The transparency and immutability properties offered by the burgeoning field of blockchain technology make it an increasingly popular choice for applications across many domains. One such application is online gaming. Blockchain offers participants the ability to verify the fairness of games in a manner previously unattainable by the classical centralized approach. However, the introduction of blockchain incurs additional overhead and poses unique challenges necessary to overcome in order to be a viable alternative. The transparency blockchain provides opens potential avenues for collusion, particularly in multiplayer games, which must be addressed. Furthermore, the emulation of random state, a core component of online gaming, is rendered difficult in a decentralized context. No approach exists which manages random state for multiplayer gaming in a completely decentralized manner. We propose a novel approach toward this end, significantly extending the utility of blockchain technology to online gaming.

IoT has proven valuable in many industries such as Supply Chain, Shipping and Transportation providing updates on the status of shipments in real time. This has resulted in a large amount of data created by IoT devices that require real time processing. Blockchain has also emerged as a trusted common data storage among multiple participants in IoT systems. However, it is essential to verify the authenticity of data before they go in to the blockchain. Some IoT data must be also encrypted in a secure manner. In this paper, we propose a method to collect sensor data from IoT devices and use blockchain to store and retrieve the collected data in a secure and decentralized fashion. A testbed private blockchain system with Ethereum and Raspberry Pi has been developed to test the feasibility and performance of blockchain-based secure IoT system. We employ private PKI for key management and TPM for storing keys, signing, verifying and encrypting data in a trusted way. We describe the structure of the testbed, decentralized storage system based on Ethereum Swarm and IPFS, TPM configuration processes, smart contract codes, and detailed operation sequences. Performance has been measured with 5000 test transactions in 8 different configurations. The result shows that Ethereum blockchain with Swarm can offer about 9 transactions per second. Encryption and decryption is shown to reduce the performance slightly to about 8 transactions per second. The proposed system can provide a secure IoT Blockchain platform for small industrial applications at low cost.

Apache Hadoop is a famous framework for big data science. Most of the research for improving the speed of big data analysis is researching based on Hadoop modules such as Hadoop common, Hadoop Distribute File System (HDFS), Hadoop Yet Another Resource Negotiator (YARN) and Hadoop MapReduce. The paper focuses on data locality on HDFS and MapReduce to improve the performance. The input data is divided into several blocks and stored in HDFS. Each block has sever key-value fair in map stages. The paper use the keys in block to make key-based Deep Data Locality (DDL). The MapReduce with key-based DDL reduce some steps on map stage, shuffle stage, and reducer stages to improve the performance of MapReduce. We tested the performance of MapReduce with block-based DDL and key-based DDL to compare with default MapReduce. According to the test, MapReduce with key-based DDL is 28% faster than default MapReduce and 15.4 % faster than MapReduce with block-based DDL. Additionally, key-based DDL can be combined other data locality methods to improve the Hadoop. Combined key-based DDL and block-based DDL improve the Hadoop performance up to 52.5%.The paper also introduced the simulator for testing the performance of MapReduce with applied data locality methods on Hadoop. The simulator display a performance of each stage of MapReduce using graph. Key-based DDL can be combined with other data locality research to get optimized performance in various data types and node' status in the simulator.

A satellite communication environment has a high latency and a high data error rate, and thus the performance of TCP is greatly impaired. To overcome this, Performance Enhancing Proxies (PEPs) are commonly deployed around the satellite links. However, the operation of PEPs is disabled when TCP traffic is encrypted by High Assurance Internet Protocol Encryptions (HAIPE). As a result the performance of the HAIPE-encrypted TCP traffic across satellite links becomes very low. Numerous approaches have been proposed to resolve this problem, but a practical solution is yet to be developed. In this research, we developed a method that can achieve the high performance offered by PEPs for HAIPE-encrypted TCP traffic across satellite links. This method encodes and relays the original TCP flow information across HAIPE without any modification to the existing HAIPE while preserving the same level of security. It then reconstructs new TCP streams and encapsulates HAIPE-encrypted original TCP packets in them. These new TCP streams can be natively handled by PEPs and thus the full TCP performance can be achieved. This method is also applicable to both IPv4 and IPv6. However, this scheme faces a challenge of handling TCP-over-TCP that suffers from a phenomenon called TCP meltdown. We propose a method that can prevent TCP meltdown and briefly describe it.

