A hybrid genetic artificial neural network (G-ANN) algorithm for optimization of energy component in a wireless mesh network toward green computing
Wireless mesh networks are a special class of wireless networks that are implemented as a collection of radio nodes in a mesh pattern or topology. Unlike MANETs, the mobility of nodes is very less in the topology. Quality of service is an essential metric in the performance of mesh networks which are attributed to several parameters including optimal routing through shortest path, ability for other nodes to communicate even if a particular node in the mesh fails, minimization of packet loss and time delay, computational complexity and cost, energy. This research paper is focused toward minimization of energy taken as the objective function and a five-stage neural network is used and trained after optimizing with a genetic algorithm. The experiments have been conducted in NS2 and Qualnet environment with a varying number of mesh routers and energy computed. The performance of energy savings has been compared against conventional routing techniques like AODV and a bee colony optimization technique presented in the literature. An energy savings of 51% have been reported in the paper justifying the superiority of the hybrid G-ANN algorithm.
KeywordsWireless mesh networks Quality of service Genetic optimization Intelligent algorithms Neural networks Activation function Energy savings
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interests.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Bheemalingaiah M, Naidu MM, Rao DS, Varaprasad G (2009) Energy aware node disjoint multipath routing in mobile ad-hoc network. J Theor Appl Inf Technol 5(4):416–419Google Scholar
- Cardei M, Cheng MX, Cheng X, Du D-Z (2002) Connected domination in ad hoc wireless networks. In: Proceedings of the sixth international conference on computer science and informaticsGoogle Scholar
- Girgis MR, Mahmoud TM, Abdullatif BA, Rabie A (2014) Solving the wireless mesh network design problem using genetic algorithm and simulated annealing optimization methods. Int J Comput Appl 96(11):1–10Google Scholar
- He B, Xie B, Agrawal DP (2007) Optimizing the internet gateway deployment in a wireless mesh network. In: Mobile adhoc and sensor systems, pp 1–9Google Scholar
- Kumar N, Kumar M, Patel RB (2010) Coverage and connectivity aware neural network based energy efficient routing in wireless sensor networks. J Appl Graph Theory Wirel Ad Hoc Netw Sens Netw 2(1):45–60Google Scholar
- More A, Raisinghani V (2017) A survey on energy efficient coverage protocols in wireless sensor networks. J Comput Inf Sci 29(4):428–448Google Scholar
- Praveena A, Smys S (2016) Efficient cryptographic approach for data security in wireless sensor networks using MES VU. In: 2016 10th International conference on intelligent systems and control (ISCO). IEEE, pp 1–6Google Scholar
- Pries R, Staehle D, Staehle B, Tran-Gia P (2010) On optimization of wireless mesh networks using genetic algorithms. Int J Adv Internet Technol 3(2):13–28Google Scholar
- Sharma S, Kumar S, Singh B (2014) Hybrid intelligent routing in wireless mesh networks: soft computing based approaches. Int J Intell Syst Appl 1:45–57Google Scholar
- Smys S, Bala GJ, Raj JS (2010) Self-organizing hierarchical structure for wireless networks. In: 2010 International conference on advances in computer engineering (ACE). IEEE, pp 268–270Google Scholar
- Yie W, Heidemann J, Estrin D (2002) An energy-efficient MAC protocol for wireless sensor networks. In: Proceedings of the 21st annual joint conference of the IEEE computer and communications societies (INFOCOM), New York, USA, pp 1567–1576Google Scholar