Future Internet Of Things Applications Research Articles

High power triboelectric nanogenerator based on nanofibers of silk protein and PVBVA and its motion sensing applications

Rapid expansion of the Internet of Things (IoT)-based healthcare and biomedical research has surged the demand for versatile electronics with seamless human–machine interfaces. However, chemical batteries hinder interfacing with the human body, and this limitation can be mitigated by using self-powered devices. Here, we introduce a novel strategy to develop a high-power and protein-based triboelectric nanogenerator (TENG) suitable for self-powered human–machine interfacial applications in daily life. The TENG consists of electrospun PEO-Silk nanofibers (PEO-Silk-NFs) and poly(vinyl butyral-co-vinyl alcohol-co-vinyl acetate) nanofibers (PVBVA-NFs). Owing to large surface areas and electrical affinity differences between the two NFs, a high TENG performance with an open-circuit voltage (Voc) of ∼ 2.1 kV, a short-circuit current (Isc) of ∼ 6.5 µA, and an impressive power density of ∼ 196 mW/cm2 are obtained. The PVBVA-NF/PEO-Silk-NF TENG is highly sensitive to impact forces and thus allows effective and continuous monitoring of body joint movements. Additionally, the PVBVA-NF/PEO-Silk-NF TENG is water-resistant, completely recyclable, and wearable, which make it an excellent option for future IoT applications. Our research paves the way for high-voltage output and motion-sensing solutions for the next phase of wearable electronics based on self-powered and sustainable technology.

AN ULTRA-WIDEBAND PATCH ANTENNA FOR FUTURE INTERNET OF THINGS APPLICATIONS

The Internet of Things (IoT) era is driving up demand for wireless connectivity, changing the tech sector. The creation of a compact, inexpensive, and effective antenna is crucial for supporting IoT devices. In this study, a hybrid strategy (HS)-based dense rectangular patch antenna for future Internet of Things applications (IoT) is demonstrated. A slotted patch and a partial ground with defected ground structure (DGS) are combined to create HS, leading to in a small antenna with a large impedance bandwidth, good impedance matching, increased radiation efficiency, and high gain. The antenna is designed on an FR4_epoxy substrate with a compact size of 30 × 20 × 0.8 mm3, a dielectric constant of 4.4, a thickness of 0.8 mm, a loss tangent of 0.02 and fed by a 50 Ω microstrip feed line. It is analyzed and simulated at the operating frequency of 5.2 GHz for ultra-wideband (UWB) (3.1–10.6 GHz) applications using high-frequency structure simulator (HFSS) software. The simulation results reveal that the antenna offers a broad bandwidth of 19.9 GHz (3.10 GHz to 23 GHz), a gain of 7.97 dB with an efficiency of 0.9651 (96.51%). The radiation pattern is bidirectional in both the E-plane and H-plane. Hence, the proposed antenna is very promising for future IoT applications due to its compact size and wide bandwidth characteristics. It also enhances connectivity and performance, lowers interference, opens up new applications, and encourages the widespread use of IoT technology, ultimately leading to the development of a more sophisticated and connected society.

EEDC: An Energy Efficient Data Communication Scheme Based on New Routing Approach in Wireless Sensor Networks for Future IoT Applications.

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing "Region based Hierarchical Clustering for Efficient Routing (RHCER)"-a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio.

Simulation and analysis of edge ghosting for microcapsule e‐paper based on particle dynamics and light scattering model

AbstractElectronic paper (e‐paper) is a reflective display technology with unique advantages, such as bistability, low‐power consumption, and high ambient contrast ratio. These features make e‐paper a promising candidate for future Internet of Things applications. Among different technologies of e‐paper, electrophoretic display (EPD) is the most successful one for commercialization. However, the edge ghosting (also known as the fringing effect) still limits the performance of EPD. Herein, we established a model of particle dynamics of electrophoresis, simulated the edge ghosting of microcapsule EPD, analyzed the edge ghosting effect, and revealed the relationship between thicknesses, dielectric constants of the back binder layer, and the edge ghosting. Two EPD panels with different thicknesses of back binder layer were demonstrated, which verifies the accuracy of this simulation model. With the proposed model, many device mechanisms and product issues can be analyzed and illustrated, which is supposed to guide the researchers in optimizing the device structure design of EPD.

