Transmission Power Of Nodes Research Articles

Reducing Transmission Signal Collisions on Optimized Link State Routing Protocol Using Dynamic Power Transmission

Many devices connected to a network inevitably result in clashes between communication signals. These collisions are an important factor that causes a decrease in network performance, especially affecting Quality of Service (QoS) like throughput, Packet Delivery Ratio (PDR), and end-to-end de- lay, which has a direct impact on the success of data transmission by potentially causing data loss or damage. The aim of this research is to integrate the Dynamic Power Transmission (DPT) algorithm into the Optimized Link State Routing (OLSR) routing protocol to regulate the communication sig- nal strength range. The DPT algorithm dynamically adapts the signal coverage distance based on the density of neighboring nodes to reduce signal collisions. In our protocol, the basic mechanism of a DPT algorithm includes four steps. The Hello message structure of OLSR has been modified to incorporate the ”x-y position” coordinate field data. Nodes calculate distances to neighbors using these coordinates, which is crucial for route discovery, where all nearby nodes can process route re-quests. The results of this research are that DPT-OLSR improves network efficiency in busy areas. In particular, the DPT-OLSR routing protocol achieves an average throughput enhancement of 0.93%, a 94.79% rise in PDR, and reduces end-to-end delay by 45.69% across various variations in node density. The implication of this research result is that the algorithm proposed automatically adapts the transmission power of individual nodes to control the number of neighboring nodes within a de-fined range. This effectively avoids unwanted interference, unnecessary overhearing, and excessive processing by other nodes, ultimately boosting the network’s overall throughput.

Optimal interference mitigation with deep learningbased channel access in wireless body area networks

SummaryWireless body area networks (WBANs) are essential for medical applications, especially in remote health monitoring, as they transmit crucial and time‐sensitive data collected by nodes positioned around or within the body. However, the coexistence of WBANs with wireless channels can degrade performance due to interference. This study introduces OIM‐DLCAM, an optimal interference mitigation scheme for WBANs, which utilizes a deep learning‐based channel access method. OIM‐DLCAM addresses interference through the multiobjective Hungarian optimization (MOHO) algorithm, considering design constraints such as node transmission power, packet delivery ratio, and interference range. Additionally, it employs a deep probabilistic neural network‐based channel access method (DPNN‐CAM) to effectively mitigate interference by making decisions regarding contention window size, frame length, and buffer size. The proposed OIM‐DLCAM scheme ensures fairness between users while enhancing system performance. Simulation results from both static and dynamic sensor node scenarios demonstrate its effectiveness under various conditions, showcasing its potential to improve WBAN performance in medical applications. The simulations reveal that OIM‐DLCAM outperforms existing state‐of‐the‐art schemes across various scenarios, with efficiency gains of up to 86.187%, 72.452%, and 47.954% for WBAN node density, mobility, and packet arrival rate, respectively. Moreover, it significantly reduces the average end‐to‐end delay and packet drop rate while improving throughput and packet delivery ratio compared with existing schemes. Additionally, comparisons with industry standards, such as the IEEE 802.15.4e norm, validate the suitability of OIM‐DLCAM for cofounded WBANs.

ACTOR: Adaptive Control of Transmission Power in RPL.

RPL-Routing Protocol for Low-Power and Lossy Networks (usually pronounced "ripple")-is the de facto standard for IoT networks. However, it neglects to exploit IoT devices' full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent-child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%.

