###### Abstract
Hence, this paper proposes a novel energy-efficient swarm intelligence IoV-based method for opportunistic data collection and traffic engineering for SC waste management. This method involves deploying multiple data collector vehicles (DCVs) traversing the entire network to gather sensed data via a single-hop transmission for SC waste management operations. None of the existing waste management strategies has considered this approach to optimize energy consumption for big data collection and transmission in an LS-WSN. Previous studies have shown multiple ways by which sensor data can be transmitted from sensors to the base station (BS) or access point (AP). For a waste management scenario, the traditional method involves data transmission from sensors directly to the sink for processing via a multi-hop communication formed by the sensor nodes. Multi-hop communication enables nodes near the sink node to relay data from the nodes that are farther way from the sink node. While this approach may be feasible for data transmission, it creates some bottlenecks in the network. The major drawback is the energy-hole problem created as a result of frequent data-forwarding activities of the sensor nodes that are near the sink node [4; 5; 6]. Such nodes usually dissipate their energies faster than their counterparts that are stationed farther away from the sink node. Hence, multi-hop data-forwarding strategy can be ineffective due to the energy-hole problem occasioned by frequent data forwarding resulting in high transmission overheads [2; 7]. Furthermore, the emergence of LS-WSNs with sensor-based big data systems makes this approach inefficient for SC waste management strategies. Inspired by the breakthroughs in recent studies that have shown the efficacy of mobile elements [8; 9; 10; 11; 12] over traditional static multi-hop routing, this paper proposes a novel energy-efficient swarm intelligence (SI) IoV-based model exploiting multiple DCVs for opportunistic data collection and traffic engineering for SC waste management. We propose analytically based methods to investigate some of the critical issues affecting efficient data collection and transmission for SC waste management strategies, which include: (1) determining 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. Previous studies exploring waste management strategies have not considered this approach to optimize energy consumption for big data collection. The focus of this paper is the deployment of vehicular networks for opportunistic data collection and traffic engineering for waste management operations in an SC. The proposed approach aims to address the two major challenges of efficient data gathering and transmission for DCVs in large-scale wireless sensor networks (LS-WSNs) in SC scenarios. We also consider the challenge of data collection from spatially separated regions (unconnected regions), which requires network partitioning into multiple sizes and groups prior to data collection. This issue poses a great challenge that makes it infeasible to deploy a single DCV for data-gathering activities, since a single DCV does not have the energy capacity to traverse the entire network for the purpose of data gathering and delivery to the BS or AP. Based on this scalability issue with a single DCV in an LS-WSN scenario, this work considers exploiting multiple DCVs to minimize latency times since there may be many data collection points (DCPs) to be visited. Thus, the proposed approach leverages the advantages inherent in the use of multiple mobile collectors to address some of the limitations of the single data collector scheme. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Hence, there is a need to create a balance to guarantee network stability by determining the optimal number of DCVs required for continuous network operation.