Link failure cause packet loss, increased delays, disconnection from the network or even the shutdown of the inter-vehicle communication system vehicle-to-vehicle. Routing protocols suffer from this situation due to the lack of fault tolerance mechanisms that are intended to make the vehicular ad hoc network capable of remaining functional over time with low response time and/or high throughput; despite failures. Through this paper, we propose a version of the Greedy Perimeter Stateless Routing (GPSR) protocol that is tolerant to link failures based on the prediction of the future position of each vehicle (position prediction-GPSR) and a minimum threshold of the vehicle coverage area (position predictive with threshold-GPSR) to elect the next hops. Simulation results show that the proposed approach offers good performance.

We are experiencing the true dawn of an Internet of Things society, in which all things are connected to the Internet. While this enables us to receive a wide variety of useful services via the Internet, we cannot ignore the fact that this means the number of devices targeted for Internet attacks has also increased. One known method for handling such issues is the utilization of a darknet monitoring system, which urgently provides information on attack trends occurring on the Internet. This system monitors and analyzes malicious packets in the unused IP address space and provides security related information to both network administrators and ordinary users. In this paper, Topological Data Analysis (TDA) Mapper is utilized to analyze malicious packets on the darknet, which grow increasingly complexity every day from a new perspective. TDA Mapper is a method of TDA that has continued to attract attention in recent years. In an evaluation experiment, by applying TDA to malicious packets monitored using the actual darknet, the malicious packets were able to be visualized. In this study, the author considers the overall image of the visualized malicious packets and examples extracted from the relationships among packets and reports on the effectiveness of the proposed method.

The Personal Health Record (PHR) could be seen as a preventive care solution to the incoming aging society. The blockchain-based PHR system has been proposed recently to enhance the security and privacy for the PHR data. Consequently, the performance becomes a concern for blockchain-PHR integration because of the blockchain performance issues in the past. Thus, this article presents the performance analysis of the blockchain-based PHR system to ensure the usability in practice. The proposed blockchain-based PHR system prototype is implemented and the architectural model for the blockchain-based PHR system is also constructed. The key parameters for the architectural model are extracted from the prototype. Experiments are conducted with various data sizes including 128, 512 KB, 2, 8 and 32 MB. The result shows that storing 32 MB of the PHR data takes 4.84 s and retrieving the same PHR data takes 5.19 s. The result of simulating the architectural model shows that the proposed blockchain-based PHR system can response within 4 min for 60,000 accesses each day. The performance results indicated that the proposed blockchain-based PHR system can work within the emergency response time of 8 min and it is usable with an efficient computational cost. Further evaluation on the distributed design of the proposed blockchain-based PHR system is planned for our future work.

Testing automation is one of the challenges facing the software development industry, especially for large complex products. This paper proposes a mechanism called Multi Stage Failure Detector (MSFD) for automating black box testing using different machine learning algorithms. The input to MSFD is the tool’s set of parameters and their value ranges. Te parameter values are randomly sampled to produce a large number of parameter combinations that are fed into the software under test. Using neural networks, the resulting logs from the tool are classified into passing and failing logs and the failing logs are then clustered (using mean-shift clustering) into different failure types. MSFD provides visualization of the failures along with the responsible parameters. Experiments on and results for two real-world complex software products are provided, showing the ability of MSFD to detect all failures and cluster them into the correct failure types, thus reducing the analysis time of failures, improving coverage, and increasing productivity.

In this work, we extended the original Traveling Salesman Problem (TSP) to cover not only the case of multiple vehicles but also to constrain the minimum and maximum numbers of cities each vehicle can visit. Our algorithm is a modified Ant Colony Optimization (ACO) algorithm which has the ability to avoid local optima; our algorithm can be applied to transportation problem that covers either a single vehicle or multiple vehicles. To the original ACO, we added a new reproduction method, a new pheromone updating strategy, and four improved local search strategies. We tested our algorithm on several standard datasets in the TSP library. Its single-vehicle performance was compared to that of ant system (AS) and elitist ant system (EAS) algorithms. Its multiple-vehicle performance was evaluated against that of ant colony system variants reported in the literature. The experiments show that our proposed ACO’s single-vehicle performance was superior to that of AS and EAS on every tested dataset and its multiple-vehicle performance was excellent.