Joint Optimization of Trajectory and Node Access in UAV-Aided Data Collection System

With the continuous development of Internet of Things, data transmission presents an exponential rising trend. Due to the limited energy storage and low transmission power of sensor nodes, it is impossible to continuously transmit a large amount of collected data to the remote cloud center for a long time. To guarantee the data reliability and extend the lifetime of sensor nodes, we use unmanned aerial vehicle (UAV) assisted data collection for data transmission to improve the performance of the network. As each sensor node has a certain business requirement for unloaded data volume, the flight trajectory and node access scheme are jointly optimized to minimize the energy consumed by all the sensing nodes. First, a model regarding the minimization problem of the maximum energy consumption was constructed. To effectively solve this nonconvex problem, the block coordinate descent method and slack variable method are used to transform the original optimization problem into two subproblems: trajectory optimization problem and node access scheme optimization problem. Second, an algorithm is proposed using the successive convex approximation to jointly optimize the flight trajectory and node access scheme. This algorithm acquires the optimal node access scheme and UAV flight trajectory successively in each iteration. When the number of iterations is great enough, the minimum–maximum energy consumption of sensing nodes is gradually converged. The simulation results showed that in comparison with the basic UAV flight scheme, the proposed algorithm can effectively reduce the energy consumption of nodes and improve the data transmission rate, and meanwhile, this algorithm is characterized by favorable convergence and tolerable complexity.

Optimized Resource Allocation for IoT-D2D Communication Using WSN

The need for a strong system to access radio resources demands a change in operating frequency in wireless networks as a part of Radio Resource Management (RRM). In the fifth-generation (5G) wireless networks, the capacity of the system is expected to be enhanced by Device-to-Device (D2D) communication. The cooperation and Resources Allocation (RA) in the development of Internet of Things (IoT) enabled 5G wireless networks are investigated in this paper. Developing radio RA methods for D2D communication while not affecting any Mobile Users’ (MU) communication is the main challenge of this research. Distinct performance goals such as practising equality in the rates of user data, increasing Network Throughput (NT), and reducing End-to-End Delay (EED) are achieved by RA. The study undertaken on optimising performance for various wireless networks is focused on in this research work. Proposing a polynomial-time Proportional Fair Resource Allocation Method (PFRAM), which considers the MU’s rate requirements, is the prime objective of this paper. Any Resource Allocation Method (RAM) can be used by the proposed method for MU, and the time and differing location channel conditions are the factors to be adapted with. Allotting more than one resource block is allowed by our PFRAM to a D2D pair. The automatic maintenance of battery-less IoT wireless devices’ energy level is done potentially using an Extensible Energy Management System (EEMS). Finally, the device’s Node Transmission Power (NTP) can be managed using an Energy-Saving Algorithm (ESA) designed in this work for Node Uplink Data Transmission (NUDT). The trade-off between the Packet Loss Rate (PLR) and NTP is balanced by the algorithm. The cost of NUDT’s average Energy Consumption (EC) is reduced by locating the optical NTP. In order to free much bandwidth for wireless information, NUDT conserves the harvested energy for minimising Radio Frequency (RF) Energy Transmission (ET). MATLAB simulations are used to assess the proposed EEMS. The IoT device’s NTP is managed using ESA designed for NUDT. The significant minimisation of channel hopping EED and the selection of the premium quality communication channel by the proposed framework are indications of the simulation results. 67.19% is the bandwidth to transmit DPs with the Bandwidth Allocation Algorithm (BAA), which is greater than the cases in its absence.

Routing Protocol for a Heterogeneous MSN With an Intermittent Mobile Sink

Choosing the first broadcaster to be the parent node as proposed by the leader-based routing (LBR) enables its Polling packet to be received earliest in a heterogeneous mobile sensor network (MSN) with short and intermittent availability of the mobile sink but may lead to an unreachable parent node. Furthermore, employing the three-way-handshaking as proposed by the link quality indicator (LQI)-based beaconless routing (LQI-BLR) may lead to a longer packet delivery delay. To avoid the aforementioned drawbacks simultaneously, the transmission power of the candidate parent node is included in the Polling packet in our proposed protocol. Along with the LQI, a mobile sensor can estimate its distance to the candidate parent node and its packet-receive ratio (PRR). If the PRR is higher than a preset threshold, the candidate can be chosen accordingly. Via simulations, we demonstrate that our proposed protocol can outperform LBR and LQI-BLR in a heterogeneous environment. In terms of the packet delivery ratio, our proposed protocol is able to achieve at least 35% and 20% of improvement when compared to LBR and LQI-BLR, respectively. Regarding the energy consumption and packet delivery delay, more than 20% improvement can be observed.

