Due to intrinsic properties of aqueous environments, routing protocols for underwater wireless sensor network (UWSN) have to cope with many challenges such as long propagation delay, bad robustness, and high energy consumption. Basic ant colony optimization algorithm (ACOA) is an intelligent heuristic algorithm which has good robustness, distributed computing and combines with other algorithms easily. But its disadvantage is that it may converge at local solution, not global solution. Artificial fish swarm algorithm (AFSA) is one kind of intelligent algorithm that can converge at global solution set quickly but has lower precision in finding global solution. Therefore we can make use of AFSA and ACOA based on idea of complementary advantages. So ACOA-AFSA fusion routing algorithm is proposed which possesses advantages of AFSA and ACOA. As fusion algorithm has aforementioned virtues, it can reduce existing routing protocols’ transmission delay, energy consumption and improve routing protocols’ robustness theoretically. Finally we verify the feasibility and effectiveness of fusion algorithm through a series of simulations.
Rapid evolvement of wireless communication technology, electronic technique, sensor technology, micro-electro-mechanical system, and other computer technologies promotes research in wireless sensor network. Underwater wireless sensor network (UWSN), one kind of special and promising wireless sensor network, gains more and more attention. With the development of ocean exploitation, UWSN is becoming one of hot topics in research fields. UWSN is the development trend of future underwater communication and detection techniques, for instance, seabed resource detection and exploration, oil spill, tsunami, underwater earthquake, and underwater environment monitoring. Therefore we can say that marine development degree determines future world development degree while UWSN determines marine development.
Electromagnetic wave and light wave are not suitable for underwater communication since their signals can be absorbed by water. As wavelength of acoustic wave is long enough and it is cost effective, acoustic communication is the only ideal medium for underwater information transmission. That is to say, acoustic communication is the best communication style for UWSN to date [
Ant colony optimization algorithm (ACOA) is one kind of heuristic bionic algorithm based on ant colony finding its way in the population’s foraging process. ACOA algorithm is parallel algorithm in essence that has the following features: robustness, universality, fast convergence, easiness of combining with other algorithm, and so forth [
The remainder of the paper is organized as follows. In Section
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The aforementioned researches are closely related with sensor network routing and its algorithms. As characteristics of subaqueous environments, routing algorithms with short propagation delay, good robustness, low energy consumption, and high network throughput can be really challenged for UWSN. To address the above-mentioned challenges, we propose one hybrid ACOA-AFSA routing algorithm for underwater wireless sensor network.
Ant will leave pheromones in their passing roads so that other ants can find the previous ants easily. What is more, ant is going to take the path which has higher pheromones, left by other ants, than other pathways nearby. So ant colony can find their end readily with the help of pheromones’ positive information feedback. That’s to say, although ant is nonintelligent species, activities of ant colony represent living intelligent. Advantages of ACOA are self-organization, distributed process, positive feedback, and good robustness. Thus ACOA is suitable for solution of NP-hard problems like routing protocol in UWSN. But we notice that ACOA may converge at local optimal solution instead of global optimal solution sometimes.
Fish can find specific areas that are rich in nutrients in their living waters by themselves or tagging along with other fish quickly. Thus the area that has the greatest number of fish is the most nutritious area in the water. According to the characteristics, artificial fish swarm algorithm is proposed by Xiaoli [
Basic idea of the fusion algorithm is that fusion algorithm takes artificial fish swarm algorithm as subject, and it introduces idea of ant colony optimization algorithm. So ant colony algorithm takes advantage of AFSA’s speediness and global solution to fulfill quick convergence and get global solution. At the same time ACOA covers disadvantages of AFSA. As ACOA has strong ability of positive feedback for ants search for pheromones left by previous ants fully, every next state of ACOA will be better than its current status. Such superiority can cover disadvantage of AFSA effectively. With ACOA’s calibration, AFSA can modify its routing path more slightly and accurately.
AFSA can get optimal solution domain quickly with lower precision. Meanwhile an important feature of ACOA is that the algorithm makes use of pheromones’ positive feedback to choose optimal solution. Thus based on the idea of offsetting each other’s weakness, ACOA-AFSA fusion algorithm is proposed which can converge at optimal solution effectively and quickly. To summarize the realization of the proposed fusion routing algorithm, fish swarm tries to find routing path through data delivering from source node to destination node. And each node in the routing path then compares its energy, path length with nearby nodes with the assistance of ACOA algorithm. Algorithm’s basic steps are as follows while its flow diagram is shown in Figure
Fusion algorithm’s flow diagram.
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Similarly the two conditions indict that
So the same work is done to improve environment consistence like foraging activity and clustering activity. At last AFSA updates content of bulletin board.
