Wireless sensor networks have increasingly become contributors of very large amounts of data. The recent deployment of wireless sensor networks in Smart City infrastructures has led to very large amounts of data being generated each day across a variety of domains, with applications including environmental monitoring, healthcare monitoring and transport monitoring. To take advantage of the increasing amounts of data there is a need for new methods and techniques for effective data management and analysis to generate information that can assist in managing the utilization of resources intelligently and dynamically. Through this research, a Multi-Level Smart City architecture is proposed based on semantic web technologies and DempsterShafer uncertainty theory. The proposed architecture is described and explained in terms of its functionality and some real-time context-aware scenarios.

A traffic signal system established due to the congestion of vehicles at road intersections. Traffic choking at road convergence becomes a controversy for daily riders and proportionally allowing the violation of traffic rules or other hazards. The conventional traffic signal system has lots of disadvantages. Even the system is automatic but due to fixed time range it gives rise to other problem like traffic violation, air pollution indirectly this would lead to deceitful affair. The signal system has been executed using different types of controller some have fast execution time, a greater number of inputs and outputs, reliability also plays an important role in traffic signal system design. To overcome such issues, we proposed a design in this paper which works on the compactness of vehicles on traffic signals and also detects the traffic signal breach done by the vehicles. This paper shows the simulation of dynamic traffic signal and traffic breach control system with IR sensor or piezo sensor to detect the density of vehicles and traffic breach or jumping of red light. This paper related with an FPGA controller as a VLSI design using Verilog in Xilinx software.

Precise forecast of municipal strong waste era has a critical part in future arranging and squander management framework. The attributes of the created strong waste are distinctive at better places (region to district or nation to nation). The precise forecast of municipal solid waste (MSW) era turns into an essential errand in present day period. Its prediction requires accurate MSW data. The point of the present review is to outline the time series model for foreseeing month to month based strong waste production in Greater Noida city of Uttar Pradesh State (India) utilizing artificial neural network (ANN) with time series autoregressive method. The gathered municipal waste perceptions have been organized month to month from 2012 to 2016. The 60 months data set has been classified into 42 training data sets, 9 testing data sets and 9 validating data sets. An assortment of models of ANN has been examined by altering the number of hidden layer neurons. Ultimately, paramount enhanced architecture of neural network is established. The least value of performance parameters is validated in the proposed model as mean square error 0.0004, root mean square error 0.0203 and the high value of the coefficient of regression 0.8123. On the premise of these execution parameters it is reasoned that the ANN model provides precise prescient outcomes.

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