Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management

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.

Big data challenges and opportunities in Internet of Vehicles: a systematic review

“…These insights can be used to dynamically optimize network resources and adapt communication protocols based on the changing environment. This dynamic network management allows IoV systems to respond to different demands and conditions efficiently (Ijemaru et al , 2023; Kang et al , 2019). Data load balancing: Big data insights can be used to analyze traffic patterns and load balance communication resources to avoid network congestion.…”

“…The most significant challenges and opportunities of big data in IoV are listed as follows: Traffic management and optimization : Big data analysis enables real-time traffic handling, congestion prediction and traffic flow optimization. By analyzing data from various sources, including vehicles, customized infrastructures, traffic signals and road sensors, traffic management systems can provide up-to-date information that leads to reduced congestion and improved traffic flow (Nallaperuma et al , 2019; Ijemaru et al , 2023; Abdollah and Zarei, 2021; Abbasi et al , 2022). Predictive maintenance : Using vehicle sensor data, big data analytics can predict maintenance needs and potential breakdowns.…”

“…The collaborative data gathering proposal introduces a scheme for efficient data gathering (Das and Dash, 2023), energy preservation (Das and Dash, 2023), real-time monitoring (Sahraoui et al , 2022) and fast convergence (Xu et al , 2023) through simulation. However, challenges include coordination complexity and real-world deployment (Das and Dash, 2023), data privacy and integration (Sahraoui et al , 2022; Ijemaru et al , 2023). The opportunistic data collection and traffic engineering proposal (Ijemaru et al , 2023), using MATLAB and RMASE, showcases traffic flow optimization and real-time data insights, but faces challenges in implementation complexity and data privacy/security.…”

“…However, challenges include coordination complexity and real-world deployment (Das and Dash, 2023), data privacy and integration (Sahraoui et al , 2022; Ijemaru et al , 2023). The opportunistic data collection and traffic engineering proposal (Ijemaru et al , 2023), using MATLAB and RMASE, showcases traffic flow optimization and real-time data insights, but faces challenges in implementation complexity and data privacy/security. The disease spread monitoring proposal (Sahraoui et al , 2022), simulated using NS3, emphasizes early detection, real-time monitoring and effective communication, yet grapples with challenges related to data privacy, integration and infrastructure deployment.…”

“…The system involved two main components: data gathering and sensor recharging. Ijemaru et al (2023) explored the application of swarm intelligence and IoV approaches in smart city waste management. The researchers focused on using swarm intelligence techniques to optimize opportunistic data collection and traffic engineering for efficient waste management in urban environments.…”