Radio frequency identification (RFID) is a key enabling technology for large-scale Internet of Things (IoT). Its deployment and management impact significantly on the operational effectiveness and scalability of IoT. Redundant reader elimination is of great importance to reduce the system's overhead and prolong the lifetime of RFID networks. It helps to reduce unnecessary reader-tag interactions and the cost of collision avoidance algorithm in distributed data collection of RFID network. In large-scale distributed RFID networks, one of the most challenging tasks for redundant reader elimination is to improve the performance of distributed algorithms. This paper proposes a novel distributed redundant reader elimination algorithm based on the threshold selection process, named threshold selection algorithm (TSA), for RFID networks. TSA algorithm selects effective reader iteratively based on the threshold sequence determined by expected tag coverage. This paper also introduces an optimization mechanism into TSA based on detected movement, named TSA with movement detection algorithm. By preliminary simulation, we determine the suggested parameter of linear multiplier for TSA. Our experiments show that TSA algorithm can provide 30%-60% better performance than other major distributed algorithms and also multiphase schemes in both dense and sparse environments. The overhead of TSA algorithm is 30%-50% lower than other selected algorithms, especially in tag-write operations.

Event processing is a crucial cornerstone supporting the revolution of Internet of Things (IoT) and Cyber-Physical Systems (CPS) by integrating physical-layer networking and providing intelligent computation and real-time control abilities. In various IoT and CPS application scenarios, the event processing systems are required to detect complex event patterns using large time window, namely long-term events. The detection of long-term event usually leads to a large number of redundant runtime instances and calculations that significantly deteriorates the system efficiency. In this article, we propose an efficient long-term event processing model, named Long-Term Complex Event Processing (LTCEP). It leverages the semantic constraints calculus to split long-term event into sub-models. We establish a long-term query and intermediate result buffering mechanism to optimize the real-time response ability and throughput performance. Experimental results show that LTCEP can effectively reduce more than 50% redundant runtime states, which provides over 60% faster response performance and around 30% higher system throughput comparing to other selected benchmarks. The results also imply that LTCEP model has better stability and scalability in large-scale event processing applications.

Research shows that despite organisational efforts to achieve privacy compliance, privacy breaches continue to rise. The extant studies on organisational privacy compliance concentrate on the extent to which privacy threats can be alleviated through a combination of technical and human controls and the positive (and often intended) influences of these controls. This focus inadvertently neglects unintended consequences such as impeded workflow in medical practices. To address this research conflict, this study uses an interpretive grounded theory research approach to investigate the consequences of privacy safeguard enactment in medical practices, including whether it influences their ability to meet privacy requirements and whether workflows are impeded. Our central contribution is a theoretical framework, the unintended consequences of privacy safeguard enactment (UCPSE) framework, which explicates the process by which privacy safeguards are evaluated and subsequently bypassed and the resulting influence on organisational compliance. The UCPSE highlights the importance of the imbalance challenge, which is the result of unintended consequences outweighing the intended consequences of privacy safeguard enactment. Failure to address the imbalance challenge leads to the adoption of workarounds that may ultimately harm the organisation’s privacy compliance. Despite several research calls, the consequences and effectiveness of organisational privacy efforts are largely missing from both information systems and health informatics research. This study is one of the first attempts to both systematically identify the impacts of privacy safeguard enactment and to examine its implications for privacy compliance in the healthcare domain. The findings also have practical implications for healthcare executives on the UCPSE and how they could alleviate the imbalance challenge to thwart workarounds and the subsequent negative effects on privacy compliance.

Big data is becoming a key basis for productivity growth, innovation, and consumer surplus, but also bring us great challenges in its volume, velocity, variety, value, and veracity. The notion of event is an important cornerstone to manage big data. High-speed railway is one of the most typical application domains for event-based system, especially for the train onboard system. There are usually numerous complex event patterns subscribed in system sharing the same prefix, suffix, or subpattern; consequently, multipattern complex event detection often results in plenty of redundant detection operations and computations. In this paper, we propose a multipattern complex event detection model, multipattern event processing (MPEP), constructed by three parts: 1) multipattern state transition; 2) failure transition; and 3) state output. Based on MPEP, an intelligent onboard system for high-speed train is preliminarily implemented. The system logic is described using our proposed complex event description model and compiled into a multipattern event detection model. Experimental results show that MPEP can effectively optimize the complex event detection process and improve its throughput by eliminating duplicate automata states and redundant computations. This intelligent onboard system also provides better detection ability than other models when processing real-time events stored in high-speed train Juridical Recording Unit (JRU).

