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Socionomic Modelling in Wireless Sensor Networks

Sinha S., Chaczko Z.

International Journal of Electronics and Telecommunications

2011

Vol. 57, No. 3

301-308

The performance and efficiency of a Wireless Sensor Network (WSN) is typically subject to techniques used in data routing, clustering, and localization. Being primarily driven by resource constraints, a Socionomic model has been formulated to optimize resource usage and boost collaboration among sensor nodes. In this paper, we present several experimental results to ascertain the underlying philosophy of the Socionomic model for improving network lifetime of resource constrained devices - such as, sensor nodes.

An inflationary inventory model with time dependent demand with Weibull distribution deterioration and partial backlogging under permissible delay in payments

Basu M., Sinha S.

Control and Cybernetics

2007

Vol. 36, no. 1

203-217

This paper proposes to present a general inventory model with due consideration to the factors of time dependent partial backlogging and time dependent deterioration. It also takes into account the impact of inflation, time-dependent demand and permissible delay in payments.

Chaotic dynamics in iterated map neutral networks with piecewise linear activation function

Sinha S.

Fundamenta Informaticae

1999

Vol. 37, Nr 1,2

31-50

The paper examines the discrete-time dynamics of neuron models (of excitatory and inhibi-tory types) with piecewise linear activation functions, which are connected in a network. The properties of a pair neurons (one excitatory and the other inhibitory) connected with each other, is studied in detail. Even such a simple system shows a rich variety of behavior, includ-ing high-period oscillations and chaos. Border-collision bifurcations and multifractal fragmentation of the phase space is also observed for a range of parameter values. Extension of the model to a larger number of neurons is suggested under certain restrictive assumptions, which makes the resultant network dynamics effectively one-dimensional. Possible applica-tions of the network for information processing are outlined. These include using the network for auto-association, pattern classification, nonlinear function approximation and periodic sequence generation.