Measurement of Power Grid Resilience Based on a Dynamic Inoperability Input–Output Model

The increase in global natural disasters, emergencies, and terrorist attacks brings great challenges to the flexible operation of power grids. The research on the flexibility of power grids becomes more and more important. At the same time, the rapid development of renewable energy and smart grids also provides new opportunities for research of flexible power grids. Based on the dynamic inoperability input–output model (DIIM), this study creatively uses the node transmission power to represent the relationship between nodes in the power grid and then studies the resilience measurement of the power grid. First, the significance of studying power grid resilience is discussed, and the absorption and recovery capacity of power grid resilience is described. Second, the power grid resilience measurement model based on the DIIM is established, and a theoretical analysis is carried out. Using the characteristics of inoperability, the power grid resilience is measured from the time aspect, and the characteristics of the model are analyzed at the same time. On the basis of theoretical analysis, this study also provides a control strategy to improve the grid resilience, which has good convenience and applicability. Finally, the theoretical analysis method is applied to a power grid example, and the correctness and effectiveness are verified. The measurement model can intuitively display the resilience characteristics of power grids and provide theoretical support for management and emergency response.

Transceiver Optimization for Two-Hop AF MIMO Relay Systems With DFE Receiver and Direct Link

In this paper, we consider precoding and receiving matrices optimization for a two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system with a decision feedback equalizer (DFE) at the destination node in the presence of the direct source-destination link. By adopting the minimum mean-squared error (MMSE) criterion, we develop two new transceiver design algorithms for such a system. The first one employs an iterative procedure to design the source, relay, feed-forward, and feedback matrices. The second algorithm is a non-iterative suboptimal approach which decomposes the optimization problem into two tractable subproblems and obtains the source and relay precoding matrices by solving the two subproblems sequentially. Simulation results validate the better MSE and bit-error-rate (BER) performance of the proposed algorithms and show that the non-iterative suboptimal method has a negligible performance loss when the ratio of the source node transmission power to the relay node transmission power is small. In addition, the computational complexity analysis suggests that the second algorithm and one iteration of the first algorithm have the same order of complexity. As the first algorithm typically converges within a few iterations, both proposed algorithms exhibit a low complexity order.

AI‐driven efficient and reliable routing mechanism for optical fiber communication and wireless sensor networks

Wireless sensor networks is a comprehensive intelligent information system which integrates information acquisition, information transmission and information processing. It has been widely used in many fields. How to guarantee the quality of service is an important topic to make effective use of wireless sensor networks. Additionally, the applications of wireless sensor networks also put forward new requirements which are different from traditional networks. This paper adopts an energy‐efficient routing protocol with QoS guarantee for real‐time communication in wireless sensor networks. The energy‐efficient routing protocol transforms the real‐time requirements of application communication into the speed requirements of data packets and dynamically adjusts the transmission power and routing decision of nodes according to the data packet speed and network state. When the real‐time requirement of communication is high, it increases the transmission power of nodes and switching speed with increasing energy consumption and reducing system throughput. When the real‐time requirement of communication is low, it saves energy consumption and increases system throughput by reducing the transmission power of nodes. The associated structure includes dynamic speed allocator, delay estimator, forwarding policy, queue manager and neighbor manager. Experimental results show that the energy‐efficient routing protocol greatly reduces the loss rate and energy consumption of real‐time data communication delay.

