No abstract

In the world of digital era, an advance development with internet of things (IoT) were initiated, where devices communicate with each other and the process are automated and controlled with the help of internet. An IoT in an agriculture framework includes various benefits in managing and monitoring the crops. In this paper, an architectural framework is developed which integrates the internet of things (IoT) with the production of crops, different measures and methods are used to monitor crops using cloud computing. The approach provides real-time analysis of data collected from sensors placed in crops and produces result to farmer which is necessary for the monitoring the crop growth which reduces the time, energy of the farmer. The data collected from the fields are stored in the cloud and processed in order to facilitate automation by integrating IoT devices. The concept presented in the paper could increase the productivity of the crops by reducing wastage of resources utilized in the agriculture fields. The results of the experimentation carried out presents the details of temperature, soil moisture, humidity and water usage for the field and performs decision making analysis with the interaction of the farmer.

We propose an efficient mixture classification technique, which uses electroencephalography (EEG) signals for establishing a communication channel for the physically challenged or immobilized people, by the usage of the brain signals. In order to identify the emotion expressions by an immobilized person, we introduce a novel approach for emotion recognition based on the generalized mixture distribution model. The main benefit of utilizing this model is that it is an asymmetric distribution, which helps to extract the EEG signals, which are either in symmetric or asymmetric form. The skew Gaussian distribution helps to identify the small duration EEG signal sample and helps toward better recognition of emotions in both clean and noisy EEG signals. The proposed method is particularly well suited for the high variability of the EEG signal allowing the emotions to be identified appropriately. The features of the brain signals are extracted by using cepstral coefficients. The extracted features are classified into different emotions using mixture classification techniques. In order to validate the model, six mentally impaired subjects are considered in the age group of 60-68, and an 8-channel EEG signal is utilized to collect the EEG signals under audio-visual stimuli. The basic emotions considered in this study include happy, sad, neutral, and boredom and an average emotion recognition accuracy of 89% is achieved.INDEX TERMS Brain-computer interaction (BCI), emotion recognition, affective computing, electroencephalography (EEG), Gaussian mixture, cepstral analysis.

Clustering is considered as one of the most prominent solutions to preserve the energy in the wireless sensor networks. However, for optimal clustering, an energy efficient cluster head selection is quite important. Improper selection of cluster heads (CHs) consumes high energy compared to other sensor nodes due to the transmission of data packets between the cluster members and the sink node. Thereby, it reduces the network lifetime and performance of the network. In order to overcome the issues, we propose a novel cluster head selection approach using grey wolf optimization algorithm (GWO) namely GWO-CH which considers the residual energy, intra-cluster and sink distance. In addition to that, we formulated an objective function and weight parameters for an efficient cluster head selection and cluster formation. The proposed algorithm is tested in different wireless sensor network scenarios by varying the number of sensor nodes and cluster heads. The observed results convey that the proposed algorithm outperforms in terms of achieving better network performance compare to other algorithms.

Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies. Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, the Cloud users' requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers. We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of mlearning systems. Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users. Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments. INDEX TERMS Cloud computing, dominant firefly algorithm, load balancing, mobile learning (m-learning), virtual machines.