High transmission rates in 5G cellular networks can effectively promote the application of the Internet of Things (IoT) and smart cities. However, the demand for large-capacity data transmission may overload 5G networks due to limited spectrum resources, as current mobile communication systems cannot meet the exponentially growing demand for wireless data traffic. To enable efficient Device-to-Device (D2D) communication within cellular networks, effective resource management is critical. Therefore, designing a reasonable and efficient resource allocation method remains a key research focus. This paper addresses the power control problem for both cellular and D2D users by proposing a hierarchical game-based joint power allocation and transmission rate control algorithm. First, the maximum transmit power is calculated for cellular users and D2D users, followed by a mathematical modeling of the joint transmit power optimization problem. Second, Stackelberg hierarchical game theory is applied to solve the optimization problem. Finally, simulations validate the proposed method. The results demonstrate that the proposed algorithm improves the network transmission rate by approximately 8.93
With the development of cancer detection technologies, medical image analysis with image processing assists in diagnosing cancer even better. Most of the previously existing methods transmit a large quantity of information and it consumes more time for detecting the disease. In this paper, a newly developed optimization-based routing and detection of lung cancer disease in the Internet of Things (IoT) is demonstrated by reducing the time complexity. Here, the nodes in IoT are simulated and this node collects patient’s data. For transmitting the collected data to the base station and the optimal path is determined by using the proposed Walruses Remora Optimization Algorithm (WaROA) with measures like energy, delay, distance, and link quality. This approach is the integration of two optimizations, such as the Walruses Optimization Algorithm (WaOA) and Remora Optimization Algorithm (ROA). The nature of the WaOA approach mimics the walrus behavior and it is a new bio-inspired metaheuristic technique. The essential motivations engaged in the method of WaOA are the course of migrating, feeding, fighting, and escaping predators. The ROA approach is based on nature-inspired, new bionics and meta-heuristic. Due to the parasitic activities of remora, the ROA approach is motivated. By combining both approaches of WaOA and ROA, the best path is chosen and the performance of routing is enhanced. Then, the lung cancer detection at Base Station (BS) is carried out with the use of Computed Tomography (CT) images. The pre-processing is done by utilizing a Mean filter, which is used to eliminate the noise from the images. The lung lobe is segmented using Ethernet (E-Net) in which the features are, Gabor, Local Vector Pattern (LVP) and statistical features are extracted. Finally, lung cancer detection takes place with the help of SqueezeNet, where the weight of SqueezeNet is trained by the proposed WaROA. Finally, the WaROA approach for routing is compared with other existing techniques in terms of energy and delay, which is considered to obtain a maximum energy is 0.047J and a minimum delay is 0.107 ms in 50 nodes based on the Lung Image Dataset. Furthermore, the detection of lung cancer using WaROA-SqueezeNet is assessed by the metrics of accuracy, sensitivity, and specificity in which it attains the values of 93
Software Defined Network (SDN) which is labeled as a new network archetype that decouples the data plane and control plane is capable to solve today’s network problems and improve network performance. Yet, among numerous challenges and research openings in software-defined networks, Controller Placement Problem (CPP) is supposed to be the most vital issue which can directly affect the whole network’s performance. In this thesis, we deliver a comprehensive review of various metaheuristic CPP-optimized models in SDNs. In this regard, we propose a methodology named Ant Colony Optimization Controller Placement (ACO-CP), to solve the optimal controller location. Ant Colony Optimization is a population-based meta-heuristic algorithm proposed for the optimal location of the controllers, which takes a precise set of the objective function and returns the best potentially location out of the possible set of locations. The objective function is defined by considering, the average and maximum controller-to-controller, switch-to-controller latency, and load balance. By comparing the network performance our proposed algorithm, provides better performance compared with Pareto Simulated Annealing and k-medoid method, Specially on its overall global latency and controller-to-controller latency.
