https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.

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Internet of Things for Smart Cities

Increasing population density in urban centers demands adequate provision of services and infrastructure to meet the needs of city inhabitants, encompassing residents, workers, and visitors. The utilization of information and communications technologies to achieve this objective presents an opportunity for the development of smart cities, where city management and citizens are given access to a wealth of real-time information about the urban environment upon which to base decisions, actions, and future planning. This paper presents a framework for the realization of smart cities through the Internet of Things (IoT). The framework encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system. This IoT vision for a smart city is applied to a noise mapping case study to illustrate a new method for existing operations that can be adapted for the enhancement and delivery of important city services.

An Information Framework for Creating a Smart City Through Internet of Things

The Internet of Things vision: Key features, applications and open issues

Wireless sensor networks (WSNs) have emerged as an effective solution for a wide range of applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been proposed. Most of them exploit mobility to address the problem of data collection in WSNs. In this article we first define WSNs with MEs and provide a comprehensive taxonomy of their architectures, based on the role of the MEs. Then we present an overview of the data collection process in such a scenario, and identify the corresponding issues and challenges. On the basis of these issues, we provide an extensive survey of the related literature. Finally, we compare the underlying approaches and solutions, with hints to open problems and future research directions.

Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey

Review: Coverage and connectivity issues in wireless sensor networks: A survey

We investigate the following fundamental question-how fast can information be collected from a wireless sensor network organized as tree? To address this, we explore and evaluate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as convergecast. We first consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a convergecast. Next, we combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. We give lower bounds on the schedule length when interference is completely eliminated, and propose algorithms that achieve these bounds. We also evaluate the performance of various channel assignment methods and find empirically that for moderate size networks of about 100 nodes, the use of multifrequency scheduling can suffice to eliminate most of the interference. Then, the data collection rate no longer remains limited by interference but by the topology of the routing tree. To this end, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant improvement in scheduling performance over different deployment densities. Lastly, we evaluate the impact of different interference and channel models on the schedule length.

Fast Data Collection in Tree-Based Wireless Sensor Networks

Wireless sensor networks offer an attractive choice for low- cost and easy-to-deploy solutions for intelligent traffic guidance systems and parking lot applications. In this paper, we propose the use of a combination of magnetic and ultrasonic sensors for accurate and reliable detection of vehicles in a parking lot. We describe a modified version of the min-max algorithm for detection of vehicles using magnetometers, and also an algorithm for ultrasonic sensors. Through extensive real world experiments conducted in a multi-storied university parking space we compare the pros and cons of using different sensing modalities, and show that ultrasonic sensors along with magnetometers is an excellent choice for accurate vehicle detections. We demonstrate the efficacy of our proposed approach by conducting an elaborate car counting experiment lasting over a day, and show promising results using these two sensing modalities.

Intelligent parking lot application using wireless sensor networks

Area coverage is one of the most fundamental problems in ad hoc wireless sensor networks because it directly relates to optimization of resources in a sensing field. Maximizing the coverage area while maintaining a lower cost of deployment has always been a challenge, especially when the monitoring region is unknown and possibly hazardous. In this paper, we present a method to deterministically estimate the exact amount of coverage holes under random deployment using Voronoi diagrams and use the static nodes to collaborate and estimate the number of additional mobile nodes needed to be deployed and relocated to optimal positions to maximize coverage. We follow a two-step deployment process in a mixed sensor network and we argue by simulation and analysis that our collaborative coverage enhancing algorithm (COVEN) can achieve a tradeoff between the cost of deployment and percentage of area covered.

Estimating coverage holes and enhancing coverage in mixed sensor networks

Wireless sensor networks [33,34] have inspired tremendous research interest in since the mid-1990s. Advancement in wireless communication and microelectromechanical systems (MEMSs) have enabled the development of low-cost, lowpower, multifunctional, tiny sensor nodes that can sense the environment, perform data processing, and communicate with each other untethered over short distances. A typical wireless sensor network consists of thousands of sensor nodes, deployed either randomly or according to some predefined statistical distribution, over a geographic region of interest. A sensor node by itself has severe resource constraints, such as low battery power, limited signal processing, limited computation and communication capabilities, and a small amount of memory; hence it can sense only a limited portion of the environment. However, when a group of sensor nodes collaborate with each other, they can accomplish a much bigger task efficiently. One of the primary advantages of deploying a wireless sensor network is its low deployment cost and freedom from requiring a messy wired communication backbone, which is often infeasible or economically inconvenient. Wireless sensor networks ensure a wide range of applications [2], starting from security surveillance in military and battlefields, monitoring previously unobserved environmental phenomena, smart homes and offices, improved healthcare, industrial diagnosis, and many more. For instance, a sensor network can be deployed in a remote island for monitoring wildlife habitat and animal behavior [25], or near the crater of a volcano to measure temperature, pressure, and seismic activities. In many of these applications the environment can be hostile where human

Amitabha Ghosh

Papers

Review: Coverage and connectivity issues in wireless sensor networks: A survey

Fast Data Collection in Tree-Based Wireless Sensor Networks

Intelligent parking lot application using wireless sensor networks

Estimating coverage holes and enhancing coverage in mixed sensor networks

Coverage and Connectivity Issues in Wireless Sensor Networks