Borja Martinez

Bootstrapping smart cities through a self-sustainable model based on big data flows

We have a clear idea today about the necessity and usefulness of making cities smarter, the potential market size, and trials and tests. However, it seems that business around Smart Cities is having difficulties taking off and is thus running short of projected potentials. This article looks into why this is the case and proposes a procedure to make smart cities happen based on big data exploitation through the API stores concept. To this end, we first review involved stakeholders and the ecosystem at large. We then propose a viable approach to scale business within that ecosystem. We also describe the available ICT technologies and finally exemplify all findings by means of a sustainable smart city application. Over the course of the article, we draw two major observations, which are seen to facilitate sustainable smart city development. First, independent smart city departments (or the equivalent) need to emerge, much like todays well accepted IT departments, which clearly decouple the political element of the improved city servicing from the underlying technologies. Second, a coherent three-phase smart city rollout is vital, where in phase 1 utility and revenues are generated; in phase 2 only-utility service is also supported; and in phase 3, in addition, a fun/leisure dimension is permitted.

Understanding the Limits of LoRaWAN

Low-power wide area networking technology offers long-range communication, which enables new types of services. Several solutions exist; LoRaWAN is arguably the most adopted. It promises ubiquitous connectivity in outdoor IoT applications, while keeping network structures and management simple. This technology has received a lot of attention in recent months from network operators and solution providers. However, the technology has limitations that need to be clearly understood to avoid inflated expectations and disillusionment. This article provides an impartial and fair overview of the capabilities and limitations of LoRaWAN. We discuss those in the context of use cases, and list open research and development questions.

Mixed SW/SystemC SoC Emulation Framework

Developing HW modules for standard platforms like PCs or embedded devices requires a complete system emulator availability to detect and fix bugs on developed HW, Operating Systems (OS) drivers and applications. This paper presents a set of plug-ins to an open-source CPU emulator that enables mixed simulations between platforms emulators and hardware (HW) modules described in SystemC. In this paper three plugins for QEMU are described: one for connecting TLM SystemC modules to any bus QEMU emulates, one for connecting SystemC to PCI bus for PC based platform and one plug-in for connecting SystemC to AMBA bus for ARM platforms. With this framework, it is possible to develop OS drivers at the same time HW is developed and final application tested running in this virtual platform.

On the Performance of Lossy Compression Schemes for Energy Constrained Sensor Networking

Lossy temporal compression is key for energy-constrained wireless sensor networks (WSNs), where the imperfect reconstruction of the signal is often acceptable at the data collector, subject to some maximum error tolerance. In this article, we evaluate a number of selected lossy compression methods from the literature and extensively analyze their performance in terms of compression efficiency, computational complexity, and energy consumption. Specifically, we first carry out a performance evaluation of existing and new compression schemes, considering linear, autoregressive, FFT-/DCT- and wavelet-based models , by looking at their performance as a function of relevant signal statistics. Second, we obtain formulas through numerical fittings to gauge their overall energy consumption and signal representation accuracy. Third, we evaluate the benefits that lossy compression methods bring about in interference-limited multihop networks, where the channel access is a source of inefficiency due to collisions and transmission scheduling. Our results reveal that the DCT-based schemes are the best option in terms of compression efficiency but are inefficient in terms of energy consumption. Instead, linear methods lead to substantial savings in terms of energy expenditure by, at the same time, leading to satisfactory compression ratios, reduced network delay, and increased reliability performance.

The Power of Models: Modeling Power Consumption for IoT Devices

Low-energy technologies in the Internet of Things (IoTs) era are still unable to provide the reliability needed by the industrial world, particularly in terms of the wireless operation that pervasive deployments demand. While the industrial wireless performance has achieved an acceptable degree in communications, it is no easy task to determine an efficient energy-dimensioning of the device in order to meet the application requirements. This is especially true in the face of the uncertainty inherent in energy harvesting. Thus, it is of utmost importance to model and dimension the energy consumption of the IoT applications at the pre-deployment or pre-production stages, especially when considering critical factors, such as reduced cost, life-time, and available energy. This paper presents a comprehensive model for the power consumption of wireless sensor nodes. The model takes a system-level perspective to account for all energy expenditures: communications, acquisition and processing. Furthermore, it is based only on parameters that can empirically be quantified once the platform (i.e., technology) and the application (i.e., operating conditions) are defined. This results in a new framework for studying and analyzing the energy life-cycles in applications, and it is suitable for determining in advance the specific weight of application parameters, as well as for understanding the tolerance margins and tradeoffs in the system.

