Fog computing in enabling 5G-driven emerging technologies for development of sustainable smart city infrastructures

Cluster Computing - The usage of 5G-enabled IoT devices is rising exponentially as humans tend to shift towards a more automated lifestyle. A significant amount of IoT devices is expected to join...     

Privacy protection for fog computing and the internet of things data based on blockchain

Cluster Computing - With the development of the Internet of Things (IoT) field, more and more data are generated by IoT devices and transferred over the network. However, a large amount of IoT data...     

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak. Therefore, advanced inference techniques must be developed to address the limitations of IoT devices. This review proposes that IoT technology in biological and medical applications should be based on a new data assimilation process that fuses multiple data scales from several sources to provide diagnoses. Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology. Furthermore, cross-disciplinary integration has emerged with advancements in IoT. For example, the multiscale modeling of systems biology from proteins and cells to organs integrates current developments in biology, medicine, mathematics, engineering, artificial intelligence, and semiconductor technologies. Based on the monitoring objectives of IoT devices, researchers have gradually developed ambulant, wearable, noninvasive, unobtrusive, low-cost, and pervasive monitoring devices with data assimilation methods that can overcome the limitations of devices in terms of quality measurement. In the future, the novel features of data assimilation in systems biology and ubiquitous sensory development can describe patients’ physical conditions based on few but long-term measurements.     

Smart contract based policies for the Internet of Things

Cluster Computing - Internet of Things (IoT) is one of the most powerful platforms that incorporates several other technological components within itself. The IoT ecosystem comprises devices,...     

Middleware-based multi-agent development environment for building and testing distributed intelligent systems

The spread of the Internet of Things (IoT) is demanding new, powerful architectures for handling the huge amounts of data produced by the IoT devices. In many scenarios, many existing isolated solutions applied to IoT devices use a set of rules to detect, report and mitigate malware activities or threats. This paper describes a development environment that allows the programming and debugging of such rule-based multi-agent solutions. The solution consists of the integration of a rule engine into the agent, the use of a specialized, wrapping agent class with a graphical user interface for programming and testing purposes, and a mechanism for the incremental composition of behaviors. Finally, a set of examples and a comparative study were accomplished to test the suitability and validity of the approach. The JADE multi-agent middleware has been used for the practical implementation of the approach.

     

A fuzzy approach for optimal placement of IoT applications in fog-cloud computing

Fog-cloud computing is a promising distributed model for hosting ever-increasing Internet of Things (IoT) applications. IoT applications should meet different characteristics such as deadline, frequency rate, and input file size. Fog nodes are heterogeneous, resource-limited devices and cannot accommodate all the IoT applications. Due to these difficulties, designing an efficient algorithm to deploy a set of IoT applications in a fog-cloud environment is very important. In this paper, a fuzzy approach is developed to classify applications based on their characteristics then an efficient heuristic algorithm is proposed to place applications on the virtualized computing resources. The proposed policy aims to provide a high quality of service for IoT users while the profit of fog service providers is maximized by minimizing resource wastage. Extensive simulation experiments are conducted to evaluate the performance of the proposed policy. Results show that the proposed policy outperforms other approaches by improving the average response time up to 13%, the percentage of deadline satisfied requests up to 12%, and the resource wastage up to 26%.

     

A multi-layer trust-based middleware framework for handling interoperability issues in heterogeneous IOTs

Cluster Computing - Traditional wireless technologies have evolutionary converged to Internet of Thing (IoT) for devices and service interactions. In the past decade, the academia, industry 4.0 and...     

Security framework for smart cyber infrastructure

Cluster Computing - The rapid deployment of the Internet of Things (IoT) devices have led to the development of innovative information services, unavailable a few years ago. To provide these...     

Lattice-based cryptosystems for the security of resource-constrained IoT devices in post-quantum world: a survey

Cluster Computing - The concept of the Internet of Things (IoT) arises due to the change in the characteristics and numbers of smart devices. Communication of things makes it important to ensure...     

An intelligent trust sensing scheme with metaheuristic based secure routing protocol for Internet of Things

Cluster Computing - The Internet of Things (IoT) defines the network of physical objects, commonly used to interconnect and communicate with other devices through the internet. Security is highly...     

An efficient content source verification scheme for multi-receiver in NDN-based Internet of Things

Cluster Computing - Internet of Things (IoT) is a heterogeneous environment where multiple devices/consumers can interest/request for the same chunk of content at the same time with the need for...     

