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With the rapid development of wireless technology and Mobile communication technology worldwide, Mobile Learning represents a new learning trend in the field of higher education, and has gradually become the development trend and research hotspot of independent learning and lifelong learning. Based on modern wireless network mobile communication terminal technology, this paper proposes a Granott model-driven mobile learning system. Firstly, the Granott model is studied and analyzed, and the ubiquitous computing with physical integration and spontaneous interoperation is used to control the Android mobile terminal, which greatly improves the interactivity of M-Learning. Then JSON is selected to optimize the data transmission format in Ajax, and j Query is used to parse JSON data. Ajax + JSON + j Query can shield the complexity of DOM parsing XML files, greatly shorten the data transmission time and improve the efficiency of Granott model. Finally, the Fast kernel density estimation (Fast KDE) algorithm is introduced to compress the data transmitted by Ajax + JSON + j Query, so as to obtain a reasonable number of training samples. Through experiments and relevant data analysis, the proposed mobile learning system based on modern wireless network mobile communication terminal technology, optimized by JSON and Fast KDE algorithm, driven by Granott model, can efficiently complete the learning task, and improve the ubiquity, interactivity, timeliness of mobile learning. [ABSTRACT FROM AUTHOR]

One of the networking technologies is called a wireless sensor network, and what it does is link sensor nodes by using wireless communication as the channel of connection. A haphazard and random distribution of nodes is carried out throughout the deployment process within the network area. As a result, the distances between the nodes do not remain constant. The nodes have a high sensing capacity, but their range of perception and battery life are also severely limited. The wireless data may be sensed by all of the nodes inside their respective sensing regions. Consequently, there are a variety of new applications that make use of WSN applications, particularly in surveillance monitoring applications such as those used in hospitals, buildings, forests, and airspace monitoring. The transmission of the data to the appropriate server or data centre that is linked with the cloud or the internet is experiencing greater difficulties as a result of the growing amount of monitoring data. This indicates that a significant quantity of data is lost as a result of the fact that wireless transmission is affected by environmental factors such as precipitation, humidity, and wind storms. It is vital to create and execute an innovative routing protocol to deliver greater throughput and raise the overall Quality of Service of the WSN applications in order to solve the difficulties that have been outlined above. This study paper presents a metaheuristic algorithm based optimal routing protocol for WSN application, which boosts the overall quality of service of any surveillance monitoring applications. To achieve this goal, the protocol was designed for wireless sensor networks. It does so in an effective manner, resulting in decreased delay and energy consumption as well as enhanced throughput and PDR. Based on the findings of the experiments, it has been hypothesised that the proposed metaheuristic algorithm-based routing protocol is capable of performing better routing for any type of surveillance monitoring and remote monitoring, as well as successfully transmitting any type of data, including numerical, video, and image data. [ABSTRACT FROM AUTHOR]

A large number of interconnected smart devices or objects interact with the physical environment known as the Internet of Things (IoT). These networks efficiently perform complex tasks with a high level of intelligence without human intervention and require run-time processing and computation. Edge computing is introduced where devices are placed at the edge between the data source and the cloud. These devices can provide more powerful computational and storage capabilities to IoT users. With various benefits, one of the significant requirements is secure and trustworthy data communication. Various trust management schemes are introduced to guarantee the trustworthiness and isolation of the malicious nodes and give preference to the most trustworthy ones. The existing schemes selected trustworthy nodes again and again from the path without considering the load factor which leads them to be more vulnerable for the Denial of Services (DoS) attacks. In this paper, we propose a Link-based Penalized Trust Management scheme to provide trust management and consider load factors for the selection of most trustworthy nodes and provide a preemptive measure to secure the network from DoS attacks based on its link's association. Simulation results indicate the better performance of the proposed scheme as compared with existing schemes. [ABSTRACT FROM AUTHOR]

In 5G, users can easily enjoy services by accessing the edge devices (EDs) deployed around the base stations. Generally, due to the uneven distribution and mobility of users, one ED is required to serve multiple users simultaneously, that results in the ED overload, seriously affecting the quality of experience (QoE) of the users. By responding to users' requests, service migration realizes the cross edge device migrations, that dynamically provides services for mobile users, which effectively improves the resource utilization rate of edge servers and the QoE of users. However, during the service migrations, malicious users may deploy pseudo base stations, gateways and other devices to illegally eavesdrop or tamper with user service data, that causes user information loss or disclosure. To prevent these security issues, a fountain-coding based cooperative jamming strategy is proposed in this paper. Specifically, the fountain coding technology is introduced to construct a three-node transmission model, including an original node, a target node and a malicious node, to realize the secure data transmission process in service migration. Besides, a group of relay nodes are employed to carry out cooperative jamming, that deteriorate the illegal eavesdropping quality of malicious nodes on service data. Then secrecy rate and outage probability are utilized to evaluate the security and reliability of the whole service migration. Finally, the theoretical analysis is shown by simulation results. [ABSTRACT FROM AUTHOR]

In recent years, wireless sensor networks (WSNs), as the underlying basic interface of the Internet of Things, have developed rapidly, and the performance requirements of WSNs are constantly improved in various application environments. However, in the practical application, the traditional wireless sensor network has the problem of uneven and limited energy consumption. To solve this problem, a non-uniform clustering algorithm based on node classification and multi-level data transmission (NCMLT) is proposed in this paper. The algorithm divides the nodes into S1 and S2 levels according to the distance from the base station, and transmits data in chain and cluster structures respectively, effectively combining the advantages of the two structures. In the stage of cluster head election, the threshold formula of low energy adaptive clustering hierarchy (LEACH) is improved, and the remaining energy of nodes and other factors are added into the candidate cluster head election function. When clustering, considering the rationality of the competitive radius, the distance and energy factors are adjusted to form non-uniform clustering, which reduces the energy cost of the network. Based on the greedy algorithm, the chain structure is constructed according to the defined chain head election formula, and the optimal chain node is selected by calculating the relay value of the cluster chain as the relay node for data transmission between the cluster head that is closer to the base station and the base station, and the data transmission distance between the nodes is optimized by means of hierarchical multi-hop. Through simulation experiments, compared with the energy-efficient uneven clustering algorithm (EEUC) and the uneven clustering routing algorithm based on ant colony optimization (URACO), the network lifetime of NCMLT algorithm is increased by 47.43% and 5.02%, respectively, which proves that NCMLT algorithm can effectively balance node energy consumption and extend network lifetime. [ABSTRACT FROM AUTHOR]

