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In recent years, with the continuous derivative of the Internet of Things (IoT) business, the use of various types of information sensing equipment needs to continue to increase. However, power supply conditions are getting tougher. On the other hand, as the function of the devices itself increases, the power consumption also increases, and the lack of energy demand is more obvious. To solve these problems, a dual-frequency RF energy harvesting system is designed in this paper. First of all, in order to expand the effective receiving range of RF energy, this paper designed a 920 MHz hexagonal windmill circular-polarized antenna, and added an energy receiving circuit in the 2.45 GHz band on this basis, which fully alleviated the problem of insufficient energy collection for small devices in the Internet of Things while ensuring a small volume wavelength ratio. In addition, in order to improve the energy conversion efficiency, this paper also designed a dual-frequency rectifier circuit based on conjugate separation matching, using the harmonic signal of 920 MHz signal to broaden the bandwidth of 2.45 GHz. The experimental results show that the peak conversion rates of 920 MHz and 2.45 GHz channels are 72.8%@0dBm and 38.7%@-2dBm, respectively. Overall testing in a Wireless Fidelity (Wi-Fi) room with a commercial Radio Frequency Identification (RFID) reader. The experimental results show that the system can steadily output 3.29 V–3.3 V voltage within 6 m charging distance, which is 20% higher than the output voltage of the single-channel receiving system, and the receiving angle is 36.84% larger. [ABSTRACT FROM AUTHOR]

In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. As a result, massive amounts of data are transmitted to the cloud, but the latency generated in edge-to-cloud communication is unacceptable for many tasks. In response to this, this paper introduces a novel contribution—a layered computing network built on the principles of fog computing, accompanied by a newly devised algorithm designed to optimize user tasks and allocate computing resources within rechargeable networks. The proposed algorithm, a synergy of Lyapunov-based, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO), allows for predictive task allocation. The fog servers dynamically train LSTM networks to effectively forecast the data features of user tasks, facilitating proper unload decisions based on task priorities. In response to the challenge of slower hardware upgrades in edge devices compared to user demands, the algorithm optimizes the utilization of low-power devices and addresses performance limitations. Additionally, this paper considers the unique characteristics of rechargeable networks, where computing nodes acquire energy through charging. Utilizing Lyapunov functions for dynamic resource control enables nodes with abundant resources to maximize their potential, significantly reducing energy consumption and enhancing overall performance. The simulation results demonstrate that our algorithm surpasses traditional methods in terms of energy efficiency and resource allocation optimization. Despite the limitations of prediction accuracy in Fog Servers (FS), the proposed results significantly promote overall performance. The proposed approach improves the efficiency and the user experience of Internet of Things systems in terms of latency and energy consumption. [ABSTRACT FROM AUTHOR]

Internet of things establishes communication among heterogeneous devices. IoT network is low power and lossy network known as LLN. The components in LLN use low power for its operations. The Internet Engineering Task Force (IETF) has defined routing protocol for standardized LLN, i.e., routing protocol for low-power and lossy networks (RPL). One of the major challenges in RPL is efficient conservation of node energy to improve the life of the LLN network. In the RPL network, most of the energy is consumed while regulating and controlling the packets rather than transmission. The algorithm used for regulating and controlling packet in RPL is called trickle timer algorithm. Hence to improve the lifetime of network it is essential to modify the existing trickle timer algorithm. In this paper, we have proposed a new algorithm called EE-trickle. The performance of EE-trickle is compared with existing trickle using the simulator Cooja and using open test bed of future Internet of things lab. From the experiments, it is identified that EE-trickle provides better PDR along with less energy consumption than the existing trickle. Hence, the paper helps the future researchers who work on energy consumption in RPL to make use of EE-trickle in their experiment rather than existing trickle. [ABSTRACT FROM AUTHOR]