Despite two decades of intensive research, it remains a challenge to design a practical anonymous two-factor authentication scheme, for the designers are confronted with an impressive list of security requirements (e.g., resistance to smart card loss attack) and desirable attributes (e.g., local password update). Numerous solutions have been proposed, yet most of them are shortly found either unable to satisfy some critical security requirements or short of a few important features. To overcome this unsatisfactory situation, researchers often work around it in hopes of a new proposal (but no one has succeeded so far), while paying little attention to the fundamental question: whether or not there are inherent limitations that prevent us from designing an “ideal” scheme that satisfies all the desirable goals? In this work, we aim to provide a definite answer to this question. We first revisit two foremost proposals, i.e. Tsai et al.'s scheme and Li's scheme, revealing some subtleties and challenges in designing such schemes. Then, we systematically explore the inherent conflicts and unavoidable trade-offs among the design criteria. Our results indicate that, under the current widely accepted adversarial model, certain goals are beyond attainment. This also suggests a negative answer to the open problem left by Huang et al. in 2014. To the best of knowledge, the present study makes the first step towards understanding the underlying evaluation metric for anonymous two-factor authentication, which we believe will facilitate better design of anonymous two-factor protocols that offer acceptable trade-offs among usability, security and privacy.

An adaptive process management system (APMS) allows for flexible, dynamic, and even ad hoc adaptation of processes based on case data, context, and events. These processes may arise in various domains such as business, healthcare, etc. In knowledge-intensive environments, it is important that APMS technology ensures error-free process execution and compliance with semantic constraints. However, most process design tools handle only syntactic constraints. This restricts their value in real-world applications considerably. This paper proposes a novel approach to check the compliance of process models against semantic constraints and the validity of process change operations using mixed-integer programming (MIP). The MIP formulation allows us to describe existential, dependency, ordering, and various other relationships among tasks along with business policies in a standard way. In addition to incorporating the semantic constraint specifications into an MIP formulation, we introduce three novel ideas in this paper: (1) the notion of degree of compliance of processes to constraints based on a penalty function, (2) the concepts of full and partial validity of change operations, and (3) the idea of compliance by compensation. Thus, compensation operations derived from compliance degree can transform a noncompliant process into a compliant one both at design and execution time. We illustrate our approach in the context of a healthcare workflow as a way to reduce medical errors and argue that it is more elegant and superior to a pure logic-based approach. Complex scenarios with multiple concurrent processes (and constraints across them) for a single patient are also considered.

Today's organizations raise an increasing need for information sharing via on-demand access. Information brokering systems (IBSs) have been proposed to connect large-scale loosely federated data sources via a brokering overlay, in which the brokers make routing decisions to direct client queries to the requested data servers. Many existing IBSs assume that brokers are trusted and thus only adopt server-side access control for data confidentiality. However, privacy of data location and data consumer can still be inferred from metadata (such as query and access control rules) exchanged within the IBS, but little attention has been put on its protection. In this paper, we propose a novel approach to preserve privacy of multiple stakeholders involved in the information brokering process. We are among the first to formally define two privacy attacks, namely attribute-correlation attack and inference attack, and propose two countermeasure schemes automaton segmentation and query segment encryption to securely share the routing decision-making responsibility among a selected set of brokering servers. With comprehensive security analysis and experimental results, we show that our approach seamlessly integrates security enforcement with query routing to provide system-wide security with insignificant overhead.

