Smart cities play a vital role in limiting the ill-effects of rapid urbanization on the environment without compromising on benefits such as improving infrastructure, standard of living, and productivity. However, the collection, storage, and sharing of data from the plethora of sensor networks in a typical smart city deployment warrants a well-defined data platform architecture. In this paper, we propose a multi layer architecture compliant with the oneM2M standards and the Indian Urban Data Exchange (IUDX) framework. The proposed architecture consists of Data Monitoring (DML), Data Storage (DSL), and Data Exchange (DEL) layers. The DML employs oneM2M as the middleware platform to achieve interoperability. The DSL uses a multi-tenant architecture with multiple logical databases, enabling efficient and reliable data management. The DEL utilizes standard data schemas and open APIs of IUDX to avoid data silos, and enables secure data sharing. Further, we present a proof-of-concept implementation of our architecture deployed in a university campus using OM2M, PostgreSQL, and Django. Finally, simulations mimicking real-time data insertion and retrieval showed that the DML can handle 600 concurrent users with an average latency under 100 milli seconds. The DSL improved the latency compared to a single database architecture and the DEL could handle 100 concurrent users with zero failed requests.
<p>With increasing connectivity and sophisticated software, the modern vehicles are able to leverage different kinds of services provided by the environment. One such service recommended by Automotive Edge Computing Consortium (AECC) is the downloading of high-definition map data by vehicles. This high volume of data can be provided to the vehicles when they are moving by allocating resources on edge server nodes or roadside units if the route is known apriori. However, this is not a realistic assumption to make in general. Therefore, in this work, we propose a two-stage optimization framework for efficient data delivery to connected vehicles via edge nodes while considering dynamic route changes. We have evaluated the efficiency of this proposed approach (considering a real-world dataset) with respect to (a) offline optimization strategies considering fixed routes and (b) two heuristics considering route changes. Our proposed approach works considerably better than the existing approaches in the context of dynamic route changes.</p>
<p>With increasing connectivity and sophisticated software, the modern vehicles are able to leverage different kinds of services provided by the environment. One such service recommended by Automotive Edge Computing Consortium (AECC) is the downloading of high-definition map data by vehicles. This high volume of data can be provided to the vehicles when they are moving by allocating resources on edge server nodes or roadside units if the route is known apriori. However, this is not a realistic assumption to make in general. Therefore, in this work, we propose a two-stage optimization framework for efficient data delivery to connected vehicles via edge nodes while considering dynamic route changes. We have evaluated the efficiency of this proposed approach (considering a real-world dataset) with respect to (a) offline optimization strategies considering fixed routes and (b) two heuristics considering route changes. Our proposed approach works considerably better than the existing approaches in the context of dynamic route changes.</p>
<p>Smart cities play a vital role in limiting the ill-effects of rapid urbanization on the environment without compromising on benefits such as improving infrastructure, standard of living, and productivity. However, the collection, storage, and sharing of data from the plethora of sensor networks in a typical smart city deployment warrants a well-defined data platform architecture. In this paper, we propose a multi layer architecture compliant with the oneM2M standards and the Indian Urban Data Exchange (IUDX) framework. The proposed architecture consists of Data Monitoring (DML), Data Storage (DSL), and Data Exchange (DEL) layers. The DML employs oneM2M as the middleware platform to achieve interoperability. The DSL uses a multi-tenant architecture with multiple logical databases, enabling efficient and reliable data management. The DEL utilizes standard data schemas and open APIs of IUDX to avoid data silos, and enables secure data sharing. Further, we present a proof-of-concept implementation of our architecture deployed in a university campus using OM2M, PostgreSQL, and Django. Finally, simulations mimicking real-time data insertion and retrieval showed that the DML can handle 600 concurrent users with an average latency under 100 milli seconds. The DSL improved the latency compared to a single database architecture and the DEL could handle 100 concurrent users with zero failed requests. </p>
<p>Smart cities play a vital role in limiting the ill-effects of rapid urbanization on the environment without compromising on benefits such as improving infrastructure, standard of living, and productivity. However, the collection, storage, and sharing of data from the plethora of sensor networks in a typical smart city deployment warrants a well-defined data platform architecture. In this paper, we propose a multi layer architecture compliant with the oneM2M standards and the Indian Urban Data Exchange (IUDX) framework. The proposed architecture consists of Data Monitoring (DML), Data Storage (DSL), and Data Exchange (DEL) layers. The DML employs oneM2M as the middleware platform to achieve interoperability. The DSL uses a multi-tenant architecture with multiple logical databases, enabling efficient and reliable data management. The DEL utilizes standard data schemas and open APIs of IUDX to avoid data silos, and enables secure data sharing. Further, we present a proof-of-concept implementation of our architecture deployed in a university campus using OM2M, PostgreSQL, and Django. Finally, simulations mimicking real-time data insertion and retrieval showed that the DML can handle 600 concurrent users with an average latency under 100 milli seconds. The DSL improved the latency compared to a single database architecture and the DEL could handle 100 concurrent users with zero failed requests. </p>
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