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• Development of the Depth-Based Stable Election Routing Protocol, which is specifically designed for underwater environments, by combining the probabilistic CH election approach and stable election protocol to improve the stability and efficiency of the proposed routing protocol. • Consideration of energy efficiency as a priority, in addition to tackling the difficulty of energy imbalance in varied IoUT scenarios, by recognizing efficient energy utilization as a major issue in IoUT. • Use fitness functions adapted to underwater nodes with varying energy levels and depths. • The suggested depth-based SEP-IoUT protocol was compared to conventional SEP and SEP-IoUT protocols. Furthermore, higher performance in terms of network lifetime, stability period, and energy consumption reduction must be demonstrated. Internet of underwater things (IoUT) is a technology enabling underwater sensor nodes to communicate with the IoT networks to support various applications. Efficient energy utilization and network lifetime are the two main critical issues related to the IoUT. Both performance factors are related to how efficiently the routing protocol is tailored especially for heterogeneous IoUT. In this context, efficient routing protocols that consider underwater nodes (UNs) energy heterogeneity can adjust the energy imbalance, especially in heterogeneous IoUT scenarios. In IoUT, both the UN's energies and their depths can greatly affect the energy efficiency and clustering-based routing protocol. This paper provided an energy and depth-based stable election routing protocol (SEP) to suit the requirements of underwater communications. The objective of the depth-based SEP protocol is to enhance energy-efficient routing while considering the varying energy levels of UNs in IoUT. The proposed protocol depends on the depth-based cluster head (CH) election approach by defining distinct functions tailored for UNs with different energy levels and depths, in addition to the distance between the selected CH and the surface base station. Experimental results indicate that depth-based SEP-IoUT protocol outperforms network lifetime more than conventional SEP and SEP-IoUT. It gives an average stability period of 19.6 % in addition to reducing energy consumption by 39 % and 15.7 % when compared to SEP and SEP-IoUT respectively. [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]

With the emergence of Internet of Things (IoT) and allied applications for smart cities, sustainability goals have seen a prominent emphasis. This paper focuses on the energy efficiency aspect of such sustainable smart city goals. Although energy efficiency has been studied at different levels of a smart city's Information and Communication Technology (ICT) infrastructure, this paper specially focuses on device level energy minimization strategy by means of modelling the energy consumption while accounting for the Clusterheads (CluH) and duty cycling and thereby using evolutionary algorithms. In this paper, a Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO) has been used to solve the energy minimization problem. Simulation experiments carried out for different scenarios with varying configuration demonstrate the efficacy of the hybrid neuro evolutionary scheme. [Display omitted] • IoT-based energy optimization model for smart city scenarios. • Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO). • Several output metrics, such as the number of alive nodes, load, residual energy, and cost function, were used to pick the best cluster head nodes in IoT network clusters. • The proposed method enacts an intelligent duty cycling by predicting sleep–wake cycles. [ABSTRACT FROM AUTHOR]

In this paper, CMOS technology-based neural session key generation is proposed for integration with the Internet-of-Things (IoT) to enhancing security. Recent technological developments in the IoT era enable improved strategies to exacerbate the maintenance of energy efficiency and stability issues. The existing security solutions do not properly address IoT's security. A small logic area ASIC implementation of a re-keying enabled Triple Layer Vector-Valued Neural Network (TLVVNN) using CMOS architectures with measurements of 65 and 130 nanometers are proposed for integration with IoT. The paper aims to defend IoT devices using TLVVNN synchronization to enhance security. For a 20% weight misalignment in the re-keying phase, the synchronization period may be decreased from 1.25 ms to less than 0.7 ms, according to behavioral simulations. Experiments to verify the proposed technique's performance are conducted, and the findings demonstrate that the proposed method has greater performance benefits than the existing related techniques. [ABSTRACT FROM AUTHOR]

The Internet-of-Things (IoT) represents the next groundbreaking change in information and communication technology (ICT) after the Internet. IoT is concerned with making everything connected and accessible through the Internet. However, IoT objects (things) are characterized by constrained computing and storage resources. Therefore, the Cloud of Things (CoT) paradigm that integrates the Cloud with IoT is proposed to meet the IoT requirements. In CoT, the IoT capabilities (e.g., sensing) are provisioned as services. Unfortunately, the two-tier CoT model is not efficient in the use cases sensitive to delays and energy consumption (e.g., in healthcare). Consequently, Fog Computing is proposed to support such IoT services and applications. This paper reviews the most relevant Fog-enabled CoT system models and proposes an energy-aware allocation strategy for placing application modules (tasks) on Fog devices. Finally, the performance of the proposed strategy is evaluated in comparison with the default allocation and Cloud-only policies, using the iFogSim simulator. The proposed solution was observed to be more energy-efficient, saving approximately 2.72% of the energy compared to Cloud-only and approximately 1.6% of the energy compared to the Fog-default. [ABSTRACT FROM AUTHOR]