An Opportunistic Routing for Energy-Harvesting Wireless Sensor Networks With Dynamic Transmission Power and Duty Cycle

Opportunistic routing (OR) is widely used in energy-harvesting wireless sensor networks (EH-WSNs). The transmission power of sensor nodes in EH-WSNs is usually adjusted dynamically to make full use of the harvested energy. Many OR algorithms with dynamic transmission power adjustment have been proposed for EH-WSNs. However, fewer studies consider the non-bidirectional communication and the increase in packet retransmission and delay caused by the heterogeneous transmission power/radii. To this end, we propose an opportunistic routing algorithm for EH-WSNs with dynamic transmission power and duty cycle (ORDPD). It adjusts the transmission power of the sensor nodes at the end of each time slot, according to the predicted available energy. It also adjusts the transmission power and duty cycle of the sensor nodes in time slots if the nodes receive packets from other nodes outside their current transmission range. ORDPD also adopts an improved transmission model and information exchange mechanism to dynamically update relay sets and forwarding paths. A series of experiments were conducted to verify the effectiveness of the proposed algorithm. The experimental results demonstrate that the proposed ORDPD reduces non-bidirectional communication and retransmission significantly and performs better than its corresponding competitors.

A stable community detection approach for complex network based on density peak clustering and label propagation

Dividing a network into communities has great benefits in understanding the characteristics of the network. The label propagation algorithm (LPA) is a fast and convenient community detection algorithm. However, the community initialization of LPA does not take advantage of topological information of networks, and its robustness is poor. In this paper, we propose a stable community detection algorithm based on density peak clustering and label propagation (DS-LPA). First, the local density calculation method in density peak clustering algorithm is improved in finding the community center of the network, so as to build a suitable initial community, which can improve the quality of community partition. Then, the label update order is determined reasonably by computing the information transmission power of nodes, and the solutions for multiple candidate labels are provided, which greatly improved the robustness of the algorithm. DS-LPA is compared with other seven algorithms on the synthetic network and real-world networks. NMI, ARI, and modularity are used to evaluate these algorithms. It can be concluded that DS-LPA has a higher performance than most comparison algorithms on synthetic network with ten different mixed parameters by statistical testing. And DS-LPA can quickly calculate the best community partition on different sizes of real-world networks.

Reliable Over-the-Air Computation by Amplify-and-Forward Based Relay

In typical sensor networks, data collection and processing are separated. A sink collects data from all nodes sequentially, which is very time consuming. Over-the-air computation, as a new diagram of sensor networks, integrates data collection and processing in one slot: all nodes transmit their signals simultaneously in the analog wave and the processing is done in the air. This method, although efficient, requires that signals from all nodes arrive at the sink, aligned in signal magnitude so as to enable an unbiased estimation. For nodes far away from the sink with a low channel gain, misalignment in signal magnitude is unavoidable. To solve this problem, in this paper, we investigate the amplify-and-forward based relay, in which a relay node amplifies signals from many nodes at the same time. We first discuss the general relay model and a simple relay policy. Then, a coherent relay policy is proposed to reduce relay transmission power. Directly minimizing the computation error tends to over-increase node transmission power. Therefore, the two relay policies are further refined with a new metric, and the transmission power is reduced while the computation error is kept low. In addition, the coherent relay policy helps to reduce the relay transmission power by half, to below the limit, which makes it one step ahead towards practical applications.

Comparative Analysis of Nodes Transmission Power in Wireless Sensor Network

Wireless Sensor Networks are one of the major technology boons which has revamped the whole standard of transmission phenomenon. Sensor nodes are the backbone of WSN which carry out huge responsibility of transmission between neighbouring nodes for effective communication and task performance. The major challenge faced by sensor nodes is the optimal transmission power which leads to low battery life. Moreover, altering the power for nodes transmission after their installation in a network is a very tedious task. In this paper, a comparative analysis is being done on different node transmission power methods and various techniques are studied which can strengthen the network connectivity for an efficient communication.

CDS-Based Topology Control in FANETs via Power and Position Optimization

In a flying ad hoc network (FANET), reliable maintenance of topology confronts a huge challenge due to the highly time-varying connectivity between flying nodes. Aiming at mitigating this issue, this letter proposes a connected dominating set (CDS) based topology control mechanism iteratively for the FANETs by solving a jointed optimization problem on adjusting the node's transmission power and position during the construction of CDS. In particular, a well-tailored group of the topology constructing and maintaining algorithms are developed with a time-discretized approach. Simulation results show that the proposed mechanism has a better performance with respects to overheads and network stability than a general particle swarm optimization (PSO) algorithm, especially in a typical swarm pattern for unmanned aerial vehicle (UAV) team.

