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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]

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]

A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively. [ABSTRACT FROM AUTHOR]

One of the biggest challenges for Internet of Things (IoT) systems is traffic congestion in large networks. For this reason, the bandwidth should be increased in such systems. In addition, the issue of routing is raised in sending packets from the origin to the destination. Therefore, if there are many IoT devices in the network, it will increase the traffic, which makes faultless routing important in these networks. In this paper, a novel routing method based on Routing Protocol for Low-Power (RPL) is presented to minimize the energy consumption of the Internet of Things. Using the backward method based on the A* method to reduce energy consumption in a large graph, promising nodes are selected. A coordinate node is used to manage packets and transfer them. The selection of the coordinator node helps to receive packets with less energy and less delay from its neighbors, and the head node selects the best coordinator node with the shortest distance and the highest residual energy. The proposed method improves the energy consumption criteria, the delay between nodes, and the network overhead criterion by considering the estimated energy to the destination with the A* method. [ABSTRACT FROM AUTHOR]

In healthcare (HC), Internet of Things (IoT) integrated cloud computing provides various features and real-time applications. However, owing to the nature of IoT architecture, their types, various modes of communication and the density of data transformed in the network, security is currently a critical issue in the IoT healthcare (IoT-HC) field. This paper proposes a deep learning (DL) model, namely Adaptive Swish-based Deep Multi-Layer Perceptron (ASDMLP) that identifies the intrusions or attacks in the IoT healthcare (IoT-HC) platform. The proposed model starts by clustering the patients' sensor devices in the network using the Probability-based Fuzzy C-Means (PFCM) model. After clustering the devices, the cluster heads (CHs) among the cluster members are selected based on the energy, distance and degree of the sensor devices for aggregating the data sensed by the medical sensor devices. The base station (BS) sends the patient's data collected by the CHs to the cloud server (CS). At the cloud end, the proposed model implements an IDS by applying training of the DL model in publicly available databases. The DL approach first performs preprocessing of the data and then selects optimal features from the dataset using the Opposition and Greedy Levy mutation-based Coyotes Optimization Algorithm (OGCOA). The ASDMLP trains these optimal features for the detection of HC data intrusions. The outcomes confirm that the proposed approach works well on real-time IoT datasets for intrusion detection (ID) without compromising the energy consumption (EC) and lifespan of the network. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient energy management approach for managing the demand response and energy forecasting in a smart grid using Internet of Things (IoT). The proposed energy management approach is the hybrid technique that is the joint execution of adaptive neuro fuzzy inference system (ANFIS) and balancing composite motion optimization (BCMO), thus it is called ANFIS-BCMO technique. An energy management approach is developed using price-based demand response (DR) program for IoT-enabled residential buildings. Then, we devised a approach depends on ANFIS-BCMO technique to systematically manage the energy use of smart devices in IoT-enabled residential buildings by programming to relieve peak-to-average ratio (PAR), diminish electricity cost, and increase user comfort (UC). This maximizes effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings on smart cities. The ANFIS-BCMO technique automatically responds to price-based DR programs to combat the main problem of DR programs that is the limitation of the consumer's knowledge to respond when receiving DR signals. For consumers, the proposed ANFIS-BCMO based strategy programs appliances to exploit benefit based on reduced electricity bill. By then, the proposed method increases the stability of the electrical system by smoothing the demand curve. At last, the proposed model is executed on MATLAB/Simulink platform and the proposed method is compared with existing systems. [ABSTRACT FROM AUTHOR]

