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Delay and stability are two key factors that affect the performance of multicast data transmission in a network. However, current algorithms of tree generation hardly meet the requirements of low delay and high stability simultaneously. Given a general network, the generation algorithm of a multicast tree with minimum delay and maximum stability is an NPhard problem, without a precise and efficient algorithm. To address these challenges, this paper studies the generation of low-delay and high-stability multicast trees under the model of spanning tree based on stability probability, degree-constrained, edge-weighted for multicast (T-SDE). A class of algorithms was proposed which creates the multicast tree greedy on the ratio of fan-out to delay (RFD) and probability of stability of terminal to obtain a high performance in multicast. The proposed algorithms greedily select terminals with a large RFD and a high probability of stability as forwarding nodes in the generation of the multicast tree, where the larger RFD and higher stability of upstream nodes are beneficial to achieve a low transmission delay and high stability in multicast. The proposed RFD can be compatible with the original model, which can take advantage of network connectivity during the generation of a multicast tree. This paper carries out simulation experiments on Matlab R2016b to measure the performance of the proposed algorithm. Experimental results show that the proposed algorithm can provide a smaller height, higher stability, and a lower transmission delay of the resulting multicast tree than other solutions. The spanning tree of the proposed algorithms can support low transmission delay and high stability in multicast transmission. [ABSTRACT FROM AUTHOR]

Real-time health data monitoring is pivotal for bolstering road services' safety, intelligence, and efficiency within the Internet of Health Things (IoHT) framework. Yet, delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems. We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this. This strategy is devised to streamline the data retrieval path, subsequently diminishing network strain. Crafting an adept cache processing scheme poses its own set of challenges, especially given the transient nature of monitoring data and the imperative for swift data transmission, intertwined with resource allocation tactics. This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach, facilitating nuanced health monitoring via edge devices. The system capitalizes on cloud computing for intricate health data analytics, especially in pinpointing health anomalies. Given the dynamic locational shifts and possible connection disruptions, we have architected a hierarchical detection system, particularly during crises. This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times. Furthermore, we introduce the Cache-Assisted Real-Time Detection (CARD) model, crafted to optimize utility. Addressing the inherent complexity of the NP-hard CARD model, we have championed a greedy algorithm as a solution. Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio (CHR) and data freshness, outshining its contemporaneous benchmark algorithms. The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution. To encapsulate, this paper tackles the nuances of real-time health data monitoring in the IoHT landscape, presenting a joint edge-cloud caching strategy paired with a hierarchical detection system. Our methodology yields enhanced cache efficiency and data freshness. The corroborative numerical data accentuates the feasibility and relevance of our model, casting a beacon for the future trajectory of real-time health data monitoring systems. [ABSTRACT FROM AUTHOR]

As an open network architecture, Wireless Computing Power Networks (WCPN) pose new challenges for achieving efficient and secure resource management in networks, because of issues such as insecure communication channels and untrusted device terminals. Blockchain, as a shared, immutable distributed ledger, provides a secure resource management solution for WCPN. However, integrating blockchain into WCPN faces challenges like device heterogeneity, monitoring communication states, and dynamic network nature. Whereas Digital Twins (DT) can accurately maintain digital models of physical entities through real-time data updates and self-learning, enabling continuous optimization of WCPN, improving synchronization performance, ensuring real-time accuracy, and supporting smooth operation of WCPN services. In this paper, we propose a DT for blockchain-empowered WCPN architecture that guarantees real-time data transmission between physical entities and digital models. We adopt an enumeration-based optimal placement algorithm (EOPA) and an improved simulated annealing-based near-optimal placement algorithm (ISAPA) to achieve minimum average DT synchronization latency under the constraint of DT error. Numerical results show that the proposed solution in this paper outperforms benchmarks in terms of average synchronization latency. [ABSTRACT FROM AUTHOR]

The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing linkage is proposed in this paper. The primary objective of this strategy is to maximize the confidentiality capacity of the system. To tackle this, an optimization problem is formulated, taking into consideration interruption probability and interception probability as constraints. To efficiently solve this optimization problem, a low-complexity algorithm rooted in deep reinforcement learning is designed, which aims to derive a suboptimal solution for the problem at hand. Ultimately, through simulation results, the validity of the proposed strategy in guaranteed communication security, stability, and timeliness is substantiated. The results confirm that the proposed intelligent security transmission strategy significantly contributes to the safeguarding of communication integrity, system stability, and timely data delivery. [ABSTRACT FROM AUTHOR]

Asynchronous federated learning (AsynFL) can effectively mitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security. However, the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning (FL) algorithm uses synchronous or asynchronous aggregation. Therefore, there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction. This paper proposes a novel Fixed-point Asynchronous Federated Learning (FixedAsynFL) algorithm, which could mitigate the resource consumption caused by frequent data communication while alleviating the effect of device heterogeneity. FixedAsynFL uses fixed-point quantization to compress the local and global models in AsynFL. In order to balance energy consumption and learning accuracy, this paper proposed a quantization scale selection mechanism. This paper examines the mathematical relationship between the quantization scale and energy consumption of the computation/communication process in the FixedAsynFL. Based on considering the upper bound of quantization noise, this paper optimizes the quantization scale by minimizing communication and computation consumption. This paper performs pertinent experiments on the MNIST dataset with several edge nodes of different computing efficiency. The results show that the FixedAsynFL algorithm with an 8-bit quantization can significantly reduce the communication data size by 81.3% and save the computation energy in the training phase by 74.9% without significant loss of accuracy. According to the experimental results, we can see that the proposed AsynFixedFL algorithm can effectively solve the problem of device heterogeneity and energy consumption limitation of edge nodes. [ABSTRACT FROM AUTHOR]

The paper addresses the challenge of transmitting a big number of files stored in a data center (DC), encrypting them by compilers, and sending them through a network at an acceptable time. Face to the big number of files, only one compiler may not be sufficient to encrypt data in an acceptable time. In this paper, we consider the problem of several compilers and the objective is to find an algorithm that can give an efficient schedule for the given files to be compiled by the compilers. The main objective of the work is to minimize the gap in the total size of assigned files between compilers. This minimization ensures the fair distribution of files to different compilers. This problem is considered to be a very hard problem. This paper presents two research axes. The first axis is related to architecture. We propose a novel pre-compiler architecture in this context. The second axis is algorithmic development. We develop six algorithms to solve the problem, in this context. These algorithms are based on the dispatching rules method, decomposition method, and an iterative approach. These algorithms give approximate solutions for the studied problem. An experimental result is implemented to show the performance of algorithms. Several indicators are used to measure the performance of the proposed algorithms. In addition, five classes are proposed to test the algorithms with a total of 2350 instances. A comparison between the proposed algorithms is presented in different tables discussed to show the performance of each algorithm. The result showed that the best algorithm is the Iterative-mixed Smallest-Longest-Heuristic (ISL) with a percentage equal to 97.7% and an average running time equal to 0.148 s. All other algorithms did not exceed 22% as a percentage. The best algorithm excluding ISL is Iterative-mixed Longest-Smallest Heuristic (ILS) with a percentage equal to 21,4% and an average running time equal to 0.150 s. [ABSTRACT FROM AUTHOR]