Self-C2AD: Enhancing CA Auditing in IoT with Self-Enforcement Based on an SM2 Signature Algorithm

Malicious certificate authorities (CAs) pose a significant threat to the security of the Internet of Things (IoT). Existing CA auditing schemes primarily rely on passive detection and public data collection, lacking real-time and comprehensive monitoring. In this paper, we propose a novel double-authentication preventing signature scheme based on an SM2 algorithm, referred to as Dap-SM2. We further enhance its functionality by introducing Self-C2AD, a CA auditing mechanism with self-enforcement. In our proposed mechanism, any malicious CA that generates two certificates with different descriptions (such as public key and basic information) for the same IoT device will immediately lose its private key. To ensure the security of our proposed scheme, we provide a detailed security analysis of Dap-SM2. The analysis demonstrates that our Self-C2AD mechanism meets the necessary security requirements, offering robust protection against malicious CAs. Additionally, we conduct an efficiency evaluation and present experimental data to illustrate the promising potential of our construction for future IoT applications. By introducing the Dap-SM2 scheme and the Self-C2AD mechanism, we address the critical issue of malicious CAs in the IoT domain. Our approach provides real-time and comprehensive auditing capabilities, surpassing the limitations of existing schemes. The security analysis confirms the effectiveness of Dap-SM2, while the efficiency evaluation and experimental data demonstrate its suitability for practical IoT applications. In summary, our work presents a novel solution to combat the threat of malicious CAs in the IoT context. The Dap-SM2 scheme, coupled with the Self-C2AD mechanism, offers enhanced security and real-time auditing capabilities. The security analysis validates the robustness of our approach, while the efficiency evaluation and experimental data showcase its potential for future IoT deployments.

A Multibranch U-Shaped Tunable Encoding Chipless RFID Strain Sensor for IoT Sensing System

Structural health monitoring (SHM) is essential for modern large buildings and infrastructure. Radio frequency identification (RFID) widen a new paradigm for SHM in the Internet of Things (IoT) era. This article introduces a low-cost intelligent RFID monitoring system for future IoT applications. Through adding a multiparameter sensing strategy to the passive RFID tags that can be deployed on a large scale to form a sensor network, which expands the coverage of IoT in key positions health monitoring of buildings. In this work, a novel chipless RFID strain sensing tag is designed to characterize the magnitude and direction of metal surface strain, and a low-cost detection method suitable for large mechanical structures is proposed. The traditional strain antennas focus on identifying the strain characteristics without the function of encoding, and there are limitations, such as the rigid measurement methods and the expensive measurement instruments. The tag designed in this article integrates both strain sensing and encoding functions, and has the advantages of small size, high data capacity, and information reading is not easily affected by environmental noise. This article proposes an economical and flexible tag spectrum extraction method, which realizes the intelligent collection and transmission of tag data by connecting lightweight vector network analyzer (VNA) with microcontroller. Combined with the IoT, this method can be well applied to the security assessment and damage detection of large infrastructure structures, which provides a new approach to modern building health monitoring applications from integrated tag design to intelligent detection and risk assessment schemes.

Efficient data transmission using trusted third party in smart home environments

Currently, deployed Internet of Things (IoT) technology acts as a passive observer of the environment that sends data to a remote location. Developing and deploying future IoT applications will need re-tasking this one-way behaviour in a reliable manner. A novel computationally tractable optimization technique that can accept cross-layer resource configurations and focus on network enhancement with longevity should be created for the smart home, one of the heterogeneous IoT applications. This study shows different smart home architectures in static and mobile environments, taking into account some of the challenges like orchestration, mobility, and range in IoT. For network communication, routing protocol over 6LoWPAN (RPL) is used. The goal of the work is to optimize the communication network in both static and mobile environment. To attain the goal, this paper proposes an algorithm that improves the path selection by modifying the existing objective functions of RPL. The proposed smart home architectures are analysed and compared based on different parameters such as packet reception ratio, network overhead, throughput, average latency, and total energy consumption. Even when some of the devices in the smart home are mobile, the modified smart home-optimized path (MSHOP) is found to achieve a packet reception ratio of 99.93%, minimum latency of 0.9 s, and the highest total energy usage in the network of 3373 millijoules. In conclusion, proposed MSHOP outperforms all existing smart home architectures when considering network efficiency, time, and usability.

