Wireless communication networks and technologies are witnessed a huge improvement which gain a large number of users. In addition, the choice and using methods of the network are depending on the environment in which it is created. Although the network in each case is unique, many of them share a lot of common. To this end, we propose a new approach for planning the structure of the Internet of Things (IoT) network based on fractals, where fractal figures are utilized to describe the structure of the target environment. Moreover, fractal dimension’s estimation, fraction area occupied by the target environment, and network model are used in the planning process. This approach allows you to choose a model that accurately describes the properties of the environment. Finally, the results proved the suitability of this approach for the IoT network structure planning in an urban or other environment based on the target environment’s data.

Data-driven industrial manufacturing services are proliferating. They use large amounts of data generated from Industrial-Internet-of-Things (IIoT) devices for intelligent services to end-service-users. However, cloud data centers hosting these services consume a huge amount of energy, resulting in a high operational cost. To address this issue, an energy-efficient resource allocation framework is proposed in this article for cloud services. It operates in two phases. First, a multithreshold-based host CPU utilization classification scheme is developed to classify hosts into four groups for improved CPU resource allocation. It is designed through analyzing CPU utilization data by using the least median squares regression technique. Thereby, the scheme limits search space, thus reducing time complexity. In the second phase, with a metaheuristic search, an energy- and thermal-aware resource allocation method is developed to find an energy-efficient host for allocating resources to services. From real data center workload traces, extensive experiments show that our framework outperforms existing baseline approaches with 6.9%, 33.75%, and 34.1% on average in terms of temperature, energy consumption, and service-level-agreement violation, respectively.

Mobile Edge Computing (MEC) is a promising and fast-developing paradigm that provides cloud services at the edge of the network. MEC enables IoT devices to offload and execute their real-time applications at the proximity of these devices with low latency. Such applications include efficient manufacture inspection, virtual/augmented reality, image recognition, Internet of Vehicles (IoV), and e-Health. However, task offloading experiences security and privacy attacks such as data tampering, private data leakage, data replication, etc. To this end, in this paper, we propose a new blockchain-based framework for secure task offloading in MEC systems with guaranteed performance in terms of execution delay and energy consumption. First, blockchain technology is introduced as a platform to achieve data confidentiality, integrity, authentication, and privacy of task offloading in MEC. Second, we formulate an integration model of resource allocation and task offloading for a multi-user with multi-task MEC systems to optimize the energy and time cost. This is an NP-hard problem because of the curse-of-dimensionality and dynamic characteristics challenges of the considered scenario. Therefore, a deep reinforcement learning-based algorithm is developed to derive the close-optimal task offloading decision efficiently. Theoretical analysis and experimental results demonstrate that the proposed framework is resilient to several task offloading security attacks and it can save about 22.2% and 19.4% of system consumption with respect to the local and edge execution scenarios. Moreover, the benchmark analysis proves that the framework consumes few resources in terms of memory and disk usage, CPU utilization, and transaction throughput.

Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies than existing taxonomy techniques, and also highlighted the demerit of Support Vector Machine (SVM). These proposed techniques, therefore, will be of relevance in detection and correction of errors in large contextual data (Big data).

Mobile-edge computing (MEC) has emerged as a new computing paradigm with great potential to alleviate resource limitations attributed to mobile device users (MDUs) by offloading intensive computations to ubiquitous MEC server. However, most of the current offloading policies allow MDUs to transmit their tasks to the same connected small base stations (sBSs), which invariably increases latency and limits performance gain due to overload. Moreover, the security issue mitigating sensitive communication of information is not adequately addressed. Therefore, in this study, in addition to proposing a joint load balancing and computation offloading (CO) technique for MEC systems, we introduce a new security layer to circumvent potential security issues. First, a load balancing algorithm for efficient redistribution of MDUs among sBSs is proposed. In addition, a new advanced encryption standard (AES) cryptographic technique suffused with electrocardiogram (ECG) signal-based encryption and decryption key is presented as a security layer to safeguard the vulnerability of data during the transmission. Furthermore, an integrated model of load balancing, CO and security is formulated as a problem whose goal is to decrease the time and energy demands of the system. Detailed experimental results prove that our model with and without the additional security layers can save about 68.2% and 72.4% of system consumption compared to the local execution.

Smart cities provide citizens with smart and advanced services to improve their quality of life. However, it has been observed that the collection, storage, processing, and analysis of heterogeneous data that are usually borne by citizens will bear certain difficulties. The development of the Internet of Things, cloud computing, social media, and other Industry 4.0 influencers pushed technology into a smart society’s framework, bringing potential vulnerabilities to sensor data, services, and smart city applications. These vulnerabilities lead to data security problems. We propose a decentralized data management system for smart and secure transportation that uses blockchain and the Internet of Things in a sustainable smart city environment to solve the data vulnerability problem. A smart transportation mobility system demands creating an interconnected transit system to ensure flexibility and efficiency. This article introduces prior knowledge and then provides a Hyperledger Fabric-based data architecture that supports a secure, trusted, smart transportation system. The simulation results show the balance between the blockchain mining time and the number of blocks created. We also use the average transaction delay evaluation model to evaluate the model and to test the proposed system’s performance. The system will address residents’ and authorities’ security challenges of the transportation system in smart, sustainable cities and lead to better governance.