The energy consumption of cloud robotics is an important design factor to be considered early in system development. Software as the main enabler of cloud robots, its energy consumption will directly influence the energy consumption level of the entire system. In this paper, we propose a green software model for cloud robotics based on energy consumption time state transition matrix (ETSTM), which can effectively integrate the logic functions, energy consumption, and execution time of software into a single model. To improve the practicability of ETSTM, we provide a software energy consumption function based on software characteristic fitting by a backpropagation (BP) neural network, which can be used to predict software energy consumption. Furthermore, we provide two types of energy consumption analysis algorithms based on explicit execution path search and bounded model checking (BMC) for ETSTM. The experiment results show that the proposed method can achieve good performance. [ABSTRACT FROM AUTHOR]

This paper proposes LBA, a lifetime balanced data aggregation scheme for the Internet of Things (IoT) under an application-specified end-to-end delay requirement. In contrast to existing aggregation schemes, LBA aims to prolong the IoT network lifetime under network heterogeneity and dynamics, while ensuring the required data delivery delay. To achieve this goal in a distributed manner, LBA adaptively adjusts the aggregation delays of neighboring devices to balance the lifetime between them. As such balancing takes place in all neighborhoods, the minimal device lifetime in the network is increased gradually, thus prolonging the lifetime of the entire network. The effectiveness of LBA is demonstrated via extensive experiments on a testbed. Generally, when the network presents a higher degree of heterogeneity and dynamics, LBA’s performance gain over a state-of-the-art non-adaptive data aggregation scheme becomes more significant, and the gap between LBA’s performance and its theoretical upper bound gets smaller. [ABSTRACT FROM AUTHOR]

Green and reliable communication has great significance for Industrial Internet of Things. Unfortunately, because of the loss nature, achieving reliable transmission is challenging. In this paper, a novel hybrid transmission protocol (HTP) is proposed to maximize lifetime while the reliability is still guaranteed. The proposed protocol adopts Send-Wait automatic Repeat-Request protocol in hotspot areas to reduce the energy consumption and network coding based redundant transmission approach with adaptive redundancy level in non-hotspot areas to guarantee the reliability. The proposed protocol could improve the lifetime and shorten the delay on the premise of ensuring the reliability. Comparing with Send-Wait automatic Repeat-Request protocol, it can improve lifetime by 15%–30% under the same reliability and improve the reliability by 12%–45% under the same lifetime. Comparing with network coding based redundant transmission approach, the lifetime has increased by more than one time under the same reliability. [ABSTRACT FROM AUTHOR]

• This paper aims to provide an overview of the Internet of Energy concept in the Industrial Internet of Things paradigm. • Internet of Energy is a decentralized, smart and viable energy solution that is yet unexplored in the industrial paradigm. The concept is emphasized in close relation to the Internet of Things, Industrial Internet of Things and Industry 4.0. • Internet of Energy integration in the industry is focused to provide key requirements, applications, architecture frameworks and open challenges. The Internet of Energy (IoE) transforms energy production, supply, and consumption to fulfill high energy demands via intelligent automation of industrial energy producers and consumers. This paper emphasizes the concept of the IoE in the Industrial Internet of Things (IIoT) perspective in order to ensure productivity, control, reduced cost, real-time decision making and monitoring, customer satisfaction, and innovative experience. Furthermore, industrial applications and requirements for optimizing performance, transmission, and consumption for efficient energy utilization and increased productivity have also been discussed. Industrial services, technologies, prerequisites, and application requirements are focused on outlining the architectural framework for achieving net-zero energy efficiency, applicability, and limitations. The taxonomy of communication layer protocols is discussed, compared, and evaluated in depth along with their limitations, to help with pre-technology implementations. Moreover, open challenges such as middleware, mobility, data integrity, and scalability are also discussed in depth with their potential solutions. [ABSTRACT FROM AUTHOR]

