Cognitive Engine Research Articles

Retiring the “Cinderella view”: the spinal cord as an intrabodily cognitive extension

Within the field of neuroscience, it is assumed that the central nervous system is divided into two functionally distinct components: the brain, which does the cognizing, and the spinal cord, which is a conduit of information enabling the brain to do its job. We dub this the “Cinderella view” of the spinal cord. Here, we suggest it should be abandoned. Marshalling recent empirical findings, we claim that the spinal cord is best conceived as an intrabodily cognitive extension: a piece of biological circuitry that, together with the brain, constitutes our cognitive engine. To do so, after a brief introduction to the anatomy of the spinal cord, we briefly present a number of empirical studies highlighting the role played by the spinal cord in cognitive processing. Having so done, we claim that the spinal cord satisfies two popular and often endorsed criteria used to adjudicate cases of cognitive extension; namely the parity principle and the so-called “trust and glue” criteria. This, we argue, is sufficient to vindicate the role of the spinal cord as an intrabodily mental extension. We then steel our case considering a sizable number of prominent anti-extension arguments, showing that none of them poses a serious threat to our main claim. We then conclude the essay, spelling out a number of far-from trivial implications of our view.

PHYRON: cognitive computing for the creation of an innovative Intelligence Experience Center

<p>This research presents the results of a project called “PHYRON: Cognitive Computing for the creation of an innovative Intelligence Experience Center”, funded by the Basque Government (Economic Development, Sustainability and Environment Department). The project started in April 2019 and it will end in December 2021. Its main objective was to arrange an industrial research about cognitive computing. The main aim was the application of these systems for the development of an Intelligent Experience Center (IExC) to facilitate: i) enrichment of processes, products and services, in general client experiences, ii) automatic generation of technical predictions related to the product and the client behaviour through the exploitation of acquired knowledge, and iii) rationalization and automation of the processes that are involved in the after sale services both at technical and management level. The technological outcome presented in this paper is built using cognitive engines to enable learning from the client experience, and predictive models to anticipate client necessities.</p>

AI business model: an integrative business approach

Artificial intelligence is the ecosphere’s prevalent and most comprehensive general acquaintance common-sense cognitive engine. The artificial intelligence (AI) business platform model is virtually at affluence with cloud SaaS model. It concerns AI solutions that can work together on the top layer of the other digital systems, like a Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) business system. AI admittances in the digital data fluid through the coordination, fueling business enhancements over phases. In this business model, the business will safekeep a recurrent subscription. This paper endeavors to emphasize on the preventative side of the use of AI and machine learning (ML) technology to enterprise digital platform business model innovation and business dynamics. We acme the strategic implications and innovations with analytics. We explore the derivations of data-driven insights, models, and visualizations.

Spectrum-Agile Cognitive Radios Using Multi-Task Transfer Deep Reinforcement Learning

This work proposes a cognitive engine design that enables a radio to find transmission opportunities in non-contiguous wideband spectrum to avoid interference. The radio’s objective is to apply both frequency hopping and transmit power adjustment to maintain a required level of quality-of-service (QoS). The spectrum is partitioned into sub-bands each made of a number of narrowband channels. A multi-task deep Q-network (DQN) is utilized to solve the underlying problem where communications over each sub-band represents a single task. The proposed technique exploits transfer learning between tasks to speed up learning operation for new tasks. The proposed multi-task transfer DQN is proved to be converged. It is shown through simulations that the radio is able to learn an efficient strategy to evade interference signals in a partially observable environment. The experimental results indicate that the proposed approach offers up to 24% improvement to the percentage of successful communications when compared to other RL-based approaches found in existing literature.

Security-Enhanced Content Caching for the 5G-Based Cognitive Internet of Vehicles

With the explosive growth of multimedia contents in vehicular networks and the development of 5G technology come great challenges to the Internet of Vehicles system. By utilizing the storage capability of roadside units, popular contents can be cached in advance during nonpeak periods, which can bring better quality of experience to users. However, it is difficult for the existing content caching schemes in traditional vehicular networks to achieve global control when designing content caching mechanisms. Moreover, some content caching schemes also bring security concerns. Thus, in this article, we first discuss the content caching problem in the 5G-based cognitive Internet of Vehicles. Then we design security enhancement methods with the cognitive engine. Finally, we use a special case to study how to design a secure and delay-sensitive content caching scheme in cognitive vehicular networks. Extensive experiments show that the proposed algorithm outperforms traditional content caching methods.

Coronavirus Disease 2019: Exploring Media Portrayals of Public Sentiment on Funerals Using Linguistic Dimensions.

