Earticle

The Delay Tolerant Networks (DTNs) generally contain relatively sparse nodes that are frequently disconnected. Message Ferrying (MF) is a mobility-assisted approach which utilizes a set of mobile elements to provide communication service in ferry-based DTNs. In this paper, we propose a Density-Aware Route Design (DARD) algorithm using partitive clustering algorithms along a validity index for identifying the suitable node clusters and assigning ferries to these clusters. In the proposed algorithm, unlike using multiple ferries in a single route (SIRA algorithm) or dividing the deployment area into grid as static (NRA and FRA algorithms), the manner of node’s distribution and their density in network are regarded as clustering metric. Evaluation results for comparing our scheme with existing routing algorithms demonstrate that DARD either minimizes message delivery delay or by preserving message delay, it reduces resource requirements in both ferries and nodes resulting in increasing ferries efficiency.

This paper proposes a method to realize sensor function allocation and effective data aggregation simultaneously in wireless sensor networks. This method realizes dynamic allocation of sensor functions so as to balance the distribution of each sensor function in a target monitoring area. In addition, effective data aggregation is performed by using a tree network topology and time division multiple access (TDMA), which is a collision-free communication scheme. By comparing the results from the proposed method with the results from non-optimized methods, it can be validated that the proposed method is 1.7 times more efficient than non-optimized methods in distributing sensor functions. With this method, the network lifetime is doubled, and the number of data packets received at a base station is considerably increased by avoiding packet collisions.

Authentication of communicating entities and confidentiality of transmitted data are fundamental procedures to establish secure communications over public insecure networks. Recently, many researchers proposed a variety of authentication schemes to confirm legitimate users. Among the authentication schemes, a one-time password authentication scheme requires less computation and considers the limitations of mobile devices. The purpose of a one-time password authentication is to make it more difficult to gain unauthorized access to restricted resources. This paper discusses the security of Kuo-Lee's one-time password authentication scheme. Kuo-Lee proposed to solve the security problem based on Tsuji-Shimizu's one-time password authentication scheme. It was claimed that their proposed scheme could withstand a replay attack, a theft attack and a modification attack. Therefore, the attacker cannot successfully impersonate the user to log into the system. However, contrary to the claim, Kuo-Lee's scheme does not achieve its main security goal to authenticate communicating entities. We show that Kuo-Lee's scheme is still insecure under a modification attack, a replay attack and an impersonation attack, in which any attacker can violate the authentication goal of the scheme without intercepting any transmitted message. We also propose a scheme that resolves the security flaws found in Kuo-Lee's scheme.

Medical images have become the recent key investigation tools for medical diagnosis and treatment planning. Due to the advent of digital imaging the need of data storage and retrieval of medical images increased rapidly. Some difficulties in retrieving the medical images are: medical images have only intensity images that carry less information, more noise and records of medical images are large and complex to analyze. To address these problems the present work proposes a novel scheme based on Texture Unit. The proposed scheme reduces texture unit values from 0 to 6561 to 0 to 255 based on Lag values. The Similarity measures are extracted on both schemes and a good comparison is made. The experimental results on MRI images indicate reliability, feasibility and efficacy of the proposed method.

Self-localization is a fundamental problem in mobile robotics. It consists of estimating the position of a robot given a map of the environment and information obtained by sensors. Among the algorithms used to address this issue, the Monte Carlo technique has obtained a considerable attention by the scientific community due to its simplicity and efficiency. Monte Carlo localization is a sample-based technique that estimates robot´s pose using a probability density function represented by samples (particles). The complexity of this algorithm scales proportionally to the number of particles used. The larger the environment, the more particles are required for robot localization. This fact limits the use of this algorithm in large size environments. In order to improve the efficiency of the Monte Carlo technique and allow it to be used in large environments we propose a parallel implementation of it. Our implementation is based on OpenMP and MPI message passing interface. Experimental results are used to show the efficiency of our approach.