Wireless sensor networks (WSN) are one of the significant technologies due to their diverse applications such as health care monitoring, smart phones, military, disaster management, and other surveillance systems. Sensor nodes are usually deployed in large number that work independently in unattended harsh environments. Due to constraint resources, typically the scarce battery power, these wireless nodes are grouped into clusters for energy efficient communication. In clustering hierarchical schemes have achieved great interest for minimizing energy consumption. Hierarchical schemes are generally categorized as cluster-based and grid-based approaches. In cluster-based approaches, nodes are grouped into clusters, where a resourceful sensor node is nominated as a cluster head (CH) while in grid-based approach the network is divided into confined virtual grids usually performed by the base station. This paper highlights and discusses the design challenges for cluster-based schemes, the important cluster formation parameters, and classification of hierarchical clustering protocols. Moreover, existing cluster-based and grid-based techniques are evaluated by considering certain parameters to help users in selecting appropriate technique. Furthermore, a detailed summary of these protocols is presented with their advantages, disadvantages, and applicability in particular cases.

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process.

No abstract

Wireless sensor networks (WSN) empower applications for critical decision-making through collaborative computing, communications, and distributed sensing. However, they face several challenges due to their peculiar use in a wide variety of applications. One of the inherent challenges with any battery operated sensor is the efficient consumption of energy and its effect on network lifetime. In this paper, we introduce a novel grid-based hybrid network deployment (GHND) framework which ensures energy efficiency and load balancing in wireless sensor networks. This research is particularly focused on the merge and split technique to achieve even distribution of sensor nodes across the grid. Low density neighboring zones are merged together whereas high density zones are strategically split to achieve optimum balance. Extensive simulations reveal that the proposed method outperforms state-of-the-art techniques in terms of load balancing, network lifetime, and total energy consumption.

Walnut has been regarded as a health food that is delicious and nutritious. Both preventive and therapeutic effects of walnut are well documented. Walnuts are rich in omega-3 fatty acids that are reported to have beneficial effects on brain function. The present work was designed to evaluate the effects of walnuts on learning and memory in male rats. The effect of oral intake of walnut was also monitored on food intake. Walnut was given orally to rats for a period of 28 days. Memory function in rats was assessed by elevated plus maze (EPM) and radial arm maze (RAM). A significant improvement in learning and memory of walnut treated rats compared to controls was observed. Walnut treated rats also exhibited a significant decrease in food intake while the change in growth rate (in terms of percentage) remained comparable between the two groups. Analysis of brain monoamines exhibited enhanced serotonergic levels in rat brain following oral intake of walnuts. The findings suggest that walnut may exert its hypophagic and nootropic actions via an enhancement of brain 5-HT metabolism.

No abstract

.Vehicular ad hoc network (VANET) is a wireless emerging technology that aims to provide safety and communication services to drivers and passengers. In VANETs, vehicles communicate with other vehicles directly or through road side units (RSU) for sharing traffic information. The data dissemination in VANETs is a challenging issue as the vehicles have to share safety critical information in real time. The data distribution is usually done using broadcast method resulting in inefficient use of network resources. Therefore, to avoid the broadcast storm and efficiently use network resources, next forwarder vehicle (NFV) is selected to forward data to nearby vehicles. The NFV selection is based on certain parameters like direction, distance, and position of vehicles, which makes it a multicriteria decision problem. In this paper, analytical network process (ANP) is used as a multicriteria decision tool to select the optimal vehicle as NFV. The stability of alternatives (candidate vehicles for NFV selection) ranking is checked using sensitivity analysis for different scenarios. Mathematical formulation shows that ANP method is applicable for NFV selection in VANETs. Simulation results show that the proposed scheme outperforms other state-of-the-art data dissemination schemes in terms of reachability, latency, collisions, and number of transmitted and duplicate data packets.

.Vehicular ad hoc networks (VANETs) are the preferable choice for Intelligent Transportation Systems (ITS) because of its prevailing significance in both safety and nonsafety applications. Information dissemination in a multihop fashion along with privacy preservation of source node is a serious but challenging issue. We have used the idea of the phantom node as the next forwarder for data dissemination. The phantom node (vehicle) hides the identity of actual source node thus preserving the location privacy. The selection of the phantom node among the set of alternatives' candidate vehicles is considered as a multicriteria-based problem. The phantom node selection problem is solved by using an analytical network process (ANP) by considering different traffic scenarios. The selection is based on different parameters which are distance, speed, trust, acceleration, and direction. The best alternative (target phantom vehicle) is selected through an ANP where all the alternatives are ranked from best to worst. The vehicle having maximum weight is considered to be the best choice as a phantom node. In order to check the stability of the alternatives' ranking, sensitivity analysis is performed by taking into account different traffic scenarios and interest level of candidate vehicles.