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Summary: The Internet of Things (IoT) has provided us with various applications that need processing in real‐time. Fog computing has arisen as a highly effective computing paradigm that provides services closer to the IoT devices and hence fulfills the need for rapid response time and delay requirements of IoT applications. However, the heterogeneity of IoT tasks and the limited resources of fog nodes pose considerable challenges to task scheduling. Therefore, the effective utilization of fog resources is highly significant and necessitates an optimal and intelligent approach to task scheduling. This paper presents the "deadline‐cost aware task scheduling" (DCaTS) algorithm, which is designed for fog‐based IoT applications. The primary objective of this algorithm is to minimize the monetary cost while taking into account the deadline requirements and priority of tasks. The efficacy of DCaTS is assessed by analyzing various parameters, including completion time, monetary cost, response time, and waiting time. The experimental results show that in comparison with traditional policies, the proposed algorithm shows superiority in all the parameters. The proposed algorithm reduces the monetary cost up to 11.07% compared with the second best result. The results also indicate that DCaTS achieves a reduction of 10.05% in completion time, 10% in response time, and 10.12% in waiting time compared with the second best result. [ABSTRACT FROM AUTHOR]

Summary: With the development of 5G technology, the Internet of Things (IoT) system is becoming more and more widely used in various fields. However, the reliability issue still hinders the wide applications. For instance, the transmission reliability of the IoT system will be significantly affected by the status of end devices, wireless channel quality, and the environment. Moreover, according to the Automatic Repeat‐reQuest (ARQ) retransmission mechanism in the 802.15.4 protocol, if the IoT end devices pursue a conservative power model causing low signal transmission power, it is likely that the signal power will lose too much during the transmission process. This will result in the increase of the network packet loss rate and the power consumption during the data retransmission. To solve this problem, this paper proposes a reasonable transmission power allocation algorithm to ensure the transmission reliability, considering the ARQ retransmission mechanism. The algorithm intends to find the optimal transmission power of each IoT end device, so as to minimize the total energy consumption. The simulation results demonstrate that the power allocation algorithm improves the reliability of the IoT system, compared with other algorithms. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) is a novel paradigm that consists of a wide network of machines and intelligent devices with the capability of communication and interaction with each other. One of the significant issues for the IoT is its healthcare applications. The positioning systems based on their environment can be classified into two main categories: Indoor and outdoor. Recently, IoT indoor positioning attracts too much attention among researchers for patient monitoring. Hence, in this paper, a stress‐free floor plan indoor positioning algorithm for patient monitoring is proposed, called THIP. The history of the patient's movement is stored in a trajectory history, and this information is employed to estimate the patient's more accurate position. The proposed THIP algorithm is simulated in MATLAB software. Several experiments are conducted to evaluate the performance of the proposed algorithm. Simulation results show that the proposed THIP algorithm raises patient positioning accuracy and declines the distance error cumulative compared to LiFS by about 4.68% and 60.6 m, respectively. Moreover, it can identify the floor level of the moving user. [ABSTRACT FROM AUTHOR]

