In this paper, we investigate the joint impact of task priority and mobile computing service on the mobile edge computing (MEC) networks, in which one unmanned aerial vehicle (UAV) provides mobile computing service to help compute the tasks from users in multiple hotspots where the task priority is time-varying. For such a system, we firstly measure the system performance by the computing utility of multiples users, where the effect of a wide-range task priority is incorporated. We then analyze the impact of the network wireless bandwidth and UAV computational capability on the system performance, from which we optimize the system through UAV hotspot selection and user task offloading. To solve the optimization problem, we further employ deep Q-learning algorithm to learn an effective solution by continuous interaction between the UAV agent and system environment. Simulations are finally conducted to verify the superiority of the proposed studies in this paper.

Dynamicity is one of the most challenging, yet, central aspects of wireless networks. Dynamicity can come in many guises, such as churn (node insertion/deletion) and node mobility. Although the study of dynamic networks has been popular in distributed computing domain, previous works considered only partial factors causing dynamicity. We here propose a dynamic model that is comprehensive to include crucial dynamic factors on nodes and links. Our model defines dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a localized dynamic model suits distributed algorithm studies better than a global one. The proposed dynamic model uses the more realistic SINR model to describe wireless interference, instead of the oversimplified graph-based models adopted in most existing works. Under the proposed dynamic model, we provide an efficient distributed algorithm accomplishing local broadcast services in the abstract MAC layer that was first presented by Kuhn et al.\ \cite{DBLP:conf/wdag/KuhnLN09}. Our solution paves the road for many new fast algorithms for solving high-level problems in dynamic networks, such as consensus, single-message broadcast, and multiple-message broadcast. Extensive simulations show that our algorithm exhibits good performance in realistic environments with dynamic behaviors.

Nodes in Wireless Sensor Networks (WSNs) are usually deployed in an unattended even hostile environment. What is worse, these nodes are equipped with limited battery, storage, computation and communication resources. Therefore, it is challenging to ensure the security of a WSN without decreasing its network performance. Data Aggregation (DA) combined with security mechanism can provide a good scheme for solving the aforementioned problems. This paper presents a comprehensive review of Secure Data Aggregation (SDA) in WSNs, including its security goals together with existing problems. The traditional network topologies as well as new emerging ones are discussed and compared in order to indicate the application scenes and security levels of different topologies. Meanwhile, the contrastive analyses of security strategies are presented which divides SDA protocols into five categories according to different security mechanisms, security goals and network topologies. Besides, the discussion points out some open issues which may be valuable topics of SDA in the future.

Emotion detection in online social networks (OSNs) can benefit kinds of applications such as personalized advertisement services, recommendation systems, etc. Conventionally, emotion analysis mainly focuses on the sentence level polarity prediction or single emotion label classification, however, ignoring the fact that emotions might co-exist from users’ perspective. To this end, in this work, we address the multiple emotions detection in OSNs from user level view, and formulate this problem as a multi-label learning problem. First, we discover emotion labels correlations, social correlations, and temporal correlations from an annotated Twitter dataset. Second, based on the above observations, we adopt a factor graph based emotion recognition model to incorporate emotion labels correlations, social correlations and temporal correlations into a general framework, and detect the multiple emotions based on multi-label learning approach. Performance evaluation demonstrates that the factor graph based emotion detection model can outperform the existing baselines.

Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a crowd of workers are recruited to perform sensing tasks collaboratively. Although it has stimulated many applications, an open fundamental problem is how to select among a massive number of workers to perform a given sensing task under a limited budget. Nevertheless, due to the proliferation of smart devices equipped with various sensors, it is very difficult to profile the workers in terms of sensing ability. Although the uncertainties of the workers can be addressed by conventional Combinatorial Multi-Armed Bandit (CMAB) framework through a trade-off between exploration and exploitation, we do not have sufficient allowance to directly explore and exploit the workers under the limited budget. Furthermore, since the sensor devices usually have quite limited resources, the workers may have bounded capabilities to perform the sensing task only few times, which further restricts our opportunities to learn the uncertainty. To address the above issues, we propose a Context-Aware Worker Selection (CAWS) algorithm in this paper. By leveraging the correlation between the context information of the workers and their sensing abilities, CAWS aims at maximizing the expected cumulative sensing revenue efficiently with both budget constraint and capacity constraints respected, even when the number of the uncertain workers is massive. The efficacy of CAWS can be verified by rigorous theoretical analysis and extensive experiments.

Recent large-scale deployments of wireless sensor networks have posed a high demand on network throughput, forcing all (discrete) orthogonal ZigBee channels to be exploited to enhance transmission parallelism. However, the interference from widely deployed WiFi networks has severely jeopardized the usability of these discrete ZigBee channels, while the existing CSMA-based ZigBee MAC is too conservative to utilize each channel temporally. In this paper, we propose ART (Adaptive fRequency-Temporal co-existing) as a framework consisting of two components: FAVOR (FrequencyAllocation for Versatile Occupancy of spectRum) and P-CSMA (Probabilistic CSMA), to improve the co-existence between ZigBee and WiFi in both frequency and temporal perspectives. On one hand, FAVOR allocates continuous (center) frequencies to nodes/links in a near-optimal manner, by innovatively converting the problem into a spatial tessellation problem in a unified frequency-spatial space. This allows ART to fully exploit the “frequency white space” left out by WiFi. On the other hand, ART employs P-CSMA to opportunistically tune the use of CSMA for leveraging the “temporal white space” of WiFi interference, according to real-time assessment of transmission quality. We implement ART in MicaZ platforms, and our extensive experiments strongly demonstrate the efficacy of ART in enhancing both throughput and transmission quality.