Wireless sensors networks (WSN) become more popular in recent years. For their efficient use, it is necessary to define energy-efficient routing protocols. In WSN, it sometimes arrives that some sensors have items that belong or not to them. The aim being to send each item to its real destination. This problem is known as the permutation routing problem. Some sensors may fail during the routing process. These particular cases must not prevent the operation of routing as a whole. Thus, it becomes important to ensure the fault-tolerance of the routing mechanism. In this paper we propose an energy-efficient and fault-tolerant permutation routing protocol for WSN. The proposed PEFTOSPRO protocol is realized in four stages. Firstly, we partition the sensors in cliques within which Cluster Heads (CH) are elected. Secondly, we achieve the hierarchical clustering of CH. Thirdly, we route items to their belonging cliques. And finally, we route each item to it real destination within all the cliques. We use the wake and sleep technique and the fault-tolerance in the data routing process in order to save the sensors’ energy. Simulations show that the wake and sleep technique and the fault tolerance management during the data routing process help to save the sensors’ energy, and by doing so, prolong the lifetime of the WSN. PEFTOSPRO is a suited protocol to solve the permutation routing problem in a multi-hop environment for wireless sensor networks, because it ensures that sensors efficiently use their energy and takes into account the fault tolerance.
According to a recent study, adding intelligent reflecting surfaces (IRS) to propagation environments improves the performance of wireless devices. The capabilities of cognitive radio’s spectrum sensing over environments with assisted wireless propagation via IRS are examined in this research. Taking channel fading effects into consideration, we derive closed-form analytical expressions for the average probability of detection (APD) for single user and cooperative users. Further, the derived APD expression is used to derive the average area under the receiver operating characteristics curve ( AUC ) expression. Monte Carlo simulations are also carried out in order to validate the derived expressions. Results demonstarte the extent of detectability performance in cognitive radio on utilizing the IRS aided wireless propagation environment therefore witnessing its significance for upcoming wireless technological advancement by offering spectrum efficiency with good quality of service (QoS).
Flood-based communication protocols are attractive due to their easy and fast network startup, resiliency to communication or node loss, and node mobility. Concurrent transmissions are flooding-based protocols that enable low-latency, network-wide communication synchronously. They also provide energy-efficient data dissemination with higher delivery reliability over traditional flooding. This paper proposes the Good Flood Bed (GFB) method to reduce further the energy consumption of networks operating based on concurrent transmission by preventing flood in parts of the network where there is no need for flood data. GFB starts with a distributed leader-election process to choose a root node. Then, the root node builds a spanning tree among nodes. The tree establishes a parent-child relationship and forms a new controlled flood bed. We show the effectiveness of the proposed method by simulation and experimenting on a BLE5 small-scale test bed. Experiments prove that this method has the potential to cut network energy consumption by 50%.
We have recently witnessed the rapid development of several emerging technologies, including the internet of things, which lead to a high interest in wireless sensor networks. Tiny sensor nodes are now important parts of a large number of complex systems, with numerous applications, including military, environment monitoring, and surveillance and body area sensor networks. A wireless sensor network builds the core part for IoT. Besides this, lifetime maximization is the biggest challenge in the wireless sensor network. Also, In a wireless sensor network, it is difficult to find an optimal node deployment approach that would minimize costs, be robust to node failures, decrease computing overhead and communication, and maintain a high degree of coverage and network connectivity. There is numerous literature addressed this challenge which is discussed in this paper; still there are lot many challenges yet to be addressed. Considering this scenario, in this paper, we propose a scheduled-based node deployment algorithm using Firefly Optimization (FA) to offer a circumstance where we have a group of target points that satisfy p-coverage and sensor nodes that satisfy q-connectivity, with subject to the selection of the optimal number of a sensor node that has the highest energy and minimum distance. The multiple parameters as no. of sensor nodes, distance, survivability factors, coverage, and connectivity of the sensor nodes are considered for designing the fitness function. A comprehensive statistical analysis is done using the simulation results to prove the proposed scheme’s efficiency with other existing state-of-the-art methods under various p-coverage and q-connectivity configurations.