Vocal learning has found its proliferation in recent years due to the advancement in wireless networking. Vocal art encompasses profound cultural values and aesthetic preferences, making wireless platforms, e.g. Wi-Fi and ZigBee, essential for the enhancement of online vocal learning. ZigBee, as a wireless platform, has more recently been used quite frequently for online vocal learning and music. This paper aims to design an efficient wireless-enabled platform by using the lightweight features of ZigBee to support online vocal learning and expressions. The platform enables data transmission, storage, analysis, and playback of vocal learning sounds through the ZigBee network. It is built on a cloud platform, utilizing Docker virtualization technology to deploy a Hadoop distributed cluster, effectively simulating a distributed environment. The platform incorporates a model for recognizing musical sound and noise signals, empowering the online learning control platform to detect various characteristics of vocal music tones. Vocal music learning is enhanced by extracting endpoint fusion spectrum features and employing a tone adjustment and dynamic detection model. The proposed method improves anti-interference capability, online learning control, vocal tone recognition, and emotional expression accuracy. Simulation results demonstrate its effectiveness in vocal music, online learning control, and tone recognition, leading to more precise vocal music pronunciation and intonation registration. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc network (VANET) plays a vital role in emerging Intelligent Transportation System (ITS). VANET's deal with the group of network that are designed in ad-hoc model in which various moving devices and other stationary linking objects that are connected over a wireless manner which interchange effective data to each other. Since, the communication between the VANET and other communication devices take place in open channel, there exist many threats caused by the intruder. Moreover, most of the existing authentication schemes suffer from computational and communicational overhead. In order to provide efficient lightweight authentication with enhanced privacy. In this paper, a novel lightweight authentication protocol named as Lightweight Authentication Protocol with Enhanced Privacy (LAPEP) has been proposed to provide efficient end to end security in VANET. The proposed protocol employs Elliptic Curve Cryptography (ECC) along with Bilinear pairing for efficient generation of keys and provides secured multiparty key exchange. Moreover, the security analysis of the proposed scheme is carried out by Burrows Abadi Needham (BAN) logic. The advantages of the proposed system are reduction of computation overhead and provide resistance to various security attacks during data transmission. Moreover, the proposed system enhances the Quality of Service (QoS) namely latency, overhead, delivery ratio, loss ratio and delay rate. The implementation of the proposed protocol is carried out in NS3 simulator. The simulation result proves that the proposed protocol improves the computational cost by 42.85%, communication cost by 35.82%, message end to end delay by 30.65%, signing time by 25.45% and verification time by 32.45%. [ABSTRACT FROM AUTHOR]

In path-constrained multi-hop sensor networks (M-WSNs), maximizing data collection with minimal energy consumption is critical, especially for delay-sensitive applications. Because a mobile element (ME) moving at a constant speed along a constrained-path must receive data from sensor nodes (SNs) within a given time bound. This issue can be addressed by efficiently scheduling the SNs' data transmission. The shortest path data transmission (SPT) or its variations are a simple and energy-efficient technique for scheduling data transmission from SNs. Although this method considerably minimizes total energy spent, it does not enhance data collection efficiency due to uneven data forwarding load. To solve this issue, this paper proposes a novel efficient heuristic method. The proposed method first computes a set of discrete sub-paths on a given path based on the start and end distance points of nearby SNs, the speed of ME, and the given time delay. Then, for each sub-path and SN-communication model, a network flow graph is used to schedule and optimize SN data transmission to ME. Finally, a sub-path is chosen among those which return maximum data with minimum energy consumption. The network flow graphs are created by considering two distinct SN-communication models: (1) energy-unrestricted SN-model and (2) energy-restricted SN-model. Finally, the simulation reports demonstrate the proposed method's efficacy over baseline schemes in terms of data collected, energy usage efficiency, and success-delivery ratio. [ABSTRACT FROM AUTHOR]

Considering that one of the goals of the future network generations is to provide ubiquitous communication in the most diverse scenarios to achieve high connection coverage, it is foreseen that the use of unmanned aerial vehicles as flying base stations (UAV-BSs) can potentially extend the network and communication range. UAVs as flying base station can bring the potential to assist user devices and vehicles by carrying communication resources that can accommodate clients that were not previously planned by the ground infrastructure design due to flash crowd events, sudden natural disasters, or any other event that let to an overloaded environment. Allocating UAVs as flying base station still poses significant challenges in their deployment and the effectiveness of information transmission through UAVs as flying base station in the context of wireless communication since it is necessary to deal with both wireless communication capability and interference in the presence of terrestrial infrastructures already present. Besides, it is essential to understand how communication resources affect network performance. This paper studies the feasibility of using UAVs as flying base station in the assistance of wireless communication in a scenario where there is a sudden demand for data transmission due to possible congestion of local infrastructure. We show how the number of communication resources provided by the UAV-BS, the interference caused by the presence of multiple next generation node Bs (gNBs), and the UAV as flying base station positioning affect the network performance. We also highlight the need for a better next generation node B (gNB) and UAVs placement criteria since the received signal power prevents the user equipments (UEs) from using most of the available resources. [ABSTRACT FROM AUTHOR]

Wireless body area network applications for health monitoring need to transfer data to the coordinator node with the highest quality of service (QoS). However, the sensors may generate redundant data, which can overload the transmission and processing of data and negatively impact the network's QoS parameters. Maintaining the network QoS parameters such as network lifetime, network stability, energy consumption, throughput, data transmission reliability, and packet delivery delay is a challenging task. This paper proposes a QoS supported Redundancy Balanced Data Transmission scheme (RBDT) to address the above maintained problem. RBDT comprises three steps: 1- Node behavioral recognition, 2- Cooperative relay selection, and 3- Redundancy reduction based on compressive sampling. Simulations were performed on a real-time dataset. The results show that the proposed RBDT scheme performs better than state-of-art methods available in the literature to ensure network QoS parameters as mentioned above. [ABSTRACT FROM AUTHOR]

This paper discusses the challenges of mobile edge computing with low latency and energy consumption caused by the explosive growth of communication traffic and data generated by mobile devices. To address the issue of selecting edge servers, a multi-user-oriented edge server selection strategy is proposed. The strategy minimizes the weighted sum of network latency and total energy consumption and develops an improved SPEA2-based algorithm to choose the most suitable edge server. The resource allocation problem is also considered, and a resource allocation strategy is proposed that maximizes the energy efficiency ratio. A DQN-based resource allocation strategy is devised to find the best resource allocation strategy. Experimental results demonstrate that the proposed server selection strategy reduces system overhead in terms of energy consumption and latency while improving resource utilization. The resource allocation strategy improves the computational efficiency of edge servers while reducing latency and total energy consumption. [ABSTRACT FROM AUTHOR]

The demand for wireless connectivity has grown tremendously over the last few decades. A new perspective of wireless communication will soon be positioned worldwide. In the years beyond, the millimeter-wave spectrum for the mobile communication infrastructure has improved spectral efficiency. The problem of the technical challenges may be detected, identified, and resolved in different designs as traditional solutions involve the software tricks that prevent the modes of operation that trigger the trouble. In this research, we carry out the proposed Millimetre assisted wave technologies using 6G federated learning and high-performance computing for 6G assisted wireless communication systems for voice data optimization. Millimeter-wave technology is a specific part of the radio frequency spectrum, between 24 and 100 GHz. The outcome of this proposed technique will be voice data optimization in the 6G cellular networks for next-generation networks to improve the 6G assisted networks federated learning from existing cellular network technology generation, established on the tool of Flight stack or Autopilot. The observational results express to assess the performance of the device, we acquire theMillimetre assisted wave technologies and federated learning and high-performance computing at various data transmission rates and modulations, including real-time data transfer up to 96 Gbit/s at 420 GHz, with a low bit error rate and good signal-to-noise ratio. With 18 dBm emitted power at 0.4 THz, this method creates and transmits a signal with a 160 Gbits per second net rate across a 15 m distance. This monolithic dual-DFB PIC-based THz generating technique represents an important advancement toward completely integrated, economically viable, and energy-efficient THz transmitters. [ABSTRACT FROM AUTHOR]

