Single Internet Of Things Research Articles

Exploring the potential of swarm intelligence for optimal energy efficiency in IoT downlink system

This study delves into the energy optimization problem in Internet of Things (IoT) networks. We consider the downlink from multiple antenna Gateway (GW) and single antenna IoT devices. For this challenging nonconvex problem, we initially introduced the well-known zero-forcing beamforming (ZFBF) to eliminate inter-user interference, thereby transforming the energy efficiency maximization problem into a concave-convex fractional problem. Then, instead of applying a combination of ZFBF with power allocation, we propose the Particle Swarm Optimization (PSO) algorithm to allocate power to find the optimized beamforming matrix. Through extensive numerical analysis, we demonstrate the effectiveness of our proposed scheme in terms of energy efficiency and power achieved at the GW. The results underscore the significant benefits of our approach over conventional methods, paving the way for practical and efficient energy optimization in IoT networks.

An extended review of the application layer messaging protocol of the internet of things

In the internet of things (IoT), there are resource-constrained and immense heterogeneous electronic gadgets worldwide. Till now, no single IoT application layer messaging protocol is the best, nor is anyone axiomatic for every requirement. This paper exhaustively summarizes information on the messaging protocols from the available previous research sources online. Our goal is to encapsulate a simple guideline so that users can choose an optimal messaging protocol quickly according to development requirements and specifications. For this purpose, we have reviewed the literature on six enabling and evolving application layer messaging protocols used for IoT systems namely, message queuing telemetry transport (MQTT), advanced message queuing protocol (AMQP), the constrained application protocol (CoAP), extensible messaging and presence protocol (XMPP), data distribution service (DDS), and simple text-oriented messaging protocol (STOMP) in terms of some interrelated metrics. Additionally, we represented a critical analysis of the application layer messaging protocols. This study will be helpful to readers with valuable insights and guide research scholars and developers in choosing optimal application layer messaging protocols based on development specifications and requirements.

Optimizing Service Redeployment in Migration-Oriented IoT Networks

The Internet of Things (IoT) paradigm has established an effective platform to promote the collaboration of resource-limited and duty-cycle IoT nodes, in order to support relative complex service requests that can hardly be achieved by any single IoT node. The functionalities of IoT nodes are typically encapsulated into IoT services, and the satisfaction of service requests is implemented as IoT service composition. Generally, IoT nodes work in turn in terms of their pre-specific working cycles, and IoT network topology is constantly varied due to their active/sleeping behaviour switching, causing unscheduled response latency. IoT service composition instantiation should be dynamically adjusted and partially re-deployed on-demand for supporting request processing efficiently. To remedy this issue, this paper proposes a Migration-oriented Service re-Deployment (MSrD) mechanism by migrating certain IoT services from their hosted IoT nodes to neighboring ones, in order to support functionally continuous availability.We formulate this problem as a game-theoretic approach, which is reduced to a potential game, where a Nash equilibrium solution is searched for optimizing this service re-deployment game. Extensive experiments are conducted, and numerical results show that our MSrD mechanism is promising, compared with the state-of-art techniques, in achieving service re-deployment optimization with efficient energy consumption and timely response latency simultaneously.

IEEE P2668-Compliant Multi-Layer IoT-DDoS Defense System Using Deep Reinforcement Learning

The Internet of Things (IoT) has been attracting people to its capability to deal with smart applications. However, with the development of IoT, there are more attacks to threaten IoT systems. Especially, the distributed denial of service (DDoS) attack can lead to mighty destruction to IoT servers, causing the whole IoT network to be out-of-service. Hitherto, given the lack of a common standard for defining the IoT-driven DDoS (IoT-DDoS) attack, the DDoS defense system for IoT is developed without accurate guidance. Additionally, defense against multi-layer IoT-DDoS attacks is rarely covered by previous works. To address these issues, a deep reinforcement learning-based multi-layer IoT-DDoS defense system (DRL-MLDS) is proposed with the reward metrics in compliance with IEEE P2668 – the first of its kind. In addition, to provide a resilient blocking time configuration for false-positive samples, a new power-law-based blocking time mechanism is developed to cooperate with the DRL-MLDS. The outcome reveals that the DRL-MLDS can reach the same accuracy level (i.e., more than 96%) as previous works under single protocol-based IoT DDoS attack, as well as providing around 97% defense accuracy on multi-layer IoT-DDoS attack, which was rarely discussed in previous works. Additionally, by applying the IEEE P2668-compliant reward metrics, the applicability index (ADex) of DRL-MLDS can be improved from 3.2 to 4.4, fulfilling the recommendation of ADex (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {&gt;}3.5$ </tex-math></inline-formula> ) toward IoT best practices. The DRL-MLDS can be extended to Metaverse design and applications.

