Changlin Yang

The operators and nodes of Internet of things (IoT) networks often perform resource allocation, where they may solve a mathematical program to compute the optimal allocation of resources or decision(s), e.g., energy usage of devices, using sensed data as parameter values. Further, these parameter values may be stored/predicted using a neural network given one or more features, e.g., historical energy arrival rates at nodes. In this respect, to date, there are no IoT specific tutorials or works that focus on the smart predict and optimize (SPO) resource allocation framework. This paper thus fills this gap. Briefly, it shows the workflow of SPO, which involves using a machine learning model and a mathematical program to compute decisions. A key feature of SPO is that unlike prior approaches that only aim to predict the parameter values of a mathematical program, it maps the features of a system to decisions in an end-to-end manner. This paper also outlines various potential IoT applications that would benefit from SPO. It then presents a case study where SPO is used to decide task offloading from a device that has to contend with other users in a WiFi network. Lastly, it outlines some future research directions.

Blockchain data encoding has been proposed as a solution to reduce the storage load on full nodes in blockchain systems. However, the high latency and cost associated with the decoding process make coding techniques difficult to apply to frequently accessed blockchain data. This limitation is particularly significant in blockchain applications for the Internet of Things (IoT), where nodes generally have limited storage capabilities. To address this issue, we propose a novel blockchain encoding storage architecture. We classify the frequently used state data in blockchain into hot data and cold data, with hot data stored normally and cold data encoded. In addition, we designed a new transaction processing mechanism that prioritizes transactions involving specific data within a single block production process, thereby reducing the frequency of data decoding. At the same time, we use erasure codes and Merkle trees to ensure the security of the stored data. Experimental results show that in our approach, the account data storage on full nodes is significantly lower than that of normal full nodes, while the block transaction volume and block generation time are not substantially different from the normal state. Finally, we verify the robustness of our method against node dropouts through experiments.

Complete targets coverage is required by many Internet of Things (IoT) applications. In this respect, an important goal is to maximize the number of time slots with complete targets coverage. Achieving such coverage is challenging when devices experience spatio-temporal energy arrivals. To this end, this paper outlines a deep learning assisted approach that has an offline stage whereby it determines and stores an exhaustive collection of optimal activation schedules based the energy levels and arrivals of devices. In addition, it presents a network partitioning and training strategy, and outlines an algorithm to mend coverage holes in its online stage. We have compared the proposed approach with the optimal solution, and also a state-of-the-art heuristic algorithm. The results show that our solution achieves 94% of the optimal coverage lifetime. Moreover, the proposed approach has a 35% smaller optimality gap as compared with the said heuristic algorithm.

Artificial Intelligence (AI) integration in communication is crucial for 6G. It optimizes terrestrial communication and computing resource usage in the Internet of Things (IoT) using AI techniques, such as supervised learning for data analysis and reinforcement learning for resource allocation. However, in remote areas, i.e., oceans and deserts, IoT devices lose connection due to limited terrestrial coverage. Low Earth Orbit Satellite (LEOS) offers low-latency, high-bandwidth access in these unconnected regions. However, power and computing limitations on both IoT devices and LEOSs present challenges for continuous service. To this end, we present an LEOS-assisted Open Radio Access Network (RAN) Architecture (LO-RAN) where a RAN Intelligence Controller (RIC) is integrated to provide AI abilities. We formulate a joint Offloading decision, Path selection, and Resource allocation problem (OPR) to minimize the weighted energy consumption and latency of LO-RAN. We proposed a Joint Optimization for the Offloading decision, Path selection, and Resource allocation (JOOPR) algorithm. It selects contact and processing LEOSs for path selection, uses Proximal Policy Optimization (PPO) for offloading decisions, and applies Karush-Kuhn-Tucker (KKT) to solve resource allocation. The outputs from path selection and resource allocation contribute to the reward that feeds into the PPO. We conduct numerical simulations to compare the proposed JOOPR with the state-of-the-art approaches. The results show that JOOPR reduces energy consumption and latency by at most 28.75% and 33.01%, respectively.

