Membership models and the design of authentication protocols for MANETs

Authentication protocols are very much essential for secure communication in mobile ad hoc networks (MANETs). A number of authentication protocols for MANETs have been proposed in the literature which provide the basic authentication service while trying to optimize their performance and resource consumption parameters. A problem with most of these protocols is that the underlying networking environment on which they are applicable have been left unspecified. As a result, lack of specifications about the networking environments applicable to an authentication protocol for MANETs can mislead about the performance and the applicability of the protocol. In this paper, we first characterize networking environment for a MANET as its 'Membership Model' which is defined as a set of specifications related to the 'Membership Granting Server' (MGS) and the 'Membership Set Pattern' (MSP) of the MANET. We then identify various types of possible membership models for a MANET. In order to illustrate that while designing an authentication protocol for a MANET, it is very much necessary to consider the underlying membership model of the MANET, we study a set of six representative authentication protocols, and analyze their applicability for the membership models as enumerated in this paper. The analysis shows that the same protocol may not perform equally well in all membership models. In addition, there may be membership models which are important from the point of view of users, but for which no authentication protocol is available.

A Secure and Efficient Authentication Protocol (SEAP) for MANETs with Membership Revocation

In this paper, we propose a novel authentication protocol for MANETs requiring stronger security. The protocol works on a two-tier network architecture with client nodes and authentication server nodes, and supports dynamic membership. We use an external membership granting server (MGS) to provide stronger security with dynamic membership. However, the external MGS in our protocol is semi-online instead of being online, i.e., the MGS cannot initiate a connection with a network node but any network node can communicate with the MGS whenever required. To ensure efficiency, the protocol uses symmetric key cryptography to implement the authentication service. However, to achieve storage scalability, the protocol uses a pseudo random function (PRF) to bind the secret key of a client to its identity using the secret key of its server. In addition, the protocol possesses an efficient server revocation mechanism along with an efficient server re-assignment mechanism, which makes the protocol robust against server node compromise.

Fuzzy optimization model for water quality management of a river system - Closure

A fuzzy waste-load allocation model, FWLAM, is developed for water quality management of a river system using fuzzy multiple-objective optimization. An important feature of this model is its capability to incorporate the aspirations and conflicting objectives of the pollution control agency and dischargers. The vagueness associated with specifying the water quality criteria and fraction removal levels is modeled in a fuzzy framework. The goals related to the pollution control agency and dischargers are expressed as fuzzy sets. The membership functions of these fuzzy sets are considered to represent the variation of satisfaction levels of the pollution control agency and dischargers in attaining their respective goals. Two formulations—namely, the MAX-MIN and MAX-BIAS formulations—are proposed for FWLAM. The MAX-MIN formulation maximizes the minimum satisfaction level in the system. The MAX-BIAS formulation maximizes a bias measure, giving a solution that favors the dischargers. Maximization of the bias measure attempts to keep the satisfaction levels of the dischargers away from the minimum satisfaction level and that of the pollution control agency close to the minimum satisfaction level. Most of the conventional water quality management models use waste treatment cost curves that are uncertain and nonlinear. Unlike such models, FWLAM avoids the use of cost curves. Further, the model provides the flexibility for the pollution control agency and dischargers to specify their aspirations independently.

An algebraic theory of functional and multivalued dependencies in relational databases

Computation of the dependency basis is the fundamental step in solving the membership problem for functional dependencies (FDs) and multivalued dependencies (MVDs) in relational database theory. We examine this problem from an algebraic perspective. We introduce the notion of the inference basis of a set M of MVDs and show that it contains the maximum information about the logical consequences of M. We propose the notion of a dependency-lattice and develop an algebraic characterization of inference basis using simple notions from lattice theory. We also establish several interesting properties of dependency-lattices related to the implication problem. Founded on our characterization, we synthesize efficient algorithms for (a): computing the inference basis of a given set M of MVDs; (b): computing the dependency basis of a given attribute set w.r.t. M; and (c): solving the membership problem for MVDs. We also show that our results naturally extend to incorporate FDs also in a way that enables the solution of the membership problem for both FDs and MVDs put together. We finally show that our algorithms are more efficient than existing ones, when used to solve what we term the ‘generalized membership problem’.