Software-defined vehicular networks (SDVN) is a promising technology for wireless data transmissions between vehicles. SDVN inherits software-defined networking principles and aims to improve the typical performance of safety and non-safety applications of vehicular adhoc networks. Consequently, enhancing the performance of Intelligent Transportation System (ITS). However, the performance of these ITS applications largely depends on the computational capability of the controller node, which involves creating or destroying a data path from the source vehicle to the destination vehicle and generating flow rules for the requests coming from the data plane elements. As a result, SDVN often suffers from the problems of overburdening the controller node with route requests under heavy traffic generation at vehicles and single-point controller failure. To counter these problems, solutions based on multiple controllers are proposed. In fact, the load-balancing problem remains an important issue. So, routing of data with multiple controllers and load-balancing, both topics in SDVN, go hand in hand. In this paper, we survey this state-of-the-art that discusses the above-mentioned challenges, starting with the SDVN preliminaries. We scrutinize the existing routing methodologies and also discuss load-balancing techniques. Furthermore, we provide real-time applications and services of SDVN, discuss trending research, potential future research directions, and the real-life applicability of SDVN that have not been addressed previously. [ABSTRACT FROM AUTHOR]

Metal oxide arrester (MOA) is an electric power equipment used to limit overvoltage and cooperate insulation between generator substation and direct current converter station. In the actual operation of MOA, due to the abnormal leakage current will produce various faults, the significant increasing of its resistive component will cause thermal effect, and even explosion, so as to lose the protection for the MOA. Particularly, in the transition from preventive maintenance to state maintenance, the importance of its safe operation or fault diagnosis is undoubted. Since the 1990s, fault diagnosis of MOA in operation has been very popular, and it is still popular today. After decades of unremitting efforts, we have accumulated rich experience in fault diagnosis of MOA, and also explored some new fault diagnosis methods and means. With the rise of wireless network technology, its network construction, maintenance, and other aspects have incomparable advantages of wired. Therefore, adopting wireless online monitoring to track MOA status can greatly improve the flexibility, real-time, and accuracy of fault diagnosis, and reduce the error and cost of wired data transmission. In this study, the monitoring module can realize the real-time transmission of MOA operation characteristics to the control terminal through wireless networks, judge the running status and insulation performance of the MOA by the change of the basic component of the resistance current, and then diagnose the fault of the MOA by the proposed weighted k-nearest neighbors algorithm. The simulation results show that the proposed method has very high precision and accuracy in fault diagnosis of MOA, as well as high recall and F1. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) gather active sensor data within a specified period to the sink node. The data transmission in restricted resource utilization in wireless surroundings is a primary issue. Compressive sensing enables resource utilization based on spatial data by exploiting the transfer of limited measurements according to the original signals. In this paper, an Adaptive Adjacent based Compressive Sensing (AACS) methodology is proposed for effective data construction in spatial-related wireless sensor networks. A sparse Matrix is constructed with the coordinates of location and position for data transmission. The fuzzy logic is used to find the best forwarder among the sensor nodes in the network using the parameters of Mobility, Energy, and Fuzzy cost. Within a sensing time, the sensor node forwards the data around the time to the adjacent node according to a correlation. The communication time provides proficient enhancement with compressed data with AACS and sparse index. Therefore, AACS gives a reduced amount of communication and maximizes accuracy. AACS is compared with the related techniques and the results prove that the proposed methodology performed well in the performance metrics. The performance analysis shows that the proposed technique has produced 54.7% of network throughput than the relevant technique, 76.9% lesser routing overhead, and 44% of minimized relative error. [ABSTRACT FROM AUTHOR]

Using new methodologies such as Blockchain in data communications in wireless sensor networks (WSN) has emerged owing to the proliferation of collaborative technologies. However, the WSN is still vulnerable to denial of service cyber attacks, in which jamming attack becomes prevalent in blocking data communications in WSN. The jamming attack launches malicious sensor nodes to block legitimate data communications by intentional interference. This can in turn cause monitoring disruptions, data loss and other safety-critical issues. In order to address the malicious attacks, this paper proposes an adaptive anti-jamming solution based on Hyperledger Fabric-based Blockchain, named as ABAS, to ensure the reliability and adaptivity of data communication in case of jamming attacks. In order to validate the ABAS solution, we applied the algorithm in healthcare WSN and showed that ABAS has significantly reduce the jamming coverage and energy consumption while maintaining high computational performance. [ABSTRACT FROM AUTHOR]

One of the wireless sensor networks applications is to sense a discrete set of targets lying on the field and maintain connectivity with the sink for data transmission. In addition, it needs to minimize energy consumption to maximize the coverage lifetime. One such solution for coverage maximization is to group sensor nodes into cover sets. Each cover set remains active at a time to keep track of all the targets in the field until one of its active nodes depletes energy completely. Therefore, maximizing the number of cover sets and enhancing each set's coverage lifetime is a challenging issue. In this paper, we propose a new energy-aware algorithm for the coverage and connectivity of the sensor nodes. In the algorithm, we devise an energy-efficient strategy to maximize the number of cover sets and energy-aware connectivity. Extensive simulation runs show that the proposed algorithm outperforms the existing ones. [ABSTRACT FROM AUTHOR]

In a Wireless Sensor Network (WSN), sensor nodes are deployed to detect physical parameters in hostile environments. The sensor nodes rely on each other for effective data transmission. When the articulation points fail, the network is split into disjoint segments, resulting in the network partitioning problem. In this situation, connecting the disjoint partitions using a mobile relay node (MD-carrier) is a must to maintain the functional operations of WSN. However, the presence of obstacles in the trajectory path makes touring a challenging task. Consequently, finding tour paths by avoiding obstacles has always been one of the most common WSN problems. An Obstacle-avoiding Connectivity Restoration Strategy for partitioned WSN using MD-carriers (OCRS-MD) to re-establish connectivity in the presence of obstacles has been proposed in this paper. OCRS-MD includes a path-finding algorithm that bypasses obstacles for the effectual touring of the MD-carrier. An algorithm has also been proposed for identifying the MD-carriers' sojourn locations. Hence, the MD-carrier follows the optimal touring path, pausing at sojourn locations to collect data from the segments. As a result, OCRS-MD restores the partitioned WSN's connectivity by reporting the collected data to the sink node. The primary goal of OCRS-MD is to restore network connectivity in the presence of obstacles with reduced travel distance and latency. The computational complexity of OCRS-MD is O (n 2) , where 'n' is the number of disjoint segments in the network. The simulation results indicate that OCRS-MD reduces tour length and latency by an average of 16.89% and 15.47%, respectively, when compared to the existing OACE and EEDRM approaches. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the energy efficiency is a big challenge and this can be defeated using various techniques such as clustering and routing. In the clustering approach, clusters are made up of the sensor nodes. In most of the applications, the sensor nodes are battery-powered. The sensor nodes may carry an uneven load of data and consume an unequal amount of energy. This leads to the formation of an energy hole in the network. Therefore, load balancing in the network with respect to the distance of data transmission is important in WSN. Hence, this paper proposes an energy-efficient as well as Load Balanced Clustering algorithm using Fuzzy logic (LBCF). The values of load and distance between the sensor node and BS are associated using rule-based fuzzy logic. The output values of the fuzzy system decides the cluster heads (CHs) and the size of the cluster. The sensor nodes get assigned to the CHs with respect to the capacity of CH. The simulations are conducted under different scenarios and network parameters and the proposed LBCF shows out performance to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Spectrum sharing (SS) and energy harvesting (EH) basically improve spectrum-and-energy efficiencies required by modern communications systems. Nonetheless, most publications have focused on the linear (L) EH model which did not characterize practically non-linearities of EH circuits. Moreover, SS induces secondary receivers to suffer primary interference (PI) and facilitates eavesdroppers to overhear legitimate data. To secure communications in energy harvesting spectrum sharing networks (EHSSNs), this paper proposes a full-duplex (FD) secondary destination which not only receives legitimate signal but also jams signal reception of the eavesdropper. Joint effects of non-linear (NL) EH, PI, and FD destination-assisted jamming on security-and-reliability performances of EHSSNs are first analyzed in terms of outage and intercept probabilities that are then endorsed by Monte-Carlo simulations. Next, the proposed analysis is exploited to achieve the asymptotic analysis and the security-and-reliability analysis for two baseline EHSSNs (half-duplex (HD) destination and L EH). Finally, numerous results demonstrate joint effects of NL EH, PI, and FD destination on data security and communication reliability by comparing three EHSSNs: (i) L EH and FD destination; (ii) NL EH and FD destination; (iii) NL EH and HD destination. Notably, the proposed communications scheme with the FD destination is more secure than the baseline scheme with the HD destination. In addition, the reliability of the communications scheme with the NL EH is inferior to that with the L EH but the data security of the former is better than the latter. Additionally, the security-reliability trade-off exists for all the considered communications schemes. Moreover, the PI enhances the security but degrades the reliability. Furthermore, the time fraction for EH can be optimized to attain the best security-reliability trade-off. [ABSTRACT FROM AUTHOR]

