coverage

The successful operation of a wireless sensor network depends on the proper coverage of the environment, which in turn is affected by the number and location of sensors. In general , when the sensors are deployed randomly , the initial coverage is not high . One of the major challenges for network design is to determine the placement strategy of the sensors so that the deployed nodes can cover as m any regions as possible. On the other hand, the power supply of each sensor node is a non - rechargeable battery. Th erefore, t he objective of this study is to solve th e coverage problem in such a way that the energy consumption of the nodes is minimal , too . The proposed approach uses division and detection of uncovered regions. Then a greedy method based on the topology and properties of the nodes and the network deployment region is presented to select the optimal nodes and cover the region. The proposed approach is simulated and the evaluation results show a decrease in the displacement of the sensors for more coverage and a reduction in energy consumption compared to similar works.

In this paper, a fuzzy multi-objective model is presented to select and allocate the order to the suppliers in uncertainty conditions, considering multi-period, multi-source, and multi-product cases at two levels of a supply chain with pricing considerations. Objective functions considered in this study as the measures to evaluate the suppliers are the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects. Partial and general coverage of suppliers in respect of distance and finally supplier's weights make the amounts of products orders more realistic. Deference and coverage parameters in the model are considered as uncertain and random triangular fuzzy number. Since the proposed mathematical model is NP-hard, multi-objective particle swarm optimization (MOPSO) algorithm is presented. To validate the performance of MOPSO, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms. A practical case study in an agricultural industry is shown to demonstrate that the proposed algorithm applies to the real-world problems. The results are analyzed using quantitative criteria, performing parametric, and non-parametric statistical analysis.

Wireless sensor networks(WSN) have unique properties that distinguished them from other wireless networks and have special challenges. Not-chargeable, not-changeable and limited power supplies of sensor nodes is the most important challenge of this networks, and if the power supply of node expired, a part of data maybe lost. Because of the importance of covers in wireless sensors, in this work we have presented approaches that in their designs nodes are in special and different areas so that the hole of environment be covered and for saving power consumption, in each active period only one node is active and others are in inactive. For implementing the task cycle and avoiding from over-load of one node, the active node which have the task of sensing and maintaining the cover of area, in the next period, another node will replace it. It seems that by zoning the environment and cycle of tasks, the overall cover of wireless network improves.

In this paper, we investigate the bi-objective grid patrol routing problem (BGPRP) in which multiple patrol vehicles have to cooperatively patrol several nodes for a given specific time during a certain planning horizon in a grid map. The BGPRP is an NP-hard problem and an extended application of periodic vehicle routing problem (PVRP). However, unlike PVRP, in addition to minimizing the total routing distance, the considered BGPRP also aims to maximize the routing coverage of lanes. The BGPRP involves both the combination and permutation of nodes simultaneously. In this paper, an efficient encoding scheme is developed to tackle both the combination and the permutation of nodes simultaneously, and then we apply an immune based evolutionary approach for solving the BGPRP with the objective of minimizing total routing distance. Finally, an integer programming approach is utilized to maximize the routing coverage of lanes. Numerical results of multiple patrol vehicles in a grid map show the performance of the approach.

In Wireless Sensor Networks (WSNs), localization algorithms could be range-based or range-free. The Approximate Point in Triangle (APIT) is a range-free approach. We propose modification of the APIT algorithm and refer as modified-APIT. We select suitable triangles with appropriate distance between anchors to reduce PIT test errors (edge effect and non-uniform placement of neighbours) in APIT algorithm. To reduce the computational load and avoid useless anchors selection, we propose to segment the application area to four non-overlapping and four overlapping sub-regions. Our results show that the modified-APIT algorithm has better performance in terms of average error and time requirement for all sizes of network with random and grid deployments. For increasing the accuracy of localization and reduction of computation time, every sub-region should contain minimum 5 anchors. The modified-APIT has better performance for different sizes of network for both grid and random deployments in terms of average error and time requirement. Variations of the size of a network and radio communication radius of anchors affect the value of average error and time requirement. To have more accurate location estimation, 5 to 10 anchors per sub-region are effective in modified-APIT.

Systems of computers and their application in the lives of modern human beings are vastly expanding. In any kind of computer application، failure in computer systems can lead to a range of financial and mortal losses. Indeed، the major origin of software failure can be located in designing or implementing software. With regard to these statistics، 30% of the software projects have been prosperous and successful. The proposed method is intended to reduce the cost and time of testing and it focuses on enhancing the efficiency of software testing methods. In this paper، we investigated the effect of slicing techniques on the reduction rate of testing cost and time. The results of experiments show that we can cover a large number of program instructions، branches and paths by a small number of test cases in the sliced program.