An adaptive data coding scheme for energy consumption reduction in SDN-based Internet of Things

In recent decades, most internet-connected devices were personal computers. However, everything is connecting to the Internet over time. Today, the Internet of Things (IoT) is expanding rapidly, including everything from clothing to agricultural equipment. The Internet of Everything (IoE) is very complex and heterogeneous due to the independent development of its various sections. Hence, the coordination and management of its different equipment are challenging, and the energy consumption efficiency is very low, which is the bottleneck of battery-operated IoT equipment. So far, various methods have been proposed to reduce energy consumption in IoT devices. However, these methods are used independently and individually on a single IoT node, and none of them consider all objects in the IoT hierarchy together. Whereas the IoT network arises when various objects interact with each other. Therefore, focusing on energy consumption in a single node should be transformed into energy management at the whole network administrative level. In this paper, considering the IoT network features and the data exchange all over the network, a data manipulation method is proposed to reduce energy consumption and network traffic by decreasing the amount of data exchanged. Furthermore, we take advantage of Software-Defined Networking (SDN) to enhance the efficiency of this method by adapting our energy management decisions to the environmental conditions’ dynamicity. Simulation results using real data workloads demonstrate that the proposed method decreases energy consumption by up to 38.1% without an SDN. This reduction is significantly improved by up to 80.0% when utilizing SDN. We also experimentally evaluate the proposed method by its hardware implementation in the real environment and illustrate that a maximum of only 1.3% difference is observed between the simulation results and the experiments, which indicates the consistency of the results.

Game-Theory-Based Multimode Routing Protocol for Internet of Things

Various routing protocols have been proposed for ad hoc networks such as the Internet of Things (IoT). Most of the routing protocols introduced for IoT are specific to applications and networks. In the current literature, it is essential to configure all the network nodes with a single proposed protocol. Moreover, it is also possible for a single IoT network to consist of different kinds of nodes. Two or more IoT networks can also be connected to create a bigger heterogeneous network. Such networks may need various routing protocols with some gateway nodes installed. The role of gateway nodes should not be limited to the interconnection of different nodes. In this paper, a multi-mode hybrid routing mechanism is proposed that can be installed on all or a limited number of nodes in a heterogenous IoT network. The nodes configured with the proposed protocols are termed smart nodes. These nodes can be used to connect multiple IoT networks into one. Furthermore, a game-theory-based model is proposed that is used for intercommunication among the smart nodes to gain optimal efficiency. Various performance matrices are assessed under different network scenarios. The simulation results show that the proposed mechanism outperforms in broader heterogeneous IoT networks with diverse nodes.

Enhancing Distributed In-Situ CNN Inference in the Internet of Things

Convolutional neural networks (CNNS) enable machines to view the world as humans and become increasing prevalent for Internet of Things (IoT) applications. Instead of streaming the raw data to the cloud and executing CNN inference remotely, it would be very attractive to use local IoT devices to process as it enables IoT applications with independent decision-making ability. Since a single IoT device can hardly match the requirements of the CNN inference, especially for time-sensitive and high-accuracy tasks, the distributed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> CNN inference becomes a potential solution. However, because of the inherently tightly coupled structure of existing CNN models, it is difficult to distribute the inference efficiently. In this article, we enhance the distributed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> CNN inference in the IoT. We fundamentally reduce the communication overhead of distributed CNN inference by designing new loosely coupled structure (LCS). Experimental results demonstrate that LCS achieves the leading performance compared with other popular structures. Next, based on the LCS, we customize the partitioning method to reduce the synchronization points and design the decentralized asynchronous method to optimize communication in each synchronization point. To evaluate the effectiveness, we build a prototype system. When the number of IoT devices increases from 1 to 4, our system accelerates by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.85\times $ </tex-math></inline-formula> and reduces the memory footprint in each device by 70% with achieving a competitive accuracy and significantly outperforming other approaches.