This paper examines the performance of Full-Duplex Cooperative Rate Splitting (FD-CRS) with Simultaneous Wireless Information and Power Transfer (SWIPT) support in Multiple Input Single Output (MISO) networks. In a Rate Splitting Multiple Access (RSMA) multicast system with two local users and one remote user, the common data stream contains the needs of all users, and all users can decode the common data stream. Therefore, each user can receive some information that other users need, and local users with better channel conditions can use this information to further enhance the reception reliability and data rate of users with poor channel quality. Even using Cell-Center-Users (CCUs) as a cooperative relay to assist the transmission of common data can improve the average system speed. To maximize the minimum achievable rate, we optimize the beamforming vector of Base Station (BS), the common stream splitting vector, the cooperative distributed beam vector and the strong user transmission power under the power budget constraints of BS and relay devices and the service quality requirements constraints of users. Since the whole problem is not convex, we cannot solve it directly. Therefore, we propose a low complexity algorithm based on Successive Convex Approximation (SCA) technology to find the optimal solution to the problem under consideration. The simulation results show that FD C-RSMA has better gain and more powerful than FD C-NOMA, HD C-RSMA, RSMA and NOMA. [ABSTRACT FROM AUTHOR]

Effective data communication is a crucial aspect of the Social Internet of Things (SIoT) and continues to be a significant research focus. This paper proposes a data forwarding algorithm based on Multidimensional Social Relations (MSRR) in SIoT to solve this problem. The proposed algorithm separates message forwarding into intra- and cross-community forwarding by analyzing interest traits and social connections among nodes. Three new metrics are defined: the intensity of node social relationships, node activity, and community connectivity. Within the community, messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity. When a node performs cross-community forwarding, the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities. The proposed algorithm was compared to three existing routing algorithms in simulation experiments. Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context. [ABSTRACT FROM AUTHOR]

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications, such as remote health monitoring, industrial monitoring, transportation, and smart agriculture. Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes. This paper presents a traffic-aware, cluster-based, and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks. The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies. The protocol considers multi-criteria attributes, i.e. energy, traffic load, and distance parameters to select the next hop for data delivery towards the base station. The performance of the proposed protocol is evaluated through the network simulator NS3.40. For different traffic rates, number of nodes, and different packet sizes, the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio, energy consumption, end-to-end delay, and network lifetime. For 100 nodes, the proposed protocol achieved a 13% improvement in packet delivery ratio, 10 ms improvement in delay, and 10 mJ improvement in average energy consumption over LoRaWAN. [ABSTRACT FROM AUTHOR]

Large-scale wireless sensor networks (WSNs) play a critical role in monitoring dangerous scenarios and responding to medical emergencies. However, the inherent instability and error-prone nature of wireless links present significant challenges, necessitating efficient data collection and reliable transmission services. This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs. The primary goal is to enhance the reliability of data collection and transmission services, ensuring a comprehensive and practical approach. Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability. Additionally, it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops. These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios. Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes. The evaluation spans varying sensor densities, wireless channel conditions, and packet transmission rates, showcasing the protocol's superiority in ensuring reliable and efficient data transfer. Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs. By prioritizing fairness, reliability, and efficiency, the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services, thereby offering a valuable contribution to the field of medical event-drivenWSNs. [ABSTRACT FROM AUTHOR]

The Internet of thing (IoT) is a growing concept for smart cities, and it is compulsory to communicate data between different networks and devices. In the IoT, communication should be rapid with less delay and overhead. For this purpose, flooding is used for reliable data communication in a smart cities concept but at the cost of higher overhead, energy consumption and packet drop etc. This paper aims to increase the efficiency in term of overhead and reliability in term of delay by using multicasting and unicasting instead of flooding during packet forwarding in a smart city using the IoT concept. In this paper, multicasting and unicasting is used for IoT in smart cities within a receiver-initiated mesh-based topology to disseminate the data to the cluster head. Smart cities networks are divided into cluster head, and each cluster head or core node will be responsible for transferring data to the desired receiver. This protocol is a novel approach according to the best of our knowledge, and it proves to be very useful due to its efficiency and reliability in smart cities concept because IoT is a collection of devices and having a similar interest for transmission of data. The results are implemented in Network simulator 2 (NS-2). The result shows that the proposed protocol shows performance in overhead, throughput, packet drop, delay and energy consumption as compared to benchmark schemes. [ABSTRACT FROM AUTHOR]

Orthogonal Frequency Division Multiplexing (OFDM) is a wireless communication technology that is used for highly reliable and high data rate communication. In a multi-user OFDM system, the interference has occurred in the receiver side between the consecutive OFDM symbols. This interference reduces the performance of the OFDM system. To achieve good quality in received symbols the interference level should be minimized. The conventional cancellation system requires higher interference reduction time and power. These limitations of the conventional interference cancellation architectures for OFDM systems are overcome by proposing efficient and low power interference cancellation architecture. Hence, this paper proposes a novel and efficient architecture based on logic gates for interference cancellation in multi-user OFDM systems. The proposed design consist of multiplexers, inverters and OR gate. The heuristic parameters for the proposed cancellation architecture are computed by performing an XOR operation. Compared to existing architecture, the proposed interference cancellation architecture consumes 7 mW of power consumption in the Virtex processor, 33.59 mW of power consumption in Spartan 3E processor and 0.029 mW of power consumption in the CPLD processor. This proposed interference cancellation architecture consumes fewer hardware resources and consumes low power. The proposed system is designed using Verilog High Definition Language (HDL) and synthesized in Xilinx Project Navigator 12.1i. Further, this paper also proposes gate diffusion input (GDI) based implementation of proposed interference cancellation architecture to analyse a delay and power consumption compared to other logic style implementation. [ABSTRACT FROM AUTHOR]