Sputtered (Fe,Mn)3O4 Spinel Oxide Thin Films for Micro-Supercapacitor

The scaling up of wireless operating microelectronics for upcoming Internet of Things (IoT) applications demands high-performance micro-supercapacitors (MSCs) with corresponding high-energy and power capabilities. Indeed, this necessitates the quest for MSC’s electrode materials capable of delivering high energy density at high charge/discharge rates. Many multicationic oxides, such as spinel manganese-iron compounds, demonstrate good pseudocapacitive properties as positive electrodes in conventional supercapacitors. However, fulfilling the required fabrication techniques is a challenge for their applications in MSCs. Hence, this study, for the first time, demonstrates the successful deposition of spinel Mn-Fe thin films on a functional platinum-based current collector. The deposition is achieved in a reactive oxygen environment via reactive DC magnetron sputtering techniques and subsequently annealed ex situ at 600 °C in a nitrogen environment. The electrochemical signature in neutral 1 M Na2SO4 aqueous electrolyte is comparable to those reported for spinel type Mn-Fe bulk counterparts. The areal capacitance at 10 mV.s−1 is 15.5 mF.cm−2 for 1 μm thick film, exhibiting excellent coulombic efficiency (close to 100%) and long-term cycle stability after 10,000 cycles. Thus, the synthesis of the multicationic pseudocapacitive oxides via compatible microelectronic deposition methods has set a prospective path to achieve very high-performance MSCs for future IoT applications.

ELIoT: enhancing LiFi for next-generation Internet of things

Communication for the Internet of things (IoT) currently is predominantly narrowband and cannot always guarantee low latency and high reliability. Future IoT applications such as flexible manufacturing, augmented reality and self-driving vehicles rely on sophisticated real-time processing in the cloud to which mobile IoT devices are connected. High-capacity links that meet the requirements of the upcoming 6G systems cannot easily be provided by the current radio-based communication infrastructure. Light communication, which is also denoted as LiFi, offers huge amounts of spectrum, extra security and low-latency transmission free of interference even in dense reuse settings. We present the current state-of-the-art of LiFi systems and introduce new features needed for future IoT applications. We discuss results from a distributed multiple-input multiple-output topology with a fronthaul using plastic optical fibre. We evaluate seamless mobility between the light access points and also handovers to 5G, besides low-power transmission and integrated positioning. Future LiFi development, implementation and efforts towards standardization are addressed in the EU ELIoT project which is presented here.

Low-Complexity Channel Estimation and Multi-User Detection for Uplink Grant-Free NOMA Systems

Grant-free non-orthogonal multiple access (NOMA) scheme is a promising candidate to accommodate massive connectivity with reduced signalling overhead for Internet of Things (IoT) services in massive machine-type communication (mMTC) networks. In this letter, we propose a low-complexity compressed sensing (CS) based sparsity adaptive block gradient pursuit (SA-BGP) algorithm in uplink grant-free NOMA systems. Our proposed SA-BGP algorithm is capable of jointly carrying out channel estimation (CE), user activity detection (UAD) and data detection (DD) without knowing the user sparsity level. By exploiting the inherent sparsity of transmission signal and gradient descend, our proposed method can enjoy a decent detection performance with substantial reduction of computational complexity. Simulation results demonstrate that the proposed method achieves a balanced trade-off between computational complexity and detection performance, rendering it a viable solution for future IoT applications.

Reliable Internet of Things: Challenges and Future Trends

The Internet of Things (IoT) is a vital component of many future industries. By intelligent integration of sensors, wireless communications, computing techniques, and data analytics, IoT can increase productivity and efficiency of industries. Reliability of data transmission is key to realize several applications offered by IoT. In this paper, we present an overview of future IoT applications, and their major communication requirements. We provide a brief survey of recent work in four major areas of reliable IoT including resource allocation, latency management, security, and reliability metrics. Finally, we highlight some of the important challenges for reliable IoT related to machine learning techniques, 6G communications and blockchain based security that need further investigation and discuss related future directions.

Performance Analysis of Ambient Backscatter Systems With LDPC-Coded Source Signals

Ambient backscatter communication (AmBC), a promising solution to future Internet of Things (IoT), has attracted increasing attention from both academia and industry in recent years. AmBC systems enable communications between RF-powered devices (e.g., tags and sensors) and AmBC receivers by utilizing ambient RF source signals from existing communication systems. Thus it offers a low-cost and energy-efficient means of communications for future IoT applications. In the current AmBC research, the impact of using channel coding on existing primary networks in terms of the bit-error-rate (BER) performance of the AmBC systems are mostly ignored. Low-density parity-check (LDPC) codes that offer fast encoding and decoding process have been proposed as promising channel codes in the fifth-generation (5 G) networks. In this paper, we investigate the BER performance of an AmBC system that uses LDPC-coded RF source signals. We have analytically derived the BER upper and lower bounds of both the RF source and tag signals for our proposed AmBC system. Simulation results show that the BERs of both signals decrease significantly, compared to conventional AmBC systems with uncoded RF source signals.

