The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting

Wireless fingerprinting refers to a device identification method leveraging hardware imperfections and wireless channel variations as signatures. Beyond physical layer characteristics, recent studies demonstrated that user behaviours could be identified through network traffic, e.g., packet length, without decryption of the payload. Inspired by these results, we propose a multi-layer fingerprinting framework that jointly considers the multi-layer signatures for improved identification performance. In contrast to previous works, by leveraging the recent multi-view machine learning paradigm, i.e., data with multiple forms, our method can cluster the device information shared among the multi-layer features without supervision. Our information-theoretic approach can be extended to supervised and semi-supervised settings with straightforward derivations. In solving the formulated problem, we obtain a tight surrogate bound using variational inference for efficient optimization. In extracting the shared device information, we develop an algorithm based on the Wyner common information method, enjoying reduced computation complexity as compared to existing approaches. The algorithm can be applied to data distributions belonging to the exponential family class. Empirically, we evaluate the algorithm in a synthetic dataset with real-world video traffic and simulated physical layer characteristics. Our empirical results show that the proposed method outperforms the state-of-the-art baselines in both supervised and unsupervised settings

Secrecy Constrained Distributed Inference in Wireless Sensor Networks

Comprised of a large number of low-cost, low-power, mobile and miniature sensors, wireless sensor networks are widely employed in many applications, such as environmental monitoring, health-care, and diagnostics of complex systems. In wireless sensor networks, the sensor outputs are transmitted across a wireless communication network to legitimate users such as fusion centers for final decision-making. Because of the wireless links across the network, the data are vulnerable to security breaches. For many applications, the data collected by local sensors are extremely sensitive, and care must be taken to prevent that information from being leaked to any malicious third parties, e.g., eavesdroppers. Eavesdropping is one of the most significant threats to wireless sensor networks, where local sensors are tapped by an eavesdropper in order to intercept information. I considered distributed inference in the presence of a global, greedy and informed eavesdropper who has access to all local node outputs rather than access. My goal is to develop secured distributed systems against eavesdropping attacks using a physical-layer security approach instead of cryptography techniques because of the stringent constraints on sensor networks energy and computational capability. The physical-layer security approach utilizes the characteristics of the physical layer, including transmission channels noises, and the information of the source. Additionally, physical-layer security for distributed inference is scalable due to the low computational complexity. I first investigate secrecy constrained distributed detection under both Neyman-Pearson and Bayesian frameworks. I analyze the asymptotic detection performance and proposed a novel way of analyzing the maximum performance trade-off using Kullback-Leibler divergence ratio between the fusion center and eavesdropper. Under the Neyman-Pearson framework, I show that the eavesdropper\u27s detection performance can be limited such that her decision-making is no better than random guessing; meanwhile, the detection performance at the fusion center is guaranteed at the prespecified level. Similar analyses and proofs are provided under the Bayesian framework, where it was shown that an eavesdropper can be constrained to an error probability level equal to her prior information. Additionally, I derive the asymptotic error exponent and show that asymptotic perfect secrecy and asymptotic perfect detection are possible by increasing the number of sensors under both frameworks if the fusion center has noiseless channels to the sensors. For secrecy constrained distributed estimation, I conducted similar analysis under both a classical setting and Bayesian setting. I derived the maximum achievable secrecy performance and show that under the condition that the eavesdropper has noisy channels and the fusion center has noiseless channels, both asymptotic perfect secrecy and asymptotic perfect estimation can be achieved under a classical setting. Similarly, under a Bayesian setting, I derived the performance trade-off using Fisher information ratio and show that the fusion center outperforms the eavesdropper significantly in the simulation section. Secrecy constrained in distributed inference with Rayleigh fading binary symmetric channel is considered as well. Similarly, I derive the maximum achievable secrecy performance ratio for both detection and estimation. The maximum achievable trade-off turns out to be almost the same in distributed estimation as in distributed detection. This suggests that a universal framework for generally structured inference problems are feasible. Further investigations are needed to justify this conjecture for more general applications

Motorized cart

Motorized cart is known as an effective tool and timeless that help people carry heavy loads. For farmers, it has an especially vital tool for moving goods. Oil palm farmers typically uses the wheelbarrow to move the oil palm fruit (Figure 10.1). However, there is a lack of equipment that should be further enhanced in capabilities. Motorized carts that seek to add automation to wheelbarrow as it is to help people save manpower while using it. At present, oil palm plantation industry is among the largest in Malaysia. However, in an effort to increase the prestige of the industry to a higher level there are challenges to be faced. Shortage of workers willing to work the farm for harvesting oil palm has given pain to manage oil palm plantations. Many have complained about the difficulty of hiring foreign workers and a high cost. Although there are tools that can be used to collect or transfer the proceeds of oil palm fruits such as carts available. However, these tools still have the disadvantage that requires high manpower to operate. Moreover, it is not suitable for all land surfaces and limited cargo space. Workload and manpower dependence has an impact on farmers' income

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Space-Time Hierarchical-Graph Based Cooperative Localization in Wireless Sensor Networks

It has been shown that cooperative localization is capable of improving both the positioning accuracy and coverage in scenarios where the global positioning system (GPS) has a poor performance. However, due to its potentially excessive computational complexity, at the time of writing the application of cooperative localization remains limited in practice. In this paper, we address the efficient cooperative positioning problem in wireless sensor networks. A space-time hierarchical-graph based scheme exhibiting fast convergence is proposed for localizing the agent nodes. In contrast to conventional methods, agent nodes are divided into different layers with the aid of the space-time hierarchical-model and their positions are estimated gradually. In particular, an information propagation rule is conceived upon considering the quality of positional information. According to the rule, the information always propagates from the upper layers to a certain lower layer and the message passing process is further optimized at each layer. Hence, the potential error propagation can be mitigated. Additionally, both position estimation and position broadcasting are carried out by the sensor nodes. Furthermore, a sensor activation mechanism is conceived, which is capable of significantly reducing both the energy consumption and the network traffic overhead incurred by the localization process. The analytical and numerical results provided demonstrate the superiority of our space-time hierarchical-graph based cooperative localization scheme over the benchmarking schemes considered.Comment: 14 pages, 15 figures, 4 tables, accepted to appear on IEEE Transactions on Signal Processing, Sept. 201