With the growing quantity of wireless devices, the demands for wireless transmission are increasing exponentially. More and more WiFi access points (APs) have been built in the environment to serve these stations (STAs). As the density of devices increases, two or more basic service sets (BSSs) are too close to hear each other, which leads to overlapping BSS (OBSS) problems. When the OBSS problem occurs, the STAs in the OBSS area are interfered with by the AP signals from the overlapping BSSs, which causes the data transmission delay, and the quality of service of the user cannot be guaranteed. In this paper, we designed a scheme with a controller which maintains the Overlapping Area Record (OAR) of each BSS. By utilizing the OAR, we proposed a mechanism that reduces the transmission power of the interfering APs and extends the transmission opportunity limits of the low-QoS stations (LQ-STAs). Moreover, we give priority to the LQ-STAs in the multi-user multiple-input multiple-output transmission to deal with the OBSS problem. Through simulations, when the data arrival rate is 128 kbits/s, our method reduces the average downlink transmission delay of interfered high-efficiency STAs and legacy STAs by 21.4% and 25% compared to the base case scenario. When the data arrival rate is 512 kbits/s, our method outperforms the base case scenario up to 99%, which greatly ameliorates the OBSS problem in IEEE 802.11ax. [ABSTRACT FROM AUTHOR]

A patient's body gets exposed to RF radiation due to node data transmissions in a Wireless Body Area Network (WBAN). Therefore, in addition to energy efficiency, the adverse effect of node radiation on human health is also an important concern for WBAN. The quantum of radiation exposure is measured in terms of the specific absorption rate (SAR). SAR indicates the rate of absorption of radiation energy per kilogram of tissue by the human body when exposed to radiation. The majority of existing data routing schemes for WBAN neglect radiation-factor during data path selection. This paper presents the Fuzzy based Energy Efficient and Low SAR routing protocol (FEELS) for WBAN. FEELS is a clustering-based protocol that incorporates the provision of reducing the radiation effect on the human body. In cluster routing, the nodes transmit data packets to an intermediary cluster head (CH) node instead of transmitting directly to the hub. Hence, the nodes require lower signal power for data transmission. CH compiles the received packets and passes them to the hub node. Low signal power helps in reducing the radiation effect. However, body tissues near CH feel increased radiation as it receives and transmits a larger data set for the entire transmission round. For reducing the radiation effect of CH, the proposed scheme considers the sensitivity of a node's body location to RF radiation along with node signal transmission power for CH selection. A Fuzzy-logic based approach is utilized for CH selection. The selected CH node requires minimum transmission power and its body location is least sensitive to RF radiation to minimize the radiation effect. The proposed scheme, as compared to other relevant schemes is computationally simpler that offers energy-efficient and low SAR performance. [ABSTRACT FROM AUTHOR]

Wireless medical sensor networks (WMSNs) play a major role in remote medical monitoring systems. Generally, in a WMSN, professionals need to obtain real-time physiological data of patients, and these data often encounter various security and privacy issues during the transmission process. Thus, the secure transmission of data is particularly critical. To ensure data security and patient privacy, many authentication schemes have been proposed. However, most of the existing schemes either cannot withstand known attacks (such as privileged-insider attack, desynchronization attack, etc.) or require more communication and computation costs, and are not suitable for resource-constrained WMSNs. Therefore, this paper proposes a new anonymous physically unclonable function (PUF)-based authentication protocol for WMSNs by using PUFs, fuzzy extractor, cryptographic one-way hash functions, and bitwise XOR operations. Formal security analysis under the real-or-random model shows that this scheme is provably secure. And informal security analysis shows that our scheme is secure against various known attacks. At the same time, compared with other existing related schemes, the proposed scheme not only provides more security and functionality features, but also requires less communication (5360 bits) and computation costs (57.047 ms). [ABSTRACT FROM AUTHOR]

Underwater sensor networks (UWSN) include a set of sensor nodes equipped with limited batteries. These batteries are not rechargeable and replacement of them is difficult and costly due to difficult access and condition of the underwater environment. So, the optimization and management of energy are the most important issues for UWSNs. Many studies have been presented in this field that the most focus of them has been on improving and optimizing routing and data transmission because of the highest energy consumption for routing and sending data. While each of these studies was effective in improving energy-related issues, the need to develop an energy-efficient and reliable routing protocol for UWSNs is still under investigation. In this paper, a method called an energy-efficient grid-based routing protocol for underwater wireless sensor networks (EEGBRP) using TOPSIS technique and 3-dimensional cell division is introduced. EEGBRP is a multi-hop method which in the first step the network is divided into three-dimensional cells. In the second step head-cell nodes or gateways are selected and data routing and communication is performed in the third step. The simulation results using NS2 indicated the successful performance and significant superiority of EEGBRP compared to previous researches. This improvement for different scenarios compared to EMGGR method was 10.65% for successful delivery, 9% for the optimization of energy consumption and 8.8% for end-to-end delay. [ABSTRACT FROM AUTHOR]

Underwater Wireless Sensor Networks (UWSN) has received more attention in exploring promising technologies for scientific data collection of underwater natural resources with maximum link reliability. For effective communication among the sensor nodes, reliable data delivery based routing protocols have been designed to avoid long-distance communication in large network areas. But the network-based protocols suffer from many constraints like limited distance-dependent bandwidth, defective channels and high delay. Furthermore, the supplied batteries have limited power and also data transmission cannot be exploited in the long-distance network areas due to the harsh underwater environment. Therefore, a clustering-based mobility pattern routing protocol is required to link long-distance communication over depth areas with less consumed energy and less delay. This paper proposed an energy-efficient Distributed Node Clustering Mobility Pattern Routing Protocol (DNC-MPRP) to reach the long-distance depth area for data transmission with less energy consumption and avoid inaccurate routing paths using mobility patterns in UWSN. This approach consists of several phases: network initialization, Cluster Head (CH) formation and data transmission. In DNC-MPRP, the network is partitioned into two dense areas for near and far distance communications. Then the network configuration initializes the rectangle mobility pattern which is used to reach the surface sink with less delay. Next, the CH formation initializes the cluster area in the large and common area to maintain energy in the collected member nodes. Lastly, the data transmission is adopted only when the coverage area is in the transmission range of the network field. Simulation result depicts that the DNC-MPRP approach would be evaluated in terms of Energy Consumption (EC), Packet Delivery Ratio (PDR), End to End Delay (E2ED) and Network Lifetime (NL) based on the variation of nodes, different transmission range, mobile sink, data rate and payload data with high PDR of 95%, high NL of 1200 s, low E2ED of 9.5 s and less EC than existing methods. [ABSTRACT FROM AUTHOR]