Despite the widespread adoption of the Routing Protocol for Low-power and Lossy Networks (RPL) in IoT environments, its inherent limitations in addressing security vulnerabilities have left IoT networks vulnerable to ongoing attacks. This paper introduces a novel intrusion detection system tailored specifically for IoT networks, with a focus on mitigating attacks at the network's edge. The study presents the Hybrid Rank Attack (HRA), a sophisticated threat exploiting RPL vulnerabilities by alternately advertising decreased and increased rank values in control messages. Extensive experimentation evaluates the detrimental effects of HRA on critical network metrics including exchanged messages, energy consumption, PDR, latency, and memory footprint. Additionally, a lightweight and distributed countermeasure algorithm is proposed to effectively mitigate the impact of HRA. Simulation-based evaluations demonstrate significant reductions in control overhead (68.7%) and energy consumption (61.83%), with minimal additional RAM utilization (1.05%). This lightweight solution enhances the resilience of RPL-based IoT networks against HRA threats. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices has given rise to applications that demand real-time responses and minimal delay. Fog computing has emerged as a suitable platform for processing IoT applications, extending cloud computing services to the edge of the network. This enables more cost-effective and time-efficient processing at the network's edge. However, determining how to allocate tasks to fog nodes presents a fundamental challenge, involving factors like energy consumption and limited fog server capacity, impacting quality of service parameters such as delay. This paper introduces a mathematical formula for resource allocation to minimize delay and energy consumption while considering quality of service criteria. The subsequent step involves presenting a hybrid genetic algorithm (GA) and the gray wolf optimization (GWO), constituting an improved hybrid approach where the GA exhaustively explores the solution space to reduce the risk of converging to a locally optimal point. The combination of these algorithms produces multiple solutions. Despite incurring processing costs and computation delays, the implementation of these algorithms is crucial for enhancing the Quality of Service (QoS). In conclusion, the results indicate that the simultaneous use of positive aspects from both algorithms significantly improves execution time, final task completion time compared to the other methods. [ABSTRACT FROM AUTHOR]

An industry-wide paradigm change has been sparked by the growth of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs), which has made reliable and effective routing methods necessary. This thorough analysis looks at how Machine Learning (ML) techniques may be used to solve the problems that come with WSN routing. A summary of standard routing algorithms and an examination of their shortcomings comprise the first portion of the paper. The integration of ML approaches, such as reinforcement learning and supervised and unsupervised learning, is then explored in order to improve WSN routing efficiency. The article examines the difficulties and factors related to ML-based routing, including data quality, energy efficiency, scalability, and security. Applications and case studies show how ML is really used in WSN routing, offering insights into effective tactics and lessons discovered. Evaluation metrics and performance assessments are included in a separate section that uses simulation and experimental data to compare ML-based and conventional techniques. Looking forward, the study describes new breakthroughs in ML for WSNs and points out unresolved issues, providing a guide for future research paths. The important results and their consequences are outlined in the conclusion, which also highlights how ML has the potential to revolutionize WSN routing in the future. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) refers to a vast network of interconnected devices, objects, and systems powered by sensors, software, and connectivity capabilities. The interconnectivity of IoT devices has led to a substantial increase in data production. Efficiently managing and analyzing large data volumes is a significant challenge for IoT systems. To address this challenge, data aggregation is the primary process. IoT data aggregation aims to provide high-quality service by ensuring fast data transmission, high reliability, minimal energy consumption, and data priority consideration. Data aggregation involves collecting data from multiple sensors and devices and then integrating it using a function to minimize system traffic. This paper thoroughly examines data aggregation techniques in the IoT context. Techniques are grouped according to underlying principles, and their potential applications, advantages, and limitations are discussed. [ABSTRACT FROM AUTHOR]

This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed intelligence network for Internet of Things (IoT) devices. The heterogeneous nature of clients and data presents challenges for effective federated learning (FL) techniques, prompting our exploration of federated transfer learning (FTL) on Raspberry Pi, Odroid, and virtual machine platforms. Our study provides a detailed examination of the design, implementation, and evaluation of the FTL framework, specifically adapted to the unique constraints of various IoT platforms. By measuring the accuracy of FTL across diverse clients, we reveal its superior performance over traditional FL, particularly in terms of faster training and higher accuracy, due to the use of transfer learning (TL). Real-world measurements further demonstrate improved resource efficiency with lower average load, memory usage, temperature, power, and energy consumption when FTL is implemented compared to FL. Our experiments also showcase FTL's robustness in scenarios where users leave the server's communication coverage, resulting in fewer clients and less data for training. This adaptability underscores the effectiveness of FTL in environments with limited data, clients, and resources, contributing valuable information to the intersection of edge computing and DL for the 6G IoT. [ABSTRACT FROM AUTHOR]

Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems. [ABSTRACT FROM AUTHOR]

