We propose a novel relay augmentation strategy for extending the lifetime of a certain class of wireless sensor networks. In this class sensors are located at fixed and pre-determined positions and all communication takes place via multi-hop paths in a fixed routing tree rooted at the base station. It is assumed that no accumulation of data takes place along the communication paths and that there is no restriction on where additional relays may be located. Under these assumptions the optimal extension of network lifetime is modelled as the Euclidean @math -bottleneck Steiner tree problem. Only two approximation algorithms for this NP-hard problem exist in the literature: a minimum spanning tree heuristic (MSTH) with performance ratio 2, and a probabilistic 3-regular hypergraph heuristic (3RHH) with performance ratio @math . We present a new iterative heuristic that incorporates MSTH and show via simulation that our algorithm performs better than MSTH in extending lifetime, and outperforms 3RHH in terms of efficiency.
Sensors are often overtaxed due to an uneven distribution of traffic flow. Many lifetime extending strategies in the literature therefore strive to dynamically adjust the routing topology in order to relieve the burden on these sensors; see @cite_8 , @cite_14 , @cite_25 . Unfortunately, these techniques are only germane if multiple available paths exist between nodes in the network. In WSNs with low connectivity or where routing protocols are limited by other application-specific or topological factors, deployment based strategies may be more valuable.
@cite_14 @cite_25 @cite_8
1965271273 2124415567 2141573624
Performing tasks energy efficiently in a wireless sensor network (WSN) is a critical issue for the successful deployment and operation of such networks. Gathering data from all the sensors to a base station, especially with in-network aggregation, is an important problem that has received a lot of attention recently. The Maximum Lifetime Data Gathering with Aggregation (MLDA) problem deals with maximizing the system lifetime T so that we can perform T rounds of data gathering with in-network aggregation, given the initial available energy of the sensors. A solution of value T to the MLDA problem consists of a collection of aggregation trees together with the number of rounds each such tree should be used in order to achieve lifetime T. We describe a combinatorial iterative algorithm for finding an optimal continuous solution to the MLDA problem that consists of up to n-1 aggregation trees and achieves lifetime T"o, which depends on the network topology and initial energy available at the sensors. We obtain an @a-approximate optimal integral solution by simply rounding down the optimal continuous solution, where @a=(T"o-n+1) T"o. Since in practice T"[email protected]?n,@a 1. We get asymptotically optimal integral solutions to the MLDA problem whenever the optimal continuous solution is @w(n). Furthermore, we demonstrate the efficiency and effectiveness of the proposed algorithm via extensive experimental results. Unbalanced energy consumption is an inherent problem in wireless sensor networks characterized by multihop routing and many-to-one traffic pattern, and this uneven energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of maximizing network lifetime through balancing energy consumption for uniformly deployed data-gathering sensor networks. We formulate the energy consumption balancing problem as an optimal transmitting data distribution problem by combining the ideas of corona-based network division and mixed-routing strategy together with data aggregation. We first propose a localized zone-based routing scheme that guarantees balanced energy consumption among nodes within each corona. We then design an offline centralized algorithm with time complexity O(n) (n is the number of coronas) to solve the transmitting data distribution problem aimed at balancing energy consumption among nodes in different coronas. The approach for computing the optimal number of coronas in terms of maximizing network lifetime is also presented. Based on the mathematical model, an energy-balanced data gathering (EBDG) protocol is designed and the solution for extending EBDG to large-scale data-gathering sensor networks is also presented. Simulation results demonstrate that EBDG significantly outperforms conventional multihop transmission schemes, direct transmission schemes, and cluster-head rotation schemes in terms of network lifetime. Wireless sensor networks (WSNs) require protocols that make judicious use of the limited energy capacity of the sensor nodes. In this paper, the potential performance improvement gained by balancing the traffic throughout the WSN is investigated. We show that sending the traffic generated by each sensor node through multiple paths, instead of a single path, allows significant energy conservation. A new analytical model for load-balanced systems is complemented by simulation to quantitatively evaluate the benefits of the proposed load-balancing technique. Specifically, we derive the set of paths to be used by each sensor node and the associated weights (i.e., the proportion of utilization) that maximize the network lifetime.