Cooperative Non-Orthogonal Multiple Access (Cooperative NOMA) is a communication technique used in wireless networks to enhance spectral efficiency and overall system performance. Non-Orthogonal Multiple Access is a multiple access scheme that allows multiple users to share the same time-frequency resources by allocating different power levels to each user. This paper presents a comprehensive performance validation of NOMA empowered by optimal relay selection and deep learning-based power allocation techniques. To achieve effective data transmission to far users, the optimal relay selection process employs the Maximum Energy Harvested Relay (MEHR) selection approach, which intelligently selects relays based on their energy harvesting capabilities. Furthermore, an enhanced Deep Convolutional Optimized Neural Network (DCOnN) is utilized for optimal power allocation among users in a cooperative NOMA system. To efficiently optimize the power allocation process with the DCOnN model, the Grey Wolf (GW) optimization is utilized to fine-tune the network weights. The presented approach significantly increases the energy efficiency in cooperative NOMA networks. The combination of optimal relay selection and deep learning-based power allocation enhances outage probability performance, ensuring better coverage and communication reliability while reducing the bit error rate for users in challenging channel conditions. Finally, the integration of optimal relay selection using DCOnN with GW optimization and cooperative cognitive NOMA provides a powerful framework to achieve superior communication performance in wireless networks. The proposed scheme achieves higher performance in terms of energy efficiency (519.257 Bits/J/Hz) and CPU time (0.012 seconds). The experimental results proved that the proposed performance is enhanced than the different existing schemes. [ABSTRACT FROM AUTHOR]

The traditional laboratory LAN technology has the problem that the transmission efficiency is lower than the error rate. In this paper, a data acquisition and transmission method based on fuzzy DEMATEL algorithm for laboratory LAN is proposed. By laboratory LAN Internet data acquisition model, the laboratory LAN statistical characteristics of the original data, extract the laboratory LAN of higher order spectral characteristics of sampled data, the fuzzy c-means clustering model applied in the laboratory LAN data information fusion processing, and use the wireless sensor network detection technology, to achieve optimum laboratory LAN data transmission. The simulation results show that the method can accurately realize the prediction and evaluation of the laboratory LAN data, the data transmission accuracy is high, the characteristics of the laboratory LAN transmission information can be accurately calibrated, the prediction and evaluation of the laboratory LAN transmission information has a good application value in the laboratory LAN transmission control. [ABSTRACT FROM AUTHOR]

This research addresses the problem of redundant data transmission and improves load-balance routing in wireless sensor network (WSN). Redundant data generates higher data transfer and additional traffic loads, which degrade the network performance. The sub-clustering approach is used to minimize redundant transmissions by grouping overlapped or closely located nodes in a cluster into several sub-clusters such that only one node is required to sense the surroundings and send data to the cluster head (CH). The remaining sub-cluster members turn off their radios to save energy. However, the grouping of nodes into non-overlapping clusters and sub-clusters as well as proper selection of nodes to be awaked within a sub-cluster remained a challenging issue. Moreover, using a single node in a cluster performing the role of CH and relay could lead to load-balancing issues as the position of the selected CH may not ensure balanced intra-cluster and inter-cluster transmissions at the same time. In this paper, we proposed a Two-Step-Clustering (TSC) to improve the performance of WSN. In TSC, in the first step, the sensor nodes of minimum distance from each other were grouped into balanced non-overlapping clusters and sub-clusters. Then, a sleep-awake mechanism was employed among the members of the sub-cluster such that the sub-cluster members take turns according to their remaining energy. This is done to minimize redundant transmission to achieve energy efficiency. Furthermore, two CHs were selected, i.e., primary, and secondary CHs. The primary CH is responsible for intra-cluster data collection and the secondary CH is responsible for inter-cluster data transmission. This improves load-balanced routing within the network. In addition, single-hop and multi-hop routing were used to send data to BS. The result shows that the TSC has 54% lifetime improvements against SEED and 60% against DHSCA. [ABSTRACT FROM AUTHOR]

Underwater Acoustic Networks are gaining in popularity because of their usage in underwater communication. The communication is facilitated with sensors and AUVs (Autonomous Underwater Vehicles), also termed as UWCNs (Underwater Wireless Communication Networks). The QoS expected of underwater networks is effective transmission along with an extended lifespan of the network. This can be achieved with strategically placed clusters underwater which help in transmitting effectively. Clustering can be strategically used to securely transmit the data while conserving the node energy.This paper proposes an Optimal and Energy Efficient Framework for Cluster Communication Based Routing Protocol for UWCNs (OEEFCP).This scheme works in two phases. Phase-1 focuses on selecting an optimal CH (a Robust Cluster Head) which will be responsible for selecting an efficient carrier of data. Additionally, we have used probability in selecting the optimum number of clusters and CHs. Phase-2 uses an effective multipath energy model which will focus on transmitting data through effective communication channels. A RSSI (Received Signal Strength Indicator) value is used to determine a gateway node out of the optimal CHs. The robust gateway nodes thus selected will supply data to the AUVs (Autonomous Underwater Vehicles) nearest to them. The AUV nodes will then transfer the data via a communication link and thereon towards a destination on land. The proposed technique focuses on a cost effective solution for underwater transmission. The proposed OEEFCP is analyzed with existing schemes such as OC-TARE-TOPSIS and TARE-TOPSIS protocols, using the NS2 simulator. The analysis confirms that the proposed OEEFCP is capable of achieving a good performance in achieving an extended network lifespan, throughput, energy conservation and a maximized PDR. Our proposed OEEFCP scheme shows a 65% increase in PDR over the other existing methods. [ABSTRACT FROM AUTHOR]

