Purpose

Wireless Body Area Network (WBAN) technology envisions a network in which sensors continuously operate on and obtained critical physical and physiological readings. Sensors deployed in WBANs have restricted resources such as battery energy, computing power and bandwidth. We can utilize these resources efficiently. By devising a mechanism that is energy efficient with following characteristics, i.e. computational complexity is less, routing overhead is minimized, and throughput will be maximum. A lot of work has been done in this area but still WBAN faces some challenges like mobility, network lifetime, transmission range, heterogeneous environment, and limited resources. In the present years well, contemplative studies have been made through a large body to reach some holistic points pertaining to the energy consumption in WBAN. Thus we/put forward appropriate algorithm for energy efficiency which can vividly corroborate the advances in this specific domain. We have also focused on various aspects and phases of the studies like study computational complexity, routing overhead and throughput type of characteristics. There is still a room for improvement to get the desired energy optimization in WBAN. The network performance mainly relies upon the algorithm used for optimization process. In this work, we intended to develop an energy optimization algorithm for energy consumption in WBAN which is based on evolutionary algorithms for inter-BAN communications using cluster-based routing protocol.

Design/methodology/approach

In this paper we propose a meta heuristics algorithm Goa to solve the optimization problem in WBAN. Grasshopper is an insect. Generally, this insect is viewed individually and creating large swarm in nature. Figure 5 shows the individual grasshoppers' primitive patterns in swarm. Figure 7 depicts the pseudo code of Goa. In Goa, experiments are done to view the behavior of grasshoppers in swarm. How they gradually move towards the stationary and mobile target. Through experimentation it is conceived that swarm gradually converge towards their target. Another interesting pattern related to convergence of grasshopper is that it slowly towards its target. This shows that grasshopper does not trapped in local optima. In starting iterations of exploration process Goa, search globally and in last iterations it searches local optima. Goa makes the exploration and exploitation process balanced while solving challenging optimization problems.

Findings

Energy efficiency is achieved in the optimization process of cluster formation process. As the use of proposed algorithm Goa creates the optimal number of clusters. Shorter cluster lifetime means more times clustering procedure is called. It increases the network computational cost and the communication overhead. Experimentation results show that proposed Goa algorithm performs well. We compare the results of Goa with existing optimization Algorithms ACO and MFO. Results are generated using MATLAB.

Originality/value

A lot of work has done for the sake of energy optimization in WBAN. Many algorithms are proposed in past for energy optimization of WBAN. All of them have some strengths and weaknesses. In this paper we propose a nature inspired algorithm Goa. We use the Goa algorithm for the sake of energy optimization in WBAN.

中文翻译：

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基于物联网 (IoT) 的无线体域网的高效集群优化框架

目的

无线体域网 (WBAN) 技术设想了一个网络，其中传感器持续运行并获取关键的物理和生理读数。部署在 WBAN 中的传感器具有有限的资源，例如电池能量、计算能力和带宽。我们可以有效地利用这些资源。通过设计一种具有以下特征的节能机制，即计算复杂度较低，路由开销最小，吞吐量最大。在这方面已经做了很多工作，但 WBAN 仍然面临一些挑战，如移动性、网络寿命、传输范围、异构环境和有限的资源。近年来，已经通过大型机构进行了深入研究，以得出与 WBAN 中的能源消耗有关的一些整体观点。因此，我们/提出了合适的能源效率算法，可以生动地证实这一特定领域的进展。我们还专注于研究的各个方面和阶段，例如研究计算复杂性、路由开销和吞吐量类型的特性。要在 WBAN 中获得所需的能量优化，仍有改进空间。网络性能主要取决于用于优化过程的算法。在这项工作中，我们打算开发一种 WBAN 能源消耗的能源优化算法，该算法基于使用基于集群的路由协议的 BAN 间通信的进化算法。我们还专注于研究的各个方面和阶段，例如研究计算复杂性、路由开销和吞吐量类型的特征。要在 WBAN 中获得所需的能量优化，仍有改进空间。网络性能主要取决于用于优化过程的算法。在这项工作中，我们打算开发一种 WBAN 能源消耗的能源优化算法，该算法基于使用基于集群的路由协议的 BAN 间通信的进化算法。我们还专注于研究的各个方面和阶段，例如研究计算复杂性、路由开销和吞吐量类型的特性。要在 WBAN 中获得所需的能量优化，仍有改进空间。网络性能主要取决于用于优化过程的算法。在这项工作中，我们打算开发一种 WBAN 能源消耗的能源优化算法，该算法基于使用基于集群的路由协议的 BAN 间通信的进化算法。

设计/方法/途径

在本文中，我们提出了一种元启发式算法 Goa 来解决 WBAN 中的优化问题。蚱蜢是一种昆虫。通常，这种昆虫被单独观察并在自然界中形成大群。图 5 显示了个体蝗虫在群体中的原始模式。图 7 描述了 Goa 的伪代码。在果阿，进行了实验以观察蝗虫群中的行为。他们如何逐渐向静止和移动目标移动。通过实验，可以设想群体逐渐向他们的目标汇聚。另一个与 Grasshopper 收敛相关的有趣模式是它缓慢地朝向目标。这表明 Grasshopper 没有陷入局部最优。在探索过程 Goa 的开始迭代中，全局搜索并在最后一次迭代中搜索局部最优值。

发现

能源效率是在集群形成过程的优化过程中实现的。由于使用建议的算法，Goa 创建了最佳数量的集群。更短的集群生命周期意味着调用更多次集群过程。它增加了网络计算成本和通信开销。实验结果表明，所提出的Goa算法性能良好。我们将 Goa 的结果与现有优化算法 ACO 和 MFO 进行比较。结果是使用 MATLAB 生成的。

原创性/价值

为了WBAN 中的能量优化，已经做了很多工作。过去提出了许多算法用于WBAN的能量优化。他们都有一些优点和缺点。在本文中，我们提出了一种受自然启发的算法 Goa。为了 WBAN 中的能量优化，我们使用 Goa 算法。