In current information systems, a huge number of IoT (Internet of Things) devices are interconnected with various kinds of networks like WiFi and 5G networks. A large volume of data is gathered into servers from a huge number of IoT devices and is manipulated to provide application services. Gathered data is encapsulated along with methods to manipulate the data as an object like a database system. In object-based systems, each application is composed of multiple objects. In addition, each object is replicated on multiple physical servers in order to increase availability, reliability, and performance of an application service. On the other hand, replicas of each object is required to be mutually consistent in presence of multiple transactions. Here, a larger amount of electric energy and computation resources are consumed in physical servers than non-replication approaches to serialize conflicting transactions on multiple replicas. Many algorithms to synchronize conflicting transactions are so far proposed like 2PL (Two-Phase Locking) and TO (Timestamp Ordering). However, the electric energy consumption is not considered. In this paper, an EEQBL-OMM (Energy-Efficient Quorum-Based Locking with Omitting Meaningless Method) protocol is newly proposed to reduce not only the average execution time of each transaction but also the total electric energy consumption of servers by omitting the execution of meaningless methods on replicas of each object. Evaluation results show the total electric energy consumption of servers, the average execution time of each transaction, and the number of aborted instances of transactions in the EEQBL-OMM protocol can be on average reduced to 79%, 62%, and 80% of the ECLBQS (Energy Consumption Laxity-Based Quorum Selection) protocol which is proposed in our previous studies in a homogeneous set of servers, respectively. In addition, the evaluation results show the total electric energy consumption of servers, the average execution time of each transaction, and the number of aborted instances of transactions in the EEQBL-OMM protocol can be on average reduced to 73%, 50%, and 67% of the ECLBQS protocol in a heterogeneous set of servers, respectively. The evaluation results also show at most 48% and 51% of the total number of methods can be omitted as meaningless methods in a homogeneous set and heterogeneous set of servers, respectively, in the EEQBL-OMM protocol. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) services are directly deployed on resource-constrained smart devices. Smart devices are characteristic by spatial and temporal constraints and energy limitations. A single IoT service cannot meet the complex requirements of users, so multiple IoT services need to be combined to provide services to users. As more and more smart devices are deployed in IoT, how to select IoT services with lower energy consumption and better quality of service (QoS) for service composition becomes a challenging problem. Combined with the characteristics that the data information of IoT is closely related to geographical location, the GeoHash algorithm is used to locally screen services based on geographical location, so as to narrow the range of candidate services. For smart devices with energy constraints, this paper proposes a combined optimization model. The model considers not only the transmission energy consumption and switching energy consumption, but also the execution energy consumption when the device provides services. In order to balance QoS attributes and energy consumption, the composition problem is regarded as a multi-objective optimization problem and solved using a genetic algorithm (GA). The simulation results show that service composition scheme selected by this service composition optimization model has lower energy consumption and higher service quality. [ABSTRACT FROM AUTHOR]

Due to the mobility and frequent topology changes, routing the data poses a challenging issue in the Internet of Things (IoT). Security poses a fundamental requirement factor in most IoT applications particularly, secure routing among the IoT nodes. The aim of this paper is to develop secure routing in IoT with minimal delay, minimal energy consumption and high throughput. Thus, a new algorithm, named Rider Foraging Optimization algorithm (RFO) is designed by the incorporation of standard Rider Optimization algorithm (ROA) and Bacterial Foraging optimization algorithm (BFO) that render an optimal solution, which is the optimal path for transmitting the information. Initially, the trust of the nodes is evaluated and the cluster head (CH) is chosen based on Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol to securely transmit the data. Then, the proposed RFO algorithm selects the optimal path using the fitness factors, such as trust, energy, and delay constraints. The results revealed that the energy consumption of the nodes is reduced and the lifetime of the network is enhanced through the implementation of the proposed RFO, which attained a maximal throughput of 1, and minimal delay and energy consumption of 0.1169 and 0.0002, respectively. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of poor data classification accuracy and effectiveness of traditional data monitoring methods, this paper designs a data security monitoring method based on narrow-band Internet of things. Firstly, the model of network data acquisition and sensor node's optimal configuration is established to collect intranet data. Based on the analysis of data characteristics, dynamic intranet data analysis indexes are designed from three aspects: establishing security incident quantity index, establishing address entropy index and data diversion. According to the above-mentioned narrow-band data aggregation rate, the security index of the Internet of things is calculated to realize the security of monitoring data. The experimental results show that: whether the network attack exists or not, the accuracy rate of the method is always higher than 90%, the classification time is less than 4 s, and the energy consumption of monitoring process is always less than 150 J, which fully proves that the method achieves the design expectation. [ABSTRACT FROM AUTHOR]

