Igor Burago

Urban IoT Edge Analytics

The Urban Internet of Things (IoT) supports city-scale data collection and processing. It’s practical deployment poses several technical and technological challenges to overcome. In this chapter, we illustrate the main aspects of Urban IoT solutions based on Edge computing architectures. The potential to boost efficiency granted by such architectures, which bridge the gap between local and global information and processing resources, is discussed. Within this context, the key aspects introduced in this chapter are: (1) Context and computation-aware data selection and compression to minimize network load and energy expense; (2) Content-aware wireless networking protocols, where the network layer, informed by the edge processor(s), is adapted to the content and processing needs of the supported applications and algorithms, and (3) Improved data search and information availability via layered data transportation and processing architectures.

Driving behavior modeling and estimation for battery optimization in electric vehicles: work-in-progress

Battery and energy management methodologies such as automotive climate controls have been proposed to address the design challenges of driving range and battery lifetime in Electric Vehicles (EV). However, driving behavior estimation is a major factor neglected in these methodologies. In this paper, we propose a novel context-aware methodology for estimating the driving behavior in terms of future vehicle speeds that will be integrated into the EV battery optimization. We implement a driving behavior model using a variation of Artificial Neural Networks (ANN) called Nonlinear AutoRegressive model with eXogenous inputs (NARX). We train our novel context-aware NARX model based on historical behavior of real drivers, their recent driving reactions, and the route average speed retrieved from Google Maps in order to enable driver-specific and self-adaptive driving behavior modeling and long-term estimation. Our methodology shows only 12% error for up to 30-second speed prediction which is improved by 27% compared to the state-of-the-art. Hence, it can achieve up to 82% of the maximum energy saving and battery lifetime improvement possible by the ideal methodology where the future vehicle speed is known.

Automated Attacks on Compression-Based Classifiers

Methods of compression-based text classification have proven their usefulness for various applications. However, in some classification problems, such as spam filtering, a classifier confronts one or many adversaries willing to induce errors in the classifiers judgment on certain kinds of input. In this paper, we consider the problem of finding thrifty strategies for character-based text modification that allow an adversary to revert classifiers verdict on a given family of input texts. We propose three statistical statements of the problem that can be used by an attacker to obtain transformation models which are optimal in some sense. Evaluating these three techniques on a realistic spam corpus, we find that an adversary can transform a spam message (detectable as such by an entropy-based text classifier) into a legitimate one by generating and appending, in some cases, as few additional characters as 11% of the original length of the message.

Network Estimation in Cognition-Empowered Wireless Networks

An approach to parametric identification of the transmission processes of the terminals in a wireless network is proposed, presenting a tradeoff between accuracy of capturing the temporal dependencies in observations of transmission processes and the time complexity of the estimation procedure. The maximum likelihood estimator is built for an approximation of the true likelihood function for the observed network activity. A complex network where terminals store packets in a finite buffer and implement a backoff-based random channel access protocol is considered. Minimal information is available for observation to the cognitive terminals, in the form of energy readings mapped to the number of transmitting nodes in each time instant. The entanglement of the transmission processes induced by interference and the filtering effect of packet buffering make this task particularly difficult. It is shown how, based on the estimated parameters, the cognitive terminals, operating in the same channel resource, can predict the transmission trajectories of the other nodes and devise smart transmission strategies controlling the interference generated to the network.

Bandwidth-Aware Data Filtering in Edge-Assisted Wireless Sensor Systems

By placing processing-capable devices at the edge of local wireless access networks, Edge Computing architectures have been recently proposed to connect mobile devices to computational power through a one-hop low-latency wireless link. In this paper, we propose a new design where edge assistance is used to control local data filtering at the mobile devices in bandwidth and energy constrained systems. We focus on real-time monitoring applications, where the video input from mobile devices is processed to centrally detect and recognize objects. The edge processor controls the activation and deactivation of local classifiers implemented by the mobile devices to remove useless portions of video frames. The objective is to adapt the video stream to time-varying bandwidth constraints, while minimizing the additional energy consumption introduced by local processing. To this end, an optimization problem is formulated for a loss function embodying the balance between the risk of violating the available bandwidth and the cost of overly-conservative data filtering. The edge assists the local decision by extracting parameters of the video, such as density of objects of interest in a frame, which influence the output of the sensor. Numerical results, obtained by performing a measurement campaign based on a real implementation, illustrate the tension between energy and bandwidth use for a Haar-feature based cascade classifier.

Semantic compression for edge-assisted systems

A novel semantic approach to data selection and compression is presented for the dynamic adaptation of IoT data processing and transmission within “wireless islands”, where a set of sensing devices (sensors) are interconnected through one-hop wireless links to a computational resource via a local access point. The core of the proposed technique is a cooperative framework where local classifiers at the mobile nodes are dynamically crafted and updated based on the current state of the observed system, the global processing objective and the characteristics of the sensors and data streams. The edge processor plays a key role by establishing a link between content and operations within the distributed system. The local classifiers are designed to filter the data streams and provide only the needed information to the global classifier at the edge processor, thus minimizing bandwidth usage. However, the better the accuracy of these local classifiers, the larger the energy necessary to run them at the individual sensors. A formulation of the optimization problem for the dynamic construction of the classifiers under bandwidth and energy constraints is proposed and demonstrated on a synthetic example.