A Fuzzy Approximation Scheme for Sequential Learning in Pattern Recognition

An adaptive learning scheme, based on a fuzzy approximation to the gradient descent method for training a pattern classifier using unlabeled samples, is described. The objective function defined for the fuzzy ISODATA clustering procedure is used as the loss function for computing the gradient. Learning is based on simultaneous fuzzy decisionmaking and estimation. It uses conditional fuzzy measures on unlabeled samples. An exponential membership function is assumed for each class, and the parameters constituting these membership functions are estimated, using the gradient, in a recursive fashion. The induced possibility of occurrence of each class is useful for estimation and is computed using 1) the membership of the new sample in that class and 2) the previously computed average possibility of occurrence of the same class. An inductive entropy measure is defined in terms of induced possibility distribution to measure the extent of learning. The method is illustrated with relevant examples.

Estimation of fuzzy memberships from histograms

Based on the conclusions drawn in the bijective transformation between possibility and probability, a method is proposed to estimate the fuzzy membership function for pattern recognition purposes. A rational function approximation to the probability density function is obtained from the histogram of a finite (and sometimes very small) number of samples. This function is normalized such that the highest ordinate is one. The parameters representing the rational function are used for classifying the pattern samples based on a max-min decision rule. The method is illustrated with examples.

Chemical communication in Ropalidia marginata: Dufour's gland contains queen signal that is perceived across colonies and does not contain colony signal

Queens of the primitively eusocial wasp Ropalidia marginata appear to maintain reproductive monopoly through pheromone rather than through physical aggression. Upon queen removal, one of the workers (potential queen, PQ) becomes extremely aggressive but drops her aggression immediately upon returning the queen. If the queen is not returned, the PQ gradually drops her aggression and becomes the next queen of the colony. In a previous study, the Dufour's gland was found to be at least one source of the queen pheromone. Queen-worker classification could be done with 100% accuracy in a discriminant analysis, using the compositions of their respective Dufour's glands. In a bioassay, the PQ dropped her aggression in response to the queen's Dufour's gland macerate, suggesting that the queen's Dufour's gland contents mimicked the queen herself. In the present study, we found that the PQ also dropped her aggression in response to the macerate of a foreign queen's Dufour's gland. This suggests that the queen signal is perceived across colonies. This also suggests that the Dufour's gland in R. marginata does not contain information about nestmateship, because queens are attacked when introduced into foreign colonies, and hence PQ is not expected to reduce her aggression in response to a foreign queen's signal. The latter conclusion is especially significant because the Dufour's gland chemicals are adequate to classify individuals correctly not only on the basis of fertility status (queen versus worker) but also according to their colony membership, using discriminant analysis. This leads to the additional conclusion (and precaution) that the ability to statistically discriminate organisms using their chemical profiles does not necessarily imply that the organisms themselves can make such discrimination. (C) 2010 Elsevier Ltd. All rights reserved.

Quasicrystals:Indian research accomplishments and imperatives

: Within two months of the first report on quasicrystals in PRL in November 1984, Indian research which had a 'premature discovery' in 1978 in this area got under way, In the past nine years these efforts have led to original discoveries relating to new types of quasicrystalline phases as well as extensive investigations involving tiling theory, hyperspace, positron annihilation and electrical properties, These researches have been multi-institutional and multi-disciplinary. Enlightened and generous funding was extended by DST from 1986 by recognizing it as a thrust area in basic research via SERC and US-India Funds. International recognition, subjective though it is, in the form of citation of Indian papers, invited lectures and reviews, books as well as the membership of International Advisory Committee has followed and is among the highest in the fields of condensed matter science covered at the Bangalore meeting, Future directions pertaining to the exploration of mechanical and electronic properties as well as structures beyond the quasicrystalline order will be pointed out.

A Coalitional Game Framework for Cooperative Secondary Spectrum Access

We consider a framework in which several service providers offer downlink wireless data access service in a certain area. Each provider serves its end-users through opportunistic secondary spectrum access of licensed spectrum, and needs to pay primary license holders of the spectrum usage based and membership based charges for such secondary spectrum access. In these circumstances, if providers pool their resources and allow end-users to be served by any of the cooperating providers, the total user satisfaction as well as the aggregate revenue earned by providers may increase. We use coalitional game theory to investigate such cooperation among providers, and show that the optimal cooperation schemes can be obtained as solutions of convex optimizations. We next show that under usage based charging scheme, if all providers cooperate, there always exists an operating point that maximizes the aggregate revenue of providers, while presenting each provider a share of the revenue such that no subset of providers has an incentive to leave the coalition. Furthermore, such an operating point can be computed in polynomial time. Finally, we show that when the charging scheme involves membership based charges, the above result holds in important special cases.