In formula (
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Usually UWSN consists of many underwater nodes for monitoring underwater environments. But characteristics of underwater environments, such as long propagation delay, multipath interference, and limited channel width, decrease UWSN’s network performance. Nodes and network link may join in UWSN or be unusable at any time. Namely, protocols of UWSN may have bad robustness, higher energy consumption, longer transmission delay, lower throughput of network, and so forth.
Thus efficient routing protocol algorithm for UWSN should resolve the aforementioned deficiencies effectively. The conspicuous characteristics of efficient routing algorithm are that the algorithm has autocatalysis and positive feedback mechanism. Fusion algorithm proposed in this paper is the very algorithm that possesses the aforementioned characteristics. As fusion algorithm is based on ant colony algorithm and artificial fish swarm algorithm, the fusion algorithm has advantages of both algorithms like positive feedback mechanism, self-adaptive mechanism, converging at global solution quickly, and good robustness.
Fusion algorithm’s self-adaptive mechanism enhances robustness of UWSN’s routing protocol. In this case routing protocol will not be affected if partial nodes become invalid or some network links are disabled suddenly. Thus robustness of routing protocol is strengthened. What is more, the positive feedback mechanism makes message routes from source node to destination node in shortest path. That means overhead of information routing will be decreased naturally. The more nodes deployed in acoustic environment and further distance that message need to be delivered, the more overhead will be saved. Taking all the aforementioned contents into consideration, we can employ the fusion algorithm to improve UWSN’s network robustness, reduce its energy consumption and propagation delay. Because of reasons given above, we will use ACOA-AFSA fusion algorithm for finding optimal link in UWSN.
For simplicity, we give communication links in protocol table of UWSN as shown in Figure
Topology link of UWSN.
We ran series of simulations to evaluate performance of the proposed algorithm by comparing with other popular algorithms which were discussed in Section
The simulation experiments were realized through C++ to verify feasibility and validity of the proposed algorithm. The compared schemes during the simulations included the proposed ACOA-AFSA fusion algorithm, a representative clustering algorithm called low-energy adaptive clustering hierarchy (LEACH) [
We compared the three algorithms in the following aspects. (1) energy consumption: we compared energy consumption in the same application and condition, such as same nodes, same velocity, same packets need to be delivered. (2) Loss ratio of data packets: we got each algorithm’s packets loss probability through quantity loss of the sent packets and received packets between same source node and destination node in same application and condition, such as same nodes, same velocity, and same packets that need to be delivered. (3) Propagation time and delay: propagation time and delay were measured by transmission time of data packets from same source node and destination node in the same application and condition such as same nodes, same velocity, same packets need to be delivered.
Figure
energy consumption versus data receive at sink node.
Total data packets received at sink node.
Figure
Packet dropping rate versus remaining energy.
We have also conducted the simulation to observe the average delay over number of nodes, namely, the relationship between network scale and average delay. VBF protocol is kind of UWSN routing protocol that performs well while scale of network is small. The major reason is that VBF protocol empowers most nodes in network to transfer data with less data retransmission if UWSN is dense network. As nodes’ number increases, the protocol’s shortage of data retransmission handicaps network’s normal operation of data receiving and delivering seriously. Simulation result in Figure
Average transmission delay versus number of nodes.
In this paper, a novel ACOA-AFSA fusion algorithm for UWSN routing protocol has been presented. It is a useful routing algorithm for underwater sensor networks owing to its local acknowledge and global view offered by ACOA and AFSA, respectively. The proposed algorithm also introduces a self-adaptive mechanism to fuse such two algorithms for searching better routing path. ACOA algorithm’s parallel feature makes routing search in proposed protocol quickly. As AFSA algorithm finds candidate optimal routing path roughly in the fusion algorithm for optimizing and ACOA algorithm helps AFSA to calibrate routing path, fusion algorithm’s robustness is stronger than VBF and LEACH. The fusion algorithm’s global view and local optimal routing path makes its energy consumption for data transmission and reception more efficiently. With the increment of nodes and iteration number, advantages of swarm intelligence play greater role in finding best transmission route as well. The whole train of simulations proves ACOA-AFSA routing algorithm outperforms VBF and LEACH marginally in the way of energy consumption, packet loss rate, and delay. For future research work in the field, we will devote our efforts to the optimal tuning of parameters in ACOA and AFSA algorithm to realize the optimal performance in underwater wireless sensor network’s data routing.
This paper was supported by Foundation of Shanghai Maritime University (20110028), Shanghai Science and Technology Committee Program (11PJ1404300, 09170502000), Shanghai Leading Academic Discipline Project (S30602), and a Grant from the National Natural Science Foundation of China (40801174).