A Distributed Algorithm for Topology Control in VANETs: Distributed Distant Node Graphical Structure (DDGST) Algorithm

Vehicle ad hoc networks—a subset of MANETs—allow cars to directly connect. These networks are independent of previous infrastructure. VANETs promote traffic safety, efficiency, and ITS application development. Their link allows this. Vehicular Ad-hoc Networks (VANETs) have grown in popularity due to its ability to efficiently and reliably connect automobiles. Vehicular networks evolve rapidly, making network topology maintenance problematic. VANET topologies are managed decentralized here. The algorithm uses neighbor numbers and distances to calculate vehicle transmission range. Simulations indicate that the suggested method reduces network partitions and maximizes connection. Due to the great mobility of VANET vehicles, the network architecture changes quickly. As this network topology is continually changing, creating strong and effective routing protocols is difficult. Topology control can overcome this difficulty. We present a distributed topology control mechanism that keeps VANETs online, reduces control messages, and prevents collisions. Node centrality and network density are used to fine-tune each node's transmission power. Computer simulations compare the proposed approach to different topology control algorithms. The results show that the suggested method outperforms the alternatives in network connectivity, longevity, and control overhead.

Multi-objective optimization framework complying IEEE 802.15.6 communication standards for wireless body area networks

Wireless body area network (WBAN) routing protocols are primarily designed for improvement of network performance parameters such as network lifetime, throughput and latency. However, these designs do not take into account the health hazards posed due to WBAN radiation. The focus of implementing IEEE 802.15.6 standards is to develop an energy-efficient, reliable and health-conscious wireless communication system around human body. This paper presents the implementation of a clustering-based routing protocol for WBAN that targets in achieving overall optimization of WBAN performance parameters including network lifetime, throughput, latency, node signal power and specific absorption rate (SAR) of human body for emf radiation. The suggested protocol applies a multi-objective NSGA-II optimization heuristic for cluster formation while taking into account various IEEE 802.15.6 standard constraints. A unique chromosome structure has been proposed for NSGA-II population in which each chromosome represents a random network topology for determining the node clusters, cluster heads and node transmission power. Furthermore, three system-level models for conducting multi-objective fitness evaluation of each chromosome have been proposed. These models predict the global transmission energy consumption; the average received signal strength (RSSI) and end to end network latency respectively. The developed NSGA-II model evolves into a non-dominated set of Pareto-optimal network topologies that renders the desired objectives of minimization of network energy consumption and network latency besides maximization of average RSSI. Enhancement in RSSI further leads to a tremendous improvement in the throughput rate. The node transmission power specifications of Pareto-optimal network topologies meet the standard SAR constraints too. The performance results shows that the suggested protocol strictly meets IEEE 802.15.6 compliance.

Cognitive routing optimization protocol based on multiple channels in wireless sensor networks

With the development of modern communication, available spectrum resources are becoming increasingly scarce, which reduce network throughput. Moreover, the mobility of nodes results in the changes of network topological structure. Hence, a considerable amount of control information is consumed, which causes a corresponding increase in network power consumption and exerts a substantial impact on network lifetime. To solve the real-time transmission problem in large-scale wireless mobile sensor networks, opportunistic spectrum access is applied to adjust the transmission power of sensor nodes and the transmission velocity of data. A cognitive routing and optimization protocol based on multiple channels with a cross-layer design is proposed to study joint optimal cognitive routing with maximizing network throughput and network lifetime. Experimental results show that the cognitive routing and optimization protocol based on multiple channels achieves low computational complexity, which maximizes network throughput and network lifetime. This protocol can be also effectively applied to large-scale wireless mobile sensor networks.