Resilient Deployment of Smart Nodes for Improving Confident Information Coverage in 5G IoT

The development of 5G has brought new opportunities for the application of Internet of Things (IoT). The integration of 5G and IoT technologies promote high availability, resilience, and reliability of the network infrastructures. IoT deployment optimization is the core issue of 5G IoT. Traditionally, IoT node deployment methods mostly used disk coverage model or probabilistic detection coverage model, which only utilizes the sensing capability of a single IoT node, which results in higher deployment costs. In this article, we study the network resilience of coverage estimation error and solve the coverage problem of resilient deployment of smart nodes in 5G IoT. The coverage formulation in the deployment optimization method is defined based on the confident information coverage (CIC). In order to obtain the optimal deployment with a given coverage quality and with a given budget, the mixed-integer linear programming models (CICILP-COST) and (CICILP-ERROR) are proposed based on the CIC model. After analyzing the model complexity, the proposed models are solved by the variable relaxation algorithm (CICVR-COST) and dichotomous search algorithm (CICDS-ERROR), respectively. Simulations on air pollution datasets in Lyon, France, show that the proposed model yields a lower cost optimal deployment than existing peer schemes.

Energy consumption-based services composition optimization for internet of things

Internet of Things (IoT) services are directly deployed on resource-constrained smart devices. Smart devices are characteristic by spatial and temporal constraints and energy limitations. A single IoT service cannot meet the complex requirements of users, so multiple IoT services need to be combined to provide services to users. As more and more smart devices are deployed in IoT, how to select IoT services with lower energy consumption and better quality of service (QoS) for service composition becomes a challenging problem. Combined with the characteristics that the data information of IoT is closely related to geographical location, the GeoHash algorithm is used to locally screen services based on geographical location, so as to narrow the range of candidate services. For smart devices with energy constraints, this paper proposes a combined optimization model. The model considers not only the transmission energy consumption and switching energy consumption, but also the execution energy consumption when the device provides services. In order to balance QoS attributes and energy consumption, the composition problem is regarded as a multi-objective optimization problem and solved using a genetic algorithm (GA). The simulation results show that service composition scheme selected by this service composition optimization model has lower energy consumption and higher service quality.

Finite Block Length Analysis of RIS-Assisted UAV-Based Multiuser IoT Communication System With Non-Linear EH

Reconfigurable intelligent surface (RIS) has emerged as an important transmission technology for numerous applications in Internet of Things (IoT) systems. Thus, in this paper, we investigate the application of RIS in energy harvesting (EH) based unmanned aerial vehicle (UAV) communication network with finite block length (BL) codes, where a rotary wing type flying UAV communicates with the multiple single antenna IoT users with the aid of multiple RISs mounted on several skyscraper buildings. To transmit the signal to a particular IoT user, the UAV selects an RIS on the basis of either UAV-RIS (i.e., partial) or UAV-RIS-IoT (i.e., full) channel state information (CSI) and then transmits the signal through the selected RIS along with the direct link transmission. In particular, we derive (i) the expression for probability of RIS selection, (ii) the statistical distribution of instantaneously received information signal-to-noise ratio (SNR) at the IoT user. Based on the derived statistics, we analyze the performance of the considered system under finite BL codes in terms of the average outage probability, average block error rate (ABLER) and goodput averaged over entire flying duration. Moreover, the BLER performance with finite BL codes is also compared with the infinite BL codes scenario. Additionally, we also investigate the impact of various channel and system parameters like imperfect CSI, number of RISs and the number of reflecting elements at each RIS, location of IoT users, variable altitude of the UAV, and the severity of channel fading of UAV-RIS link on the system performance. Furthermore, we have obtained the optimum UAV location in each time slot which minimizes the ABLER per time slot over all the users in the network. The analytical results are corroborated with Monte Carlo simulations.

