## 1 Introduction  
The smart city is being equipped with many smart devices, driven by the advancement of digital technologies and the ever-increasing demand of end-user applications. However, energy-efficiency is one of the recent demands toward the development of the green smart city. It is estimated that smart cities will be equipped with possibly 40,000 million smart devices for 100,000 million global connections in different areas, such as health care, transportation, and finance, etc. These smart devices will be responsible for generating big data in the smart city, which is already increasing at a compound annual growth rate (CAGR) of 47%. It has been estimated that 90 ZB of data will be created on IoT devices by 2025 [1]. As companies currently transfer massive amounts of data across wide-area networks to backup their data, sync search indexes between data centers, or provide high-definition surveillance video records to governments and access audio and video across social media sites, a large amount of data is transferred over wide-area networks. Since the data volume and complexity of big data [2] are extremely large, the survival of big data is impossible without the underlying technical support of networking. A new connectivity method is, therefore, required to overcome this biggest challenge. By finding alternative data-transmission network architectures, researchers aim to reduce traffic congestion. Cellular base stations, T2T approaches, WI-FI hotspots, and vehicular networks are a few examples of data offloading techniques used. Cities' bus networks [3] have characteristics such as wide coverage and fixed routes, granting them the potential to form the backbone of communication, alongside traditional networks. In recent years, vehicular-assisted networks hold the utmost importance in the smart city to improve the quality of life, reliability, operational efficiency, and service quality in urban areas. Vehicles are used as data carriers in network communication. In addition to this, the data-offloading approach has been utilized to offload data from one network to another as per different criteria and priorities. Mobile Computation Offloading (MCO) [4] is a popular emerging technology to offload computation-intensive data to the servers to increase the capacity of devices and conserve battery energy. Through opportunistic contacts between moving vehicles and Road-Side Units (RSUs) placed on roads, it is possible to offload data onto vehicles for further delivery. In particular, public transport is a category of vehicular networks with several exclusive properties, such as regular and scheduled movements and reliable physical coverage in all urban centers. The main contributions of this paper are the following:  
1. We designed the Public Transport-Assisted Data-Dissemination (PTDD) System in a smart city which will be equipped with wireless sensors and data centers to handle massive data using wired, wireless, and public transport networks;
2. We applied a Multi-Attribute Decision making (MADM) algorithm for best network selection based upon different user requirements and different attributes;
3. We applied the Capacitated Vehicle Routing Problem (CVRP) to minimize energy consumption using public transport as a data carrier. We will use buses to offload the entire set of demands of each bus stop. Our model constrains the objective by the maximum capacity of the bus;
4. For the evaluation of the best network selection, different services are considered, based upon user requirements, to find the best network in the heterogeneous network. Next, a detailed comparative analysis of energy consumption is performed for traditional and public transport networks for the various demands of users. The rest of the paper is organized as follows. The PTDD is presented in Section 2, along with MADM and CVRP, for network selection and for allocating data onto buses. In Section 3, we perform a numerical analysis and include two case studies to present the results. Next, we have a brief discussion section in Section 4.