CN105101331A - Traffic perception routing optimization method based on transmission cost - Google Patents

Abstract

The invention provides a traffic perception routing optimization method based on a transmission cost. The traffic perception routing optimization method comprises the following steps: determining a forward neighbor node of a wireless sensor node; calculating the energy cost and the load index of the forward neighbor node; and determining the transmission cost of the forward neighbor node, and determining a next hop routing node. According to the traffic perception routing optimization method provided by the invention, the energy efficiency of the wireless sensor node and the traffic balance are comprehensively considered, and a transmission cost function of the wireless sensor node is constructed based on the energy cost and the load index to optimize and match the energy efficiency and the traffic balance. The wireless sensor node selects a forward neighbor node with the least transmission cost as the next hop according to the transmission cost of the forward neighbor node. The method is used for significantly reducing the total energy consumption of the wireless sensor network, and has good instantaneity performance and reliability at the same time.

Description

A kind of traffic aware routing optimization method based on transmission cost

Technical field

The present invention relates to a kind of optimization method, be specifically related to a kind of traffic aware routing optimization method based on transmission cost.

Background technology

In power information acquisition system, acquisition terminal node has the feature such as position dispersion, application scenarios complexity, in this case, the wireless sensor network (wirelesssensornetworks, WSN) being feature with low-power consumption, low cost is applied widely gradually.Multiple acquisition terminal node forms wireless sensor network at random, and then the data collected are sent to concentrator in a multi-hop fashion, realizes teletransmission and data storage by concentrator.Method for routing determines data how relaying and route, directly affects energy consumption and the performance of wireless sensor network.

Traditional routing algorithm generally for the situation of wireless sensor node finite energy, and does not consider that the inhomogeneities of wireless sensor node distribution and wireless sensor network flow is on the impact of the deemed-to-satisfy4 energy of route.But in power information acquisition system, wireless sensor node by using electric line continued power, can there will not be wireless sensor node depleted of energy in the past and the situation of death.Meanwhile, the features such as Information Monitoring data flow generally has periodically, flow is large, the transmittability of wireless sensor network that usually caused wireless sensor network load to exceed, and then cause wireless sensor network congested.

Summary of the invention

The present invention is directed to the unrestricted and wireless sensor network flow great Yi of wireless sensor node energy in power information acquisition system and produce congested feature, a kind of traffic aware routing optimization method based on transmission cost is provided, consider the validity of wireless sensor node energy and the harmony of flow, build the transmission cost function of wireless sensor node based on energy cost and load factor, realize the Optimized Matching of energy efficiency and flow equalization.Wireless sensor node is according to the transmission cost of forward direction neighbor node, and the forward direction neighbor node therefrom selecting transmission cost minimum is as down hop.The method significantly reduces wireless sensor network total energy consumption, has good real-time and reliability simultaneously.

In order to realize foregoing invention object, the present invention takes following technical scheme:

The invention provides a kind of traffic aware routing optimization method based on transmission cost, said method comprising the steps of:

Step 1: the forward direction neighbor node determining wireless sensor node;

Step 2: the energy cost and the load factor that calculate forward direction neighbor node;

Step 3: the transmission cost determining forward direction neighbor node, and determine down hop routing node.

In described step 1, described forward direction neighbor node is the set of the down hop both candidate nodes of wireless sensor node; Specifically comprise the following steps:

Step 1-1: set up wireless sensor network; Specifically comprise:

1) N number of wireless sensor node is evenly deployed in the square monitored area that the length of side is L at random, and each wireless sensor node has unique identification, and has power control capabilities and be adjusted to maximum communication distance R;

2) sink node deployment is in fixed position, the periodic perception environmental information of wireless sensor node, and sends data to sink node by the routing mode of multi-hop;

Step 1-2: all forward direction neighbor nodes determining wireless sensor node; Specifically comprise:

Wireless sensor network non-directed graph G (V, E) represents, V represents the set of wireless sensor node, and E represents can wireless connections between the wireless sensor node of direct communication, are expressed as:

