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There has been significant interest in the use of HTTP adaptive streaming for live or on-demand video over the Internet in recent years. To mitigate the streaming transmission delay and reduce the networking overhead, an effective and critical approach is to utilize cache servers between the origin servers and the heterogeneous clients. As the underlying protocol for web transactions, HTTP has great potentials to explore the resources within state-of-the-art CDNs tor caching; yet distinct challenges arise in the HTTP adaptive streaming context. After examining a long-term and large-scale adaptive streaming dataset as well as statistical analysis, we demonstrate that the switching requests among the different qualities frequently emerge and constitute a significant portion in a per-day view. Consequently, they have substantially affected the performance of cache servers and Quality-of-Experience (QoE) of viewers. In this paper, we propose a novel cache model that captures the dependency among the segments in the cache server for adaptive HTTP streaming. Our work does not assume any specific selection algorithm on the client's side and hence can be easily incorporated into existing streaming cache system. Its centralized nature is also well accommodated by the latest DASH specification. The performance evaluation shows our dependency-aware strategy can significantly improved the cache hit-ratio and QoE of HTTP streaming as compared to previous methods.

Digital media covers larger parts of our daily lives nowadays. Mobile services enable a better connected society where citizens can easily access public services, discover events, and obtain important information in the city. We observe the popularity of mobile car sharing applications, such as Uber and Didi Dache. Mobile social applications provide new ways of developing and optimizing public transportation. In this paper, we present a mobile platform for timetable-free traveling. It can capture the traffic demand of citizens in real-time, and support efficient planning and scheduling for vehicles on-demand. At the moment, the platform is targeted for public bus services, but it has great potential to be extended for self-driving vehicles in the future.

WISENET, ProFuN

Location information is a key element of participatory sensing. Many mobile and sensing applications require location information to provide better recommendations, object search and trip planning. However, continuous GPS positioning consumes much energy, which may drain the battery of mobile devices quickly. Although WiFi and cell tower positioning are alternatives, they provide lower accuracy compared to GPS. This paper solves the above problem by proposing a novel localization scheme through the collaboration of multiple mobile devices to reduce energy consumption and provide accurate positioning. Under our scheme, the mobile devices are divided into three groups, namely the broadcaster group, the location information receiver group and the normal participant group. Only the broadcaster group and the normal participant group use their GPS. The location information receiver group, on the other hand, makes use of the locations broadcast by the broadcaster group to estimate their locations. We formulate the broadcaster set selection problem and propose two novel algorithms to minimize the energy consumption in collaborative localization. Simulations with real traces show that our proposed solution can save up to 68% of the energy of all of the participants and provide more accurate locations than WiFi and cellular network positioning.

In recent years, Information-centric Networking (ICN) has received much attention from both academic and industry participants. ICN offers a data-centric means of inter-networking that is radically different from today's host-based IP networks. Security and privacy issues in ICN have become increasingly important as ICN technology gradually matures and nears real-world deployment. As is well known, in today's Internet, security and privacy features were originally not present and had to be incrementally and individually retrofitted (with varying success) over the last 35 years. In contrast, since ICN-based architectures (e.g., NDN, CCNx, etc.) are still evolving, it is both timely and important to explore ICN security and privacy issues as well as devise and assess possible mitigation techniques. This report documents the program and outcomes of the Dagstuhl Seminar 16251 "Information-centric Networking and Security." The goal was to bring together researchers to discuss and address security and privacy issues particular to ICN-based architectures. Attendees represented diverse areas of expertise, including: networking, security, privacy, software engineering, and formal methods. Through presentations and focused working groups, attendees identified and discussed issues relevant to security and privacy, and charted paths for their mitigation.

