The increasing number of vehicles on the road along with the mismanagement of available parking space leads to the parking related problems as well as increased traffic congestion in urban areas. Thus it is highly required to develop an automated smart parking management system that would help the driver to find out some suitable parking space for his/her vehicle very quickly. Although ample amount of research works on the development of smart parking system exist in literature, but most of them have not addressed the problem of real-time detection of improper parking and automatic collection of parking charges. In this paper, a prototype of internet-of-thing based E-parking system is proposed. The proposed E-parking system uses an integrated component called parking meter to address the above-mentioned issues as well as to provide smart parking management throughout the city.

Traffic congestion is one of the major issues with the public transportation system in recent time. The traffic congestion has a negative impact on the productivity, competitiveness and economic growth of a country. Hence traffic congestion control has become an important area of research and significant number of solutions to this problem came out of various research efforts in the said field over the past few decades. Among these, vehicle-to-vehicle (V2V) communication based approaches cannot accurately estimate the density of traffic congestion. On the other hand, the traffic signaling systems having predetermined fixed operation time cannot manage the traffic volume changing over time and thus, long traffic queues are generated at the road crossings. To address the above mentioned issue, this paper proposes an internet-of-things (IoT) based intelligent traffic congestion control system that dynamically sets the signal operation time based on the measured values of traffic congestion density. Moreover, a novel technique of measuring the density of traffic congestion created at the road crossings is also presented in this paper.

Smart city is an emerging concept that aims to improve the quality of city life, to enhance the efficiency of urban operations and services as well as to create a sustainable economic growth of the city. Internet-of-Things (IoT) and big data have emerged as key enabling technologies for smart city services infrastructure. A major issue in the design of smart city services is the provision of Internet connectivity anywhere within the cities through some suitable network infrastructure. Although the use cases for various smart city services and business models for delivering smart city services have been proposed in the literature over the past few years, none of them attempts to address the problem of dealing with heterogeneous technologies required for collecting and processing data to offer such services. Thus, this paper proposes a suitable framework for smart city services based on IoT to address the above-mentioned issues.

The received signal strength (RSS) based Wi-Fi fingerprint technique is not only a cost-effective means for indoor positioning but also provides reliable positioning accuracy in the indoor settings. Thus, such positioning technique has drawn many researchers’ attention to address its several limitations like degraded positioning accuracy due to continuous changes in surrounding environment, high positioning overhead, storage overhead etc. To address these issues, we propose a novel weighted fusion based efficient clustering strategy (WF-ECS) for fingerprint positioning system in this paper. Our proposed technique WF-ECS computes a weighted average of the group of reference points (RPs) having similar RSS patterns and thus, creates a more perfect match between fused positional co-ordinates and RSS patterns considered for merging to a single entry. Extensive experimentation have been carried out to evaluate and compare the performances of our proposed system WF-ECS with the contemporary fingerprint positioning systems including our prior work using the simulation test bed, the dataset collected from our departmental building and also the benchmark dataset . The experimental results depict that our newly proposed technique WF-ECS can outperform the contemporary techniques in terms of positioning accuracy and positioning overhead while reducing the storage overhead in real indoor settings.