Energy Efficient Optimal Routing for Communication in VANETs via Clustering Model

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Mohamed Elhoseny ◽  
K. Shankar
Keyword(s):  
Energy Efficient ◽  
Optimal Routing ◽  
Clustering Model

Energy Efficient Sensor Configuration by Fishing Spider Inspired Mechanism

2013 ◽  
Vol 284-287 ◽  
pp. 2049-2055
Author(s):  
Kyu Hong Lee ◽  
Hee Sang Lee
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Cluster Heads ◽  
Self Organized ◽  
Clustering Model ◽  
Dolomedes Triton ◽  
Fishing Spider

Wireless sensor networks have inherent characteristics that differ from other wireless networks. Therefore, topology configuration and routing methods in WSNs must address these characteristics. In this paper, we propose an energy efficient clustering model. This model was inspired by the behaviors and capabilities of the six-spotted fishing spider, Dolomedes triton. The suggested model performs cluster-heads selection and clustering in self-organized ways. In order to determine the cluster-heads and the cluster-members, each sensor node uses the local information and simple rules that have been inspired by the Dolomedes triton. We compared our model with a well-known cluster-based routing protocol that uses random fairness for the selection of sensor node cluster-heads. In our computational experiments, we have showed that the energy efficiency and lifetimes of our bio-inspired model exceeds those of the comparison protocol by only using simple bio-inspired mechanism. We also demonstrate our model’s good performance in terms of scalability, which is one of the important indicators of performance for self-organized wireless sensor networks.

scholarly journals Energy Potent Shortcut Tree Routing in Zigbee Networks

2018 ◽  
Vol 7 (4.10) ◽  
pp. 857
Author(s):  
B. Rajeshkanna ◽  
M. Anitha
Keyword(s):  
Performance Evaluation ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Ieee 802.15.4 ◽  
Optimization Method ◽  
Link Quality ◽  
Optimal Routing ◽  
Physical Layers ◽  
Tree Routing ◽  
Table Data

ZigBee is an international standard for low-power and low-data rate WPAN. ZigBee works on IEEE 802.15.4 MAC and physical layers. Its network layer is liable to facilitate routing in networks. To provide a near optimal routing path as well as to balance the load and energy over the nodes, Energy-efficient shortcut tree routing (ESTR) protocol suggested an optimization method with three criteria such as minimum hop-counts, minimum congestion and maximum link quality. This paper proposes an Energy potent shortcut tree routing (EPSTR) that adds one more criterion called minimum failure-transmissions with EPSTR’s criteria. The values for these criteria can be derived from the neighbor table data of a node. Performance evaluation reveals that EPSTR appreciably expands the network lifetime and shows better routing performances compared to ESTR.  

Request-based, secured and energy-efficient (RBSEE) architecture for handling IoT big data

2018 ◽  
Vol 45 (2) ◽  
pp. 227-238 ◽  
Author(s):  
Mohd Abdul Ahad ◽  
Ranjit Biswas
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Distributed System ◽  
Energy Efficient ◽  
Optimal Routing ◽  
The Core ◽  
Huge Data ◽  
Open Source Framework ◽  
Energy Efficient Architecture

The technological advancements in the field of computing are giving rise to the generation of gigantic volumes of data which are beyond the handling capabilities of the conventionally available tools, techniques and systems. These types of data are known as big data. Moreover with the emergence of Internet of Things (IoT), these types of data have increased in multiple folds in 7Vs (volume, variety, veracity, value, variability, velocity and visualisation). There are several techniques prevalent in today’s time for handling these types of huge data. Hadoop is one such open source framework which has emerged as a de facto technology for handling such huge datasets. In an IoT ecosystem, real-time handling of requests is an imperative requirement; however, Hadoop has certain limitations while handling these types of requests. In this article, we present an energy-efficient architecture for effective, secured and real-time handling of IoT big data. The proposed approach adopts atrain distributed system (ADS) to construct the core architecture. This study uses software-defined networking (SDN) framework for energy-efficient and optimal routing of data and requests from source to destination, and vice versa. Furthermore, to ensure secured handling of IoT big data, the proposed approach uses ‘Twofish’ cryptographic technique for encrypting the information captured by the sensors. Finally, the concept of ‘request-type’ identifying unit has been proposed. Instead of handling all the requests in an identical way, the proposed approach works by characterising the requests on the basis of certain criteria and parameters, which are identified here.

ENERGY EFFICIENT CLUSTERING MODEL: MODIFIED LEACH-R PROTOCOL

2016 ◽  
Vol 16 (2) ◽  
pp. 283-296
Author(s):  
J. Kaur ◽  
G. S. Gaba ◽  
A. Singh ◽  
P. Singh
Keyword(s):  
Energy Efficient ◽  
Clustering Model ◽  
Energy Efficient Clustering

scholarly journals MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 791
Author(s):  
Nader Ajmi ◽  
Abdelhamid Helali ◽  
Pascal Lorenz ◽  
Ridha Mghaieth
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Swarm Optimization ◽  
Adaptive Clustering ◽  
Clustering Model ◽  
Chicken Swarm Optimization

Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.

scholarly journals Energy Efficient Data Distribution on Cloud with Optimal Routing Path Based Congestion Control in WSN Environment

2021 ◽  
Vol 23 (08) ◽  
pp. 421-436
Author(s):  
Pampapathi B M ◽  
◽  
Nageswara Guptha M ◽  
M S Hema ◽  
◽  
...  
Keyword(s):  
Congestion Control ◽  
Energy Efficient ◽  
Industrial Applications ◽  
Base Station ◽  
Sensor Data ◽  
Control Technique ◽  
Optimal Routing ◽  
Minimal Distance ◽  
Cloud Data ◽  
Efficient Data

Presently, Wireless Sensor Network (WSN) is the extremely standard services utilized in commercial in addition to industrial applications, owing to its technical development in the embedded computing devices’ processor, communication, along with less-power usage. Moreover, in the WSN’s advantages, the sensor data’s energy-efficient distribution onto the cloud data centre is amid the core challenges caused by higher data loads and also congestion. Aimed at overcoming these problems, this work proffers an energy-efficient Data Packet (DP) distribution protocol comprising an optimal routing path centred congestion control technique in the WSN environment. This system not just ponders the energy-efficient DP distribution nevertheless increments the sensor’s lifetime by choosing the Cluster Head (CH) centred on the sensor’s residual energy, packet loss factor, data forwarding rate, and minimal distance. Next, the numerous routing paths prevalent in inter-cluster and intra-clusters are detected utilizing the QLA2ODV method; the congested nodes are detected as of these paths utilizing LM-NN classifier; if congestion happens, the QLA2ODV distributes the DP onto the Base Station (BS) via the optimum congestion-free routing path. The optimal routing paths were elected as of the congestion-free paths utilizing the WFCSO method. Past obtaining the sensor data, the BS transmits the DPs onto the Fog Nodes (FNs). At last, the sensor data are effectively distributed onto the cloud data centres grounded on the data’s features and cloud features utilizing the BD-SBO technique. The experiential outcomes determine the proposed system’s effectiveness.

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