Transmission Power Control Algorithm Research Articles

Multi-virtual wireless mesh networks through multiple channels and interfaces

The high flexibility of the wireless mesh networks (WMNs) physical infrastructure can be exploited to provide communication with different technologies and support for a variety of different services and scenarios. Context information may trigger the need to build different logical networks on top of physical networks, where users can be grouped according to similarity of their context, and can be assigned to the logical networks matching their context. When building logical networks, network virtualization can be a very useful technique allowing a flexible utilization of a physical network infrastructure. Moreover, dynamic resource management using multiple channels and interfaces, directional antennas and power control, is able to provide a higher degree of flexibility in terms of resource allocation among the available virtual networks, to enable isolated and non-interfering communications while maximizing the network efficiency. In this paper we propose a resource management approach that uses transmit power control algorithm with both omnidirectional and directional antennas, to determine the resources of each virtual network while minimizing interference between virtual networks, considering the support of multiple services and users. Each virtual network can be extended to include the nodes of the WMN required by new users. The results of the proposed approach show that the support of multiple virtual networks for multiple services highly improves the network performance when compared to the support of the services in only one virtual network, with no interference minimization nor dynamic resource control.

Feasibility Investigation on Cognitive Radio Network Systems: Hearsay Algorithm

In this paper Specifies, how effectively utilization of radio spectrum is important for accommodating the rapid growth of wireless communications. Cognitive Radio (CR) refers to a wireless architecture that enables dynamic spectrum access, where unlicensed devices are allowed to operate in temporally/spatially unused licensed channels. The main challenge for CR is a robust protection mechanism to guarantee an adequate Licensed Users (LU) system performance at all times. This requires a reliable spectrum sensing technique to accurately identify frequency opportunities; and an autonomous transmit power control algorithm that remains effective in severe fading environments. Cooperation among CR devices to assist the LU identification process is shown to be an imperative CR attribute for maximising the CR performance and the overall spectral utilisation The Hearsay Algorithm is a novel Transmit Power Control algorithm for CRs in a cooperative network. Each node seeking to transmit is informed of the status of the LU channel by a chain of other CR nodes, hence the term “Hearsay”.

Energy efficiency comparison between data rate control and transmission power control algorithms for wireless body sensor networks

This article presents comparison between data rate or rate control, that is, video transmission rate control algorithm and transmission power control algorithms for two different cases. First, energy consumption due to high peak variable data rates in video transmission. Second, energy depletion due to high transmission power consumption and dynamic nature of wireless on-body channel. The former one focuses on constant (fixed) transmission power level and variable data rate (“severe” conditions), for example, medical monitoring of the emergency patients. The latter considers variable transmission power level and constant (fixed) data rate (“less severe” conditions), for example, electrocardiography measurement for patients in wireless body sensor networks. Besides, energy efficiency comparison analysis of battery-driven or video transmission rate control algorithm and transmission power control–driven or power control algorithm is presented. Finally, proposed algorithms are analyzed and categorized as energy-efficient and battery-friendly for medical applications in wireless body sensor networks.

A Joint Transmission Power Control and Duty-Cycle Approach for Smart Healthcare System

Emerging revolution in the healthcare has caught the attention of both the industry and academia due to the rapid proliferation in the wearable devices and innovative techniques. In the mean-time, body sensor networks (BSNs) have become the potential candidate in transforming the entire landscape of the medical world. However, large battery lifetime and less power drain are very vital for these resource-constrained sensor devices while collecting the bio signals. Hence, minimizing their charge and energy depletions are still very challenging tasks. It is examined through large real-time data sets that due to the dynamic nature of the wireless channel, the traditional predictive transmission power control (TPC) and a constant transmission power techniques are no more supportive and potential candidates for BSNs. Thus, this paper first proposes a novel joint TPC and duty-cycle adaptation-based framework for pervasive healthcare. Second, an adaptive energy-efficient transmission power control algorithm is developed by adapting the temporal variation in the on-body wireless channel amid static (i.e., standing and walking at a constant speed) and dynamic (i.e., running) body postures. Third, a feedback control-based duty-cycle algorithm is proposed for adjusting the execution period of tasks (i.e., sensing and transmission). Fourth, system-level battery and energy harvesting models are proposed for body sensor nodes by examining the energy depletion of sensing and transmission tasks. It is validated through Monte Carlo experimental analysis that the proposed algorithm saves more energy of 11.5% with reasonable packet loss ratio by adjusting both the transmission power and the duty cycle unlike the conventional constant TPC and PTPC methods.