Lean Sensing: Exploiting Contextual Information for Most Energy-Efficient Sensing

Cyber-physical technologies enable event-driven applications, which monitor in real-time the occurrence of certain inherently stochastic incidents. Those technologies are being widely deployed in cities around the world and one of their critical aspects is energy consumption, as they are mostly battery powered. The most representative examples of such applications today is smart parking. Since parking sensors are devoted to detect parking events in almost-real time, strategies like data aggregation are not well suited to optimize energy consumption. Furthermore, data compression is pointless, as events are essentially binary entities. Therefore, this paper introduces the concept of Lean Sensing, which enables the relaxation of sensing accuracy at the benefit of improved operational costs. To this end, this paper departs from the concept of instantaneous randomness and it explores the correlation structure that emerges from it in complex systems. Then, it examines the use of this system-wide aggregated contextual information to optimize power consumption, thus going in the opposite way; from the system-level representation to individual device power consumption. The discussed techniques include customizing the data acquisition to temporal correlations (i.e, to adapt sensor behavior to the expected activity) and inferring the system-state from incomplete information based on spatial correlations. These techniques are applied to real-world smart-parking application deployments, aiming to evaluate the impact that a number of system-level optimization strategies have on devices power consumption.

Data Analytics for Smart Parking Applications

We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset.

Balancing Power Consumption in IoT Devices by Using Variable Packet Size

Currently, IoT devices are becoming more and more popular, being deployed in different scenarios such as monitoring the power consumption of a house or the state of an outdoor parking spot. These networks tend to be densely populated by a huge amount of sensors that send really short messages. Given these characteristics, their main problem is the great variability and unpredictability of battery lifetime, when they cannot be plugged to a power outlet. In this paper, we analyze the mote behaviour on a real IoT network and use the extracted data to propose a mechanism to distribute the power consumption more equally between all motes, regardless the number of messages each one sends. This new proposal decreases the numbers of interventions required to replace batteries, minimizing costs and increasing network lifetime.

I3Mote: An Open Development Platform for the Intelligent Industrial Internet

In this article we present the Intelligent Industrial Internet (I3) Mote, an open hardware platform targeting industrial connectivity and sensing deployments. The I3Mote features the most advanced low-power components to tackle sensing, on-board computing and wireless/wired connectivity for demanding industrial applications. The platform has been designed to fill the gap in the industrial prototyping and early deployment market with a compact form factor, low-cost and robust industrial design. I3Mote is an advanced and compact prototyping system integrating the required components to be deployed as a product, leveraging the need for adopting industries to build their own tailored solution. This article describes the platform design, firmware and software ecosystem and characterizes its performance in terms of energy consumption.

Energy-Aware MPEG-4 Single Profile in HW-SW Multi-Platform Implementation

Developers of next generation Multi-Processor Systems-on-a-chip (MPSoC) silicon platforms used in multimedia mobile devices should design efficient systems for diverse execution time vs. energy consumption trade-offs for a given quality of service. By exploiting Dynamic Voltage and Frequency Scaling (DVFS) techniques we can obtain singular computational/power trades offs points and thus design energy efficient platforms. This paper presents a high level methodology to acquire an optimal set of working points for an MPEG-4 Single Profile (SP) Video encoder implementation. The flow starts from a MPEG-4 encoder described in C++ language which is translated to a SystemC hard/soft description which will be analyzed and further mapped into different platforms. Refined code is migrated to four different processor architectures: a processor research framework (CRISP-Trimaran), a soft core processor with specific functional units implemented on an Altera FPGA, an ASIC and a classic DSP.