Energy Efficient Internet Of Things Research Articles

Underwater Backscatter: What is its Practical Communication Distance?

Recent years have seen mounting interest in low-power, distributed subsea internet-of-things (IoT) networks because of their potential in environmental, industrial, and defense applications [1,2]. Energy-efficient ocean IoT networks can enable long-term sensing of ocean variables (temperature, pH, pressure, salinity, etc.) to create more accurate climate and weather prediction models and monitor the impact of climate change on the ocean [3]. Similarly, low-cost and efficient ocean IoT networks can help boost the growth of the world's blue economy by enabling active monitoring of marine infrastructures ranging from oil/gas pipelines to underwater tunnels [4]. Real-time distributed underwater sensor networks can help boost aquaculture (seafood farm) production by monitoring the farm vitals (water temperature, dissolved nutrients, pH, etc.) and detecting environmental hazards (such as harmful algae blooms) early [5].

A State-of-the-Art Survey for IoT Security and Energy Management based on Hashing Algorithms

The Internet of Things (IoT) has developed as a disruptive technology with wide-ranging applications across several sectors, enabling the connecting of devices and the acquisition of substantial volumes of data. Nevertheless, the rapid expansion of networked gadgets has generated substantial apprehensions pertaining to security and energy administration. This survey paper offers a detailed examination of the present state of research and advancements in the field of Internet of Things (IoT) security and energy management. The work places special emphasis on the use of hashing algorithms in this context. The security of the Internet of Things (IoT) is a crucial element in safeguarding the confidentiality, integrity, and availability of data inside IoT environments. Hashing algorithms have gained prominence as a fundamental tool for enhancing IoT security. This survey reviews the state of the art in cryptographic hashing techniques and their application in securing IoT devices, data, and communication. Furthermore, the efficient management of energy resources is essential to prolong the operational lifespan of IoT devices and reduce their environmental impact. Hashing algorithms are also instrumental in optimizing energy consumption through data compression, encryption, and authentication. This survey explores the latest advancements in energy-efficient IoT systems and how hashing algorithms contribute to energy management strategies. Through a comprehensive analysis of recent research findings and technological advancements, this survey identifies key challenges and open research questions in the fields of IoT security and energy management based on hashing algorithms. It provides valuable insights for researchers, practitioners, and policymakers to further advance the state of the art in these critical IoT domains.

Evaluation of Correlation between Temperature of IoT Microcontroller Devices and Blockchain Energy Consumption in Wireless Sensor Networks.

Blockchain technology is an information security solution that operates on a distributed ledger system. Blockchain technology has considerable potential for securing Internet of Things (IoT) low-powered devices. However, the integration of IoT and blockchain technologies raises a number of research issues. One of the most important is the energy consumption of different blockchain algorithms. Because IoT devices are typically low-powered battery-powered devices, the energy consumption of any blockchain node must be kept low. IoT end nodes are typically low-powered devices expected to survive for extended periods without battery replacement. Energy consumption of blockchain algorithms is an important consideration in any application that combines both technologies, as some blockchain algorithms are infeasible because they consume large amounts of energy, causing the IoT device to reach high temperatures and potentially damaging the hardware; they are also a possible fire hazard. In this paper, we examine the temperatures reached in devices used to process blockchain algorithms, and the energy consumption of three commonly used blockchain algorithms running on low-powered microcontrollers communicating in a wireless sensor network. We found temperatures of IoT devices and energy consumption were highly correlated with the temperatures reached. The results indicate that device temperatures reached 80 °C. This work will contribute to developing energy-efficient blockchain-based IoT sensor networks.

