T. Revathi

Minimal complexity attack classification intrusion detection system

In general, the kind of users and the injection of network packets into the internet sectors are not under specific control. There is no clear description as to what packets can be considered normal or abnormal. If the invasions are not detected at the appropriate level, the loss to system may be some times unimaginable. Although many intrusion detection system (IDS) methods are used to detect the existing types of attacks within the network infrastructures, reducing false negative and false positives is still a major issue. In our paper an intrusion detection system is designed to classify by the incorporation of enhanced rules as learnt from the network behavior with less computational complexity of O(n). The method demonstrates the achievements of promising classification rate. The bench mark data KDD Cup99 data is used in our method.

Heuristic routing with bandwidth and energy constraints in sensor networks

We implement homogeneous sensor network.Four models with and without control transmission power, with and without data aggregation are considered.Increase in network lifetime is found using distributed NNT along energy and bandwidth constraints.Heuristic-I and Heuristic-II takes less energy to route data to the sink node since it deploys NNT. Most of the routing algorithms devised for sensor networks considered either energy constraints or bandwidth constraints to maximize the network lifetime. In the real scenario, both energy and bandwidth are the scarcest resource for sensor networks. The energy constraints affect only sensor routing, whereas the link bandwidth affects both routing topology and data rate on each link. Therefore, a heuristic technique that combines both energy and bandwidth constraints for better routing in the wireless sensor networks is proposed. The link bandwidth is allocated based on the remaining energy making the routing solution feasible under bandwidth constraints. This scheme uses an energy efficient algorithm called nearest neighbor tree (NNT) for routing. The data gathered from the neighboring nodes are also aggregated based on averaging technique in order to reduce the number of data transmissions. Experimental results show that this technique yields good solutions to increase the sensor network lifetime. The proposed work is also tested for wildfire application.

Fuzzy enabled congestion control for Differentiated Services Networks

The requirement of services at different levels of Quality of Service has necessitated the classification of them. The Differentiated Services architecture has been proposed for providing different levels of services and has recently received wide attention. Packets are classified into a class of service according to its Service Level Agreement and treated differently by its class. Hence policy is setup about the kind of actions to be taken such as classifying, shaping, dropping and marking on the packets requiring different level of services. We propose a Fuzzy Enabled Differentiated Services at three precedence levels in the link queue to decide the packet drop. The performance of Differentiated Services with three precedence levels (DS3) is compared with the proposed method. It is found that the number of packets dropped has been reduced in the proposed method compared to the existing Differentiated Services with three precedence levels and hence throughput has been shown to be increased.

Game multi objective scheduling algorithm for scientific workflows in cloud computing

Cloud computing is the latest utility computing that enables the dynamic provisioning for applications over the Internet. Cloud computing provide large number of opportunities to solve large scale scientific problems. Scheduling the tasks in the workflows is an NP-hard problem and in this work we focus on optimizing the scheduling process of the workflow. Scheduling of the workflow applications that satisfy the given constraints for the scientific tasks is an essential requirement for the workflow scheduling. Mapping of each task to suitable resource and allowing the task to satisfy performance constraints is the main aim of this paper. In the present work we propose game theoretic algorithm for multi-objective scheduling of scientific workflows in cloud computing environment. The scheduling problem is formulated as a new sequential cooperative game based on two user objectives that are execution time and economic cost while fulfilling two constraints network bandwidth and storage requirements. We call it as Game Multi Objective (GMO) algorithm. In this paper we apply Game Multi Objective algorithm for minimizing the execution time and cost of an workflow application.

Ensuring Privacy in Data Storage as a Service for Educational Institution in Cloud Computing

Cloud computing is an emerging computing technology that allows us to implement their own services using on-demand IT infrastructures.1 The idea behind this approach is to provide a new model of infrastructure provisioning which can create elastic on-demand IT infrastructures according to the changing requirements. In our work we have proposed to use this on-demand service for data storage typically in an Educational Institution. But, this new technology suffers security issues. To solve this problem we have proposed to incorporate public audit ability and data dynamics for Data Storage as a Service with a Trusted Third Party auditor. Thus making the owners of the data to store their data remotely in the cloud data storage and thereby making them to enjoy on-demand high-quality data storage service from the shared pool of data storage.