MapReduce has been widely used in many data science applications. It has been observed that an excessive data transfer has a negative impact on its performance. To reduce the amount of data transfer, MapReduce utilizes data locality. However, even though the majority of the processing cost occurs in the later stages, data locality has been utilized only in the early stages, which we call Shallow Data Locality (SDL). As a result, the benefit of data locality has not been fully realized. We have explored a new concept called Deep Data Locality (DDL) where the data is pre-arranged to maximize the locality in the later stages. Toward achieving stronger DDL, we introduce a new block placement paradigm called Limited Node Block Placement Policy (LNBPP). Under the conventional default block placement policy (DBPP), data blocks are randomly placed on any available slave nodes, requiring a copy of RLM (Rack-Local Map) blocks. On the other hand, LNBPP places the blocks in a way to avoid RLMs, reducing the block copying time. The containers without RLM have a more consistent execution time, and when assigned to individual cores on a multicore node, they finish a job faster collectively than the containers under DBPP. LNBPP also rearranges the blocks into a smaller number of nodes (hence Limited Node) and reduces the data transfer time between nodes. These strategies bring a significant performance improvement in Map and Shuffle. Our test result shows that the execution times of Map and Shuffle have been improved by up to 33% and 44% respectively. In this paper, we describe the MapReduce workflow in Hadoop with a simple computational model and introduce the current research directions in each step. We analyze the block placement status and RLM locations in DBPP with the customer review data from TripAdvisor and measure the performances by executing the Terasort Benchmark with various sizes of data. We then compare the performances of LNBPP with DBPP.

Recently, majority of environmental monitoring systems use Internet of Things devices [1]. The Nevada Solar Energy-Water-Environment Nexus project collects a large amount of environmental data from various remote sensors. In the previous research, we introduced REST API for communication and web service between senor and server [2]. However, current RESTful web service has several issues for authentication system. We developed an ID-based authentication algorithm to achieve a secure RESTful web service. ID-based authentication is based on Boneh-Franklin ID-based encryption and REST URI. Such process enabled server to handle client's request by acknowledging client's URI rather than storing client's entire status for stateless REST. Also, such process resolves current problems generated by implementing REST authentication.

Software vulnerabilities are the weaknesses in the software that inadvertently allow dangerous operations. If the vulnerability is in a network service, it poses serious security threats because a cyber-attacker can exploit it to gain unauthorized access to the system. Hence, rapid discovery and remediation of network vulnerabilities is critical issues in network security. In today's dynamic IT environment, it is common practice that an organization prioritizes the mitigation of discovered vulnerabilities according to their risk levels. Currently available technologies, however, associate each vulnerability to the static risk level which does not take the unique characteristics of the target network into account. This often leads to inaccurate risk prioritization and less-than-optimal resource allocation. In this research, we introduce a novel way of quantifying the risk of network vulnerability by augmenting the static risk level with conditions specific to the target network. The method calculates the risk value of each vulnerability by measuring the proximity to the untrusted network and risk of the neighboring hosts. The resulting risk value, RCR is a composite index of the individual risk, network location and neighborhood risk conditions. Thus, it can be effectively used for prioritization, comparison and trending. We tested the methodology through the network intrusion simulation. The results shows average 88.9% the correlation between RCR and number of successful attacks on each vulnerability.

Authentication to a computing system is composed of two parts, identification and verification. Traditionally, login IDs have been used for identification and passwords for verification. Many schemes have been proposed to improve both parts, but they may require specialized devices or they may not be always reliable. We propose a method that can augment the current password-based system by strengthening the identification process. It utilizes personal secret data instead of a login ID to identify a user uniquely, hence mindmetrics. It then asks the user to choose a correct login ID among multiple choices of partially obscured IDs. Since it does not accept a login ID during the authentication process, a stolen or cracked password cannot be used for gaining an access to the computing system unless the attacker provides a correct identification material, i.e., mindmetrics token. This additional step raises the security of an authentication system considerably over single or double password systems. Since the stolen passwords cannot be used immediately by the attackers, account holders can have extra time to change their passwords before the attackers gain an access. This scheme does not require any specialized hardware and can be implemented easily. It may be used where biometrics schemes cannot be used cost-effectively, e.g., on public e-commerce web sites. Mindmetrics scheme separates the identification server and the verification server, thus it is scalable to a large system. We implemented a proof-of-concept system and evaluated it with test users. The survey indicates that the system is not as intrusive as other schemes, users feel better protected, and they are willing to use the scheme on public web sites.

