An efficient power control scheme and joint adaptive modulation for wireless sensor networks☆
Introduction
Wireless sensor networks (WSNs) have recently attracted research interest. They comprise numerous sensors equipped with limited power and radio communication capabilities. Sensors can be deployed in extremely hostile environments: battlefield target zones, earthquake disaster areas, inaccessible areas inside a chemical plant, or a nuclear reactor to measure environmental changes or acquire necessary information. Such sensors are usually battery operated; it is important that they have an acceptable lifetime to accomplish intended objectives. Energy consumption as a critical factor makes it important to optimize (minimize) power usage [1], [2], [3]. Currently, how to optimize both power usage and Quality-of-Service (QoS) constraint is a key problem in wireless networking. Here a QoS is usually required to meet a fixed rate on packet loss, tolerance of packet delay, or traffic throughput. Such sensor nodes typically operate with small batteries for a long time in many applications [4], [5], [6]. Reducing power consumption under power constraint thus poses a major challenge in wireless sensor networks. Analytical model presented in [4] analyzes system performance in terms of network capacity, power consumption and data delivery delay, against sensor dynamics in on/off modes. Since power consumed by individual nodes in the sensor network that affects network lifetime is pivotal in all WSN applications, different algorithms for minimizing power consumption show that there exist trade-offs between energy consumption and data delivery delay [6]. There are several studies in adaptive modulation schemes for current wireless communication systems [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], most unsuitable for wireless sensor networks. Their main objectives are increasing system capacity and overcome impact of wireless fading channel rather than save the energy. This motivates us to study the problem of adaptive modulation scheme under limited power and bit error rate constraints in wireless sensor networks.
However, power is a previous resource in wireless network; efficient allocation of power looms ever more important in design of wireless communication systems. Distributed power control has drawn much attention in recent years, especially in wireless networks [7], [8]. Effective use of transmit power not only conserves energy to maximize battery life but also minimizes interference introduced by other transmit sides to enhance capacity.
Various studies probe areas of distributed power control and adaptive modulation. In [8], the author proposed simple distributed power control algorithm, wherein power level at next iteration only depends on target and actual Signal to Interference plus Noise Ratio (SINR). The goal is to minimize total power consumption subject to the target SINR requirement. In addition, a power distribution algorithm was designed to adjust transmit power with joint congestion conditions; its simulation was run on TCP Vegas-based network. Thus, utilization of the wireless system is raised by properly scheduling link occupation and efficiently minimizing power consumption. Unhappily, both require exhaust search for a fixed point, making it computationally unaffordable when the number of players in the game is large.
We first cite adaptive modulation without power control scheme (AM without PC) [10]: simple closed form used to approximate bit-error-rate (BER) function for M-QAM over additive white Gaussian noise (AWGN) channel. Switching threshold of signal-to-noise-ratio (SNR) parameter for each transmission mode is derived based on closed form function and target BER. By computing SNR received, the transmitter can select suitable transmission mode for current data modulation. Next, we introduce adaptive modulation with power control scheme (AM with PC 1) [9] to maximize the system capacity. To achieve the main aim, a parameter is added in the closed form in [10] to adjust transmission power. Hence, the way of assigning switching thresholds differs from the preceding scheme. Due to average transmission power constraint, the optimal switching thresholds are obtained by Lagrange method, after which we get a power control scheme to adjust transmission power based on the received SNR. The main concept is to increase transmission rate when the channel is good, while decrease the transmission rate when the channel is bad. This paper presents a power minimization scheme with centralized architecture of wireless sensor networks that involves base station, cluster head nodes, and sensor nodes [17]; we focus on wireless uplink between mobile cluster head and base station.
Adaptive modulation with power control scheme aims at maximum channel capacity by adjusting transmission power under a constraint of constant long-term average transmission power. The AM PC1 with and without PC schemes are not suitable for wireless sensor networks. We propose a simple adaptive modulation with power control scheme (AM with PC 2) to prolong the system lifetime of wireless sensor networks. The transmitter can decrease transmission power to attain energy efficiency as received SNR increases within two adjacent switching thresholds. We present simulations testing effectiveness of our proposed approach and compare its performance with that of [11] based on the same constraint. Numerical results show that our algorithm obtains transmitting powers more accurately than [11].
The rest of this paper is organized as follows. Section 2 presents the system model. A new scheme including power control named as AM with PC 2 is presented in Section 3. Section 4 analyzes simulation results to evaluate effectiveness of our proposed scheme. Conclusions appear in Section 5.
Section snippets
System description
Proposed architecture is a medical monitoring wireless sensor networks which provides doctor to get important and instantaneous information of patients’ statuses from patients’ body [18], [19], [21], [22], [23], [24]. In this system, each sensor node that attached to patient’s body can detect important physiological information: e.g., blood pressure, heartbeat. When the sensor node detects unusual information, it must rapidly transmit information to the doctor (base station), enabling the
Adaptive modulation with power control scheme 1 (AM with PC 1)
In this section, we join an issue of power control, as shown in Fig. 3. In [11] the authors consider adaptive transmission power and modulation scheme based on the feedback SNR that received at receiver. This scheme can help the system to maximize spectral efficiency.