Thanks to the introduction of the Internet of Things (IoT), the research and the implementation of home automation are getting more popular because the IoT holds promise for making homes smarter through wireless technologies. There is a main requirement that make a wireless protocol ideal for use in the IoT, that is the energy efficiency. Bluetooth Low Energy (BLE) has a high potential in becoming an important technology for the IoT in low power, low cost, small devices. However, specific techniques can be used in such a way as to further reduce the energy consumption of BLE. To this end, this paper proposes a fuzzy logic based mechanism that determine the sleeping time of field devices in a home automation environment based on BLE. The proposed FLC determines the sleeping time of field devices according to the battery level and to the ratio of Throughput to Workload (Th/Wl). Simulation results reveal that using the proposed approach the device lifetime is increased by 30% with respect to the use of fixed sleeping time. [ABSTRACT FROM AUTHOR]

The new era of information technologies made smart healthcare by including edge, Internet of Things (loT), Internet of Medical Things (IoMT), cloud computing etc. This adaptation has partially replaced the old medical system, making healthcare more efficient, easy, and customized. Wireless medical sensors are used in the smart healthcare sector to capture the patient's health data and transmit it to the remote server for treatment or diagnostic purposes. Every time a cloud-based solution is adopted to collect and preserve the sensitive and personal health information. This collected patient data is shared between the different stakeholders across the network. Due to this, healthcare systems are vulnerable to confidentiality, integrity and privacy threats. Secure data access is a big challenge in such systems. In this paper, a lightweight authentication technique with the privacy-preserving schema using fully homomorphic encryption is proposed. This approach can be used for a secure access of patient data through online mode, and also the data can be shared in an encrypted format for desired purposes of stakeholders. The proposed authentication scheme is for Internet of Medical Things that can resist all network attacks and is easy to implement. The performance analysis of the proposed work shows that overall execution time (80.03%), communication cost (15.62%) and energy consumption (79.19%) is reduced in comparison with existing work. [Display omitted] • A lightweight mutual authentication scheme for smart healthcare systems is designed and implemented using on login ID, password, and perceptual hash based biometric security. • A formal security analysis is introduced using Burrows–Abadi–Needham (BAN) logic that resists all well-known attacks and provide anonymity of patient identity. • A performance comparison of the proposed scheme with existing schemes is also carried out in terms of security features and communication overheads, computation overheads, storage and network throughput. [ABSTRACT FROM AUTHOR]

Internet of Things has been a pivotal technology enabler for realizing smart cities vision through the provision of connectivity for everyday objects by means of wireless sensor networks (WSNs). Scalability, flexibility, route efficiency, mobility support and reduced overhead in routing protocols are desired functions in large-scale WSNs. Given the several geographic routing schemes proposed, mainly focusing on positioning, location error, and energy consumption, still exhibit higher delays and overhead which significantly affect the performance of the network. In this paper, an energy-efficient geographic (EEG) routing protocol has been proposed that focuses on network throughput and energy consumption of sensor nodes. Moreover, this strategy also helps in avoiding void region's creation in the network. The proposed protocol reduces the energy hole problem by efficiently balancing the energy consumption among sensor nodes. The extensive simulations illustrate that the proposed scheme manages energy consumption and packet delivery ratio more efficiently in comparison to a state-of-the-art geographic routing protocol. [ABSTRACT FROM AUTHOR]

Green energy Internet is a new concept for future power systems based on high-level interconnection among different systems for energy efficient delivery and manage energy resources. Smart grid is one of the core energy management system based on multiple routing and data transferring services for energy management, distribution, pricing, and demand services. The traditional energy networks have suffered from high and complex computing operations and lead to degradation of services and the overall performance of all operations. Software-Defined Networking (SDN) is one of the platform to enhance the flexibility and efficiency by decoupled the control plane from its data plane. A detachment of these two-planes improved dynamic configuration, controller programmability, centralized and decentralized network operations. In this paper, a distributed SDN approach is used to address the scalability and robustness issues and improve the overall energy efficiency in smart grid systems. The SDN controller logically centralized but it works on multiple nodes that physically distributed. Distributed nodes are communicating with each other and improve the network performance to assist data flow. When flow arrives through end devices to any data plane in distributed SDN, the proposed approach supports the data plane to increase the controller response time. Also, the controller checks, whether the data needs to prioritize flow to send it locally or globally with the help of Elephant or Mice flow. The proposed approach is used to reduce the controller workload, response time, and manage Quality of Services (QoS) and enhanced energy efficiency among controllers. This approach also improves the green energy utilization in smart grid systems. The experimental results indicate that the proposed approach is more suitable and feasible for smart grid systems. [ABSTRACT FROM AUTHOR]