Efficient Data Transfer in Internet of Things (IoT) Networks: Privacy-Preserving Techniques for Edge-Cloud Collaborative Computing in Resource-Limited Settings

It has never been seen before that opportunities for data-driven automation, monitoring, and intelligent decision-making have evolved as a result of the rapid proliferation of ecosystems that are connected to the Internet of Things (IoT). The adoption of the Internet of Things can be difficult in settings where resources are restricted, such as rural areas, low-power industrial installations, and remote sensor networks. This is because of obstacles such as limits on bandwidth and computation capacity, as well as issues around privacy. An investigation into data-transfer protocols that are specifically geared to ensure the privacy of users in edge–cloud collaboration systems is the objective of this study. Application development for the Internet of Things that is scalable and adheres to the principles of data security, dependable connectivity, and resource efficiency is the goal of this project. The hybrid computing architecture is utilized in the research project in order to reduce the quantity of data that is collected at the edge as a result of Internet of Things sensors. This is done prior to the transmission of the raw data to the cloud for further analysis. Differential privacy, homomorphic encryption, secure multi-party computation, and federated learning are all examples of techniques that can maintain the usefulness of the information that is extracted while simultaneously protecting sensitive material. These measures lower the danger of unauthorized access and data loss, especially in limited environments with poor security. The proposed method was tested using simulated IoT networks. Poor bandwidth, unpredictable connectivity, and varied device capabilities plagued these networks. We assessed latency, energy, data reduction, prediction accuracy, and privacy. Edge-based preprocessing, adaptive compression, and encrypted feature extraction cut transmission load by 60% without compromising accuracy. Privacy-enhanced approaches were hard to eavesdrop, guess, and analyze metadata. The collaborative architecture makes the system more scalable and less cloud-dependent, allowing data processing virtually in real time even with poor networks. This study emphasises privacy-preserving and resource-efficient communication in modern IoT systems. Collaborative edge-cloud computing and strengthened data transfer protocols are used to build strong IoT infrastructures in resource-constrained contexts. The findings enable safe, rapid, and easy IoT ecosystem growth for smart agriculture, remote healthcare, and smart transportation systems.