学习自动机优化机会网络中n-流行病路由协议的动态调整策略

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"动态调整n-流行病路由协议策略：一种学习自动机方法" 这篇研究论文主要探讨了在机会网络（Opportunistic Networks）中提高n-流行病路由协议（n-Epidemic Routing Protocol）能源效率的动态调整策略，该策略采用学习自动机（Learning Automata）原理。机会网络是一种特殊的移动 ad hoc 网络，其中节点间的通信是不可预测的，通常不存在稳定端到端的转发路径。传统的n-流行病路由协议在节点间传播数据时，往往忽视了网络环境中的能源消耗问题，可能导致不必要的能量损耗。论文作者提出了一个新颖的参数n动态调整策略，以解决这一问题。首先，网络中的节点会动态地更新当前环境的平均能量水平。这一过程意味着节点能够感知到周围环境的能量状态，从而更好地决策路由选择。学习自动机是一种随机决策机制，它通过试错学习来优化其行为。在这个上下文中，每个节点被看作是一个学习自动机，通过与环境的交互来学习最优的n值。当节点接收或发送数据包时，它会根据学习自动机的规则更新其状态，并基于这些经验调整n值。这样，节点可以自我适应地调整其传播策略，以最大化数据传输效率并减少能源浪费。论文指出，这种策略的实施包括以下几个关键步骤： 1. 状态定义：节点根据其能量状态和环境条件定义不同的状态。 2. 动作选择：节点可以选择传播数据包或者不传播，这取决于当前的能量水平和n值。 3. 反馈机制：根据传播的成功与否，学习自动机提供正或负的反馈，影响节点未来的行为。 4. 学习过程：通过不断尝试和反馈，节点逐渐学习到最优的n值，即在能源效率和数据传输成功率之间找到平衡。此外，论文还可能涵盖了算法的设计、仿真结果和性能分析，以证明所提出的策略在实际网络环境中的有效性和优越性。可能的性能指标包括能源效率、数据包交付率、网络延迟等。通过与其他路由协议的比较，作者可能展示了新策略如何在各种网络条件下显著提高机会网络的性能。总体来说，这篇研究论文提出了一个创新的方法，利用学习自动机理论来动态调整n-流行病路由协议的参数，以优化机会网络的能源效率和数据传输性能。这种方法对于未来设计能源高效的机会网络路由策略具有重要的参考价值。

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 11, NO. 4, April 2017 2023

only takes the parameter n from a static and predefined parameter set determined by energy

value and node density. It is irrespective of current energy situation of neighbors. The value

in the set can also not be changed for different type of nodes. It is obviously that this method

is not suitable for all the conditions as the energy value of various nodes is not identical.

In opportunistic networks scenarios, it is impossible to replace or recharge batteries while

nodes are moving around. However, nodes are aware of distinct remaining energy levels of

themselves, and can get this information from their current neighbors by the inquiry method.

Hence, detrimental replication decisions for adjacent nodes can be canceled ahead of

schedule for saving energy. There must be a tradeoff between energy conservation and

message replication. This paper not only considers energy constraints of mobile nodes, but

also takes the current local energy state information of neighbors into account during making

message replication decisions.

2.2 Learning Automata

Learning Automata (LA) is an adaptive decision-making mathematical model which

operates on an unknown random environment [13]. There is a finite set of actions that could

be chosen at every period by a learning automaton. An action probability vector is used to

describe the possibility of each action. The environment will evaluate the chosen action and

give a reinforcement signal with the probability distribution. Then the automaton updates its

action probability vector depending on the reinforcement signal at each cycle, and then

evolves to next desired behavior. The mathematical expression of learning automata is called

variable structure, and is represented by a quadruple (α, β, p, T), in which α = {α

, α

, … α

}

represents the action set of the automata, β = {β

, β

, … β

} represents the input set, p = {p

,…p

} denotes the action probability set, and p(n + 1) = T[α(n), β(n), p(n)] represents the

learning algorithm.

The environment depending on the class of the reinforcement signal β can be classified

into P-model, Q-model and S-model. The environment in which the reinforcement signal can

only take two binary values 0 and 1 are referred to P-model environment. Another class of

the environment allows a finite number of values in the interval [0, 1] can be taken by the

reinforcement signal. Such an environment is referred to Q-model environment. In S-model

environment, the reinforcement signal lies in the interval [a, b] [15].

Let α

be the action chosen at time n, then the recurrence equation for updating p is defined

as:

( 1) () ()(1 ())

( 1) () () () ,

+= + ⋅−

+= − ⋅ ∀ ≠

ii i

jj j

pn pn an pn

pn pn an pn jj i

(1)

for favorable responses, and

( 1) (1 ()) ()

( 1) (1 ()) () ,

+=− ⋅

+= +− ⋅ ∀ ≠

−

pn bn pn

pn bn pn jj i

(2)

for unfavorable ones, where a and b are reward and penalty parameters respectively. If a = b,

learning algorithm is called L

R-P

; if a << b, it is called L

; if b=0, it is called L

R-I

[16]. The

relationship between the learning automaton and its random environment is shown in Fig. 1.

2.3 Dynamic Irregular Cellular Learning Automata (DICLA)

Cellular Automata (CA) is a dynamic model that is consisted of a lot of identical

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学习自动机优化机会网络中n-流行病路由协议的动态调整策略

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