Wireless Sensor Network (WSN) has wide range of applications including next generation intelligent IoT applications. Sensor nodes in WSNs don’t allow replacing batteries as phenomenon under consideration is not frequently accessible or inaccessible. Resource constrained WSN nodes run on batteries with limited capacity. For long running of the WSNs, energy consumption on each node and lifetime of overall network is a concern for IoT applications. To prolong lifetime of WSNs, use of energy efficient method is necessary and a challenging task. Most of the on-demand routing protocols use metrics like number of hops for the selection of path from the source node to the destination node. Purely hop count-based metrics leads to frequent broken paths as well as more energy consumption and results into reduced lifetime of IoT applications. Method proposed in this paper is a cross layer variant of AODV by replacing hop count metric with link quality and collision count. To establish a path, proposed method fetches link quality information from Physical layer and collision information from MAC layer to aid Network layer for making intelligent routing decisions using ZScore method. Using this information, proposed routing metric caters to select stable and sustainable routing path. Method proposed in this paper is implemented in well-known network simulator, NS-2 by incorporating necessary changes into existing physical and MAC layers as well as AODV protocol. A comparative analysis with existing methods is an indicator of improved performance in terms of energy efficiency, network lifetime, path stability and delay.

The Internet of Things (IoT) is empowering the field of agriculture. The IoT application helps the farmer and makes him aware about all the latest information which is related to agriculture. In this study, authors have found that the implementation of IoT is very important in the field of agriculture that makes farming smarter. IoT applications make farmers smarter with the latest information about the crop and weather. By using the IoT application the farmer can prepare a plan for next season’s crop. With the help of IoT based agriculture application the farmers already know the information about the weather, soil, wind speed and direction, relative humidity, and temperature. The farmers know about the disease of crops and then consult the expert and get an appropriate solution to the disease. The experts may advise the farmer how farmers can protect the crop from the disease. The IoT agriculture application helps to increase the production of crops and reduce the loss of crops from the disease. The IoT based agriculture application is like a friend of a farmer because the IoT based agriculture applications are helping the farmer at every moment of agriculture activities. India has set a food grain production target of 298.3 million tonnes for the current fiscal year 2020-21 which is 2.5% higher compared to previous fiscal year’s targets. Agriculture sector contribution to Indian economy is 17%. To empower Indian farmers and in turn to empower Indian economy, IoT based agriculture applications are essential for the farmers.

Data clustering is a basic technique to show the structure of a data set. K-means clustering is a widely acceptable method of data clustering, which follow a partitioned approach for dividing the given data set into non-overlapping groups. Unfortunately, it has the pitfall of randomly choosing the initial cluster centers. Due to its gradient nature, this algorithm is highly sensitive to the initial seed value. In this paper, we propose a kernel density-based method to compute an initial seed value for the k-means algorithm. The idea is to select an initial point from the denser region because they truly reflect the property of the overall data set. Subsequently, we are avoiding the selection of outliers as an initial seed value. We have verified the proposed method on real data sets with the help of different internal and external validity measures. The experimental analysis illustrates that the proposed method has better performance over the kmeans, k-means++ algorithm, and other recent initialization methods.