scholarly journals PEDTARA: Priority-Based Energy Efficient, Delay and Temperature Aware Routing Algorithm Using Multi-Objective Genetic Chaotic Spider Monkey Optimization for Critical Data Transmission in WBANs

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 68
Author(s):  
Omar Ahmed ◽  
Min Hu ◽  
Fuji Ren
Keyword(s):  
Network Lifetime ◽  
Data Transmission ◽  
Energy Efficient ◽  
Routing Algorithm ◽  
Spider Monkey ◽  
Area Network ◽  
Multi Objective ◽  
Critical Data ◽  
On Demand ◽  
Spider Monkey Optimization

Software-Defined Wireless Body Area Network (WBAN)s have gained significance in emergency healthcare applications for remote patients. Prioritization of healthcare data traffic has a high influence on the congestion and delay in the WBAN routing process. Currently, the energy constraints, packet loss, retransmission delay and increased sensor heat are pivotal research challenges in WBAN. These challenges also degrade the network lifetime and create serious issues for critical health data transmission. In this context, a Priority-based Energy-efficient, Delay and Temperature Aware Routing Algorithm (PEDTARA) is presented in this paper using a hybrid optimization algorithm of Multi-objective Genetic Chaotic Spider Monkey Optimization (MGCSMO). This proposed optimized routing algorithm is designed by incorporating the benefits of chaotic and genetic operators to the position updating function of enhanced Spider Monkey Optimization. For the prioritized routing process, initially, the patient data transmission in the WBAN is categorized into normal, on-demand and emergency data transmissions. Each category is ensured with efficient routing using the three different strategies of the suggested PEDTARA. PEDTARA performs optimal shortest path routing for normal data, energy-efficient emergency routing for high priority critical data and faster but priority verified routing for on-demand data. Thus, the proposed PEDTARA ensures energy-efficient, congestion-controlled and delay and temperature aware routing at any given period of health monitoring. Experiments were performed over a high-performance simulation scenario and the evaluation results showed that the proposed PEDTARA performs efficient routing better than the traditional approaches in terms of energy, temperature, delay, congestion and network lifetime.

scholarly journals Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

2021 ◽  
Author(s):  
Duraimurugan Samiayya ◽  
Avudaiammal Ramalingam
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Fitness Function ◽  
Heavy Traffic ◽  
Spider Monkey ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Multi Objective ◽  
Spider Monkey Optimization

Abstract In wireless sensor network (WSN), the gateways far away from the base station (BS) uses the gateways nearer to the BS to forward the data. It causes heavy traffic to the gateways in proximity with the BS. They need to manage this heavy traffic load but it leads to additional energy consumption and reduction in network lifetime. In order to overcome these issues, loads around the gateways need to be balanced. In this paper, multi objective based spider monkey optimization (MOSMO) has been presented to balance the load and to improve the network lifetime through energy efficient routing and clustering. The objective functions such as routing fitness and clustering fitness have been considered for optimal routing and clustering. The routing fitness function is found by incorporating both the minimum distance traversed by the gateways and minimum number of the gateway hops. The clustering fitness function is the minimum fitness function of gateways. The fitness function of each gateway is computed based on both the mean load of gateways as well as the distance between gateways and BS. The performance of the proposed MOSMO based routing and clustering scheme is compared with the existing Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) based routing and clustering scheme. The QoS features such as delay, energy consumption, delivery ratio, throughput and network lifetime with various node density are analyzed. The proposed work is simulated using MATLAB. The results show that, the reduction in delay and energy consumption is about 18% and 17% respectively whereas improvement in delivery ratio, throughput and network life time is about 15%, 24% and 19% respectively when compared to the existing PSO and GWO methods.

Energy Efficient Clustering and Routing Algorithm for WSN

2019 ◽  
Vol 12 ◽  
Author(s):  
Mohit Kumar ◽  
Sonu Mittal ◽  
Md. Amir Khusru Akhtar
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Hot Spot ◽  
Routing Algorithm ◽  
Relay Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Clustering ◽  
Hot Spot Problem

Background: This paper presents a novel Energy Efficient Clustering and Routing Algorithm (EECRA) for WSN. It is a clustering-based algorithm that minimizes energy dissipation in wireless sensor networks. The proposed algorithm takes into consideration energy conservation of the nodes through its inherent architecture and load balancing technique. In the proposed algorithm the role of inter-cluster transmission is not performed by gateways instead a chosen member node of respective cluster is responsible for data forwarding to another cluster or directly to the sink. Our algorithm eases out the load of the gateways by distributing the transmission load among chosen sensor node which acts as a relay node for inter-cluster communication for that round. Grievous simulations show that EECRA is better than PBCA and other algorithms in terms of energy consumption per round and network lifetime. Objective: The objective of this research lies in its inherent architecture and load balancing technique. The sole purpose of this clustering-based algorithm is that it minimizes energy dissipation in wireless sensor networks. Method: This algorithm is tested with 100 sensor nodes and 10 gateways deployed in the target area of 300m × 300m. The round assumed in this simulation is same as in LEACH. The performance metrics used for comparisons are (a) network lifetime of gateways and (b) energy consumption per round by gateways. Our algorithm gives superior result compared to LBC, EELBCA and PBCA. Fig 6 and Fig 7 shows the comparison between the algorithms. Results: The simulation was performed on MATLAB version R2012b. The performance of EECRA is compared with some existing algorithms like PBCA, EELBCA and LBCA. The comparative analysis shows that the proposed algorithm outperforms the other existing algorithms in terms of network lifetime and energy consumption. Conclusion: The novelty of this algorithm lies in the fact that the gateways are not responsible for inter-cluster forwarding, instead some sensor nodes are chosen in every cluster based on some cost function and they act as a relay node for data forwarding. Note the algorithm does not address the hot-spot problem. Our next endeavor will be to design an algorithm with consideration of hot-spot problem.

scholarly journals Abstract ACO-EEAODR: An Ant-Swarm Inspired Energy-Efficient Ad Hoc On-Demand Routing Protocol for Mobile Ad Hoc Networks

2021 ◽  
Author(s):  
Waqas Shah
Keyword(s):  
Network Lifetime ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Minimum Energy ◽  
Sustainable Growth ◽  
Energy Aware ◽  
On Demand ◽  
Demand Routing

As the world’s economic activities are expanding, the energy comes to the fore to the question of the sustainable growth in all technological areas, including wireless mobile networking. Energyaware routing schemes for wireless networks have spurred a great deal of recent research towards achieving this goal. Recently, an energy-aware routing protocol for MANETs (so-called energy-efficient ad hoc on-demand routing protocol (EEAODR) for MANETs was proposed, in which the energy load among nodes is balanced so that a minimum energy level is maintained and the resulting network lifetime is increased. In this paper, an Ant Colony Optimization (ACO) inspired approach to EEAODR (ACO-EEAODR) is proposed. To the best of our knowledge, no attempts have been made so far in this direction. Simulation results are provided, demonstrating that the ACO-EEAODR outperforms the EEAODR scheme in terms of energy consumed and network lifetime, chosen as performance metrics.

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