With the emergence of Internet of Things (IoT), the research on Smart Cities with wireless sensor networks (WSNs) got leveraged due to similarities between objectives in both Smart City and IoT. Along with them, research in controlling WSN faces new challenges and opportunities for data aggregation and routing has received consistent focus from researchers. Yet new techniques are being proposed to address modern challenges in WSN and efficient resource utilization. Moreover, solutions are required to integrate existing deployed WSN with ever increasing numbers of IoT devices in Smart Cities, that benefit both. In this work, we present an approach for routing in a WSN, in which IoT is used opportunistically to reduce the communication overhead of the sensors. In our approach, WSN deployed in a Smart City interacts with the IoT devices to route the data to the sink. We build a prototype Integration Platform for the WSN that allows interaction with IoT devices and utilizes them opportunistically that results in an energy efficient routing of data. Simulation results show that the direction is quite promising and our approach offers to utilize IoT to gain unique advantages.

The ready-mixed concrete delivery (RMC) problem is a scheduling problem, where multiple trucks deliver concrete to order sites abiding by hard constraints in a dynamic environment. It is an NP-hard problem, impractical to solve using exhaustive methods. Thus, it requires heuristic-based approaches for generating sub-optimal schedules. Due to its distributed nature, we address this problem using a decentralised, scalable, cooperative MAS (multiagent system) that dynamically generates schedules. We explore the impact of teamwork by trucks on schedule optimisation. This work illustrates two novel approaches that address the dynamic RMC problem; a Delegate MAS approach and a team-extended approach. We present an empirical study, comparing our novel approaches with existing ones. The evaluation is performed by classifying the RMC case study scenarios into unique stress, scale, and dynamism characteristics. With 40% to 70% improvement over different metrics, the results show that both approaches generate better schedules, and using agent teams augments the performance. Thus, such decentralized MAS with the appropriate coordination approach and teamwork can be used for solving constrained dynamic scheduling problems.