Among various sparse array techniques, co-prime array is found to be more attractive because of its higher DoF with a smaller number of sensing elements. Generally, a co-prime array with O(N) sensors can offer $O(N^2)$ number of DoFs by exploiting the difference co-array that can be obtained from the second-order statistics of received signals. However, the number of achievable DoFs of co-prime arrays is significantly smaller than expected due to the existence of “holes” in the difference co-array. In this paper, we make three major contributions to co-prime arrays. We first introduce a k-times extended co-prime configuration which can achieve lager number of DoFs and higher flexibility in array configurations, and the later helps better meet different application needs. Second, based on our k-times extended geometry, we uncover the mysterious veil of the holes in the difference co-arrays. We find several general rules of the locations of holes and derive the close-form expressions of the exact locations of all holes in the difference co-arrays of different co-prime array structures. Finally, we propose a specific array structure called complementary sub-array that can fill all of holes existing in the difference co-array. Compared with the traditional hole-existing co-prime arrays, our k-times complementary coprime array has either similar number of sensors and DoFs with much smaller array aperture required, or much higher DoFs with the same aperture.

Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to initiate diagnosis and remediation procedures. Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are useful for anomaly detection. However, our study reveals that VAE-based methods face challenges in capturing long-periodic heterogeneous patterns and detailed short-periodic trends simultaneously. To address these challenges, we propose Frequency-enhanced Conditional Variational Autoencoder (FCVAE), a novel unsupervised AD method for univariate time series. To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data. Together with a carefully designed "target attention" mechanism, our approach allows the model to pick the most useful information from the frequency domain for better short-periodic trend construction. Our FCVAE has been evaluated on public datasets and a large-scale cloud system, and the results demonstrate that it outperforms state-of-the-art methods. This confirms the practical applicability of our approach in addressing the limitations of current VAE-based anomaly detection models.

Communications in vehicular ad-hoc network (VANET) are subject to performance degradation as results of channel fading and intermittent network connectivity. The emerging Vehicular Content Centric Network (VCCN) is promising in supporting the needs of contents and alleviating the communication problems in VANET. Specifically, to improve the cache hit ratio and reduce the access delay of content retrieval, it helps to choose the appropriate vehicles to cache the frequently accessed data items. In this paper, we propose a Cooperative Caching scheme based on Social Attributes and Mobility Prediction (CCSAMP) for VCCN. CCSAMP is based on the observation that vehicles move around and are liable to contact each other according to drivers' common interests or social similarities. A caching node sharing more social attributes with the content requester is more likely to be interested in the same contents and distribute the contents to others with similar interests. Furthermore, a caching node that frequently meets other nodes is a better candidate to keep cache copies. CCSAMP also exploits the regularity of vehicle moving behaviors to predict the chance for a vehicle to reach hot zones based on Hidden Markov Model (HMM). We evaluate CCSAMP through the ONE simulator to demonstrate its higher performance.

To enhance dependable resource allocation against increasing DDoS attacks, in this paper, we investigate interactions between a sensor device-edgeVM pair and a DDoS attacker using a game-theoretic framework, under the constraints of the task time, resource budget, and incomplete knowledge of the processing time of machine learning tasks. In this game, the sensor device expects an edgeVM to cooperate and choose its resource allocation strategy with the objective of satisfying the minimum resource required of machine learning tasks at the corresponding sensor device. Similarly, the attacker’s objective is to strategically allocate resources so that the resource constraint of the machine learning tasks is not satisfied. Owing to a lack of complete information of the processing time of the machine learning tasks, this strategic resource allocation problem between the two players is modeled as a Bayesian Q-learning game, in which the optimal strategies of the sensor device-edgeVM pair and the attacker are analyzed. Furthermore, probability distributions are employed by the corresponding players to model the incomplete nature of the game and a greedy Q-learning algorithm is proposed to dependable resource allocation against DDoS attacks. Numerical simulation results demonstrate that the proposed mechanism is superior to other dependable resource allocation mechanisms under incomplete information for DDoS attacks in the sensor edge cloud.

Matrix completion has emerged very recently and provides a new venue for low cost data gathering in Wireless sensor networks (W SNs). Existing schemes often assume that the data matrix has a known and fixed low-rank, which is unlikely to hold in a practical system for environment monitoring. Environmental data vary in temporal and spatial domain. By analyzing a large set of weather data collected from 196 sensors in ZhuZhou, China, we reveal that weather data has the features of low-rank, temporal stability, and relative rank stability. Taking advantage of these features, we propose an on-line data gathering scheme based on matrix completion theory, named MC-Weather, to adaptively sample different locations according to environmental and weather conditions. To better schedule sampling process while satisfying the required reconstruction accuracy, we propose several novel techniques, including three sample learning principles, an adaptive sampling algorithm based on matrix completion, and a uniform time slot and cross sample model. With these techniques, our MC-Weather scheme can collect the sensory data at required accuracy while largely reduce the cost for sensing, communication and computation. We perform extensive simulations based on the data traces from weather monitoring and the simulation results validate the efficiency and efficacy of the proposed scheme.