# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 1 Introduction
### Sensed Data Collection with OppNets  
Human population is growing rapidly--at an average rate of 1.2% per annum over the last 50 years--and increasingly flowing towards urban areas [7]. This necessitates the cause towards smart sustainable cities, where there are preventive maintenance activities and resource optimization for critical infrastructure such as transportation, communications, water, and energy. In this regard, 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 the realization of this goal. 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 success of IoT relies on connecting these sensors to the Internet in order to share the generated information across multiple platforms and applications. This brings about urban automation networks (UANs), in which a backhaul is required to collect and transmit sensed data to dedicated gateways that are connected to remote management centers (through the Internet) where the information can be processed and action can be taken accordingly [8]. In UANs, the desired level of coverage can be directly achieved through long-range communication technologies or by deploying numerous relay nodes. However, it is still challenging to achieve the required level of scalability (w.r.t. the cost of procuring, installing, and maintaining supporting infrastructure) for every application with finite resources and limited budgets [9]. Equipping each sensor node with cellular connectivity is not economical (e.g., [PERSON] et al. [10] report an annual cost of $4550 for 12 sensors) and also reduces sensor lifetime due to high transmission power. Setting up wireless mesh networks with ad-hoc technologies, which requires relay nodes to collect data from sensors, may not be economical as well (e.g., 1096 relays were required to collect CO\({}_{2}\) data from only 100 sensor nodes spread across Wuxi City, China [11]). Existing communication infrastructure in cities (e.g., networks offering Internet access to citizens) may also serve as the backhaul [8]. However, amidst the rapid growth of mobile data traffic due to the widespread use of mobile devices and content-centric services such as live audio and video streaming among mobile users [12], introducing data generated from sensors raises further difficulties in maintaining the quality of service. A subset of Smart City applications tolerate infrequent sensor node connectivity opportunities (e.g., twice per day) and deal with data that can afford delays of up to hours or a day (e.g., agricultural monitoring, [13] habitat monitoring [14] and environmental monitoring data for: garbage collection and green zone management [8]; analysis of noise levels and water quality [15]; river pollution management [16]; and for meter readings [17]). In this regard, OppNets present a logical backhaul solution by leveraging pervasive mobile devices as data mules in a scalable manner to reduce costs in procuring, installing, and maintaining supporting infrastructure, e.g., [18; 19; 20]. At the same time, OppNets serve as a complementary technology for offloading cellular networks and freeing bandwidth for mobile users through direct device-to-device communication using available short-range wireless communication interfaces, e.g., [21; 22; 23]. Inspired by these benefits, researchers have studied the collection of sensed delay-tolerant data with OppNets from various perspectives. For example, [PERSON] et al. [24] provide an analytical and experimental study of the performance and trade-offs of Bluetooth Low Energy as a wireless technology for collecting sensed delay-tolerant data. [PERSON] and [PERSON] [19] evaluate the feasibility of collecting sensor data with OppNets by analyzing a city-wide mobility dataset of Beijing.