###### Abstract  
Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics. s 202323286033903 202323 23 23 23 23 23 23 23 23 23 23 23 23  
Waste management strategies entail all activities and operations relating to waste, including the collection, handling, transportation, and proper disposal at appropriate locations. According to [1], waste management strategies involve a set of processes ranging from collection of waste to the recycling of the collected waste. Waste management is therefore a critical component of an SC framework. A comprehensive review of SC waste management techniques can be found in [2]. Several studies on waste management strategies have focused on exploiting IoT-based technologies. However, this is associated with the challenge of high energy consumption of sensor nodes, which limits the operational lifetime of the network. Thus, optimizing energy consumption and operational costs to meet the energy requirements of the network is a major focus of the current research approach. Realizing the harmful effects of improper refuse disposal on the environment, it has become necessary to devise efficient waste management strategies for SC scenarios. Furthermore, owing to the surge in urban population growth, waste management data are growing astronomically and are projected to account for 70 percent of the Earth's population by 2050 [3], giving rise to large cities. Such cities are required to be'smart' to meet the demands of citizens by offering advanced and efficient services including smart waste management. Thus, efficient waste management strategies are a critical aspect of an SC framework. However, new challenges associated with energy-efficient waste management have emerged due to the need for sensor-based big data collection architectures and the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities. Thus, the current traditional IoT-based methods for data collection and transmission for waste management are no longer sustainable in the context of SC waste management scenarios because the sensors are battery-operated, with several limitations on memory capacity, processing, and storage capabilities to hold large volumes of waste management data that characterize an SC ecosystem. Because data collection and transmission operations are energy-dependent, network operations will be hampered if data transmission is impacted by energy-deficient sensor nodes.