Wireless sensor networks (WSNs) are wireless communication systems integrating sensor technology, embedded computing, distributed processing, and network communication technology, which have wide applications in military, medicine, environment, biology, and industry. Sensor nodes are usually powered by non-rechargeable batteries; so energy consumption is one of the most concerned topics in the network design. Routing algorithm and sink node placement algorithm are two important factors which have direct impacts on energy consumptions in WSNs. According to the residual energy distribution in the networks, an adaptive routing protocol is designed and the appropriate placement of the sink node is selected to reduce energy consumption and prolong network lifetime effectively.Based on the characteristics of WSNs, we propose an ant routing algorithm and investigate the sink node placement algorithms in the networks with randomly distributed nodes. The major contributions in this paper are as follows:(1) This paper first analyzes the characteristics of energy consumption of the routing in WSNs and proposes a routing-cost based ant routing algorithm. The algorithm ensures that the networks choose a shorter path to transmit data. Furthermore, it can make a detour to avoid the sensor nodes and sensing a region with low residual energy, so that the energy distribution can reaches a balance in the networks. The simulation results show that, compared with other construction methods, the algorithm constructed by routing-cost adapts very well to WSNs with sensor nodes in a uniform or non-uniform random distribution. It can both reduce energy consumption and improve the energy balance in WSNs. Therefore the network lifetime can be prolonged effectively.(2) An energy-oriented placement algorithm and a lifetime-oriented placement algorithm are investigated in the single-hop WSNs. From the first-order radio model, we find that in the energy-oriented placement algorithm the optimal placement of the sink node is the place which minimizes the secong-order moment of the distance between the source nodes to the sink node, and particularly, the optimal placement is the center of gravity of the convex sensing region. This position minimizes the longest communication distance in the networks in the lifetime-oriented placement algorithm. Simulation data verify the accuracy of the simulation results.(3) This paper then investigates an energy-oriented placement algorithm and a lifetime-oriented placement algorithm in the multi-hop WSNs in a convex region. In a multi-hop network, the energy consumption on the route can be estimated by the distance between the source nodes to the sink node. In the energy-oriented placement algorithm, the best placement of the sink node is on the position which minimizes the expectation of the total distance between all sensor nodes to the sink node. In the lifetime-oriented placement algorithm, we not only consider the total distance but also take the sensor density near the sink node into account. Therefore the sensor nodes in the area which consume energy fastest have the longest lifetime. Simulation results show that the networks with a lifetime-oriented algorithm consume energy faster, but have a longer lifetime.(4) Finally the paper briefly studied the placement algorithms of the multi-hop WSNs with non-convex sensing region. The non-convex region is first divided into grids. We then use the shortest effective grid-distance instead of the Euclidean distance to estimate the energy consumption on the route. An iterative algorithm is used to calculate the total distance between all grids to the sink node and is eventually embedded into the sink node placement algorithm so that we can find the optimal placement. The algorithm reduces the computational complexity in the process of finding the shortest distance. Compared with the algorithm based on the Euclidean distance, the algorithm proposed in this paper has a smaller error. We simulated the networks with the energy-oriented and the lifetime-oriented placement algorithms based on the two distance-calculation methods, respectively. The results show that the algorithm based on the shortest effective grid-distance can reduce network energy consumption and prolong network lifetime. |