Energy Efficient Scheme for Wireless Sensor Networks

Recent advances in wireless sensor networks have commanded many new protocols specifically designed for sensor networks where energy awareness is an important concern. This routing protocols might differ from depending on the application and the network architecture. To extend the lifetime of Wireless sensor network (WSN), an energy efficient scheme can be designed and developed via an algorithm to provide reasonable energy consumption and network for WSN. To maintain high scalability and better data aggregation, sensor nodes are often grouped into disjoint, non-overlapping subsets called clusters. Clusters create hierarchical WSNs which incorporate efficient utilization of limited resources of sensor nodes to reduce energy consumption, thus extend the lifetime of WSN. The objective of this paper is to present a state of the art survey and classification of energy efficient schemes for WSNs. Keywords: Wireless Sensor Network, clustering, energy efficient clustering, network lifetime, energy efficient algorithms, energy efficient routing, and sensor networks. DOI: 10.17762/ijritcc2321-8169.15024

An Efficient Energy Harvesting Assited Clustering Scheme for Wireless Sensor Networks

One of the prominent challenges in Wireless sensor networks (WSNs) is the energy conservation of sensor nodes irrespective of the nature of the sensor applications due to the tiny, limited batteries of the nodes.One promising solution to preserve energy is the clustering phenomenon and this mechanism also requires adequate stress on overall overhead of the network. Various clustering solutions have been addressed to extricate the power constraints of the sensor networks and they fluctuate in their boundaries owing to the multifaceted nature of this problem. In a typical clustering process in a WSN, energy is consumed in three phases: data sensing, data forwarding and data aggregation. A potential green, untrammeled replacement towards the conventional clustered sensor networks is the harvesting and utilization of energy from an ambient power resource. Unlike many other solutions, this approach overcomes the customary trade-offs but hosts economic and application-specific constraints. Our proposed Efficient Energy Harvestingassisted Clustering (EEHC) scheme contributes the idea of forming effective clusters that are free of residual nodes and overlapping. In this environment each sensor node is equipped with the energy harvesting device.The cluster head effectively balances the load in a cluster based on energy budgeting and nearly reduces the need for reclustering. Our approach is compared against the conventional and modern clustering algorithms for WSNs and yields significant improvement in the scope of lifetime from the pecuniary perspective

Optimizing communication and computation for multi-UAV information gathering applications

Typical mobile agent networks, such as multi-UAV systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular, limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e. the computational and the sensing energy are small compared to the communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this work, we aim at attaining an optimal trade-off between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

MODLEACH: A Variant of LEACH for WSNs

Wireless sensor networks are appearing as an emerging need for mankind. Though, Such networks are still in research phase however, they have high potential to be applied in almost every field of life. Lots of research is done and a lot more is awaiting to be standardized. In this work, cluster based routing in wireless sensor networks is studied precisely. Further, we modify one of the most prominent wireless sensor network's routing protocol "LEACH" as modified LEACH (MODLEACH) by introducing \emph{efficient cluster head replacement scheme} and \emph{dual transmitting power levels}. Our modified LEACH, in comparison with LEACH out performs it using metrics of cluster head formation, through put and network life. Afterwards, hard and soft thresholds are implemented on modified LEACH (MODLEACH) that boast the performance even more. Finally a brief performance analysis of LEACH, Modified LEACH (MODLEACH), MODLEACH with hard threshold (MODLEACHHT) and MODLEACH with soft threshold (MODLEACHST) is undertaken considering metrics of throughput, network life and cluster head replacements.Comment: IEEE 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

From carbon nanotubes and silicate layers to graphene platelets for polymer nanocomposites

In spite of extensive studies conducted on carbon nanotubes and silicate layers for their polymer-based nanocomposites, the rise of graphene now provides a more promising candidate due to its exceptionally high mechanical performance and electrical and thermal conductivities. The present study developed a facile approach to fabricate epoxy–graphene nanocomposites by thermally expanding a commercial product followed by ultrasonication and solution-compounding with epoxy, and investigated their morphologies, mechanical properties, electrical conductivity and thermal mechanical behaviour. Graphene platelets (GnPs) of 3.5

EMEEDP: Enhanced Multi-hop Energy Efficient Distributed Protocol for Heterogeneous Wireless Sensor Network

In WSN (Wireless Sensor Network) every sensor node sensed the data and transmit it to the CH (Cluster head) or BS (Base Station). Sensors are randomly deployed in unreachable areas, where battery replacement or battery charge is not possible. For this reason, Energy conservation is the important design goal while developing a routing and distributed protocol to increase the lifetime of WSN. In this paper, an enhanced energy efficient distributed protocol for heterogeneous WSN have been reported. EMEEDP is proposed for heterogeneous WSN to increase the lifetime of the network. An efficient algorithm is proposed in the form of flowchart and based on various clustering equation proved that the proposed work accomplishes longer lifetime with improved QOS parameters parallel to MEEP. A WSN implemented and tested using Raspberry Pi devices as a base station, temperature sensors as a node and xively.com as a cloud. Users use data for decision purpose or business purposes from xively.com using internet.Comment: 6 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:1409.1412 by other author