Funerals are a reflective practice to bid farewell to the departed soul. Different religions, cultural traditions, rituals, and social beliefs guide how funeral practices take place. Family and friends gather together to support each other in times of grief. However, during the coronavirus pandemic, the way funerals are taking place is affected by the country's rules and region to avoid the spread of infection. The present study explores the media portrayal of public sentiments over funerals. In particular, the present study tried to identify linguistic dimensions associated with lexical components of social processes, affective processes, fear, and disgust. An exhaustive search of newspaper coverage of funeral and related articles was made for a specific corona period. After an initial screening for the details and language used, a total of 46 newspaper articles on funerals were finalized for the analysis. Linguistic Inquiry and Word Count (LIWC) software was used to determine the association between linguistic dimensions of function words and words related to social and affective processes, as presented in the newspaper articles. Sentiment Analysis and Cognition Engine (SEANCE) was applied for the analysis of sentiment, social cognition, and social order. Bayesian correlation analysis and regression revealed positive and significant associations between function words and affective processes, between pronouns and social processes, and between negative adjectives and psychological processes of fear and disgust. Also, significant negative associations were found between polarity nouns and psychological processes of fear and disgust and between polarity verbs and psychological processes of fear and disgust. Bayes factor 10 provides strong evidence in favor of the study hypotheses. The media is influenced by the prevailing sentiments in society and reflects their perception of the current social order and beliefs. The findings provide a glimpse into the prevailing sentiment of society through the lens of media coverage. These understandings are expected to enhance our observations of how people express their feelings over the loss of their loved ones and help mental health professionals develop their therapeutic protocols to treat the coronavirus disease 2019 (COVID-19)-affected population.

Intelligent Cognitive Radio Ad-Hoc Network: Planning, Learning and Dynamic Configuration

Cognitive radio (CR) is an adaptive radio technology that can automatically detect available channels in a wireless spectrum and change transmission parameters to improve the radio operating behavior. A CR ad-hoc network (CRAHN) should be able to coexist with primary user (PU) systems and other CR secondary systems without causing harmful interference to licensed PUs as well as dynamically configure autonomous and decentralized networks. Therefore, an intelligent system structure is required for efficient spectrum use. In this paper, we present a learning-based distributed autonomous CRAHN network system model for network planning, learning, and dynamic configuration. Based on the system model, we propose machine learning-based optimization algorithms for spectrum sensing, cluster-based ad-hoc network configuration, and context-aware signal classification. Using the sensing engine and the cognitive engine, the surrounding spectrum usage and the neighbor network operation status can be analyzed. The proposed policy engine can create network operation policies for the dynamically changing surrounding wireless environment, detect policy conflicts, and infer the optimal policy for the current situation. The decision engine finally determines and configures the optimal CRAHN configuration parameters through cooperation with a learning engine, in which we implement the proposed machine-learning algorithms. The simulation results show that the proposed machine-learning CRAHN algorithms can construct CR cluster networks that have a long network lifetime and high spectrum utility. Additionally, with high signal context recognition performance, we can ensure coexistence with neighboring systems.

Neural Network Cognitive Engine for Autonomous and Distributed Underlay Dynamic Spectrum Access

Two key challenges in underlay dynamic spectrum access (DSA) are how to establish an interference limit from the primary network (PN) and how cognitive radios (CRs) in the secondary network (SN) become aware of the interference they create on the PN, especially when there is no exchange of information between the two networks. These challenges are addressed in this paper by presenting a fully autonomous and distributed underlay DSA scheme where each CR operates based on predicting its transmission effect on the PN. The scheme is based on a cognitive engine with an artificial neural network that predicts, without exchanging information between the networks, the full adaptive modulation and channel coding configuration for the primary link that is received with highest power by a transmitting CR. By managing the effect of the SN on the PN, the presented technique maintains the relative average throughput change in the PN within a prescribed maximum value, while also finding transmit settings for the CRs that result in throughput as large as allowed by the PN interference limit. Simulation results show that the ability of the cognitive engine in estimating the effect of a CR transmission on the full adaptive modulation and coding (AMC) mode leads to a very fine resolution underlay transmit power control. This ability also provides higher transmission opportunities for the CRs, compared to a scheme that can only estimate the modulation scheme used at the PN link.

