CN108495321B - Wireless sensor network k-coverage algorithm under complex environment - Google Patents
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Abstract
The invention discloses a wireless sensor network k-coverage algorithm under a complex environment, which dynamically adjusts the working state of nodes in a target area by setting three strategies of sleep awakening, relay selection and mode switching of the nodes, and saves energy consumption under the condition of meeting the k-coverage condition. In the k-coverage algorithm of the wireless sensor network under the complex environment, more cheap static sensor nodes are randomly deployed, the state conversion mode of the nodes is established by adopting the wake-up algorithm, the energy consumption of each node in the network is balanced, and the structure of the data acquisition tree is modified by adopting the relay selection algorithm, so that the data transmission distance between the nodes is reduced, and the energy consumption of the sending node is saved. These measures maximize the K-coverage lifetime of the network. The experimental result shows that compared with other algorithms, the algorithm can effectively reduce the node energy consumption and improve the k-coverage survival time of the network to a greater extent.
Description
Technical Field
The invention relates to a k-coverage algorithm, in particular to a wireless sensor network k-coverage algorithm in a complex environment.
Background
Wireless Sensor Networks (WSNs) are composed of a large number of energy-limited sensor nodes, have been widely used in various fields of society, have the characteristics of flexible deployment, simple expansion, self-organizing networks and the like, and the performance of the wireless sensor networks depends on the monitoring quality and the survival time of the networks to a great extent. The monitoring requirements for the network may vary in different application areas. For example, in the fields of military frontier defense and field monitoring with complex environment, in order to obtain higher monitoring quality, a wireless sensor network must have good fault-tolerant capability and stability, and K-coverage needs to be implemented on a target area, that is, each monitoring grid point of the target area is at least sensed by K (K >1) sensor nodes.
At present, coverage algorithms for researching wireless sensor networks are more, such as CCP, DCOA, GAF, EECCP and the like, but most of the coverage algorithms are based on 1-coverage, the requirement for higher monitoring quality is not considered, and the defects of higher communication and calculation cost, shorter network survival time and the like exist. In the algorithm, a network must depend on external equipment such as a GPS (global positioning system), a directional antenna and the like, so that the potential safety hazard is increased undoubtedly, the energy consumption and hardware cost of the node are increased, and meanwhile, the problem of inaccurate positioning also exists. In addition, it is very difficult to achieve accurate deployment of nodes in strong interference and dangerous area conditions, and a k-coverage algorithm relying on a mobile sensor node has also been proposed in the prior art, however, when the target area includes obstacles and mud, the mobile node cannot move as expected, and the mobile node is much more expensive than the static node.
Disclosure of Invention
The invention aims to provide a k-coverage algorithm of a wireless sensor network in a complex environment. The scenario simulated by the algorithm is that the k-coverage of the whole target area is realized by scattering sensor nodes on a complex target area, and the maximization of the network survival time is realized.
The technical scheme is as follows:
a wireless sensor network k-coverage algorithm under a complex environment comprises 3 sub-algorithms of awakening, relay selection and mode switching, wherein:
bs: a sink node;
s.energy[tnow]current remaining energy of node s;
tnowthe current time;
c, energy consumption of the node s per second;
the method comprises the following specific steps:
1) after all nodes are initially deployed, the Bs determines an induction node set, a relay node set and a sleep node set, and determines a data collection tree through an awakening algorithm, a balanced boundary selection algorithm and a relay selection algorithm;
2) the Bs calculates the sleep time of all the sleep nodes according to the following formula;
wherein,
is the minimum consumption duration that the remaining energy of the node s can sustain;
3) the Bs informs all the nodes of the mode setting, the data collection tree and the next battery exhaustion time of each node in a flooding manner;
4) each node is switched to a designated mode, and a target node is set;
5) the network starts to work, and the energy of each node is gradually reduced along with the time;
6) at the next battery exhaustion moment, the sleep node finishes the sleep mode and prepares to monitor and listen to information from the Bs;
7) repeating the steps 1) to 6).
Further, the method specifically comprises the following steps:
the invention has the beneficial effects that:
in the k-coverage algorithm of the wireless sensor network under the complex environment, more cheap static sensor nodes are randomly deployed, the state conversion mode of the nodes is established by adopting the wake-up algorithm, the energy consumption of each node in the network is balanced, and the structure of the data acquisition tree is modified by adopting the relay selection algorithm, so that the data transmission distance between the nodes is reduced, and the energy consumption of the sending node is saved. These measures maximize the K-coverage lifetime of the network. The experimental result shows that compared with other algorithms, the algorithm can effectively reduce the node energy consumption and improve the k-coverage survival time of the network to a greater extent.
