The continuous demand for content-rich multimedia is pushing for high-speed and secure transmission approaches. In recent years, linear network coding (LNC) has been shown to be a promising technology to improve network throughput, transmission reliability, and information security. In this paper, we study the optimal transmission topology construction and LNC design for a secure multiple-source multicast to deliver the same content with integral link rates, which can be equivalent to the secure multicast problem with a virtual source, i.e., the integer secure virtual-source multicast (ISVM) problem. The objectives of the ISVM problem include the following: 1) satisfy the weakly secure requirements, 2) maximize the secure multicast rate (SMR), and 3) minimize the transmission cost when the SMR is maximized. First, we analyze the necessary and sufficient condition that there exist a transmission topology with integral link rates and a secure LNC that can achieve a given SMR $R$. Then, we model the ISVM problem as an integer linear programming based on the theoretical analysis and design an efficient transmission topology construction algorithm to solve the ISVM problem by utilizing the Lagrangian relaxation and subgradient method. We also analyze the size of finite field required to construct the deterministic LNC for a secure virtual-source multicast and the probability that the virtual-source multicast is weakly secure when using random LNC in the ISVM problem. Finally, we design upper and lower bounds for the ISVM problem and conduct extensive simulations to compare the performance of the proposed algorithms with these two bounds.

With the rapid development of mobile networks and widespread use of mobile devices, there is an increasing focus on assigning location-based tasks to mobile users in the context of Mobile Crowd Sensing (MCS). One of the primary challenges in MCS is task assignment, i.e., distributing tasks to suitable users for completion. However, existing work often assumes static offline scenarios where the spatiotemporal information of all users and tasks is pre-determined and known. Neglecting the dynamic spatiotemporal distribution of users and tasks can lead to suboptimal assignment results. In this study, we investigate a novel task assignment problem called Community Task Assignment (CTA). The objective is to enhance the effectiveness and precision of task distribution by considering the sociality of current users and distributing location-based tasks through communities. Initially, we partition users into different communities by abstracting and identifying behavior patterns through the computation of minimum spanning trees, connectivity parameters, and community cohesion. Subsequently, we calculate the match between perception tasks and community behavior pattern features, and task distribution is carried out by the central nodes of the communities based on this match. Experimental validation first confirms the effectiveness of the community partitioning algorithm. Compared to existing algorithms, the proposed method more accurately detects community structures with similar behavioral features in the network. Furthermore, a comparison with existing task assignment algorithms verifies the superiority of the proposed method in terms of average task completion time, task matching rate, and overall utility of task assignments.

We propose a task distribution algorithm based on agent correlation to enhance the comprehensive research on the distribution sensing tasks in mobile sensor networks. Firstly, in the proposed algorithm, the score and feature factors of the mobile agents are considered comprehensively in a direct correlation model, which is constructed by combining the direct and indirect correlation samples. Secondly, we introduce a Mobility Model based on the Exponential Distribution (MMED), and obtain a calculation method of probability parameter λ according to the analysis presented in this paper. At last, we integrate the constructed correlation model and mobility model; which are then apply to the distribution algorithm. By experimentally research, Task Distribution algorithm based on Relationships of Agents (TDRA) we proposed has a precision improvement of 57.23% over MTPS, which is a fine-grained multitask allocation framework algorithm, and 12.31% of that to Sequential MF, which is a recommendation algorithm based on collaborative filtering by exploiting sequential behaviors. These results indicate that the proposed algorithm improves the task distribution performance significantly, and offers a more accurate and reliable service.

With the popularity of the smart phones embedded in a large number of sensors, mobile crowd sensing (MCS) applications have developed rapidly. These applications often require the participants to collect the location-related data. However, this process faces the risk of the privacy leakage. The existing anonymous and cloaking methods can still analyze the user's identity and sensing locations by obtaining enough data with spatiotemporal correlation. In addition, the existing encryption methods not only take a lot of computation cost but also need to assume a full trusted sensing platform. In order to preserve the users' privacy, we need to separate the user's true identity and the sensing location. We propose a privacy-preserving method based on server-aided (or cloud-assisted) reverse oblivious transfer (ROT) protocol containing a cloud server which can compute the results of the encrypted sensing data to avoid the full trust in the sensing platform and enhance the computing efficiency in MCS. It overcomes the derivation of the malicious users in the standard malicious model through constructing the coefficients of the polynomial. Finally, the analysis demonstrates that the proposed method possesses the most efficient computing time, communication overhead and storage overhead comparing with other methods and can achieve the quality-privacy tradeoff optimization.

Collecting data with confidence of the environment is an important application of the wireless sensor networks (WSN). In this study, we used a splay tree based event region fault-tolerant detection algorithm (STERD) for WSNs to address these problems. Rather than passing large quantities of raw data through the network, our network instead sends the coefficients of a regression polynomial that simulates the readings of the sensor nodes throughout the region of interest. These coefficients essentially summarize the relevant information over the region, in effect compressing the data, leading to a reduction in both data volume and energy expenditure. Simulation results indicate that the proposed STERD can attractively obtain the high event region detection accuracy and considerably reduce the false alarm probability.