Online social networks have now become the most popular platforms for people to share information with others. Along with this, there is a serious threat to individuals' privacy. One privacy risk comes from the sharing of co-owned data, i.e., when a user shares a data item that involves multiple users, some users' privacy may be compromised, since different users generally have different opinions on who can access the data. How to design a collaborative management mechanism to deal with such a privacy issue has recently attracted much attention. In this paper, we propose a trust-based mechanism to realize collaborative privacy management. Basically, a user decides whether or not to post a data item based on the aggregated opinion of all involved users. The trust values between users are used to weight users' opinions, and the values are updated according to users' privacy loss. Moreover, the user can make a tradeoff between data sharing and privacy preserving by tuning the parameter of the proposed mechanism. We formulate the selecting of the parameter as a multi-armed bandit problem and apply the upper confidence bound policy to solve the problem. Simulation results demonstrate that the trust-based mechanism can encourage the user to be considerate of others' privacy, and the proposed bandit approach can bring the user a high payoff.

With the growing popularity of mobile social networks, point-of-interest (POI) recommendation, which utilizes users' check-in data to suggest interesting places for users, has attracted much attention in recent years. The check-in data, containing time and location information, are closely related to the user's personal life. Due to privacy concerns, users are reluctant to share check-in data with the service provider (SP), which causes a negative effect on recommendations. It is important for the user to find a balance between privacy and recommendation quality. In this paper, we consider a POI recommendation scenario where an adversary can access the data that a user reports to the SP. The user sequentially decides whether to check in for the POI he has visited. A stochastic game model is proposed to analyze the interaction between the user and the adversary. To find a good policy for the user, two value iteration algorithms are applied. The proposed game has a large state set, which makes it difficult for policy learning. To deal with this problem, we use some tricks when implementing the minimax Q-learning algorithm, and a set of neural networks are trained to approximate the Q-functions. To evaluate the performance of the learning algorithms, we conduct a series of simulations by using real-world check-in data. Simulation results show that the proposed learning algorithms can help the user to make good decisions, in the sense that the user can get a high long-term return.

This paper presents a cross-platform solution of smartphone-based mobile sink for wireless sensor networks, named uSink. With a cross-platform SD card, named uSD card, any smartphone with SD interface can be empowered with the capability to communicate with wireless sensor nodes. Furthermore, a middleware on mobile phone, named uSinkWare, is also designed to provide a typical mobile sink's functionalities, including sensor detection, topology monitoring, routing, sensor data collection and sensor control, etc. Thanks to Qt, uSinkWare can also work on many popular mobile platforms, such as Symbian, Maemo, Windows mobile and embedded Linux. uSink has been implemented, and its performance has been verified by experiments. The experiment results show that current high-end smartphones have enough capabilities to work as mobile sinks, and uSink can be regarded as an attempt on this aspect.

In this paper, a platform "uSensing" is proposed to support mobile smartphones accessing into wireless sensor networks(WSNs). Since phones have different platforms, e.g. operating systems and CPU processors, it is a challenge to provide a universal platform for smartphones. To solve it, we design novel hardware and software: 1) "uSD": a SD card with zigbee radio; 2) "uSinkWare": a software running on smartphones. The uSD card and uSinkWare consists of uSensing platform. With uSensing, any phones can communicate with WSNs and parse sensor messages. We demonstrate the proposed uSensing in Nokia 5800XM to connect with our WSNs testbed, and evaluate the performance of uSensing through measuring the phone CPU and RAM. The experiment results show that the uSensing can support the smartphones very well to access into the WSNs as the data sink.

In this paper, we present a 3D indoor navigation service system based on smartphones. To achieve an accurate indoor navigation system in a multi-storey building, it is necessary to determine the storey where the user is. By attaching an atmosphere pressure sensor on smartphones, the altitude information of the user can be obtained, and then, the storey where the user is can be calculated indirectly. With the information of storey, we project localization anchor nodes, e.g. WiFi-AP, of different storeys onto the storey where the user is, and calculate their projection distances based on received signal strength (RSS). To utilize the projection distance in 2D localization algorithms, the systematic error deviation from the storey deviation can be reduced. In addition, we develop a software running in a smartphone to implement our localization method, and provide a smartphone navigation service in our office building. Experimental results show that our method is more accurate than the traditional triangle localization method.

Packet level coding schemes are used to improve the transmission reliability and efficiency in data broadcasting applications, especially for wireless networks. However, existing coding schemes have large redundancy for packets are coded by a random way or some certain probability distributions. Although the Maximum Distance Separable (MDS) codes are designed without redundancy, they are not effective when the packet erasure probability is high. To reduce the coding redundancy, lots of work use the receiver side information to adjust the codes construction on-the-fly, which is more beneficial. However, these work have disadvantages:1) the coding redundancy is also large; 2) the feedback schemes have great affect on performance. In this paper, we design a robust feedback scheme and a novel "Rateless Online MDS Code"(ROME) to eliminate the coding redundancy. Our contributions include that our codes are throughput optimal codes without redundancy, and the analysis on theoretical finite field size bound to achieve throughput optimal codes. We also design the finite field construction to speed up the encoding and decoding process. Finally, we compare the performance between ROME and other existing codes, like RLC, LT, RT oblivious and SLT codes, and their performance when feedbacks are erased.

Advances in wireless access technologies like WiMax and LTE are driving the wireless video applications and wireless broadcasting video service in particular. Though existing erasure codes are designed to achieve high throughput for wireless broadcast/multicast, their potential for wireless video broadcasting can only be achieved if the distortions associated with video packets and their complex decoding dependency are also considered. In this paper, we propose a Joint Video Adaptation and Erasure Code(JVEC) for wireless video broadcast, taking into consideration the video decoding dependency among temporal scalable decoding dependency among I, P and B frames, and formulate a solution to minimize the average video distortion. Assuming that the source knows the erasure state of transmitted packets at the receiver side through the feedback channel, JVEC can generate erasure codes on the fly to firstly guarantee the decoding of important frames and secondly correct erased frames as many as possible. The simulation result shows that JVEC can achieve less video distortion compared with other erasure codes, such as LT code, RT oblivious code and SLT code.