Summary: Internet of Underwater Things (IoUT) comprises various resource‐constrained sensor nodes; therefore, the routing followed in acoustic medium underwater should be energy efficient to preserve their energies. Due to large area covered in the scenarios of underwater, the multi‐hop communication leads to energy hole problem. Therefore, Energy hole mitigation through Optimized Cluster Head (CH) selection and Strategic Routing (EOCSR) in IoUT is proposed in this paper. The proposed work not only optimizes CH selection using Tunicate Swarm Algorithm but also incorporates strategic routing to address energy hole problem. The simulation results show that EOCSR improves stability and lifetime of network by 16.8% and 17.7%, as compared with recently proposed Moth Flame Optimization‐based routing method. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces a new detection mechanism for defending the cyberspace with a new logic that aiding the concept of deep learning. The process involves two phases, namely, feature extraction and classification. The initial phase is the feature extraction, in which the features are extracted from the given input data by the renowned principal component analysis (PCA). Subsequently, the extracted features are subjected to the classification phase, where the deep belief network (DBN) model is used. The DBN model classifies the presence of attacks like denial of service (DoS), probe, R2L, and U2R. In order to make the performance more excellent, this paper diverts the strategy to a new concept termed "Optimization Concept." Here, the hidden neuron of DBN is optimally selected by a new algorithm termed novel mutation rate‐based lion algorithm (NMR‐LA), which is the modified model of lion algorithm (LA). The performance of proposed algorithm NMR‐LA is compared over the conventional models in terms of both positive and negative measures like accuracy, sensitivity, specificity, precision, negative predictive value (NPV), F1 score and Mathews correlation coefficient (MCC), false‐positive rate (FPR), false‐negative rate (FNR), and false‐discovery rate (FDR) and proves the betterments of proposed work. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network is an essential building block for Internet of Things (IoT) due to its usage for collecting and sensing data by the sensor nodes in a nearby environment. Energy efficiency is an important requirement for these networks as the sensors used are short on power and memory. In this paper, an enhanced energy‐efficient clustering protocol is proposed that works on three major phases under network setup: grid formation and grid head selection, clustering, and cluster head (CH) selection. Clustering is achieved using nature‐inspired grey wolf optimization (GWO) algorithm, and CH selection is accomplished using fuzzy inference‐based system. Simulation is performed in MATLAB software, and the protocol is evaluated in terms of network lifetime, the first node dead (FND), half node dead (HND), and last node dead (LND). Additionally, a prototype modeling of IoT using the Thingspeak IoT Platform provided by Mathworks is also integrated with the proposed protocol to demonstrate its usage in IoT applications. The simulation results indicated that the proposed scheme attained better performance in preserving energy and extending network lifetime. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) as an emerging technology is going to play a critical rule in the physical‐cyber ecosystem. In this environment, sensing and actuating are considered as services for building intelligent applications. From performance point of view, a main question is how to locate these services on devices can realize a remarkable cost‐efficiency and energy saving. In this paper, the goal is to specify which accompanying service with another specific service can be set on a device to optimize the energy consumption, load amount on devices, and the service response time. For this purpose, we formulate this goal as a multiobjective decision‐making problem, and then we solve it by using the PROMETHEE method. In this solution, the utility of each device is determined and estimated based on entropy technique that used for computing weights for different criteria. Numerical simulation results show that the proposed co‐locating algorithm, called p‐co‐locating, outperforms none‐co‐locating approach in terms of energy consumption, load amount, and the service response time. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensors are considered the key elements in enabling efficient Internet of Things (IoT) networking and services. One of the key objectives in designing a WSN is increasing network lifetime by reducing the overall network energy consumption. To achieve this, several techniques have been proposed to extend the network lifetime, such as clustering. Clustering divides the network into several clusters, each with its own member nodes and a cluster‐head (CH) node. In this paper, a novel clustering mechanism is developed with the objective of extending network lifetime through load balancing in wireless sensor networks (WSNs). This is achieved by electing a new node to a provides specific tasks in the WSN to decrease energy consumption and enhance network lifetime. This node is called a relay node that is responsible of delivering the received data packets from the CHs, and then forwards them to the central node (sink). Unlike previous clustering schemes, the proposed algorithm consists of three main processes: CH selection, relay node selection, and medium access control processes. These processes depend on energy level and user's distributions. The performance of the proposed algorithm is investigated through simulation experiments in terms of network lifetime under various network parameters. The results reveal that the proposed mechanism significantly enhances network lifetime compared to previously proposed algorithms. [ABSTRACT FROM AUTHOR]

Summary: Different devices with different characteristics form a network to communicate among themselves in Internet of Things (IoT). Thus, IoT is of heterogeneous in nature. Also, Internet plays a major role in IoT. So, issues related to security in Internet become issues of IoT also. Hence, the group and hierarchical management scheme for solving security issues in Internet of Things is proposed in this paper. The devices in the network are formed into groups. One of the devices is selected as a leader of each group. The communication of the devices from each group takes place with the help of the leader of the corresponding group using encrypted key to enhance the security in the network. Blom's key predistribution technique is used to establish secure communication among any nodes of group. The hierarchy is maintained such that the security can be increased further, but the delay is increased as it takes time to encrypt at every level of hierarchy. Hence, the numbers of levels of hierarchy need to be optimized such that delay is balanced. Hence, this algorithm is more suitable for delay‐tolerant applications. The performance of the proposed Algorithm is evaluated and is proved to perform better when compared with the legacy systems like Decentralized Batch‐based Group Key Management Protocol for Mobile Internet of Things (DBGK). [ABSTRACT FROM AUTHOR]

Summary: This paper deals with the lifetime problem in the Internet of Things. We first propose an efficient cluster‐based scheme named "Cuckoo‐search Clustering with Two‐hop Routing Tree (CC‐TRT)" to develop a two‐hop load‐balanced data aggregation routing tree in the network. CC‐TRT uses a modified energy‐aware cuckoo‐search algorithm to fairly select the best cluster head (CH) for each cluster. The applied cuckoo‐search algorithm makes the CH role to rotate between different sensors round by round. Subsequently, we extend the CC‐TRT scheme to present two methods for constructing multi‐hop data aggregation routing trees, named "Cuckoo‐search Clustering with Multi‐Hop Routing Tree (CC‐MRT)" and "Cuckoo‐search Clustering with Weighted Multi‐hop Routing Tree (CC‐WMRT)." Both CC‐MRT and CC‐WMRT rely on a two‐level structure; they not only use an energy‐aware cuckoo‐search algorithm to fairly select the best CHs but also adopt a load‐balanced high‐level routing tree to route the aggregated data of CHs to the sink node. However, CC‐WMRT slightly has a better performance thanks to its low‐level routing strategy. As an advantage, the proposed schemes balance the energy consumption among different sensors. Numerical results show the efficiency of the CC‐TRT, CC‐MRT, and CC‐WMRT algorithms in terms of the number of transmissions, remaining energy, energy consumption variance, and network lifetime. [ABSTRACT FROM AUTHOR]