Industrial low-power wireless networks exhibit unique requirements in terms of reliability, battery lifetime and security. Time Synchronized Channel Hopping is a networking technique created to address these needs, which was standardized by working group IETF 6TiSCH. Analog Devices’ SmartMesh product lines have been the best-in-class TSCH implementation, exhibiting over 99.999% wire-like end-to-end reliability, a decade of battery lifetime, and certified security. With over 100,000 networks deployed, SmartMesh plays a market-leading role. Today, SmartMesh runs on the LTC5800, and often requires customers to drive it from a second external micro-controller, which increases cost. This paper introduces a port of SmartMesh to the MAX32655, a dual-core microcontroller and radio System on Chip: the RISC-V core runs the communication stack, the ARM Cortex-M4 core is left available to the customer. We show how that ARM Cortex-M4 core can run a MicroPython interpreter for faster prototyping and time-to-market. A simple script, designed to transmit packets over a SmartMesh network using MicroPython, requires only an additional 6 kB of RAM. Furthermore, by compiling this script into byte code, the processing time can be reduced to a quarter of its original duration.

Time Synchronized Channel Hopping (TSCH) is a technique that enables ultra reliable and ultra low-power wireless multi-hop networks, and in which communication is orchestrated by a schedule. When building that schedule, one can cleanly trade off between latency, power consumption, throughput, and reliability. TSCH technology is commonplace in industrial applications, with increasing momentum to apply it to other application domains. This paper is the first to present a model that estimates the performance of a complete network. This model is applied to SmartMesh IP, a commercial TSCH product. The parameters of different classes of use cases for low-power mesh networks are presented, and the estimation model is used to evaluate the applicability of TSCH technology to those cases. This paper indicates how TSCH networks can be used in a wide range of applications: from urban applications which require a decade of battery lifetime, to high-density smart building networks with tens of nodes in the same radio space, and to deep multi-hop networks in an industrial setting.

Channel-specific path data for a 44-node 2.4 GHz wireless sensor network deployed in an industrial setting is presented. Each node generates one data packet every 28 seconds with the number of transmissions, received acknowledgements, average RSSI, and other metrics for a path to a single neighbor on a single channel for every 15 minutes of operation. Twenty-six days of data were recorded, revealing the scale of time-variation of stability throughout the network and how this is a frequency-dependent quantity. Particularly on low-power paths, both RSSI and stability are observed to vary in unpredictable ways that differ from other paths in the same spatial vicinity. A time-varying model is proposed for simulation of networks in low-noise environments. Channel hopping and path diversity succeed in maintaining near-perfect reliability at a delivered rate of 1.0 kb/s despite this time-and frequency-variance.

An evaluation methodology is presented for the performance of reporting node self-selection in wireless sensor networks. Five cost metrics are proposed along with several methods for self-selection that involve little or no collaboration with other nodes. These costs are used to evaluate how efficiently the various algorithms allow for node self-selection as simulated on different field complexities. Analysis of different methods over 100 test fields sampled by 2000 nodes indicates that there is no single method that is superior in all respects. Trade-offs in latency and overall energy consumption are revealed to be highly dependent on the selection method and the field complexity.

We propose a distributed on-demand power-management protocol for collecting data in sensor networks. The protocol aims to reduce power consumption while supporting fluctuating demand in the network and provide local routing information and synchronicity without global control. Energy savings are achieved by powering down nodes during idle times identified through dynamic scheduling. We present a real implementation on wireless sensor nodes based on a novel, two-level architecture. We evaluate our approach through measurements and simulation, and show how the protocol allows adaptive scheduling and enables a smooth trade-off between energy savings and latency. An example current measurement shows an energy savings of 83% on an intermediate node.

We consider the problem of efficiently transmitting sets of spatially correlated observations in a distributed sensor network without requiring inter-node communication to exploit the correlation. Specifically, we provide a construction for quantizer design given a training set, and a distributed compression scheme to efficiently relay the quantized observations to a central decoder.

A method for estimating unknown node positions in a sensor network based exclusively on connectivity-induced constraints is described. Known peer-to-peer communication in the network is modeled as a set of geometric constraints on the node positions. The global solution of a feasibility problem for these constraints yields estimates for the unknown positions of the nodes in the network. Providing that the constraints are tight enough, simulation illustrates that this estimate becomes close to the actual node positions. Additionally, a method for placing rectangular bounds around the possible positions for all unknown nodes in the network is given. The area of the bounding rectangles decreases as additional or tighter constraints are included in the problem. Specific models are suggested and simulated for isotropic and directional communication, representative of broadcast-based and optical transmission respectively, though the methods presented are not limited to these simple cases.