FQ-AGO: Fuzzy Logic Q-Learning Based Asymmetric Link Aware and Geographic Opportunistic Routing Scheme for MANETs

The proliferation of mobile and IoT devices, coupled with the advances in the wireless communication capabilities of these devices, have urged the need for novel communication paradigms for such heterogeneous hybrid networks. Researchers have proposed opportunistic routing as a means to leverage the potentials offered by such heterogeneous networks. While several proposals for multiple opportunistic routing protocols exist, only a few have explored fuzzy logic to evaluate wireless links status in the network to construct stable and faster paths towards the destinations. We propose FQ-AGO, a novel Fuzzy Logic Q-learning Based Asymmetric Link Aware and Geographic Opportunistic Routing scheme that leverages the presence of long-range transmission links to assign forwarding candidates towards a given destination. The proposed routing scheme utilizes fuzzy logic to evaluate whether a wireless link is useful or not by capturing multiple network metrics, the available bandwidth, link quality, node transmission power, and distance progress. Based on the fuzzy logic evaluation, the proposed routing scheme employs a Q-learning algorithm to select the best candidate set toward the destination. We implemented FQ-AGO on the ns-3 simulator and compared the performance of the proposed routing scheme with three other relevant protocols: AODV, DSDV, and GOR. For precise analysis, we considered various network metrics to compare the performance of the routing protocols. The simulation result validates our analysis and demonstrates remarkable performance improvements in terms of total network throughput, packet delivery ration, and end-to-end delay. FQ-AGO achieves up to 15%, 50%, and 45% higher throughput compared to DSDV, AODV, and GOR, respectively. Meanwhile, FQ-AGO reduces by 50% the end-to-end latency and the average number of hop-count.

Improving Performance of Distributed Collaborative Beamforming in Mobile Wireless Sensor Networks: A Multiobjective Optimization Method

Mobile wireless sensor networks (MWSNs) are resource constrained, and have limited energy and transmission range. Distributed collaborative beamforming (DCB) in MWSNs based on a virtual node antenna array (VNAA) can increase the transmission distance and enhance the energy efficiency of a single sensor node. To achieve a lower maximum sidelobe level (SLL), sensor nodes can move to optimal locations with optimal excitation current weights for DCB. However, this leads to an extra motion energy consumption. In this article, we construct a multiobjective optimization framework (MOF) to jointly optimize the maximum SLL, transmission power, and motion energy consumption of the DCB nodes in MWSNs. Moreover, an improved nondominated sorting genetic algorithm-II (INSGA-II) and a distributed parallel INSGA-II (DPINSGA-II) are proposed for solving the formulated MOF. In addition, a simple but practical DCB scheduling mechanism is proposed. The simulation results show that the maximum SLL, transmission power, and motion energy consumption of the VNAA can be effectively optimized by the proposed algorithms.

Novel control topology with obstacle detection using RDPSO–GBA in mobile AD-HOC network

MANET plays an irreplaceable task by providing quality aware broadcast routing technique for small infrastructure-less field of application. Depending upon arising interference, transmission power of individual node can be done by Ant Colony Optimization (ACO) algorithm. In addition, nodes probability link connectivity is estimated by using Bayesian pursuit (BPST) algorithm that determines probability to control adjustment in nodes power. In this paper, proposed to control topology with obstacle detection using RDPSO–GBA (Robotic Darwin Particle Swarm Optimization–Graph Based Algorithm) in mobile ad hoc network. The robotics DPSO is to be represented with the help of the multiple swarms of various testing results when each swarm particularly operates an ordinary PSO method with various set of rules which are governing the gather of swarms. In the graph based algorithm, the nodes are to be presented in the MANET which has been separated into the two various classes that is leaders and the followers. The leader has the base node and the explorer node and the follower has the autonomous robots which provide the communication broadcasting. The simulation and result shows that there is the analysis of the various performances which is compared with conventional methods.