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PurposeWireless Body Area Network (WBAN) technology envisions a network in which sensors continuously operate on and obtained critical physical and physiological readings. Sensors deployed in WBANs have restricted resources such as battery energy, computing power and bandwidth. We can utilize these resources efficiently. By devising a mechanism that is energy efficient with following characteristics, i.e. computational complexity is less, routing overhead is minimized, and throughput will be maximum. A lot of work has been done in this area but still WBAN faces some challenges like mobility, network lifetime, transmission range, heterogeneous environment, and limited resources. In the present years well, contemplative studies have been made through a large body to reach some holistic points pertaining to the energy consumption in WBAN. Thus we/put forward appropriate algorithm for energy efficiency which can vividly corroborate the advances in this specific domain. We have also focused on various aspects and phases of the studies like study computational complexity, routing overhead and throughput type of characteristics. There is still a room for improvement to get the desired energy optimization in WBAN. The network performance mainly relies upon the algorithm used for optimization process. In this work, we intended to develop an energy optimization algorithm for energy consumption in WBAN which is based on evolutionary algorithms for inter-BAN communications using cluster-based routing protocol.Design/methodology/approachIn this paper we propose a meta heuristics algorithm Goa to solve the optimization problem in WBAN. Grasshopper is an insect. Generally, this insect is viewed individually and creating large swarm in nature. Figure 5 shows the individual grasshoppers' primitive patterns in swarm. Figure 7 depicts the pseudo code of Goa. In Goa, experiments are done to view the behavior of grasshoppers in swarm. How they gradually move towards the stationary and mobile target. Through experimentation it is conceived that swarm gradually converge towards their target. Another interesting pattern related to convergence of grasshopper is that it slowly towards its target. This shows that grasshopper does not trapped in local optima. In starting iterations of exploration process Goa, search globally and in last iterations it searches local optima. Goa makes the exploration and exploitation process balanced while solving challenging optimization problems.FindingsEnergy efficiency is achieved in the optimization process of cluster formation process. As the use of proposed algorithm Goa creates the optimal number of clusters. Shorter cluster lifetime means more times clustering procedure is called. It increases the network computational cost and the communication overhead. Experimentation results show that proposed Goa algorithm performs well. We compare the results of Goa with existing optimization Algorithms ACO and MFO. Results are generated using MATLAB.Originality/valueA lot of work has done for the sake of energy optimization in WBAN. Many algorithms are proposed in past for energy optimization of WBAN. All of them have some strengths and weaknesses. In this paper we propose a nature inspired algorithm Goa. We use the Goa algorithm for the sake of energy optimization in WBAN.

PurposeThe objective of this paper is to perform infrared (IR) face recognition efficiently with convolutional neural networks (CNNs). The proposed model in this paper has several advantages such as the automatic feature extraction using convolutional and pooling layers and the ability to distinguish between faces without visual details.Design/methodology/approachA model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces.FindingsThe experimental results and analysis reveal high recognition rates of IR faces with the proposed model.Originality/valueA designed CNN model is presented for IR face recognition. Both the feature extraction and classification tasks are incorporated into this model. The problems of low contrast and absence of details in IR images are overcome with the proposed model. The recognition accuracy reaches 100% in experiments on the Terravic Facial IR Database (TFIRDB).