Sensor networks represent an attractive tool to observe the physical world. Networks of tiny sensors can be used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Application-specific deployments of static-sensor networks have been widely investigated. Commonly, these networks involve a centralized data-collection point and no sharing of data outside the organization that owns it. Although this approach can accommodate many application scenarios, it significantly deviates from the pervasive computing vision of ubiquitous sensing where user applications seamlessly access anytime, anywhere data produced by sensors embedded in the surroundings.
With the ubiquity and ever-increasing capabilities of mobile devices, urban environments can help give substance to the ubiquitous sensing vision through Urbanets, spontaneously created urban networks. Urbanets consist of mobile multi-sensor devices, such as smart phones and vehicular systems, public sensor networks deployed by municipalities, and individual sensors incorporated in buildings, roads, or daily artifacts. My thesis is that "multi-sensor mobile devices can be successfully programmed to become the underpinning elements of an open, infrastructure-less, distributed sensing platform that can bring sensor data out of their traditional close-loop networks into everyday urban applications". Urbanets can support a variety of services ranging from emergency and surveillance to tourist guidance and entertainment. For instance, cars can be used to provide traffic information services to alert drivers to upcoming traffic jams, and phones to provide shopping recommender services to inform users of special offers at the mall.
Urbanets cannot be programmed using traditional distributed computing models, which assume underlying networks with functionally homogeneous nodes, stable configurations, and known delays. Conversely, Urbanets have functionally heterogeneous nodes, volatile configurations, and unknown delays. Instead, solutions developed for sensor networks and mobile ad hoc networks can be leveraged to provide novel architectures that address Urbanet-specific requirements, while providing useful abstractions that hide the network complexity from the programmer.
This dissertation presents two middleware architectures that can support mobile sensing applications in Urbanets. Contory offers a declarative programming model that views Urbanets as a distributed sensor database and exposes an SQL-like interface to developers. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous and semantically correct interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. In addition, they allow on-demand collection of sensor data with the accuracy and at the frequency required by every application.
These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in ad hoc networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption.Deploying a network of sensors to monitor an environment is a common practice. For example, cameras in museums, supermarkets, or buildings are installed for surveillance purposes. However, while a decade ago, most deployed sensor networks involved a limited number of sensors, wired to a central processing unit, nowadays, the focus is on wireless, distributed, sensing nodes. Sensor technology has greatly advanced in terms of size, power consumption, processing capabilities, and low cost, thus fostering deployments of self-organizing wireless sensor networks over large geographical areas. For example, sensor networks have been used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Yet, sensor networks are usually perceived as ``something'' remote in the forest or on the battlefield, and regular users do not yet benefit from them.
With the ubiquity and ever-increasing capabilities of mobile devices, such as smart phones and computers embedded in cars, urban environments offer the elements necessary to create people-centric mobile sensor networks and support a large variety of so-called sensing applications ranging from emergency and surveillance to tourist guidance and entertainment. For example, near-ubiquitous smart phones with audio and video sensing capabilities and more sensors in the near future can be used to provide shopping recommender services to inform users of special offers at the mall. Sensor-equipped cars can be used to provide traffic information services to alert drivers to upcoming traffic jams. However, urban mobile sensor networks are challenging programming environments due to the dynamism of mobile devices, the resource constraints of battery-powered devices, the software and hardware heterogeneity, and the large number of concurrent applications that they need to support. These requirements hinder the direct adoption of traditional distributed computing platforms developed for static resource-rich networks.
This dissertation presents two architectures that can support the development of mobile sensing applications in urban environments. Contory offers a declarative programming model that views the urban network as a distributed sensor database. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption.
The proposed architectures offer many opportunities to flexibly and quickly establish customized services that can greatly enhance the users' urban experience. Further steps to fully accomplish people-centric mobile sensing applications will have to address more technical issues as well as social and legal concerns
