Billions of devices are connected to the Internet of Things (IoT). These devices generate a large volume of data, which poses an enormous burden on conventional networking infrastructures. As an effective computing model, edge computing is rise to collaborative with cloud computing by moving part intensive computation and storage resources to edge devices, thus optimizing the network latency and energy consumption. Meanwhile, software defined networks (SDN) technology is promising in improving quality of service (QoS) for complex IoT-driven applications. However, building SDN-based computing platform faces great challenges, making it difficult for the current computing models to meet the low latency, high complexity and high reliability requirements of emerging applications. Therefore, a Cloud-MEC Collaborative Task Offloading scheme with Service Orchestration (CTOSO) is proposed in this paper. First, the CTOSO scheme models the computational consumption, communication consumption and latency of task offloading, and implements differentiated offloading decisions for tasks with different resource demand and delay sensitivity. What’s more, the CTOSO scheme introduces Orchestrating Data as Services (ODaS) mechanism based on SDN technology. The collected metadata are orchestrated as high quality services by MEC servers, which greatly reduces the network load caused by uploading resources to the cloud on the one hand, and on the other hand, the data processing is completed at the edge layer as much as possible, which achieves the load balancing and also reduces the risk of data leakage. Experimental results demonstrate that, compared to the random decision-based task offloading scheme and the maximum cache-based task offloading scheme, the CTOSO scheme reduces delay by approximately 73.82-74.34%, reduces energy consumption by 10.71%-13.73%.

Through well-designed counterfeit websites, phishing induces online users to visit forged web pages to obtain their private sensitive information, e.g., account number and password. Existing antiphishing approaches are mostly based on page-related features, which require to crawl content of web pages as well as accessing third-party search engines or DNS services. This not only leads to their low efficiency in detecting phishing but also makes them rely on network environment and third-party services heavily. In this paper, we propose a fast phishing website detection approach called PDRCNN that relies only on the URL of the website. PDRCNN neither needs to retrieve content of the target website nor uses any third-party services as previous approaches do. It encodes the information of an URL into a two-dimensional tensor and feeds the tensor into a novelly designed deep learning neural network to classify the original URL. We first use a bidirectional LSTM network to extract global features of the constructed tensor and give all string information to each character in the URL. After that, we use a CNN to automatically judge which characters play key roles in phishing detection, capture the key components of the URL, and compress the extracted features into a fixed length vector space. By combining the two types of networks, PDRCNN achieves better performance than just using either one of them. We built a dataset containing nearly 500,000 URLs which are obtained through Alexa and PhishTank. Experimental results show that PDRCNN achieves a detection accuracy of 97% and an AUC value of 99%, which is much better than state-of-the-art approaches. Furthermore, the recognition process is very fast: on the trained PDRCNN model, the average per URL detection time only cost 0.4 ms.

Localization plays a pivotal role in wireless sensor networks (WSNs). Many range-based localization algorithms have been proposed for 2-dimensional (2D) sensor networks or densely deployed 3-dimensional sensor (3D) networks. However, range-based localization in sparse 3D sensor networks is still a challenging problem, because the sparseness of the network makes it difficult to obtain a proper order of nodes to be sequentially localized. The patch-and-stitching localization strategy can conquer the sparseness problem in 2D networks, but for 3D networks it is still unknown how to uniquely merge two patches when there are not enough common nodes. In this paper, we solve this challenging problem by deriving the conditions under which two sub-networks can be uniquely merged. In the proposed approach, we treat the translation parameters as unknowns and form a set of equations with which the unknowns can be uniquely solved. The novelty of our algorithm also lies in that we exploit both common nodes and connecting edges among adjacent sub-networks to merge them, resulting in very high chances that two sub-networks can be merged. We conduct extensive simulation experiments to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm could localize more than 90 percent of nodes in sparse 3D networks with average node degree of 11 and anchor ratio of 5 percent, while the best existing solution can localize only 52 percent of nodes in the same situation.

Vital signs (e.g., respiration rate or heartbeat rate) sensing is of great importance to implement pervasive in-home healthcare. Traditional vital signs monitoring approaches usually require users to wear some dedicated sensors. These approaches are intrusive and inconvenient to use, especially for elderly people. Some non-intrusive vital signs monitoring approaches based on wireless sensing have been proposed in recent years. However, these approaches require the target user to be in situ during the monitoring process, which greatly limits their utilization in practical scenarios where the target users usually move around. In this paper, we propose RF-RMM, an RFID-based approach to accurate and continuous respiration monitoring for mobile users. The major challenge in respiration monitoring for moving people is that the tiny body displacement caused by the user’s respiration is overwhelmed by the user’s entire body movement. To address this issue, we propose a novel approach that uses a pair of tags to eliminate the effect of the user’s body movement. We fuse the data from the paired tags to cancel the effect of the user’s entire body movement and retain only the displacement caused by the user’s respiration. Another challenging issue in implementing RF-RMM is how to resolve the phase ambiguity problem when the target user moves around, which becomes more serious than in the static case. We propose a distance tracking algorithm to track the phase transition during the user’s movement, according to which the phase ambiguity problem can be well handled. We implement RF-RMM on commercial RFID devices and conduct extensive real-world experiments to evaluate its performance. The results show that RF-RMM achieves accurate respiration rate monitoring with an average error of 0.54 BPM in estimating different users’ respiration rate and an average relative error of less than 13% in estimating the user’s individual breath length.

Backdoor attacks have severely threatened deep neural network (DNN) models in the past several years. In backdoor attacks, the attackers try to plant hidden backdoors into DNN models, either in the training or inference stage, to mislead the output of the model when the input contains some specified triggers without affecting the prediction of normal inputs not containing the triggers. As a rapidly developing topic, numerous works on designing various backdoor attacks and developing techniques to defend against such attacks have been proposed in recent years. However, a comprehensive and holistic overview of backdoor attacks and countermeasures is still missing. In this paper, we provide a systematic overview of the design of backdoor attacks and the defense strategies to defend against backdoor attacks, covering the latest published works. We review representative backdoor attacks and defense strategies in both the computer vision domain and other domains, discuss their pros and cons, and make comparisons among them. We outline key challenges to be addressed and potential research directions in the future.

Tag scanning is an important issue to dynamically manage tag IDs in radio frequency identification (RFID) systems. Different from tag identification that collects IDs of all the tags, tag scanning first verifies whether or not a responding tag has already been identified and retrieves its ID when the answer is yes, and collects the tag's ID only when it is unidentified. In this paper, we present the first study on spot scanning with a handheld reader, which aims to scan tags in the reader's interrogation range at an arbitrarily specified position in the system. Existing studies mainly focus on continuous scanning, and they are highly time inefficient in performing spot scanning. The inefficiency stems from the small overlap between tag populations in different spot scanning operations, in which case existing solutions cannot efficiently recognize unidentified tags. We develop a novel technique called LOCK to efficiently recognize unidentified tags even when the overlapped tags are few. LOCK does not simply use a tag's reply slot index but also compact short responses from tags to efficiently distinguish unidentified tags from identified ones. The valuable compact short responses are firstly investigated, which are the keys for efficient tag identification in the paper. Based on LOCK, three tag scanning protocols are proposed to solve the spot scanning problem. Simulation results show that, for spot scanning, our best protocol reduces per tag scanning time by up to 70 percent when compared with the state-of-the-art solution. Moreover, the proposed protocols can also be employed to perform continuous scanning with better time efficiency than the best existing solutions.