Swarm Intelligence to Face IoT Challenges

Abstract

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.

Bio-Inspired Internet of Things: Current Status, Benefits, Challenges, and Future Directions

“…In general, IoT systems raise concerns about security and privacy, and bio-inspired IoT is no exception. The interconnected nature of IoT devices and the use of bio-inspired algorithms can introduce vulnerabilities, such as attacks on swarm intelligence or genetic processes [ 73 ]. Thus, ensuring the security and privacy of data, communications, and algorithms in bio-inspired IoT is an ongoing challenge that many researchers are currently working on [ 34 , 35 , 36 ].…”

“…The integration of bio-inspired algorithms with other computational techniques, such as machine learning, deep learning, and data analytics, can enhance the capabilities of bio-inspired IoT [ 63 , 64 , 73 , 74 , 75 , 76 , 77 , 78 ]. Hybrid approaches that combine the strengths of different algorithms and methodologies are likely to emerge, enabling more powerful and adaptable solutions [ 74 , 75 , 79 ].…”

“…As bio-inspired IoT systems become more complex and autonomous, the need for explainable and interpretable models and algorithms becomes critical. Research focusing on understanding and interpreting the decision-making processes of bio-inspired algorithms can lead to more trustworthy and transparent IoT systems, enabling users to comprehend and validate the outputs and behavior of such systems [ 48 , 49 , 50 , 73 , 74 , 75 ].…”

“…Exploring the collective behavior and cooperation among multiple bio-inspired IoT systems can lead to innovative solutions [ 76 , 77 ]. Research on collaborative algorithms, coordination mechanisms, and collective decision making can enable IoT systems to work together as intelligent collectives, sharing information and optimizing tasks in a cooperative manner [ 73 ].…”

“…As bio-inspired IoT becomes more integrated into various aspects of daily life, addressing ethical and social implications is crucial. Hence, future research should focus on understanding and addressing issues related to bias, fairness, accountability, transparency, and the social impact of bio-inspired IoT systems [ 49 , 73 , 74 , 75 ].…”