Geocognitive Technologies Improve Basin- and Petroleum-System Analyses

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 197610, “Application of Geocognitive Technologies to Basin- and Petroleum-System Analyses,” by Paolo Ruffo, Marco Piantanida, SPE, and Floriana Bergero, Eni, et al., prepared for the 2019 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11-14 November. The paper has not been peer reviewed. Eni and IBM developed a cognitive engine exploiting a deep-learning approach to scan documents searching for basin geology concepts, extracting information about petroleum system elements (e.g., formation name; geological age; and lithology of source rocks, reservoirs, and seals), and enabling basin geologists to perform automated queries to collect all information related to a basin of interest. The cognitive engine succeeded in identifying the correct formations, lithologies, and geological ages of the petroleum systems with an accuracy in the range of 75 to 90%. Introduction While commercial databases often provide summary information about basins that can be extracted easily with queries or even interactive tools, the explorationist needs to integrate such information with more up-to-date and in-depth descriptions of structural and sedimentary events occurring in the basin, descriptions that can be found only in unstructured documents. Key information about basins can be scattered across paragraphs, tables, and image captions of hundreds of technical articles, or can be embedded within pictures. Even when exploiting a traditional search engine with the name of the desired basin, the results can be unsatisfactory: first, not all the results might be relevant; second, many different variants of the basin name are often used within publications. In the optimistic hypothesis that the subset of relevant documents is found by the search engine, all key concepts related to a basin need to be understood by the geologist by careful examination of the paper text and images. Moreover, even if the published information (structured and unstructured) on a basin is found, there are different opinions expressed by different authors, in addition to the uncertainty of the data itself (such as the age of a formation or the details of the geological evolution of the basin), so that multiple conceptual models of the basin can be drawn from the uncertain and scarce information available. Some of these conceptual models are more probable while others are less probable, but sometimes the latter happen to be economically more valuable. Therefore, recovery of all relevant information about a basin is crucial, but also important is the preservation of differing opinions about the data - what might be termed minority reports.

Adaptive Multi-Input Medium Access Control (AMI-MAC) Design Using Physical Layer Cognition for Tactical SDR Networks

Tactical software defined radio (SDR) networks demand stringent requirements of latency, throughput, and reliability. In the past, significant efforts have been made to achieve maximal efficiency with modifications and improvements either in an individual layer or through the cross-layer design of its working protocol. In this paper, we propose a novel cross-layer design consisting of adaptive multi-input medium access control (AMI-MAC) layer along with an intelligent channel allocation scheme supported by a multiband multimode physical layer. A cognitive engine further empowers this cross-layer design approach to achieve high throughput, improved quality of service (QoS), and adaptive range capabilities. The proposed physical layer exhibits a mixed use of narrowband and wideband waveforms accommodating different range requirements as per demanded QoS. The uniqueness of the proposed physical layer enables SDR to operate in hybrid topology by receiving multiple narrowband signals of different bandwidths with the same configuration of wideband RF front end. The proposed AMI-MAC design ensures a reduction in both control and data phase latency. MAC layer ensures the maximal utilization of the time and frequency spectrum. Bandwidth and delay optimization is also managed by the proposed trio of the physical layer, MAC, and cognition to reduce latency and achieve desired QoS. Simulation results are presented to show the superiority of the proposed design over conventional tactical radio MAC.

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

The spectrum low utilization and high demand conundrum created a bottleneck towards fulfilling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became the first step towards the intelligentization of mobile and wireless networks, and in order to strengthen its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of artificial intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at filling the gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies of spectrum management. This is a prospective review whose primary objective is to provide an in-depth exploration of the recent trends in AI strategies employed in mobile and wireless communication networks. The existing reviews in this area have not considered the relevance of incorporating the mathematical fundamentals of each AI strategy and how to tailor them to specific mobile and wireless networking problems. Therefore, this review addresses that problem by detailing how deep architectures can be integrated into spectrum management problems. Beyond reviewing different ways in which deep architectures can be integrated into spectrum management, model selection strategies and how different deep architectures can be tailored into the CR space to achieve better performance in complex environments are then reported in the context of future research directions.

LoRa based architecture for smart town traffic management system

While traffic congestion has been pointed out as everyday driving stress, few attempts are specialized in traffic management by using current IoT technology. In order to help alleviate traffic stress from drivers, this article proposes a cross-layer LoRa architecture and a machine-learning algorithm for smart town’s traffic management systems. LoRa is selected since it has strengths in range and power when compared to other wireless communication technologies. We introduce the cross-layer LoRa architecture, which is devised to facilitate its cognitive analysis. By dynamically allocating network and information resources, it complements the limitations of the standard LoRa protocol. We also have designed the logistic regression algorithm-which runs above its cognitive engine. The proposed algorithm outputs traffic coefficients based on density and travel time. This algorithm has achieved 97% of accuracy in the simulation. With further research, we believe the proposed system could be an excellent solution for smart traffic management.