E＝{(i,j)|i∈V,j∈V∪{sink}}(1)

In formula (1), i, j represent wireless sensor node;

If the neighbor node of wireless sensor node i represents with m, neighbor node m forms neighbor node set N (m) and is defined as:

N(m)＝{j|j∈V,d(i,j)＜R}(2)

In formula (2), d (i, j) represents the distance between wireless sensor node i, j;

Choose the forward direction neighbor node q of wireless sensor node i from neighbor node set N (m) of wireless sensor node i, form forward direction neighbor node set FN (q), FN (q) is defined as:

FN(q)＝{j|d(j,s)＜d(i,s),j∈N(m)}(3)

In formula (3), d (i, s) represents the distance of wireless sensor node i to sink node, and d (j, s) represents the distance of wireless sensor node j to sink node.

Described step 2 specifically comprises the following steps:

Step 2-1: the gross energy cost calculating forward direction neighbor node;

Step 2-2: the load factor calculating forward direction neighbor node.

In described step 2-1, by the energy cost EC of forward direction neighbor node q _iqcalculate the gross energy cost EC of forward direction neighbor node q _{i, q, p}; Specifically comprise:

The energy cost EC of forward direction neighbor node q _iqbe expressed as:

${\mathrm{EC}}_{iq}=\frac{E_{tx}(l,d(i,q))}{d(i,s)-d(q,s)}---\left(4\right)$

In formula (4), d (q, s) represents the distance of forward direction neighbor node q to sink node, E _tx(l, d (i, q)) expression wireless sensor node i sends the energy that lbit data consume to forward direction neighbor node q, is expressed as:

$E_{tx}\left(l,d\left(i,q\right)\right)=\{\begin{array}{c}{\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{fs}d{\left(i,q\right)}^2,d\left(i,q\right)<d_0\\ {\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{mp}d{\left(i,q\right)}^4,d\left(i,q\right)\&GreaterEqual;d_0\end{array}---\left(5\right)$

In formula (5), l represents that wireless sensor node i is transferred to the bit number of forward direction neighbor node q; E _txelecrepresent the energy that transmitter circuitry process unit bit data consume; D (i, q) represents the distance of wireless sensor node i to forward direction neighbor node q; E _fsthe energy that large processing of circuit unit bit data consume transferred by expression free space model; E _mpthe energy that processing of circuit unit bit data consume is amplified under representing multipath attenuation model; d ₀represent distance threshold, and

So, according to the gross energy cost EC of formula (4) by forward direction neighbor node q _{i, q, p}be expressed as:

${\mathrm{EC}}_{i,q,p}={\mathrm{EC}}_{iq}+\stackrel{\&OverBar;}{{\mathrm{EC}}_{qp}}---\left(6\right)$

In formula (6), represent the estimation energy cost of forward direction neighbor node q forwarding data, be expressed as:

$\stackrel{\&OverBar;}{{\mathrm{EC}}_{qp}}=\frac{\underset{p\&Element;FN\left(q\right)}{\&Sigma;}{E}_{tx}(l,d(q,p\left)\right)}{\left|FN\left(q\right)\right|}---\left(7\right)$

In formula (7), | FN (q) | represent the number of forward direction neighbor node, E _tx(l, d (q, p)) expression forward direction neighbor node q sends the energy that lbit data consume to down hop routing node p, is expressed as:

$E_{tx}\left(l,d\left(q,p\right)\right)=\{\begin{array}{c}{\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{fs}d{\left(q,p\right)}^2,d\left(q,p\right)<d_0\\ {\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{mp}d{\left(q,p\right)}^4,d\left(q,p\right)\&GreaterEqual;d_0\end{array}---\left(8\right)$

In formula (8), d (q, p) represents the distance of forward direction neighbor node q to down hop routing node p.

