With the development of the micro-electro-mechanical system (MEMS) technology, wireless communication,. Routing in wireless sensor networks (WSNs) is very.
An improved ant-based routing protocol in Wireless Sensor Networks Ge Chen, Tian-De Guo, Wen-Guo Yang, and Tong Zhao Dept. ofMathematics, Graduate University of Chinese Academy of Sciences, Beijing 100080, China tchengeO4, yangwg}@mails.gucas. ac. cn tzhaotong, tdguo}@gucas. ac. cn Abstract Routing in wireless sensor networks (WSNs) is very challenging due to their inherent characteristics of large scale, no global identification, dynamic topology, and very limited power, memory, and computational capacities for each sensor. Recent research on WSNs routing protocol has proved that data-centric technologies are needed for performing in-network aggregation of data to yield energy-efficient dissemination. As an effective distributed approach, Ant Colony Optimization (ACO) algorithms have been introduced to the design of data-centric routing protocol and have got many achievements, but still have some shortcomings blocking their further application in the large scale WSNs. To overcome theflaws of conventional ant-based data-centric routing algorithms, we proposed an improvedprotocol by adding a new type of ant, search ant, to supply prior information to the following ants. Besides, we introduced the strategy of simulating global pheromone update to accelerate the convergence of our algorithm and defined a "retry" rule to avoid dead-lock of the protocol. All of these modifications made the routing protocol scalable, practicable and energyconservative. Simulation results showed the great advantages of the new protocol.
1. Introduction A typical sensor network consists of a large number of multifunctional sensor nodes which are equipped with sensing, information collecting, processing, and communicating components. These sensor nodes can be scattered randomly and need not be pre-determined in the remote terrains or even many dangerous relief such as craters and cataclysm regions, and the sensing elements embedded in the sensor nodes can detect ambient conditions such as sound, light, temperature, smell or vibration, and then transform them into electric signals. By analyzing the signals, we can get much information of the location where event happened under the collaborative effort of the sensors, since the sensors can either communicate among each other or directly with a
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1-4244-0429-O/06/$20.OO ©2006 IEEE
With the development of the micro-electro-mechanical system (MEMS) technology, wireless communication, materials, and digital electronics, wireless sensor network has attracted tremendous interests in the research communities and has been applied in the area of environmental monitoring, military security, disease surveillance, hearth care, and so on [1][2]. However, there are many obstacles baffling the application of WSNs, although researchers and engineers have proposed and implemented many protocols for the conventional wireless ad-hoc networks. Main reason relies on the inherent characteristics that distinguish these networks from other wireless networks [1]. Among all the challenges encountered by WSNs, routing issue is one of the problems that significantly block the scalability, robustness and energy-efficient. So the design of routing protocol for large scale network is very difficult for the following reasons: * Due to the extremely low cost of the sensors, it is not unrealistic to scale the WSNs to be composed of hundreds of thousands of sensors. However, scalability causes another problem that it is difficult to address such large number of nodes globally. Traditional end-to-end communication IP theme which base on unique identification failed, so we need to work out new routing protocols to improve the system performance that don't need to identify *
the sensor nodes by their identifications.
Because of the limitation of energy and storage capability in each sensor, we should reduce the redundant activities. For the specific task, we need to choose the proper subset of the nodes to invoke so as to save the battery energy which is the key factor of the lifetime of the whole network. By the way, implosion problems should be avoid when flooding protocols lead types of data transmitted to the same
destination.
WSNs are usually dynamic and self-organized mainly because of two reasons. Firstly, the nodes are deployed static or mobile. Secondly, the limited power supply and environmental change lead to the frequent update of the system. So we need a stable routing protocol that can solve these problems. In order to overcome these challenges mentioned above, a great many of implementation and proposal such as data aggregation, which can be applied for eliminating *
redundancy and minimizing the hop numbers so as to save energy, have been made but still far from the requirement of the widely application of WSNs. The technical paradigm of data aggregation shifts the routing protocol research focus from address-centric routing to data-centric routing [4] (we will give the detail in the follow section). In this paper, we focus on a new datacentric routing algorithm and have great improvement in and scalability, practicability energy-efficiency comparing to the former similar algorithms. The rest of the paper is organized as follows. Some background knowledge on Ant Colony Optimization (ACO) algorithms and data-centric routing algorithms is reviewed in Section 2. Then our routing protocol is presented in Section 3. Section 4 demostrates its simulation results. Some concluding remarks and future works are summarized in the last section.