The capacity to update firmware is a vital component in the lifecycle of Internet of Things (IoT) devices, even those with restricted hardware resources. This paper explores the best way to wirelessly (Over The Air, OTA) update low-end IoT nodes with difficult access, combining the use of unicast and broadcast communications. The devices under consideration correspond to a recent industrial IoT project that focuses on the installation of intelligent lighting systems within ATEX (potentially explosive atmospheres) zones, connected via LoRa to a gateway. As energy consumption is not limited in this use case, the main figure of merit is the total time required for updating a project. Therefore, the objective is to deliver all the fragments of the firmware to each and all the nodes in a safe way, in the least amount of time. Three different methods, combining unicast and broadcast transmissions in different ways, are explored analytically, with the aim of obtaining the expected update time. The methods are also tested via extensive simulations, modifying different parameters such as the size of the scenario, the number of bytes of each firmware chunk, the number of nodes, and the number of initial broadcast rounds. The simulations show that the update time of a project can be significant, considering the limitations posed by regulations, in terms of the percentage of airtime consumption. However, significant time reductions can be achieved by using the proper method: in some cases, when the number of nodes is high, the update time can be reduced by two orders of magnitude if the correct method is chosen. Moreover, one of the proposed methods is implemented using actual hardware. This real implementation is used to perform firmware update experiments in a lab environment. Overall, the article illustrates the advantage of broadcast approaches in this kind of technology, in which the transmission rate is constant despite the distance between the gateway and the node. However, the advantage of these broadcast methods with respect to the unicast one could be mitigated if the nodes do not run exactly the same firmware version, since the control of the broadcast update would be more difficult and the total update time would increase. [ABSTRACT FROM AUTHOR]

Smart objects in the Internet of Things (IoT) communicate with one another, gather information, and respond to users requests. In these systems, wireless sensors are used to collect data and monitor the environment at the lowest level. In IoT applications, wireless sensor networks play a pivotal role. Since IoT devices often use batteries, efficiency is important to them such that IoT-related standards and research efforts focus more on energy saving or conservation. In this paper, we have used two meta-heuristics algorithm for clustering and routing in IoT. We cluster the network using a clustering method called WOA-clustering based on the meta-heuristic Whale Optimization Algorithm (WOA) and select the optimal cluster heads. We then use a routing method called HHO-Routing based on the Harris Hawks Optimization (HHO) algorithm, a novel meta-heuristic algorithm, to route the cluster heads to BS. The use of the above methods results in reduced power consumption for reaching the base station (BS). Also, to prove the optimal performance of the proposed methods, these methods were simulated and compared with five different methods in a similar context. It was observed that the consumed energy, the number of survival cycles for the death of the first node, and the data transmission rate were improved. The proposed method is simulated in cooja simulator, and for a more accurate evaluation, we compare it with UCCGRA, PSO-SD, PUDCRP, EECRA, EEMRP algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan. [ABSTRACT FROM AUTHOR]

In today's smart communities, small-scale energy systems are essential for sustainable development and efficient resource management. However, ensuring the confidentiality, safety, and accurate prediction of energy consumption patterns in energy trading is a major challenge. To address these issues, an innovative solution that synergistically combines two cutting-edge technologies: blockchain and machine learning is proposed. This paper unveils a novel approach that harmoniously merges blockchain with the Recalling-Enhanced Recurrent Neural Network (RERNN) to revolutionize energy trading systems called 'Blockchain-Enhanced Energy Trading with Recalling-Enhanced Recurrent Neural Network (BET-RERNN).' Data from IoT-enabled smart devices is securely stored in blockchain blocks, ensuring data integrity and immutability. Blockchain's decentralized nature creates a trust-less environment for energy trading, protecting the privacy and anonymity of participants while maintaining transparency. At the heart of our system lies the advanced machine-learning capabilities of the RERNN model. By processing the data stored on the blockchain, RERNN accurately predicts optimal power generation for small-scale energy systems, enabling smart communities to make informed decisions and optimize their energy consumption. The BET-RERNN scheme provides a plethora of strengths. First, participants can securely engage in energy trading without compromising sensitive information, fostering a more resilient and efficient market. Second, blockchain technology ensures that all energy-related data is protected from tampering and unauthorized access, ensuring system reliability and trust. An in-depth comparison of RERNN's performance to traditional General Regression Neural Network (GRNN) and Gradient Boost Decision Tree (GBDT) methods is conducted. To verify the strategy's effectiveness, MATLAB simulations are employed, demonstrating its real-world applicability and scalability. By combining blockchain and machine learning, a secure and privacy-preserving smart community is established, promoting sustainable energy practices for a greener future. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel DILTS algorithm that uses a new approach inspired by the energy efficiency of dragonflies. The algorithm optimizes the energy-harvesting mechanisms in IoT devices, inspired by the way dragonflies use wind energy to fly. A sophisticated algorithm optimizes power consumption during task execution, saving energy and speeding up tasks while maintaining the application throughput. The algorithm leverages lazy task scheduling (LTS) to enhance task execution performance. The proposed algorithm evaluates the energy levels of each task and implements an LTS method. This LTS approach improves performance and task management by streamlining scheduling data and reducing overhead. The LTS model reliably optimizes the energy across microbenchmarks and real-time IoT devices. To assess the efficiency and practicality of our algorithm, we compared it to four alternatives. Our novel algorithm outperformed the others with a chip area of 856 μm2, performance speed of 7.11 ns, scheduling accuracy of 94%, and response time of 2.61 ns. Our simulations showed that our proposed method reduced energy consumption by up to 10.02% compared to existing methods. We evaluated the performance of the algorithms on a Zynq 7000 FPGA using the Xilinx Vivado platform via simulations. Our novel algorithm can improve the energy efficiency of green data centers. [ABSTRACT FROM AUTHOR]