Research on Supervision System of Power Safety Tools and Equipment Based on Internet of Things Technology

A power-system protection device built using Internet-of-Things (IoT) technologies in an intelligent environment. IoT supports electrical and physical parameters monitoring. One of the characteristics that must be checked is electricity usage from electronic gadgets. It is a complex problem to design energy-efficient IoT methods. IoT gets more complicated because of its vast size, and current wireless sensor network approaches cannot be used directly to IoT. Information gathering on the area is monitored by intelligent cellular terminals, intelligent security tools, and other multi-source sensing equipment. That is the foundation for the combined analysis and evaluation of security risk extensive data by cloud computing and edge computing. The IoT-based Power safety tools management (IoT-PSTM) system has been developed to integrate it into intelligent settings, such as smart homes or smart cities, to safeguard electrical equipment. It is meant to increase power security by quickly disconnecting in failure events such as leaking current. The system allows for real-time monitoring and alerting of events using a sophisticated data-concentration architecture communication interface. The goal is to progress and merge several technologies technically and integrate them into a personal safety system to increase security, preserve their availability, eliminate mistakes, and reduce the time required for scheduled or ad hoc interventions. Real-time data transmission, instant data processing from diverse sources, local intelligence in low-power embedded systems, interaction with many on-site users, sophisticated user interfaces, portability, and wearability are the main difficulties for the research project. This article offers a comprehensive explanation of the design and execution of the proposed system and the test findings. The results denote the higher performance of the suggested IoT-PSTM system with IoT module and enhanced performance of 94.7%.

Developing a region-based energy-efficient IoT agriculture network using region- based clustering and shortest path routing for making sustainable agriculture environment

The current technological developments have paved the path for various fields to thrive, explore and enrich their current applications with the help of technology such as Artificial Intelligence (AI), Internet Technology, Wireless Technology, and the Internet Of Things (IoT). This research focused on providing energy-efficient software and IoT applications for sustainability. Sustainability is integrating the environmental, social, and economic resources to thrive healthy and different requirements to fulfill human needs simultaneously. It can also be referred to as the maintenance of the environment, especially the natural resources maintaining social equality and economic stability to create a healthy community for this generation and the next generation. For sustainability, this paper proposed a Region-based clustering and cluster-head election model to improve the Energy efficiency of IoT networks deployed in the Agriculture environment (REAN). The proposed methodology uses the Shortest Routing and Less Cost algorithm (SRLC) and Region Clustering and Cluster Head Selection (RCHS) algorithm to provide energy-efficient software and IoT application. The experimental results are used to verify the performance of IoT-based sustainable applications in a real-time environment.

Green IoT: A Review and Future Research Directions

The internet of things (IoT) has a significant economic and environmental impact owing to the billions or trillions of interconnected devices that use various types of sensors to communicate through the internet. It is well recognized that each sensor requires a small amount of energy to function; but, with billions of sensors, energy consumption can be significant. Therefore, it is crucial to focus on developing energy-efficient IoT technology and sustainable solutions. The contribution of this article is to support the implementation of eco-friendly IoT solutions by presenting a thorough examination of energy-efficient practices and strategies for IoT to assist in the advancement of sustainable and energy-efficient IoT technologies in the future. Four framework principles for achieving this are discussed, including (i) energy-efficient machine-to-machine (M2M) communications, (ii) energy-efficient and eco-sustainable wireless sensor networks (WSN), (iii) energy-efficient radio-frequency identification (RFID), and (iv) energy-efficient microcontroller units and integrated circuits (IC). This review aims to contribute to the next-generation implementation of eco-sustainable and energy-efficient IoT technologies.

A Review: Image Processing Techniques’ Roles towards Energy-Efficient and Secure IoT

The goal of this review paper is to highlight the image processing techniques’ role in the Internet of Things (IoT), aiming to attain an energy-efficient and secure IoT. IoT-dependent systems (IoTSs) cause heavy usage of energy. This is one of the biggest issues associated with IoTSs. Another issue is that the security of digital content is a big challenge and difficulty. Image processing has recently played an essential role in resolving these difficulties. Several researchers have made efforts to improve future IoTSs, which are summarized in this article. Day-by-day, proposed methods are developed, and thus IoT deployment has been plainly engaged in our everyday activities. Several efficient image-processing techniques that can be utilized by IoTSs to overcome such issues have been proposed. This review paper aims to highlight those proposed methods that can make contributions in this direction. Thus, this study aims to review numerous research studies on this subject. This study looks at 36 publications relevant to image-processing techniques utilized by several types of IoTSs. The innovative work of this review paper is to provide readers with a map of suitable image processing techniques to be used with certain types of IoT systems (i.e., scenarios). Both methodology and analysis have come out with a suggested mind map highlighting a number of proposed solutions (i.e., image processing techniques) that can be suitable to help design an energy-efficient, secure, and intelligent IoT system. We have made some conclusions and projections for future research work.