colony algorithms were first proposed by Dorigo et al [7] as a multi-agent approach to difficult combinatorial optimization problems like the traveling salesman problem (TSP) and the quadratic assignment problem (QAP), and later introduced the ACO meta-heuristic [8]. ACO algorithms are a class of constructive meta-heuristic algorithms that mimic the cooperative behavior of real ants to achieve complex computations and have been proven to be very efficient to many different discrete optimization problems. Many theoretical analyses related to ACO show that this optimization can converge to the global optima with non-zero probability in the solution space [9] and their performance have greatly matched many well-studied stochastic optimization algorithms, for example, genetic algorithm, pattern search, GPASP, and annealing simulations [14]. Due to the intrinsic characteristics of the algorithms, such as distribute computation, stochastic search and
collaborate cooperation, ACO is particularly suitable for
2. Related work
large scale self-organize system and exceeds the traditional metrics in three aspects: scalability, robustness Recently, researchers in the fields of WSNs attempt to and suitability for dynamic environment [13]. Based on apply data-centric routing rather than address-centric the aforementioned advantages, ACO algorithms have routing which is more suitable for traditional networks. gained popularity in network-based applications. The Generally, we can formalize the data-centric routing in earliest such applications were ABC and CAD, which WSNs as follows: * The network is represented by a weighted graph G assumed symmetric costs across links and were useful in telephone and packet-switching networks [15][ 16][ 17]. (V, E) where V is the set of nodes in which each AntNet algorithm was very successfully applied to routing node stands for a sensor and E is the set of edges in packet-switching network later that consider the case An edg (.) in E indicates that node i and jc jcan of asymmetric costs [18][19][20]. Other similar commnicate directly he i approaches have begun to make their appearance. th Das et al have given an on-line ACO algorithm to D *i AntNet using techniques for MSDC [10] which has been Centri ing a Centric rotng SC to be a typically Minimum Steiner Tree which is composed of some source nodes s and a single dstinatin (sink)node d.problems. There are two types of ants applied in the algorithms, forward ants and backward ants. Forward breaksout,the rasot h exploring thee path and ants, a nts whose main actions are source nodes collect the information and then send it to are from the source nodes to the information the sink node. In this case, we neglect the diversity of the data which means that all sensors send the same type of same asth urcea nodes. The paths that forward ants travel will construct information,~~~~~~~~~~~ so daafso.a ~epromdi h tree when they merge into each other or reach the no Unerth p destination and data is transmitted along the tree paths. There are two key factors that conduct the movement of problem to construct an optimal tree of which every tefradat:oei hrmn rista r source nodes and the destination node are the leaf nodes. theforward e is is tha This typical Steiner Tree problem was first proposed by deposited along the edges, and the other is the nodes' potential which provides an estimate of how far an ant Krsnmcaie*l[] hofraie n vlae will have to travel from any the node to either reach the s for generating aggregation optimization schemes destination d or to aggregate data with another node. While the backward ants, traveling back from destination node to source nodes to the forward ants, sprea butth WS gasned great popularity in the and spINa[7], perform their uppermostcontrary function of updating the
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deliverer, whose main task is to dissipate information gathered at the nodes among other neighboring nodes. Comparing their algorithms with standard addresscentric routing, it is obviously that more co-operations adopted in these metrics, though all of them support distributed computation. This collaborating work means data aggregation or data fusion. Practically, simulation results also show that their algorithms are significantly better than address-centric routing. But both of the two algorithms have several shortcomings as follows: * The forward ants normally spend a long time to reach the destination in the early iterations of each algorithm. Because the MSDC model have only one destination node and the AntNet system also lack the useful heuristic information in the beginning of each algorithm, the forward ants are hard to find the destination. * There is a bug of dead lock in their algorithms. To prevent ants' travel from forming cycles, each forward ant can't move to the nodes that it has visited. So if one forward ant locates in a sensor node all of whose neighboring nodes have been visited by this ant, it will have no choice for next movement. There is other even worse instance that in their algorithms one ant must merge itself into other ants' paths if it reaches a node that already visited by other ants. But other ants probably arrive at the nodes that ant visited and must follow that ant' trail, which could form a complicated cycle involving several ants. Both of these two circumstances result in their algorithms dead-lock for the reason of some forward ants can't reach the destination. So in their simulative experiments, they selected 20% of the nodes as destination nodes to reduce the probability of dead-lock happening. It will greatly increase the cost of the network and not be in accord with the definition of MSDC. * In their improved algorithm, a large amount of random ants (almost equal to 5%-01.0% of total nodes number) are needed. The experiments result implied that the more random ants' amount will result in better routing path. However, a large quantity of random ants really spends much energy of sensor networks, which conflicts with the main principle of eliminating the redundant energy. In fact, their algorithms are not practicable for real wireless sensor networks, especially for large scale WSNs.