In the world of connected devices with enormous application market, wireless sensor network comes with inherent challenges. Energy consumption balancing among the sensors affects its lifetime and connectivity. A hybrid approach for the randomly deployed sensor network is proposed in this paper with better consumption energy balance through the lifetime of the sensor network. An optimized threshold probability is analyzed for the improvement of network lifetime in comparison with stable election protocol. Simulation results provide improved consumption energy distribution and prolonged network lifetime of heterogeneous wireless sensor network. The proposed protocol is reactive to the change in the network, making it suitable for the Internet of Things applications. [ABSTRACT FROM AUTHOR]

It is well known that buildings have a sizeable energy and environmental footprint. In particular, in environments like university campuses, the occupants as well as occupancy in shared spaces varies over time. Systems for cooling in such environments that are centrally controlled are typically threshold driven and do not account for occupant feedback and thus are often relying on a reactive approach (fix after identifying problems). Therefore, having a fixed thermal operating set point may not be optimal in such cases—both from an occupant comfort and well-being as well as an energy efficiency perspective. To address this issue, a study was conducted which involved development and deployment of an experimental Internet of Things (IoT) prototype system and an Android application that facilitated people engagement on a university campus located in the UAE which typically exhibits hot climatic conditions. This paper showcases data driven insights obtained from this study, and in particular, how to achieve a balance between the conflicting goals of improving occupant comfort and energy efficiency. Findings from this study underscore the need for regular reassessments and adaptation. The proposed solution is low cost and easy to deploy and has the potential to reap significant savings through a reduction in energy consumption with estimates indicating around 50–100 kWh/day of savings per building and the resulting environmental impact. These findings would appeal to stakeholders who are keen to improve energy efficiency and reduce their operating expenses and environmental footprint in such climatic conditions. Furthermore, collective action from a large number of entities could result in significant impact through this cumulative effect. [ABSTRACT FROM AUTHOR]

Smart homes are characterized by the integration of technology and services using home networking, with the goal of improving the overall quality of life. When designing these intricate systems, it is important to consider the experiences and needs of the occupants. Smart homes consist of various devices that offer comfort, security, convenience, energy efficiency, and promote intelligent living. These devices communicate and interact with each other, forming a connected ecosystem. In order to ensure prompt assistance during emergencies such as fires, medical emergencies, or distress calls, interactivity and technology within smart homes should enable emergency service providers to quickly respond. To achieve this, a modeling language that supports the semantic aspect is employed, allowing for modeling that closely aligns with end user requirements. This language typically utilizes the concept of ontology. However, the use of ontologies in smart home design is still limited in current research. As a result, this paper aims to enhance the EUPont ontology by integrating Arduino devices and implementing an algorithm that simplifies the analysis of detected alarm signals and user decision-making. The execution of this algorithm relies on the definition of several SWRL rules (Semantic Web Rule Language). [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) underpinning the smart-grid Internet of Things (SG-IoT) has been a popular research topic in recent years due to its great potential for enabling a wide range of important applications. However, the energy consumption (EC) characteristic of sensor nodes is a key factor that affects the operational performance (e.g., lifetime of sensors) and the total cost of ownership of WSNs. In this paper, to find the modulation techniques suitable for WSNs, we investigate the EC characteristic of continuous phase modulation (CPM), which is an attractive modulation scheme candidate for WSNs because of its constant envelope property. We first develop an EC model for the sensor nodes of WSNs by considering the circuits and a typical communication protocol that relies on automatic repeat request (ARQ)-based retransmissions to ensure successful data delivery. Then, we use this model to analyze the EC characteristic of CPM under various configurations of modulation parameters. Furthermore, we compare the EC characteristic of CPM with that of other representative modulation schemes, such as offset quadrature phase-shift keying (OQPSK) and quadrature amplitude modulation (QAM), which are commonly used in communication protocols of WSNs. Our analysis and simulation results provide insights into the EC characteristics of multiple modulation schemes in the context of WSNs; thus, they are beneficial for designing energy-efficient SG-IoT in the beyond-5G (B5G) and the 6G era. [ABSTRACT FROM AUTHOR]

