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The Impact of Random Power Assignment in Handshaking on Wireless Sensor Network Lifetime
Abstract
Optimization of data and acknowledgement (ACK) packets' transmission power levels is necessary to prolong the lifetime of wireless sensor networks (WSNs). Utilizing the maximum power level available results in the minimum level of handshake failures, however, such a strategy will also result in energy waste because it may not be necessary to utilize the maximum power level at each link. Alternatively, the optimal data and ACK packet transmission levels can be determined for each link that minimizes the energy cost of handshaking. Accomplishing this task requires that the packet failure rates for each combination of power levels for data and ACK packets are measured through a close loop scheme which brings extra overhead when compared to the strategy of employing the highest power level. In this paper, we propose a novel strategy based on random power level assignments that do not require the overhead necessary to measure the handshake failure rate, yet, at the same time performs better than the maximum power level assignment strategy. To evaluate the performance of the proposed strategy we built a novel mixed integer programming (MIP) framework. Our results show that random power assignment performs better than maximum power assignment in terms of network lifetime.
... In [27], a novel strategy has been proposed based on random power level assignments. These assignments avoid the overhead required to measure the handshake failure rate. ...
Efficient utilization of energy is a hot topic of research in the field of wireless sensor networks. Limited battery resource at a sensor node coupled with the hostile multi-path fading propagation environment makes the task of the network to provide reliable data services with an enhanced lifetime, challenging. In order to achieve this goal, a number of efforts have been made by the researchers; one such key strategy is an energy-aware routing embedded with transmission power control mechanism. In this strategy, every sensor node in a network transmits at a lowest possible power level to maintain on one hand reliable wireless links with other neighboring nodes and saves the energy on the other. In this paper, we propose a novel routing technique embedded with transmission power control for wireless sensor networks while considering a realistic radio fading environment. The proposed strategy considers channel fading in the propagation environment and mitigate it through transmission power control mechanis
In naturally deaf wireless sensor networks or generally when there is no feedback channel, the fixed-level transmit power of all nodes is the conventional and practical power allocation method. Using random power allocation for the broadcasting nodes has been recently proposed to overcome the limitations and problems of the fixed power allocation. However, the previous work discussed only the performance analysis when uniform power allocation is used for quasi-static channels. This paper gives a general framework to evaluate the performance (in terms of outage and average transmit power) of any truncated probability density function of the random allocated power. Furthermore, dynamic Rayleigh fading channel is considered during the performance analysis which gives more realistic results that the AWGN channels assumed in the previous work. The main objective of this paper is to evaluate the communication performance when general random power allocation is used. Furthermore, the truncated inverse exponential probability distribution of the random power allocation is proposed and compared with the fixed and the uniform power allocations. The performance analysis for the proposed schemes are given mathematically and evaluated via intensive simulations.
Optimization of flows to maximize Wireless Sensor Network (WSN) lifetime is a problem already investigated in various aspects. However, most studies ignored the effects of finite bandwidth. As source data rate of sensor nodes increases, flow patterns that balance energy dissipation optimally might need more bandwidth than available. As a result, ignoring bandwidth limitations may lead to infeasible solutions. In this study, we make a comprehensive evaluation of the impacts of finite bandwidth by building a linear programming framework. The objective is the minimization of the energy expenditure in the maximum energy-dissipating node to achieve the maximum lifetime under bandwidth constraints. Our analysis reveals that energy consumption values may stay constant until a threshold data rate is reached. But, after the threshold the energy values increase because suboptimal paths are used due to bandwidth constraints. The bandwidth for optimal energy dissipation is limited approximately by twice the minimum bandwidth requirement.
Despite their increasing sophistication, wireless sensor networks still do not exploit the most powerful of the human senses: vision. Indeed, vision provides humans with unmatched capabilities to distinguish objects and identify their importance. Our work seeks to provide sensor networks with similar capabilities by exploiting emerging, cheap, low-power and small form factor CMOS imaging technology. In fact, we can go beyond the stereo capabilities of human vision, and exploit the large scale of sensor networks to provide multiple, widely different perspectives of the physical phenomena.To this end, we have developed a small camera device called Cyclops that bridges the gap between the computationally constrained wireless sensor nodes such as Motes, and CMOS imagers which, while low power and inexpensive, are nevertheless designed to mate with resource-rich hosts. Cyclops enables development of new class of vision applications that span across wireless sensor network. We describe our hardware and software architecture, its temporal and power characteristics and present some representative applications.
