New Means for ERP Systems by eContracting

Nowadays business to business (B2B) coordination is characterized by long term contracts albeit the fact that most interactions are already made electronically based on ERP-systems. Especially in cooperations, the flexibility offered by electronic coordination is rarely used. With the term eContracting we refer to electronic coordination among participants in the sense of the alienation and acquisition, between individuals, of rights of property and liberty in supply chains which are influenced by new information technologies. An interdisciplinary approach, i.e. incorporating economics as well as computer science, will provide new means to the alienation and acquisition of short to medium term contracts in supply chains

SSthreshless Start: A Sender-Side TCP Intelligence for Long Fat Network

Measurement shows that 85% of TCP flows in the internet are short-lived flows that stay most of their operation in the TCP startup phase. However, many previous studies indicate that the traditional TCP Slow Start algorithm does not perform well, especially in long fat networks. Two obvious problems are known to impact the Slow Start performance, which are the blind initial setting of the Slow Start threshold and the aggressive increase of the probing rate during the startup phase regardless of the buffer sizes along the path. Current efforts focusing on tuning the Slow Start threshold and/or probing rate during the startup phase have not been considered very effective, which has prompted an investigation with a different approach. In this paper, we present a novel TCP startup method, called threshold-less slow start or SSthreshless Start, which does not need the Slow Start threshold to operate. Instead, SSthreshless Start uses the backlog status at bottleneck buffer to adaptively adjust probing rate which allows better seizing of the available bandwidth. Comparing to the traditional and other major modified startup methods, our simulation results show that SSthreshless Start achieves significant performance improvement during the startup phase. Moreover, SSthreshless Start scales well with a wide range of buffer size, propagation delay and network bandwidth. Besides, it shows excellent friendliness when operating simultaneously with the currently popular TCP NewReno connections.Comment: 25 pages, 10 figures, 7 table

A new splitting-based displacement prediction approach for location-based services

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Several location prediction models have been proposed to enhance and increase the relevance of the information retrieved by users of mobile information systems, but none of them studied the relationship between accuracy rate of prediction and the performance of the model in terms of consuming resources and constraints of mobile devices. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. One such technique is the Prediction Location Model (PLM), which deals with inner cell structure. The PLM technique suffers from memory usage and poor accuracy. The main goal of this paper is to propose a new path prediction technique for Location-Based Services. The new approach is competitive and more efficient compared to PLM regarding measurements such as accuracy rate of location prediction and memory usage

Towards Understanding Risk Perceptions of Online Consumers

Although electronic commerce (e-commerce) is expanding, online sales account for only a small percentage of the total retail sales. Perceived risk is one of the factors affecting online consumers’ purchasing intentions. Reduction of online consumers’ risk perceptions is critical in order to attract new customers and retain existing ones. Therefore, understanding of online consumers’ risk perceptions and attitudes is desperately needed. This research initiative will utilize the psychometric paradigm to study online consumers’ risk perceptions and reveal a “cognitive map” of their attitudes and perceptions to online risks that will aid researchers to understand and predict consumers’ responses to risks posed by online hazards and activities. The goal of this research is to uncover a cognitive map of people’s attitudes and perceptions related to online risks

KISS: Stochastic Packet Inspection Classifier for UDP Traffic

This paper proposes KISS, a novel Internet classifica- tion engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming appli- cations, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square-like test, which extracts the protocol "format," but ignores the protocol "semantic" and "synchronization" rules. The signatures feed a decision process based either on the geometric distance among samples, or on Sup- port Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asym- metry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server proto- cols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are al- most perfect when dealing with new P2P streaming applications

Channel Sampling Strategies for Monitoring Wireless Networks

Monitoring the activity on an IEEE 802.11 network is useful for many applications, such as network management, optimizing deployment, or detecting network attacks. Deploying wireless sniffers to monitor every access point in an enterprise network, however, may be expensive or impractical. Moreover, some applications may require the deployment of multiple sniffers to monitor the numerous channels in an 802.11 network. In this paper, we explore sampling strategies for monitoring multiple channels in 802.11b/g networks. We describe a simple sampling strategy, where each channel is observed for an equal, predetermined length of time, and consider applications where such a strategy might be appropriate. We then introduce a sampling strategy that weights the time spent on each channel according to the number of frames observed on that channel, and compare the two strategies under experimental conditions

Location prediction based on a sector snapshot for location-based services

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shaped cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the new Markov-based mobility prediction (NMMP) and prediction location model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression, and insufficient accuracy. In this paper, a novel cell splitting algorithm is proposed. Also, a new prediction technique is introduced. The cell splitting is universal so it can be applied to all types of cells. Meanwhile, this algorithm is implemented to the Micro cell in parallel with the new prediction technique. The prediction technique, compared with two classic prediction techniques and the experimental results, show the effectiveness and robustness of the new splitting algorithm and prediction technique

Adaptive Synchronization of Semantically Compressed Instructional Videos for Collaborative Distance Learning

The increasing popularity of online courses has highlighted the need for collaborative learning tools for student groups. In addition, the introduction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources available to students. We present an e-Learning architecture and adaptation model called AI2TV (Adaptive Interactive Internet Team Video), which allows groups of students to collaboratively view a video in synchrony. AI2TV upholds the invariant that each student will view semantically equivalent content at all times. A semantic compression model is developed to provide instructional videos at different level-of-details to accommodate dynamic network conditions and usersäó» system requirements. We take advantage of the semantic compression algorithmäó»s ability to provide different layers of semantically equivalent video by adapting the client to play at the appropriate layer that provides the client with the richest possible viewing experience. Video player actions, like play, pause and stop, can be initiated by any group member and and the results of those actions are synchronized with all the other students. These features allow students to review a lecture video in tandem, facilitating the learning process. Experimental trials show that AI2TV successfully synchronizes instructional videos for distributed students while concurrently optimizing the video quality, even under conditions of fluctuating bandwidth, by adaptively adjusting the quality level for each student while still maintaining the invariant

Robot Local Network Using TQS Protocol for Land-to-Underwater Communications, Journal of Telecommunications and Information Technology, 2019, nr 1

This paper presents a model and an analysis of the Tag QoS switching (TQS) protocol proposed for heterogeneous robots operating in different environments. Collaborative control is topic that is widely discussed in multirobot task allocation (MRTA) – an area which includes establishing network communication between each of the connected robots. Therefore, this research focuses on classifying, prioritizing and analyzing performance of the robot local network (RLN) model which comprises a point-to-point topology network between robot peers (nodes) in the air, on land, and under water. The proposed TQS protocol was inspired by multiprotocol label switching (MPLS), achieving a quality of service (QoS) where swapping and labeling operations involving the data packet header were applied. The OMNET++ discrete event simulator was used to analyze the percentage of losses, average access delay, and throughput of the transmitted data in different classes of service (CoS), in a line of transmission between underwater and land environments. The results show that inferior data transmission performance has the lowest priority with low bitrates and extremely high data packet loss rates when the network traffic was busy. On the other hand, simulation results for the highest CoS data forwarding show that its performance was not affected by different data transmission rates characterizing different mediums and environments