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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 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]

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]

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]

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]

In this paper, we introduce an efficient user-association and bandwidth-allocation scheme based on an actor-critic deep learning framework for downlink data transmission in dense mobile networks. In this kind of network, small cells are densely deployed in a single macrocell, and share the same spectrum band with the macrocell. The small-cell base stations are also called green base stations since they are powered solely by solar-energy harvesters. Therefore, we propose an actor-critic deep learning (ACDL) algorithm for the purpose of maximizing long-term network performance while adhering to constraints on harvested energy and spectrum sharing. For this purpose, the agent of the ACDL algorithm tries to obtain an optimal user-association and bandwidth-allocation policy by interacting with the network's environment. We first formulate the optimization problem in this paper as a Markov decision process, during which the agent learns about the evolution of the environment through trial and error experience. Then, we use a deep neural network to model the policy function and the value function in the actor and in the critic of the agent, respectively. The actor selects an action based on the output of the policy network. Meanwhile, the critic uses the output of the value network to help the actor evaluate the taken action. Numerical results demonstrate that the proposed algorithm can enhance network performance in the long run. [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]

This paper investigates the secrecy performance of the Cell-Free massive multiple-input multiple-output network with finite resolution analog-to-digital converters at the access points (APs) and users in presence of an active eavesdropper. Using the additive quantization noise model, the uplink minimum mean squared error channel estimation and downlink data precoding are respectively operated. Specifically, the lower bound on the achievable ergodic rate and upper bound for the information leakage to the eavesdropper are theoretically derived in details. Thereby, the closed-form expression for the achievable ergodic secrecy rate is accordingly obtained with respect to the number of APs, number of each APs antenna, number of users, pilot and data transmission power and quantization bits, etc. In addition, the asymptotic approximation for the ergodic secrecy rate has been presented. Moreover, the path-following power control algorithm has been proposed aiming at maximizing the secrecy rate subject to both power and achievable rate constraints. Finally, extensive simulations are provided to corroborate the theoretical analytical results and the validity of the proposed power allocation scheme. [ABSTRACT FROM AUTHOR]

Optical fiber access systems are one of the driving forces behind the success of the Internet. Time- and wavelength-division multiplexing passive optical network (TWDM-PON) is regarded as the key technology for future Internet access networks. In this paper, we propose an ITU TWDM-PON module for the ns-3 network simulator and describe its concept and design specifications. The proposed module is developed based on the XG-PON module for ns-3. It can simulate G.989 standard-compliant data packet transmission in the upstream and downstream directions using multiple wavelength channels. It enables us to evaluate the performance issues that arise with the TWDM-PON development, including various dynamic bandwidth allocation and dynamic wavelength allocation algorithms. The proposed module is expected to become a good platform for studying future access networks composed of TWDM-PON and mobile networks by enabling us to simulate dynamic wavelength and bandwidth allocation. [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]

In recent years Terahertz (THz) Band communications have gained even greater interest and higher expectations to meet an ever increasing demand for the speed of wireless communications. This paper provides the characteristics of electromagnetic waves propagating in the THz Band, which is one of the key technology to satisfy the increasing demand for Terahertz Wireless Data Communication (ThWDC). The performance of future terabit super channels implemented using bipolar phase-shift-keying which gives the best BER (Bit Error Rate) with today's technology is investigated through the simulations for ThWDC. The objective of this paper is to describe the important issues related to the transmission in of ThWDC in air environment and to determine the best transmission windows in the THz range. In particular, ThWDC channel is modeled considering effects like capacity, channel performance and BER is investigated through simulation. The simulation results and the theoretical analysis show that data communication is possible from 0.01 to 0.5 THz frequency range and the best transmission window in this range have been found ω1 = [0.01-0.05 THz], ω2 = [0.06-0.16 THz] and ω3 = [0.2-0.3 THz] in this paper. [ABSTRACT FROM AUTHOR]

Nowadays wireless sensor networks enhance the life of human beings by helping them through several applications like precision agriculture, health monitoring, landslide detection, pollution control, etc. The built-in sensors on a sensor node are used to measure the various events like temperature, vibration, gas emission, etc., in the remotely deployed unmanned environment. The limited energy constraint of the sensor node causes a huge impact on the lifetime of the deployed network. The data transmitted by each sensor node cause significant energy consumption and it has to be efficiently used to improve the lifetime of the network. The energy consumption can be reduced significantly by incorporating mobility on a sink node. Thus the mobile data gathering can result in reduced energy consumption among all sensor nodes while transmitting their data. A special mobile sink node named as the mobile data transporter (MDT) is introduced in this paper to collect the information from the sensor nodes by visiting each of them and finally it sends them to the base station. The Data collection by the MDT is formulated as a discrete optimization problem which is termed as a data gathering tour problem. To reduce the distance traveled by the MDT during its tour, a nature-inspired heuristic discrete firefly algorithm is proposed in this paper to optimally collect the data from the sensor nodes. The proposed algorithm computes an optimal order to visit the sensor nodes by the MDT to collect their data with minimal travel distance. The proposed algorithm is compared with tree-based data collection approaches and ant colony optimization approach. The results demonstrate that the proposed algorithm outperform other approaches minimizing the tour length under different scenarios. [ABSTRACT FROM AUTHOR]