Fog computing integrates cloud and edge resources. According to an intelligent and decentralized method, this technology processes data generated by IoT sensors to seamlessly integrate physical and cyber environments. Internet of Things uses wireless and smart objects. They communicate with each other, monitor the environment, collect information, and respond to user requests. These objects have limited energy resources since they use batteries to supply energy. Also, they cannot replace their batteries. As a result, the network lifetime is limited and short. Thus, reducing energy consumption and accelerating the data transmission process are very important challenges in IoT networks to reduce the response time. In the data transmission process, selecting an appropriate cluster head node is very important because it can reduce the delay when sending data to the fog. In this paper, cluster head nodes are selected based on several important criteria such as distance, residual energy, received signal strength, and link expiration time. Then, objects send the processed data to the server hierarchically through a balanced tree. The simulation results show that the proposed method outperforms the energy-efficient centroid-based routing protocol (EECRP) and the Emergency Response IoT based on Global Information Decision (ERGID) in terms of packet delivery rate, delay, response time, and network lifetime. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), coverage is a significant indicator of the quality of service. However, full coverage is not feasible to achieve due to coverage holes (CH) in the targeted area. CH occurs for many reasons, such as energy depletion, occlusions and random deployment of nodes. Most of the existing CH restoration methods have huge limitations like high energy consumption (EC), low coverage ratio (CR) and less network lifetime. To address these issues, an Enhanced Mobile Sink-based Coverage Optimization and Link-stability Estimation based Routing (EMSCOLER) protocol is proposed in this paper. The proposed EMSCOLER provides a solution to the coverage restoration issue optimally and avoids network transmission faults. This work consists of two phases such as coverage restoration and Link Stability Estimation based Routing (LSER). Initially, the grid is constructed in the coverage area and randomly deploys the nodes over the grid based coverage area. Grid-based Red Deer Simulated Annealing (GRDSA) model detects the CH in the sensing field and moves the redundant nodes to the hole area to solve the restoration problem. LSER algorithm estimates the link quality and chooses the relay nodes for the transmission of data in order to maximize the whole network lifetime and provide an energy efficient routing. MATLAB tool is used for implementing the proposed protocol. The results of the proposed EMSCOLER are analysed using CR, EC, average residual energy (ARE), moving EC and network lifetime by comparing with the state of the art techniques. The proposed EMSCOLER approach achieved 98.53% CR, 5.842 J EC, 5.94 J moving EC, 0.1980 J ARE and 99.61% network lifetime. From the experimental analysis, the proposed method achieved better results than other techniques. Therefore, the proposed approach can effectively solve the coverage restoration problem and perform energy efficient routing. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the secrecy performance of a massive MIMO NOMA network. Specifically, we demonstrate a detailed training process in the network and derive downlink ergodic secrecy rates of legitimate users. In order to gain system's insights, discussions on secrecy performance of the network in two special cases, i.e., large number of antennas and high transmit power at the BS, are provided. Furthermore, from the analysis, an optimization to maximize the users' minimum secrecy rate in each NOMA cluster is proposed to aid the weak users' secrecy performance. The correctness of our analysis and the efficiency of the proposed are confirmed through computer simulations. [ABSTRACT FROM AUTHOR]

Cooperative spectrum sensing (CSS) has been considered as an essential paradigm of cognitive radio (CR) to provide ever-growing wireless applications with the available spectrum band. However, the following problems degrade the security and efficiency of CSS in cognitive radio networks (CRNs), such as, (i) the open facet of the CRN provides an opportunity for the Byzantine attacker to falsify the sensing information, (ii) imperfect reporting channels may distort the sensing information during the decision-making process, (iii) a large amount of sensing information transmission incurs communication overhead. Confronting these problems, we propose a secure and efficient CSS scheme, called generalized voting-sequential and differential reporting (GV-SDR) in this paper. For this aim, we first design a data transmission monitoring process in the periodic spectrum sensing frame structure to defend against Byzantine attack. Furthermore, we exploit the on/off signaling characteristic and propose a single signaling transmission method to mitigate the negative impact of the imperfect reporting channel. Moreover, based on the generalized voting (GV) rule, the sensing information fusion is carried out in the sequential and differential reporting (SDR) mechanism to improve the spectrum sensing efficiency. Simulation results show that in front of Byzantine attack and imperfect reporting channels, the proposed GV-SDR not only significantly reduces the sensing information required by the FC, but also provides the better detection performance. [ABSTRACT FROM AUTHOR]

Clustering improves network stability and scalability significantly by efficiently handling the fast topological change in VANET. Multi-hop clustering is introduced in VANET to handle some significant issues observed in the single-hop clustering, i.e., low cluster coverage, high number of clusters formed, and inefficient bandwidth utilization in routing decisions and internetwork data processing. Among many, cluster forming based on neighbourhood following strategy is the most recent and advanced way to form multi-hop clustering. By exploring the weaknesses and limitations of the existing neighbourhood following strategy, this paper introduced an advanced multi-hop clustering (AMC) algorithm. The proposed algorithm is empowered with an efficient cluster formation procedure that helps the vehicle to select and follow the most suitable target vehicle from one-hop neighbours. The suggested CH selection and cluster merging procedure contribute to strengthening stability. Further, we suggested the routing protocol of AMC to check the data transmission performance. Here, one vehicle appointed as a cross node at the road junction assigns the weight to the road segments by proper connectivity analysis. The lowest weight route signifies the best data forwarding path. The algorithm performance is tested in the realistic type scenario where vehicles' speed and arrival time are randomized. The detailed simulation result shows the strength of the AMC algorithm. [ABSTRACT FROM AUTHOR]

To improve the average remaining energy of the network, reduce the routing overhead, extend the network lifecycle and reduce the average end-to-end delay, a new multi-frequency communication routing protocol for mobile Internet of things device terminal wireless network is proposed. Construct the topology of a multi-frequency wireless communication network and measure and analyze its routing; According to the routing measurement results, the communication routing cluster head is elected; The RPBUC algorithm is used to establish the wireless communication energy consumption model, and the greedy algorithm is used to establish the wireless network communication data transmission mechanism. Based on the wireless network communication energy consumption model and the wireless network communication data transmission mechanism, the ant colony algorithm is used to select the optimal wireless network multi-frequency communication routing protocol. The experimental results show that the average network residual energy of the protocol is greater than 0.01 J, the routing overhead is kept within 0.2, the network life cycle is prolonged, and the average end-to-end delay is effectively reduced. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) has made an indelible mark on the lives of everyone by assisting in agriculture, education, manufacturing, and environmental monitoring etc. One of the biggest problems for WSNs is achieving security and protecting data in a hostile environment against nodes. To address these problems, a novel Reliable Fuzzy based Generic Detection (RFGD) method has been proposed in this paper. The suggested RFGD framework consists of 2 stages. Initially, a reliable cluster head is selected by using the Parameter Optimized Fuzzy Model. Secondly, a Generic Detection method is used to detect compromised nodes to ensure secure transmission of data. The proposed RFGD technique is equated with state-of-arts techniques in terms of packet delivery ratio, energy consumption, number of alive nodes and detection accuracy. The proposed RFGD framework achieves 26.66%, 10.76%, 17.43% and 10.76% more detection accuracy than ACOPSO improved ABC, FGF and GAPSO-H methods. [ABSTRACT FROM AUTHOR]

