Computation offloading from a mobile device to edge server is an emerging paradigm to reduce completion latency of intensive computations in mobile edge computing (MEC). In order to satisfy the delay-sensitive computing tasks, offloading time including task uploading time, task execution time and results downloading time is adopted as the computational performance metrics for offloading nodes that perform offloaded computing tasks for mobile devices. Therefore, how to minimize the offloading time by selecting an optimal offloading node in MEC is of research importance. This work first investigates an MEC system consisting of mobile devices and heterogeneous edge severs that support various radio access technologies. Then, based on the available bandwidth of heterogeneous edge severs and the location of mobile devices, an optimal offloading node selection strategy is formulated as a Markov decision process (MDP), and solved by employing value iteration algorithm (VIA). Finally, extensive numerical results demonstrate the effectiveness of the proposed strategy over classic strategies in terms of offloading time.

Fuzzy $c$ -means (FCM) clustering is a promising method to handle uncertainties in data clustering. However, the traditional FCM and most of its variants cannot address incomplete inputs. To this aim, a novel fuzzy clustering framework is put forward to perform highly accurate clustering on incomplete data. It adopts twofold ideas: 1) Utilizing a nonnegative latent factor model to prefill the missing data in the inputs by rigidly extracting involved entities’ latent features, where the principle of a minibatch gradient descent algorithm is incorporated into a single latent factor-dependent, nonnegative and multiplicative update algorithm to accelerate the convergence rate; and 2) integrating the distribution of inputs and the weights of local features into the objective function through sparse self-representation and weighting allocation to focus on crucial features. In this way, a NLF analysis-incorporated and feature-weighted fuzzy double $c$ -means clustering (NF $^2$ D) method is achieved, where the data distribution and instance correlation are simultaneously considered with care. Experiments on 12 real-world datasets including both data and images with different missing rates show that the proposed NF $^2$ D method has a significant superiority over state-of-the-art fuzzy clustering methods.

In Wireless Sensor Networks, the node reliability can affect the reliability of data transmission. To analyze the node reliability, this paper defines both the betweenness centrality and the dependency degree for a node to reflect its global reliability and local reliability, respectively. Based on the above definition, a Global and Local Reliability based Routing (GLRR) protocol is proposed to guarantee the reliability of data transmission between a source and destination node in network. In GLRR, at first, some nodes, usually with greater betweenness centrality among their neighbors in a limited range, will be selected as the backtracking node. Moreover, all backtracking nodes then construct their backtracking paths from themselves to the source node separately, and each node on a backtracking path should calculate the dependency degree on its previous hop node. All the betweenness centrality and dependency degree will be forwarded to the source node along the backtracking paths, and be combined to design the routing metric, based on which, the source node can calculate an optimal backtracking path to forward packets to the corresponding backtracking node, meanwhile, this backtracking node will act as a new source node to launch another routing process until the packets be forwarded to the destination node. At last, the simulation results demonstrate that the proposed protocol is superior to the classical algorithms in terms of the network reliability and the network efficiency.

Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers' mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers' competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit.

Sparse mobile crowdsensing, driven by the increasing ubiquity of smartphones, has emerged as a popular method for data collection. This approach can reconstruct the whole sensing map by inferring missing temporal data from their spatial-temporal correlations to sparse samples. Traditional methods face challenges in capturing non-linear correlations in sensors and error accumulation. To address these issues, we propose a dynamic signal map reconstruction model, i.e., Spatial-Temporal Graph Attention (STGA). Specifically, STGA first applies graph embedding to convert sensing points into graph structures, followed by using a message-passing mechanism based on Graph Attention Networks (GAT) to capture complex spatial-temporal dependent features in sensing data. Then, we propose hierarchical attention mechanism, where different nodes are selected and weighted, and the temporal and spatial sequences of the nodes are stratified separately. By collecting a small amount of observation data, the spatial-temporal characteristics of nodes in the current sparse sensing data graph are effectively learned and the signal graph is updated. Attention mechanism quantitatively evaluates and weights the measurement points to further reduce the measurement error. Evaluations conducted on three typical urban sensing datasets demonstrate the effectiveness of the proposed methods in enhancing the inference accuracy of sparse sensing data.