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Topology control in the mobile ad hoc networks in order to intensify energy conservation

机译：移动自组织网络中的拓扑控制，以增强节能效果

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Although studied for years, due to their dynamic nature, research in the field of mobile ad hoc networks (MANETs) has remained a vast area of interest. Since once distributed, there will be less to no plausibility of recharge, energy conservation has become one of the pressing concerns regarding this particular type of network. In fact, one of the main obligations of designers is to make efficient use of these scarce resources. There has been tremendous work done in different layers of protocol stack in order to intensify energy conservation. To date, numerous topology control algorithms have been proposed, however, only a few have used meta-heuristics such as genetic algorithms, neural networks and/or learning automata to overcome this issue. On the other hand, since nodes are mobile and thus in a different spatial position, as time varies, we can expect that by regulating time intervals between topology controls, one may prolong the network's lifetime. The main initiative of this paper is to intensify energy conservation in a mobile ad hoc network by using weighted and learning automata based algorithms. The learning automata, regulates time intervals between which the topology controls are done. The represented learning automata based algorithm uses its learning ability to find appropriate time-intervals so that the nodes would regulate the energy needed in order to exchange the information to their neighbors, accordingly. Moreover, at first we have represented two weighted based algorithms which extend two prominent protocols, namely K-Neigh and LMST. Then these algorithms are combined with a learning based algorithm which regulates time intervals between which the topology controls are done. In comparison with approaches that are based on periodic topology controls, proposed approach shows enhanced results. On the other hand, considering the learning ability of the learning automata based algorithms, composition of the aforementioned algorithms has been proven to be enhanced, in the respect of energy consumed per data transmitted, over those compared with.
机译：尽管已经进行了多年研究，但由于其动态性质，移动自组织网络（MANET）领域的研究仍然引起了广泛的兴趣。由于一旦分配，将几乎没有或几乎没有充电的可能性，关于这种特定类型的网络，节能已成为紧迫的问题之一。实际上，设计师的主要义务之一是有效利用这些稀缺的资源。为了加强节能，在协议栈的不同层上进行了大量工作。迄今为止，已经提出了许多拓扑控制算法，但是，只有少数几个使用元启发式算法，例如遗传算法，神经网络和/或学习自动机来克服这个问题。另一方面，由于节点是可移动的，因此在不同的空间位置（随时间变化），我们可以预期，通过调节拓扑控制之间的时间间隔，可以延长网络的寿命。本文的主要倡议是通过使用基于加权和学习自动机的算法来加强移动自组织网络中的节能。学习自动机可调节完成拓扑控制的时间间隔。基于表示的学习自动机的算法使用其学习能力来找到适当的时间间隔，以便节点相应地调节所需的能量，以便与邻居交换信息。此外，首先，我们代表了两种基于加权的算法，它们扩展了两个突出的协议，即K-Neigh和LMST。然后，将这些算法与基于学习的算法相结合，该算法可调节完成拓扑控制的时间间隔。与基于定期拓扑控制的方法相比，建议的方法显示出增强的结果。另一方面，考虑到基于学习自动机的算法的学习能力，就每个传输数据所消耗的能量而言，与之相比，上述算法的组成已被证明是增强的。

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