Offloading strategy with PSO for mobile edge computing based on cache mechanism

Cluster Computing - With the development of Internet of Things (IoT) devices and the growth of users’ demand for computation and real-time services, artificial intelligence has been applied...     

Trust management for internet of things using cloud computing and security in smart cities

Many consumers participate in the smart city via smart portable gadgets such as wearables, personal gadgets, mobile devices, or sensor systems. In the edge computation systems of IoT in the smart city, the fundamental difficulty of the sensor is to pick reliable participants. Since not all smart IoT gadgets are dedicated, certain intelligent IoT gadgets might destroy the networks or services deliberately and degrade the customer experience. A trust-based internet of things (TM-IoT) cloud computing method is proposed in this research. The problem is solved by choosing trustworthy partners to enhance the quality services of the IoT edging network in the Smart architectures. A smart device choice recommendation method based on the changing networks was developed. It applied the evolutionary concept of games to examine the reliability and durability of the technique of trust management presented in this article. The reliability and durability of the trustworthiness-managing system, the Lyapunov concept was applied.A real scenario for personal-health-control systems and air-qualitymonitoring and assessment in a smart city setting confirmed the efficiency of the confidence-management mechanism. Experiments have demonstrated that the methodology for trust administration suggested in this research plays a major part in promoting multi-intelligent gadget collaboration in the IoT edge computer system with an efficiency of 97%. It resists harmful threads against service suppliers more consistently and is ideal for the smart world's massive IoT edge computer system.

     

Indoor Thin‐Film Photovoltaics: Progress and Challenges

Energy generation and consumption have always been an important component of social development. Interests in this field are beginning to shift to indoor photovoltaics (IPV) which can serve as power sources under low light conditions to meet the energy needs of rapidly growing fields, such as intelligence gathering and information processing which usually operate via the Internet‐of‐things (IoT). Since the power requirements for this purpose continue to decrease, IPV systems under low light may facilitate the realization of self‐powered high‐tech electronic devices connected through the IoT. This review discusses and compares the characteristics of different types of IPV devices such as those based on silicon, dye, III‐V semiconductors, organic compounds, and halide perovskites. Among them, specific attention is paid to perovskite photovoltaics which may potentially become a high performing IPV system due to the fascinating photophysics of the halide perovskite active layer. The limitations of such indoor application as they relate to the toxicity, stability, and electronic structure of halide perovskites are also discussed. Finally, strategies which could produce highly functional, nontoxic, and stable perovskite photovoltaics devices for indoor applications are proposed.     

SHARE-ABE: an efficient and secure data sharing framework based on ciphertext-policy attribute-based encryption and Fog computing

Attribute-based encryption (ABE) is an access control mechanism that ensures efficient data sharing among dynamic groups of users by setting up access structures indicating who can access what. However, ABE suffers from expensive computation and privacy issues in resource-constrained environments such as IoT devices. In this paper, we present SHARE-ABE, a novel collaborative approach for preserving privacy that is built on top of Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Our approach uses Fog computing to outsource the most laborious decryption operations to Fog nodes. The latter collaborate to partially decrypt the data using an original and efficient chained architecture. Additionally, our approach preserves the privacy of the access policy by introducing false attributes. Furthermore, we introduce a new construction of a collaboration attribute that allows users within the same group to combine their attributes while satisfying the access policy. Experiments and analyses of the security properties demonstrate that the proposed scheme is secure and efficient especially for resource-constrained IoT devices.

     

High Power Magnetic Field Energy Harvesting through Amplified Magneto‐Mechanical Vibration

Internet of Things (IoT) is driving the development of new generation of sensors, communication components, and power sources. Ideally, IoT sensors and communication components are expected to be powered by sustainable energy source freely available in the environment. Here, a breakthrough in this direction is provided by demonstrating high output power energy harvesting from very low amplitude stray magnetic fields, which exist everywhere, through magnetoelectric (ME) coupled magneto‐mechano‐electric (MME) energy conversion. ME coupled MME harvester comprised of multiple layers of amorphous magnetostrictive material, piezoelectric macrofiber composite, and magnetic tip mass, interacts with an external magnetic field to generate electrical energy. Comprehensive experimental investigation and a theoretical model reveal that both the magnetic torque generated through magnetic loading and amplification of magneto‐mechanical vibration by ME coupling contributes toward the generation of high electrical power from the stray magnetic field around power cables of common home appliances. The generated electrical power from the harvester is sufficient for operating microsensors (gyro, temperature, and humidity sensing) and wireless data transmission systems. These results will facilitate the deployment of IoT devices in emerging intelligent infrastructures.     

