Abstract-We present a new information dissemination protocol for wireless sensor networks. This protocol uses location information to reduce redundant transmissions, thereby saving energy. The sensor network is divided into virtual grids and each sensor node associates itself with a virtual grid based on its location. Sensor nodes within a virtual grid are classified as either gateway nodes or internal nodes. While gateway nodes are responsible for forwarding the data across virtual grids, internal nodes forward the data within a virtual grid. The proposed approach, termed location-aided flooding (LAF), achieves energy savings by reducing the redundant transmissions of the same packet by a node. We study the performance of LAF for different grid sizes and different node densities and compare it to other well-known methods. We show that LAF can save a significant amount of energy compared to prior methods.

We present a two-tier distributed hash table-based scheme for data-collection in event-driven wireless sensor networks. The proposed method leverages mobile sinks to significantly extend the lifetime of the sensor network. We propose localized algorithms using a distributed geographic hash-table mechanism that adds load balancing capabilities to the data-collection process. We address the hotspot problem by rehashing the locations of the mobile sinks periodically. The proposed mobility model moves the sink node only upon the occurrence of an event according to the evolution of current events, so as to minimize the energy consumption incurred by the multihop transmission of the event-data. Data is collected via single-hop routing between the sensor node and the mobile sink. Simulation results demonstrate significant gains in energy savings, while keeping the latency and the communication overhead at low levels for a variety of parameter values.

We propose a novel data-delivery method for delay-sensitive traffic that significantly reduces the energy consumption in wireless sensor networks without reducing the number of packets that meet end-to-end real-time deadlines. The proposed method, referred to as SensiQoS, leverages the spatial and temporal correlation between the data generated by events in a sensor network and realizes energy savings through application-specific in-network aggregation of the data. SensiQoS maximizes energy savings by adaptively waiting for packets from upstream nodes to perform in-network processing without missing the real-time deadline for the data packets. SensiQoS is a distributed packet scheduling scheme, where nodes make localized decisions on when to schedule a packet for transmission to meet its end-to-end real-time deadline and to which neighbor they should forward the packet to save energy. We also present a localized algorithm for nodes to adapt to network traffic to maximize energy savings in the network. Simulation results show that SensiQoS improves the energy savings in sensor networks where events are sensed by multiple nodes, and spatial and/or temporal correlation exists among the data packets. Energy savings due to SensiQoS increase with increase in the density of the sensor nodes and the size of the sensed events.

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