On-demand routing protocols discover routes through network-wide searches. Route requests are broadcast to a large number of nodes, and route replies may contain long routes. In this paper, we address the route discovery problem and aim to reduce route discovery overhead. We propose using data packets to discover routes. A source sets a boolean variable in a data packet to be true when it has only one route to the destination. This variable is a new form of a route request. The nodes forwarding the data packet send route replies containing cached routes. To prevent nodes from sending duplicate routes to the source, we define a forward list and a backward list in the data packet. The node sending a route reply records route diverging and converging information about the route in the route reply. Subsequent nodes use the information in the data packet to decide whether to send a route reply. Our algorithm reduces route discovery latency and discovers routes shorter than or having the same length as the active data path. Due to these shorter routes, it reduces the total size of route requests and route replies significantly. Routing overhead increases slowly as mobility or network load increases. Our algorithm is independent of node movement. It improves packet delivery ratio by 15% and reduces latency by 54% for the 100-node networks at node mean speed of 20 m/s. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication stands as a pivotal technology revolutionizing conventional base station-to-device (B2D) communication paradigms. It facilitates direct data exchange among Internet of Things (IoT) devices without intermediaries such as servers or cloud services. However, the direct link between devices poses significant security risks, such as data leaks and unauthorized access. In this paper, a novel Secure data Transmission using Crystals Kyber (STUCK) technique has been proposed to secure device-to-device communication efficiently. STUCK encompasses four pivotal phases: token generation, device discovery, link configuration, and secure data transmission, ensuring robust protection against potential security breaches. Leveraging the Crystals Kyber cryptographic technique, STUCK adeptly manages key operations, encrypts and decrypts data, thus ensuring the integrity of data transmission across devices. Experimental validation conducted in MATLAB validates the efficacy of the proposed system, revealing noteworthy performance metrics encompassing processing time, communication overhead, and encryption duration. Comparative analysis demonstrates superior performance of STUCK, showcasing processing time increases of 23.07%, 44.23%, and 65.38% compared to existing techniques such as B-IoMV, LMECC, and QSAP, respectively. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is made up of many sensor nodes with insufficient energy, storage, and processing capabilities. Data gathering and transmission to the base station are two of the main responsibilities of the sensor nodes (BS). As a result, the network lifespan becomes the key factor in the successful design of data collection strategies in WSN. In this study, we provide the Enriched energy optimized LEACH (EE-OLEACH) protocol for data transfer. Through a combination of efficient optimum clustering and an optimal route selection mechanism, it provides a means for energy-efficient routing in WSN. The Homogeneous Hunter-Wolf optimization (HHWO) is used for clustering, and a cluster head is chosen for each cluster to reduce energy loss among sensor nodes and maximize efficiency in their use of available resources. Nodes with the highest residual energy may receive the most energy-efficient routing. In order to send the data to BS, the nodes with the greatest residual energy are chosen. The pheromone-profound Ant optimization (PPAO) method was then used to reduce energy consumption throughout the path-selection process. It contributes to a higher packet delivery ratio while reducing power consumption. According to the experimental findings, the proposed EE-OLEACH performs better than the current Protocol in terms of packet delivery ratio, end-to-end latency, and energy usage. In this paper, we compare the performance of the existing hierarchical routing protocols under varying conditions (packet size, starting energy level, etc.) and demonstrate how the optimal CH selection based on a suggested algorithm improves both network lifetime and energy consumption. The Simulation results shows that the EE-OLEACH enhances energy efficiency by 30%, delay by 35%, node survived by 45%, network lifetime by 56%, packet delivery ratio by 47% and throughput by 38% when compared with other existing protocols. The results clearly show that the suggested EE-OLEACH extends the lifespan of the network and reduce the energy consumption. [ABSTRACT FROM AUTHOR]

To deal with the problem of complex changes of topology in UAVs (Unmanned Aerial Vehicles) network and the increasing need of data transmission, the combination of self-organizing and SDN (Software Defined Network) is a usefull paradigm. However, the control node's position in SDN will influence the transmission of command between controller and relay nodes, thus determining the quality of communication. In this context, we present a network construction, named SDN-FANET (Flying Ad-Hoc Network). We formulate the problem as a classical network center location model. To solve this problem, we propose a particle swarm algorithm based on the graph theory. Simulation results show the algorithm performs better in terms of accuracy and stability, compared with several benchmarks. Particularly, with the initial particle position optimized, the proposed algorithm converges faster. [ABSTRACT FROM AUTHOR]

In-Game theory Applications, the 6G-assisted federated learning in continuous monitoring applications with wireless sensor networks (WSN) is a significant concern. With increased applications comes the increased demand for advanced resource allocation and energy management systems. WSN can be determined as a self-configured, infrastructure-less wireless network monitoring physical or other surrounding conditions. In this study, the proposed system is concentrated on applying game theory to 6G-assisted federated learning for continuous monitoring in wireless sensor networks. The techniques imposed by the dual sink, such as Static and dynamic moving nodes approaches, are applied to the tentative node selection based on aggregated data transmission techniques. Based on the Static nodes and trusted nodes, the Aggregated data transmission is achieved high-level data transmission by combining individual-level data, i.e., the aggregate of the output data. This technique is performed with the wireless sensor network (WSN) platform with a future 6G network coordinating with the tool of NS4-Programmable Data Plane simulation. The proposed system simplifies the development of a behavioral model and bridges the gap between simulation and deployment. Finally, the combination of game theory with 6G-assisted federated learning for continuous monitoring applications in WSN solves problems and identifies several future directions. The outcome analysis of the proposed system is to design the wireless sensor network to yield a high network lifetime of more than 20 h and low power (less than 0.2 kJ energy) consumption for efficient communication in the future 6G cellular network. [ABSTRACT FROM AUTHOR]

With the advancement of ICT technology today, we can equip every single object with tiny and inexpensive radio modules; hence, they can interact and cooperate to perform complex tasks that have not been possible previously. During recent years several practical IoT-based applications have been proposed by deploying this improvement. With the expansion of IoT-based systems, many challenges began to emerge in this area. Energy optimization has been considered, as one of the most important problems in this domain, and data transmission has been identified as the primary accused. To tackle this problem, clustering has been suggested as a promising solution for reducing transmission distance and consequently energy consumption. The non-deterministic polynomial-time hard problem nature of clustering; alongside a variety of considerable parameters, limitations, and their contradiction, have made this problem more complex. As a result, various approaches have been proposed during recent years, each of which considered some parameters and real-world constraints. Here we present an efficient improvement on the existing energy-aware distributed unequal clustering protocol (EADUC). Our solution deploys a well-known swarm intelligence algorithm (SI), named Biogeography-based optimization (BBO) in a distributed manner to achieve heterogeneous load balancing. Our proposed work reflected a variety of real-world limitations such as energy, time, communication radius, and buffer size that have not been considered in many previous works simultaneously. Our simulations show approximately a 26% drop in the total number of dead nodes and a 1.59 % drop in energy consumption in comparison to the existing EADUC algorithm. [ABSTRACT FROM AUTHOR]

With the continuous maturity and adoption of mobile devices enabled by wireless communication technology, people are more apt to record their sport exercise data or healthcare data through various lightweight and smart devices, e.g., mobile phones and smart watches. Meanwhile, massive sport data or healthcare data keep being produced with time, which forms a main source of big healthcare data. Deep mining and analysis of such healthcare data are of positive significance for accurately recognizing the real-time health condition of mobile users and further recommend appropriate sport items to them. However, traditional centralized healthcare data mining and recommendation approaches require mobile users to transmit their health data collected by mobile devices to a remote cloud platform, which often involves heavy data transmissions from mobile devices to cloud platform. As a consequence, the transmission cost is high and the time delay is long. Moreover, long-distance data transmissions are prone to disclose user privacy. Considering these limitations, we bring forth a novel time-efficient and privacy-preserving healthcare data integration and mining approach for sport item recommendation, based on edge-cloud collaboration mechanism. At last, we design a group of simulation experiments to validate the effectiveness and efficiency of our approach. Experimental comparisons indicate a good balance between different evaluation metrics. [ABSTRACT FROM AUTHOR]