Remarkable resource management and energy efficiency improvements can be achieved in greenhouses using innovative technological advancements and modern agricultural methods. Deployment of Internet of Things (IoT) and optimization algorithms in greenhouse farming is highly desirable for real-time monitoring and controlling various parameters with optimal solutions. However, IoT based greenhouses require more energy as compared to traditional farming. This paper proposes an optimal greenhouse water supplement mechanism with efficient energy consumption based on IoT and optimization techniques. The first contribution of this study is to gather the actual water and soil moisture levels from the greenhouse and tank using IoT devices. Secondly, the formulation and deployment of an objective function to compute the optimal water and soil moisture levels for greenhouse and tank based on user-desired settings, the system constraints and actual sensing values. We applied a rule-based expert system to activate water pumps with the required flow rate and operational duration to achieve efficient energy consumption. To prove the effectiveness of the proposed concept, embedded IoT devices and objective function for optimization are deployed as well as, a number of experiments are conducted to provide the optimal water and soil moisture levels in a real greenhouse and water tank environment. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has been emerged as one of the most optimistic solution that reducing the gap between physical and cyber world. In IoT, sensors and actuators gather the sensory information; communicate with each other to fulfill application requirements. As sensor nodes are running on batteries, which cannot be replaced, so the development of the green model in IoT for shifting the real world data into the cyber world is challenging task. As IoT is deployed in large area and become more complex, so wireless sensor network current techniques cannot be applied in it directly. To achieve energy efficient network in the field of Internet of Thing, this paper addresses these challenging issues by introducing an efficient deployment scheme. This paper contributes: (1) A hierarchical cluster model for IoT network deployment; (2) An optimal Relay node ( ℝ ℕ ) election technique with respect to node density in the network and their deployment strategy by using their communication area as threshold distance for next ℝ ℕ selection within the cluster; (3) An efficient energy routing algorithm to meet the requirement of proposed model. The analysis of experimental results show that proposed method is more efficient in terms of energy consumption and suits IoT requirements. [ABSTRACT FROM AUTHOR]

The total number of pilgrims for the Hajj Season of 1438H reached 2,352, 122 — according to the General Authority for statistics Kingdom of Saudi Arabia. Pilgrims data analysis and prediction help concerned entities of the country in the future planning programs for the purpose of ensuring the necessary services — social, health, security, food and transportation services to name a few. Predictive analytics is the process of using data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events. Predictive analytics is often discussed in the context of big data as businesses apply algorithms to derive insights from large datasets using a framework like Hadoop, HDFS, and Spark. Building MATLAB-based framework for data analytics applied to Hajj dataset is the main aim of this research paper. The proposed framework is mainly relying on four main concepts; namely the cloud-based Internet of things (IoT), fog, Edge-of-Things (EoT), and predictive analytics. This proposed framework helps in reducing the amount of data sent, lowering network traffic, increasing bandwidth, and reducing power energy consumption. On top that, the framework including regression has the potential to predict how likely Hajj is susceptible to illness or even death. [ABSTRACT FROM AUTHOR]

For the current shipborne anti-collision sounding system, when multiple detection signals are transmitted, it is difficult to avoid collision with each other. In addition, there are shortcomings of insufficient energy consumption, low sounding precision, and slow response. To address this problem, a shipborne anti-collision sounding system based on ACT algorithm and Internet of things is designed in this paper. With ZigBee wireless communication technology and embedded technology, the function of anti-collision and sounding is realized by modular design. For the problem of the signal collision of each node of the wireless network, the ACT algorithm is introduced for system optimization to prevent signals from conflict when receiving, and ensure the synchronization and accuracy of the whole system. STM32F103 VET6 embedded chip is used as the control core of the system. CC2530 is responsible for the implementation of ZigBee wireless network communication. Experimental results show that the designed system has the advantages of low energy consumption, fast response, and high precision. [ABSTRACT FROM AUTHOR]

With the advancement of multimedia, Internet of things, and cloud computing technologies, technology enhanced learning applications such as smart school and smart learning at home have been paid more attentions to. Cloud computing services, which provide a novel collaborative and personalized learning style, have an important impact on learning and teaching. However, with the rapid development of cloud computing, the energy consumption problem for cloud computing networks cannot be ignored and the idea of energy efficiency comes into being. Currently, the main method to solve energy consumption problem is to turn the idle routers and links into sleeping, but this approach cannot be suited for cloud computing networks and may bring down network resource utility and network performance. This paper combining the Sleeping Redundant Links Algorithm (SRLA) with the Minimum Criticality Routing Algorithm (MCRA), proposes a Robust Energy Efficiency Routing Algorithm (REERA) used for cloud computing networks. The REERA uses the SRLA algorithm to sleep the redundant links in cloud computing backbone network, and then improves the robustness of the entire network by the MCRA algorithm. The REERA is able to dynamically change the link weight, ensure that most of the link can be used uniformly, and avoid the traffic congestion when certain links of cloud computing networks was excessively used. It thus improves the performance of the entire network. Comparing REERA with OSPF-based algorithm, the simulation results show that REERA outperforms Open Shortest Path First (OSPF) -based algorithm, and REERA can realize the energy-efficient routing algorithm under the premise of network robustness constraints. [ABSTRACT FROM AUTHOR]