The wireless sensor networks community, has now an increased understanding of the need for realistic link layer models. Recent experimental studies have shown that real deployments have a "transitional region" with highly unreliable links, and that therefore the idealized perfect-reception-within-range models used in common network simulation tools can be very misleading. In this paper, we use mathematical techniques from communication theory to model and analyze the low power wireless links. The primary contribution of this work is the identification of the causes of the transitional region, and a quantification of their influence. Specifically, we derive expressions for the packet reception rate as a function of distance, and for the width of the transitional region. These expressions incorporate important channel and radio parameters such as the path loss exponent and shadowing variance of the channel; and the modulation and encoding of the radio. A key finding is that for radios using narrow-band modulation, the transitional region is not an artifact of the radio non-ideality, as it would exist even with perfect-threshold receivers because of multi-path fading. However, we hypothesize that radios with mechanisms to combat multi-path effects, such as spread-spectrum and diversity techniques, can reduce the transitional region.
One of the main design issues for a sensor network is conservation of the energy available at each sensor node. We propose to deploy multiple, mobile base stations to prolong the lifetime of the sensor network. We split the lifetime of the sensor network into equal periods of time known as rounds. Base stations are relocated at the start of a round. Our method uses an integer linear program to determine new locations for the base stations and a flow-based routing protocol to ensure energy efficient routing during each round. We propose four metrics and evaluate our solution using these metrics. Based on the simulation results we show that employing multiple, mobile base stations in accordance with the solution given by our schemes would significantly increase the lifetime of the sensor network.
In this letter, we present stochastic analysis of two power control schemes for wireless ad hoc networks: random-vs. fixed power controls. Using numerical examples, we show that randomizing transmit power has positive effect of reducing high interferences to the other nodes, and improves network connectivity, in high-density networks. However, the fixed power control is more favorable in low-density environments. In this letter, we derive a formula of such cross-over (density) point at which superiority of each power control is switched.
In recent years power control becomes one of research hotspots in wireless sensor networks to reduce total energy consumption in packet interference and improve channel spatial reuse. For the random power control approach, the wireless module of a sensor node picks up one stochastic power value between the minimum and the maximum to send data packet. Connectivity of the random power control approach in wireless sensor networks where sensor nodes are distributed densely is analyzed in this paper. The minimum and the maximum can be selected to meet the performance requirement of networks. Simulation results show that random power control can improve the energy efficiency compared to the conventional fixed power control under the tiny difference of transmission successful probability.
In this paper we provide a method to analytically compute the energy saving provided by the use of Transmission Power Control (TPC) at the MAC layer in Wireless Sensor Networks (WSN). We consider a classical TPC mechanism: data packets are transmitted with the minimum required power to achieve a given packet error probability, whereas the additional MAC control packets are transmitted with the nominal (maxi- mum) power. This scheme has been chosen because it does not modify the network topology, since control packet transmission range does not change. This property also allow us to compute analytically the expected energy savings. Besides, this type of TPC can be implemented in the current sensor hardware, and can be applied directly to several MAC protocols already proposed for WSN. The foundation of our analysis is the evaluation of L ratio, defined as the total energy consumed by the network using the original MAC protocol divided by the total energy consumed if the TPC mechanism is employed. In the L computation we emphasize the basic properties of sensor networks. Namely, the savings are calculated for a network that is active a very long time, and where the number of sensors is supposed to be very large. The nodes position is assumed to be random -for the sake of example a normal bivariate distribution is assumed in the paper- and no node mobility is considered. In the analysis we stress the radio propagation and the distribution of the nodes in the network, that will ultimately determine the performance of the TPC. Under these conditions we compute the mean value of L. Finally, we have applied the method to evaluate the benefits of TPC for TDMA and CSMA with two representative protocols, L-MAC and S-MAC using their implementation reference parameters. The conclusion is that, while S-MAC does not achieve a significant improvement, L- MAC may reach energy savings up to 10-20%.
In multihop wireless sensor networks that are often characterized by many-to-one (convergecast) traffic patterns, problems related to energy imbalance among sensors often appear. Sensors closer to a data sink are usually required to forward a large amount of traffic for sensors farther from the data sink. Therefore, these sensors tend to die early, leaving areas of the network completely unmonitored and reducing the functional network lifetime. In our study, we explore possible sensor network deployment strategies that maximize sensor network lifetime by mitigating the problem of the hot spot around the data sink. Strategies such as variable-range transmission power control with optimal traffic distribution, mobile-data-sink deployment, multiple-data-sink deployment, nonuniform initial energy assignment, and intelligent sensor/relay deployment are investigated. We suggest a general model to analyze and evaluate these strategies. In this model, we not only discover how to maximize the network lifetime given certain network constraints but also consider the factor of extra costs involved in more complex deployment strategies. This paper presents a comprehensive analysis on the maximum achievable sensor network lifetime for different deployment strategies, and it also provides practical cost-efficient sensor network deployment guidelines.