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Purpose -The purpose of this paper is to determine the factors that predict green purchase behaviour of young educated consumers in Delhi. Design/methodology/approach -A survey was carried out on a sample of 1,502 young educated consumers. Structural equation modelling was used to assess the predictive power of considered variables towards green purchasing. Findings -Results indicate that the variables under study predicted green purchase behaviour of young educated consumers of Delhi in the following descending order: social influence, attitude towards green purchase, perceived environmental knowledge, recycling participation, ecolabelling and exposure to environmental messages through the media.Research limitations/implications -The sample considered in the study was restricted to Delhi only. Further, the predictive power of only a few variables was examined. Practical implications -The paper identifies key predictors of consumers' green purchase behaviour, enabling practitioners to understand which factors influence young educated consumers in their decision making regarding green purchases. This knowledge will help marketing managers design effective strategies to encourage green purchase behaviour among such consumers. Social implications -Policy makers and government organizations may use the findings of this study to run awareness campaigns for disseminating information and promoting green purchase behaviour among larger sections of society. Such initiatives may help in minimizing the negative consequences of irresponsible consumption practices on environment and society. Originality/value -The present study is the first which applies reciprocal deterministic theory to predict green purchase behaviour of educated young consumers in India. Moreover, this is the first study to investigate the influence of consumers' exposure to environmental messages through the media on their green purchase behaviour.

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Wireless Sensor Networks (WSNs) in applications like battlefield surveillance or environmental monitoring are usually deployed in inhospitable environments, in which their constituent nodes are susceptible to an increased risk of failure due to hazardous operating conditions or adversary attacks. In these scenarios it is possible for multiple nodes to fail at the same time and partition the WSN into disjoint segments. Such loss of connectivity may cause service disruptions and render the WSN useless. Given the critical role a WSN plays and the fact that deployment of additional nodes may be infeasible, the WSN must have the ability to self-heal and restore connectivity by utilizing surviving resources. In this paper we present a distributed Resource Constrained Recovery (RCR) approach that reconnects a network partitioned into disjoint segments by strategically repositioning nodes to act as relays. In case the number of surviving relocatable nodes are insufficient to form a stable intersegment topology, some of them are employed as mobile data collectors with optimized tours to reduce data latency. The performance of RCR is validated through mathematical analysis and simulation.

Wireless Sensor Networks (WSNs) often serve mission-critical applications in inhospitable environments such as battlefield and territorial borders. Inter-node communication is essential for WSNs to effectively fulfill their tasks. In hostile setups, the WSN may be subject to damage that breaks the network connectivity and disrupts the application. The network must be able to recover from the failure and restore connectivity so that the designated tasks can be carried out. Given the unattended operation of the network, the recovery should be performed autonomously. In this paper we present a distributed algorithm for Autonomous Repair (AuR) of damaged WSN topologies in the event of multiple node failures. AuR models connectivity between neighboring nodes as electrostatic interaction between charges based on Coulomb's law. The recovery process is initiated locally at the neighbors of failed nodes by moving in the direction of loss to reconnect with other nodes. The performance of AuR is validated through simulation.

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