The alarming rate of fatality and injuries recorded through road accidents call for the deployment of intelligent transportation system (ITS). The internet of vehicles (IoV), being the backbone of ITS, provides vehicles with standards and protocols to disseminate basic safety messages (BSMs) containing kinematic information to other vehicles and infrastructures, making the IoV a complex network and therefore susceptible to cyberattacks. Despite employing public-key infrastructure (PKI) to ensure BSMs are digitally signed and authenticated, insider attackers can still falsify BSMs and cause chaos in the network. The research community has contributed by proposing data-centric approaches however, the over-reliance on one vehicle BSM data for training and inference gives the attacker an upper hand. To address these drawbacks, we proposed a machine learning-based neighbour-vehicle approach for anomaly detection of BSM falsification in IoV (NAIBI) and demonstrate its superiority over the state-of-the-art which exceeds 99% in accuracy, precision, recall and F1-score.

Mobile Ad-hoc Networks (MANETs) are challenged by highly dynamic network conditions like delayed or frequently disrupted communication. Traditional MANETs typically rely on a single Delay-Tolerant Network (DTN) routing protocol across all nodes, which can limit efficiency in unfavourable network conditions. To address this, our research introduces a dynamic approach, employing multiple DTN routing protocols concurrently within a MANET. Each node can adaptively operate on a distinct DTN routing protocol based on the prevailing network conditions. This strategy harnesses the strengths of each protocol while mitigating their limitations, resulting in a notable enhancement in the delivery ratio, as evidenced by simulations of different network scenarios. Comparatively, this multi-protocol approach outperforms single-protocol MANETs, as simulated by the Opportunistic Network Environment (ONE) simulator. The findings emphasize that a multi-protocol strategy enhances adaptability, robustness, and overall performance in MANETs, offering a transformative approach to wireless communication network management and operation.

We propose to extend the concept of difference lists to general data structures and call them difference data structures. Difference lists are well-known in logic programming as a data structure that realizes constant-time concatenation by retaining a reference to the tail as well as the head of the list. In recent years, they are also called list segments and appear as a typical example of separation logic, a logic for handling heaps and pointers. Difference data structures can be utilized to discuss various important concepts in programming languages including functions, evaluation contexts, and continuations, in a unified setting. To handle such structures succinctly and safely, we base this work on a graph rewriting language. We propose a typing method for graph rewriting languages based on graph grammar, which covers structures beyond algebraic data types, to allow users to define difference data types for two different major applications, i.e., (i) users can describe powerful pattern matching with user-defined shapes and (ii) the implementation can statically type-check operations on difference data structures efficiently. Our method introduces the concept of difference at the meta-level, i.e., difference data types are automatically derived from the base data type defined by graph grammar so that we can treat the base structure and difference structure uniformly without type conversion.

The Smart Systems and Services Innovative Professional Education (Smart SE) program is a certification program developed as part of the education network for the Practical information Technologies (enPiT-Pro) project, which is funded by the Japan Ministry of Education, Culture, Sports, Science and Technology. The Smart SE program provides industry professionals working in fields related to information and communication technology (ICT) with additional training and education in smart systems and services that utilize various technologies such as IoT, Cloud, Big Data, and Artificial Intelligence (AI) for businesses. Here, we illustrate its purpose, curriculum and features to respond to the needs of industrial professional education.

In scalable storage systems, there are two kinds of methods for data redundancy: mirroring and parity. Each has its pros and cons. Mirroring creates a large amount of redundancy data, resulting in less usable space. Write performance degrades proportionally to the redundancy level due to an increase in communication. Parity-based methods partition data into multiple pieces, add parity information, and distribute the pieces of data and parity information. Parity-based methods are not often used with memory class media that are faster than the network, because distributing the data across servers results in low read performance. This research aims to establish an efficient data protection method that can be applied to fast, memory class media. We propose a new parity-based method called Multi-stage Erasure Coding (MEC), which creates two different erasure codes: one at the data transmission source server, and the other at the destination server. We show that our method reduces the space required to achieve redundancy while achieving high performance by making the amount of write communication independent of the redundancy level. We built a prototype program using MEC on a commodity cluster server. We show that compared with conventional parity-based methods with redundancy level 2, read I/O throughput is over one order of magnitude higher thanks to local reads and that write I/O throughput is almost the same due to network bottleneck.

We propose the multiple branch collection routing (MBCR), a routing method for wireless mesh networks (WMNs), and the main purpose of which is to reduce control communications. The virtual address of a node is generated when the node joins a network through MBCR. Nodes that join the network can obtain necessary information only from their neighboring nodes. When data are transmitted to a destination node, each node uses information of its virtual address, and the virtual addresses of its neighboring and destination nodes. However, MBCR does not include a method to retrieve a destination virtual address.

Substitution in the λ-calculus is a subtle operation. In a formal description, Barendregt's variable convention isassumed to avoid variable capture, but such an assumption is notwell suited for implementation on computers. We introduce graphrepresentation and manipulation of λ-terms, in which bound andfree variables are encoded by using hyperlinks with differentattributes. A graph type called hlground is generalized to identifythe scope of a term n in substitution m[x:= n], which enablesbound variables and free variables to have suitable behaviorduring the substitution. Our representation of the λ-terms arereadable and the definition of substitution in this technique isfree from any side conditions on the freeness and freshness of variables.

