High-Level Design of Wireless Sensor Networks for Performance Optimization Under Security Hazards

2017 ◽  
Vol 13 (3) ◽  
pp. 1-37 ◽  
Author(s):  
Pablo Peñil ◽  
Alvaro Díaz ◽  
Hector Posadas ◽  
Julio Medina ◽  
Pablo Sánchez
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Performance Optimization ◽  
Wireless Sensor ◽  
Level Design ◽  
High Level

scholarly journals A Preemptive Priority-Based Data Fragmentation Scheme for Heterogeneous Traffic in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4473 ◽  
Author(s):  
Anwar Khan ◽  
Sayeed Ghani ◽  
Shama Siddiqui
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fragment Size ◽  
Heterogeneous Traffic ◽  
Wireless Sensor ◽  
Packet Size ◽  
Lower Priority ◽  
Data Fragmentation ◽  
High Level ◽  
Performance Challenge

Prioritizing the heterogeneous traffic for Wireless Sensor Networks (WSNs) imposes an important performance challenge for Internet of Things (IoT) applications. Most past preemptive MAC schemes are based on scheduling the high priority packets earlier than those of lower priority. However, in a majority of these schemes, high priority traffic must wait for the ongoing transmission of lower priority traffic due to the non-availability of an interruption mechanism. This paper presents the design and high-level implementation details of a fragmentation scheme (FROG-MAC) for heterogeneous traffic in WSN. FROG-MAC aims at guaranteeing quick transmission of high priority/emergency traffic by interrupting ongoing on channel transmissions. High level implementation of FROG-MAC has been developed in MATLAB as a proof of concept. Traffic of two priorities was generated and a single hop star topology of 100 nodes was used for the experiments. Effect of the proposed fragmentation scheme has been evaluated on delay and Packet Drop Ratio (PDR) for both traffic types, by varying the packet size and fragment size. Simulation results have suggested that with the increasing packet size, the delay and PDR increase for both traffic types. When fragmentation was applied, the performance of high priority traffic significantly improved as compared to the low priority for both the parameters, delay and PDR. Furthermore, it has been found that decreasing the fragment size for low priority traffic results in reducing the delay for high priority traffic.

scholarly journals An Architecture for Performance Optimization in a Collaborative Knowledge-Based Approach for Wireless Sensor Networks

Sensors ◽  
2011 ◽  
Vol 11 (10) ◽  
pp. 9136-9159 ◽  
Author(s):  
Manuel Angel Gadeo-Martos ◽  
Jose Angel Fernandez-Prieto ◽  
Joaquin Canada-Bago ◽  
Juan Ramon Velasco
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Performance Optimization ◽  
Wireless Sensor ◽  
Knowledge Based ◽  
Collaborative Knowledge

C-POEM: comprehensive performance optimization evaluation model for wireless sensor networks

2016 ◽  
Vol 21 (12) ◽  
pp. 3377-3385 ◽  
Author(s):  
Nan Jiang ◽  
Bin Li ◽  
Pingan Pan ◽  
Tao Wan ◽  
Lingfeng Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Performance Optimization ◽  
Evaluation Model ◽  
Wireless Sensor ◽  
Comprehensive Performance

scholarly journals A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink

2020 ◽  
Author(s):  
Zhixing Huang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Genetic Programming ◽  
Network Lifetime ◽  
Building Blocks ◽  
Mobile Sink ◽  
Wireless Sensor ◽  
Programming Algorithm ◽  
Computational Time ◽  
High Level

Maximizing the lifetime of wireless sensor networks (WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks, are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.

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