International Journal of Networking and Computing Vol. 7(2017) No. 1

The 18th Workshop on Advances in Parallel and Distributed Computational Models (APDCM) - held in conjunction with the International Parallel and Distributed Processing Symposium (IPDPS) on May 23-27, 2016, in Chicago, USA, - aims to provide a timely forum for the exchange and dissemination of new ideas, techniques and research in the field of the parallel and distributed computational models.

The APDCM workshop has a history of attracting participation from reputed researchers world-wide. The program committee has encouraged the authors of accepted papers to submit full-versions of their manuscripts to the International Journal of Networking and Computing (IJNC) after the workshop. After a thorough reviewing process, with extensive discussions, three articles on various topics have been selected for publication on the IJNC special issue on APDCM.

On behalf of the APDCM workshop, we would like to express our appreciation for the large efforts of reviewers who reviewed papers submitted to the special issue. Likewise, we thank all the authors for submitting their excellent manuscripts to this special issue. We also express our sincere thanks to the editorial board of the International Journal of Networking and Computing, in particular, to the Editor-in-chief Professor Koji Nakano. This special issue would not have been possible without his support.

We propose a definition of parallel state, derive a phase space from this state, and calculate the entropy of states and full executions using combinatorial analysis. A main contribution of this work is the introduction of an experimentally measurable phase space, which we then use to analyze execution states, ensembles of states, and ensembles of complete executions. We show that the entropy analysis reveals both expected and unexpected features of execution, and application of principal component analysis shows capability to extract execution details at the level of individual process states, as well as reveal hardware properties such as network or memory communications.

A Mobile Ad hoc Network (MANET) is a network that consists of mobile nodes and is autonomously managed without infrastructure base stations such as access points. MANETs have started being used as part of safety critical applications. A Vehicular Ad hoc Network (VANET) used in automated driving systems is such an example. In such applications, defects in the network protocol may cause serious social problems. Model checking, a state search-based verification technique, has proven to be effective in finding faults in complex system designs, such as communication protocols. However it is challenging to apply this technique to MANET protocols, because a MANET can have a number of different network topologies, thus resulting in the state explosion problem very easily. In this paper we propose a modeling technique to mitigate this problem using the AODV protocol as a running example. MANET protocols, such as AODV, typically enforce a source node that wishes to establish a route to the destination to retry the route establishing process some fixed number of times in face of failures. We show that to model check the protocol's behavior in these retries it suffices to consider only the last trial. The results of experiments using the SPIN model checker show that using the proposed technique significantly reduced the time and memory usage compared to standard full state exploration and allowed us to model check the protocol with up to five nodes.

Cooperative MIMO communication is a promising technology which enables realistic solution for improving communication performance with MIMO technique in wireless networks that are composed of size and cost constrained devices. However, the security problems inherent to cooperative communication also arise. Cryptography can ensure the confidentiality in the communication and routing between authorized participants, but it usually cannot prevent the attacks from compromised nodes which may corrupt communications by sending garbled signals. In this paper, we propose a cross-layered approach to enhance the security in query-based cooperative MIMO sensor networks. The approach combines efficient cryptographic technique implemented in upper layer with a novel information theory based compromised nodes detection algorithm in physical layer. In the detection algorithm, a cluster of K cooperative nodes are used to identify up to K - 1 active compromised nodes. When the compromised nodes are detected, the key revocation is performed to isolate the compromised nodes and reconfigure the cooperative MIMO sensor network. During this process, beamforming is used to avoid the information leaking. The proposed security scheme can be easily modified and applied to cognitive radio networks. In such cognitive radio network, if we assume that in cooperative transmission the unlicensed users use one licensed user's frequency, a cluster of K cooperative nodes can identify up to K - 2 active compromised nodes. Simulation results show that the proposed algorithm for compromised nodes detection is effective and efficient, and the accuracy of received information is significantly improved.

The main contribution of this paper is to present an implementation that performs the exhaustive search to verify the Collatz conjecture using a GPU. Consider the following operation on an arbitrary positive number: if the number is even, divide it by two, and if the number is odd, triple it and add one. The Collatz conjecture asserts that, starting from any positive number m, repeated iteration of the operations eventually produces the value 1. We have implemented it on NVIDIA GeForce GTX TITAN X and evaluated the performance. The experimental results show that, our GPU implementation can verify 1.31×10¹² 64-bit numbers per second. While the sequential CPU implementation on Intel Core i7-4790 can verify 5.25×10⁹ 64-bit numbers per second. Thus, our implementation on the GPU attains a speed-up factor of 249 over the sequential CPU implementation. Additionally, we accelerated the computation of counting the number of the above operations until a number reaches 1, called delay that is one of the mathematical interests for the Collatz conjecture by the GPU. Using a similar idea, we achieved a speed-up factor of 73.

This study considers the problem of selecting a small number of important persons from social media. Skyline query has been utilized for selecting key persons. Based on certain criteria from social media, this query selects persons who are not dominated by any other. Owing to the complex structure of social media, selecting a key person is more complicated and its application is quite different from conventional skyline queries. We need to consider various metrics in the social media. In addition, social media contains massive data, and the data increase is huge. It is collection of online communication channels dedicated to community-based inputs, interactions, content sharing, and collaboration. We use MapReduce framework to speed up the computation and introduce parallelism in the processing. An extensive set of experiments shows that the analysis of social activities, social relationships, and socially shared contents helps finding a key person. The experimental results also confirm the efficiency and scalability of our algorithm on a synthetic dataset.