A Comprehensive Dataset for Job Allocation in Internet of Things Networks

Abstract This paper presents the Network and Application Dataset for Internet of Things (NADIoT), a dataset built to support research on job allocation in large-scale IoT networks. The dataset provides instances containing between 10 (101) and 1,000,000,000 (109) network nodes, each associated with multiple jobs that represent application demands. Nodes and jobs of the dataset have essential attributes such as processing capacity, bandwidth, and latency, allowing researchers to explore performance under a wide range of operating conditions. NAD-IoT is organized into scenarios defined by the ratio between jobs and nodes: lightweight scenarios, where jobs represent up to 20% of the nodes, and heavyweight scenarios, where jobs correspond to 70% of the nodes. It enables systematic assessments of heuristics, metaheuristics, and optimization strategies, supporting analyzes of scalability, efficiency, and runtime. It provides a reliable foundation for benchmarking algorithmic behavior, examining job-distribution patterns, and evaluating resource-management techniques.Beyond optimization studies, NAD-IoT can be used for experiments, simulation design, model training, and validation in investigations involving large-scale, heterogeneous, and resource-constrained IoT environments.