Based on [11], denotes average transmission power, N0/2 denotes the noise density, B denotes received signal bandwidth, and denotes average channel gain. With appropriate scaling of , we assume that . For a constant
Simulation results
This section compares performance of the proposed scheme with two schemes, one based on adaptive modulation with fixed transmission power (AM without PC) and the other adaptive modulation with variable transmission power (AM with PC 1). The following simulation assumes target BER is 10−3.
According to Eq. (5), we can partition received SNR level into four regions (no transmit, BPSK, QPSK, 16-QAM), as in Fig. 5. Table 2 plots points of switching thresholds for AM with PC 1; we learn that this
Conclusions and discussion
We address the problem of energy consumption by wireless sensor networks. This paper proposes adaptive modulation with power control scheme which mainly reduces power consumption to achieve energy efficiency for wireless sensor networks. This scheme decreases transmission power to attain energy efficiency as received SNR increases within two adjacent switching thresholds. Numerical and simulation results validate that our proposed adaptive modulation with power control scheme can reduce more
Chien-Erh Weng received his M.S. in Electrical Engineering from National Yunlin University of Science & Technology and Ph.D. in Electrical Engineering from National Chung Cheng University at Chiayi in 2000 and 2007, respectively. In September 2010, he joined the Department of Electronic Communication Engineering at National Kaohsiung Marine University at Kaohsiung. His search interest is in the fields of performance study of UWB communication systems, wireless sensor networks and cooperative
References (24)
- et al.
Wireless sensor networks: a survey
ELSEVIER Comput Netw
(2002) - et al.
Wireless sensor network survey
ELSEVIER Comput Netw
(2008) - et al.
Using hierarchical agglomerative clustering in wireless sensor network: an energy-efficient and flexible approach
Ad Hoc Netw
(2010) - et al.
Performance analysis of joint diversity combining, adaptive modulation, and power control schemes
IEEE Trans Commun
(2011) - et al.
Wireless sensing system-on-chip for near-field monitoring of analog and switch quantities
IEEE Trans Industr Electron
(2012) - et al.
An analytical model for wireless sensor networks with sleeping nodes
IEEE Trans Mob Comput
(2006) - et al.
A survey on sensor network
IEEE Commun Mag
(2002) - et al.
Sensor networks: evolution, opportunities, and challenges
Proc IEEE
(2003) - et al.
Sensor networks: an overview
IEEE Potentials Mag
(2003) - Caponi L, Chiti F, Fantacci R. A dynamic rate allocation technique for wireless communication systems. In: Proceedings...
Adaptive wireless transceivers
Wireless communications
Cited by (9)
Joint power and bandwidth allocation in IEEE802.22 based cognitive LTE network
2014, Computer NetworksCitation Excerpt :Such poor performance of PCAM can be explained as follows. Recall that in PCAM the power is allocated in the way minimizing power consumptions of the user terminals, whereas buffer size (and therefore delay and loss) in the network nodes is not considered [3]. Thus, when the network is fully loaded, PCAM takes no action against the growing buffers (and queuing delay) of the users and the eNBs.
Optimum Trans–Receiver Scheme Using Cross Layer Design for Acoustic Communication
2022, Wireless Personal CommunicationsAnti-sybil Attack Detection Algorithm RSSI-Based Wireless Sensor Networks
2021, Lecture Notes on Data Engineering and Communications TechnologiesControlling interferences in smart building IoT networks using machine learning
2019, International Journal of Sensor Networks
Chien-Erh Weng received his M.S. in Electrical Engineering from National Yunlin University of Science & Technology and Ph.D. in Electrical Engineering from National Chung Cheng University at Chiayi in 2000 and 2007, respectively. In September 2010, he joined the Department of Electronic Communication Engineering at National Kaohsiung Marine University at Kaohsiung. His search interest is in the fields of performance study of UWB communication systems, wireless sensor networks and cooperative radio networks.
Ho-Lung Hung received his M.S. in Electrical Engineering from the University of Detroit at Mercy in 1994 and Ph. D. in Electrical Engineering from National Chung Cheng University at Chia-Yi in 2007. From1995 to 2006, he was a lecturer with the Department of Electrical Engineering, Chienkuo Technology University. Since 2007, he has been an associate professor with the Department of Electrical Engineering, Chienkuo Technology University, Taiwan. His current research interests are in wireless communications, detection of spread-spectrum signal, wireless sensor networks, evolutionary computation, and intelligent systems. He serves as Associate Editor for TELECOMMUNICATION SYSTEMS.
- ☆
Reviews processed and recommended for publication to Editor-in-Chief by Associate Editor Dr. Paul Cotae.