Abstract-Wireless sensor networks are often deployed over a large area of interest and therefore the quality of the sensor signals may vary significantly across the different sensors. In this case, it is useful to have a measure for the importance or the so-called 'utility' of each sensor, e.g., for sensor subset selection, resource allocation or topology selection. In this paper, we consider the efficient calculation of sensor utility measures for four different signal estimation or beamforming algorithms in an adaptive context. We use the definition of sensor utility as the increase in cost (e.g., mean-squared error) when the sensor is removed from the estimation procedure. Since each possible sensor removal corresponds to a new estimation problem (involving less sensors), calculating the sensor utilities would require a continuous updating of K different signal estimators (where K is the number of sensors), increasing computational complexity and memory usage by a factor K. However, we derive formulas to efficiently calculate all sensor utilities with hardly any increase in memory usage and computational complexity compared to the signal estimation algorithm already in place.

No abstract

No abstract

The recent trend towards the use of low-power wide-area-networks (LPWAN) communication technologies in the Internet of Things such as SigFox, Lora and Weightless gives rise to promising applications in smart grids, smart city, smart logistics, etc. where tens of thousands of sensors in a large area are connected to a single gateway. However, to manage such a sheer number of deployed devices, solutions to provide over-the-air firmware updates are required. This paper analyses the feasibility of over-the-air (partial) software updates for three LPWAN technologies (LoRa,) and discusses the best suited update method for different scenarios: full system updates, application updates and network stack updates.The results indicate that full firmware upgrades consume a substantial amount of energy, especially for the lowest bit-rate LPWAN technologies such as SigFox which drains a single AA battery with 2% when performing a version update.However, technologies with a similar range (i.e. LoRa SF12) require only 0.12%.The trade-off between range and energy (or bit-rate) becomes clear when considering that the least sensitive technology (IEEE-802.15.4g-OFDM) consumes only 0.0001%. Partial updates require significantly less energy for all technolo- * Corresponding author Preprint submitted to Journal of Internet of Things September 19, 2018 gies. Adding a single application uses 6 to 38 times less energy compared to a firmware update, depending on the update method and LPWAN technology. Even partial network stack updates (i.e. MAC) cost 3 to 8 times less energy, making over-the-air updates feasible. Keywords: LPWAN; Internet-of-Things; partial over-the-air software updates; network management; SigFox; LoRa; IEEE-802.15.4g 1. Introduction In recent years low-power wide-area-networks (LPWAN) such as NB-IoT LoRa, SigFox and IEEE-802.15.4g gained increasing interest from industry and the scientific community in fields related to the Internet-of-Things (IoT). The promise of providing large coverage for low power devices is a key enabler for 5 many use cases in application domains such as smart grids, smart city, smart logistics, etc. because a single LPWAN gateway can serve thousands of sensors within a range of several kilometres. To this end, most LPWANs operate in the sub-1 GHz frequency bands and therefore experience less attenuation and multipath fading.10Although the increased range of LPWAN technologies is appealing for many use cases, LPWAN technologies also have disadvantages. (i) Firstly, they achieve a longer range by using more energy per transmitted bit. The coverage of LP-WAN devices is increased by using a lower modulation rate, effectively putting more energy in each transmitted bit (or symbol), thereby resulting in a higher 15 link budget. (ii) Secondly, low power operation is achieved by using a simple star topology, applying an ultra low radio duty cycle, and using a simple, non-synchronised lightweight medium access control protocol such as slotted aloha. As a result, most LPWAN devices only listen sporadica...

A wireless sensor network is envisaged that performs signal estimation by means of the distributed adaptive nodespecific signal estimation (DANSE) algorithm. This wireless sensor network has constraints such that only a subset of the nodes are used for the estimation of a signal. While an optimal node selection strategy is NP-hard due to its combinatorial nature, we propose a greedy procedure that can add or remove nodes in an iterative fashion until the constraints are satisfied based on their utility. With the proposed definition of utility, a centralized algorithm can efficiently compute each nodes's utility at hardly any additional computational cost. Unfortunately, in a distributed scenario this approach becomes intractable. However by using the convergence and optimality properties of the DANSE algorithm, it is shown that for node removal, each node can efficiently compute a utility upper bound such that the MMSE increase after removal will never exceed this value. In the case of node addition, each node can determine a utility lower bound such that the MMSE decrease will always exceed this value once added. The greedy node selection procedure can then use these upper and lower bounds to facilitate distributed node selection.

Due to the fast pace at which Internet-of-Things (IoT) protocols and applications evolve, there is an increasing need to support over-the-air 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 an overhead, especially in terms of energy consumption, thereby impacting the operational lifetime of the constrained battery powered devices. Up until now, a comprehensive overview describing the different update steps and quantifying the impact of each step is missing in scientific literature, making it hard to assess the overall feasibility of an over-the-air update. To remedy this, our article (i) analyzes which parts of an IoT operating system are most updated after device deployment, (ii) proposes a step-by-step approach to integrate software updates in IoT solutions, and (iii) quantifies the energy cost of each of the involved step. 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 135.026mJ, of which the main portions are data dissemination (63.11%) and encryption (5.29%). However, when modular updates are used instead, the energy cost (e.g. for a MAC update) is reduced to 26.743mJ (of which 48.69% for data dissemination and 26.47% for encryption).

Abstract-This paper presents empirical path loss models for an environment of stacked shipping containers. Specifically, a system for wireless monitoring of containers is considered for which three different types of wireless links are identified, namely intra-, inter-, and extra-container links. Furthermore, the intercontainer link is investigated for the two most common types of container stacking: row and block stacking. Intra-and intercontainer path loss is investigated at IEEE 802.15.4 frequencies of 433, 868, and 2400 MHz. Extra-container path loss is examined at GSM/UMTS frequencies of 900, 1850, and 2100 MHz. Distancedependent path loss models are proposed for the inter-and extracontainer links (high correlation coefficients between 0.76 and 0.86). The resulting path loss models can be used in link budget calculations for container monitoring systems.

Abstract-The paradigm shift towards the Internet-of-Things results in an increasing number of wireless applications being deployed. Since many of these applications contend for the same physical medium (i.e. the unlicensed ISM bands), there is a clear need for beyond-state-of-the-art solutions that coordinate medium access across heterogeneous wireless networks. Such solutions demand fine-grained control of each device and technology, which currently requires a substantial amount of effort given that the control APIs are different on each hardware platform, technology and operating system.In this paper an open architecture is proposed that overcomes this hurdle by providing unified programming interfaces (UPIs) for monitoring and controlling heterogeneous devices and wireless networks. The UPIs enable to create and test advanced coordination solutions while minimizing the complexity and implementation overhead. The availability of such interfaces is also crucial for the realization of emerging software-defined networking approaches for heterogeneous wireless networks. To illustrate the use of UPIs, a showcase is presented that simultaneously changes the medium access control (MAC) behavior of multiple wireless technologies in order to mitigate cross technology interference taking advantage of the enhanced monitoring and control functionality.An open source implementation of the UPIs is available for wireless researchers and developers. It currently supports multiple widely used technologies (IEEE-802.11, IEEE-802.15.4, LTE), operating systems (Linux, Windows, Contiki) and radio platforms (Atheros, Broadcom, CC2520, Xylink Zynq, …), as well as advanced reconfigurable radio systems (IRIS, GNURadio, WMP, TAISC).