Multi-channel ad hoc networks are promising technology to alleviate the spectrum scarcity in dense networks. The main challenge for channel access in distributed multi-channel ad hoc networks is channel synchronization and competition with neither common control channel nor dedicated control transceiver. In this paper, a Tight Synchronized MAC protocol with distributed synchronization and competition process without any dedicated control channel is proposed. The protocol uses a simple handshaking mechanism to create a channel table for each node, which contains its neighbors and channels' status. By this, any node knows the priority of its channels and also the priority of neighbors' channels. By assigning priorities, a sender node starts synchronization. If sender and receiver nodes have a common channel, the proposed method finds it directly or indirectly using the common neighbor mechanism. The other feature of the proposed scheme is using a fair preemptive data transmission phase, which reduces the number of switchings along data transmission. The fair preemptive mechanism is achieved by an analytical solution to estimate the number of competing nodes and thus, estimates the number of assigned data slots. Simulation results show the performance improvements in terms of synchronization delay and packet delivery ratio over HMMAC and MSRMMAC protocols. [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]

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]

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 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]

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]

In this paper, we analyze the performance of wireless networks subject to random loss. In this regard, we revisit our TCP Congestion Control Enhancement for Random Loss (CERL) mechanism that has been introduced earlier in a previous study. We call our new developed TCP variant as CERL Plus (CERL+). TCP CERL+ is a modification of TCP Reno at sender-side. TCP CERL+ is a new generation that works similarly as TCP CERL but its main idea is to use a dynamic threshold in terms of RTT. In doing this, we employ the average RTT and its minimum measurements made over the connection to estimate the queue length of the bottleneck link. As a result, we can use this queue length to evaluate the congestion status and distinguish it from the random loss status. This in turn would not reduce the window size leading to enhance the performance of the proposed algorithm in the sense of increasing the amount of data transmission. Additionally, CERL+ alleviates the congestion in the bottleneck obviously. In this paper, we present simulation experiment for TCP CERL+ considering a two-way transmission assuming a heavy load and wide range of random loss rates compared to TCP NewReno, TCP New Jersey + , TCP mVeno, TCP Westwood + , TCP Cubic and TCP YeAh using the network simulation ns-2. Simulation results prove that CERL+ outperforms other TCP variants and achieves excellent throughput gain. [ABSTRACT FROM AUTHOR]

Early prediction of a forest fire is one of the critical research challenges of the wireless sensor network (WSN) to save our ecosystem. In WSN based forest fire detection system, sensor nodes are deployed in the remote forest area for transmitting the sensed data to the base station, which is accessible by the forest department. Though sensor nodes in the forest are localized through GPS connection, the high deployment cost for it motivates the authors of this paper to design a novel localization technique applying the Support Vector Machine. Forest fire prediction in an energy efficient way is another concern of this paper. The semi-supervised classification model is proposed to address this problem by dividing the forest area into different zones [High Active (HA), Medium Active (MA), and Low Active (LA)]. It is designed in such a way that it can be able to predict the state of the (HA, MA, LA) fire zone with 90% accuracy when only one parameter is sensed by sensor nodes due to energy constraints. The greedy forwarding technique is used to transmit the packets from the HA zone to the base station continuously, and the MA zone transmits packets periodically, whereas, LA zone avoids transmitting the sensed data to the base station. This technique of data forwarding enhances network lifetime and reduces congestion during data transmission from the forest area to the base station. [ABSTRACT FROM AUTHOR]

This paper describes a novel design and wet laboratory implementation of reliable end-to-end molecular communication. In the reliable end-to-end molecular communication described in this paper, source and destination bio-nanomachines exchange molecular packets through intermediate bio-nanomachines that are capable of packet replication. A source bio-nanomachine forms a molecular packet and transmits the molecular packet into the environment. An intermediate bio-nanomachine detects a molecular packet and produces its copies through packet replication. A destination bio-nanomachine, upon reception of a molecular packet, produces an acknowledgment molecular packet and transmits back to the source bio-nanomachine. This paper describes how the reliable end-to-end molecular communication can be biochemically implemented with RNA (ribonucleic acid) molecules and artificial cell systems. It also describes a simulation-based performance evaluation study showing the impact of model parameters on propagation delay in the reliable end-to-end molecular communication. [ABSTRACT FROM AUTHOR]

