Shashikala Tapaswi

An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch

Economic dispatch is carried out at the energy control center to find out the optimal output of thermal generating units such that power balance criterion is met, unit operating limits are satisfied and the fuel cost is minimized. With growing environmental awareness and strict government regulations throughout the world, it has become essential to optimize not only the total fuel cost but also the harmful emissions, both, under static as well as dynamic conditions. The static environment economic dispatch finds the optimal output of generating units for a fixed load demand at a given time, while the dynamic environmental economic dispatch schedules the output of online generators with changing power demands over a certain time period (normally one day) so as to minimize these two conflicting objectives, simultaneously. In this paper, the price penalty factor approach is employed for simultaneous minimization of cost and emission. The generator ramp rate constraints, non-convex and discontinuous nature of cost function and the large number of generators in practical power plants, make this problem very difficult to solve. Here, a fuzzy ranking approach is employed to identify the solution which offers the best compromise between cost and emission objectives. This paper proposes an improved bacterial foraging algorithm (IBFA) in which a parameter automation strategy and crossover operation is used in micro BFA to improve computational efficiency. The performance of IBFA is compared with classical BFA and with previously published literature on four standard test systems and is found to be better.

Robust Smart Card Authentication Scheme for Multi-server Architecture

In a traditional single server smart card authentication scheme, one server is responsible for providing services to all the registered remote users. Though if a user wishes to access network services from different servers, he or she has to register with these servers separately. To handle this issue, multi-server authentication scheme has been proposed. However, almost all these schemes available in the literature are exposed to one or the other potential attack. This paper proposes robust multi-server authentication scheme using smart cards. It eliminates the use of verification table and permits the registered remote users to access multiple servers without separate registration. Moreover, users can choose and change the password securely without any assistance from the server or registration center, supports mutual authentication and session key agreement between user and the server. Furthermore, the proposed scheme withstands present potential network attacks. Besides, our scheme is validated by using BAN logic. Comparative analysis of existing schemes with our proposed scheme is also presented in terms of various security features provided and computational complexity.

A Power Aware Minimum Connected Dominating Set for Wireless Sensor Networks

Connected Dominating Set (CDS) problem in unit disk graph has a significant impact on an efficient design of routing protocols in wireless sensor networks. In this paper, an algorithm is proposed for finding Minimum Connected Dominating Set (MCDS) using Dominating Set. Dominating Sets are connected by using Steiner tree. The algorithm goes through three phases. In first phase Dominating Sets are found, in second phase connectors are identified, connected through Steiner tree. In third phase the CDS obtained in second phase is pruned to obtain a MCDS. MCDS so constructed needs to adapt to the continual topology changes due to deactivation of a node due to exhaustion of battery power. These topological changes due to power constraints are taken care by a local repair algorithm that reconstructs the MCDS i.e. Power Aware MCDS, using only neighbourhood information. Simulation results indicate both the heuristics are very efficient and result in near optimal MCDS.

A centralized detection and prevention technique against ARP poisoning

The Man-in-the-Middle (MITM) attack on ARP is presently a common attack and nuisance to the typical LAN environment. This type of MITM is brought to effect by ARP cache poisoning which is achieved using forged ARP packets. ARP poisoning is a mechanism in which a node poisons ARP cache table of another node forcing it to send packets to a destination other than the intended one. This paper presents a feasible solution to the ARP cache poisoning, removing inconsistencies from all ARP tables of all hosts in the network. This paper uses a centralized system and ARP Central Server (ACS) to manage ARP table entries in all hosts. All hosts in the network uses the ACS to validate their ARP table entries. The ACS validates and corrects the poisoned ARP entries of the attacked hosts and hence prevents ARP poisoning in the network.

Comments on symmetric key encryption based smart card authentication scheme

Recently smart card authentication scheme based on symmetric key encryption is proposed. It provides mutual authentication between user and server and generates a session key. Its security depends on the one-way hash function and symmetric cryptosystem. It has been claimed that the scheme is secure and provides mutual authentication. In addition, it withstands replay attack, impersonation attack and parallel session attack. This paper shows that the scheme is vulnerable to perfect forward secrecy and Denial-of-Service attack.

SmartCQL: Semantics to Handle Complex Queries over Data Streams

SmartCQL, enhanced version of CQL is developed so as to provide semantics to handle complex queries. Simple SPJ (Select Project Join) queries are no longer useful and the existing CQL supports complex queries only to a certain extent. But the main goal of SmartCQL is to enhance this feature to a large extent. We achieved this goal by providing semantics to handle complex queries like sub queries which was not handled by CQL. We solved the famous Linear Road problem, benchmark for data streams with minimum number of queries as compared to CQL. Here we will show how SmartCQL increased the expressiveness to a greater extent.

A Heuristic for Minimum Connected Dominating Set with Local Repair for Wireless Sensor Networks

Connected Dominating Set (CDS) problem in unit disk graph has a significant impact on an efficient design of routing protocols in wireless sensor networks. In this paper, an algorithm is proposed for finding Minimum Connected Dominating Set (MCDS) using Dominating Set. Dominating Sets are connected by using Steiner tree. The algorithm goes through three phases. In first phase Dominating Set is found, in second phase connectors are identified, connected through Steiner tree. In third phase the CDS obtained in second phase is pruned to obtain a MCDS. MCDS so constructed needs to adapt to the continual topology changes due to deactivation of a node due to exhaustion of battery power. These topological changes due to power constraints are taken care by a local repair algorithm that reconstructs the MCDS using only neighborhood information. Simulation results indicate both the heuristics are very efficient and result in near optimal MCDS.

Virtual machine introspection: towards bridging the semantic gap

Virtual machine introspection is a technique used to inspect and analyse the code running on a given virtual machine. Virtual machine introspection has gained considerable attention in the field of computer security research. In recent years, it has been applied in various areas, ranging from intrusion detection and malware analysis to complete cloud monitoring platforms. A survey of existing virtual machine introspection tools is necessary to address various possible research gaps and to focus on key features required for wide application of virtual machine introspection techniques. In this paper, we focus on the evolution of virtual machine introspection tools and their ability to address the semantic gap problem.

Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so, the RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user’s location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.

Integrated evolutionary neural network approach with distributed computing for congestion management

Electric supply industry is facing deregulation all over the world. Under deregulated power supply scenario, power transmission congestion has become more intensified and recurrent, as compared to conventional regulated power system. Congestion may lead to violation of voltage or transmission capacity limits, thus threatens the power system security and reliability. Also the growing congestion may lead to unanticipated divergent electricity pricing. Owing to these facts congestion management has become a crucial issue in the deregulated power system scenario. Fast and precise prediction of nodal congestion prices in real time deregulated/spot power market may enable market participants and system operators to keep pace with the congestion by taking preventive measures like transaction rescheduling, bids (both for supplying and consuming electricity) modification, regulated dispatch of electric power, etc. This paper proposes an integrated evolutionary neural network (ENN) approach to predict nodal congestion prices (NCPs) for congestion management in spot power market. Distributed computing is employed to tackle the heterogeneity of the data in the prediction of NCP values. Developed ENNs have been trained and tested under distributed computing environment, using a message passing paradigm. Proposed hybrid approach for NCP prediction is demonstrated on a 6-bus test power system with and without distributed computing. The proposed approach not only demonstrated the computing efficiency of the developed ENN model over the conventional optimal power flow method but also shows the time saving aspect of distributed computing.