Clustering routing protocols currently have problems such as Single point of failure of cluster head nodes, poor network dynamics, uneven data transmission, etc., which are critical to the optimization of energy efficiency, network lifespan and network topology control. However, this optimization problem is an NP hard problem that conventional algorithms are difficult to solve. This paper proposes a new multi-objective cluster routing protocol (CHEABC-QCRP) aimed at optimizing network energy consumption, system lifespan, and quality of services (QoS). The protocol is based on a new chaotic hybrid elite artificial bee colony algorithm (CHEABC) proposed in this paper, which has strong search ability and greatly reduces convergence time. At the same time, a new chaotic strategy was designed to effectively prevent falling into local optima and premature convergence. In simulation experiments, compared with multiple routing protocols, a large number of test results show that this protocol significantly reduces network energy consumption, greatly improves system lifespan, and effectively improves QoS in IWSN. [ABSTRACT FROM AUTHOR]

• Proposing a novel reversible QCA-based half-adder structure based on the coplanar layout and simple quantum cells, focusing on low occupied area and energy consumption;. • Recommending a single-layer reversible QCA-based full adder design based on the coplanar layout and simple quantum cells with emphasis on the low-occupied area and low latency;. • Suggesting the reversible QCA-based multiplier design using the suggested structures for IoT devices such as printed devices and microcontroller units;. • Designing the proposed reversible QCA-based multiplier using a powerful quantum tool for evidence of its near zero-energy dissipation. An enormous variety of items and things are connected via wired or wireless connections and specific addressing schemes, which is known as the Internet of Things (IoT). However, IoT devices that adopt aggressive duty-cycling for high power efficiency and prolonged lifespan necessitate the incorporation of ultra-low power consumption always-on blocks. The multiplier plays a crucial role in enhancing the capabilities of low-power IoT devices, particularly those operating with energy-efficient batteries that offer extended battery life. The previous multipliers have a struggling speed, enormous occupied area, and high energy consumption; therefore, all prior flaws must be fixed by implementing it in a suitable technology, like the quantum computing. Therefore, this paper examines the ultra-low power circuit for nano-scale IoT platforms. It also suggests novel quantum-based adders for multiplier structure. The proposed designs are simulated using the QCADesignerE 2.2 tool by focusing on energy-efficient and occupied areas for miniaturizing IoT systems. [Display omitted] [ABSTRACT FROM AUTHOR]

Internet Protocol version 6 (IPv6) address configuration is needed to meet the needs of current portable devices and wearable applications, where each device needs its own unique identification number. High complexity in Wireless Sensor Network (WSN) and Internet of Things (IoT) environments has increased challenges, such as low energy consumption and high overhead in IPv6 address configuration. The proposed IPv6 generation is based on a hybrid mathematical approach that retains the IP address's confidentiality and prevents Denial of Service (DoS) attacks from new and existing nodes in the network. In this paper, the proposed hybrid protocol for DoS attacks from new and existing nodes is analyzed. It is evident from the results that the rate of successful configuration, overhead, and energy consumption is found to be better than the existing approach, namely, Programming Protocol-independent Packet Processors Duplicate Address Detection (P4DAD), match prevention technique, etc. • Focuses on IPv6 using a hybrid Duplicate Address Detection (DAD). • Reduces the energy consumption of the IPv6 as compared to P4DAD, etc. • Reduces the long wait time compared to EUI-64. • ASR is investigated for the Hybrid DAD under three different scenarios [ABSTRACT FROM AUTHOR]

