Anindita Ray

Energy efficient cluster head selection in wireless sensor network

This paper presents an improved version of LEACH protocol which aims to reduce energy consumption within the wireless sensor network and prolong the lifetime of the network. This paper improves LEACH protocol by improving the election strategy of the cluster-head nodes based on remaining energy of sensor nodes, distance from base station and the number of consecutive rounds in which a node has not been a cluster head. Also it considers the factor that whether the nodes remaining energy is sufficient enough to send the aggregate data to the base station or not. If the nodes remaining energy is not sufficient enough it cannot be selected as cluster head. Simulation results shows that the proposed protocol could better reduce energy consumption and prolong lifetime of the wireless sensor network with respect to the parameters FND (First Node Dies), HND (Half Node Dies) and LND (Last Node Dies). The proposed protocol is 41.7% better than LEACH with respect to FND, 31.6 % better than LEACH with respect to HND and 25.5% better than LEACH with respect to LND.

An energy efficient sensor movement approach using multi-parameter reverse glowworm swarm optimization algorithm in mobile wireless sensor network

Abstract In mobile wireless sensor network, coverage and energy conservation are two prime issues. Sensor movement is required to achieve high coverage. But sensor movement is one of the main factors of energy consumption in mobile wireless sensor network. Therefore, coverage and energy conservation are correlated issues and quite difficult to achieve at the same time. In this paper, these conflicting issues are considered, using one of the latest Bio-inspired algorithms, known as Glowworm Swarm Optimization algorithm. Considering the limited energy of sensors, this paper presents an Energy Efficient Multi-Parameter Reverse Glowworm Swarm Optimization (EEMRGSO) algorithm, to move the sensors in an energy efficient manner. Our proposed algorithm reduces redundant coverage area by moving the sensors from densely deployed areas to some predefined grid points. In this proposed algorithm, energy consumption is reduced by decreasing the number of moving sensors as well as the total distance traversed. Simulation results show that, our proposed EEMRGSO algorithm reduces total energy consumption utmost 60% compared to the existing approach based on Glowworm Swarm Optimization algorithm. At the same time, our proposed algorithm reduces the number of overlapped sensors significantly and achieves an effective coverage of 80–89% approximately.

Analysis of Energy Consumption for IARP, RIP and STAR Routing Protocols in Wireless Sensor Networks

The era of Wireless Sensor Network (WSN) emerged due to the advancement in information technology for internet of things. In WSN large numbers of sensor nodes are deployed to sensing and collecting information from its surrounding and send them to the base station with the help of routing protocol. Routing protocols are used to maintain and identify the routes in the network. Energy consumption is a major challenge in WSN. There are various routing protocols for handling routing problems as well as optimizing energy consumption in WSN. In this paper we have used Qual Net 7.1 simulator to perform simulation analysis of IARP, RIP and STAR routing protocols based on different performance metrics such as number of update packets sent, node wise energy consumption as well as percentage of time in different modes, average energy consumed in transmit, receive and idle mode with different terrain sizes and varying simulation time in transmit mode.

Energy efficient clustering protocol based on K-means (EECPK-means)-midpoint algorithm for enhanced network lifetime in wireless sensor network

Wireless sensor networks (WSNs) consist of an enormous number of tiny sensor nodes deployed in huge numbers which are able to sense, process and transmit environmental information to the base station (BS) for a variety of applications. Energy efficiency is one of the primary concerns for maintaining WSN in operation. In this study, an energy efficient clustering protocol based on K-means algorithm named EECPK-means has been proposed for WSN where midpoint algorithm is used to improve initial centroid selection procedure. The proposed approach produces balanced clusters to ultimately balance the load of cluster heads (CHs) and prolong the network lifetime. It considers residual energy as the parameter in addition to Euclidean distance used in basic K-means algorithm for appropriate CH selection. Multi-hop communication from CH nodes to BS takes place depending on their distances from BS. Simulation result shows that the proposed approach outperforms LEACH-B, balanced parallel K-means (BPK-means), Parks approach and Mk-means with respect to network lifetime and energy efficiency. Simulation result also demonstrates that the proposed approach can reduce the energy consumption at most 50% compared to LEACH-B, 14% compared to BPK-means protocol, 10% compared to Parks approach and 6% compared to Mk-means.

Architecture of green sensor mobile cloud computing

This study presents an architecture of green sensor mobile cloud computing which integrates sensor network and mobile network with cloud computing. In the proposed scheme, the sensor data are transmitted to the cloud through a mobile device. To develop the proposed architecture both the indoor and outdoor region are considered. For indoor region light weight access point and home node base station are used by the mobile device for sensor data transmission to the cloud, whereas for outdoor region macrocell and microcell base stations are used. Simulation results present that using home node base station the power consumption at indoor region can be reduced by ∼10% than the light weight access point, and using microcell base station at outdoor region the power consumption can be reduced by ∼30% than the macrocell base station. Hence, using home node base station and microcell base station green sensor mobile cloud computing can be obtained at indoor and outdoor regions, respectively. In this study, an experimental analysis of the proposed architecture is also performed.

Maximizing lifetime and coverage for minimum energy wireless sensor network using corona based sensor deployment

Wireless sensor network consist of an enormous range of sensor nodes positioned in any environmental area for collecting and transfer information. These days wireless sensor networks are widely used in different areas of technology, such as health care system, environment monitoring, smart city implementation, military surveillance and many more. To provide a good quality of service it is essential to position the sensor nodes in an effective manner such that the region of interest is fully monitored with minimum number of coverage holes. It is also required to reduce the consumption of energy by the network since the battery lifetime is very limited in the sensor node. This paper presents a Corona based sensor node deployment strategy which provides effective monitoring in WSN. Furthermore it reduces the complete energy depletion of the WSN. Simulation outcomes show that in the proposed strategy the region of interest is fully monitored with low power consumption. Simulation results also demonstrate that network lifetime and coverage can be enhanced to a great extent using proposed corona based deployment.