based on scientific literature and represents hierarchical molecular pathways that lead from receptor activation to cardiac pathologies. To test the ability of our approach to detect cardiotoxic outcomes from transcriptomic data, we have selected three publicly available data sets that reported chemically induced heart pathologies in zebrafish larvae for five different chemicals. Network-based analysis detected cardiac perturbations for four out of five chemicals tested, for two of them using transcriptomic data collected up to 3 days before the onset of a visible phenotype. Additionally, we identified distinct molecular pathways that were activated by the different chemicals. The results demonstrate that the proposed integrational method can be used for evaluating the effects of chemicals on the zebrafish cardiac function and, together with observed cardiac apical end points, can provide a comprehensive method for connecting molecular events to organ toxicity. The computable network model is freely available and may be used to generate mechanistic hypotheses and quantifiable perturbation values from any zebrafish transcriptomic data.

Let H d (n,p) signify a random d-uniform hypergraph with n vertices in which each of the \({n}\choose{d}\) possible edges is present with probability p = p(n) independently, and let H d (n,m) denote a uniformly distributed d-uniform hypergraph with n vertices and m edges. We establish a local limit theorem for the number of vertices and edges in the largest component of H d (n,p) in the regime, thereby determining the joint distribution of these parameters precisely. As an application, we derive an asymptotic formula for the probability that H d (n,m) is connected, thus obtaining a formula for the asymptotic number of connected hypergraphs with a given number of vertices and edges. While most prior work on this subject relies on techniques from enumerative combinatorics, we present a new, purely probabilistic approach.KeywordsJoint DistributionConnected GraphRandom GraphLarge ComponentRandom StructureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Previous results show that a node's throughput scales poorly as the network size increases when every node has traffic. However, in many cases, only a fraction of nodes in large networks have data to send or receive at any given time, while other nodes can act as relays/routers. Therefore, in this paper, we study the scaling behavior from a user's viewpoint (a user is a node with traffic). We first derive an upper bound on per user throughput. To derive the lower bound, we propose a simple scheduling scheme that enables users to cooperate with relay nodes and fully utilize the networks capacity. We show that per user throughput depends on the network size, the number of users, and the node deployment schemes, and is in general much more optimistic. Our scheme also sheds light on designing efficient cooperation protocols in heterogeneous networks and cognitive radio networks.

We present the Expert Network Designer an automated system for large structured computer network design, modeling, simulation and optimization. Obtaining the network environment from the user END employs the formalized network design experience to recommend the network solutions suitable for such environment, explains why such solutions were chosen, and further ranks and evaluates such solutions using artificial intelligence optimization techniques and engineering simulation tools.

We demonstrate the transmission of a millimeter-wave radio signal over a seamless fiber–terahertz-wave system in the 300 GHz band. The system utilizes a simple optical heterodyning method at the transmitter and a direct detection at the receiver. As a proof-of-concept demonstration, we successfully transmitted a 100-MHz bandwidth 5G new radio compliant signal at 24.5 GHz over a seamless fiber-terahertz system in the 300 GHz band. The proposed system is promising to facilitate the deployment of ultra-dense small cells in high-frequency bands in 5G and beyond networks.

We propose and demonstrate transmission of MIMO radio signals over optical fibre using a novel phase quadrature double sideband frequency translation technique which employs fewer LO sources than SCM and does not require WDM.

This paper proposes an energy-efficient dynamic decision model for wireless multi-sensor network, which is based on the dynamic analysis of the energy consumption characteristics of wireless multi-sensor nodes. We analyze the behaviors of the nodes in wireless multi-sensor network and introduce the existing energy-efficient decision methods, then propose a simple dynamic decision model and prove it theoretically. This paper uses MATLAB 2015 to carry out simulation experiments under the condition of fixed routing protocol based on tree topology and two low-power routing protocols based on mesh topology, and simulation results show that the network lifetime is obviously prolonged. Extending the application of the proposed decision model to aquaculture environmental monitoring system, testing results show that the proposed decision model can effectively reduce the network energy consumption, and be promising when generalized to other applications.

There are two kinds of fading models for large and small scale fading in the propagation process of electromagnetic waves, and this paper analyzes the root-mean-square delay expansion and Les K factor of two kinds of small scale fading characteristics under line-of-sight and non-line-of-sight scenarios channels by combining the statistical method with the fitting. A channel model library is established, and multiple large-scale fading models are used to compare the residual sum of squares and the goodness of fit to analyze the large-scale fading characteristics in HF signal transmission. Combined with the performance of the characteristics existing in HF signal transmission, we propose a wireless channel multipath identification algorithm based on D-S evidence theory, analyze the algorithm operation flow, and test the algorithm for multi-channel scene identification. The CDF of the rms delay extensions under the line-of-sight and non-line-of-sight scene channels can be fitted well with shape parameters of 0.727 and 0.272 and scale parameters of 655.33 and 1173.83, respectively. The CDFs of the Rice K factors for the 2 scenarios fit the normal distribution well, with standard deviations less than 3 dB. The wireless channel multipath identification algorithm based on D-S evidence theory can improve the wireless signal transmission identification and optimize the frequency response of wireless signal transmission.

Signal Transmission and Reception in Wireless Smart Cities

We have constructed a stochastic model of a water supply network with leaks, which, compared to the previously proposed models (excluding leaks), more adequately describes the processes of transportation and water distribution in water supply systems. Mathematical modeling of water supply networks is associated with difficulties related to the huge dimensionality of actual water supply networks, limited information resources and operational data, which does not make it possible to assess parameters of the technological equipment and structure of a water supply network adequately enough. Therefore, an equivalent scheme is built for an actual water supply network based on its dictation points, which is then used for subsequent calculations. The task on building a scheme for an equivalent water supply network consists of three problems: identification of the structure, parameters, and state of a water supply network. The proposed method for leaks detection is based on the comparison of change in the magnitude of head at pumping stations and at the dictation points of a water supply network. Based on the stochastic model of a water supply network with leaks, we have constructed a method for calculating the magnitude of leaks, which implies the following: by knowing the head of water at the nodes of an equivalent water supply network and the approximate diameters of leaks at nodes, new values of heads at nodes of the equivalent water supply network are calculated. Then we again compute the magnitude of a leak by knowing the new head at the node and the diameter of the leaks. Upon completion of several such iterations, a conclusion is drawn on that starting at a certain step the magnitude of leaks and the heads at the nodes of the equivalent water supply network stop changing. By knowing the magnitude of leaks and head at each node within the equivalent water supply network, we determine the actual diameter of fistulas at each node. The proposed method for calculating the magnitude of leaks does not require financial costs or the use of additional equipment; it could be used by water utilities to detect and calculate the magnitude of leaks