Earticle

Background: Radiation portal monitors (RPMs) involving plastic scintillators installed at the border inspection sites can detect illicit trafficking of radioactive sources in cargo containers within seconds. However, RPMs may generate false alarms because of the naturally occurring radioactive materials. To manage these false alarms, we previously suggested an energy-weighted algorithm that emphasizes the Compton-edge area as an outstanding peak. This study intends to evaluate the identification of radioactive sources using an improved energy-weighted algorithm. Materials and Methods: The algorithm was modified by increasing the energy weighting factor, and different peak combinations of the energy-weighted spectra were tested for source identification. A commercialized RPM system was used to measure the energy-weighted spectra. The RPM comprised two large plastic scintillators with dimensions of 174 × 29 × 7 cm3 facing each other at a distance of 4.6 m. In addition, the in-house-fabricated signal processing boards were connected to collect the signal converted into a spectrum. Further, the spectra from eight radioactive sources, including special nuclear materials (SNMs), which were set in motion using a linear motion system (LMS) and a cargo truck, were estimated to identify the source identification rate. Results and Discussion: Each energy-weighted spectrum exhibited a specific peak location, although high statistical fluctuation errors could be observed in the spectrum with the increasing source speed. In particular, 137Cs and 60Co in motion were identified completely (100%) at speeds of 5 and 10 km/hr. Further, SNMs, which trigger the RPM alarm, were identified approximately 80% of the time at both the aforementioned speeds. Conclusion: Using the modified energy-weighted algorithm, several characteristics of the energy weighted spectra could be observed when the used sources were in motion and when the geometric efficiency was low. In particular, the discrimination between 60Co and 40K, which triggers false alarms at the primary inspection sites, can be improved using the proposed algorithm.

Complexity and energy-efficiency are viewed as two main factors of design error correct coding for the wireless sensor networks. First of all, this paper presents a Weighted Hard-Input Hard-Output (WHIHO) iterative decoding algorithm with the low complexity when the required bit error rate (BER) is satisfied for wireless sensor nodes. A reliability matrix from four different aspects of the decoding process, properly weighs and combines their respective reliability measures, and then employs the combined measure to make a judgment with regard to whether each received bit is correct or not. Next, the energy efficiencies of turbo product codes using HIHO and WHIHO decoding algorithm are analyzed. Simulation results in a BER curve indicating that the WHIHO decoding algorithm can achieve a better tradeoff between reliability and complexity, which compared with conventional HIHO and soft-input and soft-output (SISO) decoding algorithms. The simplicity and effectiveness of WHIHO decoding algorithm makes it a promising candidate for the application for wireless sensor network.

Clustering problem is one of the significant issues for wireless sensor networks concerned with energy consumption and large-scale deployment. Several energy-efficient clustering algorithms have been proposed to improve the energy utilization efficiency and prolong the network lifetime. In this paper, we propose a new clustering scheme after a comprehensive analysis on existing protocols. In our algorithm, named WPCA (Weighted Probabilistic Clustering Algorithm), every node independently decides whether to be a cluster head according to a weighted probability, which is related to the ratio between node’s residual energy and average remaining energy. The nodes with more residual energy are assigned larger weight value to further increase the chances to be elected as cluster heads. In addition, the rotation procedure of cluster heads in previous algorithms is totally abandoned. Simulation results show that WPCA achieves longer lifetime than previous probabilistic-based clustering algorithms and gets a very close approximation compared with a deterministic clustering method.

In this paper, we present a virtual laboratory platform (VLP) baptized Mercury allowing students to make practical work (PW) on different aspects of mobile wireless sensor networks (WSNs). Our choice of WSNs is motivated mainly by the use of real experiments needed in most courses about WSNs. These experiments require an expensive investment and a lot of nodes in the classroom. To illustrate our study, we propose a course related to energy efficient and safe weighted clustering algorithm. This algorithm which is coupled with suitable routing protocols, aims to maintain stable clustering structure, to prevent most routing attacks on sensor networks, to guaranty energy saving in order to extend the lifespan of the network. It also offers a better performance in terms of the number of re-affiliations. The platform presented here aims at showing the feasibility, the flexibility and the reduced cost of such a realization. We demonstrate the performance of the proposed algorithms that contribute to the familiarization of the learners in the field of WSNs.

본 논문에서는 이동 에드 혹 네트워크(Mobile Ad-hoc network: MANET)에서의 상황인식 기반의 에너지 스케쥴링 기법인 ESWC(Energy Scheduling Weighted-value Control) 알고리즘을 제안한다. 기존 MANET에서 노드의 에너지를 관리하기 위한 여러 가지의 방법들이 제안 되고 있다. 특히 이동노드들의 경우 이동성에 따라 에너지 소모율이 다르며, 이때 상호 노드간 전송하는 데이터와 상호 위치에 따라 에너지 소모율은 다르게 된다. 본 논문에서는 계층적 클러스터 단위의 동적인 토폴로지에서 전송 거리와 전송 데이터의 패킷량이 어떠한 상호관계에 따라 노드의 소모 에너지에 미치는 영향이 있는지를 분석한다. ESWC에서 도출된 에너지의 가중치 결과는 최종적으로 전송 경로에 있어 에너지 소모율을 분석하여 네트워크상에서 전체적인 노드의 라이프타임을 향상 시킬 수 있다. 본 연구의 주어진 모의실험환경에서 노드의 방향성 및 시간에 따른 이동성을 반영함으로써 에너지 효율적인 클러스터링 및 라우팅 경로 알고리즘이 제공되어 네트워크의 최적화된 에너지 소모 결과를 보여주었다.