Internet of Things (IoT) systems are increasingly widespread across various fields. Concurrently, vulnerabilities in IoT devices are continuously emerging, potentially leading to severe consequences, such as information leakage, system failure, or even resource abuse. For IoT system developers, understanding the characteristics of these critical vulnerabilities is crucial for safeguarding the security of IoT systems. However, existing studies on IoT vulnerabilities have not specifically focused on such severe or critical vulnerabilities, i.e., high-risk vulnerabilities. Moreover, previous works analyzed IoT vulnerabilities based on unofficial information sources, such as online reports, GitHub issues, or open-sourced projects. To fill this gap, this article presents the first large-scale empirical study on high-risk IoT vulnerabilities based on the well-known vulnerability data source, i.e., the national vulnerability database (NVD), which is maintained by the U.S. government. We constructed a database consisting of 1739 IoT-related vulnerabilities archived over the last two decades (from 1999 to 2023), including 1076 high-risk vulnerabilities for analysis. We classified the high-risk vulnerabilities into four categories, consisting of 25 different weakness types. We further collected 11 detection tools and summarized their capabilities in detecting IoT vulnerabilities. Our study sheds lights on new findings and insights for developers to secure the IoT system.

In this article, we consider an unmanned aerial vehicle (UAV) with an in-band full-duplex radio that is used to interconnect industrial Internet of things (IIoT) devices and exploit their computation and energy resources to help process data. We formulate a mixed integer linear program to optimize the first sampling rate of each device, second amount of data the UAV transmits and receives to/from a device, third position of the UAV over time, and finally the number of virtual machines used by devices and UAV to compute data. We also propose a distributed protocol to determine quantity using aforementioned points. Our results show that an IBFD-UAV has a higher max–min computed sampling rate as compared to when the UAV uses a half duplex radio. Moreover, the said protocol achieves a max–min rate that is 80% optimal.

This article considers maximizing coverage quality in Internet of Things (IoT) networks using unmanned aerial vehicles (UAVs) to augment the link from solar-powered devices to a sink/gateway. Specifically, it aims to jointly optimize the assignment of UAVs to hovering points or a charging station, time in which devices monitor targets, and the amount of data transmitted by devices. These quantities are optimized over a given planning horizon using a mixed integer linear program (MILP). Further, this article presents a heuristic method named decoupled energy aware algorithm (DEAA) to optimize the said quantities. In addition, it outlines a model predictive control (MPC) approach that only requires current and historical energy arrivals information of devices. The simulation results showed that DEAA and MPC achieved 80.58% and 61.19% of the optimal results computed by MILP.

The Internet of Things (IoT) system provides sensing and computing services via terrestrial networks. However, the restricted coverage of terrestrial networks, such as base stations, limits the ubiquitous IoT services. Low-Earth orbit (LEO) satellites are able to provide network coverage for terrestrial IoT devices in unconnected scenarios, e.g., maritime. IoT devices in such scenarios usually have restricted onboard computation and power resources. In this article, we present an LEO-assisted IoT network (L-IoT) architecture where a device splits its task and offloads a portion of its task to the LEO to process within the coverage time. We formulate a task split problem with communication and computation resource allocation (SCC) to minimize the L-IoT energy consumption. We proposed an alternating optimization for split ratio and resource allocation (AOSR) algorithm. In particular, we use the outputs of Karush-Kuhn–Tucker (KKT) for resource allocation as part of the reward that feeds twin-delayed deep deterministic policy gradient. Lastly, the results of numerical simulations show that the proposed AOSR approach reduces 12.7% energy consumption compared to soft actor-critic (SAC) and 15% to deep deterministic policy gradient (DDPG).

Devices in Internet of Things (IoT) networks are likely to operate over a shared medium, and thus they have to use a channel access protocol to minimize or avoid collision. Further, they may have to harvest radio frequency (RF) energy in order to transmit or/and receive data. To this end, this survey presents the first comprehensive review of prior works that employ contention-based and contention-free protocols in IoT networks with one or more dedicated RF energy sources. Specifically, these protocols work in conjunction with RF-energy sources to deliver energy delivery or/and data. In this respect, this survey covers protocols based on Aloha, carrier sense multiple access (CSMA), polling, and dynamic time division multiple access (TDMA). Further, it covers protocols that assume a receiver with successive interference cancellation capability. It highlights key issues and challenges addressed by prior works, and provides a qualitative comparison of these works. Finally, it identifies gaps in the literature and presents a list of future research directions.