Proceedings of IC-BNMT2009

SURVEY ON NETWORK LIFETIME RESEARCH FOR WIRELESS SENSOR NETWORKS

Long Zhaohua, Gao Mingjun

Dept of computer, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. Gao_mingjun@163.com

AbstractWireless sensor network consists of large number of sensor nodes randomly distributed in some regions. Each node has a limited energy supply and generates information when some events that need to be communicated to a sink node occur. It has been proved that the nodes closer to the sink node will use up their energy more quickly,as a result, the network lifetime will be affected, some bad consequences will happen.Therefore how to optimize the network lifetime become an important problem.According to the reasearch fruit many reaserchers have contributed,we concluded that the method of optimizing the network life included keeping energy effectifcy,improving or creating some optimized algorithms,making use of the inherent mechanism of nodes and so on. The network lifetime can depend on many other factors too.In the paper we give out the survey of the sensor network lifetime and do some analysis.

Keywords: wireless sensor networks; energy limited; opeimizing network lifetime

1 Introduction A wireless sensor network (WSN) is an ad hoc network formed from many sensor nodes that gather data and uses wireless communication to transmit the information that they collect and the number of nodes dependes on the application [24]. Because sensor nodes are battery powered, and their lifetime should be maximized, one of the most important design criteria for this type of network is energy efficiency [19]. Sensor network interfaces with the other networks(eg wired network) by one or several sinks the sensory data collected by the sensors is sent to the closest sink where it is further aggregated, it was noticed that the sensors closest to the sink are easier to use up their energy than other sensors[21],[22],[23], as a result,it is clear that lifetime of the network will be significantly affected.

Network lifetime is commonly defined as the length from the time network begins working to the one when one node appears to use up its own energy. It is very meanful to research the network lifetime.It has

become a hot problem in wireless sensor network. By researching the network lifetime, we can arrive at the purpose of improving the energy efficiency ,also we can has a optimized network systme, and so on.On improving the network lifetime, many researchers have made great efforts. But few sum-ups had been done on it.In the paper we will show the survey on network lifetime for wireless sensor network, mainly on Lifetime Optimization.

The main contribution of this paper is to classify and analyse the latter research fruits of the network lifetime for wireless sensor network, the important point is to introduce the method the reseachers uses to maximize network lifetime.

The remainder of the paper is organized as follows. First we briefly introduce the importance of network lifetime and then in section 2 we begin to classify the later research fruit, including the research related to energy efficiency, the algorithm and nodes or sensors. In section 3 we analysis the later reasearch fruit simply, as well as give out our advice.Finally section 4, we conclude the paper with a summary and discussion of future work.

2 Related research of network lifetime for wireless sensor network 2.1 The research of nework life related to energy efficiency

The works on energy efficiency mainly contain three aspects, area coverage, request spreading and data aggregation; at each layer of protocol stack by proposing new algorithms and protocols, some research works has been done for energy efficiency [19],[20]..

The authors in [4], considering the theory of communication complexity, extended the notions of ambiguity set and ambiguity to compute the worst and average case communication Complexities and applied these results to estimate the worst and average case lifetimes of the sensor networks.

In [6], the authors explored the energy-latency tradeoffs in wireless communication using techniques such as modulation scaling, abstractthe data ___________________________________

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aggregation tree was also used for abstracting the packet flow. Meanwhile, the authors gave out the DP-Algo to minimize the overall energy dissipation of the sensor nodes in the aggregation tree subject to the latency constraint. For the off-line problem, the authors also offered an Off-Line algorithm for the optimal solution.

Several node scheduling approaches [28][29] have been proposed to minimize energy consumption while maintaining sensing coverage at the desired level. In [14], the authors, considering the approach where sensor nodes are over-deployed in a given area (only a subset of the nodes are in active mode to maintain a certain degree of sensing coverage) and the remaining ones are put in sleeping mode, studied the lifetime-coverage tradeoff in a random topology and illustrated that providing partial coverage can more significantly improve the lifetime of a given sensor network compared to the cases that provided full coverage by using pCover protocol (partial coverage) in which a sensor node is either in working mode or sleeping mode, performed similarly as the algorithm in [28] when used to provide full coverage.

In [15], the authors investigated the optimization of network lifetime for approximate data aggregation, differentiated the quality of data collected from different sensor nodes to balance their energy consumption and analyzed the optimal precision allocation in terms of network lifetime and have proposed an adaptive precision allocation scheme that dynamically adjusts the error bounds of sensor nodes.

2.2 The algorithm research on improving the network lifetime

The problem of maximizing the data extracted from an energy-limited sensor network consisting of heterogeneous nodes was examined in [5].The authors introduced an additional element of “data-awareness” that must be considered in addition to “energy-awareness.” The authors formulated the maximum data extraction problem as a linear program and present a 1+w iterative approximation algorithm for it.

A polynomial-time algorithm by exploiting the parametric analysis (PA) technique from linear programming (LP), called as serial LP with Parametric Analysis (SLP-PA), was presented in [7]. SLP-PA proved by the authors can be also used to address the so-called LMM node lifetime problem much more efficiently than an existing technique proposed in the literature. The authors used numerical results to tell SLP-PA algorithm to the LMM rate allocation problem and pointed out that there existed an underlying duality relationship between the LMM rate allocation problem and the LMM node lifetime problem.