A self‐adaptive algorithm for super‐peer selection considering the mobility of peers in cognitive mobile peer‐to‐peer networks

SummaryCognitive peer‐to‐peer networks are obtained from a combination of cognitive networking concepts and peer‐to‐peer networks. These networks are able to improve their performance while operating under dynamic and unknown environments. A cognitive peer‐to‐peer network tries to learn an appropriate configuration for itself considering the unknown physical properties of peers. Cognitive mobile peer‐to‐peer networks refer to cognitive peer‐to‐peer networks which are built over mobile ad hoc networks. In these networks, heterogeneity of the mobility of peers and resource limitation in wireless networks create challenges for network management algorithms. Because of the dynamicity of these networks, the management algorithms should be designated in self‐adaptive manner. In one type of these networks, some peers, called super‐peers, undertake to perform network managerial tasks. The mobility of peers leads to connection failure among peers and reselection of new super‐peers. Therefore, the selection of super‐peers, due to their influential role, requires an algorithm that considers the peers' mobility. Up to now, no self‐adaptive algorithm has been designated for super‐peer selection considering the mobility of peers in a self‐adaptive manner. This paper proposes M‐SSBLA, a self‐adaptive algorithm for super‐peer selection considering the mobility of peers based on learning automata. The proposed algorithm is obtained from cooperation between a learning automata‐based cognitive engine and MIS. MIS is a well‐known super‐peer selection algorithm in mobile peer‐to‐peer networks. We compared the proposed algorithm with recently reported algorithms, especially for a network with high mobility. Simulation results show that the proposed algorithm can cover maximum ordinary‐peer with a few super‐peer and improve robustness against super‐peer failures while decreasing maintenance overhead.

Intelligent spectrum management based on reinforcement learning schemes in cooperative cognitive radio networks

Cognitive Radio (CR) and Cooperative Communication provide key technologies for efficient utilization of available unused spectrum bands (called resources) to achieve a spectral efficient system with high throughput. But to achieve its full potential, it is essential to empower the brain of CR that is Cognitive Engine (CE), using machine learning algorithms to control the operation and adapt parameters according to the dynamic environment. However, in practical scenarios, it is difficult to formulate network model beforehand due to complex network dynamics. To address this issue, the most favorable machine learning scheme, Reinforcement Learning (RL) based schemes are proposed to empower CE without forming an explicit network model. The proposed schemes, Comparison based Cooperative Q-Learning (CCopQL) and Comparison based Cooperative State-Action-Reward-(next) State-(next) Action (CCopSARSA) for resource allocation, allows each CR to learn cooperatively. The cooperation among CRs is in the form of comparing and then exchanging Q-values to obtain an optimal policy. Though these schemes involve information exchange among CRs as compared to independent Q-Leaning and SARSA but it provides improved system performance with high system throughput. Numerical results reveal the significant benefits from exploiting the cooperative feature with RL, both proposed schemes outperform other existing schemes in terms of system throughput and expedite the convergence than individual CR learning with CCopSARSA and CCopQL respectively.

Communication-Efficient Offloading for Mobile-Edge Computing in 5G Heterogeneous Networks

The unified management of IoT devices with interoperability can be inspired by cloud computing. In addition, sinking the 5G core network to the edge brings chances for the deployment of end-to-end ultralow-latency services. However, the resource efficiency brought by heterogeneous computing devices in 5G spectrum multiplexing environments has encountered challenges. To discuss this issue from a comprehensive perspective, this article first proposes an ultralow-latency service deployment architecture in 5G heterogeneous networks, and three cognitive engines are the key components for efficient service communication across the terminal/edge/cloud computing structure. Then we give an analysis of application task model in the proposed architecture, and following the service response time models are established. In addition, it is efficient to deploy multiuser tasks with constraint resources when the differentiated user requirements are met. Finally, we conducted some experiments and the result statistics are up to our expectations. The first one is the system performance under two microcloud covered cells, and the second one is the performance comparison of the proposed solution with three single scenes of terminal computing, edge computing and cloud computing.

Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments \u2013 cyber risk in the colonisation of Mars

Multiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

A novel hybrid learning based Ada Boost (HLBAB) classifier for channel state estimation in cognitive networks

Spectrum sensing, through efficient channel estimation methods utilizing cognitive radio networks, has become an increasingly researched area in recent times. This has become more pronounced in recent times, especially with increasing scarcity in availability of radio frequency spectrum. Wireless state of the art communication standards demand high bandwidth to provide seamless connectivity and high degree of mobility, which requires more of radio frequency band for functioning. Hence, intelligent methods of spectrum allocation have been an increasing challenge in recent times. This paper proposes a hybrid learning based-Ada Boost classifier model for efficient spectrum allocation through channel state estimation through a learning and double classification approach. The proposed algorithm has been experimented in a high bandwidth characterized 5G communication simulation settings and observed for its performance measures namely collision rate analysis, throughput, probability of detection, false alarm detection and bit error rate. The proposed technique has been compared against benchmark techniques such as conventional fast Fourier transform based energy detector, fuzzy cognitive engine and adaptive neuro fuzzy inference model without Ada Boost and found to exhibit superior performance in all performance measures. The proposed technique exhibits a spectral efficiency of nearly 90% and considered to be a suitable spectrum sensing scheme for high bandwidth and narrowband utilities.

Design Foundations for Emotional Game Characters

Recent Computer Role Playing Games such as Bethesda’s The Elder Scrolls V: Skyrim and Nintendo’s The Legend of Zelda: Breath of the Wild, have entranced us with their expansive, complex worlds. However, the Non-Player Characters (NPCs) in these games remain stale and lackluster outside of scripted events. This is, in part, because game engines generally do not simulate emotions in their NPCs while they wander in the world. Wouldn't these games be much more interesting, potentially even more re-playable, if NPCs reacted more appropriately to the situations they find themselves in?To be able to do this, designers need an engine that models emotion, based on inputs available in the game world and from other designer-defined character elements such as personality, goals, and mood. A full-fledged cognitive architecture could fulfill this task, but it would likely be much too inefficient for use in a real-time environment like a game.There are many psychological models of emotion but only a few have been explored for video game applications. A game requires an emotion engine which generates believable results to enhance NPC agency and player engagement. Unlike AI agents and simulations of cognitive psychology theories, an emotion engine for games does not need to be correct or even justifiable. This enables the exploration of a variety of emotion theories that have not been actively considered for games. One such theory is Plutchik's psychoevolutionary synthesis. He proposes a method of organizing emotions into a cone, where the intensity of an emotion increases as one moves up the sides. It also postulates that primary emotions in the model can be arranged in opposing pairs and that other emotions can be composed from the primary emotions and their intensities. This allows for greater flexibility in the number and type of emotions to include, whereas most models that have been used before define a closed set of emotion types—a serious constraint on designer's freedom. A second theory, Lazarus's cognitive appraisal, better describes emotion elicitation and behaviour selection, and appears to integrate well with Plutchik's work.An emotion engine based on simplified versions of psychoevolutionary synthesis and cognitive appraisal is an understudied approach towards emotional NPCs. Together with readily identifiable elements of emotion processing, such as attention and action selection, an engine can be designed and customized to meet the needs of game designers with minimal impact on computational resources.We will present an overview of some existing cognitive architectures and emotion engines followed by a description of key elements in psychoevolutionary synthesis and cognitive appraisal. Next we list some requirements for an emotion engine for NPCs and how our selected emotion theories meet them. Finally, we propose a design and a collection of game-oriented test scenarios to illustrate how our design handles various facets of NPC emotional responses.

Artificial intelligence-based load optimization in cognitive Internet of Things

The Internet of Things (IoT) comprises smart objects capable of sensing, processing, and transmitting application-specific data. These objects collect and transmit a huge amount of correlated and redundant data due to overlapped sensing regions, causing unnecessary exploitation of spectral bands and load balancing issues in the network. As a result, time-critical and delay-sensitive data experience a higher delay, lower throughput, and quality of service degradation. To circumvent these issues, in this paper, we propose a model that is energy efficient and is capable of maximizing the spectrum utilization with optimal Device-to-Gateway configuration. Initially, the network gateways perform spectrum sensing for available channels using an energy detection technique and forward them to a cognitive engine (CE). The CE assigns the best available channels in the licensed band to the network devices for communication. Each channel is divided into equal-length time slots for the timely delivery of critical data. In addition, the CE calculates the load on each gateway and uses particle swarm optimization algorithm for optimal load distribution among the network gateways. Our experimental results show that the proposed model is efficient for the resource-constrained IoT devices in terms of packet drop ratio, delay, and throughput of the network. Moreover, the proposed scheme also achieves optimal Device-to-Gateway configuration with efficient spectrum utilization in the licensed band.

Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) – Forecasting

Multiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.