The purpose of this research is to develop a highly reliable simulator of hybrid systems, i.e., systems involving both discrete change and continuous evolution. In particular, we aim at rigorous simulation of parametrized hybrid systems, which enables not only the analysis of model's possible behavior but also the design of parameters that realize desired properties. Simulators with interval arithmetic can reliably compute a reachable set of states, but preserving the dependency of uncertain quantities in models is still challenging. In this paper, we discuss a simulation method that is based on symbolic computation and cooperates with the interval Newton method and affine arithmetic, which is able to preserve first-order dependency of uncertain quantities. We implemented the algorithm on the symbolic simulator we have been developing and evaluated the performance of the method with example models.

Many peer-to-peer network applications are used by multiple users or peers. All these network applications require similar information about the network. In our proposed system, all peer-to-peer applications share information through a single overlay network. The system provides information about the overlay network to the peers or applications and thus enables each application to access the network information that they need to transfer data. The overlay network is constructed by collecting information from those peers that use peer-to-peer network applications. In this paper, we present a conceptual model of the proposal system. The network manager of our proposal system collects and offers information to the peers or networks. If there are many peers that use multiple peer-to-peer network applications, the number of message packets in the networks can be reduced by omitting the redundant data transfer. This paper also presents the results of simulating an evaluation of the proposal system. From the results, we could conclude that our proposal system would be effective when there is a large number of whole peers and/or peers that use multiple peer-to-peer network applications.

In recent data centres, large-scale storage systems storing big data comprise thousands of large-capacity drives. Our goal is to establish a method for building highly reliable storage systems using more than a thousand low-cost large-capacity drives. Some large-scale storage systems protect data by erasure coding to prevent data loss. As the redundancy level of erasure coding is increased, the probability of data loss will decrease, but the increase in normal data write operation and additional storage for coding will be incurred. We therefore need to achieve high reliability at the lowest possible redundancy level. There are two concerns regarding reliability in large-scale storage systems: (i) as the number of drives increases, systems are more subject to multiple drive failures and (ii) distributing stripes among many drives can speed up the rebuild time but increase the risk of data loss due to multiple drive failures. These concerns were not addressed in prior quantitative reliability studies based on realistic settings. In this work, we analyze the reliability of large-scale storage systems with distributed stripes, focusing on an effective rebuild method which we call Dynamic Refuging. Dynamic Refuging rebuilds failed storage areas from those with the lowest redundancy and strategically selects blocks to read for repairing lost data. We modeled the dynamically changing amount of storage at each redundancy level due to multiple drive failures, and performed reliability analysis with Monte Carlo simulation using realistic drive failure characteristics. When stripes with redundancy level 3 were sufficiently distributed and rebuilt by Dynamic Refuging, we found that the probability of data loss decreased by two orders of magnitude for systems with 384 or more drives compared to normal RAID. This technique turned out to scale well, and a system with 1536 inexpensive drives attained lower data loss probability than RAID 6 with 16 enterprise-class drives.

This special issue contains selected papers from the Eighth Asian Symposium on Programming Languages and Systems (APLAS 2010), held from 28 November – 1 December 2010, in Shanghai, China. The symposium was sponsored by the Asian Association for Foundation of Software (AAFS) and Shanghai Jiao Tong University.

Peers search contents with information of contents such as keywords in many peer-to-peer contents sharing systems. In many peer-to-peer contents search architectures, queries are forwarded to peers which belong to clusters related with the keywords. Since clusters are basically constructed regardless of physical network topology, a peer receives contents from peers that are located far from the peer in some cases. To solve this problem, the authors proposed an architecture in which clusters were constructed on multiple logical layers. And furthermore, the authors verified effects of switching clusters.

Network traffic on the Internet has been increasing in recent years. Some reports by government institutions indicate that Peer-to-Peer (P2P) traffic consumes 30% of network bandwidth on the Internet. In most current P2P file-sharing software, computers, which are called "peer" get files not only from computers that are nearby on the network but also from those that are far away. This is because the computers do not consider the relative location of other computers on the Internet. We regard Round Trip Time (RTT) or Time To Live (TTL) as the network distance and design a new P2P content delivery system. In this system, to decrease waste, peers on the P2P file-sharing network prioritize nearby peers when obtaining files. Peers search for other peers that have desired files using clusters, based on network distance and trend of contents which a peer has. Peers search for popular files from other peers in distance-based clusters and search for less popular files from peers in trend-based clusters. Simulation results indicate our system facilitates a reduction in traffic in the whole network.

In this article, we propose a personalized sports digest composition system with scenario templates and show its architecture. While conventional methods only select and show important events in a simple series, our system is capable to add a direction to them with considering a story of a whole game. We apply our prototype system to the summer Japan championship tournament of high school baseball. We provided the personalized digest service of the summer championship tournament of high school baseball in 2000 to the limited audience of the Internet and received the positive feedbacks of our system from about 100 users.

We review the design of the concurrent logic language GHC, the basis of the kernel language for the Parallel Inference Machine being developed in the Japanese Fifth Generation Computer Systems project, and the design of the parallel language KL1, the actual kernel language being implemented and used. The key idea in the design of these languages is the separation of concurrency and parallelism. Clarification of concepts of this kind seems to play an important role in bridging the gap between parallel inference systems and knowledge information processing in a coherent manner. In particular, design of a new kernel language has always encouraged us to re-examine and reorganise various existing notions related to programming and to invent new ones.