The authors considered the joint optimal design of the physical, medium access control (MAC), and routing layers to maximize the lifetime of energy-constrained wireless sensor networks in [8].For the case of general link schedules, the author proposed an iterative algorithm to approximate the optimal solution.The predominance of load balancing, multi hop routing and frequency reuse in extending the lifetime of energy constrained networks are also proved.

It has been proved that in the applications of sensor network where the traffic load is very light most of the time, when a node does not take part in any data delivery, turning off the radio is necessary. In [10], an algorithmic to point out the sleep latency can be essentially eliminated (by having a periodic cycle with level-by-level offset schedules)was given out.

Differently from former research in this area that paid mainly attention to intuitive MAC protocol designs, [11] used a provided algorithmic to explain some important problems, for example one can obtain significant savings in the latency at the same duty cycling by choosing multiple wake up slots carefully.

The authors [17] researched the tradeoff problem, considering a cross-layer perspective, and gave algorithms to get the best tradeoff.

2.3 The research of network life considering the model and node design and other factors

Extending the lifetime of battery-operated devices is considered a key design mechanism. In [25], a data routing algorithm has been proposed with an aim to maximize the minimum lifetime over all nodes in wireless sensor networks. In [26], the network lifetime has been maximized by employing the accumulative broadcast strategy. The author [27] has considered provisioning additional energy in the existing nodes and deploying relays to extend the lifetime.

In view of the fact the problem of maximizing network lifetime was not almost addressed precisely, in [1], the authors presented a mixed model, considering it no longer adequate to simply optimize transmission power, pointed out that turning off as many transceivers as much as possible was necessary so that energy consumption during overhearing and idle periods was avoided and analyzed a variety of network lifetime maximization problems in the time based model as well as the intensively researched packet based model. Fowllowing is the simple introduce of the mixed model the authors have presented, in the model Communication is disposed by a backbone not sleeping and connecting every pair of nodes in the network; a sleeping node can occasionally wake up to send out data over the backbone. Where communication is frequent

relatively, energy consumption can be divided into two parts. On one hand, sensor nodes consume as much power as each other on a per time unit basis. On the other hand, they may consume dramatically different energy on a per packet basis.

In [2], the authors considered the network lifetime which is based on the ratio of dead nodes to the total number of nodes, focusing on the event-driven networks with Poisson model for packet generation. In this model, the authors considered that the events were independent temporally and happen with equal probability in the area. As a result, Poisson distribution can be used effectively to model the generation of data packets [3].

In [9], the authors pointed out that the problem how the optimal sensor can be enabled represents a complex semi-Markv decision problem; the authors let ),( tAnp denote the number of active sensors that cover area element A at time t, under policy P. The time-average utility under policy P, is given out

by ����� A

t

tdAdttAnpU

t)),((1lim

0, dxdydA �

and ),,,(),( tyxnptAnp � in the above expression. The decision problem is that finding P so that the objective function is maximized.

In [12], the authors defined the network lifetime, the duration that all targets are watched .Meanwhile, the authors provided a solution consisting three steps to find the target watching schedule for sensors that achieves the maximal Lifetime. In [13],the authors provided an explanation on the uneven energy depletion phenomenon, which was noticed in sink-based wireless sensor networks theoretically, but the author also point out that their study was limited to the case where each sensor reports to one sink.In [18],the authors studied the problem of multiple directional cover whose algorithms designed for omni-directional sensor networks can not be suitable for directional sensor networks sets, gave out the Progressive algorithm based on LP, as a result got a longer network lifetime.

3 Analysis Paper [4] computed the worst and average case communication complexities for “multiple correlated informants - single recipient” communication, but the authors didn’t consider other interesting variations of the interactive communication problem. [5] [6][7][8][10][11[16][17]improved network lifetime by using the algorithm essentially. Differently [11] used the link scheduling algorithm,[10] using a new algorithm by defining a graph-theoretic combinatorial optimization problem formulation for delay efficient sleep scheduling[16] presented an algorithm for determining the wake-up frequency of the nodes in a

sensor network.[17] discussed the lifetime with a cross layer approach. [9] considered the case in that sensors can be recharged.[12] have more advantages in the situation that senses are densely deployed or sensors have larger coverage ranges.[6][14] [15]paid attention to energy efficiency mainly, considering coverage request spreading and data aggregation.

4 Conclusions and future work

In the paper, we study the later fruits of the network lifetime and compare them simply. In view of the recent study many researches have done, we find that the network lifetime is important for sensor network, especially the problem how to optimize the network lifetime is more complicated and important.In the sensor network,because of its own characteristic,so much work have been required to do so that the network lifetime can been improved. Until now,although the research fruit we can use is so much,many researchers has contributed much, but still it is a challenge for researching the network lifetime. Therefore it is meanful for us to reasearch the network lifetime ,of course it is very important to study the later fruit.The survey may not be especially completed,but we has tried our best to study the later and especially good research fruit.

We are do a project on wireless sensor network.Recently we are doing some optimal work, for example we are trying our best to relize the routing protocol better than other research.Therefore, future work is to do more in-depth research, and fight for our own creationary research on the basis of the existing research fruit, developing a new algorithm or new models to improve network lifetime and putting it into the application of project on wireless sensor network we are doing recently,of course it is necessary to do some simulations so that we can have a legibler recognition to the network lifetime of wiress sensor networking.

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