A small set of pilots in underwater acoustics OFDM (UWA-OFDM) systems tends to be insufficient for recovering the channel impulse response (CIR). This may result from the fast changes in the propagation environment and the requirement for high transmission data rates. A previous work, namely PE, considered the received subcarriers whose distances to their closest constellation points below the predetermined threshold T as potential pilots. However, extracting these pilots at the same time gives no chance to use some of them to facilitate searching for others. Fixing threshold T is also another limitation for searching pilot candidates. This paper proposes a reliable pilot search (RPS) method that consists of a multi-iterations pilot searching process with an adaptive threshold T_a to improve the estimator in UWA-OFDM. The pilot search process gradually extracts reliable pilots at each iteration based on the threshold T_a and evaluates them by performing channel estimation. Our method is compared to the MMSE and PE estimators on a wide range of settings, including the number of channel taps, pilot spacing, and various modulation schemes (i.e., MPSK and MQAM). The experimental results show that the RPS method often outperforms the MMSE and PE methods in terms of bit error rate (BER).
In this paper, a practical scenario of multicasting through heterogeneous cellular network has been considered. The macro base station of macro cell transmits confidential information to its intended multicast macro users and multiple eavesdroppers try to decode this information and pico base station of pico cell transmits confidential information to its intended multicast pico users. Authors are interested to develop a mathematical model consists of the closed-form analytical expression for the secrecy multicast capacity (SMC) to protect that confidential information from eavesdropping. The results show that the simultaneous effects of double scatterer and correlation significantly reduce the security in multicasting but these reduction in the security can be compensated by antenna diversity at the multicast users. The results also show the effect of double scatterer on different correlation scenario (Constant Correlation and Jakes Correlation) in terms of the correlation figures of transmit, receive and scatterer correlation matrices.
A mobile ad hoc network (MANET) is a cluster of wireless mobile devices that can create a temporary network without seeking the support of any central management or infrastructure. Due to such issues, wireless communications require energy consumption, frequent data transmission, and nodes' mobility. Secondly, loss of data packets occurs because of various reasons such as traffic congestion, node mobility, or unexpected losses. An efficient vehicular ad hoc network (VANET) routing protocol is proposed in this study using a genetic algorithm (GA) and evolution-based techniques while considering all the parameters. A new fitness function (F.F) to obtain the optimum route is proposed in this study by using the routes provided by the Ad hoc on-demand multipath distance vector AOMDV routing protocol. This study uses a new type of routing mechanism based on cryptography to demonstrate how to secure V2V and V2I communications from various network threats in a VANET environment. For VANET communications, the transmission message must meet the requirements for integrity, confidentiality, and non-repudiation to ensure that a trustworthy third party can identify users with a pattern of misbehavior. While ensuring optimized route selection and secured communications, a hybrid approach AOMDV-RGA and ABC is proposed. AOMDV-RGA (AOMDV Routing using Genetic Algorithm) is used for selecting optimal routes among the routes, and ABSC (Authentication Based Secured Communication) is proposed for performing secured communication between V2V and V2I. The experimental results demonstrate the proposed technique's effectiveness compared with other previously developed techniques to address the routing and security problems of VANETs.
Non-orthogonal multiple access-unmanned aerial vehicles (NOMA-UAV) are the enabling technologies for 5G network in increasing the spectral efficiency as well as enhancing the computing capability for the massive machine type communication (mMTC) devices. To support mMTC devices, machine learning (ML) based algorithms can efficiently exploit the user pairing (UP) scheme for NOMA-UAV system. In this paper, we have proposed an improved user pairing (IUP) scheme using k-mean clustering algorithm and derived the lower and/or upper bound conditions on power allocation coefficient under imperfect successive interference cancellation (Im-SIC). The objective of the proposed scheme is to maximize the system sum-rate capacity under the minimum transmission rate constraints and total remaining UAV transmission power. Based on the user distance from the base station and channel condition of the users, a hybrid approach (where paired users utilize the NOMA technique and unpaired users will utilize the OMA technique) is used to maximize the system throughput for a NOMA-UAV system. Simulation results reveals that the proposed user pairing scheme achieve the high spectral efficiency and ensure good fairness among users compare to the other user pairing schemes such as Near-Far (NF), Near-Median (NM) and Random user pairing schemes.