Drawings
Fig. 1 is an example of a wake-up process for implementing 1-overlay, where fig. 1(a) is an initial state, fig. 1(b) is a first round of node selection, fig. 1(c) is a second round of node selection, and fig. 1(d) is a selection end state;
FIG. 2 is 1-time to live as a function of number of nodes for coverage;
FIG. 3 is 2-time to live as a function of number of nodes for coverage;
figure 4 is the 3-coverage time-to-live as a function of the number of nodes.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to the following embodiments.
1 network model
Each node in the network has three modes of induction, relay and sleep, and switches the working mode of the node according to a time schedule set by a base station, and the node periodically induces surrounding environment data such as a moving object, temperature and humidity, illumination intensity and the like and transmits the data to a sink node (BS) in a multi-hop mode. S ═ S1,………,slDenotes a set of nodes, U ═ U1,u2,………}、V＝{v1,v2,………}、W＝{w1,w2… … … denotes a sensing mode, a relay mode, and a sleep mode node set, respectively, and vu ═ V ═ W ═ S. After network deployment, the sink node acquires the position information of all nodes, calculates the communication paths from all induction and relay nodes to the sink node, and broadcasts new path information to all induction and relay nodes through flooding. The algorithm adopts a node energy consumption model, and each node consumes energy when sensing data, receiving and transmitting data and sleeping in an idle state.
Trans(x,d)＝Eelec×x+Eamp×x×dn
(1)
Recep(x)＝Eelec×x
(2)
The relevant parameters are shown in table 1. (1) The formula represents the energy consumption required by the node when transmitting the data d (m) of x (bit), the value of n (n is more than or equal to 0 and less than or equal to 2) is determined by the antenna attribute, when the antenna is a directional antenna, n is 0, and when the antenna is an omnidirectional antenna, n is 2. (2) The formula represents the energy consumption required by the node to receive the data of x (bit).
Sens()＝Eelec×D+Esens
(3)
Listen(y)＝Elisten×y
(4)
Sleep(y)＝Esleep×y
(5)
The above expressions (3), (4) and (5) respectively represent energy consumption for sensing d (bit) data, energy consumption for listening to y (sec) time, and energy consumption for sleeping y (sec) time.
2 algorithm analysis
2.1 object definition
If a group of nodes is in the sensing mode for a long time, the energy of the group of nodes is exhausted quickly, and the working mode of the group of nodes must be dynamically switched. A schedule (schedule) is established for the algorithm to give the mode conversion mode of the single node. Let t0For initial deployment time of the network, tendIndicating the time at which network k-coverage terminates due to node energy depletion. When S is equal to S, t is equal to [ t ]0,tend]When the node s is in the working Mode, the Mode (s, t) is set to represent the working Mode of the node s at the time t, and the working Mode is set to be in the working Mode at the time t0,tend]The mode transition schedule of inner s is defined as
In combination with the above definitions, conditions can be found to ensure that the target area is k-covered:
wherein:
range in the above formula represents the sensing radius of the node s (s ∈ U), energy (t) represents the residual energy of the node s at time t, and s.pos and bs.pos represent the positions of the s node and the sink node.
Thus, the objective function is defined as: maximize (t)life)subjectto(6)
(7)
Wherein t islifeIndicating k-coverage lifetime, i.e. the time interval from the start of the network to the point where the condition (6) is no longer satisfied by any set of sensing mode nodes, the algorithm will depend on tlifeTo specify the time of each node s under the condition of satisfying the formula (6)Table schedule (s, [ t ]0,tend])。
2.2 target analysis
The algorithm solves three problems: (1) find satisfies condition (6) and let tlifeA maximized set of sense nodes. Since the sensing nodes perform tasks periodically, they consume more energy than the relay and sleep nodes; (2) how to determine the set of relay nodes after determining the set of sense nodes such that tlifeMaximization, some 'residual' nodes can reduce the transmission distance and the transmission data volume of key nodes, and the survival time of the whole network is prolonged; (3) after the set of sense nodes and the set of relay nodes are determined, how to determine the data collection tree such that tlifeAnd (4) maximizing. The energy consumption of all nodes on the tree must be balanced because nodes that are close to the sink node will consume more energy and are prone to premature "death". The above three problems are interrelated and will be solved by the following steps:
1) finding out the minimum sensing node set U meeting the condition (6)
2) And generating a data collection tree with the sink node as a root node, wherein the tree comprises all the sensing nodes U and part of the relay nodes (V is less than or equal to S-U). Maximizing the predicted lifetime of the network.
The network predicted lifetime is an approximate network lifetime when the node mode change is not considered, as shown in equation (9).
Wherein, tnowIs the current time, c(s) is the energy consumption of the node s per second, and the size of c(s) is defined by the formula (10), the formula (11), and the formula (12).