Energy-Efficient UAV-Based IoT Communications With WiFi Suppression in 5 GHz ISM Bands

Unmanned Aerial Vehicle (UAV) based communication is a promising technology for offering wireless communication services. UAVs can typically be adopted as aerial base stations to collect data from ground Internet of Things (IoT) devices. However, the Ground-to-Air link of UAV-based IoT communications is prone to be interfered by surrounding WiFi devices in the Industrial, Scientific, Medical (ISM) band. This paper aims to enable reliable uplink communications for IoT devices in 5 GHz ISM bands, thereby reducing the hover time and energy consumption of the UAV. To this end, we first study and design suppression frames suitable for WiFi devices. Then a suppression method is proposed, which is shown to be effective for all the tested devices, based on emulation performance verification of the frames for multiple commercial off-the-shelf (COTS) WiFi devices. Moreover, by taking the energy consumption of the UAV and multiple transmission parameters of the suppression method into account, we formulate an optimization problem for the desired uplink, which is designed to minimize the energy consumption of the UAV, under the constraints of satisfying the link channel capacity. Given that the optimization problem is non-convex mixed-integer nonlinear programming, this paper proposes an efficient algorithm that takes advantage of the suppression performance of the utilized frames. Numerical simulations demonstrate the performance of the proposed algorithm in terms of the WiFi device density, UAV weight, channel capacity requirement and parameters of the suppression method. The communication quality of the desired Ground-to-Air link is shown to be significantly improved, while the consumed energy is demonstrated to be obviously reduced.

An energy efficient Dantzig Wolfe Decomposition based parallel optimization for software-defined industrial IoT

The industrial world including the merits of Internet of Things (IoT) paradigm has wide opened the evolution of new digital technology to facilitate promising and revolutionizing dimensions in diversified industrial application. However, handling the deployment challenges of security awareness, energy consumption, resource optimization, service assurance and real-time big data analytics in Industrial IoT Networks is a herculean task. In this paper, Dantzig Wolfe Decomposition Algorithm-based Service Assurance and Parallel Optimization Algorithm (DWDA-SAPOA) is proposed for guaranteeing QoS in energy efficient Software-Defined Industrial IoT Networks. This DWDA-SAPOA is proposed for achieving minimized energy consumption on par with the competitive network routing algorithms which fails in satisfying the strict requirements of heterogeneous Quality of Service (QoS) during the process of optimizing resources under industrial communications. It is proposed as a service assurance and centralized route optimization strategy using the programmability and flexibility characteristics facilitating by the significant Software Defined Networking (SDN) paradigm which is implemented over a multi-layer programmable industrial architecture. It supports bandwidth-sensitive service and ultra-reliable low-latency communication type of heterogeneous flows that represents a routing optimization problem which could be potentially modelled as a multi-constrained shortest path problem. It further adopts Dantzig Wolfe Decomposition Algorithm (DWDA) to handle the complexity of NP-hard involved in solving the multi-constrained shortest path problems. The simulation experiments of the proposed DWDA-SAPOA prove its predominance in minimizing energy consumption by 24.28%, flow violation by 19.21%, packet loss by 21.28%, and end-to-end delay by 29.82%, and bandwidth utilization by up to 26.22% on par with the benchmarked QoS provisioning and energy-aware routing problem.