The occurrence of 'sensing holes' not only hinders seamless data collection but also leads to misinterpretation of information in certain areas under extensive data analysis. In order to overcome this, various sensor relocation strategies have been proposed, but the existing relocation strategies revealed problems such as the ping-pong, shaded area, network disconnection, etc. This paper conducted research on relocation protocols in a distributed environment that is very suitable for real-world situations and efficiently recovering the problem of sensing holes. First, a simulation was performed on the distribution of the shaded area for data collection, which is a problem with the existing representative relocation protocol. After that, a data collection capability was newly added to the relay node, which had been in charge of only communication between cluster zones so far, and with this additional functionality, the performance of the revised sensor relocation algorithm was dramatically improved to overcome the existing problems. In addition, the performance and validity of the proposed algorithm were verified through various simulations. [ABSTRACT FROM AUTHOR]

The mixed reality conference system proposed in this paper is a robust, real-time video conference application software that makes up for the simple interaction and lack of immersion and realism of traditional video conference, which realizes the entire process of holographic video conference from client to cloud to the client. This paper mainly focuses on designing and implementing a video conference system based on AI segmentation technology and mixed reality. Several mixed reality conference system components are discussed, including data collection, data transmission, processing, and mixed reality presentation. The data layer is mainly used for data collection, integration, and video and audio codecs. The network layer uses Web-RTC to realize peer-to-peer data communication. The data processing layer is the core part of the system, mainly for human video matting and human-computer interaction, which is the key to realizing mixed reality conferences and improving the interactive experience. The presentation layer explicitly includes the login interface of the mixed reality conference system, the presentation of real-time matting of human subjects, and the presentation objects. With the mixed reality conference system, conference participants in different places can see each other in real-time in their mixed reality scene and share presentation content and 3D models based on mixed reality technology to have a more interactive and immersive experience. [ABSTRACT FROM AUTHOR]

In the development of technology in various fields like big data analysis, data mining, big data, cloud computing, and blockchain technology, security become more constrained. Blockchain is used in providing security by encrypting the sharing of information. Blockchain is applied in the peer-to-peer (P2P) network and it has a decentralized ledger. Providing security against unauthorized breaches in the distributed network is required. To detect unauthorized breaches, there are numerous techniques were developed and those techniques are inefficient and have poor data integrity. Hence, a novel technique needs to be implemented to tackle the new breaches in the distributed network. This paper, proposed a hybrid technique of two fish with a ripple consensus algorithm (TF-RC). To improve the detection time and security, this paper uses efficient transmission of data in the distributed network. The experimental analysis of TF-RC by using the metric measures of performance in terms of latency, throughput, energy efficiency and it produced better performance. [ABSTRACT FROM AUTHOR]

The development of human-robot interaction has been continuously increasing for the last decades. Through this development, it has become simpler and safe interactions using a remotely controlled telepresence robot in an insecure and hazardous environment. The audio-video communication connection or data transmission stability has already been well handled by fast-growing technologies such as 5Gand 6G. However, the design of the physical parameters, e.g., maneuverability, controllability, and stability, still needs attention. Therefore, the paper aims to present a systematic, controlled design and implementation of a telepresence mobile robot. The primary focus of this paper is to performthe computational analysis and experimental implementation design with sophisticated position control, which autonomously controls the robot's position and speed when reaching an obstacle. A system model and a position controller design are developed with root locus points. The design robot results are verified experimentally, showing the robot's agreement and control in the desired position. The robot was tested by considering various parameters: driving straight ahead, right turn, self-localization and complex path. The results prove that the proposed approach is flexible and adaptable and gives a better alternative. The experimental results show that the proposed method significantly minimizes the obstacle hits. [ABSTRACT FROM AUTHOR]

In recent scenario of Wireless Sensor Networks (WSNs), there are many application developed for handling sensitive and private data such as military information, surveillance data, tracking, etc. Hence, the sensor nodes of WSNs are distributed in an intimidating region, which is non-rigid to attacks. The recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and threats. In traditional models, the solution has been made for defending the networks, only to specific attacks. However, in real-time applications, the kind of attack that is launched by the adversary is not known. Additionally, on developing a security mechanism for WSN, the resource constraints of sensor nodes are also to be considered. With that note, this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism (IDRM) for defending the sensor network from various attacks. Moreover, for efficient model design, the work includes the part of feature evaluation of some general attacks of WSNs. The IDRM also includes determination of optimal secure paths and Node security for secure routing operations. The performance of the proposed model is evaluated with respect to several factors; it is found that the model has achieved better security levels and is efficient than other existing models in WSN communications. It is proven that the proposed IDRM produces 74% of PDR in average and a minimized packet drop of 38% when comparing with the existing works. [ABSTRACT FROM AUTHOR]

By the analysis of vulnerabilities of Android native system services, we find that some vulnerabilities are caused by inconsistent data transmission and inconsistent data processing logic between client and server. The existing research cannot find the above two types of vulnerabilities and the test cases of them face the problem of low coverage. In this paper, we propose an extraction method of test cases based on the native system services of the client and design a case construction method that supports multi-parameter mutation based on genetic algorithm and priority strategy. Based on the above method, we implement a detection tool-BArcherFuzzer to detect vulnerabilities of Android native system services. The experiment results show that BArcherFuzzer found four vulnerabilities of hundreds of exception messages, all of them were confirmed by Google and one was assigned a Common Vulnerabilities and Exposures (CVE) number (CVE-2020-0363). [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is vulnerable to data-tampering (DT) attacks. Due to resource limitations, many anomaly detection systems (ADSs) for IoT have high false positive rates when detecting DT attacks. This leads to the misreporting of normal data, which will impact the normal operation of IoT. To mitigate the impact caused by the high false positive rate of ADS, this paper proposes an ADS management scheme for clustered IoT. First, we model the data transmission and anomaly detection in clustered IoT. Then, the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device. In the presence of a high false positive rate in ADSs, to deal with the trade-off between the security and availability of data, we develop a linear programming model referred to as a security trade-off (ST) model. Next, we develop an analysis framework for the ST model, and solve the ST model on an IoT simulation platform. Last, we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis. Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS. [ABSTRACT FROM AUTHOR]

Many Internet of things application scenarios have the characteristics of limited hardware resources and limited energy supply, which are not suitable for traditional security technology. The security technology based on the physical mechanism has attracted extensive attention. How to improve the key generation rate has always been one of the urgent problems to be solved in the security technology based on the physical mechanism. In this paper, superlattice technology is introduced to the security field of Internet of things, and a high-speed symmetric key generation scheme based on superlattice for Internet of things is proposed. In order to ensure the efficiency and privacy of data transmission, we also combine the superlattice symmetric key and compressive sensing technology to build a lightweight data transmission scheme that supports data compression and data encryption at the same time. Theoretical analysis and experimental evaluation results show that the proposed scheme is superior to the most closely related work. [ABSTRACT FROM AUTHOR]