User Selection for NOMA-Based MIMO With Physical-Layer Network Coding in Internet of Things Applications

Nonorthogonal multiple access (NOMA)-based multiple-input-multiple-output (MIMO), which has the potential to provide both massive connectivity and high spectrum efficiency, is considered as one of the efficient techniques for sixth-generation (6G) wireless systems. In massive Internet of Things (IoT) networks, the user-set selection is crucial for enhancing the overall performance of NOMA-based systems when compared with orthogonal multiple access (OMA) techniques. In this article, we propose a user-set selection algorithm for IoT uplink transmission to improve the sum data rate of the NOMA-based MIMO systems. In order to exchange data between the selected IoT pairs, we propose to employ wireless physical-layer network coding (PNC) to further improve the spectral efficiency and reduce the delay to fulfill the requirements of future IoT applications. Performance evaluations are provided based on both the sum data rate and bit error rate for the proposed NOMA-based MIMO with PNC in the considered massive IoT scenarios.

Novel printing techniques to print flexible radio frequency devices such as antennas

Energy harvesting devices such as solar panels and wind generators collect energy sources and convert them to generate power. Such devices are economical and efficient and also allow energy to be generated and devices and applications powered in places without conventional power sources, such as underwater. Energy harvesting also has the potential to be used to satisfy the need for energy autonomy that autonomous electronics, the Internet of Things (IoT) and wearable devices demand. At the University of Surrey, UK, Dr Simon King is collaborating with Dr Bilal Malik, Dr Maxim Shkunov and Dr Pavlos Giannakou on a project called flexible smart SURFaces for Augmented indoor communicationS (SURFAS) to design energy harvesting surfaces (antennas) for zero-power consuming electronic devices. Each team member has their own speciality and all share the common goal of revolutioning the ways that devices access and consume energy. The goal is to reduce energy consumption and provide cost benefits. Part of the team's current work involves the use of novel printing techniques to fabricate flexible radio frequency (RF) devices, such as rectifying antennas. The team believes the development of fully integrated printed energy harvesting devices will lead to countless future IoT applications. The ultimate objective of SURFAS is to enable zero-power consumption electronic devices and smart surfaces that are capable of optimally redirecting Wi-Fi signals and enhancing the performance of receivers. The team is also working to develop a manufacturing process for rapidly and cost-effectively producing such devices.

Application Domains, Evaluation Data Sets, and Research Challenges of IoT: A Systematic Review

We are at the brink of Internet of Things (IoT) era where smart devices and other wireless devices are redesigning our environment to make it more correlative, flexible, and communicative. IoT is now evolving to Internet of Everything, as it incorporates and builds a system that includes wireless networks, sensors, cloud servers, analytics, smart devices, and advanced technologies. The IoT systems are equipped with embedded devices having network connectivity and sensors to have a machine-to-machine interaction. The field of IoT is advancing to provide smart solutions to various applications, such as transportation, healthcare, farming, to name a few. Considering the diverse perspective of IoT systems/network, this article aims at discussing different applications of IoT across various research domains and to highlight the use of IoT in a variety of applications. We have systematically reviewed applications of IoT with the data sets to understand the characteristics of the data collected by the IoT devices. In this article, a comprehensive survey on the role of IoT in various application domains is presented for the years 2015-2019. This article lists different commercialized solutions developed in various IoT application domains. The aim to discuss commercialized IoT paradigms is to highlight research efforts performed in the field of IoT that have been transformed into real-life solutions. This article also discusses research challenges in the field of IoT to strengthen the research for developing future IoT applications.

Federated Data Cleaning: Collaborative and Privacy-Preserving Data Cleaning for Edge Intelligence

As an important driving factor of emerging Internet-of-Things (IoT) applications, machine learning algorithms are currently facing the challenge of how to “clean” data noise, that is introduced during the training process (e.g., asynchronous execution and lossy data compression and quantization). In an attempt to guarantee data quality, various data cleaning approaches have been proposed to filter out abnormal data entries based on the global data distribution. However, most existing data cleaning approaches are based on a centralized paradigm and thus cannot be applied to future edge-based IoT applications, where each edge node (EN) has only a limited view of the global data distribution. Moreover, the increasing demand for privacy preservation largely prevents ENs from combining their data for centralized cleaning. In this study, we propose a federated data cleaning protocol, coined as FedClean, for edge intelligence (EI) scenarios that is designed to achieve data cleaning without compromising data privacy. More specifically, different ENs first generate Boolean shares of their data and distribute them to two noncolluding servers. These two servers then run the FedClean protocol to privately and efficiently compute the attribute value frequency (AVF) scores of the collected data entries, which are then sorted in ascending order via a bitonic sorting network without revealing their values. As a result, data entries with lower AVF scores are considered as abnormal and filtered out. The security, efficiency, and effectiveness of the proposed approach are then demonstrated via concrete security analysis and comprehensive experiments.

Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard.

Integrating Low-Power Wide-Area Networks for Enhanced Scalability and Extended Coverage

Low-Power Wide-Area Networks (LPWANs) are evolving as an enabling technology for Internet-of-Things (IoT) due to their capability of communicating over long distances at very low transmission power. Existing LPWAN technologies, however, face limitations in meeting scalability and covering very wide areas which make their adoption challenging for future IoT applications, especially in infrastructure-limited rural areas. To address this limitation, in this paper, we consider achieving scal-ability and extended coverage by integrating multiple LPWANs. SNOW (Sensor Network Over White Spaces), a recently proposed LPWAN architecture over the TV white spaces, has demonstrated its advantages over existing LPWANs in performance and energy-efficiency. In this paper, we propose to scale up LPWANs through a seamless integration of multiple SNOWs which enables concurrent inter-SNOW and intra-SNOW communications. We then formulate the tradeoff between scalability and inter-SNOW interference as a constrained optimization problem whose objective is to maximize scalability by managing white space spectrum sharing across multiple SNOWs. We also prove the NP-hardness of this problem. To this extent, We propose an intuitive polynomial-time heuristic algorithm for solving the scalability optimization problem which is highly efficient in practice. For the sake of theoretical bound, we also propose a simple polynomial-time 1/2-approximation algorithm for the scalability optimization problem. Hardware experiments through deployment in an area of (25x15)sq. km as well as large scale simulations demonstrate the effectiveness of our algorithms and feasibility of achieving scalability through seamless integration of SNOWs with high reliability, low latency, and energy efficiency.

UHF RFID Handset Antenna Design With Slant Polarization for IoT and Future 5G Enabled Smart Cities Applications Using CM Analysis

Radio frequency identification (RFID) is a pivotal technology to realize the Internet of Things (IoT) dreams and considered as a last-mile solution. Therefore, this paper presents a novel compact antenna design for UHF RFID handset with angle-adjustable linear polarization using characteristic mode analysis. The proposed antenna design consists of a multi-orientation dipole partially printed on three sides of C shaped F4B substrate and an internal ground behind the handset screen. First of all, different characteristic modes of multi-orientation dipole and ground plane are analyzed using characteristic mode analysis, which shows that the vertical polarization is provided by ground plane, and the horizontal polarization is produced by the dipole. Secondly, the proposed multi-orientation dipole is connected with internal ground using two metal shorting strips to adjust the polarization angle. Finally, a suitable feeding position is selected to excite the required characteristic modes to get the radiation pattern with slant polarization. This antenna covers whole US UHF RFID band ranging from 902 - 928 MHz. The advantage behind slant polarization lies in reading of RFID tags with both horizontal and vertical orientations. A prototype of antenna with 45-degree linear polarization angle is fabricated to prove the concept, and the measurement results are matched well with simulated results. Furthermore, this paper paves the way to use typical android phone-sized handheld reader as terminal device for future IoT applications ranging from consumer terminal devices to infotainment solutions by providing capability of reading RFID tag with different orientations.

Dynamic decision support for resource offloading in heterogeneous Internet of Things environments

Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices’ resource restrictions. In the past, offloading would always utilise remote cloud infrastructures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time condition of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network.Here, we model the expected time and energy costs for the different options of offloading a task to the edge or the cloud, as well as of carrying out on the device itself. We use this model to allow the device to take the offloading decision dynamically as a new task arrives and based on the available information on the network connections and the states of the edge and the cloud. Having extended EdgeCloudSim to provide support for such dynamic decision making, we are able to compare this approach against IoT-first, edge-first, cloud-only, random and application-oriented probabilistic strategies. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, and the energy consumption not only of the individual IoT devices but also of the system as a whole. This paves the way for future IoT devices that optimise their application response times, as well as their own energy autonomy and overall energy efficiency, in a decentralised and autonomous manner.