Secured healthcare monitoring for remote patient using energy-efficient IoT sensors

The Internet of Things (IoT) and cloud computing are found to be effective in providing on-time medical support for patients by storing and accessing their clinical data on servers. There are some issues, such as communication delay, security, and energy loss in IoT sensors, that affect the quality of the service. In this research, a remote patient monitoring system using energy-efficient IoT sensors is proposed to meet security demands. The optimization of network clustering and cluster head selection is performed based on trust, inter- and intra-cluster distance, and energy level by the proposed SS-ROW algorithm, which is formulated by integrating the characteristics of the squirrel and sparrow with Ciphertext Key 2 Policy (CK2P) encryption on data. Further, an effective trust mechanism based on CP-ABE (Ciphertext-Policy Attribute-Based Encryption) and KP-ABE (Key-Policy Attribute-Based Encryption) is established to attain better results in terms of the number of alive nodes, energy left, and throughput.

Deep Learning of Sparse Patterns in Medical IoT for Efficient Big Data Harnessing

The long-term and continuous streaming of big data from medical Internet of Things (IoT), poses a great challenge for the battery-limited tiny devices. To address this challenge, we propose a novel framework for medical IoT data sparsification, leveraging both deep learning and optimal space searching. More specifically, the deep sparsification networks are designed to learn to extract key sparse patterns in the medical IoT data, by projecting the original data stream to a sparsified data representation. Further, the principles for designing deep encoding networks have been analyzed by an optimal space searching strategy, aiming to determine the best deep sparsification architecture that meets the energy constraint or sparsification error constraint. Compared with state-of-the-art approaches, our deep learning-based and space search-optimized framework shows a dramatic capability to tackle the power hungriness problem on medical IoT big data. This novel study, by enabling energy-efficient medical IoT big data sparsification, is expected to boost the continuous and long-term medical IoT applications, such as cardiac monitoring, thereby advancing precision medicine.

Adiabatic Logic Based Energy Efficient Architecture of 1-Bit Magnitude Comparator for IOT Applications

<p>The Internet of Things (IoT) applies the sensors and microcontrollers and links them through the internet. The eventual objective of low-power devices for Internet of Things is to lesser the overall system power and to extend battery life. For the development of energy efficient IoT devices, novel adiabatic techniques are proposed. By improving the performance of the comparator, one can improvise the whole system performance. The efficacy of computing devices depends on the performance of arithmetic circuits, including comparator. This paper proposes 1-bit comparator design using adiabatic techniques such as DC-DB PFAL (Direct current diode-based positive feedback adiabatic logic) and MPFAL (Modify positive feedback adiabatic logic) which are well-suited with an extensive range of applications (e.g. IoT sensors and an inbuilt analog to digital converter). For performance analysis, the results are compared together along with the other adiabatic and non adiabatic designs already reported in the literature. This paper proposes a way to decrease the dissipation of power and transistor count in binary circuits as it is one of the primary concerns. From the results, it is found that the design using DC-DB PFAL logic shows an improvement in power-delay-product of 69%, 94% and 90% compared to MPFAL, PFAL and ECRL techniques respectively.</p> <p> </p>

Low-Power High-Bandwidth Non-Polar InGaN Micro-LEDs at Low Current Densities for Energy-Efficient Visible Light Communication

Recently, the rapidly developing Internet of Things (IoT) needs to support massive connectivity with high transmission data rate, which requires IoT transmitters with high speed and low power consumption. In this work, non-polar micro-LED operating at low current density of 10 A/cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> can still maintain a high bandwidth of 508 MHz and near-peak external quantum efficiency (EQE), making it promising for low-power, energy-efficient visible light communication (VLC) to address the challenge. At 10 A/cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> , non-polar micro-LEDs can achieve a low power consumption and high energy efficiency, still maintaining the data rate of 0.25 Gbps. In addition, data rates of 2 Gbps and 1.1 Gbps were also successfully achieved for non-polar micro-LED operating at 320 and 50 A/cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> , respectively, to evaluate the relationship between power consumption and achievable data rate. The capability of non-polar micro-LED that can realize low-power consumption and high bandwidth at low current density is expected to play a great role in the field of energy-efficient VLC, multi-function display and IoT in the future.