Intelligent transportation systems (ITS) have emerged as an efficient and effective way of alleviating the traffic congestion and improving the performance of transportation systems. Key challenges of ITS in recent years include the pervasive data collection, data security, privacy preserving, large volume data processing, and intelligent analytics. These challenges lead to a revolution in ITS development by leveraging the crowdsourcing scheme and cloud computing architecture. In this paper, we propose a user-driven Cloud Transportation system (CTS) which employs a scheme of user-driven crowdsourcing to collect user data for traffic model construction and congestion prediction including data collection, filtering, modeling, intelligent computation and publish. We describe in details the application scenario, system architecture, and core CTS services model. To verify the feasibility of our approach, we have developed a prototype system which elaborated the cloud architecture and other implementation details. This paper aims to inspire further research of user-driven CTS on intelligent data processing model for smarter utilization of transportation infrastructure.

With rising concerns on user privacy over the Internet, anonymous communication systems that hide the identity of a participant from its partner or third parties are highly desired. Existing approaches either rely on a relative small set of pre-selected relay servers to redirect the messages, or use structured peer-to-peer systems to multicast messages among a set of relay groups. The pre-selection approaches provide good anonymity, but suffer from node failures and scalability problem. The peer-to-peer approaches are subject to node churns and high maintenance overhead, which are the intrinsic problems of P2P systems. In this paper, we present CAT, a node-failure-resilient anonymous communication protocol. In this protocol, relay servers are randomly assigned to relay groups. The initiator of a connection selects a set of relay groups instead of relay servers to set up anonymous paths. A valid path consists of relay servers, one from each selected relay group. The initiator explores valid anonymous paths via a probing process. Since the relative positions of relay servers in the path are commutative, there exist multiple anonymous yet commutative paths, which form an anonymous tunnel. When a connection encounters a node failure, it quickly switches to a nearest backup path in the tunnel through "path hopping", without tampering the initiator or renegotiating the keys. Hence, the protocol is resilient to node failures. We also show that the protocol provides good anonymity even when facing types of active and passive attacks. Finally, the operating cost of CAT is analyzed and shown to be similar to other node-based anonymous communication protocols.

Wireless broadcast is an effective approach for disseminating data to a number of users. To provide secure access to data in wireless broadcast services, symmetric-key-based encryption is used to ensure that only users who own the valid keys can decrypt the data. With regard to various subscriptions, an efficient key management for distributing and changing keys is in great demand for access control in broadcast services. In this paper, we propose an efficient key management scheme, namely, key tree reuse (KTR), to handle key distribution with regard to complex subscription options and user activities. KTR has the following advantages. First, it supports all subscription activities in wireless broadcast services. Second, in KTR, a user only needs to hold one set of keys for all subscribed programs instead of separate sets of keys for each program. Third, KTR identifies the minimum set of keys that must be changed to ensure broadcast security and minimize the rekey cost. Our simulations show that KTR can save about 45 percent of communication overhead in the broadcast channel and about 50 percent of decryption cost for each user compared with logical-key-hierarchy-based approaches.

A Distributed Information Brokering System (DIBS) is a peer-to-peer overlay network that comprises diverse data servers and brokering components helping client queries locate the data server(s). Many existing information brokering systems adopt server side access control deployment and honest assumptions on brokers. However, little attention has been drawn on privacy of data and metadata stored and exchanged within DIBS. In this paper, we address privacy-preserving information sharing via on-demand information access. We propose a flexible and scalable system using a broker-coordinator overlay network. Through an innovative automaton segmentation scheme, distributed access control enforcement, and query segment encryption, our system integrates security enforcement and query forwarding while preserving system-wide privacy. We present the automaton segmentation approach, analyze privacy preservation in details, and finally examine the end-to-end performance and scalability through experiments and analysis.

Peer-to-Peer (P2P) systems have become an im- portant application in the Internet for information sharing. However, recent studies showed that pollution in P2P file sharing systems is very serious. Although some measurements have been done to estimate the pollution in P2P networks, how a polluted file is propagated among nodes and why the pollution is serious are not studied. This paper proposes a model to capture the major features of the pollution. The model describes the propagation of a polluted file and the competition among the polluted version and the good version of a file in a P2P network. The model shows that correct user strategies based on publisher's reputation can effectively stop pollution at the expense of some initial propagation.

Ad hoc networks are usually unreliable and have limited bandwidth resources. In such networks, packet injection attacks can cause serious denial-of-service via wireless channel contention and network congestion. To defend against this type of injection attacks, we propose SAF, an efficient and effective Source Authentication Forwarding protocol. The protocol can either immediately filter out injected junk packets with very high probability or expose the true identity of an injector. Differing from other forwarding defenses, this protocol is designed to fit in the unreliable environment of ad hoc networks. Our simulation shows that SAF incurs very lightweight overhead in communication and computation.

