Dynamic Load Balancing Techniques in the IoT: A Review

Abstract

The Internet of things (IoT) extends the Internet space by allowing smart things to sense and/or interact with the physical environment and communicate with other physical objects (or things) around us. In IoT, sensors, actuators, smart devices, cameras, protocols, and cloud services are used to support many intelligent applications such as environmental monitoring, traffic monitoring, remote monitoring of patients, security surveillance, and smart home automation. To optimize the usage of an IoT network, certain challenges must be addressed such as energy constraints, scalability, reliability, heterogeneity, security, privacy, routing, quality of service (QoS), and congestion. To avoid congestion in IoT, efficient load balancing (LB) is needed for distributing traffic loads among different routes. To this end, this survey presents the IoT architectures and the networking paradigms (i.e., edge–fog–cloud paradigms) adopted in these architectures. Then, it analyzes and compares previous related surveys on LB in the IoT. It reviews and classifies dynamic LB techniques in the IoT for cloud and edge/fog networks. Lastly, it presents some lessons learned and open research issues.

Energy efficient reliable data transmission for optimizing IoT data transmission in smart city

“…The symbol ∝ is used to represent the proportionality between variables, specifically in this case, the average number of CH. The suggested threshold 𝐻(𝑑) for IoT sensor node 𝑑 can be reformulated using (14).…”

“…The probability of an IoT sensor node getting designated as a CH is inversely proportional to the measurement of 𝜇(𝑑) it possesses. Conversely, an IoT sensor node having a lower 𝜇(𝑑) value is assigned a higher 𝑟(𝑑) value, which is subsequently used as an input parameter in (14). As a result, the IoT sensor node will assume the role of CH, although for a limited duration.…”

“…Despite the transformative potential of IoT-Cloud integration in smart cities, challenges persist in achieving optimal data collection, analysis, and transmission [13]. Existing frameworks may fall short in addressing the specific needs of heterogeneous IoT environments, leading to inefficiencies, increased energy consumption, and compromised reliability [14]. Therefore, there is a pressing need for innovative solutions that can holistically tackle the challenges of data collection and transmission in smart cities [15].…”

A New WRR Algorithm for an Efficient Load Balancing System in IoT Networks under SDN

“…Furthermore, the data generated by IoT devices is continuously growing and evolving, leading to an increased demand for various services and resources. This necessitates the need to balance the load in order to meet the high requirements of the IoT network, such as high performance, lower latency, high throughput, and high availability [18] [19]. In traditional networks, load-balancing algorithms do not adequately address the virtualization of network operations and rapid scaling.…”

A Communication-Efficient, Privacy-Preserving Federated Learning Algorithm Based on Two-Stage Gradient Pruning and Differentiated Differential Privacy

“…FL is now being used in the IoT, which has a particularly large and complex network. The cost of communication between various devices can be very high, making it necessary to find a solution to this problem [9][10][11]. Although Deep Neural Networks (DNNs) have achieved good performances in many real-world scenarios, their high computation and memory requirements make them unsuitable for wider applications on resource-limited devices [12].…”