3) The next battery exhaustion time is a sleep period according to which the sleep node (W ═ S-U-V) is set.
4) At the next battery depletion time, stages (1), (2) and (3) are performed.
The next battery depletion time represents the energy depletion time of a single node, and is defined as shown in equation (10).
Wherein,
is the elapsed time that the remaining energy of node s can sustain.
When the node s belongs to U
C(s)＝I×(Sens()+Recep(D×s.desc)+Listen(1) +Trans(D×(s.desc+1),Dist(s,s.send))
(10)
When the node s is belonged to V
When the node s belongs to W
C(s)＝Sleep(1)
(12)
Where s.desc is the number of nodes in the subtree with s as root node except s in sensing mode, s.send is the destination node of data transmission of node s, Dist(s)1,s2) Is s1And s2The distance between them.
3 description of the Algorithm
The algorithm comprises three parts of awakening, relay selection and mode switching. The algorithm is executed at the initial deployment time and the next battery exhaustion time of each round, and when no sensing node set meets the condition (6), the survival time of the whole network is ended.
3.1 Wake-Up strategy
The awakening principle is as follows: the method comprises the following steps of selecting sleep nodes to become induction nodes one by one, searching a minimum set of induction nodes which can meet k-coverage of a target area, and specifically comprising the following steps:
1) initially all sensor nodes are treated as sleeping nodes.
2) Each sleeping node calculates the area size ca (contentionarea) left by subtracting the area already covered by k from its sensing range.
3) And selecting the sleeping node with the maximum CA to wake up to an induction mode, and randomly selecting one node when a plurality of nodes with the maximum CA values exist.
4) If there are no sleeping nodes, the algorithm terminates unconditionally.
5) If the entire target area is in k-coverage, the algorithm is terminated using the selected set of sensing nodes. Otherwise, go to step 2.
Fig. 1 shows a method for selecting a sensing node by a wake-up algorithm. The letters in the figure represent sensing nodes, the circles represent sensing ranges of the nodes, "a (65)" represents that the id of the node is "a" and the CA size is "65". FIG. 1(b) shows the results of the first round of the algorithm. By selecting node F as the sense node, the corresponding CA has been 1-covered (indicated by the gray circle), and then making other node selections, fig. 1(c) gives the result after the second cycle of the algorithm. In this case, node E and node J have the same maximum CA value of "66", node J is randomly selected to be the sense node, and FIG. 1(d) is the final result after the algorithm is finished.
3.2 Relay selection
The transmission distance and the data volume have great influence on the energy consumption of the nodes, and because the distribution of the nodes in the target area is uneven, and although some intervals can realize k-coverage, the time is very short, the invention adopts a balanced boundary selection algorithm to balance the data transmission volume of each node. In order to reduce the transmission distance, the invention adopts a relay selection algorithm, selects a relay node to modify a data tree generated by a balanced boundary selection algorithm, reduces the transmission distance of the node, saves energy consumption and improves the network survival time, and the algorithm is described as follows:
if at node s1(s1E to U and U V) and node s2(s2E U V)) is longer, then at s1And s2Is selected to be a distance s1Shortest sleeping or relay node srelay(srelayE.g. V.U.W) such that s1And srelayThe distance ratio s between1And s2The distance of (2) is short. As can be seen from equation (1), ins1And s2Passing through srelayReduces s1But if the objective function value is deteriorated after selecting a relay node, the selection of the relay node is abandoned.
3.3 mode switching
The main description of the algorithm is as follows:
1) after all the nodes are initially deployed, the Bs determines sensing, relaying and sleeping node sets, and determines a data collection tree through a wake-up algorithm, a balanced boundary selection algorithm and a relay selection algorithm.
2) The Bs calculates the sleep time of all the sleep nodes by the formula (9);
3) the Bs informs all nodes of the mode setting, the data collection tree, and the next battery exhaustion time of each node by means of flooding.
4) Each node switches to the designated mode and sets the target node.
5) The network starts to work and the energy of each node is gradually reduced along with the time.
6) At the next battery drain time, the sleep node ends the sleep mode and prepares to listen for information from the Bs.
7) Repeating the steps 1) to 6).
4. Simulation experiment
The invention takes NS-2 as an experimental platform, carries out 30 times of simulation experiments on the proposed k-overlay algorithm (deployed), and compares the k-overlay algorithm with a BalancedEdgelly algorithm, an LICA algorithm and an AASR algorithm under different conditions. Setting the size of a target region to be 50m multiplied by 50m, and setting sink node positions to be (50, 50); deploying 100, 200, 300, 400 and 500 nodes in a target area in a random distribution mode respectively; the coverage k takes the values of 1, 2 and 3 respectively; the remaining parameter settings are shown in table 1. The results of the experiments were averaged over 30 experiments, as shown in fig. 2, 3, and 4.