Service Provisioning for Multi-source IoT Applications in Mobile Edge Computing

We are embracing an era of Internet of Things (IoT). The latency brought by unstable wireless networks caused by limited resources of IoT devices seriously impacts the quality of services of users, particularly the service delay they experienced. Mobile Edge Computing (MEC) technology provides promising solutions to delay-sensitive IoT applications, where cloudlets (edge servers) are co-located with wireless access points in the proximity of IoT devices. The service response latency for IoT applications can be significantly shortened due to that their data processing can be performed in a local MEC network. Meanwhile, most IoT applications usually impose Service Function Chain (SFC) enforcement on their data transmission, where each data packet from its source gateway of an IoT device to the destination (a cloudlet) of the IoT application must pass through each Virtual Network Function (VNF) in the SFC in an MEC network. However, little attention has been paid on such a service provisioning of multi-source IoT applications in an MEC network with SFC enforcement. In this article, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements and aiming at minimizing the cost of such service provisioning, where each IoT application has multiple data streams from different sources to be uploaded to a location (cloudlet) in the MEC network for aggregation, processing, and storage purposes. To this end, we first formulate two novel optimization problems: the cost minimization problem of service provisioning for a single multi-source IoT application, and the service provisioning problem for a set of multi-source IoT applications, respectively, and show that both problems are NP-hard. Second, we propose a service provisioning framework in the MEC network for multi-source IoT applications that consists of uploading stream data from multiple sources of the IoT application to the MEC network, data stream aggregation and routing through the VNF instance placement and sharing, and workload balancing among cloudlets. Third, we devise an efficient algorithm for the cost minimization problem built upon the proposed service provisioning framework, and further extend the solution for the service provisioning problem of a set of multi-source IoT applications. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

IoT Based Antenna Positioning System

Over the past few years, IOT has become one of the most important technologies of 21st century. Every single Internet Of Things (IOT) device needs an antenna. Proper positioning of antennas is necessary for wireless communication. So here we are giving a project on an IOT based antenna positioning system that allows for remotely positioning of antennas based over IOT. The method that is used here to control the antennas position is by using Node MCU and Stepper motors. Here, in this system sensors will be mounted on the antenna to detect its direction and its direction will be changed by motors using IOT. The Blynk app acts as a remote and is used to send the signal to the MCU through the internet. The project helps to control the antenna’s position by the user’s command.

WISE: MQTT-based Protocol for IP Device Provisioning and Abstraction in IoT Solutions

Although numerous consumer devices use Transmission Control Protocol/Internet Protocol (TCP/IP) protocol stack to connect and communicate over the Internet, their integration into a single Internet of Things (IoT) solution represents a challenge, primarily due to the lack of the standardized interoperability framework on the application layer. Devices produced by different manufacturers cannot operate together out of the box, thus raising the cost of system setup and maintenance. In this paper, a novel protocol is proposed that aims to bridge this gap. It is based on Message Queuing Telemetry Transport (MQTT) protocol and allows the seamless integration of different kinds of IP devices into the connected system. The proposed protocol is complete as it covers the aspects of device discovery and association in the IoT network. It provides mechanisms for IoT network maintenance and defines the abstract device model and communication patterns to enable system-wide device interoperability. The other goal of the protocol is to be portable to resource-constrained platforms. To validate the proposed protocol, it was integrated into the existing smart home hub, and for testing and validation purposes, prototype devices were developed.

Evaluation of Performance of Cloud of Things (CoT) for Transferring Multimedia and Bulk-set Data

As the number of context-aware and unique data extracting and computing has grown leaps and bounds, paving way to their application in many platforms, Internet of Things (IoT) has gained a lot of importance in recent years. Using IoT, it is possible to connect any number of objects and in any context, giving rise to a diverse range of services. Hence power management, data storage, service discovery, service management, service creation and resource management need a more sophisticated mechanism and a better infrastructure. However, a single IoT that is power-constrained will not be able to tackle all the data that is generated. Hence cloud computing plays a crucial role by integrating with the IoT in such a way that the future internet and envisioned IoT is achievable. There are a number of challenges involved of which data trimming is common challenge. Due to unnecessary communication, the data this sent to the cloud may be interrupted. Hence to prevent this, data is initially preprocessed following which it is trimmed and then transferred to the cloud. This aspect of data processing can be carried out through Fog Computing or Smart Network that serves as a smart gateway. In this paper, we have introduced a novel concept Fog computing as the smart gateway. The results have been analysed and tested with respect to bulk-data synchronizstion Delay, bulk-data upload delay, jitter, synchronization delay and upload delay.