In described step 2-2, the load factor LI of forward direction neighbor node q _qrepresent, have:

${\mathrm{LI}}_q=\frac{Q_q}{Q_{max}}---\left(9\right)$

In formula (9), Q _qrepresent the queue length of forward direction neighbor node q in MAC layer buffering area, Q _maxrepresent the queue maximum length of forward direction neighbor node q in MAC layer buffering area.

Described step 3 specifically comprises the following steps:

Step 3-1: the transmission cost TC of forward direction neighbor node q _iqrepresent, have:

TC _iq＝αEC _i,q,p+(1-α)LI _q(10)

In formula (10), α represents adjustment factor, and α >0; LI _qrepresent the load factor of forward direction neighbor node q, EC _{i, q, p}represent the gross energy cost EC of forward direction neighbor node q _{i, q, p};

Step 3-2: choose the minimum forward direction neighbor node of transmission cost as down hop routing node.

Compared with prior art, beneficial effect of the present invention is:

The invention provides a kind of traffic aware routing optimization method based on transmission cost, consider the validity of wireless sensor node energy and the harmony of flow, build the transmission cost function of wireless sensor node based on energy cost and load factor, realize the Optimized Matching of energy efficiency and flow equalization.Wireless sensor node is according to the transmission cost of forward direction neighbor node, and the forward direction neighbor node therefrom selecting transmission cost minimum is as down hop.The method significantly reduces wireless sensor network total energy consumption, has good real-time and reliability simultaneously.

Accompanying drawing explanation

Fig. 1 is forward direction neighbor node schematic diagram in the embodiment of the present invention;

Fig. 2 is embodiment of the present invention interior joint position view.

Embodiment

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

The invention provides a kind of traffic aware routing optimization method based on transmission cost, said method comprising the steps of:

Step 1: the forward direction neighbor node determining wireless sensor node;

Step 2: the energy cost and the load factor that calculate forward direction neighbor node;

Step 3: the transmission cost determining forward direction neighbor node, and determine down hop routing node.

In described step 1, described forward direction neighbor node is the set of the down hop both candidate nodes of wireless sensor node; Specifically comprise the following steps:

Step 1-1: set up wireless sensor network; Specifically comprise:

1) N number of wireless sensor node is evenly deployed in the square monitored area that the length of side is L at random, and each wireless sensor node has unique identification, and has power control capabilities and be adjusted to maximum communication distance R;

2) sink node deployment is in fixed position, the periodic perception environmental information of wireless sensor node, and sends data to sink node by the routing mode of multi-hop;

The sustainable power supply of each wireless sensor node, not by energy limited, there will not be node energy to exhaust and the situation of death.

Step 1-2: all forward direction neighbor nodes determining wireless sensor node; Specifically comprise:

Wireless sensor network non-directed graph G (V, E) represents, V represents the set of wireless sensor node, and E represents can wireless connections between the wireless sensor node of direct communication, are expressed as:

E＝{(i,j)|i∈V,j∈V∪{sink}}(1)

In formula (1), i, j represent wireless sensor node;

If the neighbor node of wireless sensor node i represents with m, neighbor node m forms neighbor node set N (m) and is defined as:

N(m)＝{j|j∈V,d(i,j)＜R}(2)

In formula (2), d (i, j) represents the distance between wireless sensor node i, j;

Choose the forward direction neighbor node q of wireless sensor node i from neighbor node set N (m) of wireless sensor node i, form forward direction neighbor node set FN (q), FN (q) is defined as:

FN(q)＝{j|d(j,s)＜d(i,s),j∈N(m)}(3)

In formula (3), d (i, s) represents the distance of wireless sensor node i to sink node, and d (j, s) represents the distance of wireless sensor node j to sink node.

As Fig. 1, ⊙ O ₁be with wireless sensor node i for the center of circle, maximum communication distance R is the border circular areas of radius, ⊙ O ₂be sink node be the center of circle, the border circular areas that d (i, s) is radius.⊙ O ₁region contains all neighbor nodes with wireless sensor node i direct communication, and ⊙ O ₂in fact eliminate i sends possibility from data (reverse transfers) to the node far away apart from aggregation node than oneself, ensure that the appearance not having loop.Two circle overlapping regions then contain the lower all possible down hop routing node of wireless sensor node i of routing optimization method of the present invention setting.

