The IEEE Internet of Things Magazine (IEEE IoTM) is soliciting articles for its mini-series on Signal Processing for IoT. The Internet of Things (IoT) lies at the core of the unprecedented, connected era we are currently speeding towards. Both Academia and the Industry strive to push the limits of technology fueled by vertical applications, such as factory 4.0, eHealth, human centric and tactile IoT, wearable IoT, smart city/building, digital twin, etc., which will shape our future society. Signal Processing (SP) plays a significant role in expanding the number of IoT technologies and capitalizing on their applications. In this context, the unique challenges from the SP point of view are: (a) system design taking into account the applications’ practical limitations and requirements, (b) gathering collective intelligence from heterogeneous and inexpensive IoT sensors/devices (e.g. to perform an inference task in decentralized fashion), (c) minimize device cost and energy consumption while allowing perpetual monitoring/sensing, (d) grant adaptivity to cope with device mobility use cases, and (e) leverage fine-grained “programming” of wireless environments to cope with (possibly-massive) IoT scenarios. These challenges give rise to the need to develop pervasive, inexpensive, fast, and low-power algorithms, as well as novel theoretical tools, evaluation benchmarks, and methodologies to characterize the performance of the proposed algorithms.

The main goal of this mini-series is to address these challenges by presenting advanced and innovative SP tools and algorithms for IoT systems.  Accordingly, articles for this mini-series are invited to cover a wide range of topics (addressing both theoretical and practical applications), including, but not limited to:

• SP for efficient and accurate data collection from IoT devices: Exploration of novel methodologies to enhance the efficiency and accuracy of data acquisition in IoT environments, including compressed sensing techniques, censoring and sleeping procedures, quantization strategies, mixed-resolution sensors, as well as semantic and goal-oriented schemes.
• SP techniques enabling seamless multiple access in IoT:  Design and analysis of novel schemes focused on dense and/or interfering scenarios, with the aim of mitigating interference and improving spectral efficiency in IoT communication systems, e.g., NOMA, over-the-air computation, and unsourced random access. Improvements of existing communication protocols (e.g. NB-IoT, LoRA) by novel SP methodologies are also encouraged.
• SP fostering green IoT and “programming” the wireless environment: Adoption of sophisticated SP algorithms to leverage novel technologies which ensure self-sustainability of IoT systems, also capitalizing fine-grained programmability of the wireless environment. Relevant examples include ambient backscattering, energy-harvesting, wireless power transfer, as well as reconfigurable intelligent and holographic meta-surfaces.
• Distributed inference and Data Fusion for IoT:  Design and analysis of distributed inference and data fusion techniques tailored for IoT devices and applications, also accounting for diverse sensing modalities (viz. multimodal). Inference tasks encompass estimation, detection, classification, and filtering/tracking. The ultimate objective is to enable accurate and reliable decision-making, also leveraging different architectural flavors (e.g. edge-fog-cloud).
• SP aspects of Machine/Deep Learning for IoT-based analytics: Exploration of SP in the design of machine/deep learning techniques for IoT data processing. Topics may include federated learning, graph-based methods, distributed learning algorithms, and other SP techniques applied to learning from IoT-originated data.
• SP for the design of Digital Twins and Cyber-physical systems: Use of tailored SP tools to foster the coupled interconnection between physical assets and digital replicas, interconnection between twins to foster value, and SP approaches to close the sensing-actuation loop in the IoT context.
• Theoretical analysis and performance bounds for IoT systems: Contributions offering insights into fundamental limits and performance benchmarks for various aspects of IoT systems, i.e., at the communication, inference, or system/goal level. Topics of interest include the use of statistical SP tools, such as mean squared error/variance bounds, uncertainty quantification, Cramer-Rao bounds, and related theoretical frameworks. The final objective is to provide trust and reliability of the IoT-focused designed systems.
• SP techniques for multi-functional and adaptive IoT systems: Design and Analysis of reduced-complexity optimization and SP tools to design IoT systems having “swiss-knife” capabilities, such as integrated sensing, communications, localization, and analytics and able to cope with adaptivity and mobility, including UAVs, mobile sensors, and robots.
• SP-enabled vertical applications for IoT: Showcase of innovative SP solutions tailored for specific IoT verticals, with a clear focus on practical applications and case studies. Examples include, but are not limited to, Factory 4.0, smart cities, agri-food, and e-health.

Authors should keep in mind that the intended audience consists of all the members of the IoT community. Hence, articles must be understandable by the general IoT practitioner/researcher, independent of technical or business specialty, and are expected to add to the knowledge base or best practices of the IoT community. Mathematical material should be avoided, instead, references to papers containing the relevant mathematics should be provided when applicable.

In addition to the above, the IEEE IoTM general paper submission guidelines, author guidelines for manuscript development, and submission over manuscript central must be carefully abided by.

Note: IoTM does not have a specific template and does not require manuscripts to be submitted in any specific layout. However, authors can use the template for IEEE Transactions to get a rough estimate of the page count.