Climate change and urbanization have resulted in several societal challenges for urban areas. Nature-based solutions (NBS) have been positioned as solutions for enhancing urban resilience in the face of these challenges. However, the body of conceptual and practical knowledge regarding NBS remains fragmented. This study addresses this gap by means of a systematic review of the literature, to define NBS as a theoretical concept; its broader significance with respect to societal challenges; the key stakeholders in NBS planning, implementation and management; and major barriers to and enablers of NBS uptake. The results of this review reveal that, despite a lack of consensus about the definition of NBS, there is a shared understanding that the NBS concept encompasses human and ecological benefits beyond the core objective of ecosystem conservation, restoration or enhancement. Significant barriers to and enablers of NBS are discussed, along with a proposed strategic planning framework for successful uptake of NBS.

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Abstract-Worker selection is a key issue in Mobile Crowd Sensing (MCS). While previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, multi-task-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multi-task MCS environments. We study the problem of multi-task worker selection under two situations: worker selection based on workers' intentional movement for time-sensitive tasks and unintentional movement for delay-tolerant tasks. For time-sensitive tasks, workers are required to move to the task venue intentionally and the goal is to minimize the total distance moved. For delay-tolerant tasks, we select workers whose route is predicted to pass by the task venues and the goal is to minimize the total number of workers. Two Greedy-enhanced Genetic Algorithms are proposed to solve them. Experiments verify that the proposed algorithms outperform baseline methods under different experiment settings (scale of task sets, available workers, varied task distributions, etc.).

Current focus of sensor networks is on systems dedicated for a specific application. Wide variety of circumstances point to the need for sensor network deployments capable of sharing the physical network resources. We introduce the concept of Virtual Sensor Networks (VSN) to MotivationContinuing advances in the computational power, radio components, and reduction in the cost of high-performance processing and memory elements has led to the proliferation of portable devices (e.g., intelligent sensors, actuators, and sensory prosthetics) with substantial processing capabilities. Such devices are rapidly permeating a variety of applications domains such as avionics, environmental monitoring, structural sensing, telemedicine, space exploration, and command and control. Perhaps the single largest catalyst has been the emergence of micro-sensor nodes (e.g., Mica motes from Crossbow, Tmote Sky from Moteiv, the MKII nodes from UCLA, SunSpot from Sun, etc.) that integrate computation, networking, and sensing capabilities into a single device [6]. By providing the ability to monitor phenomena in close proximity with multihop wireless communication (enabled by on-board radios) such devices have created the possibility to build reactive systems that have the ability to monitor and react to physical events/phenomena. Given the importance and potential of the impact of sensor technologies, over the past decade, significant progress has been made on techniques to architect and program largescale sensor systems. Important developments include design of light-weight operating systems for sensor devices, powerful programming frameworks that isolate application logic from the complexities of optimizing the computation over sensor networks, techniques for in-network processing that exploit computational resources at the sensors to reduce communication and preserve energy.While substantial progress has been made, current research has primarily considered dedicated sensor networks each supporting one specific application. Such systems are being developed for a vast array of applications ranging from monitoring environmental phenomena, monitoring animals, location and tracking of targets, and emergency rescue [2,8]. Reasons for using dedicated sensor networks include the limited sensing, processing, and communication abilities of the nodes, severe power constraints, and most of all, the lack of algorithms, protocols, and techniques for International Conference on Information Technology (ITNG'07) 0-7695-2776-0/07 $20.00