To improve the efficiency of big data feature learning, the paper proposes a privacy preserving deep computation model by offloading the expensive operations to the cloud. Privacy concerns become evident because there are a large number of private data by various applications in the smart city, such as sensitive data of governments or proprietary information of enterprises. To protect the private data, the proposed model uses the BGV encryption scheme to encrypt the private data and employs cloud servers to perform the high-order back-propagation algorithm on the encrypted data efficiently for deep computation model training. Furthermore, the proposed scheme approximates the Sigmoid function as a polynomial function to support the secure computation of the activation function with the BGV encryption. In our scheme, only the encryption operations and the decryption operations are performed by the client while all the computation tasks are performed on the cloud. Experimental results show that our scheme is improved by approximately 2.5 times in the training efficiency compared to the conventional deep computation model without disclosing the private data using the cloud computing including ten nodes. More importantly, our scheme is highly scalable by employing more cloud servers, which is particularly suitable for big data.

Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning

This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities as an important application of the Internet of Things (IoT) paradigm. This type of services will become an integral part of a generic IoT operational platform for smart cities due to its pure business-oriented features. A high-level view of the proposed middleware is outlined and the corresponding operational platform is illustrated. To demonstrate the provision of car parking services, based on the proposed middleware, a cloud-based intelligent car parking system for use within a university campus is described along with details of its design, implementation, and operation. A number of software solutions, including Kafka/Storm/Hbase clusters, OSGi web applications with distributed NoSQL, a rule engine, and mobile applications, are proposed to provide 'best' car parking service experience to mobile users, following the Always Best Connected and best Served (ABC&S) paradigm.

/pdf/a-cloud-based-car-parking-middleware-for-iot-based-smart-unrop79x3m.pdf

A Cloud-Based Car Parking Middleware for IoT-Based Smart Cities: Design and Implementation

This book introduces the latest thinking on the use of Big Data in the context of urban systems, including research and insights on human behavior, urban dynamics, resource use, sustainability and spatial disparities, where it promises improved planning, management and governance in the urban sectors (e.g., transportation, energy, smart cities, crime, housing, urban and regional economies, public health, public engagement, urban governance and political systems), as well as Big Datas utility in decision-making, and development of indicators to monitor economic and social activity, and for urban sustainability, transparency, livability, social inclusion, place-making, accessibility and resilience.

Seeing Cities Through Big Data

Municipalities' understanding of the Smart City concept: An exploratory analysis in Belgium

Next generation IEEE 802.11 Wireless Local Area Networks : Current status, future directions and open challenges

Local governments have decided to take advantage of the presence of wireless sensor networks (WSNs) in their cities to efficiently manage several applications in their daily responsibilities. The enormous amount of information collected by sensor devices allows the automation of several real-time services to improve city management by using intelligent traffic-light patterns during rush hour, reducing water consumption in parks, or efficiently routing garbage collection trucks throughout the city [1]. The sensor information required by these examples is mostly self-consumed by city-designed applications and managers.

Public Open Sensor Data: Revolutionizing Smart Cities

Although it is clear that the evolution of fixed access networks requires the installation of Fiber-to-the-Home (FTTH) to all households, the business case for deploying such networks is very uncertain. It requires significant upfront investment, while not being assured of sufficient timely revenues to cover for it. Although many studies focus on modeling the costs and forecasting the revenues, limited research deals with how both sides can impact the overall business case. This paper compares different deployment strategies used in current FTTH network rollouts, and examines their impact on the overall business case. Deployment strategies are defined as ways to plan the installation of the network and uptake over time, influenced by a variety of situation-dependent characteristics. By studying the different parameters that allow characterizing and grouping ongoing FTTH projects worldwide, we can analyze what drove the choice for the deployment strategy, and how the latter impacts both cost and revenue side of the business case. The paper concludes that deployments driven by demand are less risky and therefore more sustainable. On the other hand, publicly funded or initiated projects obey to different goals than demand and can be unsustainable if they are not able to engage the planned penetration rates.

https://www.econstor.eu/bitstream/10419/106863/1/816637695.pdf

Deployment strategies for FTTH networks and their impact on the business case: A comparison of case studies

https://www.econstor.eu/bitstream/10419/88482/1/773358536.pdf

Will broadband pricing support 1Gbps services

Spectrum sensing is one of the key topics towards the implementation of future wireless services like SuperWiFi. This new wireless proposal aims at using the freed spectrum resulting from the analog-to-digital transition of TV channels for wireless data transmission (UHF TV White Spaces). The benefits range from better building penetration to longer distances when compared to the set of IEEE 802.11 standards. Nevertheless, the effective use of the available spectrum is subject to strict regulation that prohibits unlicensed users to interfere with incumbents (like wireless microphones). Cognitive Radios (CR) and dynamic spectrum allocation are suggested to cope with this problem. These techniques consist on frequency sweeps of the TV-UHF band to detect White Spaces that could be used for SuperWiFi transmissions. In this paper we develop and implement algorithms from GNURadio in the Ettus USRP-E110 to build a standalone White Spaces detector that can be consulted from a centralized location via IP networks.

Spectrum Sensing with USRP-E110

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Papers

Public Open Sensor Data: Revolutionizing Smart Cities

Deployment strategies for FTTH networks and their impact on the business case: A comparison of case studies

Will broadband pricing support 1Gbps services

Deployment strategies for FTTH networks and their impact on the business case: A comparison of case studies

Spectrum Sensing with USRP-E110