Underwater wireless sensor networks (UWSNs) enable various oceanic applications which require effective packet transmission. In this case, sparse node distribution, imbalance in terms of overall energy consumption between the different sensor nodes, dynamic network topology, and inappropriate selection of relay nodes cause void holes. Addressing this problem, we present a relay-based void hole prevention and repair (ReVOHPR) protocol by multiple autonomous underwater vehicles (AUVs) for UWSN. ReVOHPR is a global solution that implements different phases of operations that act mutually in order to efficiently reduce and identify void holes and trap relay nodes to avoid it. ReVOHPR adopts the following operations as ocean depth (levels)-based equal cluster formation, dynamic sleep scheduling, virtual graph-based routing, and relay-assisted void hole repair. For energy-efficient cluster forming, entropy-based eligibility ranking (E2R) is presented, which elects stable cluster heads (CHs). Then, dynamic sleep scheduling is implemented by the dynamic kernel Kalman filter (DK2F) algorithm in which sleep and active modes are based on the node’s current status. Intercluster routing is performed by maximum matching nodes that are selected by dual criteria, and also the data are transmitted to AUV. Finally, void holes are detected and repaired by the bicriteria mayfly optimization (BiCMO) algorithm. The BiCMO focuses on reducing the number of holes and data packet loss and maximizes the quality of service (QoS) and energy efficiency of the network. This protocol is timely dealing with node failures in packet transmission via multihop routing. Simulation is implemented by the NS3 (AquaSim module) simulator that evaluates the performance in the network according to the following metrics: average energy consumption, delay, packet delivery rate, and throughput. The simulation results of the proposed REVOHPR protocol comparing to the previous protocols allowed to conclude that the REVOHPR has considerable advantages. Due to the development of a new protocol with a set of phases for data transmission, energy consumption minimization, and void hole avoidance and mitigation in UWSN, the number of active nodes rate increases with the improvement in overall QoS.

Smartphone devices are widely used in our daily lives. However, these devices exhibit limitations, such as short battery lifetime, limited computation power, small memory size and unpredictable network connectivity. Therefore, numerous solutions have been proposed to mitigate these limitations and extend the battery lifetime with the use of the offloading technique. In this paper, a novel framework is proposed to offload intensive computation tasks from the mobile device to the cloud. This framework uses an optimization model to determine the offloading decision dynamically based on four main parameters, namely, energy consumption, CPU utilization, execution time, and memory usage. In addition, a new security layer is provided to protect the transferred data in the cloud from any attack. The experimental results showed that the framework can select a suitable offloading decision for different types of mobile application tasks while achieving significant performance improvement. Moreover, different from previous techniques, the framework can protect application data from any threat.

Presents corrections to author affiliation information in the above named paper.

Ultrareliable and low‐latency connection (URLLC) is one of the novel features in 5G networks and subsequent generations, in which it targets to fulfill stringent requirements on data rates, reliability, and availability. Moreover, the multiconnectivity concept is introduced to meet these requirements, where multiple different technologies are connected simultaneously, and the data packet is duplicated and transmitted from multiple transmitters. To this end, in this paper, we present an analysis, model, and method to ensure the reliability of data delivery when organizing URLLC in 5G networks. In addition, a new approach based on the organization of multiple connections (multiconnectivity) and duplication of transmitted data is considered. Further, an analytical model is presented for assessing the probability of failure, taking into account the traffic intensity, the probability of failure of elements, and the number of used connections. Moreover, an efficient method is proposed for increasing the reliability of data delivery by optimizing the number of connections. Further, a multiconnectivity‐based URLLC model has been built for evaluating the proposed method and verifies that the optimal number of routes for data delivery between the user and the point of service can be obtained, where the probability of losses and equipment reliability are jointly considered. Finally, detailed analysis of results shown that with “equal” routes in terms of load (with an equally probable traffic distribution) and the probability of equipment failure, the optimal number of routes can be found, at which the minimum probability of losses is achieved.

Mobile edge computing (MEC) is a new paradigm to alleviate resource limitations of mobile IoT networks through computation offloading with low latency. This article presents an efficient and secure multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing. It does not only investigate offloading strategy but also considers resource allocation, compression and security issues. Firstly, to guarantee efficient utilization of the shared resource in multi-user scenarios, radio and computation resources are jointly addressed. In addition, JPEG and MPEG4 compression algorithms are used to reduce the transfer overhead. To fulfill security requirements, a security layer is introduced to protect the transmitted data from cyber-attacks. Furthermore, an integrated model of resource allocation, compression, and security is formulated as an integer nonlinear problem with the objective of minimizing the weighted sum of energy under a latency constraint. As this problem is considered as NP-hard, linearization and relaxation approaches are applied to transform the problem into a convex one. Finally, an efficient offloading algorithm is designed with detailed processes to make the computation offloading decision for computation tasks of mobile users. Simulation results show that our model not only saves about 46% of system overhead consumption in comparison with local execution but also scale well for large-scale IoT networks.