The Internet Engineering Task Force (IETF) Low Power Wide Area Network (LPWAN) Working Group has developed the Static Context Header Compression (SCHC) framework to enable IPv6 over LPWAN. In order to support 1280-byte packets, as required for IPv6, SCHC includes a fragmentation functionality, since relevant LPWAN technologies offer very short data unit sizes and do not provide native fragmentation mechanisms. SCHC offers 3 fragmentation modes: No-ACK, ACK-Always, and ACK-on-Error, the latter being especially promising due to its reliability and high efficiency. In this article, we develop a mathematical model to compute the most critical performance parameters for the SCHC ACK-on-Error mode, namely, the acknowledgment traffic incurred by a fragment receiver for the successful delivery of a fragmented packet. The model is used to evaluate the SCHC ACK-on-Error mode performance, as well as to optimally tune its main parameters when used over LoRaWAN and Sigfox, for different packet sizes. Additionally, we illustrate how our derived optimal settings allow to reduce the acknowledgment traffic in a number of scenarios.

The emergence of the Internet of Things (IoT) has made wireless connectivity ubiquitous and necessary. Extending the IoT to the Industrial Internet of Things (IIoT) places significant demands in terms of reliability on wireless connectivity. The Institute of Electrical and Electronics Engineers (IEEE) Std 802.15.4-2015 standard was designed in response to these demands, and the IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) adaptation layer was introduced to address (among other issues) its payload size limitations by performing packet compression and fragmentation. However, the standardised method does not cope well with low link-quality situations and, thus, we present the state-of-the-art Forward Error Correction (FEC) methods and introduce our own contribution, Network Coding FEC (NCFEC), to improve performance in these situations. We present and analyse the existing methods as well as our own theoretically, and we then implement them and perform an experimental evaluation using the 6TiSCH simulator. The simulation results demonstrate that when high reliability is required and only low quality links are available, NCFEC performs best, with a trade-off between additional network and computational overhead. In situations where the link quality can be guaranteed to be higher, simpler solutions also start to be feasible, but with reduced adaptation flexibility.

The Internet of Things (IoT) technology is growing rapidly, while the IoT devices are being deployed massively. However, interoperability with information systems remains a major challenge for this accelerated device deployment. Furthermore, most of the time, IoT information is presented as Time Series (TS), and while the majority of the studies in the literature focus on the prediction, compression, or processing of TS, no standardized representation format has emerged. Moreover, apart from interoperability, IoT networks contain multiple constrained devices which are designed with limitations, e.g., processing power, memory, or battery life. Therefore, in order to reduce the interoperability challenges and increase the lifetime of IoT devices, this article introduces a new format for TS based on CBOR. The format exploits the compactness of CBOR by leveraging delta values to represent measurements, employing tags to represent variables, and utilizing templates to convert the TS data representation into the appropriate format for the cloud-based application. Moreover, we introduce a new refined and structured metadata to represent additional information for the measurements, then we provide a Concise Data Definition Language (CDDL) code to validate the CBOR structures against our proposal, and finally, we present a detailed performance evaluation to validate the adaptability and the extensibility of our approach. Our performance evaluation results show that the actual data sent by IoT devices can be reduced by between 88% and 94% compared to JavaScript Object Notation (JSON), between 82% and 91% compared to Concise Binary Object Representation (CBOR) and ASN.1, and between 60% and 88% compared to Protocol buffers. At the same time, it can reduce Time-on-Air by between 84% and 94% when a Low Power Wide Area Networks (LPWAN) technology such as LoRaWAN is employed, leading to a 12-fold increase in battery life compared to CBOR format or between a 9-fold and 16-fold increase when compared to Protocol buffers and ASN.1, respectively. In addition, the proposed metadata represent an additional 0.5% of the overall data transmitted in cases where networks such as LPWAN or Wi-Fi are employed. Finally, the proposed template and data format provide a compact representation of TS that can significantly reduce the amount of data transmitted containing the same information, extend the battery life of IoT devices, and improve their lifetime. Moreover, the results show that the proposed approach is effective for different data types and it can be integrated seamlessly into existing IoT systems.

Today, WiFi networks are widely deployed all over the world, with high Access Point (AP) density in urban areas. Both end-users and network operators are trying to exploit this density by combining multiple APs into “community networks,” in order to share Internet connectivity, and possibly other services. WiFi networks suffer from decentralized management and a lack of coordination in frequency allocation and power control. In order to better use and configure these uncoordinated deployments, the first step is to characterize them. This paper presents a collaborative platform, where mobile stations collect simple network measurements (e.g., the presence of an AP) and send these measurements to a central system. By gathering and processing several network measurements from different mobile users, the platform characterizes the network deployment. This paper presents two applications: 1) minimal AP set, to reduce the energy needed to provide WiFi coverage in a given area, and 2) optimal scanning parameters, to minimize the time a mobile station needs for the network discovery. These two applications show the system's viability to solve particular problems of community networks.

Metric Multidimensional Scaling is commonly used to solve multi-sensor location problems in 2D or 3D spaces. In this paper, we show that such technique provides poor results in the case of indoor location problems based on 802.11 Fine Timing Measurements, because the number of anchors is small and the ranging error asymmetrically distributed. We then propose a two-step iterative approach based on geometric resolution of angle inaccuracies. The first step reduces the effect of poor ranging exchanges. The second step reconstructs the anchor positions, starting from the distances of highest likely-accuracy. We show that this geometric approach provides better location accuracy results than other Euclidean Distance Metric techniques based on Least Square Error logic. We also show that the proposed technique, with the input of one or more known location, can allow a set of fixed sensors to auto-determine their position on a floor plan.