An improved formulation of Jaccard's theory of the electric properties of ice

In the standard derivation of Jaccard's theory of the electric properties of ice, no fundamental distinction is made between bound and free charges. This leads to some didactical problems like the ad hoc introduction of the so-called 'configuration vector' $\bf{\Omega}$. However, when the two types of charges are distinguished, it becomes clear that $\bf{\Omega}$ is redundant and proportional to the polarisation density. We also show that Jaccard's formulation contains a wrong formula for the electric susceptibility and that the correct $\Phi$ factor can be derived from a straightforward kinetic approach (which Jaccard failed to do).Comment: Submission to SciPos

Alternate marking-based network telemetry for industrial WSNs

For continuous, persistent and problem-free operation of Industrial Wireless Sensor Networks (IWSN), it is critical to have visibility and awareness into what is happening on the network at any one time. Especially, for the use cases with strong needs for deterministic and real-time network services with latency and reliability guarantees, it is vital to monitor network devices continuously to guarantee their functioning, detect and isolate relevant problems and verify if all system requirements are being met simultaneously. In this context, this article investigates a light-weight telemetry solution for IWSNs, which enables the collection of accurate and continuous flowbased telemetry information, while adding no overhead on the monitored packets. The proposed monitoring solution adopts the recent Alternate Marking Performance Monitoring (AMPM) concept and mainly targets measuring end-to-end and hopby-hop reliability and delay performance in critical application flows. Besides, the technical capabilities and characteristics of the proposed solution are evaluated via a real-life implementation and practical experiments, validating its suitability for IWSNs

Seasonal variation in soil and plant water potentials in a Bolivian tropical moist and dry forest

We determined seasonal variation in soil matric potentials (¿soil) along a topographical gradient and with soil depth in a Bolivian tropical dry (1160 mm y-1 rain) and moist forest (1580 mm y-1). In each forest we analysed the effect of drought on predawn leaf water potentials (¿pd) and drought response (midday leaf water potential at a standardized ¿pd of -0.98 MPa; ¿md) of saplings of three tree species, varying in shade-tolerance and leaf phenology. ¿soil changed during the dry season and most extreme in the dry forest. Crests were drier than slopes and valleys. Dry-forest top soil was drier than deep soil in the dry season, the inverse was found in the wet season. In the moist forest the drought-deciduous species, Sweetia fruticosa, occupied dry sites. In the dry forest the short-lived pioneer, Solanum riparium, occupied wet sites and the shade-tolerant species, Acosmium cardenasii drier sites. Moist-forest species had similar drought response. The dry-forest pioneer showed a larger drought response than the other two species. Heterogeneity in soil water availability and interspecific differences in moisture requirements and drought response suggest great potential for niche differentiation. Species may coexist at different topographical locations, by extracting water from different soil layers and/or by doing so at different moments in tim

An LSPI based reinforcement learning approach to enable network cooperation in cognitive wireless sensor networks

The number of wirelessly communicating devices increases every day, along with the number of communication standards and technologies that they use to exchange data. A relatively new form of research is trying to find a way to make all these co-located devices not only capable of detecting each other's presence, but to go one step further - to make them cooperate. One recently proposed way to tackle this problem is to engage into cooperation by activating 'network services' (such as internet sharing, interference avoidance, etc.) that offer benefits for other co-located networks. This approach reduces the problem to the following research topic: how to determine which network services would be beneficial for all the cooperating networks. In this paper we analyze and propose a conceptual solution for this problem using the reinforcement learning technique known as the Least Square Policy Iteration (LSPI). The proposes solution uses a self-learning entity that negotiates between different independent and co-located networks. First, the reasoning entity uses self-learning techniques to determine which service configuration should be used to optimize the network performance of each single network. Afterwards, this performance is used as a reference point and LSPI is used to deduce if cooperating with other co-located networks can lead to even further performance improvements

Impact of EU duty cycle and transmission power limitations for sub-GHz LPWAN SRDs : an overview and future challenges

Long-range sub-GHz technologies such as LoRaWAN, SigFox, IEEE 802.15.4, and DASH7 are increasingly popular for academic research and daily life applications. However, especially in the European Union (EU), the use of their corresponding frequency bands are tightly regulated, since they must confirm to the short-range device (SRD) regulations. Regulations and standards for SRDs exist on various levels, from global to national, but are often a source of confusion. Not only are multiple institutes responsible for drafting legislation and regulations, depending on the type of document can these rules be informational or mandatory. Regulations also vary from region to region; for example, regulations in the United States of America (USA) rely on electrical field strength and harmonic strength, while EU regulations are based on duty cycle and maximum transmission power. A common misconception is the presence of a common 1% duty cycle, while in fact the duty cycle is frequency band-specific and can be loosened under certain circumstances. This paper clarifies the various regulations for the European region, the parties involved in drafting and enforcing regulation, and the impact on recent technologies such as SigFox, LoRaWAN, and DASH7. Furthermore, an overview is given of potential mitigation approaches to cope with the duty cycle constraints, as well as future research directions

Over-the-air software updates in the internet of things : an overview of key principles

Due to the fast pace at which IoT is evolving, there is an increasing need to support over-theair software updates for security updates, bug fixes, and software extensions. To this end, multiple over-the-air techniques have been proposed, each covering a specific aspect of the update process, such as (partial) code updates, data dissemination, and security. However, each technique introduces overhead, especially in terms of energy consumption, thereby impacting the operational lifetime of the battery constrained devices. Until now, a comprehensive overview describing the different update steps and quantifying the impact of each step is missing in the scientific literature, making it hard to assess the overall feasibility of an over-the-air update. To remedy this, our article analyzes which parts of an IoT operating system are most updated after device deployment, proposes a step-by-step approach to integrate software updates in IoT solutions, and quantifies the energy cost of each of the involved steps. The results show that besides the obvious dissemination cost, other phases such as security also introduce a significant overhead. For instance, a typical firmware update requires 135.026 mJ, of which the main portions are data dissemination (63.11 percent) and encryption (5.29 percent). However, when modular updates are used instead, the energy cost (e.g., for a MAC update) is reduced to 26.743 mJ (48.69 percent for data dissemination and 26.47 percent for encryption)

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves