Intelligent Deployment Model for Target Coverage in Wireless Sensor Network

Target coverage and continuous connection are the major recital factors for Wireless Sensor Network(WSN).Several previous research works studied various algorithms for target coverage difficulties;however they lacked to focus on improving the network’s life time in terms of energy.This research work mainly focuses on target coverage and area coverage problem in a heterogeneous WSN with increased network lifetime.The dynamic behavior of the target nodes is unpredictable,because the target nodes may move at any time in any direction of the network.Thus,target coverage becomes a major problem in WSN and its applications.To solve the issue,this research work is motivated to design and develop an intelligent model named Distributed Flexible Wheel Chain(DFWC)model for efficient target coverage and area coverage in WSN applications.More number of target nodes is covered by minimum number of sensor nodes that can improve energy efficiency.To be specific,DFWC motivated at obtaining lesser connected target coverage,where every target is available in the monitoring area is covered by a smaller number of sensor nodes.The simulation results show that the proposed DFWC model outperforms the existing models with improved performance.

Reliable and Energy Efficient Target Coverage for Wireless Sensor Networks

A critical aspect of applications with Wireless Sensor Networks （WSNs） is network lifetime. Power-constrained WSNs are usable as long as they can communicate sense data to a processing node. Poor communication links and hazardous environments make the WSNs unreliable. Existing schemes assume that the state of a sensor covering targets is binary： success （covers the targets） or failure （cannot cover the targets）. However, in real WSNs, a sensor covers targets with a certain probability. To improve WSNs＇ reliability, we should consider that a sensor covers targets with users＇ satisfied probability. To solve this problem, this paper first introduces a failure probability into the target coverage problem to improve and control the system reliability. Furthermore, we model the solution as the a-Reliable Maximum Sensor Covers （a-RMSC） problem and design a heuristic greedy algorithm that efficiently computes the maximal number of a-Reliable sensor covers. To efficiently extend the WSNs lifetime with users＇ pre-defined failure probability requirements, only the sensors from the current active sensor cover are responsible for monitoring all targets, while all other sensors are in a low-energy sleep mode. Simulation results validate the performance of this algorithm, in which users can precisely control the system reliability without sacrificing much energy consumption.

Theoretical Treatment of Target Coverage in Wireless Sensor Networks

The target coverage is an important yet challenging problem in wireless sensor networks, especially when both coverage and energy constraints should be taken into account. Due to its nonlinear nature, previous studies of this problem have mainly focused on heuristic algorithms; the theoretical bound remains unknown. Moreover, the most popular method used in the previous literature, i.e., discretization of continuous time, has yet to be justified. This paper fills in these gaps with two theoretical results. The first one is a formal justification for the method. We use a simple example to illustrate the procedure of transforming a solution in time domain into a corresponding solution in the pattern domain with the same network lifetime and obtain two key observations. After that, we formally prove these two observations and use them as the basis to justify the method. The second result is an algorithm that can guarantee the network lifetime to be at least （1 - ε） of the optimal network lifetime, where ε can be made arbitrarily small depending on the required precision. The algorithm is based on the column generation （CG） theory, which decomposes the original problem into two sub-problems and iteratively solves them in a way that approaches the optimal solution. Moreover, we developed several constructive approaches to further optimize the algorithm. Numerical results verify the efficiency of our CG-based algorithm.

Optimizing Adjuvant Radiation Planning Outcomes in Patients with Synchronous Bilateral Breast Cancer

Background: Breast cancer is the most common cancer diagnosed worldwide, synchronous bilateral breast cancer accounts for unique entity of the disease, particularly post-operative radiotherapy for Synchronous Bilateral Breast Cancer (SBBC) is challenging with lack of evidence about the best irradiation technique. In this study, we tried to explore the optimum radiotherapy technique regarding the dosimetric parameters. Methods: We recruited 15 SBBC patients in whom post-operative radiotherapy was indicated and we established three plans for each patient using 3DCRT, IMRT and VMAT, and then we compared the three plans as regard target volume coverage parameters and organs at risk (OAR) doses. Results: We found that PTV coverage parameter was superior with IMRT compared with 3DCRT and VMAT in terms of Dmean (p = 0.001), D95% (p = 0.001), Dmax (p = 0.0001), conformity index (p = 0.0001) and HI (p = 0.0001). Doses to OAR were not significantly different between the three techniques in cardiac dose and LAD maximum dose, but 3DCRT was superior in LAD mean dose (p = 0.03) and lung volume receiving 20 Gy (V20) and 10 Gy (V10) (p = 0.0001), but this difference was non-significant between 3DCRT and IMRT (p = 0.4 and 0.06 respectively), while VMAT led to the highest doses to LAD and lung. Conclusions: IMRT showed the best target coverage parameters in post-operative radiotherapy for SBBC compared with 3DCRT and VMAT. For OAR doses IMRT showed comparable results with 3DCRT, while VMAT delivered a significantly higher dose to OAR.