Many scientific applications generate massive data that requires visualization. For example, the Nevada Solar Energy-Water-Environmental Nexus project has been generating a large amount of environmental monitoring data in textual format. As the data is available on the web, a web-based visualization tool is desirable for the project rather than a standalone tool. This research analyzes the processing mechanisms of four popular web-based data visualization tools, that is, Google Charts, Flex, OFC, D3, and compares their performances. A standalone visualization tool, JfreeChart, have been also used for comparison. The processing times have been divided into three segments, layout time, data transformation time, and rendering time, and separately measured. The actual temperature data from the Nevada Nexus project has been used for testing in different scales ranging from 100 to 100,000 data points. The result shows that each visualization tool has its own ideal environment.

A number of multi-channel assignment schemes have recently been proposed to improve the throughput of IEEE 802.11-based multi-hop wireless mesh networks (WMNs). In these schemes, channel coordination is done either through time synchronization across all the hosts, or through the use of a dedicated channel for the transmission of necessary control messages. Either way, excessive system overhead and/or waste of bandwidth resource become unavoidable, undermining the overall network throughput. To maximize the network throughput, we propose a synchronization-free, hybrid temporal-spatial multi-channel assignment scheme in a random heterogeneous network requiring only a single radio interface per host. In this scheme, the gateway is allowed to use all the available channels sequentially in a round-robin fashion. This temporal channel assignment approach ensures that all the neighboring hosts that communicate with the gateway directly shall have a fair access to the gateway. The channel assignment for the remaining wireless hosts is based on the geographical location and channel availability (a spatial approach) to avoid the interference within the communication region of each sender host in its transmission time period. Compared with another multi-channel scheme MMAC, extensive simulation results demonstrate that our proposed scheme can improve the network throughput substantially with the acceptable collision ratio.

In a wireless network composed of multiple access points, a long delay during roaming from one access point to another may cause a disruption for streaming traffic. Roaming in wireless LAN is generally composed of two parts, 1) searching for a new access point and 2) performing authentication at the new access point. To reduce the second part delay, we propose an innovative lightweight authentication scheme called SFRIC (secure fast /foaming using ID-based cryptography). SFRIC employs ID-based cryptography to simplify the authentication process. It performs mutual authentication for the mobile client and AP with a 3-way handshake, then generates a PTK (pairwise transient key) directly without pre-distributing PMK (pairwise master key). It does not require contacting an authentication server or exchanging certificates. SFRIC is composed of two phases. In the first phase (the preparation phase), each mobile client obtains a temporary private key from the PKG (private key generator). In the second phase (the roaming authentication phase), mutual authentication and key distribution are performed. Our preliminary analysis indicates that SFRIC can complete the roaming authentication within a period much less than the critical 20 ms threshold, required for maintaining streaming traffic, when the cryptographic operations are performed in hardware.

The Distributed Denial-of-Service (DDoS) attack is a serious threat in the Internet, and an effective method is needed for distinguishing the attack traffic from the legitimate traffic. In DDoS attacks, the large volume of attack streams cause self-induced congestion or higher utilization of the links. Based on this observation, we propose the Congestion Path Marking (CPM) scheme to identify and drop the attack packets. In this proposed scheme, we store the link utilization information in the packet header so that suspicious attack packets can be distinguished. Each router along the path records its local congestion information, and this information is accumulated to represent the overall congestion level that a packet has experienced. To enable light-weight real-time processing, we employ a RED-like random packet dropping mechanism at the victim's egress router. Through simulations, we show that when the CPM scheme is employed, most of the attack packets in excess of the link capacity are dropped while less than 4% of the legitimate packets are dropped in typical scenarios. The simulation result also shows significantly improved TCP performance when CPM is utilized.