The problem addressed in this work is the enhancement of energy efficiency in buildings, primarily focused on heating control. The paper highlights the importance of addressing this challenge, given that HVAC systems are responsible for over 60% of total energy consumption in buildings, making them the largest energy consumers in both the residential and nonresidential sectors. This paper presents a methodology for developing a model predictive control (MPC) system based on artificial neural network (ANN) models, with the aim of improving the energy efficiency of buildings. This approach involves the creation of a training dataset using dynamic thermal simulation tools, enabling the learning of the ANN model that is responsible for predicting future states within an MPC optimization loop. Additionally, the implementation leverages advanced technologies such as Field-Programmable Gate Arrays (FPGAs), specifically the PYNQ board, Wi-Fi modules, and MQTT communication to enhance scalability, deployment, and adaptability. The study evaluates the proposed Neural Network Model Predictive Control (NNMPC) strategy implemented on the PYNQ, reporting a substantial reduction of 38.53% in heating energy consumption in buildings compared to a basic On/Off control approach for 24-hour simulation. This outcome underscores the solutions significant potential for energy savings in building heating systems. [ABSTRACT FROM AUTHOR]

This paper addresses the escalating energy demand by proposing a market-based model for decentralized operation in a multi-microgrid system, aiming to enhance power distribution efficiency and reliability. Microgrids, small-scale electrical networks, exchange energy among themselves, storage systems, load collectors, and the upstream distribution network. The system encompasses uncontrollable and controllable generation units and loads. Leveraging Internet of Things (IoT) technology and cloud infrastructure, the communication structure facilitates data measurement, processing, and exchange of technical and financial information. Each microgrid, storage system, and load collector operates independently to maximize benefits by participating in the future market. The proposed model is simulated under various scenarios, and the results are systematically analyzed. To validate the model's performance, alterations in fuel cost and solar radiation intensity values are implemented, and simulation results are scrutinized. The model acknowledges interdependence and connectivity between microgrids and employs a chimpanzee optimization algorithm inspired by chimpanzee social behavior. This algorithm effectively tackles complex optimization problems by simulating the cooperative behavior of a group of chimpanzees, where each individual represents a potential solution. The numerical results show that Microgrid 1 achieves a 100% success rate in maintaining uninterrupted loads with cost-effective power. Microgrid 2 strategically preserves a 100% success rate for priority 1 load but faces occasional challenges, leading to limited load shedding. Microgrid 3 achieves an 80% success rate in keeping priority 1 load uninterrupted, adapting to market conditions. • Decentralized Optimization: Novel approach enhances energy management, distributed optimization in multi-microgrids. • IoT Integration: Real-time data sharing, responsive energy management, efficiency through IoT technology. • Chimpanzee Algorithm: Solving complex challenges in multi-microgrid systems with this unique optimization. • Flexibility & Reliability: Decentralized optimization, IoT, and chimpanzee algorithm for efficient energy management. [ABSTRACT FROM AUTHOR]

Today, fog computing is an emerging technology to support resource-constrained Internet of Things (IoT) applications. The scalability, geographic distribution, and heterogeneity of edge computing nodes, as well as the diversity of users' expectations, have made optimal module placement, considering the maximum use of fog resources, a challenging optimization problem. This paper proposes a method based on distributed learning automata to reduce the search space by using the maximum capacity of fogs in a heterogeneous fog network. In this method, fog topology is mapped to a distributed learning automata. With the cooperation of the automaton in this DLA, the problem of module placement has been solved to reduce energy consumption and delay of applications. To evaluate the amount of energy consumption and the execution time of IoT applications, two single-objective cost functions for energy and delay and another single-objective function with simultaneous consideration of energy and delay have been used. The results indicate that the average efficiency of the proposed method is 15.99%, 18.21%, and 15.53%, respectively, compared to other methods. [ABSTRACT FROM AUTHOR]

The Internet technology's rapid development makes electronic voting become an important way to ensure democracy and fairness during the election process. Compared with manual voting, electronic voting mainly relies on mobile terminals and Internet technology. Therefore, it can effectively avoid manual errors and guarantee the authenticity of the voting result. Although lots of electronic voting systems have been constructed, cryptographic protocols in these systems are vulnerable to attacks from the quantum computer. Therefore, an efficient quantum-resistant undeniable signature protocol for the electronic voting system is proposed in this paper, which is based on the Fiat–Shamir signature framework. The proposed undeniable signature protocol not only ensures the integrity of voting results but also allows voters to decide the time when the verification process happens. Additionally, the random oracle model is utilized to analyze the security of our protocol. We also make a detailed performance analysis to show the potential value of this protocol in electronic voting. [ABSTRACT FROM AUTHOR]