无线传感器网络的节能不等分簇路由协议

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"这篇研究论文提出了一种名为能量感知不等价聚类路由协议（EAUC）的方法，用于解决无线传感器网络（WSNs）中的能源消耗不平衡问题，以延长网络寿命。" 在无线传感器网络中，节点的能量消耗不均衡是一个固有的挑战，这会导致网络寿命显著缩短。该论文提出的EAUC协议在集群形成阶段采用了基于权重的模型来选择集群头，以此实现节点的不等价聚类，即将节点划分为大小不等的集群。这样可以优化能量分布，减少某些节点过早耗尽能源的情况。在数据传输阶段，EAUC协议结合了单跳模式（用于集群内部通信）和多跳模式（用于集群间通信）。此外，协议还引入了一个权重函数，用于优化集群头中继的选择，以确保能量的有效利用和负载平衡。这一策略有助于减少通信过程中的能量损耗，提高整个网络的效率。通过模拟实验，文章展示了EAUC协议相对于LEACH（低能量自组织网络）协议的优越性。LEACH是一种广泛应用的节能路由协议，但其均匀随机选择的集群头策略可能导致早期的能量耗尽。相比之下，EAUC的不等价聚类和优化的中继选择策略能更有效地管理和分配网络资源，从而延长网络的生存时间。这篇研究论文为无线传感器网络提供了一种创新的、能量高效的解决方案，它强调了对节点能量消耗的精确控制和优化，为未来WSN的设计和应用提供了重要的理论支持。通过不等价聚类和智能路由策略，EAUC协议有望在实际部署中显著提升无线传感器网络的性能和持久性。

C. Wei et al. /Journal of Computational Information Systems 10: 10 (2014) 4475–4487 4477

LEACH [3] is a typical clustering protocol proposed for periodical data gathering applications in

WSNs. In LEACH, each node independently elects itself as a CH with a probability. CHs receive

and aggregate data from cluster memb ers and forward the aggregated data to the BS by single-hop

communication. In order to balance the energy depletion, the role of CH is periodically rotated

among the nodes. Researchers further improved LEACH and proposed some new algorithms such

as centralized algorithm LEACH-C [3], multi-level clustering algorithm EEMLC [9], algorithm

based on heterogeneous networks EEHC [10], and algorithm based on multihop communication

LEACH-M [11].

HEED [12] periodically selects CHs according to a hybrid of their residual energy and the

communication cost. It terminates the clustering process within a constant number of iterations,

and achieves fairly uniform distribution of CHs across the network. In [13], the authors propose

VCA, in which sensors vote for their neighbors to elect suitable CHs, and two strategies to balance

the intra-cluster workload among CHs. CAWT [14], which uses random waiting timer to generate

clusters, was a neighbor-based clustering algorithm. In CAWT, a sensor updates its neighbor

information by Hello message. And it decreases its waiting timer inversely proportional to the

number of neighbors. When a waiting timer is expired, a sensor declares itself to become a CH.

ACAWT [15], where the CHs are selected based on residual energy, has been proposed as an

enhanced CAWT scheme. If the energy of CH is falls below a threshold level, the reselection

process of CH is started. EADEEG [16] selects CHs based on the ratio between the average

residual energy of neighbor nodes and the residual energy of the node itself, which can achieve a

good CHs distribution and prolong the network lifetime.

Although the above even clustering schemes balance the average energy consumption among

diﬀerent intra-cluster communications well, they ignore the energy balancing problem among CHs

in the inter-cluster communication. Some CHs farther away from the BS deplete more energy

with single-hop communication for long-distance transmission, and some CHs nearer the BS take

a heavier energy burden to relay data via multihop communication. To solve this problem, some

unequal clustering schemes have been proposed [17-23].

In EECS [17], a distance-based cluster formation method is proposed to produce clusters of

unequal sizes in single hop networks. Clusters farther away from the BS have smaller sizes,

thus some energy could be preserved for long-distance data transmission to the BS. Soro ad

Heinzelman [18] ﬁrst investigate an unequal clustering method for the network that was organized

into heterogeneous clusters, where some more powerful nodes act as cluster heads to control the

network operation. This approach can be expanded to homogeneous sensor networks, however,

it was demonstrated only for a circular network with two-hop inter-cluster communication. Shu

et al. [19] study an optimization problem of balancing energy consumption among CHs which

is formulated as a signomial optimization problem. The study demonstrates the signiﬁcance

of simultaneously considering the impacts of intra-cluster and inter-cluster traﬃc. In [20], the

authors propose an energy-eﬃcient unequal clustering mechanism (EEUC) for periodical data

gathering applications in WSNs. It wisely organizes the network via unequal-clustering and

multi-hop routing. The competitive range is determined on the basis of relative value of maximum

distance between the sensors and the sink. The result is that clusters closer to the base station

are expected to have smaller cluster sizes, thus the CHs will consume lower energy during the

intra-cluster data processing, and can preserve some more energy for the inter-cluster relay traﬃc.

The authors in [21] present an analytical model of cluster size based on the distance between

the CH and the sink, and propose a simple clustering algorithm called LUCA, where each cluster

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