Distributed denial-of-service (DDoS) attack is a serious threat in Internet. We propose a bit marking concept to identify and drop the DDoS attack packets. Bit marking is a variation of the packet marking technique that modifies packet headers at each router. However instead of storing the router information in the packets, bit marking alters one or more bits in the marking field. The bit marking process discussed in this paper is performed to all the packets and at all the routers along the path; hence it is called deterministic bit marking (DBM). DBM creates a common path signature for all the packets originating from the same location upon arriving at a destination. Since different source networks generate virtually unique path signatures, DBM makes it possible to isolate and discard DDoS attack traffic. From the Internet topology of autonomous systems we observe that the source networks are quite uniformly distributed over the path signature space. In our simulation over 99% of the attack traffic is blocked using DBM while up to 99% of the legitimate traffic passes. DBM can also be used for source traceback using reverse bit marking. DBM can be independently deployed for each ISP and the DBM-based networks can be protected from the attacks coming from nonDBM networks.

We present a hardware solution that can reliably block most of the malicious TCP traffic at the edge routers while passing the legitimate TCP traffic during a distributed denial-of-service (DDoS) attack on the Internet. By allocating bandwidths separately for TCP, the TCP portion of the bandwidth can be protected. In a simulation study, the filter successfully blocked 99.9% of the attack traffic while legitimate traffic showed nearly identical performance as in the non-attacked condition. This filtering is transparent to the hosts or routers and a filtering device can be easily attached to router ports.

The need to rapidly reconfigure an agile manufacturing system makes it especially difficult to test the system's control software. Although thorough testing is essential for system reliability, the time available for testing may be short. With a simulator, however, the software can be developed and tested independently from the actual workcell, while production continues or the workcell is reconfigured for the next target product. To facilitate testing of agile manufacturing software, a 3D graphical simulator has been developed at Case Western Reserve University. It permits workcell control software to be tested with a virtual workcell, which exhibits much of the behavior of the real workcell. The simulator has been extensively used in the Agile Manufacturing Project at Case Western Reserve University (CWRU), and most of the actual control software was tested and debugged with it. Considerable time and effort have been saved by simulating various workcell scenarios, some of which are difficult to create in a real workcell, e.g., device failure. In this paper, the architecture of the graphical simulator is described, and a framework for virtual testing is presented. Actual errors found during virtual testing are also described.

The flexibility required of an agile manufacturing system must be achieved largely through computer software. The system's control software must be adaptable to new products and to new system components without becoming unreliable or difficult to maintain. This requires designing the software specifically to facilitate future changes. As part of the Agile Manufacturing Project at Case Western Reserve University, we have developed a software architecture for control of an agile manufacturing workcell, and we have demonstrated its flexibility with rapid changeover and introduction of new products. In this paper, we describe the requirements for agile manufacturing software and how our software architecture addresses them.

An agile workcell has been developed for light mechanical assembly in collaboration with industrial sponsors. The workcell includes multiple Adept robots, a Bosch conveyor system, multiple flexible parts feeders at each robot's workstation, CCD cameras for parts feeding and hardware registration, and a dual VMEbus control system. Our flexible pairs feeder design uses multiple conveyors to singulate the parts and machine vision to locate them. Specialized hardware is encapsulated on modular grippers and modular worktables which can be quickly interchanged for assembly of different products. Object-oriented software (C++) running under VxWorks, a real-time operating system, is used for workcell control. An agile software architecture was developed for rapid introduction of new assemblies through code re-use. A simulation of the workcell was developed so that controller software could be written and tested off-line, enabling the rapid introduction of new products.

This paper introduces a design for agile manufacturing workcells intended for light mechanical assembly of products made from similar components (i.e. parts families). We define agile manufacturing as the ability to accomplish rapid changeover from the assembly of one product to the assembly of another product. Rapid hardware changeover is made possible through the use of robots, flexible part feeders, modular grippers and modular assembly hardware. The flexible feeders rely on belt feeding and binary computer vision for Dose estimation. This has a distinct advantage over non-flexible feeding schemes such as bowl feeders which require considerable adjustment to changeover from one part to another. Rapid software changeover is being facilitated by the use of a real-time, object-oriented software environment, modular software, graphical simulations for off-line software development, and an innovative dual VMEbus controller architecture. These agile features permit new products to be introduced with minimal downtime and system reconfiguration.