Described step 2 specifically comprises the following steps:

Step 2-1: the gross energy cost calculating forward direction neighbor node;

Step 2-2: the load factor calculating forward direction neighbor node.

The reasons such as consideration single-sensor node-node transmission is range limited, most of sensor node often can not directly communicate with base station, and needs to rely on all the other sensor nodes to adopt the mode forwarding data of multi-hop forwarding.For this reason, consider current sensor node to sink nodal distance, to neighbors distance and neighbor node to sink nodal distance three factors, build energy cost function, ensure that present node forwarding data bag is less to the energy consumed during next-hop node.

By the energy cost EC of forward direction neighbor node q _iqcalculate the gross energy cost EC of forward direction neighbor node q _{i, q, p}; Specifically comprise:

As Fig. 2, be positioned at packet that wireless sensor node i sends and want to arrive the neighbor node that sink node needs the i be forwarded to.At sink node, data are sent to forward direction neighbor node q and are equivalent to and are sent to imaginary node q '.For determining next-hop node, the energy cost EC of forward direction neighbor node q _iqbe expressed as:

${\mathrm{EC}}_{iq}=\frac{E_{tx}(l,d(i,q))}{d(i,s)-d(q,s)}---\left(4\right)$

In formula (4), d (q, s) represents the distance of forward direction neighbor node q to sink node, E _tx(l, d (i, q)) expression wireless sensor node i sends the energy that lbit data consume to forward direction neighbor node q, is expressed as:

$E_{tx}\left(l,d\left(i,q\right)\right)=\{\begin{array}{c}{\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{fs}d{\left(i,q\right)}^2,d\left(i,q\right)<d_0\\ {\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{mp}d{\left(i,q\right)}^4,d\left(i,q\right)\&GreaterEqual;d_0\end{array}---\left(5\right)$

In formula (5), l represents that wireless sensor node i is transferred to the bit number of forward direction neighbor node q; E _txelecrepresent the energy that transmitter circuitry process unit bit data consume; D (i, q) represents the distance of wireless sensor node i to forward direction neighbor node q; E _fsthe energy that large processing of circuit unit bit data consume transferred by expression free space model; E _mpthe energy that processing of circuit unit bit data consume is amplified under representing multipath attenuation model; d ₀represent distance threshold, and

So, according to the gross energy cost EC of formula (4) by forward direction neighbor node q _{i, q, p}be expressed as:

${\mathrm{EC}}_{i,q,p}={\mathrm{EC}}_{iq}+\stackrel{\&OverBar;}{{\mathrm{EC}}_{qp}}---\left(6\right)$

In formula (6), represent the estimation energy cost of forward direction neighbor node q forwarding data, be expressed as:

$\stackrel{\&OverBar;}{{\mathrm{EC}}_{qp}}=\frac{\underset{p\&Element;FN\left(q\right)}{\&Sigma;}{E}_{tx}(l,d(q,p))}{\left|FN\left(q\right)\right|}---\left(7\right)$

In formula (7), | FN (q) | represent the number of forward direction neighbor node, E _tx(l, d (q, p)) expression forward direction neighbor node q sends the energy that lbit data consume to down hop routing node p, is expressed as:

$E_{tx}\left(l,d\left(q,p\right)\right)=\{\begin{array}{c}{\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{fs}d{\left(q,p\right)}^2,d\left(q,p\right)<d_0\\ {\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{mp}d{\left(q,p\right)}^4,d\left(q,p\right)\&GreaterEqual;d_0\end{array}---\left(8\right)$

In formula (8), d (q, p) represents the distance of forward direction neighbor node q to down hop routing node p.

