Faculty Publications
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Item Energy Efficient Network Design for IoT Healthcare Applications(Springer Science and Business Media Deutschland GmbH, 2017) Sarwesh, P.; Shet, V.; Chandrasekaran, K.Internet of Things (IoT) is the emerging technology, that holds huge number of internet enabled devices and allows to share the data globally. IoT technology provides effective healthcare service by constant monitoring and reporting the chronic conditions of patients. IoT is highly greeted by healthcare sectors. IoT devices are smart in nature but constrained by energy, because most of the IoT applications uses battery operated smart devices. Hence energy is considered as valuable resource in energy constrained IoT environment. In this chapter energy efficient network architecture is proposed for IoT health care applications. Proposed network architecture describes the suitable combination of two different techniques such as, routing technique and node placement technique. In routing technique energy level of the nodes are monitored, to transmit the data in energy efficient path. In node placement technique, data traffic is balanced by varying the density of the nodes. This chapter describes the major factors that affect energy efficiency and it elaborates the suitable techniques to improve energy efficiency in IoT network. © 2017, Springer International Publishing AG.Item Energy efficient and reliable network design to improve lifetime of low power IoT networks(Institute of Electrical and Electronics Engineers Inc., 2017) Sarwesh, P.; Shekar, N.; Shet, V.; Chandrasekaran, K.Internet of Things is smart technology that is used in wide range of applications, IoT converges physical devices with cyber systems to facilitate global information sharing. In IoT network, devices are constrained by energy (limited by battery power). Thus, efficient energy utilization is the major challenge in low power IoT networks. In this paper, energy efficient and reliable network architecture is proposed to improve the lifetime of IoT networks. In proposed network architecture, routing technique and node placement technique are effectively integrated to address energy and reliability related issues. In node placement technique, density of sensor nodes are hierarchically varied to balance the energy consumption and reliability related parameters are included in routing mechanism. Hence, effective combination of these two techniques in single network architecture prolongs the lifetime of the network. In proposed work, sensor nodes and relay nodes, sensors do sensing and relay nodes handles path computation and data transmission. We included IEEE 802.15.4 PHY/MAC radio and IPv6 module in proposed work to adopt IoT Scenario. From our results, it is observed that proposed architecture prolongs the lifetime of low power IoT network. © 2017 IEEE.Item CREW: Cost and Reliability aware Eagle-Whale optimiser for service placement in Fog(John Wiley and Sons Ltd cs-journals@wiley.co.uk, 2020) Paul Martin, J.; Kandasamy, A.; Chandrasekaran, K.Integration of Internet of Things (IoT) with industries revamps the traditional ways in which industries work. Fog computing extends Cloud services to the vicinity of end users. Fog reduces delays induced by communication with the distant clouds in IoT environments. The resource constrained nature of Fog computing nodes demands an efficient placement policy for deploying applications, or their services. The distributed and heterogeneous features of Fog environments deem it imperative to consider the reliability performance parameter in placement decisions to provide services without interruptions. Increasing reliability leads to an increase in the cost. In this article, we propose a service placement policy which addresses the conflicting criteria of service reliability and monetary cost. A multiobjective optimisation problem is formulated and a novel placement policy, Cost and Reliability-aware Eagle-Whale (CREW), is proposed to provide placement decisions ensuring timely service responses. Considering the exponentially large solution space, CREW adopts Eagle strategy based multi-Whale optimisation for taking placement decisions. We have considered real time microservice applications for validating our approaches, and CREW has been experimentally shown to outperform the existing popular multiobjective meta-heuristics such as NSGA-II and MOWOA based placement strategies. © 2020 John Wiley & Sons Ltd