Multi-cell, Multi-user, and Multi-carrier Secure Communication Using Non-Orthogonal Signals’ Superposition with Dual-Transmission for IoT in 6G and Beyond

Considering the advancements of the internet of things (IoT) in 6G and beyond communications, data transmission security in IoT devices has received extensive interest because of their significant features, such as low computational complexity, led by low power requirements. In such devices, the conventional cryptographic techniques may fail to provide secure communication. To fight this drawback, physical layer security (PLS) has remarkable potential to provide security solutions suitable for such applications. In this work, a highly effective PLS technique is proposed for providing secure communication against external and internal eavesdroppers in a downlink multi-cell, multi-user, and multi-carrier IoT communication system. In our proposed system, we considered two base stations, where each base station uses a single radio frequency (RF) chain to link two antennas that are used for the transmission of data. Further, we transmit the data in two rounds, and each round of transmission occurs through a single active antenna of each base station. A different antenna is used for each round of transmission to communicate with two single antenna IoT devices/users in the presence of a passive eavesdropper. In the proposed algorithm, frequency selective channel-based pre-coder matrices and the dual transmission approach are jointly employed. The dual-transmission is performed simultaneously from two base stations to provide security against internal and external eavesdroppers. The proposed system is suitable for IoT-based applications. Also, the potential capabilities of our proposed algorithm are proved by extensive mathematical and simulation analysis.

Privacy-Preserving IoT Data Aggregation Based on Blockchain and Homomorphic Encryption.

Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers that can answer the consumer’s request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations. Therefore, group-level aggregation obfuscates IoT data, which complicates sensitive information inference from a single IoT device. Finally, we deploy the proposal on a private Ethereum blockchain and give the performance evaluation.

Receiver Energy Efficiency and Transmission Latency Analysis of Outage-Constrained Differential PSK With a Finite Resolution ADC

We consider a power constrained downlink communication scenario where energy efficiency, reliability, and latency take precedence over rate, as in some Internet of Things (IoT) applications. To reduce its receiver power consumption and complexity, we assume that the IoT device has a single RF chain and investigate the finite-resolution analog-to-digital converter (ADC) operation with differential Phase Shift Keying (PSK) modulation. A lower ADC resolution leads to an exponential decrease in power consumption, while adopting differential PSK enables the use of a low-cost detector with no channel state information (CSI) or carrier phase recovery circuitry. Our main goal in this article is to compare, both analytically and numerically using representative IoT system design parameters, the receiver energy efficiency of the proposed differential PSK system with a coherent PSK system that uses estimated CSI under reliability and latency constraints in Rayleigh slow-fading and ADC quantization distortion conditions. To mitigate the ADC finite-resolution effects without requiring CSI at the receiver or transmitter, we propose and analyze a PSK differentially-modulated Alamouti space-time block transmission scheme with only two RF chains at the transmitter while still restricting the IoT device to have a single RF chain. Our results demonstrate that in the considered low-rate, and outage and latency-constrained scenario with stringent power consumption requirements, differentially-modulated Alamouti transmissions to a single RF-chain IoT device with a low-resolution ADC is an attractive choice in terms of receiver energy efficiency, reliability, and transmission latency.

