Transformation from IoT to IoV for waste management in smart cities

Big sensor-based data systems and the emergence of large-scale wireless sensor networks (LS-WSNs), which are spatially distributed across various geographical areas in smart cities (SCs) have thrown new challenges for energy-efficient data collection. The traditional approach utilizing IoT-based techniques for data collection and transmission for waste management applications is not energy-efficient and currently infeasible for such LS-WSNs, thus necessitating the need for a transformation to an IoV-based technique, where vehicular networks can be opportunistically exploited for efficient data collection for waste management strategies in SCs. This paper gives two contributions to research in waste management for SCs. First, a comprehensive study of the various IoT-based techniques for waste management in SCs is presented. Survey studies present energy consumption of the sensor-nodes due to high routing/transmission/control overheads as a major challenge. Several IoT-based techniques have been used to optimize the energy efficiency of the sensor-nodes. However, none has effectively addressed the challenges of energy consumption and optimization. The second contribution proposes an IoV-based technique for data collection for waste management in SCs. To the best of our knowledge, this is the first of such a proposed scheme for SC waste management strategies. Thus, this paper is the first attempt to propose a novel IoV-based model for SC waste management strategies. This technique involves the use of vehicles as opportunistic MDCs to optimize energy efficiency. The paper also proposes using swarm-intelligence-based methods to increase energy efficiency and data collection for SC waste management application. An energy-efficient routing model is very critical to IoT-based applications. Hence, the paper presents an energy-efficient opportunistic model utilizing the ant-based routing algorithms for LS-WSN SC waste management applications. The paper also provides some analytical approaches to determine the energy consumption of the network model. The final part presents experiments in different application scenarios to evaluate the performance of the proposed model. The results present the proposed approach in good performance in terms of the performance metrics compared to the conventional techniques for waste management strategies.