Recently, Multi-sink Wireless Sensor Networks (WSNs) have received more and more attention due to their significant advantages over the single sink WSNs such as improving network throughput, balancing energy consumption, and prolonging network lifetime. Object tracking is regarded as one of the key applications of WSNs due to its wide real-life applications such as wildlife animal monitoring and military area intrusion detection. However, many object tracking researches usually focus on how to track the location of objects accurately, while few researches focus on data reporting. In this work, we propose an efficient data reporting method for object tracking in multi-sink WSNs. Due to the limited energy resource of sensor nodes, it seems especially important to design an energy efficient data reporting algorithm for object tracking in WSNs. Moreover, the reliable data transmission is an essential aspect that should be considered when designing a WSN for object tracking application, where the loss of data packets will affect the accuracy of the tracking and location estimation of a mobile object. In addition, congestion in WSNs has negative impact on the performance, namely, decreased throughput, increased per-packet energy consumption and delay, thus congestion control is an important issue in WSNs. Consequentially, this paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. Furthermore, data reliability is considered in our model where the sensed data can reach the sink node in a more reliable way. Finally, this paper presents a solution that sufficiently exerts the underloaded nodes to alleviate congestion and improve the overall throughput in WSNs. This work first formulates the problem as 0/1 Integer Linear Programming problem, and proposes a Reliable Energy Balance Traffic Aware greedy Algorithm in multi-sink WSNs (REBTAM) to solve the optimization problem. Through simulation, the performance of the proposed approach is evaluated and analyzed compared with the previous work which is related to our topic such as DTAR, NBPR, and MSDDGR protocols. [ABSTRACT FROM AUTHOR]

This paper investigates the energy-efficient radio resource allocation problem of the uplink smallcell networks. Different from the existing literatures which focus on improving the energy efficiency (EE) or providing fairness measured by data rates, this paper aims to provide fairness guarantee in terms of EE and achieve EE-based proportional fairness among all users in smallcell networks. Specifically, EE-based global proportional fairness utility optimization problem is formulated, taking into account each user’s quality of service, and the cross-tier interference limitation to ensure the macrocell transmission. Instead of dealing with the problem in forms of sum of logarithms directly, the problem is transformed into a form of sum of ratios firstly. Then, a two-step scheme which solves the subchannel and power allocation separately is adopted, and the corresponding subchannel allocation algorithm and power allocation algorithm are devised, respectively. The subchannel allocation algorithm is heuristic, but can achieve close-to-optimal performance with much lower complexity. The power allocation scheme is optimal, and is derived based on a novel method which can solve the sum of ratios problems efficiently. Numerical results verify the effectiveness of the proposed algorithms, especially the capability of EE fairness provisioning. Specifically, it is suggested that the proposed algorithms can improve the fairness level among smallcell users by 150-400 % compared to the existing algorithms. [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]

This paper shows a method for predicting the lifetime of a wireless sensor network based on the LoRa Ra-01 wireless modules. To develop a prediction model of the energy consumption, wireless sensor modules were assembled and it was obtained experimental data using LabView development environment. There were performed experiments to get battery discharge curve. Experimental data of power consumption depending on the packet length were obtained in transmission mode. Using experimental data, we obtained dependencies of system lifetime on sleep mode duration and packet length. The paper also considered a probabilistic approach to predict the system lifetime depending on the probability of data transmission during the day. The lifetime prediction model is based on Markov's chains. The results obtained in this work can be used to predict lifetime of sensor networks more accurately. [ABSTRACT FROM AUTHOR]

Energy is vital parameter for communication in Internet of Things (IoT) applications via Wireless Sensor Networks (WSN). Genetic algorithms with dynamic clustering approach are supposed to be very effective technique in conserving energy during the process of network planning and designing for IoT. Dynamic clustering recognizes the cluster head (CH) with higher energy for the data transmission in the network. In this paper, various applications, like smart transportation, smart grid, and smart cities, are discussed to establish that implementation of dynamic clustering computing-based IoT can support real-world applications in an efficient way. In the proposed approach, the dynamic clustering-based methodology and frame relay nodes (RN) are improved to elect the most preferred sensor node (SN) amidst the nodes in cluster. For this purpose, a Genetic Analysis approach is used. The simulations demonstrate that the proposed technique overcomes the dynamic clustering relay node (DCRN) clustering algorithm in terms of slot utilization, throughput and standard deviation in data transmission. [ABSTRACT FROM AUTHOR]

