With the proliferation of technologies such as wireless sensor networks (WSNs) and the Internet of things (IoT), we are moving towards the era of automation without any human intervention. Sensors are the principal components of the WSNs that bring the idea of IoT into reality. Over the last decade, WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care monitoring, and so on. However, the energy efficiency of the sensor nodes in WSN remains a challenging issue due to the use of a small battery. Moreover, replacing the batteries of the sensor nodes deployed in a hostile environment frequently is not a feasible option.Therefore, intelligent scheduling of the sensor nodes for optimizing its energy-efficient operation and thereby extending the life-time of WSN has received a lot of research attention lately. In particular, this article investigates extending the lifetime of the WSN in the context of target coverage problems. To tackle this problem, we propose a scheduling technique for WSN based on a novel concept within the theory of learning automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with an LA so that it can autonomously select its proper state, that is, either sleep or active, with an aim to cover all targets with the lowest energy cost possible. Our comprehensive experimental testing of the proposed algorithm not only verifies the efficiency of our algorithm, but it also demonstrates its ability to yield a near-optimal solution. The results are promising, given the low computational footprint of the algorithm.

Running a sheer virtualized data center with the help of Virtual Machines (VM) is the de facto-standard in modern data centers. Live migration offers immense flexibility opportunities as it endows the system administrators with tools to seamlessly move VMs across physical machines. Several studies have shown that the resource utilization within a data center is not homogeneous across the physical servers. Load imbalance situations are observed where a significant portion of servers are either in overloaded or underloaded states. Apart from leading to inefficient usage of energy by underloaded servers, this might lead to serious QoS degradation issues in the overloaded servers. In this paper, we propose a lightweight decentralized solution for homogenizing the load across different machines in a data center by mapping the problem to a Stable Marriage matching problem. The algorithm judiciously chooses pairs of overloaded and underloaded servers for matching and subsequently VM migrations are performed to reduce load imbalance. For the purpose of comparisons, three different greedy matching algorithms are also introduced. In order to verify the feasibility of our approach in real-life scenarios, we implement our solution on a small test-bed. For the larger scale scenarios, we provide simulation results that demonstrate the efficiency of the algorithm and its ability to yield a near-optimal solution compared to other algorithms. The results are promising, given the low computational footprint of the algorithm.

, to study the effect of post harvest treatments on quality and shelf life of sweet orange (Citrus sinensis) cultivar Local. It was carried out in Completely Randomized Design (CRD) with five treatments of different types of post harvest treatments viz. T1 = control (distilled water), T2 = bavistin (0.1%), T3 = calcium chloride (1%), T4 = Jeevatu (5%) and T5 = cinnamon oil (2%) replicated four times. Among these post harvest treatments, T1 showed highest percentage of weight loss (15.83%), lowest firmness (2.22 kg/cm 2) and highest TSS (10.70˚ Brix), lowest TA (0.395%) at final day of storage as compared to other treatments. Bavistin was found as the most effective in reducing the physiological loss in weight (10.80%), retained maximum firmness (3.13 kg/cm 2), highest tritrable acidity (0.76%), highest pH (5.08). The minimum total soluble solid (8.75˚Brix) was retained by cinnamon oil. This study revealed that sweet orange treated with bavistin recorded lowest physiological loss in weight (31.77%) and retains more firmness (24.73kg/cm 2) than that of control .Thus, present findings indicate that sweet oranges treated with bavistin increase the shelf life where as cinnamon oil also found to be promising treatment for retaining the quality of the sweet oranges stored up to 28 th days under laboratory condition.