## 1 Introduction  
The rapid growth of human population and the increasing flow towards urban areas [1] indicates a need for smart sustainable cities, where there are preventive maintenance activities and resource optimization for critical infrastructure such as transportation, communications, water, and energy. In order to achieve this, information communications technology (ICT) is tasked with developing economical and pervasive solutions to improve the quality of life through efficient urban operations and services. Wireless sensors play an important role in this regard. With the concept of Internet of Things (IoT), which would see sensors widely deployed in the environment and embedded in physical objects, the applications are almost limitless. The sensors in IoT scenarios need to be connected to the Internet in order to share the generated information across multiple platforms and facilitate various applications. To accomplish this, a backhaul is required to collect and convey sensed data to gateways that are connected to remote management centres through the Internet [2]. With sensors spatially displaced in the environment, the desired level of coverage can be directly achieved through long-range communication technologies or by deploying numerous relay nodes. However, with finite resources and limited budgets [3], it is challenging to achieve the required level of scalability with respect to the cost of procuring, installing, and maintaining supporting infrastructure for every application. For instance, it is not economical to equip each sensor node with cellular connectivity (e.g., [PERSON] et al. [4] reports an annual cost of \(\sim\)$380 per sensor) and the high transmission power reduces sensor lifetime. It is also uneconomical to deploy relay nodes to collect data from sensors via wireless mesh networks with ad hoc technologies (e.g., the sensor network across Wuxi City, China [5] for CO\({}_{2}\) data requires approximately 11 relays per sensor for proper collection). Another backhaul option is the existing communication infrastructure in cities, such as networks offering Internet access to citizens [2]. However, with mobile data traffic on the rise due to the widespread use of mobile devices and content-centric services (e.g., live audio and video streaming) among users [6], introducing sensed data shall contribute in overloading the network. By leveraging pervasive mobile devices (such as smartphones and tablets) as data mules, opportunistic networks (OppNets) provide a satisfactory level of scalability and also eliminate the costs in setting up wireless networks with ad hoc technologies. Based on the store-carry-forward (SCF) communication paradigm, data-bundles (or messages) stored in device buffers are carried from one point to another as the user moves about. When user devices encounter each other (i.e., come within radio transmission range), they exchange a list of messages in their buffers and may decide to forward some of them through available short-range wireless communication interfaces such as Bluetooth or Wi-Fi. Although delivering a message in this manner may take a relatively long time (as compared with the traditional networks [7; 8; 9; 10]), it is suitable for a subset of Smart City applications that only require occasional sensor node connectivity opportunities (e.g., twice a day) and also utilize data that can tolerate delays of up to hours or a day (e.g., agricultural monitoring [11], habitat monitoring [12] and environmental monitoring data for: garbage collection and green zone management [2]; analysis of noise levels and water quality [13]; river pollution management [14]; and for meter readings [15]). Thus, OppNets come in handy as a backhaul solution since they address two main challenges. First, the desired coverage is achieved without costs of procuring, installing and maintaining supporting infrastructure by leveraging pervasive mobile devices as data mules (e.g., [16; 17; 18]).