Energy efficient MIMO–NOMA aided IoT network in B5G communications

To accelerate future intelligent wireless systems , we designed an energy-efficient Massive multiple-input-multiple-output (MIMO)- non-orthogonal multiple access (NOMA) aided internet of things (IoT) network in this paper to support the massive number of distributed users and IoT devices with seamless data transfer and maintain connectivity between them. Massive MIMO has been identified as a suitable technology to implement the energy efficient IoT network in beyond 5G (B5G) communications due to its distinct characteristics with large number of antennas. However, to provide fast data transfer and maintain hyper connectivity between the IoT devices in B5G communications will bring the challenge of energy deficiency. Hence, we considered a massive MIMO–NOMA aided IoT network considering imperfect channel state information and practical power consumption at the transmitter. The far users of the base stations are selected to investigate the power consumption and quality of service. Then, calculate the power consumption which is non-convex function and non-deterministic polynomial problem. To solve the above problem, fractional programming properties are applied which converted polynomial problem into the difference of convex function . And then we employed the successive convex approximation technique to represent the non-convex to convex function. Effective iterative based branch and the reduced bound process are utilized to solve the problem. Numerical results observe that our implemented approach surpasses previous standard algorithms on the basis of convergence, energy-efficiency and user fairness.

On Predicting Sensor Readings With Sequence Modeling and Reinforcement Learning for Energy-Efficient IoT Applications

Prediction of sensor readings in event-based Internet-of-Things (IoT) applications is considered. A new approach is proposed, which allows turning off sensors in periods when their readings can be predicted, thus preserving energy that would be consumed for sensing and communications. The proposed approach uses a long short-term memory (LSTM) model that learns spatiotemporal patterns in sequences of sensorial data for future predictions. The LSTM model and the sensors collaboratively monitor the environment. They are controlled by a reinforcement learning (RL) agent that dynamically decides about using the LSTM prediction versus physical sensing in a way that maximizes energy saving while maintaining prediction accuracy. Two approaches are used for the RL: 1) the Markov decision process (MDP) model-based for low scale applications and 2) deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -Network-based for larger scales. Compared to the current literature, the proposed solution is unique in predicting all sensor readings for real-time event detection and providing a model capable of learning long-term spatiotemporal correlations, enabling power conservation and detection accuracy balance. We compare the proposed solutions to the most relevant state-of-the-art approaches using a large real dataset collected in a dynamic space by measuring the accuracy, consumed energy, network lifetime, latency, and missed events’ ratio. To investigate the scalability of the solutions, these parameters are calculated for different network sizes. The results show that the system achieves 50% accuracy with 32% of activation time and 75% accuracy with 60% activation time.

Joint Optimization Framework for Minimization of Device Energy Consumption in Transmission Rate Constrained UAV-Assisted IoT Network

Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be an alternative option for a scenario where Internet of Things (IoT) devices consume high energy to achieve the required data rate when they are far away from the terrestrial base station (BS). Therefore, this article has proposed an energy-efficient UAV-assisted IoT network where a low-altitude quad-rotor UAV provides mobile data collection service from static IoT devices. We develop a novel optimization framework that minimizes the total energy consumption of all devices by jointly optimizing the UAV’s trajectory, devices association, and respectively, transmit power allocation at every time slot while ensuring that every device should achieve a given data rate constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt a reinforcement learning (RL)-based solution methodology that effectively decouples it into three individual optimization subproblems. The formulated optimization problem has transformed into a Markov decision process (MDP) where the UAV learns its trajectory according to its current state and corresponding action for maximizing the generated reward under the current policy. Finally, we conceive state–action–reward–state–action, a low complexity iterative algorithm for updating the current policy of UAV, that achieves an excellent computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on optimal UAV trajectory. The proposed methodology reduces the total energy consumption of all devices by 6.91%, 8.48%, and 9.94% in 80, 100, and 120 available time slots of UAV, respectively, compared to the particle swarm optimization (PSO) algorithm.

