CN102932799B - The optimization method of a kind of cognitive sensor network model and life cycle thereof - Google Patents

Abstract

The invention discloses the optimization method of a kind of cognitive sensor network model and life cycle thereof, the method is for the unserviced feature of available channel resources finite sum under the environment of digital navigation channel, carry out joint route, channel allocation and power based on graph theory and Optimum Theory to control, thus make sensor network can observe digital navigation channel adaptively and lifecycle maximization.Method provided by the invention easily realizes, and is convenient to expansion, and comparing with the sensor network life cycle optimization method proposed more can the digital navigation channel environment of adaptive channel resource-constrained.

Description

The optimization method of a kind of cognitive sensor network model and life cycle thereof

Technical field

The invention belongs to sensor network, particularly the optimization method of a kind of cognitive sensor network model and life cycle thereof.

Background technology

Cognitive sensor network causes the very big concern of researcher in recent years, and it is that node can radio environment around perception and the sensor network of adaptive access channel.Such as, Akan etc. review the design principle of cognitive sensor network, potential advantages, application and the network architecture.Maleki etc. propose a kind of energy-conservation cognitive sensor network spectrum sensing scheme in conjunction with dormancy and examination.Han etc. propose a kind of channel management scheme of cognitive sensor network, to improve efficiency and to reduce the interference to authorized user.Liang etc. analyze cognitive sensor network for delay performance during real-time Data Transmission.Oto etc. have carried out energy saving optimizing to data package size during cognitive sensor network transfer of data.The life cycle optimization of cognitive sensor network is seldom considered in these researchs.

The life cycle of sensor network characterizes the continuous firing ability of sensor network, has obtained research widely.Such as, the life cycle irrelevant with network model of having derived such as Yunxia Chen expresses formula.The life cycle of the sensor network that Lee etc. analyze bunch.Dagher proposes a kind of iterative algorithm with maximization network life cycle.Van Hoesel proposes by cross-layer optimizing maximization network life cycle.Cunqing Hua etc. propose a kind of lifecycle maximization method of joint route and data aggregate.Chang etc. propose the maximum routing algorithm of a kind of network lifecycle.The proposition mobile relays such as Wei Wang extend network lifecycle.The feature of digital navigation channel environment lower channel resource-constrained is not considered in these researchs.The base station disposed under numeral navigation channel environment is relatively less, and signal is more easily decayed, thus can channel resource be limited.

When sensor network disposition is in the limited environment of the channel resources such as digital navigation channel, when a large amount of sensor node sends data, mutual conflict may be caused.In addition, under the environment of digital navigation channel, sensor node is unattended, changes also more difficult, and people wish that again the operating time of sensor network is long as far as possible.Therefore the necessary sensor node that allows detects the channel of deployment region, and the channel detected is distributed to sensor node with most reasonable manner, make not interfere with each other during their transmission data and also whole Network morals the longest.

Summary of the invention

The object of the invention is to, for the unserviced feature of available channel resources finite sum in the sensor network observed for Digital Aerial road, propose the optimization method of a kind of cognitive sensor network model and life cycle thereof.

In order to realize foregoing invention object, the technical scheme of employing is as follows:

A kind of cognitive sensor network model, its link is with available channel resources dynamic change, and node transmitting power is controlled, and whole network can observe digital navigation channel adaptively, and can realize network lifecycle maximization.

A kind of optimization method of life cycle of cognitive sensor network model is: do not disturb each other when ensureing the adjacent node transmission data of same jumping on different path and also on same path different jump node-node transmission data time also do not disturb each other, specifically comprise:

(1) the advanced row frequency spectrum perception of sensor node, the channel of free time detecting sensor network design region in, and the information of collection is sent to fusion center, be provided with K channel for, be designated as { B ₁, B ₂, Λ, B _k, be responsible for allotment by fusion center, have J leaf node in sensor network, be designated as { s _1,1, s _1,2, Λ, s _{1, J}, the data of leaf node collection arrive fusion center by multi-hop transmission, other node not image data, and sensor node first finds next-hop node according to minimum distance Geographic routing algorithm, form multihop path.Then we are sensor node allocated channel according to graph coloring theory, the channel that on the different and same path of the channel that makes the different nodes of same jumping corresponding, different node of jumping is corresponding is also different, graph coloring algorithm has a variety of, for the sake of simplicity, we adopt greedy algorithm, distribute first channel first to certain leaf node, then progressively give other peer distribution channel, until all nodes are assigned with channel all according to painted criterion;

(2) for after the good channel of all peer distribution, then control according to the transmitting power of Optimum Theory to each node, with maximization network life cycle; The i-th hop node on jth paths is designated as e because of the energy sending the consumption of m Bit data _{i, j}m (), the energy receiving the consumption of m Bit data is designated as ε _{i, j}m (), residue energy of node is designated as E _{i, j}; Ignore the energy ezpenditure of other parts except transfer of data in sensor node; This optimization problem of maximization network life cycle is modeled as

T _i，j＝E _i，j/(e _i，j(m)+ε _i，j(m)) (1)

T=min(T _i，j) (2)

${\underset{p_{i,j}}{\max }}^T---\left(3\right)$

Wherein P _{i, j}it is the transmitting power of the i-th hop node on jth paths; Because energy ezpenditure can connect with transmitting power, to this optimization problem, optimum transmission power level can be obtained.

