A STABLE CLUSTERING SCHEME WITH NODE PREDICTION IN MANET

The main concern in MANET is increasing network lifetime and security. Clustering is one of the approaches that help in maintaining network stability. Electing an efficient and reliable Cluster Head (CH) is a challenging task. Many approaches are proposed for efficient clustering, weight-based clustering is one among them. This paper proposes a stable clustering scheme which provides network stability and energy efficiency. Proposed Stable Clustering Algorithm with Node Prediction (SCA-NP) computes the weight of the node using a combination of node metrics. Among these metrics, Direct Trust (DT) of the node provides a secure choice of CH and Node Prediction metric based on the minimum estimated time that node stay in the cluster provides the stable clustering. Mobility prediction is considered as the probability that a node stays in the network. This metric helps in electing CH which is available in the network for a longer time. Simulation is done in NS3 to evaluate the performance of SCA-NP in terms of clusters formed, network lifetime, efficiency in packet delivery, detecting malicious nodes and avoiding them in communication

Pinning control of fractional-order weighted complex networks

In this paper, we consider the pinning control problem of fractional-order weighted complex dynamical networks. The well-studied integer-order complex networks are the special cases of the fractional-order ones. The network model considered can represent both directed and undirected weighted networks. First, based on the eigenvalue analysis and fractional-order stability theory, some local stability properties of such pinned fractional-order networks are derived and the valid stability regions are estimated. A surprising finding is that the fractional-order complex networks can stabilize itself by reducing the fractional-order q without pinning any node. Second, numerical algorithms for fractional-order complex networks are introduced in detail. Finally, numerical simulations in scale-free complex networks are provided to show that the smaller fractional-order q, the larger control gain matrix D, the larger tunable weight parameter , the larger overall coupling strength c, the more capacity that the pinning scheme may possess to enhance the control performance of fractional-order complex networks

AMCTD: Adaptive Mobility of Courier nodes in Threshold-optimized DBR Protocol for Underwater Wireless Sensor Networks

In dense underwater sensor networks (UWSN), the major confronts are high error probability, incessant variation in topology of sensor nodes, and much energy consumption for data transmission. However, there are some remarkable applications of UWSN such as management of seabed and oil reservoirs, exploration of deep sea situation and prevention of aqueous disasters. In order to accomplish these applications, ignorance of the limitations of acoustic communications such as high delay and low bandwidth is not feasible. In this paper, we propose Adaptive mobility of Courier nodes in Threshold-optimized Depth-based routing (AMCTD), exploring the proficient amendments in depth threshold and implementing the optimal weight function to achieve longer network lifetime. We segregate our scheme in 3 major phases of weight updating, depth threshold variation and adaptive mobility of courier nodes. During data forwarding, we provide the framework for alterations in threshold to cope with the sparse condition of network. We ultimately perform detailed simulations to scrutinize the performance of our proposed scheme and its comparison with other two notable routing protocols in term of network lifetime and other essential parameters. The simulations results verify that our scheme performs better than the other techniques and near to optimal in the field of UWSN.Comment: 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

Enhanced Cluster Based Routing Protocol for MANETS

Mobile ad-hoc networks (MANETs) are a set of self organized wireless mobile nodes that works without any predefined infrastructure. For routing data in MANETs, the routing protocols relay on mobile wireless nodes. In general, any routing protocol performance suffers i) with resource constraints and ii) due to the mobility of the nodes. Due to existing routing challenges in MANETs clustering based protocols suffers frequently with cluster head failure problem, which degrades the cluster stability. This paper proposes, Enhanced CBRP, a schema to improve the cluster stability and in-turn improves the performance of traditional cluster based routing protocol (CBRP), by electing better cluster head using weighted clustering algorithm and considering some crucial routing challenges. Moreover, proposed protocol suggests a secondary cluster head for each cluster, to increase the stability of the cluster and implicitly the network infrastructure in case of sudden failure of cluster head.Comment: 6 page

On the Stability and Scalability of Node Perturbation Learning

To survive, animals must adapt synaptic weights based on external stimuli and rewards. And they must do so using local, biologically plausible, learning rules – a highly nontrivial constraint. One possible approach is to perturb neural activity (or use intrinsic, ongoing noise to perturb it), determine whether performance increases or decreases, and use that information to adjust the weights. This algorithm – known as node perturbation – has been shown to work on simple problems, but little is known about either its stability or its scalability with respect to network size. We investigate these issues both analytically, in deep linear networks, and numerically, in deep nonlinear ones. We show analytically that in deep linear networks with one hidden layer, both learning time and performance depend very weakly on hidden layer size. However, unlike stochastic gradient descent, when there is model mismatch between the student and teacher networks, node perturbation is always unstable. The instability is triggered by weight diffusion, which eventually leads to very large weights. This instability can be suppressed by weight normalization, at the cost of bias in the learning rule. We confirm numerically that a similar instability, and to a lesser extent scalability, exist in deep nonlinear networks trained on both a motor control task and image classification tasks. Our study highlights the limitations and potential of node perturbation as a biologically plausible learning rule in the brain