Edge enabled IoT system model for secure healthcare

Internet of Things (IoT) is getting more attention and popularity these days as they make the place smarter and efficient. Every IoT-enabled device is communicating with one another either directly or through a gateway to the internet. Data transmission brings security challenges and concerns to retain data integrity. In the Healthcare field, it is important to ensure medical information integrity, confidentiality, and anonymity. With the increasing need to provide healthcare access in urban and rural areas, and the lack of broadband infrastructure, and the fund to build hospitals, new satellite medical centers or units suffer the challenge of accessibility and data transmission. Yet, there is a need to communicate with the main medical units for full access and provide” anytime, anywhere” access to patient records. It is vital to secure all devices and components involved in an IoT setup as it involves pervasive data collection and dissemination. In this paper, we present a latency and energy-efficient secure IoT model for healthcare systems. We illustrate the model by using Mobile Cloud Computing Simulator (MCCSim) with varying parameters to discuss the impact of security measures on power and bandwidth requirements. We found that data encryption and decryption doubled the delay time and power consumption at the cloud compared to the edge layer as we increase the packet rates.

Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: A semi-greedy approach

With the rapid advancement of Internet of Things (IoT) devices, a variety of IoT applications that require a real-time response and low latency have emerged. Fog computing has become a viable platform for processing emerging IoT applications. However, fog computing devices tend to be highly distributed, dynamic, and resource-constrained, so deploying fog computing resources effectively for executing heterogeneous and delay-sensitive IoT tasks is a fundamental challenge. In this paper, we mathematically formulate the task scheduling problem to minimize the total energy consumption of fog nodes (FNs) while meeting the quality of service (QoS) requirements of IoT tasks. We also consider the minimization of the deadline violation time in our model. Next, we propose two semi-greedy based algorithms, namely priority-aware semi-greedy (PSG) and PSG with multistart procedure (PSG-M), to efficiently map IoT tasks to FNs. We evaluate the performance of the proposed task scheduling approaches with respect to the percentage of IoT tasks that meet their deadline requirement, total energy consumption, total deadline violation time, and the system’s makespan. Compared with existing algorithms, the experiment results confirm that the proposed algorithms improve the percentage of tasks meeting their deadline requirement up to 1.35x and decrease the total deadline violation time up to 97.6% compared to the second-best results, respectively, while the energy consumption of fog resources and makespan of the system are optimized.

Spectrum and Energy-Efficiency Maximization in RIS-Aided IoT Networks

New device capabilities, supporting spectrum and energy efficient internet of things (IoT) deployments, are expected to form the evolution of 5G and beyond. In IoT networks, every IoT pair interferes with each other due to operating in the same spectrum resources. This paper utilizes reconfigurable intelligent surfaces (RISs) to mitigate inter-node interference in an IoT network through directional beamforming by adjusting the phase shifts of the passive elements of the RIS. We consider an RIS-assisted IoT network consisting of multiple pairs of IoT devices, that utilize both direct paths and one-hop reflected paths. Two resource allocation problems, namely spectrum-efficiency maximization (SEM) and energy-efficiency maximization (EEM) are studied. Since the formulated optimization problems are non-convex in nature, we divide them into two sub-problems and solve them alternately. To obtain the optimal phase shifts of the elements of the RIS for both SEM and EEM, we extend the conjugate gradient technique to Riemannian manifolds. On the other hand, to obtain the optimal transmit power, we solve the transmit power allocation sub-problem as a difference of concave functions for the SEM and use a pricing based technique for the EEM. Numerical results validate the effectiveness of deploying RISs to assist IoT networks, albeit with respect to RIS’s location, its number of elements, and the number of IoT node pairs. In particular, considerable spectrum and energy efficiency gains are achieved in comparison to baseline state-of-the-art networks.

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.