Advantage of the present invention is: the method is for the unserviced feature of available channel resources finite sum under the environment of digital navigation channel, carry out joint route, channel allocation and power based on graph theory and Optimum Theory to control, thus make sensor network can observe digital navigation channel adaptively and lifecycle maximization.Method provided by the invention easily realizes, and is convenient to expansion, and comparing with the sensor network life cycle optimization method proposed more can the digital navigation channel environment of adaptive channel resource-constrained.

Accompanying drawing explanation

Fig. 1 is cognitive sensor network illustraton of model;

Fig. 2 is cognitive sensor network life cycle optimization method schematic diagram.

Embodiment

Below in conjunction with accompanying drawing, the present invention is described further.

As shown in Figure 1, 2, channel tag to be allocated is { B ₁, B ₂, B ₃.First give the leaf node allocated channel B of leftmost 1st paths ₁.In order to avoid mutual interference, from left to right the 2nd, 3, the leaf node of 4 paths is assigned with channel B respectively ₂, B ₁, B ₂.In like manner, in order to avoid mutual interference, the 2nd hop node of the 1st paths is assigned with channel B ₂, the 2nd hop node of the 2nd paths is assigned with channel B ₁, the 2nd hop node of the 3rd paths is assigned with channel B ₂, the 2nd hop node of the 4th paths is assigned with channel B ₁.The rest may be inferred, and the 3rd hop node of the 2nd paths is assigned with channel B ₂.3rd paths and the 4th paths merge when the 3rd jumps, and therefore its 3rd hop node can only be assigned with channel B ₃.

After the good channel of each peer distribution, we consider the transfer of data between node again.The data that the leaf node of the 1st paths gathers arrive fusion center through double bounce transmission, and the data of the leaf node collection in other path are passed to through three jump set and reach fusion center.So the energy of the leaf node consumption of a data transfer jth paths is e _{1, j}m (), the data transmission times that can complete is T _{1, j}=E _{1, j}/ e _{1, j}(m).The energy of the 2nd hop node consumption of jth paths is e _{2, j}(m)+ε _{2, j}m (), the data transmission times that can complete is T _{2, j}=E _{2, j}/ (e _{2, j}(m)+ε _{2, j}(m)).Equally, the energy of the 3rd hop node consumption on the 2nd paths is e _3,2(m)+ε _3,2m (), the data transmission times that can complete is T _3,2=E _3,2/ (e _3,2(m)+ε _3,2(m)).3rd, the energy of the 3rd hop node consumption on 4 paths is 2e _3,3(m)+2 ε _3,3m (), the data transmission times that can complete is T _3,3=E _3,3/ (2e _3,3(m)+2 ε _3,3(m)).The life cycle of sensor network is T=min (T _{1, j}, T _{2, j}, T _3,2, T _3,3).The energy consumed according to the transmission of sensor node factor data and dump energy, solve optimization problem above, can obtain optimum transmission power level.

Claims (1)

1. the optimization method of the life cycle of a cognitive sensor network model, it is characterized in that: the link of this model is with available channel resources dynamic change, and node transmitting power is controlled, whole network can observe digital navigation channel adaptively, and can realize network lifecycle maximization; Do not disturb each other when ensureing the adjacent node transmission data of same jumping on different path and on same path different jump node-node transmission data time also do not disturb each other, specifically comprise:

(1) the advanced row frequency spectrum perception of sensor node, the channel of free time detecting sensor network design region in, and the information of collection is sent to fusion center, be provided with K channel for, be designated as { B ₁, B ₂..., B _k, be responsible for allotment by fusion center, have J leaf node in sensor network, be designated as { s _1,1, s _1,2..., s _{1, J}, the data of leaf node collection arrive fusion center by multi-hop transmission, other node not image data, and sensor node first finds next-hop node according to minimum distance Geographic routing algorithm, form multihop path; Then we are sensor node allocated channel according to graph coloring theory, the channel that on the different and same path of the channel that makes the same node point of same jumping corresponding, the node of identical jumping is corresponding is also different, graph coloring algorithm has a variety of, for the sake of simplicity, we adopt greedy algorithm, distribute first channel first to certain leaf node, then progressively give other peer distribution channel, until all nodes are assigned with channel all according to painted criterion;

(2) for after the good channel of all peer distribution, then control according to the transmitting power of Optimum Theory to each node, with maximization network life cycle; The i-th hop node on jth paths is designated as e because of the energy sending the consumption of m Bit data _i,jm (), the energy receiving the consumption of m Bit data is designated as ε _i,jm (), residue energy of node is designated as E _i,j; Ignore the energy ezpenditure of other parts except transfer of data in sensor node; This optimization problem of maximization network life cycle is modeled as

T _i,j＝E _i,j/(e _i,j(m)+ε _i,j(m)) (1)

T＝min(T _i,j) (2)

$\underset{\mathrm{Pi},j}{\max }T---\left(3\right)$

Wherein Pi, j are the transmitting powers of the i-th hop node on jth paths; Because energy ezpenditure can connect with transmitting power, to this optimization problem, optimum transmission power level can be obtained.