This research proposes the methodology to extract common configuration parameter set by applying multiple benchmarking applications include TeraSort, TestDFSIO, and MrBench on the Hadoop distributed file system. The parameter search space conceptually conducted named Ω(x) to hold status of all parameter values and its evaluation results for every stage to eventually reduce benchmarking cost. In the process of determining parameter set for each stage, one parameter and its associated values selected which is reduced system performance in terms of overall execution time difference that are measured by multiple applications on a Hadoop cluster. The experimental results demonstrate the proposed extended greedy manner provide a feasible benchmark model for the multiple MapReduce tasks. This model classified several candidate parameter value sets that can be reduced the overall execution time by 27% of the values against Hadoop default settings. Moreover, we propose e-heuristic greedy with alternative parameter selection model to evaluate second candidate parameter value which will lead global optimum by returning back to the previous stage if local minimum is not found at the current stage compare to the previous ones.

Neural Networks have been performing state of the art for almost a decade now; when it comes to classification and prediction domains. Within last few years, neural networks have been improved tremendously and their performance is even better than humans in some domains, e.g. AlphaGo vs Lee Sedol and Image Net Challenge-2009. It’s a beneficial factor for any parking lot to know that what would be a parking position at any given point in time. If we are able to know in advance that are we going to get parking tomorrow afternoon in a busy super store parking lot, its very beneficial to plan accordingly. In this paper, we predict customer influx in a specific departmental store by analyzing the data of its parking lot. We use this parking data to predict the customer influx and outflux for that parking lot as this parking influx is directly proportional to the customer influx in the store. We use Recurrent Neural Network on the top of two years of historical data. We generate promising results using this dataset by predicting the traffic flow for each hour for next 7 days. We further improve our performance on this dataset by incorporating three more environmental factors along with the parking logs.

This paper introduces the practical guidelines for function point analysis in the Republic of Korea. Korean government recommends that Function Point Analysis should be adopted as a standard method for measuring the functional size of software. Function Point was proposed by an international organization. We will present the procedure of counting Function Point with adjustment factors. The functional size and development cost of software can be estimated from the number of Function Point.

Distinguishing analysis is an important part of cryptanalysis. It is an important content of discriminating analysis that how to identify ciphertext is encrypted by which cryptosystems when it knows only ciphertext. In this paper, Fisher’s discriminant analysis (FDA), which is based on statistical method and machine learning, is used to identify 4 stream ciphers and 7 block ciphers one to one by extracting 9 different features. The results show that the accuracy rate of the FDA can reach 80% when identifying files that are encrypted by the stream cipher and the block cipher in ECB mode respectively, and files encrypted by the block cipher in ECB mode and CBC mode respectively. The average one to one identification accuracy rates of stream ciphers RC4, Grain, Sosemanuk are more than 55%. The maximum accuracy rate can reach 60% when identifying SMS4 from block ciphers in CBC mode one to one. The identification accuracy rate of entropy-based features is apparently higher than the probability-based features.

This paper presents ways to assure the efficacy of managing crypto links and crypto speed with respect to the dependability of blockchain. The dependability in this work is defined with reference to crypto links in a proportional manner and crypto speed in an inversely proportional manner, respectively. Also, note that dependability is proportional to the cost such that the more the number of crypto links come in place, the higher its cost is. It is a standard and intuitive practice to partition a large output file into smaller pieces at a certain threshold size provided in the blockchain analytics engine (e.g., 250 KB/ partition in Interplanetary File Systems ( IPFS)/Hadoop). Crypto link is defined by the hash address (i.e., a pointer link) stored in the blockchain for a partition of an output file off from a backend blockchain analytics engine. IPFS manages a crypto link to every partition of a file of interest whereas the proposed crypto link scheme is at a reduced number in order to address the cost and performance issue such that one crypto link to the entire file and the links between partitions within a file are maintained by local chains of links. Crypto speed is defined by the rate of the number of crypto links over the total turnaround time to encrypt all the crypto links (hash addressing) per file. Thus, notice that without loss of generality, the crypto speed increases inverse-linearly along with the number of crypto links. A binomial-based dependability model can be expressed as a rather straightforward function of the total number of partitions and the number of crypto links as proposed in this work. It is demonstrated in this work how the number of crypto links and the crypto speed influence the dependability in a numerical simulation. Also an implementation of an integrated procedure of a blockchain analytics engine is demonstrated along with Hadoop and IPFS modules.