In the data communication system, the real-time information interaction of communication device increases the risk of privacy sensitive data being tampered with. Therefore, maintaining data security is one of the most important issues in network data communication. Because the timestamp is the most important way to authenticate data in information interaction, it is very necessary to provide timestamp service in the data communication system. However, the existing centralized timestamp mechanism is difficult to provide credible timestamp service, and users can conspire with timestamping servers to forge timestamps. Therefore, this paper designs a distributed timestamp mechanism based on continuous verifiable delay functions. It utilizes multiple independent timestamp servers to provide timestamp services in a distributed model and appends the timestamp to the data once the data is generated. Thus, it can prove that the data already exists at a certain time and ensure the accuracy of the timestamp. Moreover, a digital blind signature based on elliptic curve cryptography is utilized to solve the problem of timestamp forgery in timestamp service. Finally, the security analysis of the scheme ensures the data security of data communication system and the concurrency rate of timestamp. The experimental results also show that the scheme greatly improves the efficiency of digital signatures. [ABSTRACT FROM AUTHOR]

As networked control systems continue to be widely used in large-scale industrial productions, industrial cyber-attacks have become an inevitable problem that can cause serious damage to critical infrastructures. In practice, industrial intrusion detection has been widely acknowledged to detect abnormal communication behaviors. However, unlike traditional IT systems, networked control systems have their own communication characteristics due to specific industrial communication protocols. Thus, simple cyber-attack modeling is inadequate and impractical for high-efficiency intrusion detection because the characteristics of network control systems are less considered. Based on the status information and transmission connection in industrial communication data payloads, which can properly express the characteristics of industrial control logic, this paper associates industrial communication features with transmission connection payload and status payload. Furthermore, transmission connection features include device address, context, time, and packet length, while status features cover measurement, input, distributed state, control state, and more. After designing a convolutional neural network (CNN) and a long short-term memory network (LSTM) to extract status features and transmission connection features from industrial communication data, this paper proposes a hierarchical deep learning anomaly detection approach, which can integrate the advantages of CNN and LSTM to achieve high-efficiency detection. The experimental results clearly show that the proposed approach, having the advantages of strong detection capability and low false alarm rate, is a superior means of anomaly detection when compared to its peers. [ABSTRACT FROM AUTHOR]

In many IIoT architectures, various devices connect to the edge cloud via gateway systems. For data processing, numerous data are delivered to the edge cloud. Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency. There are two types of costs for this kind of IoT network: a communication cost and a computing cost. For service efficiency, the communication cost of data transmission should be minimized, and the computing cost in the edge cloud should be alsominimized. Therefore, in this paper, the communication cost for data transmission is defined as the delay factor, and the computing cost in the edge cloud is defined as the waiting time of the computing intensity. The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs. That is, a device chooses a routing path to the selected edge cloud based on the costs. The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load. The performance of the proposed method is validated through extensive computer simulation. When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states, the proposed method reduced both the average delay and the service pause counts to about 25% of the existing method. [ABSTRACT FROM AUTHOR]

The vehicular sensor network (VSN) is an important part of intelligent transportation, which is used for real-time detection and operation control of vehicles and real-time transmission of data and information. In the environment ofVSN, massive private data generated by vehicles are transmitted in open channels and used by other vehicle users, so it is crucial to maintain high transmission efficiency and high confidentiality of data. To deal with this problem, in this paper, we propose a heterogeneous fault-tolerant aggregate signcryption scheme with an equality test (HFTASET). The scheme combines fault-tolerant and aggregate signcryption, whichnot onlymakes up for the deficiency of low security of aggregate signature, but alsomakes up for the deficiency that aggregate signcryption cannot tolerate invalid signature. The scheme supports one verification pass when all signcryptions are valid, and it supports unbounded aggregation when the total number of signcryptions grows dynamically. In addition, this scheme supports heterogeneous equality test, and realizes the access control of private data in different cryptographic environments, so as to achieve flexibility in the application of our scheme and realize the function of quick search of plaintext or ciphertext. Then, the security of HFTAS-ET is demonstrated by strict theoretical analysis. Finally, we conduct strict and standardized experimental operation and performance evaluation, which shows that the scheme has better performance. [ABSTRACT FROM AUTHOR]

With the rapid miniaturization in sensor technology, Internet-of- Drones (IoD) has delighted researchers towards information transmission security among drones with the control station server (CSS). In IoD, the drone is different in shapes, sizes, characteristics, and configurations. It can be classified on the purpose of its deployment, either in the civilian ormilitary domain. Drone's manufacturing, equipment installation, power supply, multi-rotor system, and embedded sensors are not issues for researchers. The main thing is to utilize a drone for a complex and sensitive task using an infrastructure-less/ self-organization/resource-less network type called Flying Ad Hoc Network (FANET). Monitoring data transmission traffic, emergency and rescue operations, border surveillance, search and physical phenomenon sensing, and so on can be achieved by developing a robust mutual authentication and cross-verification scheme for IoD deployment civilian drones. Although several protocols are available in the literature, they are either design issues or suffering from other vulnerabilities; still, no one claims with conviction about fool proof security mechanisms. Therefore, in this paper, the researchers highlighted the major deficits in prior protocols of the domain, i.e., these protocols are either vulnerable to forgery, side channel, stolen-verifier attacks, or raised the outdated data transmission flaw. In order to overcome these loopholes and provide a solution to the existing vulnerabilities, this paper proposed an improved and robust public key infrastructure (PKI) based authentication scheme for the IoD environment. The proposed protocol's security analysis section has been conducted formally using BAN (Burrows-Abadi-Needham) logic, ProVerif2.03 simulation, and informally using discussion/pragmatic illustration. While the performance analysis section of the paper has been assessed by considering storage, computation, and communication cost. Upon comparing the proposed protocol with prior works, it has been demonstrated that it is efficient and effective and recommended for practical implementation in the IoD environment. [ABSTRACT FROM AUTHOR]