Fuzzy Clustering Based Ad Recommendation for TV Programs

Advertisements(Ads) are the main revenue earner for Television (TV) broadcasters. As TV reaches a large audience, it acts as the best media for advertisements of products and services. With the emergence of digital TV, it is important for the broadcasters to provide an intelligent service according to the various dimensions like program features, ad features, viewers’ interest and sponsors’ preference. We present an automatic ad recommendation algorithm that selects a set of ads by considering these dimensions and semantically match them with programs. Features of the ad video are captured interms of annotations and they are grouped into number of predefined semantic categories by using a categorization technique. Fuzzy categorical data clustering technique is applied on categorized data for selecting better suited ads for a particular program. Since the same ad can be recommended for more than one program depending upon multiple parameters, fuzzy clustering acts as the best suited method for ad recommendation. The relative fuzzy score called “degree of membership” calculated for each ad indicates the membership of a particular ad to different program clusters. Subjective evaluation of the algorithm is done by 10 different people and rated with a high success score.

Similarity relations in visual search predict rapid visual categorization

How do we perform rapid visual categorization?It is widely thought that categorization involves evaluating the similarity of an object to other category items, but the underlying features and similarity relations remain unknown. Here, we hypothesized that categorization performance is based on perceived similarity relations between items within and outside the category. To this end, we measured the categorization performance of human subjects on three diverse visual categories (animals, vehicles, and tools) and across three hierarchical levels (superordinate, basic, and subordinate levels among animals). For the same subjects, we measured their perceived pair-wise similarities between objects using a visual search task. Regardless of category and hierarchical level, we found that the time taken to categorize an object could be predicted using its similarity to members within and outside its category. We were able to account for several classic categorization phenomena, such as (a) the longer times required to reject category membership; (b) the longer times to categorize atypical objects; and (c) differences in performance across tasks and across hierarchical levels. These categorization times were also accounted for by a model that extracts coarse structure from an image. The striking agreement observed between categorization and visual search suggests that these two disparate tasks depend on a shared coarse object representation.

An Optimal Fuzzy Logic Controller tuned with Artificial Immune System

In this paper, a method for the tuning the membership functions of a Mamdani type Fuzzy Logic Controller (FLC) using the Clonal Selection Algorithm(CSA) a model of the Artificial Immune System(AIS) paradigm is examined. FLC's are designed for two problems, firstly the linear cart centering problem and secondly the highly nonlinear inverted pendulum problem. The FLC tuned by AIS is compared with FLC tuned by GA. In order to check the robustness of the designed PLC's white noise was added to the system, further, the masses of the cart and the length and mass of the pendulum are changed. The PLC's were also tested in the presence of faulty rules. Finally, Kruskal Wallis test was performed to compare the performance of the GA and AIS. An insight into the algorithms are also given by studying the effect of the important parameters of GA and AIS.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

Integrated sustainable irrigation planning with multiobjective Fuzzy optimization approach

Multiobjective fuzzy methodology is applied to a case study of Khadakwasla complex irrigation project located near Pune city of Maharashtra State, India. Three objectives, namely, maximization of net benefits, crop production and labour employment are considered. Effect of reuse of wastewater on the planning scenario is also studied. Three membership functions, namely, nonlinear, hyperbolic and exponential are analyzed for multiobjective fuzzy optimization. In the present study, objective functions are considered as fuzzy in nature whereas inflows are considered as dependable. It is concluded that exponential and hyperbolic membership functions provided similar cropping pattern for most of the situations whereas nonlinear membership functions provided different cropping pattern. However, in all the three cases, irrigation intensities are more than the existing irrigation intensity.

Algebraic characterization of rainbow connectivity

The use of algebraic techniques to solve combinatorial problems is studied in this paper. We formulate the rainbow connectivity problem as a system of polynomial equations. We first consider the case of two colors for which the problem is known to be hard and we then extend the approach to the general case. We also present a formulation of the rainbow connectivity problem as an ideal membership problem.