Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq 25113, Jordan.
Institute for High Performance Computing and Networking, National Research Council, Rende, CS, Italy.
Comput Intell Neurosci. 2023 May 29;2023:4254194. doi: 10.1155/2023/4254194. eCollection 2023.
The Internet of Things (IoT) paradigm denotes billions of physical entities connected to Internet that allow the collecting and sharing of big amounts of data. Everything may become a component of the IoT thanks to advancements in hardware, software, and wireless network availability. Devices get an advanced level of digital intelligence that enables them to transmit real-time data without applying for human support. However, IoT also comes with its own set of unique challenges. Heavy network traffic is generated in the IoT environment for transmitting data. Reducing network traffic by determining the shortest route from the source to the aim decreases overall system response time and energy consumption costs. This translates into the need to define efficient routing algorithms. Many IoT devices are powered by batteries with limited lifetime, so in order to ensure remote, continuous, distributed, and decentralized control and self-organization of these devices, power-aware techniques are highly desirable. Another requirement is to manage huge amounts of dynamically changing data. This paper reviews a set of swarm intelligence (SI) algorithms applied to the main challenges introduced by the IoT. SI algorithms try to determine the best path for insects by modeling the hunting behavior of the agent community. These algorithms are suitable for IoT needs because of their flexibility, resilience, dissemination degree, and extension.
物联网 (IoT) 范式表示数十亿个物理实体连接到互联网，允许收集和共享大量数据。由于硬件、软件和无线网络可用性的进步，任何东西都可以成为物联网的组成部分。设备获得了高级别的数字智能，使它们能够在不申请人工支持的情况下实时传输数据。然而，物联网也带来了一系列独特的挑战。物联网环境中会产生大量的网络流量来传输数据。通过确定从源到目标的最短路径来减少网络流量，可以降低整体系统响应时间和能耗成本。这就需要定义有效的路由算法。许多物联网设备由电池供电，电池寿命有限，因此为了确保这些设备的远程、连续、分布式和去中心化控制和自组织，非常需要功率感知技术。另一个要求是管理大量动态变化的数据。本文综述了一组群智能 (SI) 算法，这些算法应用于物联网带来的主要挑战。SI 算法通过模拟代理群体的狩猎行为来尝试确定昆虫的最佳路径。由于其灵活性、弹性、传播程度和扩展性，这些算法非常适合物联网的需求。