In order to optimize 3D (Three Dimension) terrain wireless sensor networks, an obstacle sensing fuzzy clustering (OAFC) protocol is proposed to maintain the energy efficiency and data transmission reliability of the network in 3D terrain with obstacles. The location of sensor nodes is determined in 3D environment, and the communication quality of sensor nodes is seriously affected by undulating terrain obstacles. Improving link reliability and reducing energy consumption are key factors for 3D terrain wireless sensor networks. OAFC protocol uses Fuzzy C Means (FCM) technology and Fuzzy inference system (FIS) to search optimal cluster heads (CHs) of network. Fuzzy logic based multi-hop routing is adopted in inter cluster communication to guarantee PDR performance. The performances of OAFC protocol are simulated in three different 3D scenarios. Comparing OAFC with LEACH-3D and FCM-3D, the simulation results show that the total energy consumption of our algorithm nodes is reduced by 17.5% and 33.1% respectively. The number of dead nodes is decreased by 23.6% and 45.9% at most. Packet transfer rate is increased by 0.118 to 0.349. The experimental results show that the more complex the 3D terrain obstacle, the better the performance of OAFC in PDR (Packet Delivery Rate).
In wireless body area network health care applications, energy-constraint wearable devices are used to monitor patient physiological parameters. The security of the private health information of a person plays a significant role because if it is captured and read by an unauthorized person, the confidentiality of the patient data is lost. Therefore, there is a requirement to secure the data by performing encryption to transfer it into an unreadable form. Since the resources used for encryption should be kept to a minimum as the devices are attached to the human body, a lightweight encryption algorithm has to be used. Therefore, the generation of a unique key used for encryption plays a significant role. In work, generating a unique key uses the ECG values taken from MIT-BIH Arrhythmia database. Four unique keys are generated, which can be used for encryption. The uniqueness and randomness of the keys generated are proved using the runs test and frequency test within the block. Also, the average hamming distance calculated between the ECG keys generated from two different ECG signals is 62.5 ≈ 80 bits), which proves the distinctiveness of the keys generated. Implementation of the work is performed using Matlab.
Wireless Body Area Network (WBAN) is a wireless network of short-range communication protocols for remote healthcare monitoring with the possibility of giving freedom of human body movements. Sensor nodes (motes) are usually located under the skin, implanted deep in the body, or ingested, as in the rare case of smart pills for medical and non-medical usage. Since the necessary connections of wearable and implantable devices are wireless, and the components use low batteries, processing and transferring the critical data can deplete the nodes’ power. In most cases, it is impossible to exchange or re-power batteries. Holter monitoring, loop recorders, and wireless capsule endoscopy are some of the WBAN’s applications for saving and transferring medical data. Wireless capsule endoscopy is the application for image transmission in WBANs, and the image compression in common is a specific segment of capsule endoscopy. Since the better compression of images increases the frame rate and typically improves the diagnosis process, selecting the compression algorithm should be relevant. The considerable scope of this comprehensive study pays attention to the various biomedical data compression approaches in WBANs. This paper focuses on power-efficient schemes of remote healthcare networks. In this survey article, the energy-based biomedical data compression approaches of WBANs and remote healthcare networks are accurately classified based on lossy, lossless, and hybrid techniques; later, a comprehensive comparison of each specific method’s power consumption is presented.
This study suggests using user-initiated detecting and data gathering from power-limited and even passive wireless devices, such as passive RFID tags, wireless sensor networks (WSNs), and Internet of Things (IoT) devices, that either power limitation or poor cellular coverage prevents them from communicating directly with wireless networks. While previous studies focused on sensors that continuously transmit their data, the focus of this study is on passive devices. The key idea is that instead of receiving the data transmitted by the sensor nodes, an external device (a reader), such as an unnamed aerial vehicle (UAV), or a smartphone is used to detect IoT devices and read the data stored in the sensor nodes, and then deliver it to the cloud, in which it is stored and processed. While previous studies on UAV-aided data collection from WSNs focused on UAV path planning, the focus of this study is on the rate at which the passive sensor nodes should be polled. That is, to find the minimal monitoring rate that still guarantees accurate and reliable data collection. The proposed scheme enables us to deploy wireless sensor networks over a large geographic area (e.g., for agricultural applications), in which the cellular coverage is very poor if any. Furthermore, the usage of initiated data collection can enable the deployment of passive WSNs. Thus, can significantly reduce both the operational cost, as well as the deployment cost, of the WSN. The performance of the proposed scheme was validated by simulation. The simulation results demonstrate a significant reduction in the power consumption of the sensors, in comparison with the power consumed by sensors in conventional WSNs.