Ensuring truthfulness for scheduling multi-objective real time tasks in multi cloud environments

Cloud computing provides dynamic provisioning for real time applications over the Internet. These services are accessed by number of clients as pay per use over the internet. In this scenario, scheduling the current jobs to be executed with given constraints for the real time tasks is an essential requirement. Hence task scheduling is a major challenge in cloud computing. In general, the main aim of Cloud Service Providers (CSPs) is to earn more amount of revenue. So, the providers may provide false information about their resources to gain more profit. To enforce the genuineness of information, game theory model is used. In older approaches, a scheduling algorithm is used to schedule the task with maximum estimated gain and executes the tasks in the queue. Therefore it increases the execution time of the task. This paper presents a scheduling mechanism for real time tasks to achieve timing constraint and minimum cost for the job execution. The game theory mechanism ensures that the truthful information is provided by CSPs. we found that the induced results of the proposed algorithm are effective and our simulation results outperform the traditional scheduling algorithms with multi-objective optimization.

Improved Cluster Based Data Gathering Using Ant Lion Optimization in Wireless Sensor Networks

Wireless sensor networks play a vital role in this digital world through various applications in several domains. The sensor networks are heavily energy constrained due to limited battery power. Therefore, the energy has to be optimally exploited to improve the lifetime and throughput of the network. Among the various existing approaches, cluster based routing algorithms are more popular for its balanced and less energy consumption throughout the communication network. Improper clustering often results in numerous individual nodes (sensor nodes which are not a part of any clusters). The individual nodes will send their information to the base station with high transmission power which heavily impacts the lifetime of the sensor network. Hence, a heuristic Ant Lion Optimization clustering algorithm for wireless sensor network is proposed in this paper. In the proposed work, the cluster head selection is modeled as a fitness function of the Antlion optimization algorithm, which improves the network performance. Also, a Discrete Ant Lion Optimization algorithm is applied to find the optimal data gathering tour for a mobile sink with minimal data collection tour length. The Discrete Ant Lion optimization algorithm computes the optimal order for the mobile sink to visit the selected cluster head nodes and collects their data. The simulation results show that the proposed clustering scheme improves the network lifetime, network throughput and it also reduces the number of individual nodes when compared to existing algorithms. Also, the proposed cluster-based mobile data gathering using the Ant Lion Optimization algorithm produces an optimal tour for the mobile sink to collect data from the cluster head node with minimum data collection tour distance.

Multi-constraint multi-objective QoS aware routing heuristics for query driven sensor networks using fuzzy soft sets

Display Omitted A fuzzy based routing technique is proposed to enhance the lifetime of randomly deployed homogenous sensor network for query driven applications.Multiple broadcast query from sink to the sensor nodes considers the routing uncertainties due to link quality, remaining energy and traffic load.Routing individual unicast replies from the sensor nodes to the sink will consider only link quality and remaining energy of each node.In the link layer asynchronous scheduling algorithm is used to reduce latency due to time synchronization for network communication.Nearest neighbor tree, Fuzzy and A star algorithms are used to find optimal route in the sensor network under energy and bandwidth constraints. In this paper, a fuzzy based distributed power aware routing scheme considering both energy and bandwidth constraints, especially for query driven applications in the asynchronous duty-cycled wireless sensor networks are devised. The proposed multi-constraint, multi-objective routing optimization approach under strict resource constraints guarantees reliability and fast data delivery along with efficient power management in spite of unreliable wireless links and limited power supply. In query driven applications, the request from the sink to the individual sensor node will be a broadcast message, whereas the individual sensor nodes replies back to sink as unicast messages. In the proposed work, the fuzzy approach and A Star algorithm are utilized for satisfying energy and bandwidth constraints to route the broadcast messages of the sink while querying all the sensor nodes in the network. Every node will be provided with a guidance list, which is used to decide the next best neighbor node with good route quality for forwarding the received multi-hop broadcast messages. The route quality of the every node is estimated with fuzzy rules based on the network parameters such as maximum remaining energy, minimum traffic load and better link quality to increase the network lifetime. The provision of overhearing the broadcast messages and acknowledgements within the transmission range minimizes the effort to search for the active time of nodes while routing the broadcast messages with asynchronous scheduling. Further, in the proposed work only the time slot of its nearest neighbor relay node (to which packets are to be forwarded) is learnt to reduce the number of message transmissions in the network. For the unicast message replies, the fuzzy membership function is modified and devised based on the routing metrics such as higher residual energy, minimum traffic loads and minimum hop count under energy and bandwidth constraints. Also, the multi-hop heuristic routing algorithm called Nearest Neighbor Tree is effectively used to reduce the number of neighbors in the guidance list that are elected for forwarding. This helps to increase the individual sensor nodes lifetime, thereby maximizes the network lifetime and guarantees increased network throughput. The simulation results show that the proposed technique reduces repeated transmissions, decreases the number of transmissions, shortens the active time of the sensor nodes and increases the network lifetime for query driven sensor network applications invariant to total the number of sensor nodes and sinks in the network. The proposed algorithm is tested in a small test bed of sensor network with ten nodes that monitors the room temperature.