Health monitoring systems are now required, particularly for essential patients, following the COVID-19 pandemic, which was followed by its variants and other epidemics of a similar nature. Effective procedures and strategies are required, though, to react promptly to the enormous volume of real-time data offered by monitoring equipment. Although fog-based designs for IoT health systems typically result in enhanced services, they also give rise to issues that need to be resolved. In this paper, we propose a two-way strategy to reduce network latency and usewhile increasing real-time data transmission of device gateways used for sensors by making educated judgments for connection setup with BS and task assignment. For this, a simulation using iFogSim in the Eclipse IDE showed how effective the suggested strategy for massive IoT healthmonitoring systems is. The algorithm is analyzed for network usage and latency, and the results reveal 20%-25% improvements compared to the existing methods regarding network usage and latency. [ABSTRACT FROM AUTHOR]

The intact data transmission to the authentic user is becoming crucial at every moment in the current era. Steganography; is a technique for concealing the hidden message in any cover media such as image, video; and audio to increase the protection of data. The resilience and imperceptibility are improved by choosing an appropriate embedding position. This paper gives a novel system to immerse the secret information in different videos with different methods. An audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user's face image. A hidden message is first included in the audio from the multimedia file; using LSB Technique. The Stego-video is created in the second stage by merging the authorized user's face into the frame of the video; by using PVD technology. Stego-audio is linked again with the stego-video in the third stage. The incorporated perspective techniques (LSB-SS and PVD-SS algorithms) with more significant data immersing capacity, good robustness and imperceptibility are proposed in this research work. The spread spectrum approach is used to increase the complexity of secret data recognition. Two different video files are tested with different voice files with the results such as PSNR, SSIM, RMSE and MSE as 52.3, 0.9963, 0.0024 and 0.0000059, respectively. [ABSTRACT FROM AUTHOR]

The fifth-generation (5G) wireless technology is the most recent standardization in communication services of interest across the globe. The concept of Multiple-Input-Multiple-Output antenna (MIMO) systems has recently been incorporated to operate at higher frequencies without limitations. This paper addresses, design of a high-gain MIMO antenna that offers a bandwidth of 400 MHz and 2.58 GHz by resonating at 28 and 38 GHz, respectively for 5G millimeter (mm)-wave applications. The proposed design is developed on a RT Duroid 5880 substrate with a single elemental dimension of 9.53 × 7.85 × 0.8 mm³. The patch antenna is fully grounded and is fed with a 50-ohm stepped impedance microstrip line. It also has an I-shaped slot and two electromagnetically coupled parasitic slotted components. This design is initially constructed as a single-element structure and proceeded to a six-element MIMO antenna configuration with overall dimensions of 50 × 35 × 0.8 mm³. The simulated prototype is fabricated and measured for analyzing its performance characteristics, along with MIMO antenna diversity performance factors making the proposed antenna suitable for 5G mm-wave and 5G-operated handheld devices. [ABSTRACT FROM AUTHOR]

Cloud computing is considered to facilitate a more cost-effective way to deploy scientific workflows. The individual tasks of a scientific workflow necessitate a diversified number of large states that are spatially located in different datacenters, thereby resulting in huge delays during data transmission. Edge computing minimizes the delays in data transmission and supports the fixed storage strategy for scientific workflow private datasets. Therefore, this fixed storage strategy creates huge amount of bottleneck in its storage capacity. At this juncture, integrating the merits of cloud computing and edge computing during the process of rationalizing the data placement of scientific workflows and optimizing the energy and time incurred in data transmission across different datacentres remains a challenge. In this paper, Adaptive Cooperative Foraging and Dispersed Foraging Strategies-Improved Harris Hawks Optimization Algorithm (ACF-DFS-HHOA) is proposed for optimizing the energy and data transmission time in the event of placing data for a specific scientific workflow. This ACF-DFS-HHOA considered the factors influencing transmission delay and energy consumption of data centers into account during the process of rationalizing the data placement of scientific workflows. The adaptive cooperative and dispersed foraging strategy is included in HHOA to guide the position updates that improve population diversity and effectively prevent the algorithm from being trapped into local optimality points. The experimental results of ACF-DFS-HHOA confirmed its predominance in minimizing energy and data transmission time incurred during workflow execution. [ABSTRACT FROM AUTHOR]

Long Range Wide Area Network (LoRaWAN) has been developed to meet the requirements for the enormous device-to-device communication of Internet of Things (IoT) networks, which consist of a large number of participating devices spread over large coverage areas with low data rates and low power consumption. It supports communications in both directions, uplink, and downlink directions. However, the downlink communication in the current LoRaWAN raises the bottleneck issue at gateways due to the used gateway selection algorithm. This paper proposes a novel gateway selection algorithm based on the duty cycle time-off values for the existing gateways, Duty Cycle Gateway Selection (DCGS), to direct acknowledgment packets as downlink traffic towards the most suitable gateway. Thus, the proposed system avoids subsequent retransmission of previously sent traffic that leads to excessive traffic overloading the network. The proposed system avoids exhausting a gateway duty cycle with downlink traffic by distributing the downlink traffic among available gateways based on the duty cycle time off. DCGS is evaluated using FloRa and INET frameworks in the well-known network simulator OMNeT++. The result shows the superior performance of the proposed approach over the existing Signal-to-Noise ratio (SNR) based selection mechanism. It clearly indicates that the DCGS maintains a better confirmed packet delivery rate while reducing number of retransmissions, collisions, and power consumption. [ABSTRACT FROM AUTHOR]

Nowadays, Wireless Sensor Network (WSN) is a modern technology with a wide range of applications and greatly attractive benefits, for example, self-governing, low expenditure on execution and data communication, long-term function, and unsupervised access to the network. The Internet of Things (IoT) is an attractive, exciting paradigm. By applying communication technologies in sensors and supervising features, WSNs have initiated communication between the IoT devices. Though IoT offers access to the highest amount of information collected through WSNs, it leads to privacy management problems. Hence, this paper provides a Logistic Regression machine learning with the Elliptical Curve Cryptography technique (LRECC) to establish a secure IoT structure for preventing, detecting, and mitigating threats. This approach uses the Elliptical Curve Cryptography (ECC) algorithm to generate and distribute security keys. ECC algorithm is a light weight key; thus, it minimizes the routing overhead. Furthermore, the Logistic Regression machine learning technique selects the transmitter based on intelligent results. The main application of this approach is smart cities. This approach provides continuing reliable routing paths with small overheads. In addition, route nodes cooperate with IoT, and it handles the resources proficiently and minimizes the 29.95% delay. [ABSTRACT FROM AUTHOR]