A Trust-Based Access Control Mechanism for Intra-Sensor Network Communication in Internet of Things

The Internet of Things (IoT) can integrate both the physical and the information world to provide applications and services. IoT networks permit users, applications and devices in various physical locations for easy and seamless communication with each other. Data are sometimes quite sensitive in nature based on whether they are from one single IoT subsystem or an aggregation of multiple systems. Securing data becomes crucial and data access will have to be controlled. The IoT cloud can pose several challenges to the scale of IoT systems that help in identifying all real challenges in the cloud services. The main components of the access control mechanisms are authentication and authorization. Trust can permit the device with a natural judging method for other devices and the way in which security can be handled along with access control. A trust relationship between two different devices will help in influencing the future behaviour of interactions. Trust-based systems have been proposed in the literature, but not much work has been done to decrease the computational time in encryption system which is critical for low powered sensor devices. This work proposed a novel authentication mechanism ”Trust-Level-Public Key Authentication” which is used to connect between the nodes for data transfer which is efficient. This work was supported by a system of remote key management in the cloud and used for aggregating encrypted data. The levels of authorization in the nodes were controlled remotely. The experimental results showed that average consumption of energy (J) with the trust system to have performed better by about 11.5%, 12.6%, 17.6%, 14.2%, 13.5% and by about 15.9% when compared to the average consumption of energy (J) without a trust system that is used for the number of nodes 250, 500, 750, 1000, 1250, and 1500, respectively.

Scalable Emulated Framework for IoT Devices in Smart Logistics Based Cyber-Physical Systems: Bonded Coverage and Connectivity Analysis

In this research, scalable framework for Smart Logistics based Cyber-Physical System (SLCPS) is emulated for stable coverage and connectivity of Internet of Things (IoT) devices. This work is modern manifestation of three laws of computing. Moore's and Koomey's laws recommend performance gain and energy efficiency whereas Metcalfe's law imply network scalability. Combination of these laws suggests the research proposition that development of scalable and performance efficient IoT networks is inevitable. Although IoT has improved specific logistics modules considerably, but incorporation of IoT in complete supply chain of food and random placement of IoT devices due to which unstable coverage and connectivity occurred are major challenges in logistics. The proposed SLCPS framework is designed firstly, to develop apt IoT protocol stack for logistics. Secondly, for bonded connectivity and coverage, mathematical models are proposed instead of random placement and coverage map is based on binary coverage model. Thirdly, for scalability supply chain of food for smart logistics process is designed in terms of container, storehouse and warehouse comprising of varying number of IoT devices. The architecture of SLCPS framework has three modules i.e. internal IoT network, border router and external network, emulated in Cooja simulator. The contikimac protocol is used for efficient traffic flow and power consumption. Single hop, multiple hops and random IoT devices placement scenarios are used for results comparison and validation. The performance evaluation results, i.e. throughput, network convergence time, packet delivery ratio, average latency, power consumption and timeline investigation validated utilization of proposed framework in terms of enhanced network performance. Significance of proposed SLCPS framework results in cost minimization, reducing communication and computation overhead, resilience to IoT device failures and an interference free network connectivity and coverage. Coverage and connectivity are measure of quality of service in IoT network. Therefore, this research provided bonded coverage and connectivity in smart logistics using mathematical models. In addition, a baseline framework is provided for extended research in CPS and IoT applications.

A New Planning-Based Collision-Prevention Mechanism in Long-Range IoT Networks

Wireless communication is prone to collisions, resulting from multiple devices transmitting at the same time. It implies subsequent retransmissions, which increase energy consumption of communicating devices and reduce throughput of the network. This is especially critical in Internet of Things (IoT) networks, in which the number of connected devices grows rapidly and the available energy of IoT end devices is rather limited (e.g., energy harvesting and battery powered). The number of retransmissions must be reduced in order the networks to be sustainable. However, in long-range wireless IoT networks, the most effective collision-resolution techniques using a transmission-channel listening to detect collisions cannot be reliably used due to various problems, such as a hidden-node problem or environment interference. In this article, a new solution of this problem is proposed, which consists of a new communication-planning mechanism for low-speed long-range IoT networks with a huge number of communicating energy-constrained devices. The access points (APs) (or IoT gateways) are used to plan the periodically repeated communication into a transmission schedule, allowing only a single IoT device to communicate at a time. This approach results in reduction of collisions, which leads to the increased network throughput, smaller delays, and lower power requirements of energy-constrained devices. The experiments indicate that the proposed approach provides better communication efficiency than the LoRaWAN and Sigfox collision-resolution techniques, when more than 15, respectively 125, end devices communicate with a single AP.