In described step 2-2, the load factor LI of forward direction neighbor node q _qrepresent, have:

${\mathrm{LI}}_q=\frac{Q_q}{Q_{max}}---\left(9\right)$

In formula (9), Q _qrepresent the queue length of forward direction neighbor node q in MAC layer buffering area, Q _maxrepresent the queue maximum length of forward direction neighbor node q in MAC layer buffering area.

Work as LI _qwhen=1, illustrate that the buffering area of this node is full, the new packet arrived can not be retained, and very easily causes buffer overflow, causes congested generation;

Work as LI _qduring <1, node buffering area current queue size is less, represents that the flow load of node is lighter, produces congested possibility less.Therefore, LI _qcan the loading level of response sensor node effectively.

Described step 3 specifically comprises the following steps:

Step 3-1: the transmission cost TC of forward direction neighbor node q _iqrepresent, have:

TC _iq＝αEC _i,q,p+(1-α)LI _q(10)

In formula (10), LI _qrepresent the load factor of forward direction neighbor node q, EC _{i, q, p}represent the gross energy cost EC of forward direction neighbor node q _{i, q, p}; α represents adjustment factor, and α >0; From the definition of transmission cost in formula, which depict the flow load degree of node-node transmission energy consumption and node.Parameter alpha plays the effect of the proportion of adjusting energy cost and load factor, for making the two reasonably merge, chooses the performance that suitable α directly affects routing algorithm.

Step 3-2: choose the minimum forward direction neighbor node of transmission cost as down hop routing node.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit; those of ordinary skill in the field still can modify to the specific embodiment of the present invention with reference to above-described embodiment or equivalent replacement; these do not depart from any amendment of spirit and scope of the invention or equivalent replacement, are all applying within the claims of the present invention awaited the reply.

Claims (6)

1., based on a traffic aware routing optimization method for transmission cost, it is characterized in that: said method comprising the steps of:

Step 1: the forward direction neighbor node determining wireless sensor node;

Step 2: the energy cost and the load factor that calculate forward direction neighbor node;

Step 3: the transmission cost determining forward direction neighbor node, and determine down hop routing node.

2. the traffic aware routing optimization method based on transmission cost according to claim 1, is characterized in that: in described step 1, and described forward direction neighbor node is the set of the down hop both candidate nodes of wireless sensor node; Specifically comprise the following steps:

Step 1-1: set up wireless sensor network; Specifically comprise:

1) N number of wireless sensor node is evenly deployed in the square monitored area that the length of side is L at random, and each wireless sensor node has unique identification, and has power control capabilities and be adjusted to maximum communication distance R;

2) sink node deployment is in fixed position, the periodic perception environmental information of wireless sensor node, and sends data to sink node by the routing mode of multi-hop;

Step 1-2: all forward direction neighbor nodes determining wireless sensor node; Specifically comprise:

Wireless sensor network non-directed graph G (V, E) represents, V represents the set of wireless sensor node, and E represents can wireless connections between the wireless sensor node of direct communication, are expressed as:

E＝{(i,j)|i∈V,j∈V∪{sink}}(1)

In formula (1), i, j represent wireless sensor node;

If the neighbor node of wireless sensor node i represents with m, neighbor node m forms neighbor node set N (m) and is defined as:

N(m)＝{j|j∈V,d(i,j)＜R}(2)

In formula (2), d (i, j) represents the distance between wireless sensor node i, j;

Choose the forward direction neighbor node q of wireless sensor node i from neighbor node set N (m) of wireless sensor node i, form forward direction neighbor node set FN (q), FN (q) is defined as:

FN(q)＝{j|d(j,s)＜d(i,s),j∈N(m)}(3)

In formula (3), d (i, s) represents the distance of wireless sensor node i to sink node, and d (j, s) represents the distance of wireless sensor node j to sink node.

3. the traffic aware routing optimization method based on transmission cost according to claim 2, is characterized in that: described step 2 specifically comprises the following steps:

Step 2-1: the gross energy cost calculating forward direction neighbor node;

Step 2-2: the load factor calculating forward direction neighbor node.