A Mobile Ad hoc NETwork (MANET) is a self-configuring network that is not reliant on infrastructure. This paper introduces a new multipath routing method based on the Multi-Hop Routing (MHR) technique. MHR is the consecutive selection of suitable relay nodes to send information across nodes that are not within direct range of each other. Failing to ensure good MHR leads to several negative consequences, ultimately causing unsuccessful data transmission in a MANET. This research work consists of three portions. The first to attempt to propose an efficient MHR protocol is the design of Priority Based Dynamic Routing (PBDR) to adapt to the dynamic MANET environment by reducing Node Link Failures (NLF) in the network. This is achieved by dynamically considering a node's mobility parameters like relative velocity and link duration, which enable the next-hop selection. This method works more efficiently than the traditional protocols. Then the second stage is the Improved Multi-Path Dynamic Routing (IMPDR). The enhancement is mainly focused on further improving the Quality of Service (QoS) in MANETs by introducing a QoS timer at every node to help in the QoS routing of MANETs. Since QoS is the most vital metric that assesses a protocol, its dynamic estimation has improved network performance considerably. This method uses distance, linkability, trust, and QoS as the four parameters for the next-hop selection. IMPDR is compared against traditional routing protocols. The Network Simulator-2 (NS2) is used to conduct a simulation analysis of the protocols under consideration. The proposed tests are assessed for the Packet Delivery Ratio (PDR), Packet Loss Rate (PLR), End-to-End Delay (EED), and Network Throughput (NT). [ABSTRACT FROM AUTHOR]

Opportunistic Networks (OppNets) is gaining popularity day-by-day due to their various applications in the real-life world. The two major reasons for its popularity are its suitability to be established without any requirement of additional infrastructure and the ability to tolerate long delays during data communication. Opportunistic Network is also considered as a descendant of Mobile Ad hoc Networks (Manets) and Wireless Sensor Networks (WSNs), therefore, it inherits most of the traits from both mentioned networking techniques. Apart from its popularity, Opportunistic Networks are also starting to face challenges nowadays to comply with the emerging issues of the large size of data to be communicated and blind forwarding of data among participating nodes in the network. These issues lower the overall performance of the network. Keeping this thing in mind, ML-Fresh-a novel framework has been proposed in this paper which focuses to overcome the issue of blind forwarding of data by maintaining an optimum path between any pair of participating nodes available in the OppNet using machine learning techniques viz. pattern prediction, decision tree prediction, adamic-adar method for complex networks. Apart from this, ML-Fresh also uses the history of successful encounters between a pair of communicating nodes for route prediction in the background. Simulation results prove that the ML-Fresh outperforms the existing framework of Opportunistic Networks on the grounds of standard Quality-of-Service (QoS) parameters. [ABSTRACT FROM AUTHOR]

This paper proposes a new cross-layer communication system for the provision of Internet services and applications to mitigate the negative impacts of COVID-19, due to which the massive online demands are affecting the current communication systems’ infrastructures and capabilities. The system requirements and model are investigated where it utilizes high-altitude platform (HAP) for fast and efficient connectivity provision to bridge the communication infrastructure gap in the current pandemic. The HAP is linked to the main server or gateway station located on ground and can provide communication narrow beams towards isolated areas which suffer from poor terrestrial radio coverage or lack of communication infrastructure. The vital e-learning applications using Internet services provision from the proposed HAP system are described and modelled including system adaptation parameters such as the application and physical layers to control the data rates of different e-learning applications and the overall cell data rate. On the other hand, the provision of high-speed Internet services from the proposed system is supported by using adaptive antenna arrays onboard HAP which provides high-gain beams to achieve the required high-quality transmission data rates at the student premises and provides the capability of coverage cell area adaptation for load balancing. The concentric circular antenna arrays with tapered feeding are proposed in this adaptive antenna system to control the cell mainlobe gain and reduce the out-of-coverage radiation as well. In addition, the system feasibility has been proved in two coverage scenarios including single-beam and multibeam HAP communications. [ABSTRACT FROM AUTHOR]

An efficient trust-aware secure routing and network strategy-based data collection scheme is presented in this paper to enhance the performance and security of wireless sensor networks during data collection. The method first discovers the routes between the data sensors and the sink node. Several factors are considered for each sensor node along the route, including energy, number of neighbours, previous transmissions, and energy depletion ratio. Considering all these variables, the Sink Reachable Support Measure and the Secure Communication Support Measure, the method evaluates two distinct measures. The method calculates the data carrier support value using these two metrics. A single route is chosen to collect data based on the value of data carrier support. It has contributed to the design of Secure Communication Support (SCS) Estimation. This has been measured according to the strategy of each hop of the route. The suggested method improves the security and efficacy of data collection in wireless sensor networks. The second stage uses the two-fish approach to build a trust model for secure data transfer. A simulation exercise was conducted to evaluate the effectiveness of the suggested framework. Metrics, including PDR, end-to-end latency, and average residual energy, were assessed for the proposed model. The efficiency of the suggested route design serves as evidence for the average residual energy for the proposed framework. [ABSTRACT FROM AUTHOR]

In today's information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput, energy efficiency, and end-to-end delay, wherein low latency is considered a challenging issue in next-generation networks (NGN). This paper introduces a single and parallels stable server queuing model with amulti-class of packets and native and coded packet flowto illustrate the simple chain topology and complexmultiway relay (MWR) node with specific neighbor topology. Further, for improving data transmission capacity inMHWSNs, an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node. Finally, the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets. The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results. [ABSTRACT FROM AUTHOR]

In this paper, a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios. It is designed further to make their transmission decision that automatically adapts to the transmission dynamics to mitigate the launched jamming attacks. The generative adversarial learning neural network (GALNN) or generative dynamic neural network (GDNN) automatically learns with the synthesized training data (training) with a generator and discriminator type neural networks that encompass minimax game theory. The elimination of the jamming attack is carried out with the assistance of the defense strategies and with an increased detection rate in the generative adversarial network (GAN). The GDNN with game theory is designed to validate the channel condition with the cross entropy loss function and back-propagation algorithm, which improves the communication reliability in the network. The simulation is conducted in NS2.34 tool against several performance metrics to reduce the misdetection rate and false alarm rates. The results show that the GDNN obtains an increased rate of successful transmission by taking optimal actions to act as a defense mechanism to mislead the jammer, where the jammer makes high misclassification errors on transmission dynamics. [ABSTRACT FROM AUTHOR]