TABLE 1 simulation parameters
The experimental results show that: with the increase of the coverage degree k of the WSN, the network survival time of the four algorithms is shortened on the whole, but the advantages of the network survival time obtained by the algorithm are more obvious. Moreover, with the increase of the number of WSN nodes, the algorithm of the invention can obtain longer network survival time compared with the BalancedEdgeOnly algorithm, the LICA algorithm and the AASR algorithm. When 500 sensor nodes are deployed in a target area and the coverage k is 3, the network survival time of the algorithm is about 26.56% higher than that of the largest LICA algorithm in the other three algorithms. The awakening method adopted by the invention sorts the sensing range of the single sleep node which is not covered by k-in size, and selects the nodes to be switched in the mode from large to small, so that the energy consumption of all the nodes in the area can be balanced to the maximum extent, the energy premature exhaustion of some nodes due to the over-work can be avoided, and the awakening method is more effective than the random awakening method. Secondly, on the basis of utilizing a balanced boundary selection algorithm, the invention adopts a relay selection algorithm, reduces the data transmission distance of a single node and saves energy.
Experimental results show that the k-coverage time of the 4 algorithms is almost proportional to the number of deployed nodes, since the "remaining" nodes save energy through sleep mode before the energy of the sensing nodes is exhausted. In addition, in the experimental process, the calculation time required by the algorithm is very short, and when the number of the nodes is 600, the calculation time of the algorithm is at most 1.4 seconds, so that the calculation energy consumption of the sink nodes is directly saved, and the network efficiency is improved.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited thereto, and any simple modifications or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention are within the scope of the present invention.
Claims (1)
1. A wireless sensor network k-coverage algorithm under a complex environment is characterized in that,
the method comprises 3 sub-algorithms of awakening, relay selection and mode switching, wherein:
bs: a sink node;
s.energy[tnow]current remaining energy of node s;
tnowthe current time;
c, energy consumption of the node s per second;
wherein S ═ { S ═ S1,···,slDenotes a set of nodes, U ═ U1,u2,···}、V＝{v1,v2,···}、W＝{w1,w2The nodes in the induction mode, the relay mode and the sleep mode are respectively represented by · · · · · · · · · · · · · · · · · · · · · ·, and U ═ V ═ W ═ S;
Trans(x,d)＝Eelec×x+Eamp×x×dn (1)
Recep(x)＝Eelec×x (2)
formula (1) represents the energy consumption required by the node to transmit data d (m) of x (bit), the value of n (n is more than or equal to 0 and less than or equal to 2) is determined by the antenna attribute, when the antenna is a directional antenna, n is 0, when the antenna is an omnidirectional antenna, n is 2, and formula (2) represents the energy consumption required by the node to receive the data of x (bit).
Sens()＝Eelec×D+Esens (3)
Listen(y)＝Elisten×y (4)
Sleep(y)＝Esleep×y (5)
Equation (3), equation (4) and equation (5) represent energy consumption for sensing d (bit) data, energy consumption for listening y (sec) time, and energy consumption for sleeping y (sec) time, respectively;
the size of C(s) is defined by formula (10), formula (11) and formula (12);
when the node s belongs to U
When the node s is belonged to V
When the node s belongs to W
C(s)＝Sleep(1) (12)
Wherein s.desc is the number of sensing mode nodes except s in the subtree with s as the root node, s.send is the data transmission target node of the node s, Dist (s1, s2) is the distance between s1 and s 2;
wherein: eelecEnergy consumption per unit data processing, EampEnergy consumption per unit power amplification, EsensNode induced energy consumption, ElistenNode idle intercept energy consumption, EsleepNode sleep energy consumption, D data size and I induction frequency;
the method comprises the following specific steps:
1) after all nodes are initially deployed, the Bs determines an induction node set, a relay node set and a sleep node set, and determines a data collection tree through an awakening algorithm, a balanced boundary selection algorithm and a relay selection algorithm;
2) the Bs calculates the sleep time of all the sleep nodes according to the following formula;
wherein,
is the consumption duration that the remaining energy of the node s can be maintained;
3) the Bs informs all the nodes of the mode setting, the data collection tree and the next battery exhaustion time of each node in a flooding manner;
4) each node is switched to a designated mode, and a target node is set;
5) the network starts to work, and the energy of each node is gradually reduced along with the time;
6) at the next battery exhaustion moment, the sleep node finishes the sleep mode and prepares to monitor and listen to information from the Bs;
7) repeating the steps 1) to 6).
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