Faculty Publications

Now showing 1 - 3 of 3

Dynamic web service composition based on operation flow semantics
(2010) D’Mello, D.A.; Ananthanarayana, V.S.
Dynamic Web service composition is a process of building a new value added service using available services to satisfy the requester's complex functional need. In this paper we propose the broker based architecture for dynamic Web service composition. The broker plays a major role in effective discovery of Web services for the individual tasks of the complex need. The broker maintains flow knowledge for the composition, which stores the dependency among the Web service operations and their input, output parameters. For the given complex requirements, the broker first generates the abstract composition plan and discovers the possible candidate Web services to each task of the abstract composition plan. The abstract composition plan is further refined based on the Message Exchange Patterns (MEP), Input/Output parameters, QoS of the candidate Web services to produce refined composition plan involving Web service operations with execution flow. The refined composition plan is then transferred to generic service provider to generate executable composition plan based on the requester's input or output requirements and preferences. The proposed effective Web service discovery and composition mechanism is defined based on the concept of functional semantics and flow semantics of Web service operations. © 2010 Springer-Verlag Berlin Heidelberg.
Improved Harmony Search Algorithm for Multihop Routing in Wireless Sensor Networks
(Pleiades Publishing, 2022) Sowmya, G.V.; Manjappa, M.
Abstract: Energy efficiency is critical for prolonging the network lifetime of Wireless Sensor Network (WSN), and is the most important objective for any routing algorithm for WSN. In this article authors have proposed a Multihop harmony search algorithm for WSN with two objectives, first being increasing the throughput of the network and second being optimizing the energy consumption of the sensor nodes and thereby prolonging the lifetime of network. Finding the goodness of the communication channel/path is quite important. Sometimes, though the channel capacity is more, fewer amounts of data may be transmitted in the channel resulting in under utilization of the resources; and other times, though the channel capacity is less, more data may be dumped into the channel resulting in channel congestion and less output. Thus, if the goodness of the communication channel is known in advance, then it is easy for the algorithms to decide the upper bound of the channel and can have a congestion free and error free information transmission. Thus, the proposed algorithm employ Shannon channel capacity ‘C’ (baud rate) for finding the best next hop and the same is used for initialization of Harmony Memory. An effective local search strategy is also proposed to strengthen the local harmony search ability so that the convergence speed and the accuracy of routing algorithm is improved. Finally, an objective function model is developed by taking path length, energy consumption, and residual energy in to consideration. The proposed algorithm is compared with existing Multihop LEACH, BRM (Baud rate based Multihop routing protocol) and EEHSBR (Energy Efficient Harmony Search Based Routing) algorithm for the quantitative and qualitative analysis. The simulation results reveal that the proposed algorithm performs better than the considered algorithms in terms of network lifetime, throughput and energy consumption. © 2022, Pleiades Publishing, Ltd.
Optimizing Reinforcement Learning-Based Visual Navigation for Resource-Constrained Devices
(Institute of Electrical and Electronics Engineers Inc., 2023) Vijetha, U.; Geetha, V.
Existing work on Deep reinforcement learning-based visual navigation mainly focuses on autonomous agents with ample power and compute resources. However, Reinforcement learning for visual navigation on resource-constrained devices remains an under-explored area of research, primarily due to challenges posed by processing high-dimensional visual inputs and making prompt decisions in realtime scenarios. To address these hurdles, we propose a State Abstraction Technique (SAT) designed to transform high-dimensional visual inputs into a compact representation, enabling simpler reinforcement learning agents to process the information and learn effective navigation policies. The abstract representation generated by SAT effortlessly serves as a versatile intermediary that bridges the gap between simulation and reality, enhancing the transferability of learned policies across various scenarios. Additionally, our reward shaping strategy uses the data provided by SAT to maintain a safe distance from obstacles, further improving the performance of navigation policies on resource-constrained devices. Our work opens up opportunities for navigation assistance and other applications in a variety of resource-constrained domains, where computational efficiency is crucial for practical deployment, such as guiding miniature agents on embedded devices or aiding visually impaired individuals through smartphone-integrated solutions. Evaluation of proposed approach on the AI2-Thor simulated environment demonstrates significant performance improvements over traditional state representations. The proposed method provides 84.18% fewer collisions, 28.96% fewer movement instructions and 11.3% higher rewards compared to the best alternative options available. Furthermore, we carefully account for real-world challenges by considering noise and motion blur during training, ensuring optimal performance during deployment on resource-constrained devices. © 2013 IEEE.

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