One of the recent advancements in the electrical power systems is the smart-grid technology. For the effective functioning of the smart grid, the process like monitoring and controlling have to be given importance. In this paper, the Wireless Sensor Network (WSN) is utilized for tracking the power in smart grid applications. The smart grid is used to produce the electricity and it is connected with the sensor to transmit or receive the data. The data is transmitted quickly by using the Probabilistic Neural Network (PNN), which aids in identifying the shortest path of the nodes. While transmitting the data from the smart grid to the (Internet of Things) IoT web page, it is secured by introducing the secret keys between the neighbouring nodes through the process of key-management. In this method, the combination of Lagrange's theorem and the Location Based Key (LBK) management is used for better security performance. This approach delivers optimal performance in terms of security, throughput, packet loss and delay, which are comparatively better than the existing methods. [ABSTRACT FROM AUTHOR]

Mobile Ad Hoc Networks (MANET) is the framework for social networking with a realistic framework. In the MANET environment, based on the query, information is transmitted between the sender and receiver. In the MANET network, the nodes within the communication range are involved in data transmission. Even the nodes that lie outside of the communication range are involved in the transmission of relay messages. However, due to the openness and frequent mobility of nodes, they are subjected to the vast range of security threats in MANET.Hence, it is necessary to develop an appropriate security mechanism for the data MANET environment for data transmission. This paper proposed a security framework for the MANET network signature escrow scheme. The proposed framework uses the centralised Software Defined Network (SDN) with an ECC cryptographic technique. The developed security framework is stated as Escrow Elliptical Curve Cryptography SDN (EsECC_SDN) for attack detection and classification. The developed EsECC-SDN was adopted in two stages for attack classification and detection: (1) to perform secure data transmission between nodes SDN performs encryption and decryption of the data; and (2) to detect and classifies the attack in the MANET hyper alert based Hidden Markov Model Transductive Deep Learning. Furthermore, the EsECC_SDN is involved in the assignment of labels in the transmitted data in the database (DB). The escrow handles these processes, and attacks are evaluated using the hyper alert. The labels are assigned based on the k-medoids attack clustering through label assignment through a transductive deep learning model. The proposed model uses the CICIDS dataset for attack detection and classification. The developed framework EsECC_SDN’s performance is compared to that of other classifiers such as AdaBoost, Regression, and Decision Tree. The performance of the proposed EsECC_SDN exhibits ∼3% improved performance compared with conventional techniques. [ABSTRACT FROM AUTHOR]

IEEE 802.11 Wi-Fi networks are prone to many denial of service (DoS) attacks due to vulnerabilities at the media access control (MAC) layer of the 802.11 protocol. Due to the data transmission nature of the wireless local area network (WLAN) through radio waves, its communication is exposed to the possibility of being attacked by illegitimate users. Moreover, the security design of the wireless structure is vulnerable to versatile attacks. For example, the attacker can imitate genuine features, rendering classification-based methods inaccurate in differentiating between real and false messages. Although many security standards have been proposed over the last decades to overcome many wireless network attacks, effectively detecting such attacks is crucial in today’s real-world applications. This paper presents a novel resource exhaustion attack detection scheme (READS) to detect resource exhaustion attacks effectively. The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack. The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in the WLAN. The proposed scheme consists of four modules which make it capable to alleviates the attack impact more effectively than the related work. The experimental results show the effectiveness of the proposed technique by gaining an 89.11% improvement compared to the existing works in terms of detection. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) advances, machine-type devices are densely deployed and massive networks such as ultra-dense networks (UDNs) are formed. Various devices attend to the network to transmit data using machine-type communication (MTC), whereby numerous, various are generated. MTC devices generally have resource constraints and use wireless communication. In this kind of network, data aggregation is a key function to provide transmission efficiency. It can reduce the number of transmitted data in the network, and this leads to energy saving and reducing transmission delays. In order to effectively operate data aggregation in UDNs, it is important to select an aggregation point well. The total number of transmitted data may vary, depending on the aggregation point to which the data are delivered. Therefore, in this paper, we propose a novel data aggregation scheme to select the appropriate aggregation point and describe the data transmission method applying the proposed aggregation scheme. In addition, we evaluate the proposed scheme with extensive computer simulations. Better performances in the proposed scheme are achieved compared to the conventional approach. [ABSTRACT FROM AUTHOR]

In recent decades, several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during transmission to a shorter distance while restricting the Primary Users (PUs) interference. The Cognitive Radio (CR) system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm (ASDIC) that shows better spectrum sensing among group of multiusers in terms of sensing error, power saving, and convergence time. In this research paper, the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity. In this research, multiple random Secondary Users (SUs), and PUs are considered for implementation. Hence, the proposed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algorithms. Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646% compared to the existing algorithms. Similarly, ASDIC algorithm reduced 24.23% of SUs average node power compared to the existing algorithms. Probability of detection is higher by reducing the Signal-to-Noise Ratio (SNR) to 2 dB values. The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection. Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of network capacity. [ABSTRACT FROM AUTHOR]

Nomadic Vehicular Cloud (NVC) is envisaged in this work. The predominant aspects of NVC is, it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model (CTM). Containers are used in the place of Virtual Machines (VM), as containers' features are very apt to NVC's dynamic environment. The specifications of 5G NR V2X PC5 interface are applied to NVC, for the feature of not relying on the network coverage. Nowadays, the peak traffic on the road and the bottlenecks due to it are inevitable, which are seen here as the benefits for VC in terms of resource availability and residual in-network time. The speed range of high-end vehicles poses the issue of dis-connectivity among VC participants, that results the container migration failure. As the entire VC participants are on the move, to maintain proximity of the containers hosted by them, estimating their movements plays a vital role. To infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity, this paper proposes to apply the CTM to the container based and 5G NR V2X enabled NVC. The simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS), as an efficient data transmission method, has achieved great success in the field of data transmission such as image, video and text. It can robustly recover signals from fewer Measurements, effectively alleviating the bandwidth pressure during data transmission. However, CS has many shortcomings in the transmission of hyperspectral image (HSI) data. This work aims to consider the application of CS in the transmission of hyperspectral image (HSI) data, and provides a feasible research scheme for CS of HSI data. HSI has rich spectral information and spatial information in bands, which can reflect the physical properties of the target. Most of the hyperspectral image compressed sensing (HSICS) algorithms cannot effectively use the inter-band information of HSI, resulting in poor reconstruction effects. In this paper, A three-stage hyperspectral image compression sensing algorithm (Three-stages HSICS) is proposed to obtain intra-band and inter-band characteristics of HSI, which can improve the reconstruction accuracy of HSI. Here, we establish a multi-objective band selection (Mop-BS)model, amulti-hypothesis prediction (MHP)model and a residual sparse (ReWSR)model for HSI, and use a staged reconstruction method to restore the compressed HSI. The simulation results show that the three-stage HSICS successfully improves the reconstruction accuracy of HSICS, and it performs best among all comparison algorithms. [ABSTRACT FROM AUTHOR]

