Future IoT Systems Research Articles

Security and Privacy for Future Healthcare IoT

Computer networks are widely used in today's society, and we utilize the Internet to connect to our home networks. IoT networks are a brand-new category of these networks where we attempt to connect various household equipment and attempt to issue orders from a distance. Hash code or message authentication code (MAC) is used for authentication, and several encryptions and decryption techniques guarantee secrecy. The cost of using traditional cryptographic security techniques in terms of computing resources like memory, processor power, and power consumption is high. There is no proper model/ framework available to address the major security issues of IoT devices. The literature address that still several gaps exist regarding security issues. To secure and protect future healthcare IoT systems, this research will provide a model that combines shallow learning with deep learning.

H-HOME: A learning framework of federated FANETs to provide edge computing to future delay-constrained IoT systems

In 6G systems, it will be mandatory that the network is able to support edge computing powered by Artificial Intelligence (AI) to provide mobile devices with the opportunity of job offloading for computation, so implementing the new paradigm of Intelligent Internet of Intelligent Things (IIoIT). In areas that are very difficult to be covered by the structured networks, Flying Ad-Hoc Networks (FANET) can be considered one of the most promising technologies to enhance coverage, capacity, reliability, and energy efficiency of wireless cellular networks, also providing edge-computing services. The goal of this paper is to propose a two-layer Hierarchical Horizontal-Offload ManagEment (H-HOME) framework for horizontal offload among the Unmanned Aerial Vehicles (UAV) of the same FANET, in order to minimize processing delay and jitter. The framework exploits Federated Reinforcement Learning in order to take advantage of knowledge sharing without incurring into problems of privacy and network overloading. A Markov Decision Process (MDP) is also defined to optimize decisions of the FANET Orchestrator (FO). Simulation results, obtained from two different analyses, demonstrate that H-HOME outperforms the traditional local training approach, based on simple recursive learning, and it can be effectively used to reduce power consumption and increase FANETs flight autonomy.

Scalable Dew Computing

Dew computing differs from the classical cloud and edge computing by bringing devices closer to the end-users and adding autonomous processing independent from the Internet, but it is still able to collaborate with other devices to exchange information on the Internet. The difference is expressed also on scalability, since edge and cloud providers can provide (almost endless) resources, and in the case of dew computing the scalability needs to be realized on the level of devices, instead of servers. In this paper, we introduce an approach to provide deviceless and thingless computing and ensure scalable dew computing. The deviceless approach allows functions to be executed on nearby devices found closer to the user, and the thingless approach goes even further, providing scalability on a low-level infrastructure that consists of multiple things, such as IoT devices. These approaches introduce the distribution of computing to other smart devices or things on a lower architectural level. Such an approach enhances the existing dew computing architectural model as a sophisticated platform for future generation IoT systems.

Suitability of self-organizing service composition approach for smart healthcare ecosystem: A study

Future IoT systems will be deployed in open environments where the functionality of millions of IoT devices that are heterogeneous will be abstracted. In such a large scale system manual service composition is not feasible and often erroneous. A self-organizing service composition is a well known approach to deal with the problems in IoT systems. In a self-organizing service composition, the service composition is a runtime and autonomous process and human intervention is minimal. The atomic components will interact among themselves in a decentralized manner to form complex composites according to a set of self-organizing rules. The features of a self-organizing software composition are aptly suitable for the IoT domain. Smart healthcare has provided affordable healthcare for patients and enables them to self manage emergencies. This paper aims to establish the suitability of a self-organizing service composition for the smart healthcare ecosystem with special focus on real time monitoring applications.

Guest Editors’ Introduction: Special Issue on Architecture Advances Enabled by Emerging Technologies

As we approach the twilight of Moore’s Law and dimensional scaling of advanced CMOS technologies, many beyond-CMOS logic and memory device technologies are being explored. In order to fully exploit the promises of these emerging technologies, we need major simultaneous advances in architecture and design, especially for the new computing workloads in future intelligent IoT systems. This special issue presents five articles that describe emerging opportunities for such technologydesign- architecture co-optimizations.

Game Theoretic Mechanisms for Resource Management in Massive Wireless IoT Systems

As a result of rapid advancement in communication technologies, the Internet of Things (i.e., ubiquitous connectivity among a very large number of persons and physical objects) is now becoming a reality. Nonetheless, a variety of challenges remain to be addressed, one of them being the efficient resource management in IoT. On one hand, central resource allocation is infeasible for large numbers of entities, due to excessive computational cost as well as immoderate overhead required for information acquisition. On the other hand, the devices connecting to IoT are expected to act smart, making decisions and performing tasks without human intervention. These characteristics render distributed resource management an essential feature of future IoT. Traditionally, game theory is applied to effectively analyze the interactive decision making of agents with conflicting interests. Nevertheless, conventional game models are not adequate to model large-scale systems, since they suffer from many shortcomings including analytical complexity, slow convergence, and excessive overhead due to information acquisition/exchange. In this article, we explore some non-conventional game theoretic models that fit the inherent characteristics of future large-scale IoT systems. Specifically, we discuss evolutionary games, mean field games, minority games, mean field bandit games, and mean field auctions. We provide the basics of each of these game models and discuss the potential IoT-related resource management problems that can be solved by using these models. We also discuss challenges, pitfalls, and future research directions.

EcoSense: A Hardware Approach to On-Demand Sensing in the Internet of Things

An Internet of Things system typically contains a large number of low-cost devices that are mainly powered by batteries and designed to operate for a relatively long period of time. Due to the stagnated battery technology, energy efficiency will still be a burning issue for future IoT systems. To achieve better energy efficiency, we propose an innovative sensor architecture called EcoSense. Unlike traditional software-based approaches (e.g., duty cycling or adaptive sampling techniques), EcoSense provides a hardware-based on-demand sensing mechanism that effectively eliminates the energy waste caused by a sensor working in sleep or standby mode. When desired events are not present, an EcoSense sensor is completely turned off to save energy (i.e., drawing zero current). When desired events occur, an on-demand connection module will harvest energy from the events and reactivate the sensor. We implemented light- and RF-driven EcoSense sensors, and evaluated their performance in realworld experiments. Experimental results show that the EcoSense technique is able to immediately detect lights and RF signals, and offers reasonable reaction distances.