For pt.I see ibid., vol.6, no.3, p.12-27, 1992. A survey of wavelength-division-multiplexing (WDM)-based local lightwave networks is presented. The general characteristics of multihop systems are discussed, and various multihop approaches are reviewed. The construction of optimal structures based on minimizing the maximum link flow and optimizations based on minimization of the mean network packet delay are also reviewed. Regular topologies that have been studied as candidates for multihop lightwave networks, including the perfect shuffle, the de Bruijn graph, the toroid, and the hypercube, are discussed. Near-optimal node placement algorithms and shared-channel multihop systems are also discussed. >

WDM-based local lightwave networks. II. Multihop systems

We explore design principles for next-generation optical wide-area networks, employing wavelength-division multiplexing (WDM) and targeted to nationwide coverage. This optical network exploits wavelength multiplexers and optical switches in routing nodes, so that an arbitrary virtual topology may be embedded on a given physical fiber network. The virtual topology, which is used as a packet-switched network and which consists of a set of all-optical "lightpaths", is set up to exploit the relative strengths of both optics and electronics-viz. packets of information are carried by the virtual topology "as far as possible" in the optical domain, but packet forwarding from lightpath to lightpath is performed via electronic switching, whenever required. We formulate the virtual topology design problem as an optimization problem with one of two possible objective functions: (1) for a given traffic matrix, minimize the network-wide average packet delay (corresponding to a solution for present traffic demands), or (2) maximize the scale factor by which the traffic matrix can be scaled up (to provide the maximum capacity upgrade for future traffic demands). Since simpler versions of this problem have been shown to be NP-hard, we resort to heuristic approaches. Specifically, we employ an iterative approach which combines "simulated annealing" (to search for a good virtual topology) and "flow deviation" (to optimally route the traffic-and possibly bifurcate its components-on the virtual topology). We do not consider the number of available wavelengths to be a constraint, i.e., we ignore the routing of lightpaths and wavelength assignment for these lightpaths. We illustrate our approaches by employing experimental traffic statistics collected from NSFNET.

Some principles for designing a wide-area WDM optical network

Methods and problems of communication in usual networks

Internet of Things (IoT) plays an increasingly significant role in our daily activities, connecting physical objects around us into digital services. In other words, IoT is the driving force behind home automation, smart cities, modern health systems, and advanced manufacturing. This also increases the likelihood of cyber threats against IoT devices and services. Attackers may attempt to exploit vulnerabilities in application protocols, including Domain Name System (DNS), Hyper Text Transfer Protocol (HTTP) and Message Queue Telemetry Transport (MQTT) that interact directly with backend database systems and client–server applications to store data of IoT services. Successful exploitation of one or more of these protocols can result in data leakage and security breaches. In this paper, an ensemble intrusion detection technique is proposed to mitigate malicious events, in particular botnet attacks against DNS, HTTP, and MQTT protocols utilized in IoT networks. New statistical flow features are generated from the protocols based on an analysis of their potential properties. Then, an AdaBoost ensemble learning method is developed using three machine learning techniques, namely decision tree, Naive Bayes (NB), and artificial neural network, to evaluate the effect of these features and detect malicious events effectively. The UNSW-NB15 and NIMS botnet datasets with simulated IoT sensors’ data are used to extract the proposed features and evaluate the ensemble technique. The experimental results show that the proposed features have the potential characteristics of normal and malicious activity using the correntropy and correlation coefficient measures. Moreover, the proposed ensemble technique provides a higher detection rate and a lower false positive rate compared with each classification technique included in the framework and three other state-of-the-art techniques.

An Ensemble Intrusion Detection Technique Based on Proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things

Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

The star graph has been show to be an attractive alternative to the widely used n-cube. Like the n-cube, the star graph possesses rich structure and symmetry as well as fault tolerant capabilities, but has a smaller diameter and degree. However, very few algorithms exists to show its potential as a multiprocessor interconnection network. Many fast and efficient parallel algorithms require broadcasting as a basic step. An optimal algorithm for one-to-all broadcasting in the star graph is proposed. The algorithm can broadcast a message to N processors in O(log/sub 2/ N) time. The algorithm exploits the rich structure of the star graph and works by recursively partitioning the original star graph into smaller star graphs. In addition, an optimal all-to-all broadcasting algorithm is developed. >