In the following paper, we propose the advanced
algorithm for MSDC which can resolve those problems well and can rapidly find a good routing path just spending very low energy. Furthermore it can be applied to large scale WSNs.
3. Algorithm description
This section describes our on-line ACO algorithm for MSDC. In this paper, we denote the communicating cost between node i andj is the Euclidean distance dist (ij) if they can communicate with each other directly. We define an iteration whose duration is from the forward ants starting their travels to the backward ants reaching the source nodes. The algorithm consists of multiple iterations. To accelerate the convergence of the algorithm, an elitist strategy is used to update pheromone trail such that only the iterations that have produced the least cost tree are allowed to update pheromone trail. We define such elitist iterations as available iterations. There are three kinds of ants in our algorithm: search ants, forward ants and backward ants. Search ants travel from the destination node and add pheromone concentration on the paths which they have passed by so as to supply some prior knowledge to the forward ants. The aim of search ants is to make forward ants finding the destination node easily. They are applied in the beginning of the algorithm and have maximum life value MaxLife. Forward ants have the same function as those in Sanjoy Das et al's algorithms, excluding that they must vaporize pheromone in our paper. Backward ants travel back to the source nodes from the destination node to update the information in each sensor node which they have visited. In the absence of any global processing, all sensor nodes just know their own information and all parameters have to be stored locally in the nodes. In the algorithm, each sensor node i contains one table: the pheromone trails ir , whose size is equal to 1 nbd 11 , the number of sensors in the neighborhood of i (assumed to be known a .. th , p hero the t pheromone concentration from the trail leading form i to a neighboring sensor j, which is initialized to a predefined value -co. Each sensor node also maintains such variables: tag,, initialized to zero, records the amount of forward ants which have visited i in an iteration; next, is . . . . n l a i pheromone update just using local information, records the total available iteration number before the last pheromone evaporation in i. All the parameters stored in each node can be updated only on the time of some ants visiting this node.
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3.1. Search Ant Movement The amount of search ants should not be larger than the number of the neighboring nodes of d and they begin travel from this node. Each such ant 1 contains a tabu list T,and two variables life' and scost' . The list T'
contains the nodes already visited and is initialized to
null, while life' indicates the ant current life and scost' * from d to the node . records the distance visited currently. Both of the two variables are initialized to zero. In order to avoid the traveling paths of search ants forming cycles that may add the probability of forward ants running in cycles, search ants can't move to the nodes visited by any search ant. So when ant I arrives at i, it should examine its tabu list T' whether including its neighboring nodes ndb1. If it is true, ant I will terminate the search task. Otherwise ant I will randomly select node j from ndbk - T. Then ant I moves to j. Ant I also need check the pheromone trails in j to judge whether there is any other search ant has visited this node, and then the pheromone concentration from j to i is added by SQ , where SQ is a constant. If some search
scost' + dist(i, i) ants have visited this node, ant I should travel back to i and select next node again. Otherwise, the ant should add scost' by dist(i, j) and life' by one, then start its next movement from j until its life is exhausted. We outline the movement of search ant as follow: SearchAnt(d, 1)
while(lide' . MaxLife) { push(, I(-); ) if(ndbr e Tk break;
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two constants of the algorithm. Then ant m moves to j. If tagi isn't zero, the cost of the path from i to j is added to pCostm . This process will be repeated until ant m arrives at d. We formalize the algorithm for the movement of forward ants as follow:
i = J;
T'.++
kendb
where
scostl + dist(i, j)
lifel ++; T'.
by pi, -(-(4k)/dist(i,k) dist(i, k),8 ~~~~~~~~~~~~ ~~ ~
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