On the energy efficiency of rate and transmission power control in 802.11

Rate adaptation and transmission power control in 802.11 WLANs have received a lot of attention from the research community, with most of the proposals aiming at maximising throughput based on network conditions. Considering energy consumption, an implicit assumption is that optimality in throughput implies optimality in energy efficiency, but this assumption has been recently put into question. In this paper, we address via analysis, simulation and experimentation the relation between throughput performance and energy efficiency in multi-rate 802.11 scenarios. We demonstrate the trade-off between these performance figures, confirming that they may not be simultaneously optimised, and analyse their sensitivity towards the energy consumption parameters of the device. We analyse this trade-off in existing rate adaptation with transmission power control algorithms, and discuss how to design novel schemes taking energy consumption into account.

Transmission power control using state estimation-based received signal strength prediction for energy efficiency in wireless sensor networks

In battery-operated wireless sensor networks, the quality of service requirements such as throughput and energy efficiency must be stringently maintained while the least amount of energy is consumed. Since a major portion of energy is spent by the transceiver operations, transmission power control (TPC) in the medium access control (MAC) layer can bring about considerable energy efficiency. Since TPC algorithms will have a direct impact on the received signal strength index (RSSI) at the receiver, RSSI is the primary input parameter for any TPC algorithm. The objective of the proposed work is to decide on the exact value of transmission power required for the next transmission that will ensure an RSSI just above the threshold level at the receiver. Since this involves estimation of RSSI for the next transmission, we propose three state estimation techniques, namely Kalman filter (KF), extended KF (EKF), and unscented KF (UKF) to predict the RSSI accurately. This predicted value is used as an input for an artificial neural network (ANN)-based TPC algorithm. The effectiveness of the estimation techniques is verified by the prediction error. The accuracy of prediction is reflected in the TPC algorithm in terms of reduced power utilization.

A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels.

Traditional underground coalmine monitoring systems are mainly based on the use of wired transmission. However, when cables are damaged during an accident, it is difficult to obtain relevant data on environmental parameters and the emergency situation underground. To address this problem, the use of wireless sensor networks (WSNs) has been proposed. However, the shape of coalmine tunnels is not conducive to the deployment of WSNs as they are long and narrow. Therefore, issues with the network arise, such as extremely large energy consumption, very weak connectivity, long time delays, and a short lifetime. To solve these problems, in this study, a new routing protocol algorithm for multisink WSNs based on transmission power control is proposed. First, a transmission power control algorithm is used to negotiate the optimal communication radius and transmission power of each sink. Second, the non-uniform clustering idea is adopted to optimize the cluster head selection. Simulation results are subsequently compared to the Centroid of the Nodes in a Partition (CNP) strategy and show that the new algorithm delivers a good performance: power efficiency is increased by approximately 70%, connectivity is increased by approximately 15%, the cluster interference is diminished by approximately 50%, the network lifetime is increased by approximately 6%, and the delay is reduced with an increase in the number of sinks.

Energy‐aware quality of information maximisation for wireless sensor networks

In this study, the authors investigate the energy-aware quality of information (QoI) maximisation problem by jointly optimising the sensor selection, sampling rate, packet-dropped rate, and transmit power in wireless sensor networks. By introducing the weight parameters, the authors first present a revenue-cost (RC) function which combines the optimisation objectives of the QoI and energy expenditure into a single objective to capture the tradeoff between them. Then, a stochastic optimisation programming is formulated to maximise the long-term average RC value subject to the network stability constraint. Using the Lyapunov drift theory, the authors develop a collaborative sensing and transmit power control algorithm that can guarantee the worst-case delay for each packet. Simulation results demonstrate the advantages of the proposed algorithm.

Impact of Transmission Power Control in multi-hop networks

Many Transmission Power Control (TPC) algorithms have been proposed in the past, yet the conditions under which they are evaluated do not always reflect typical Internet-of-Things (IoT) scenarios. IoT networks consist of several source nodes transmitting data simultaneously, possibly along multiple hops. Link failures are highly frequent, causing the TPC algorithm to kick-in quite often. To this end, in this paper we study the impact that frequent TPC actions have across different layers. Our study shows how one node’s decision to scale its transmission power can affect the performance of both routing and MAC layers of multiple other nodes in the network, generating cascading packet retransmissions and forcing far too many nodes to consume more energy. We find that crucial objectives of TPC such as conserving energy and increasing network capacity are severely undermined in multi-hop networks.

An Efficient Low-Power MAC Protocol for Wireless Body Sensor Networks

The battery of a sensor node in a wireless body sensor network is small, and therefore has a short lifetime. In consequence, intensive research is being conducted on schemes to efficiently utilize the energy of a sensor node. This paper proposes a new scheme to combine a transmission power control (TPC) model, which is a technology used to heighten energy efficiency in existing body sensor networks, with a low power medium access control (MAC) protocol. This model integrates the short preamble scheme of the existing X-MAC protocol and the TPC model, by combining an early ACK packet and a transmission power control packet. Our scheme efficiently transmits data by predicting the appropriate transmission power before transmitting data packets. We experiment with a TPC algorithm, and analyze the result in order to evaluate the performance of the proposed scheme.

A Lifetime Analysis of Body Sensors Considering the Operation of Transmission Power Control Algorithm

A Lifetime Analysis of Body Sensors Considering the Operation of Transmission Power Control Algorithm

A Memory-based Transmission Power Control Algorithm in Wireless Body Sensor Networks

바디 센서 네트워크 환경에 존재하는 센서 장치는 소형 배터리 기반으로 동작하기 때문에 에너지 절약이 필수적이다. 이 때 전송 전력 조절 알고리즘은 센서 장치의 전송 전력 조절을 통해 에너지소비를 절약하고, 센서 장치의 수명을 연장시켜준다. 그러나 기존의 전송 전력 조절 알고리즘은 많은 제어 메시지를 사용하여 전송 전력을 조절하기 때문에 에너지 소비 측면에서 비효율적이다. 따라서 본 논문에서는 제어 메시지 낭비를 효율적으로 방지하기 위하여, 센서 디바이스의 전송전력을 단 한 개의 제어 메시지로 조절하는 새로운 전송전력 조절 알고리즘을 제안한다. 그리고 시뮬레이션을 통하여 제안하는 알고리즘의 성능이 기존 알고리즘에 비해 우수하다는 것을 증명한다.

ATPC

Extensive empirical studies presented in this article confirm that the quality of radio communication between low-power sensor devices varies significantly with time and environment. This phenomenon indicates that the previous topology control solutions, which use static transmission power, transmission range, and link quality, might not be effective in the physical world. To address this issue, online transmission power control that adapts to external changes is necessary. This article presents ATPC, a lightweight algorithm for Adaptive Transmission Power Control in wireless sensor networks. In ATPC, each node builds a model for each of its neighbors, describing the correlation between transmission power and link quality. With this model, we employ a feedback-based transmission power control algorithm to dynamically maintain individual link quality over time. The intellectual contribution of this work lies in a novel pairwise transmission power control, which is significantly different from existing node-level or network-level power control methods. Also different from most existing simulation work, the ATPC design is guided by extensive field experiments of link quality dynamics at various locations over a long period of time. The results from the real-world experiments demonstrate that (1) with pairwise adjustment, ATPC achieves more energy savings with a finer tuning capability, and (2) with online control, ATPC is robust even with environmental changes over time.

A Biologically-Inspired Power Control Algorithm for Energy-Efficient Cellular Networks

Most of the energy used to operate a cellular network is consumed by a base station (BS), and reducing the transmission power of a BS can therefore afford a substantial reduction in the amount of energy used in a network. In this paper, we propose a distributed transmit power control (TPC) algorithm inspired by bird flocking behavior as a means of improving the energy efficiency of a cellular network. Just as each bird in a flock attempts to match its velocity with the average velocity of adjacent birds, in the proposed algorithm, each mobile station (MS) in a cell matches its rate with the average rate of the co-channel MSs in adjacent cells by controlling the transmit power of its serving BS. We verify that this bio-inspired TPC algorithm using a local rate-average process achieves an exponential convergence and maximizes the minimum rate of the MSs concerned. Simulation results show that the proposed TPC algorithm follows the same convergence properties as the flocking algorithm and also effectively reduces the power consumption at the BSs while maintaining a low outage probability as the inter-cell interference increases; in so doing, it significantly improves the energy efficiency of a cellular network.