There are changes in the development of wireless technology systems every decade. 6G (sixth generation) wireless networks improve on previous generations by increasing dependability, accelerating networks, increasing available bandwidth, decreasing latency, and increasing data transmission speed to standardize communication signals. The purpose of this article is to comprehend the current directions in 6G studies and their relationship to the Internet of Things (IoT). Also, this paper discusses the impacts of 6G on IoT, critical requirements and trends for 6G-enabled IoT, new service classes of 6G and IoT technologies, and current 6G-enabled IoT studies selected by the systematic literature review (SLR) method published from 2018 to 2021. In addition, we present a technical taxonomy for the classification of 6G-enabled IoT, which includes self-organization systems, energy efficiency, channel assessment, and security. Also, according to the articles reviewed, we consider the evaluation factors in this domain, including data transmission, delay, energy consumption, and bandwidth. Finally, we focus on open issues and future research challenges in 6G-enabled IoT. To mention important future challenges and directions, we can point to migration, data storage, data resource, data security, data sharing, data offloading, availability, scalability, portability, user experience, reliability, authentication, and authorization. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) and blockchain receive significant interest owing to their applicability in different application areas such as healthcare, finance, transportation, etc. Medical image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public networks. This paper presents a new wind driven optimization algorithm based medical image encryption (WDOA-MIE) technique for blockchain enabled IoT environments. The WDOA-MIE model involves three major processes namely data collection, image encryption, optimal key generation, and data transmission. Initially, the medical images were captured from the patient using IoT devices. Then, the captured images are encrypted using signcryption technique. In addition, for improving the performance of the signcryption technique, the optimal key generation procedure was applied by WDOA algorithm. The goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio (PSNR). Upon successful encryption of images, the IoT devices transmit to the closest server for storing it in the blockchain securely. The performance of the presented method was analyzed utilizing the benchmark medical image dataset. The security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is an important part of the Internet of Things (IoT), which are used for information exchange and communication between smart objects. In practical applications, WSN lifecycle can be influenced by the unbalanced distribution of node centrality and excessive energy consumption, etc. In order to overcome these problems, a heterogeneous wireless sensor network model with small world characteristics is constructed to balance the centrality and enhance the invulnerability of the network. Also, a new WSN centrality measurement method and a new invulnerability measurement model are proposed based on the WSN data transmission characteristics. Simulation results show that the life cycle and data transmission volume of the network can be improved with a lower network construction cost, and the invulnerability of the network is effectively enhanced. [ABSTRACT FROM AUTHOR]

In recent years, due to the rapid progress of various technologies, wireless computer networks have developed. However, the activities of the security threats and attackers affect the data communication of these technologies. So, to protect the network against these security threats, an efficient IDS (Intrusion Detection System) is presented in this paper. Namely, optimized long short-term memory (OLSTM) network with a stacked auto-encoder (SAE) network is proposed as an IDS system. Using SAE, significant features are extracted from the databases such as input NSL-KDD database and the UNSW-NB15 database. Then extracted features are given as input to the optimized LSTM which is used as an intrusion identification system. To enhance the effectiveness of the LSTM, we present the pigeon optimization algorithm (POA). Using this algorithm, weight parameters of the LSTM are chosen optimally. Finally, the proposed IDS model decides whether the input packets are intruded or not. The results confirm that the proposed IDS model surpasses the previous machine learning-based IDS models in terms of correctness, F1-score and G mean. [ABSTRACT FROM AUTHOR]

In the era of the Internet of Things (IoT), the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge. This is prohibitive since the devices living on the edge are generally resource constrained devices in terms of energy consumption and computational power. Thus, trying to tackle this issue, in this paper, a fully automated end-to-end IoT system for real time monitoring of the status of a moving vehicle is proposed. The IoT system consists mainly of three components: (1) the ultra-lowpower consumptionWireless SensorNode (WSN), (2) the IoT gateway and (3) the IoT platform. In this scope, a selfpoweredWSN having ultra-low energy consumption (less than 10 mJ), which can be produced by environmental harvesting systems, is developed. WSN is used for collecting sensors' measurements from the vehicle and transmitting them to the IoT gateway, by exploiting a low energy communication protocol (i.e., BLE). A powerful IoT gateway gathers the sensors' measurements, harmonizes, stores temporary and transmits them wirelessly, to a backend server (i.e., LTE). And finally, the IoT platform, which in essence is a web application user interface (UI), used mainly for almost real time visualization of sensors' measurements, but also for sending alerts and control signals to enable actuators, installed in the vehicle near to the sensors field. The proposed system is scalable and it can be adopted for monitoring a large number of vehicles, thus providing a fully automatic IoT solution for vehicle fleet management. Moreover, it can be extended for simultaneous monitoring of additional parameters, supporting other low energy communication protocols and producing various kinds of alerts and control signals. [ABSTRACT FROM AUTHOR]

Due to the advanced developments in communication technologies, Internet of vehicles and vehicular adhoc networks (VANET) offers numerous opportunities for effectively managing transportation problems. On the other, the cloud environment needs to disseminate the emergency message to the vehicles which are consistently distributed on the roadway so that every vehicle gets the messages from closer vehicles in a straightforward way. To resolve this issue, clustering and routing techniques can be designed using computational intelligence approaches. With this motivation, this paper presents a new type-2 fuzzy sets based clustering with metaheuristic optimization based routing (T2FSCMOR) technique for secure communication in VANET. The T2FSC-MOR technique aims to elect CHs and optimal routes for secure intercluster data transmission in VANET. The proposed model involves T2FSC technique for the selection of CHs and construction of clusters. The T2FSC technique uses different parameters namely traveling speed (TS), link quality (LQ), trust factor (TF), inter-vehicle distance (IVD), and neighboring node count (NCC). The inclusion of trust factor helps to select the proper cluster heads (CHs) for secure data dissemination process. Moreover, trust aware seagull optimization based routing (TASGOR) approach was derived for the optimal selection of routes in VANET. In order to validate the enhanced performance of proposed technique, the set of simulations take place and the outcomes are examined interms of different measures. The experimental outcomes highlighted the improved performance of the proposed model over the other state of art techniques with a higher throughput of 98%. [ABSTRACT FROM AUTHOR]