A typical feature of MANETs is that network topology is dynamically changed by node movement. When we execute state transition testing for such protocols, first we draw the Finite State Machine (FSM) with respect to each number of neighbor nodes. Next, we create the state transition matrix from the FSMs. Then, we generate test cases from the state transition matrix. However, the state transition matrix is getting much large because the number of states and the number of transitions increase explosively with increase of the number of neighbor nodes. As a result, the number of test cases increases, too. In this paper, we propose a new method to reduce the number of test cases by using equivalent division method. In this method, we decide a representative input to each state, which is selected from equivalent inputs to the states. By using our proposed method, we can generate state transition matrix which is hard to affect increasing the number of neighbor nodes. As a consequence, the number of test cases can be reduced.

This paper discusses the collection of sensor data for power distribution systems. In current power distribution systems, this is usually performed solely by the Remote Terminal Unit (RTU) which is located at the root of a power distribution network. The recent rise of distributed power sources, such as photovoltaic generators, raises the demand to increase the frequency of data collection because the output of these distributed generators varies quickly depending on the weather. Increasing data collection frequency in turn requires shortening the time required for data collection. The paper proposes the use of aggregation points for this purpose. An aggregation point can collect sensor data concurrently with other aggregation points as well as with the RTU. The data collection time can be shortened by having the RTU receive data from aggregation points, instead of from all sensors. This approach then poses the problem of finding the optimal location of aggregation points. To solve this problem, the paper proposes a Mixed Integer Linear Problem (MILP) formulation of the problem. The MILP problem can then be solved with off-the-shelf mathematical optimization software. The results of experiments show that the proposed approach is applicable to rather large scale power distribution systems.