Location information is a key element of participatory sensing. Many mobile and sensing applications require location information to provide better recommendations, object search and trip planning. However, continuous GPS positioning consumes much energy, which may drain the battery of mobile devices quickly. Although WiFi and cell tower positioning are alternatives, they provide lower accuracy compared to GPS. This paper solves the above problem by proposing a novel localization scheme through the collaboration of multiple mobile devices to reduce energy consumption and provide accurate positioning. Under our scheme, the mobile devices are divided into three groups, namely the broadcaster group, the location information receiver group and the normal participant group. Only the broadcaster group and the normal participant group use their GPS. The location information receiver group, on the other hand, makes use of the locations broadcast by the broadcaster group to estimate their locations. We formulate the broadcaster set selection problem and propose two novel algorithms to minimize the energy consumption in collaborative localization. Simulations with real traces show that our proposed solution can save up to 68% of the energy of all of the participants and provide more accurate locations than WiFi and cellular network positioning.

The increasingly popular smartphones enable participatory sensing systems to collect location-based sensing data for different tasks. However, GPS positioning is very energy consuming, which could drain a mobile device's battery quickly. High energy consumption may threaten the participants and reduce the sustainability of the participatory sensing systems. In this paper, we propose a collaborative localization strategy with load balancing. Simulations with real traces showed that our proposed solution can save more than 80% of the energy consumption for localization in the entire network with load balancing.

ProFuN

Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this article, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which is designed to turn driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the drivers' social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driver's specific situations in an automated and intelligent manner. Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and mood-fatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease fatigue degree and negative mood degree of drivers by 49.09% and 36.35%, respectively, compared to traditional smartphone-based music player under similar driving situations.

Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.

Information Centric Networking (ICN) aims to evolve the Internet from a host-centric to a data-centric paradigm. In particular, it improves performance and resource efficiency in events with large crowds where many users in a local area want to generate and watch media content related to the event. In this paper, we present the design of a live video streaming system built on the NetInf ICN architecture and how the architecture was adapted to support live streaming of media content. To evaluate the feasibility and performance of the system, extensive field tests were carried out over several days during a major sports event. We show that our system streams videos successfully with low delay and communication overhead compared with existing Internet streaming services, by scalability tests using emulated clients we also show that it could support several thousands of simultaneous users.

Incorporating ubiquitous wireless sensor networks (WSNs) and powerful cloud computing (CC), Sensor-Cloud (SC) is attracting growing attention from both academia and industry. However, pricing for SC is barely explored. In this paper, filling this gap, five SC pricing models (i.e., SCPM1, SCPM2, SCPM3, SCPM4 and SCPM5) are proposed first. Particularly, they charge a SC user, based on 1) the lease period of the user; 2) the required working time of SC; 3) the SC resources utilized by the user; 4) the volume of sensory data obtained by the user; 5) the SC path that transmits sensory data from the WSN to the user, respectively. Further, analysis is also presented to study and demonstrate the performance of the proposed SCPMs. We believe that the pricing designs and analysis performed in this work could be a very valuable guidance for future researches regarding pricing in SC.

Web users are immersed in their roles as information producers and propagation pushers. They are unaware of being potential threats to privacy-protection towards themselves and their friends. It is necessary to know who they should beware of most in their friend-networks once their privacy information is divulged inadvertently. In this paper, we aim to identify the vulnerable friend who maximizes the dissemination of privacy information. First we develop a Privacy Receiving-Disseminating (PRD) model to simulate the iterative course of privacy information dissemination within social graph. The subgraph constituted of those users who are involved in the dissemination, called Ultimate Circle of Disseminating (UCD), is then detected by an iterative algorithm. The contribution of each direct friend could be evaluated by comparing the disseminating intensities of detected UCDs before and after unfriending himself. The performance of our work has been validated empirically with the comparison of different unfriending strategies.

Air pollution has become one of the most pressing environmental issues in many countries, including China. Fine-grained PM2.5 particulate data can prevent people from long time exposure and advance scientific research. However, existing monitoring systems with PM2.5 stationary sensors are expensive, which can only provide pollution data at sparse locations. In this paper we demonstrate for the first time that camera on smartphones can be used for low-cost and fine-grained PM2.5 monitoring in participatory sensing. We propose a LearningBased method to extract air quality related features from images taken by smartphones. These image features will be used to build the haze model that can estimate PM2.5 concentration depending on the reference sensors. We conducted extensive experiments over six months with two datasets to demonstrate the performance of the proposed solution using different models of smartphones. We believe that our findings will give profound impact in many research fields, including mobile sensing, activity scheduling, haze data collection and analysis.