Network densification and heterogeneity has attracted attention as an enabling technology for Fifth Generation (5G) communications due to the potential to enhance capacity using aggressive spatial spectrum reuse and flexibility for deployment. In the framework of Heterogeneous Networks (HetNets), densification is heavy on the pico- or femto-tiers. Therefore, the relative intensity of nodes at each tier impacts the network performance added to the different transmit powers. It could be asked for which densification levels and relative intensity of nodes can we use aggressive offloading with the established interference coordination techniques or decoupled association? In this paper, the concept of Poisson random networks were used to analytically obtain the relative densification levels corresponding to fair load distributions across tiers and intensity levels for which we need the coupled or decoupled User Association UA. The association window, where users choose to use decoupled association in terms of the relative intensity, transmit powers at each tiers and the path loss exponent of the propagation environment, is derived. Further, the ergodic rate expressions in order to study throughput performances in different densification regions, which can be computed numerically, are formulated. To validate the theoretical analysis, numerical, system level simulation and realistic network analysis were used. The analytical, simulation, and realistic test case results provide insights for the operators about the densification ranges, where to use coupled or decoupled association.
Cooperative localization plays a significant role in various applications, such as emergency rescue and navigation path planning. The advent of swarm intelligence has opened doors to agent-based cooperative localization. However, sharing data between agents during the cooperative localization process can compromise privacy. One of the key challenges is to develop a cooperative localization model that safeguards the data privacy of agents. To tackle this issue, we initially adopt a state-space model to describe the movement of an agent for single-agent dynamic localization. This model effectively handles noise and improves accuracy. For the problem of multi-agent cooperative localization, we employ the federated learning framework coupled with the alternating direction multiplier method. Within this framework, the central node aggregates local models to create a global cooperative localization model, eliminating the need for data sharing and ensuring privacy protection. When compared to the centralized model, the federated model achieves satisfactory localization accuracy while demonstrating the robustness and generalization performance across different data distributions. Furthermore, when confronted with new scenarios, the federated model exhibits excellent transfer performance.
A wireless nanosensor network framework is formed by an arrangement of wireless nanodevices with limited sensing, computation, storage and power capacity. Nanodevices can sense and collect data/events about their surrounding environement. Events are transmitted through intermediate nanonodes to nano-interface using multi-hop broadcast mechanisms. However, the communication between nanonodes and nano-interface can be interrupted by redundant transmissions, producing a performance degradation of wireless nanosensor network, which could be caused by a higher packet loss rate and unfair energy consumption. This paper introduces an adaptive diffusion protocol based on game theory by encouraging nanonodes to equally cooperate in data forwarding process. Each nanonode can adjust the broadcasting decision according to its local traffic condition. The aim is to decrease the energy consumption by balancing data transmission load between constrained-resources nanonodes. An in-depth evaluation, via simulations, shows the effectiveness of the new algorithm over related approaches in term of data delivery ratio and energy consumption.
To accomplish the targets of Beyond Fifth-Generation (B5G) networks, considerable spectral efficiency, wide coverage, better reliability, and energy efficiency are the main objectives. In order to enhance the performance of the Orthogonal Frequency Division Multiplexing (OFDM) signal in terms of Bit Error Rate (BER), powerful channel coding, such as polar coding, must be considered. In addition, a Massive Multi-Input Multi-Output (MMIMO) combined with OFDM can support better performance and spectral efficiency. On the other hand, Physical Layer Network Coding (PLNC) can increase system throughput. This paper explores the integration of Polar-Coded OFDM (PCOFDM) with MMIMO and PLNC to enhance transmission reliability and throughput. A two-way relay transmission scenario is considered at millimeter Wave (mmWave) frequencies. The consequences of the extensive simulation assessments for Multilevel Quadrature Amplitude Modulation (M-QAM) demonstrated that refinements in throughput and BER can be performed using PCOFDM, with PLNC having enough antenna elements in the massive MIMO system at the base station (the relay node). In any case of the number of antenna elements utilized in the user terminal, the BER achievement of PCOFDM-MMIMO with PLNC surpassed that of a similar system without polar code (antenna elements used in the base station are 128 and 256).