Mutual authentication scheme of IoT devices in fog computing environment

The radical shift in the technology with the advent of connected things has led to the significant proliferation in demand for IoT devices, commonly called ‘smart devices’. These devices are capable of data collection, which can help in umpteen applications, particularly in healthcare. With the tremendous growth in these resource-constrained end devices, there has been a substantial increase in the number of attack varieties. Since these end devices deal with the sensitive data that might cause severe damage if not handled properly. Hence, defending its integrity, preserving its privacy, and maintaining its confidentiality as well as availability is of utmost importance. However, there are many protocols, models, architecture tools, etc. proposed to provide security. Nevertheless, almost every solution propound so far is not fully resilient and lacks in giving full protection to the system in some way or the other. So here, we have proposed a lightweight anonymous mutual authentication scheme for end devices and fog nodes.

     

Lead-Free Perovskite-Inspired Absorbers for Indoor Photovoltaics

With the exponential rise in the market value and number of devices part of the Internet of Things (IoT), the demand for indoor photovoltaics (IPV) to power autonomous devices is predicted to rapidly increase. Lead-free perovskite-inspired materials (PIMs) have recently attracted significant attention in photovoltaics research, due to the similarity of their electronic structure to high-performance lead-halide perovskites, but without the same toxicity limitations. However, the capability of PIMs for indoor light harvesting has not yet been considered. Herein, two exemplar PIMs, BiOI and Cs₃Sb₂Cl_xI_9-x are examined. It is shown that while their bandgaps are too wide for single-junction solar cells, they are close to the optimum for indoor light harvesting. As a result, while BiOI and Cs₃Sb₂Cl_xI_9-x devices are only circa 1%-efficient under 1-sun illumination, their efficiencies increase to 4–5% under indoor illumination. These efficiencies are within the range of reported values for hydrogenated amorphous silicon, i.e., the industry standard for IPV. It is demonstrated that such performance levels are already sufficient for millimeter-scale PIM devices to power thin-film-transistor circuits. Intensity-dependent and optical loss analyses show that future improvements in efficiency are possible. Furthermore, calculations of the optically limited efficiency of these and other low-toxicity PIMs reveal their considerable potential for IPV, thus encouraging future efforts for their exploration for powering IoT devices.     

Privacy preserving personalized blockchain reliability prediction via federated learning in IoT environments

Cluster Computing - The integration of blockchain and the Internet of Things (IoT) is seen as having significant potential. In IoT Environments, Blockchain builds a trusted environment for IoT...     

A new traffic congestion prediction strategy (TCPS) based on edge computing

Real-time accurate traffic congestion prediction can enable Intelligent traffic management systems (ITMSs) that replace traditional systems to improve the efficiency of traffic and reduce traffic congestion. The ITMS consists of three main layers, which are: Internet of Things (IoT), edge, and cloud layers. Edge can collect real-time data from different routes through IoT devices such as wireless sensors, and then it can compute and store this collected data before transmitting them to the cloud for further processing. Thus, an edge is an intermediate layer between IoT and cloud layers that can receive the transmitted data through IoT to overcome cloud challenges such as high latency. In this paper, a novel real-time traffic congestion prediction strategy (TCPS) is proposed based on the collected data in the edge’s cache server at the edge layer. The proposed TCPS contains three stages, which are: (i) real-time congestion prediction (RCP) stage, (ii) congestion direction detection (CD²) stage, and (iii) width change decision (WCD) stage. The RCP aims to predict traffic congestion based on the causes of congestion in the hotspot using a fuzzy inference system. If there is congestion, the CD² stage is used to detect the congestion direction based on the predictions from the RCP by using the Optimal Weighted Naïve Bayes (OWNB) method. The WCD stage aims to prevent the congestion occurrence in which it is used to change the width of changeable routes (CR) after detecting the direction of congestion in CD². The experimental results have shown that the proposed TCPS outperforms other recent methodologies. TCPS provides the highest accuracy, precision, and recall. Besides, it provides the lowest error, with values equal to 95%, 74%, 75%, and 5% respectively.