Optimal broadcasting on the star graph

Error back propagation (EBP) is now the most used training algorithm for feedforward artificial neural networks (FFANNs). However, it is generally believed that it is very slow if it does converge, especially if the network size is not too large compared to the problem at hand. The main problem with the EBP algorithm is that it has a constant learning rate coefficient, and different regions of the error surface may have different characteristic gradients that may require a dynamic change of learning rate coefficient based on the nature of the surface. Also, the characteristic of the error surface may be unique in every dimension, which may require one learning rate coefficient for each weight. To overcome these problems several modifications have been suggested. This survey is an attempt to present them together and to compare them. The first modification was momentum strategy where a fraction of the last weight correction is added to the currently suggested weight correction. It has both an accelerating and a decelerating effect where they are necessary. However, this method can give only a relatively small dynamic range for the learning rate coefficient. To increase the dynamic range of the learning rate coefficient, such methods as the bold driver and SAB (self-adaptive back propagation) were proposed. A modification to the SAB that eliminates the requirement of selection of a good learning rate coefficient by the user gave the SuperSAB. A slight modification to the momentum strategy produced a new method that controls the oscillation of weights to speed up learning. Modification to the EBP algorithm in which the gradients are rescaled at every layer helped to improve the performance. Use of expected output of a neuron instead of actual output for correcting weights improved performance of the momentum strategy. The conjugate gradient method and self-determination of adaptive learning rate require no learning rate coefficient from the user. Use of energy functions other than the sum of the squared error has shown improved convergence rate. An effective learning rate coefficient selection needs to consider the size of the training set. All these methods to improve the performance of the EBP algorithm are presented here.

/pdf/methods-to-speed-up-error-back-propagation-learning-3zpsedrsby.pdf

Methods to speed up error back-propagation learning algorithm

All traffic models for MPEG-like encoded variable bit rate (VBR) video can be broadly categorized into (1) data-rate models (DRMs) and (2) frame-size models (FSMs). Almost all proposed VBR traffic models are DRMs. DRMs generate only data arrival rate, and are good for estimating average packet-loss and ATM buffer overflowing probabilities, but fail to identify such details as percentage of frames affected. FSMs generate sizes of individual MPEG frames, and are good for studying frame loss rate in addition to data loss rate. Among three previously proposed FSMs: (1) one generates frame sizes for full-length movies without preserving group-of-pictures (GOP) periodicity; (2) one generates VBR video traffic for news videos from scene content description provided to it; and (3) one generates frame sizes for full-length movies without preserving size-based video-segment transitions. We propose two FSMs that generate frame sizes for full-length VBR videos preserving both GOP periodicity and size-based video-segment transitions. First, two-pass algorithms for analysis of full-length VBR videos are presented. After two-pass analysis, these algorithms identify size-based classes of video shots into which the GOPs are partitioned. Frames in each class produce three data sets, one each for I-, B-, and P-type frames. Each of these data sets is modeled with an axis-shifted gamma distribution. Markov renewal processes model (size-based) video segment transitions. We have used quartile-quartile (QQ) plots to show visual similarity of model-generated VBR video data sets with original data set. Leaky-bucket simulation study has been used to show similarity of data and frame loss rates between model-generated VBR videos and original video. Our study of frame-based VBR video revealed that even a low data-loss rate could affect a large fraction of I frames, causing a significant degradation of the quality of transmitted video.

/pdf/modeling-full-length-video-using-markov-modulated-gamma-1afqdtzanb.pdf

Modeling full-length video using Markov-modulated gamma-based framework

The cognitive radio networks or CogNets poses several new challenges to the transport layer protocols, because of many unique features of cognitive radio based devices used to build them. CogNets not only have inherited all features of wireless networks, but also their link connections are intermittent and discontinuous. Exiting transport layer protocols are too slow to respond quickly for utilizing available link capacity. Furthermore, existing self-timed transport layer protocols are neither designed for nor able to provide efficient reliable end-to-end transport service in CogNets, where wide round trip delay variations naturally occur. We identify (i) requirements of protocols for the transport layer of CogNets, (ii) propose a generic architecture for implementing a family of protocols that fulfill desired requirements, (iii) design, implement, and evaluate a family of best-effort transport protocols for serving delay-tolerant applications. Results obtained from ns-2 network simulator show that the proposed protocols have potential for significantly improving end-to-end throughput. For instance, at 1% and 5% packet loss rates one of the proposed protocol has shown about 21% and 95% increase in end-to-end throughput for file transfer application.

Transport Layer Protocols for Cognitive Networks

Modeling to study the performance of wireless networks in recent years has produced sets of nonlinear equations with interrelated parameters. Because these nonlinear equations have no closed-form solution, the numerical values of the parameters are calculated by iterative algorithms. In a Markov chain model of a wireless cellular network, one commonly used expression for calculating the handoff arrival rate can lead to a sequence of oscillating iterative values that fail to converge. We present an algorithm that generates a monotonic sequence, and we prove that the monotonic sequences always converges. Lastly, we give a further algorithm that converges logarithmically, thereby permitting the handoff arrival rate to be calculated very quickly to any desired degree of accuracy.

/pdf/convergence-in-the-calculation-of-the-handoff-arrival-rate-a-330t4im5h7.pdf

Dilip Sarkar

Papers

Optimal broadcasting on the star graph

Methods to speed up error back-propagation learning algorithm

Modeling full-length video using Markov-modulated gamma-based framework

Transport Layer Protocols for Cognitive Networks

Convergence in the calculation of the handoff arrival rate: a log-time iterative algorithm