4. the traffic aware routing optimization method based on transmission cost according to claim 3, is characterized in that: in described step 2-1, by the energy cost EC of forward direction neighbor node q _iqcalculate the gross energy cost EC of forward direction neighbor node q _{i, q, p}; Specifically comprise:

The energy cost EC of forward direction neighbor node q _iqbe expressed as:

${\mathrm{EC}}_{iq}=\frac{E_{tx}(l,d(i,q\left)\right)}{d(i,s)-d(q,s)}---\left(4\right)$

In formula (4), d (q, s) represents the distance of forward direction neighbor node q to sink node, E _tx(l, d (i, q)) expression wireless sensor node i sends the energy that lbit data consume to forward direction neighbor node q, is expressed as:

$E_{tx}(l,d(i,q\left)\right)=\left\{\begin{array}{c}{\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{fs}d{(i,q)}^2,d(i,q)<d_0\\ {\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{mp}d{(i,q)}^4,d(i,q)\&GreaterEqual;d_0\end{array}\right.---\left(5\right)$

In formula (5), l represents that wireless sensor node i is transferred to the bit number of forward direction neighbor node q; E _txelecrepresent the energy that transmitter circuitry process unit bit data consume; D (i, q) represents the distance of wireless sensor node i to forward direction neighbor node q; E _fsthe energy that large processing of circuit unit bit data consume transferred by expression free space model; E _mpthe energy that processing of circuit unit bit data consume is amplified under representing multipath attenuation model; d ₀represent distance threshold, and

So, according to the gross energy cost EC of formula (4) by forward direction neighbor node q _{i, q, p}be expressed as:

${\mathrm{EC}}_{i,q,p}={\mathrm{EC}}_{iq}+\stackrel{\&OverBar;}{{\mathrm{EC}}_{qp}}---\left(6\right)$

In formula (6), represent the estimation energy cost of forward direction neighbor node q forwarding data, be expressed as:

$\stackrel{\&OverBar;}{{\mathrm{EC}}_{qp}}=\frac{\underset{p\&Element;FN\left(q\right)}{\&Sigma;}{E}_{tx}(l,d(q,p\left)\right)}{\left|FN\left(q\right)\right|}---\left(7\right)$

In formula (7), | FN (q) | represent the number of forward direction neighbor node, E _tx(l, d (q, p)) expression forward direction neighbor node q sends the energy that lbit data consume to down hop routing node p, is expressed as:

$E_{tx}(l,d(q,p\left)\right)=\left\{\begin{array}{c}{\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{fs}d{(q,p)}^2,d^2(q,p)<d_0\\ {\mathrm{lE}}_{txelec}+{\mathrm{lE}}_{mp}d{(q,p)}^4,d(q,p)\&GreaterEqual;d_0\end{array}\right.---\left(8\right)$

In formula (8), d (q, p) represents the distance of forward direction neighbor node q to down hop routing node p.

5. the traffic aware routing optimization method based on transmission cost according to claim 4, is characterized in that: in described step 2-2, the load factor LI of forward direction neighbor node q _qrepresent, have:

${\mathrm{LI}}_q=\frac{Q_q}{Q_{max}}---\left(9\right)$

In formula (9), Q _qrepresent the queue length of forward direction neighbor node q in MAC layer buffering area, Q _maxrepresent the queue maximum length of forward direction neighbor node q in MAC layer buffering area.

6. the traffic aware routing optimization method based on transmission cost according to claim 5, is characterized in that: described step 3 specifically comprises the following steps:

Step 3-1: the transmission cost TC of forward direction neighbor node q _iqrepresent, have:

TC _iq＝αEC _i,q,p+(1-α)LI _q(10)

In formula (10), α represents adjustment factor, and α >0; LI _qrepresent the load factor of forward direction neighbor node q, EC _{i, q, p}represent the gross energy cost EC of forward direction neighbor node q _{i, q, p};

Step 3-2: choose the minimum forward direction neighbor node of transmission cost as down hop routing node.