Trees In Sensor Networks Research Articles

Adapting Distributed LT codes to Y-Networks: An Abstraction of Collection Tree in Sensor Networks

The conventional approach for collecting data in sensor networks is the combination of automatic repeat-request techniques with a collection tree scheme in which all routes end up in the sink node. A high number of acknowledgments and retransmissions is the main drawback of this scheme. Erasure-correcting codes, in particular Fountain codes, can be employed to reduce the number of retransmissions. In the collection tree scheme, it is common for multiple routes to share a bottleneck. In such scenarios, to reach the optimal network throughput, it is necessary to combine the Fountain codes and Network Coding (NC) technique. Y-networks can be considered as an abstraction to this scenario. This article proposes a new algorithm, namely, Adaptive Distributed LT code (ADLT), for combining LT codes with NC in Y-networks. The ADLT algorithm enables belief propagation decoding by employing a novel technique called degree distribution updating, to preserve Robust Soliton degree distribution at destination. Unlike previously proposed algorithms in the literature, the ADLT algorithm has the flexibility to handle any number of sources with different block sizes and transmission rates, where sources perform standard LT coding. Simulation results confirm that the performance of the ADLT algorithm is close to that of standard LT code.

Analyzing the techniques that improve fault tolerance of aggregation trees in sensor networks

Sensor networks are finding significant applications in large scale distributed systems. One of the basic operations in sensor networks is in-network aggregation. Among the various approaches to in-network aggregation, such as gossip and tree, including the hash-based techniques, the tree-based approaches have better performance and energy-saving characteristics. However, sensor networks are highly prone to failures. Numerous techniques suggested in the literature to counteract the effect of failures have not been carefully analyzed. In this paper, we focus on the performance of these tree-based aggregation techniques in the presence of failures. First, we identify a fault model that captures the important failure traits of the system. Then, we analyze the correctness of simple tree aggregation with our fault model. We then use the same fault model to analyze the techniques that utilize redundant trees to improve the variance. The impact of techniques for maintaining the correctness under faults, such as rebuilding or locally fixing the tree, is then studied under the same fault model. We also do the cost-benefit analysis of using the hash-based schemes which are based on FM sketches. We conclude that these fault tolerance techniques for tree aggregation do not necessarily result in substantial improvement in fault tolerance.

An adaptive energy‐efficient and low‐latency MAC for tree‐based data gathering in sensor networks

AbstractA specific characteristic of sensor network applications is that the major traffic consists of data collection from various sensor source nodes to a sink via a unidirectional tree. In this paper, we propose DMAC, an energy efficient and low latency MAC that is designed and optimized for such data gathering trees in wireless sensor networks. We first show that previously proposed MAC protocols for sensor networks that utilize activation/sleep duty cycles suffer from a data forwarding interruption problem, whereby not all nodes on a multihop path to the sink can be notified of data delivery in progress, resulting in significant sleep delay. DMAC is designed to solve the interruption problem, by giving the active/sleep schedule of a node an offset that depends upon its depth on the tree. This scheme allows continuous packet forwarding because all nodes on the multihop path can be notified of the data delivery in progress. DMAC also adjusts node duty cycles adaptively according to the traffic load in the network by varying the number of active slots in an schedule interval. We further propose a data prediction mechanism and the use of more to send (MTS) packets in order to alleviate problems pertaining to channel contention and collisions. Our simulation results as well as experimental results with the Mote platform show that by exploiting the application‐specific structure of data gathering trees in sensor networks, DMAC provides significant energy savings and latency reduction while ensuring high data reliability. Copyright © 2007 John Wiley & Sons, Ltd.