To provide secure access to data in wireless broadcast services, symmetric key-based encryption is used to ensure that only users who own the valid keys can decrypt the data. In this paper, we propose an efficient key management scheme (namely KTR) to handle key distribution with regarding to complex subscription options and user activities. KTR has the following advantages. First, it supports all subscription activities in wireless broadcast services. Second, in KTR, a user only needs to hold one set of keys for all subscribed programs, instead of separate sets of keys for each program. Third, KTR identifies the minimum set of keys that must be changed to ensure broadcast security and minimize the rekey cost. Our simulations show that KTR can save about 45% of communication overhead in the broadcast channel and about 50% of decryption cost for each user, compared with conventional approaches based on logical key hierarchy.

In this paper, we examine the relative forecast accuracy of information markets versus expert aggregation. We leverage a unique data source of almost 2000 people's subjective probability judgments on 2003 US National Football League games and compare with the "market probabilities" given by two different information markets on exactly the same events. We combine assessments of multiple experts via linear and logarithmic aggregation functions to form pooled predictions. Prices in information markets are used to derive market predictions. Our results show that, at the same time point ahead of the game, information markets provide as accurate predictions as pooled expert assessments. In screening pooled expert predictions, we find that arithmetic average is a robust and efficient pooling function; weighting expert assessments according to their past performance does not improve accuracy of pooled predictions; and logarithmic aggregation functions offer bolder predictions than linear aggregation functions. The results provide insights into the predictive performance of information markets, and the relative merits of selecting among various opinion pooling methods.

In this paper, we present an ant colony-based heuristic to solve QoS (quality of service) constrained multicast routing problems. Our algorithm considers multiple QoS metrics, such as bandwidth, delay, delay jitter and packet loss rate, to find the multicast tree that minimizes the total cost. We also explore the scalability of the ant algorithm. Our tests show that the algorithm can find optimal (or near-optimal) solutions quickly and that it has good scalability.

Presents a hierarchical genetic algorithm (GA) to solve the cell formation and layout decisions of cellular manufacturing. The intrinsic features of our proposed GA include using a hierarchical chromosome structure to encode concurrent cell design and layout decisions, developing a new selection scheme to dynamically consider two highly correlated fitness functions, and proposing a group mutation operator to increase the probability of mutation. Our tests show that these modifications are fairly effective in improving solution quality as well as shortening the speed of convergence.

This paper presents an improved fuzzy c-means algorithm to solve the manufacturing cell formation problems. The proposed algorithm, which integrates the subtractive algorithm (to produce an initial solution), the fuzzy c-means (FCM) algorithm and a solution selecting procedure (to identify the best solution), remedies the major weaknesses of original FCM clustering. We test the performance of the proposed algorithm with 20 data sets from open literature and 60 generated data sets. Our experiments show that the proposed approach performs much better than the original FCM and the solutions are consistent with the best solutions found in references or the control solutions.

In this paper, an efficient heuristic genetic algorithm (GA), considering the domain-specific characteristics of manufacturing cell design, was developed to model the cell formation (CF) problems. Its performance was compared with an optimization model and the traditional GA approach. Our study confirmed that considering domain-specific characteristics in the crossover and mutation operators could obtain very promising results.

We explore the use of GAs for solving a network optimization problem, the degree-constrained minimum spanning tree problem. We also examine the impact of encoding, crossover, and mutation on the performance of the GA. A specialized repair heuristic is used to improve performance. An experimental design with 48 cells and ten data points in each cell is used to examine the impact of two encoding methods, three crossover methods, two mutation methods, and four networks of varying node sizes. Two performance measures, solution quality and computation time, are used to evaluate the performance. The results obtained indicate that encoding has the greatest effect on solution quality, followed by mutation and crossover. Among the various options, the combination of determinant encoding, exchange mutation, and uniform crossover more often provides better results for solution quality than other combinations. For computation time, the combination of determinant encoding, exchange mutation, and one-point crossover provides better results.