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.

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.

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 today's 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.

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.

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.