This paper proposes a novel power management solution for resource-constrained devices in the context of Internet of Things (IoT). We focus on smartphones in the IoT, as they are getting increasingly popular and equipped with strong sensing capabilities. Smartphones have complex and asynchronous power consumption incurred by heterogeneous components including their on-board sensors. Their interaction with the cloud allows them to offload computation tasks and access remote data storage. In this work, we aim at monitoring the power consumption behaviours of the smartphones, profiling both individual applications and the system as a whole, to make better decisions in power management. We design a cloud orchestration architecture as an epic predictor of behaviours of smart devices by extracting their application characteristics and resource utilization. We design and implement this architecture to perform energy profiling and data analysis on massive data logs. This cloud orchestration architecture coordinates a number of cloud-based services and supports dynamic workflows between service components, which can reduce energy consumption in the energy profiling process itself. Experimental results showed that small portion of applications dominate the energy consumption of smartphones. Heuristic profiling can effectively reduce energy consumption in data logging and communications without scarifying the accuracy of power monitoring.

In smart cities, cellular network plays a crucial role to support connectivity anywhere and anytime. However, the communication demand brought by applications and services is hard to predict. Traffic in cellular networks might fluctuate heavily over time to time, which causes burden and waste under different traffic states. Recently, small cell was proposed to enhance spectrum efficiency and energy efficiency in cellular networks. However, how green the small cell network can be is still a question because of the accompanying interference. To meet this challenge, new green technologies should be developed. In this paper, we propose a green small cell planning scheme considering dynamic traffic states. First, we predefine a set of candidate locations for base stations (BSs) in a geographical area and generate a connection graph which contains all possible connections between BSs and user equipments (UEs). Then we adopt a heuristic to switch off small cell BSs (s-BSs) and update BS-UE connections iteratively. Finally we obtain a cell planning solution with energy efficiency without reducing spectrum efficiency and quality-of-service (QoS) requirements. The simulation results show that our dynamic small cell planning scheme has low computational complexity and achieves a significant improvement in energy efficiency comparing with the static cell planning scheme.

In cloud manufacturing for regional industrial cluster, there is increasing necessity of collaboration among enterprises or facilities. It is valuable to explore the characteristics of these collaboration behaviors for effectively scheduling dispersed manufacturing facilities and organizing their collaboration. The collaborative relation of manufacturing in regional industrial cluster can be described as a generalized social collaboration network. In this paper, we introduce the relevant entities and relations of facilities collaboration, and propose the method for building Facility Collaboration Network (FCN). We further design the dynamically growing process of FCN for different facility selection strategies, including random selection, balanced selection, random selection with preference and balanced selection with preference. Based on the metrics such as network scale, node degree distribution, act degree distribution, average shortest distance and number of cliques, we present the statistical characteristics of FCN, and analyze relevant characteristics and laws for efficient facilities selection in cloud manufacturing.

Web users are immersed in their roles as information producers and propagation pushers. They are unaware of being potential threats to privacy-protection towards themselves and their friends. It is necessary to know who they should beware of most in their friend-networks once their privacy information is divulged inadvertently. In this paper, we aim to identify the vulnerable friend who maximizes the dissemination of privacy information. First we develop a Privacy Receiving-Disseminating (PRD) model to simulate the iterative course of privacy information dissemination within social graph. The subgraph constituted of those users who are involved in the dissemination, called Ultimate Circle of Disseminating (UCD), is then detected by an iterative algorithm. The contribution of each direct friend could be evaluated by comparing the disseminating intensities of detected UCDs before and after unfriending himself. The performance of our work has been validated empirically with the comparison of different unfriending strategies.