An Innovative Design Approach to Control over Ad Hoc Networks

In Mobile ad hoc networks (MANETs), the mobile nodes are communicating trough wireless medium. The protocol designs of MANETs are based on a traditional approach which has been found ineffective to deal congestion related problems. Hence, it is affecting properties of physical layer, the network layer and transport layer. This paper proposes a novel design approach, deviating from the traditional network design, towards enhancing the cross-layer interaction among different layers, namely physical, MAC and network. The novel approach for congestion control is to solve congestion and mobility related problems with a cross layer interaction. The significance of CLCC is that it has used two mechanisms: (i) Dynamic Transmission Power Control algorithm that predicts a link breakage if it is likely to happen, (ii) Dynamic Congestion Estimation Technique, which analyzes traffic fluctuation and categorize congestion status perfectly. The proposed protocol reduces the route breakage and network congestion, hence reduces the control overhead and latency, and increases the packet delivery ratio. Through simulation, it is shown that the proposed protocol is simple, robust and effective.

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

웨어러블 센서 시스템에서의 제어 패킷 전송 결정 기법

In the general transmission power control model that is used for wearable sensor systems, if RSSI value gets out of the Target RSSI Margin, then the sink node finds new transmission power by using TPC(Transmission Power Control) Algorithm. At this time, the sink node sends the control packet to the sensor node for delivering the newly calculated transmission power. However, when the wireless network channel condition is poor, even it is consuming a lot of control packets, the sink node could not find an appropriate transmission power so it only waste of energy. Therefore, we proposed a new control packet transmission decision method that the sink node changes the transmission power when the wireless network channel condition is stabilized. It makes waste of energy decline. In this paper, we apply control packet transmission decision method to Binary TPC algorithms and analyze the results to evaluate the proposed method. We propose three methods that judge the state of wireless network channel. We experiment that methods and analysis the results.

Algorithmic Methods in Wireless Sensor Network 2014

We present refinements of a novel transmission power control (TPC) algorithm based on temperature and relative humidity (TRH). Previously, we deployed a prototype TRH TPC algorithm on wireless sensor nodes operating in real harsh environmental conditions and reported promising results. Since then, we have made enhancements of the TRH TPC model, which we will show here. Furthermore, in order to develop an understanding of the nonlinear behavior that we observed from this TRH TPC scheme, we developed a simulation platform that uses real radio frequency (RF) signal and interference samples and actual T and RH sensor data acquired simultaneously. Afterwards, we logged results of repeated experiments and determined the algorithms operating ranges and behaviors, varying its main parameters, such as (1) its gain factor, (2) the average time period to recalculate power level updates, and (3) proper received signal threshold selection. We then summarize optimal parameter ranges from the analytical results that reflect where this TRH TPC technique works best. And finally, we report results of the TRH TPC algorithm running on long range WSN systems deployed in harsh environmental conditions, corroborating behaviors observed through simulation.

Parameter Optimization of a Temperature and Relative Humidity Based Transmission Power Control Scheme for Wireless Sensor Networks

We present refinements of a novel transmission power control (TPC) algorithm based on temperature and relative humidity (TRH). Previously, we deployed a prototype TRH TPC algorithm on wireless sensor nodes operating in real harsh environmental conditions and reported promising results. Since then, we have made enhancements of the TRH TPC model, which we will show here. Furthermore, in order to develop an understanding of the nonlinear behavior that we observed from this TRH TPC scheme, we developed a simulation platform that uses real radio frequency (RF) signal and interference samples and actual T and RH sensor data acquired simultaneously. Afterwards, we logged results of repeated experiments and determined the algorithms operating ranges and behaviors, varying its main parameters, such as (1) its gain factor, (2) the average time period to recalculate power level updates, and (3) proper received signal threshold selection. We then summarize optimal parameter ranges from the analytical results that reflect where this TRH TPC technique works best. And finally, we report results of the TRH TPC algorithm running on long range WSN systems deployed in harsh environmental conditions, corroborating behaviors observed through simulation.

Iterative power control based admission control for wireless networks

This paper studies transmission power control algorithms for cellular networks. One of the challenges in commonly used iterative mechanisms to achieve this is to identify if the iteration will converge since convergence indicates feasibility of transmit power allocation under prevailing network conditions. The convergence criterion should also be simple to calculate given the time constraints in a real-time wireless network. Towards this goal, this paper derives simple sufficient conditions for convergence of an iterative power control algorithm using existing bounds from matrix theory. With the help of suitable numerical examples, it is shown that the allocated transmit powers of the nodes converge when sufficient conditions are satisfied, and diverge when they are not satisfied. This forms the basis for an efficient link data-rate based admission control mechanism for wireless networks. The mechanism considers parameters such as signal strength requirement, link datarate requirement, and number of nodes in the system. Simulation based analysis shows that existing links are able to maintain their desired datarates despite the addition of new wireless links.