In the application environment having dense distribution of marginal wireless sensor network (WSN), the data transmission process will generate a large number of conflicts, which will result in loss of transmission data and increase of transmission delay. The multi-path data transmission method can effectively solve the problem of large data loss and transmission delay caused by collisions. A new approach of multi-path reliable transmission for application of marginal WSN (named RCB-MRT) is proposed in this paper. It adopts redundancy mechanism to realize the reliability of data transmission, and uses concurrent woven multi-path technology to improve the transmission efficiency of data packets. Firstly, it divides the data packets that the sensor node needs to transmit into several sub-packets with data redundancy, and then forwards the sub-packets to the aggregation node through multi-path by the intermediate nodes of marginal environment. The results of our experimental tests show that the proposed multi-path reliable transmission method can effectively reduce data packet loss rate, reduce transmission delay and increase network lifetime. The method is very useful for the applications of marginal wireless sensor network. [ABSTRACT FROM AUTHOR]

The power consumption and connectivity of mobile nodes to the network are two essential issues in smart cities. In this paper, we have proposed a decentralized approach for mobility management of mobile nodes in smart cities. We propose a customized communication protocol compliant with 6LoWPAN along with the scheme that consumes less power regarding previous solutions. The proposed approach organizes static nodes as a tree for efficient routing, automatic addressing, and handling movement of mobile nodes. The proposed approach has a hybrid data transmission capability by proactively choosing multi-hop or single-hop routing to reduce power consumption of mobile nodes. It is evaluated based on different factors. The results show the superiority of the approach compared with the previous works. The promising analytical results manifest the higher performance (about 10% compared to the best scheme) of the proposed approach specifically in reducing the power consumption of mobile nodes. [ABSTRACT FROM AUTHOR]

The core objective behind this research paper is to implement a hybrid optimization technique along with proactive routing algorithm to enhance the network lifetime of wireless sensor networks (WSN). The combination of two soft computing techniques viz. genetic algorithm (GA) and bacteria foraging optimization (BFO) techniques are applied individually on destination sequence distance vector (DSDV) routing protocol and after that the hybridization of GA and BFO is applied on the same routing protocol. The various simulation parameters used in the research are: throughput, end to end delay, congestion, packet delivery ratio, bit error rate and routing overhead. The bits are processed at a data rate of 512 bytes/s. The packet size for data transmission is 100 bytes. The data transmission time taken by the packets is 200 s i.e. the simulation time for each simulation scenario. Network is composed of 60 nodes. Simulation results clearly demonstrates that the hybrid approach along with DSDV outperforms over ordinary DSDV routing protocol and it is best suitable under smaller size of WSN. [ABSTRACT FROM AUTHOR]

This paper discusses a new method of coding and/or decoding data for wireless transmission, particularly for radio transmitted data, and examines the equipment needed for the actual implementation of the technique. The authors describe in detail the principle of the proposed approach, including possible benefits of its implementation in wireless sensor networks, and compare the given concept with the most widely used Manchester coding method. When implemented, the Time Quanta Bit coding method increases the communication range and shortens the necessary transmission time, which, in the case of battery-operated devices as a typical representative of the wireless sensor node, allows for longer operation time. The described technique can significantly improve the quality of wireless communication in small wireless sensor networks where the communication is based on infrequent small packet transfers; such target applications then include, for example, home and office automation wireless sensor networks, safety systems, street lighting, industrial manufacturing and management, and telemetry. [ABSTRACT FROM AUTHOR]

The incorporation of cloud computing in cloud radio access networks (C-RAN) implies that drawbacks of existing cloud platforms influence wireless network performance. Two of such drawbacks are power usage effectiveness and cloud content access latency. New locations such as the ocean, stratosphere and outer space are suitable alternative locations for cloud computing platforms. These new locations have cooling benefits and enhance the power usage effectiveness of future cloud computing platforms. The latency is also reduced when cloud computing platforms in these locations are closer to subscribers. This paper proposes the cloud diversity concept for C-RANs. Cloud diversity proposes combining cloud computing platforms at locations close to subscribers and with cooling benefits. Cloud computing platforms in the aforementioned locations are closer to different subscribers for which they reduce the latency of data access. This paper presents an architecture showing a future C-RAN that incorporates cloud diversity. It formulates a performance model and presents simulation results. The results obtained show that the incorporation of cloud diversity enhances power usage effectiveness by up to 74.9% on average. Cloud diversity also enhances data transmission by reducing the number of round trips associated with data access by up to 63.6% on average. [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]

In this paper, we show experimentally the feasibility of intervehicle communication of warning information. Warning messages convey significant information that might improve the safety of drivers and passengers. Intervehicle communication can be achieved by the detection of important events through a vision-based detection module, and sharing them between vehicles using a transmission module. In this paper, we developed a testbed that considers both modules in order to detect, recognize and share relevant information, such as traffic signs. To the best of our knowledge, our architecture is the first that combines detection and transmission of messages in the same platform. We detect traffic signs as blobs using the Maximally Stable Extremal Regions (MSER) algorithm, and we recognize them using Random forest classifiers. In the transmission module, we used a simplied broadcasting mechanism that avoids the use of handshaking to establish a communication. In order to assess our system, a set of indoor and outdoor experiments are considered. [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]