Applied fuzzy heuristics for automation of hygienic drinking water supply system using wireless sensor networks

About 20% of communicable infectious disease is spread by drinking contaminated water. Hence, a real-time in-pipe drinking water quality system using sensor networks is proposed. The proposed prototype Drinking Water Quality Monitoring System (DWQMS) checks for parameters such as pH, temperature, turbidity, oxidation–reduction potential, conductivity, and dissolved oxygen in the drinking water supplied through pipes by the municipality in a fast and efficient manner. In the proposed work, a sensor network that is powered by solar energy is deployed inside the water pipelines to improve the network connectivity and enhance the network lifetime. The prototype designed uses an Energy Aware Multipath Routing Protocol (EAMRP) to prevent the water flow when contamination is detected in a particular pipeline region without interrupting the supply in non-contaminated regions. The key ingredients of the proposed protocol are an energy-efficient algorithm; maximizing the data correlation among sensors; shortest path routing and fast data transmission algorithm to report the water quality to the users quickly; event detection algorithms to assess the water contamination risks in pipes; and fuzzy rule descriptors to predict the water quality as desirable/acceptable/rejected for drinking with better accuracy. The simulation results show that the designed DWQMS acts as an early warning system and outperforms in terms of energy efficiency, detects the contaminants with better accuracy, increases network lifetime, and better estimates the water quality parameters. The proposed algorithms are tested in a small test bed of wireless sensor networks with 20 nodes that monitor the drinking water quality distributed in water distribution mains, which alert the consumers/houses in the water-contaminated regions.

Fuzzy-based Security-Driven Optimistic Scheduling of Scientific Workflows in Cloud Computing

ABSTRACT Cloud computing is a new computing paradigm which is gaining wide acceptance among scientific fraternity in the recent years. The services of cloud could be effectively used for running large-scale data and computation-intensive scientific workflow applications. Finding the optimal schedule for such workflows has been a major concern among the cloud users. In the present work, a novel approach of combining both optimization of the schedule along with the allocation of the virtual machines (VMs) based on security requirements is envisaged. This paper focuses on generating an optimized schedule for the complex workflow structures. The main objective of the schedule is to minimize the makespan of the schedule. In this paper, we design the scheduling heuristic based on the cost prediction matrix (CPM) for optimized cost calculation. The CPM will estimate the execution cost by considering the child’s child task also. This leads to a prophetic estimation on the available VMs. In addition to this, we have used a fuzzy-based decision model for deciding the selection of the VMs based on security constraints in the cloud. This fuzzy model is combined with the optimized cost calculation from CPM for each and every task of the workflow. The proposed secured cost prediction-based scheduling (SCPS) algorithm then schedules the task in the best possible VM, so that the makespan is minimized. Our results show that the newly developed SCPS algorithm yields efficient schedule compared to other existing scheduling models in spite of the inclusion of security constraints besides scheduling. Nevertheless, this secured scheduling is done without much increase in the time complexity.