The transmission of data in wireless networks needs some suitable path to travel to the destination. Then, while traveling the data in some routes, they select the appropriate direction for receiving the goal that can be enabled in this study. Here, some problems in wireless network data transmission are channel errors, hidden poses, and terminal problems. This causes many problems in wireless networks. To resolve this problem, the routing must be the inserter among the routing methods, enabling the enterprise for the operations and integrating the many applications. The technique of the LEACH protocol and the Recurrent LEACH protocol is used for routing the data in the ad-hoc wireless networks. Here the LEACH protocol is used to communicate within the cluster for the perfect location in the modified location for the routing algorithm. The recurrent LEACH communication protocol has been used to compare the smart route structure. Data management and space allocation analysis have been enabled. This results from household hazardous waste for managing the smart routing and the smart bit technology. Also, a comparison of the bin structure has been enabled for the bin structure. Based on the wireless transmission, the storage, bandwidth energy, and the lack of resources allow the SNA to applications in this study. [ABSTRACT FROM AUTHOR]

Investigators are looking at radio frequency ranges that may suit cellular data consumers' growing requirements. Terahertz's (THz) frequency range is vital for high-speed data communication in wireless gadgets and has indignant the interest of researchers. A novel spiral-shaped patch and diffracted ground-based MIMO antenna structure are presented. The overall dimensions of the structure are 600 µm by 800 µm. The polyamide material is used as a substrate. The analysis among 1 × 1, 1 × 2, 2 × 1 and 2 × 2 MIMO antenna structures are carried out. Performance in reflectance response, Bandwidth, gain, radiation pattern and directivity are analyzed. The proposed structure provides the multiband response with a minimum reflectance response of − 30.51 dB, a bandwidth of 10 THz, peak isolation of 36.99 dB, a maximum normalized directivity of 1030 and a peak gain of 39 dB. ECC TARC, CCL and D.G. were considered for the performance observation. A comparison of the presented structure with other articles is included in the manuscript. The proposed work suits biomedical imaging, short-distance communication, healthcare and WBAN applications. [ABSTRACT FROM AUTHOR]

Underwater Wireless Sensor Networks (UWSNs) are the type of WSNs that transmit the data through water medium and monitor the oceanic conditions, water contents, under-sea habitations, underwater beings and military objects. Unlike air medium, water channel creates stronger communication barriers. In addition, the malicious data injection and other network attacks create security problems during data communication. Protecting the vulnerable UWSN channel is not an easy task under critical water conditions. Many research works proposed in the literature used cryptography principles and intelligent intrusion detection systems to secure the network activities from malicious nodes. However, the need for Machine Learning (ML) and Deep Learning (DL) associated Medium Access Control (MAC) principles is expected for handling the barriers in uncertain UWSN. In this regard, this article proposes a new Intrusion detection system with Integrated Secure MAC principles and Long Short-Term Memory (LSTM) architectures for organizing real-time neighbor monitoring tasks. The proposed system implements Generative Adversarial Network (GAN) driven UWSN channel assessment models and Secure LSTM-MAC principles to protect the data communication. In this regard, the proposed model creates the Intrusion Detection System (IDS) using trained distributed agents. These agents run in each legitimate sensor node contain novel LSTM-MAC engine, intrusion dataset, rule-based monitoring techniques, Secure Hashing Algorithm-3 (SHA-3), Two Fish algorithm and packet filtering tools. The proposed LSTM and agent-based model drives adaptive MAC channel operations to avoid malicious traffics in to legitimate nodes. In addition, this work implements neighbor-based packet monitoring, signal jamming and alert messaging procedures to build reliable security services against different types of attacks. The experiments and the observations reveal the performance of proposed techniques is proved to be 5% to 10% higher than existing techniques in various aspects measured with different metrics. [ABSTRACT FROM AUTHOR]

In this study, we investigate the edge offloading of sensory data in the internet of things. Multiple edge servers cooperatively offloading all or part of the sensory data initially sent to a cloud center, which improves users' experience. In the process of cooperative offloading, the transmission of sensory data and information exchange among edge servers will consume system resources, resulting in a cost of cooperation. How to maximize the offloading ratio of sensing data while maintaining a low collaboration cost is a challenge. This study first formulates a joint optimization problem of the sensing data offloading ratio and cooperative ratio satisfying the constraints of network delay and system energy consumption. Subsequently, a distributed alternating direction method of multipliers (ADMM) via constraint projection and variable splitting is proposed to solve the problem. The numerical results show that the proposed method greatly improves network delay and energy consumption compared to the fairness cooperation algorithm. [ABSTRACT FROM AUTHOR]

The efficiency of the routing strategy is essential for the sensor-based applications at the edge of internet of things. In many practical monitoring applications, many sensor networks with irregular terrain structure are ubiquitous in nature. The forwarding candidates selected by the Euclidean distance in the routing are not appropriate. To address the problem, a Geodesic Distance-based Routing (GDDR) scheme with insurance of reliable data transmission for sensor networks with irregular terrain structure is proposed. In the GDDR scheme, a novel method is proposed firstly to calculate the geodesic distance using sampling and flooding to reduce the time complexity. In the data transmission process, the geodesic distance is applied in the selection of forwarding candidates to ensure the correct transfer direction, path connectivity and avoidance of routing holes. A certain number of forwarding candidates are selected based on the remaining energy and geodesic distance. And, the number of selected candidates is variable according to the geodesic distance to reduce and balance the energy cost. The proposed scheme not only can be used in the network with irregular topology but it also can be applied in the network with a regular shape. The performance is tested and the effectiveness of the proposed GDDR scheme is evaluated by the simulation. [ABSTRACT FROM AUTHOR]

The clustering methodologies for deployed devices in the region of interest consider the similarity of sensed data or target maximization of network lifetime. Thus the clusters formed are either sensing clusters or communication clusters. In the case of motes capable of sensing multi-feature data, the similarity of sensed parameters is generally used to cluster the devices. Event detection methodologies in uni-modal sensor based IoT mainly focus on communication clusters to maximize network lifetime. Yet, the existing approaches for clustered data gathering hardly incorporate user context and preferences for sensing cluster formation and event detection. Additionally, many approaches do not incorporate both sensing and communication clusters in order to detect the event and balance it with energy efficiency. Research work that considers user context in the domain of the internet of things is also limited with a focus on network lifetime, event detection accuracy etc. The present work aims at resolving the existing constraints in 'user context' aware clustering methods and event detection. The user context parameters based on the domain knowledge of the user are used to divide the deployed region into sub-regions forming sensing clusters. The methodology allows the user to change the context and definition of events for each sub-region for accurate, context-aware, and discriminatory event detection. Additionally, the compressive gathering of detected events results in energy efficient data transmission. Simulations were performed for scalability and comparative analysis of the proposed scheme. The results show that the proposed scheme outperforms the existing schemes in terms of detection accuracy and network lifetime. [ABSTRACT FROM AUTHOR]

There are contexts where communication with TCP/IP protocol is not possible due to the lack of infrastructure or a reliable and continuous data transmission. In this cases, alternatives such as Opportunistic Networks (OPPNets) are valid. Such challenging conditions are common in rural areas and are a significant obstacle for the deployment of eHealth technologies for older adults. Considering this context, the present work introduces Interest-based System for Communication in Isolated Areas (ISCA), an OPPNet architecture for remote monitoring and emergency detection in ageing people who live alone. For this, the energetic requirements are considered, providing efficient and sustainable operation. The proposal makes use of a routing algorithm based on interests which enables asynchronous communication among entities. ISCA is evaluated over a realistic scenario and compared with similar state-of-the-art solutions. Experimental results show that ISCA notably improves the delivery probability with an enhancement of 52.25% in comparison to the second best alternative and provides a suitable average latency. Moreover, it also achieves better performance in terms of overhead and hops number compared to the other studied protocols [ABSTRACT FROM AUTHOR]