Mobile sinks were introduced in wireless sensor networks (WSNs) to mitigate the infamous hotspot problem. However, routing in mobile-sink-based WSNs requires frequent updation of sink location information to all the sensor nodes; which is an energy-expensive process for resource-constrained WSNs. Therefore, it is required to develop a green routing protocol that can minimize the energy overhead in sink location updation as well as reduce the data delivery delay. This article proposes a virtual-infrastructure-based delay-aware green routing protocol (DGRP) that creates multiple rings in the sensor field and limits the updation of mobile sink location information to the nodes belonging to the rings only. Simulation results show that DGRP outperforms existing routing protocols in terms of energy consumption and throughput. In addition to this, DGRP results in $\approx 26$ %, $\approx 39$ %, and $\approx 35$ % improvement in data delivery delay for a varying number of sensor nodes, sink speeds, and network sizes, respectively, when compared with the state of the art.

There are two major challenges in mobile sink based wireless sensor networks (WSNs) concerning to the query-driven scenarios, first, dissemination of queries to their respective region of interests (RoIs), and second, routing the data towards mobile sink. Due to sink mobility, routing of data packets to the sink becomes difficult because sink's query injection location and data collection location (current location of sink) may not be the same. Moreover, advertising mobile sink's location by flooding, introduces extensive burden on sensor nodes. In this paper, we propose a virtual ring infrastructure based query-driven ring routing protocol (QRRP) to reduce the overhead of updating mobile sink location information as well as routing the data towards current location of the sink. QRRP takes the advantage of proposed angle based routing to route the queries from mobile sink to their respective RoIs, and data from sensor nodes to the sink. Simulation results on the proposed approach show that energy consumption and data delivery delay are significantly reduced as compared to state-of-the-art mechanisms.