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首页HEED算法:节能分布式分簇协议优化传感器网络
HEED算法原文探讨了一种针对自组网(Ad-hoc Sensor Networks)的混合、节能分布式聚类方法,旨在优化网络结构并减少节点能耗。论文作者Ossama Younis和Sonia Fahmy来自普渡大学计算机科学系,他们在没有基础设施假设或对节点特定能力的要求下,仅依赖于传感器节点具备多种功率等级这一基本条件,提出了创新的解决方案。 该研究的核心是HEED(Hybrid Energy-Efficient Distributed Clustering)协议。HEED的主要目标是通过结合节点剩余能量和次要参数(如节点与邻居的距离或连接度)来动态选择集群头。这种方法的优势在于它能够有效地平衡传感器节点的工作负载,从而提升网络的可扩展性和生存期。由于其设计注重能源效率,HEED能够在每个迭代周期内快速(线性时间复杂度O(1))完成集群头的选择过程,从而降低了消息传递的开销。 HEED协议的一个关键特点是它的公平性,即能够实现网络中相对均匀的集群头分布,这有助于在整个网络中均衡地分配任务和资源,延长整体网络的寿命。此外,由于其分布式特性,HEED无需中心化控制,降低了单点故障的风险,增强了网络的健壮性。 总结来说,HEED算法在无线传感器网络的分簇策略中引入了新颖的决策机制,不仅提高了能源利用效率,还优化了网络的性能和稳定性。这对于资源受限的物联网应用具有重要意义,尤其是在需要长时间运行和低能耗的场景中。通过HEED,网络管理员和开发者可以更好地管理传感器网络,使其在满足功能需求的同时,最大限度地延长网络的生命周期。
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1) Clustering is completely distributed. Each node independently makes its decisions based only on
local information.
2) Clustering terminates within a fixed number of iterations (regardless of network diameter).
3) At the end of each T
CP
, each node is either a cluster head, or not a cluster head (which we refer
to as a regular node) that belongs to exactly one cluster.
4) Clustering should be efficient in terms of processing complexity and message exchange.
5) Cluster heads are well-distributed over the sensor field, and have relatively high average residual
energy compared to regular nodes.
III. T
HE HEED PROTOCOL
In this section, we describe the HEED protocol. First, we define the parameters used in the clustering
process. Second, we present the protocol design and pseudo-code. Finally, we prove that the protocol
meets its requirements.
A. Clustering Parameters
The overarching goal of our approach is to prolong network lifetime. For this reason, cluster head
selection is primarily based on the residual energy of each node. Measuring this residual energy is not
necessary, since the energy consumed per bit for sensing, processing, and communication is typically
known, and hence residual energy can be estimated. To increase energy efficiency and further prolong
network lifetime, we also consider intra-cluster “communication cost” as a secondary clustering parameter.
For example, cost can be a function of neighbor proximity or cluster density.
We use the primary clustering parameter to probabilistically select an initial set of cluster heads, and
the secondary parameter to “break ties” among them. A tie in this context means that a node falls within
the “range” of more than one cluster head. To understand what “range” denotes in this case, observe that
a node typically has a number (e.g., 6) of discrete transmission power levels. Thus, the cluster range
or radius is determined by the transmission power level used for intra-cluster announcements and during
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clustering. We refer to this level as the cluster power level. The cluster power level should be set to one of
the lower power levels of a node, to increase spatial reuse, and reserve higher power levels for inter-cluster
communication. These higher power levels should cover at least two or more cluster diameters to guarantee
that the resulting inter-cluster overlay will be connected. If this condition cannot be satisfied, then our
approach for clustering in conjunction with power level selection is inapplicable. We analyze inter-cluster
connectivity conditions in Section III-D. The cluster power level dictates the number of clusters in our
network. It is non-trivial to determine an optimal cluster power level, because network topology changes
due to node failures and energy depletion.
The secondary clustering parameter, intra-cluster communication cost, is a function of (i) cluster
properties, such as cluster size, and (ii) whether or not variable power levels are permissible for intra-
cluster communication. If the power level used for intra-cluster communication is fixed for all nodes,
then the cost can be proportional to (i) node degree, if the requirement is to distribute load among cluster
heads, or (ii)
1
node degree
, if the requirement is to create dense clusters. This means that a node joins the
cluster head with minimum degree to distribute cluster head load (possibly at the expense of increased
interference and reduced spatial reuse), or joins the one with maximum degree to create dense clusters.
We use the terms minimum degree cost and maximum degree cost to denote these cost types. Observe that
inter-cluster communication is not incorporated in the cost function since local information is insufficient
in this case.
Now consider the case when variable power levels are allowed for intra-cluster communication. Let
MinPwr
i
denote the minimum power level required by a node v
i
, 1 ≤ i ≤ M, to communicate with a
cluster head u, where M is the number of nodes within the cluster range. We define the average minimum
reachability power (AMRP) as the mean of the minimum power levels required by all M nodes within the
cluster range to reach u, i.e., AMRP =
P
M
i=1
MinPwr
i
M
. If each node is allowed to select the appropriate
power level to reach its cluster head, then AMRP provides a good estimate of the communication cost.
The AMRP of a node is a measure of the expected intra-cluster communication energy consumption if
this node becomes a cluster head. Using AMRP as cost in selecting cluster heads is superior to just
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