Cloud Computing and Internet of Things (IoT) are currently two of the most popular ICT paradigms that are expected to shape the next era of computing. The convergence between cloud computing and IoT has become a hot topic over the last few years because of the benefits that IoT could have from the distributed nature of cloud computing infrastructures. This paper proposes a new platform for using cloud computing capacities for provision and support of ubiquitous connectivity and real-time applications and services for smart cities' needs. We present a framework for data procured from highly distributed, heterogeneous, decentralized, real and virtual devices (sensors, actuators, smart devices) that can be automatically managed, analyzed and controlled by distributed cloud-based services.

Smart Cities Built on Resilient Cloud Computing and Secure Internet of Things

In the past few years, wireless sensor networks (WSNs) have been gaining increasing attention because of their potential of enabling of novel and attractive solutions in areas such as industrial automation, environmental monitoring, transportation business, health-care etc. If we add this collection of sensor derived data to various Web-based social networks or virtual communities, blogs etc., we can have a remarkable transformation in our ability to "see" ourselves and our planet. Our primary goal is to facilitate connecting sensors, people and software objects to build community-centric sensing applications. However, the computational tools needed to launch this exploration may be more appropriately built from the data center "Cloud" computing model than the traditional HPC approaches. In this paper, we propose a framework to enable this exploration by integrating sensor networks to the emerging data center "cloud" model of computing. But there are many challenges to enable this framework. We propose a pub-sub based model which simplifies the integration of sensor networks with cloud based community-centric applications. Also there is a need for internetworking cloud providers in case of violation of service level agreement with users. We discussed these issues and proposed reasonable solutions.

A framework of sensor-cloud integration opportunities and challenges

Wireless sensor networks (WSNs) consist of numerous small nodes that can sense, collect, and disseminate information for many different types of applications. One of these applications is subject tracking and monitoring, in which the monitored subjects often need protection. For instance, a WSN can be deployed to monitor the movement of a panda in a national park. The panda needs protection from different adversaries, such as hunters and poachers. An adversary might trace the messages in the WSN to find the source node that sensed the panda, with the final aim of killing the panda. Hence the question is: how do we hide the location of the source node from the adversary? This question is relevant in several of the scenarios related to this application, such as patient monitoring and battlefield surveillance. In other words, the problem is to provide privacy to the source node: source location privacy. In this paper, we provide a survey of the state of the art in source location privacy. We first discuss the key concepts in source location privacy, such as anonymity, unobservability, safety period, and capture likelihood. Then, we present an overview of the solutions that provide source location privacy within a WSN, in relation to the assumptions about the adversary's capabilities. In particular, we summarize the concepts and solutions, which are categorized based on the core techniques used to provide source location privacy. We mention the limitations of the algorithms as found in the literature, classify the solutions based on their approach, and provide an overview of the assumptions on the adversarial capabilities related to each solution.

Providing Source Location Privacy in Wireless Sensor Networks: A Survey

Wireless Sensor Networks (WSN) has been a focus for research for several years. WSN enables novel and attractive solutions for information gathering across the spectrum of endeavour including transportation, business, health-care, industrial automation, and environmental monitoring. Despite these advances, the exponentially increasing data extracted from WSN is not getting adequate use due to the lack of expertise, time and money with which the data might be better explored and stored for future use. The next generation of WSN will benefit when sensor data is added to blogs, virtual communities, and social network applications. This transformation of data derived from sensor networks into a valuable resource for information hungry applications will benefit from techniques being developed for the emerging Cloud Computing technologies. Traditional High Performance Computing approaches may be replaced or find a place in data manipulation prior to the data being moved into the Cloud. In this paper, a novel framework is proposed to integrate the Cloud Computing model with WSN. Deployed WSN will be connected to the proposed infrastructure. Users request will be served via three service layers (IaaS, PaaS, SaaS) either from the archive, archive is made by collecting data periodically from WSN to Data Centres (DC), or by generating live query to corresponding sensor network.

Integrating Wireless Sensor Networks with Cloud Computing

Provided are a Ubiquitous Sensor Network (USN) middleware apparatus and method for generating an information service based on heterogeneous sensor networks and a system for providing the information service using the same. In the system, a sensor node constructing a sensor network senses environment information, and a sensor network data transmitter collects sensed data sensed by the sensor node in real-time and transmits the collected sensed data in a message form. A USN middleware extracts object and environment information by cleaning, classifying and integrating the message received from the sensor network data transmitter, generates conditional events and context aware information from the extracted object and environment information, and provides the information service to an application program, the application program providing a user with information related to environment where the sensor network observes.

Usn middleware apparatus and method for generating information based on data from heterogeneous sensor networks and information service providing system using the same

Wireless sensor networks have emerged as an exciting technology for a wide range of important applications that acquire and process information from the physical world. Grid computing has evolved as a standards-based approach for coordinated resource sharing. Sensor grids combine these two promising technologies by extending the grid computing paradigm to the sharing of sensor resources in wireless sensor networks. There are several issues and challenges in the design of sensor grids. In this paper, we propose a sensor grid architecture, called the scalable proxy-based architecture for sensor grid (SPRING), to address these design issues. We also developed a sensor grid testbed to study the design issues of sensor grids and to improve our sensor grid architecture design

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P. Mukherjee

Papers

Sensor grid: integration of wireless sensor networks and the grid