The proliferation of wireless sensors has given rise to public sensing (PS) as a vibrant datasharing model. This vision can be extended under the umbrella of the Internet of Things (IoT) to include versatile data sources within smart cities such as cell phones, radio frequency identification tags and sensors on roads, and buildings and living spaces. The facilitation of such a vision faces many challenges in terms of inter operability, resource management, and pricing. In this paper, we present a priced PS framework for IoT architectures. Our framework caters for service-based applications in smart cities where data is provided via a data cloud of multifarious data sources. We propose online heuristics for public data delivery in smart city settings. We also introduce a pricing utility function for data acquisition. Our pricing function considers resource limitations in terms of delay, capacity and lifetime on the data providers' side, as well as user's quality and trust requirements from the requesters' side. We present simulation results showing the efficiency of our scheme as compared with other wireless sensor and mobile ad-hoc schemes with respect to scalability, lifetime, delay, delivery ratio, and price.INDEX TERMS Wireless sensor networks, public sensing, Internet of Things, heuristic algorithms, utility functions.

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In this paper, we propose a distributed scheme for data gathering using a mobile data collector in Wireless Sensor Networks (WSNs). In our scheme, a mobile data collector moves along a predefined track over the sensing field and data are forwarded to nodes whose transmission disks overlap with the trajectory of the data collector; these nodes are called relaying nodes. Data are classified into two categories: delay-sensitive data and delay-tolerant data. While delay-sensitive data are sent to the data collector directly, delay-tolerant data may be sent to a nearby relaying node, where they wait for the data collector to come and pick them up. We give a theoretical analysis to quantify the impact of data collector mobility on the lifetime of the network as compared to a WSN with a stationary data collector. Moreover, we use simulations to evaluate our scheme in practice. Simulation results show that our scheme has the potential to prolong the lifetime of the network significantly.

Radio Frequency IDentification (RFID) has been increasingly used to identify and track objects automatically. RFID has also been used to localize tagged objects. Several RFID localization schemes have been proposed in the literature; some of these schemes estimate the distance between the tag and the reader using the Received Signal Strength Index (RSSI). From a theoretical point of view, RSSI is an excellent approach to estimate the distance between a sender and a receiver. However, our experiments show that there are many factors that influence the RSSI value substantially and that, in turn, has a negative effect on the accuracy of the estimated distance. Another approach that has been recently proposed is utilizing transmission power control from the reader side. Our experiments show that power control results are more stable and accurate than RSSI results. In this paper, we present a test-bed comparison between the power control and the RSSI distance estimation approaches for active RFID tags. We also present the Angle of arrival Cluster Forming (ACF) localization scheme that uses both the angle of arrival of the tag's signal and the reader's transmission power control to localize active tags. Our experiments show that ACF is very accurate in estimating the location of active RFID tags.