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Background: Information communication technologies (ICT) are increasingly used in health promotion, but integration is challenging and involves complex processes. Large community health promotion events are often held but the experiences and processes have rarely been evaluated and published. No reports have described and systematically evaluated an ICT-supported health promotion event using digital games.Objective: We evaluated the development and implementation of a large community family health promotion event with ICT integration to promote family happiness with collaboration between academia (The University of Hong Kong) and the social (family) service sector, and collected feedback from participants and social service workers.Methods: We (i) conducted a systematic process evaluation, (ii) administered an on-site questionnaire survey on participant satisfaction and feedback, and (iii) collected post-event qualitative feedback from social workers on using new technologies, digital game design and overall experiences.Results: Fourteen digital games were designed and run in booths at the event by 12 non-governmental social service organizations and academia. Four gaming technologies were utilized: chroma key (green screen), somatosensory (kinect and leap motion techniques), augmented reality and virtual reality. 1,365 participants joined the event, in which 1,257 from 454 families were recruited and pre-registered through 12 NGOs. About 39.3% were male and more than half (53.3%) were aged 18 years and above. About 3,487 game booth headcounts were recorded. Games using virtual reality, kinect motion and green screen technologies were most liked. The average game satisfaction score was high (4.5 out of 5). Social service workers reported positive experiences with using new technologies in health promotion, and interests in future collaborations involving more ICT.Conclusions: Our systematic evaluation showed successful integration of ICT components in the health promotion event. This event, most likely the first of its kind, served as a capacity building and knowledge transfer platform for interdisciplinary co-sharing and co-learning of new technologies. It provided a solid foundation for further academic and social service partnerships and should be a useful model for similar community events and their evaluation. Further development and integration of ICT for health promotion among social service organizations with comprehensive evaluation are warranted.

This paper presents an argument for the necessity of a large vocabulary in automatic chord recognition systems, on the grounds of the requirements of machine musicianship. It proposes a system fram...

There are existing multi-MOOC level dropout prediction research in which many MOOCs' data are involved. This generated good results, but there are two potential problems. On one hand, it is inappropriate to use which week students are in to select training data because courses are with different durations. On the other hand, using all other existing data can be computationally expensive and inapplicable in practice. To solve these problems, we propose a model called WPSS (WPercent and Subset Selection) which combines the course progress normalization parameter wpercent and subset selection. 10 MOOCs offered by The University of Hong Kong are involved and experiments are in the multi-MOOC level. The best performance of WPSS is obtained in neural network when 50% of training data is selected (average AUC of 0.9334). Average AUC is 0.8833 for traditional model without wpercent and subset selection in the same dataset.

A mobile radio frequency identification (RFID) system consists of passive moving tags and a battery-constrained portable reader. In such a mobile RFID system, massive tags continuously enter the interrogation zone of the reader, which must quickly and reliably identify all the tags before they leave its coverage area. To meet such performance requirement, in the literature, a plethora of research works has been done in the design and optimization of anti-collision protocols, to enhance the system time efficiency and reduce the tag reading time as much as possible. However, as illustrated in this paper, optimizing the time efficiency does not necessarily lead to maximizing the energy efficiency for the reader. In fact, to efficiently utilize the limited battery energy at the reader, tag reading process should be power-aware consuming just enough energy to achieve the required performance.In this paper, we study the tradeoff between the time and energy efficiencies for tag identification. Specifically, we establish the time and energy efficiency metrics for the system and analyze the impact of frame length on the two metrics. We also consider different tag-moving scenarios, with tag density, moving velocity, prescribed identification constraint, etc., taken into consideration. We then analytically derive the most energy-efficient frame length under different tag moving scenarios. Extensive simulation results indicate that, guided by the derived numerical results, we can achieve as much as 11% improvement in energy efficiency, while satisfying the identification reliability constraint at the same time.

For a radio frequency identification (RFID) system deployed in dense environment, the reader must quickly and reliably extract information from numerous tags. To harmonize the channel access among competing tags and reduce collision probability, conventionally, framed slotted ALOHA (FSA) scheme is employed to resolve the collisions occurring when numerous tags simultaneously respond to the query of the reader. Typically, FSA algorithm consists of two parts: (i) estimating the number of available tags in vicinity, and (ii) base on the estimated value, setting the frame length accordingly. However, in the FSA scheme, when collision happens, it is impossible to estimate how many colliding tags simultaneously reply in a single slot, which, in turn, can lead to inaccuracy of the estimated cardinality and unreasonable setting of the frame length. This will further result in under-utilization of channel resource and the degradation of the system performance. The problem can be aggravated when all slots in the frame are entirely collided under a dense environment, rendering the malfunction of the estimator. To address this problem, in this paper, a prefix assisted approach, namely, PA-FSA, is proposed to enhance the estimation accuracy of the traditional FSA. Specifically, in PA-FSA, each tag appends a prefix in front of RN16 short message. When replying RN16 to the reader, a tag randomly and independently selects one bit in the prefix and sets the bit as active. By synchronizing prefixes of multiple colliding replies and counting the number of active bits in the overlapping prefixes, the reader can more precisely estimate how many tags have collided in a single slot, thus significantly improving the estimation accuracy. Extensive simulation results indicate that, compared with the traditional schemes, PA-FSA can estimate the tag cardinality more accurately and efficiently. Additionally, with PA-FSA adopted, system can reduce the communication overhead for approximately 50% and shorten the tag identification time for about 15%.

Massive Open Online Courses (MOOCs) have recently been highly popular among worldwide learners, while it is challenging to manage and interpret the large-scale discussion forum which is the dominant channel of online communication. K-Means clustering, one of the famous unsupervised learning algorithms, could help instructors identify influential users in MOOC forum, to better understand and improve online learning experience. However, traditional K-Means suffers from bias of outliers and risk of falling into local optimum. In this paper, OP-DCI, an optimized K-Means algorithm is proposed, using outlier post-labeling and distant centroid initialization. Outliers are not solely filtered out but extracted as distinct objects for post-labeling, and distant centroid initialization eliminates the risk of falling into local optimum. With OP-DCI, learners in MOOC forum are clustered efficiently with satisfactory interpretation, and instructors can subsequently design personalized learning strategies for different clusters.

Energy harvesting is a promising solution to powering billions of sensing and actuation devices in the age of the Internet of Things. However, efforts required to design and implement low-power and harvesting-aware embedded systems are not insignificant. The challenging and costly development process might even inhibit the mass adoption of energy-harvesting devices. In this paper, we present an "energy gatekeeper" architecture that decouples the task of incorporating energy harvesting from application circuit design, followed by our work-in-progress implementation. This work attempts to lower the barrier of integrating energy harvesting into applications by reducing the workload of developers.

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Powering billions of connected devices has been recognized as one of the biggest hurdles in the development of Internet of Things (IoT). With such a volume of tiny and ubiquitous smart physical objects in this new Internet paradigm, power cables or sizable battery packs are no longer a viable option to bring them online for years and decades. Energy harvesting, which enables devices to be self-sustaining, has been deemed a prominent solution to these constraints. This chapter provides a comprehensive review of IoT devices, from their roles and responsibilities, to the challenges of operating them autonomously in heterogeneous environments. The concepts, principles and design considerations for energy harvesting are introduced to aid researchers and practitioners to incorporate this key technology into their next applications.

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This paper proposes an automatic chord estimation (ACE) system with a two-layer architecture. The first layer performs chord smoothing with "GMM + HMM" approach. Then given the results of the first layer, the second layer performs chord estimation using a deep neural network, which is trained on a well chord-type balanced dataset. The system accepts exactly the "SeventhsBass" vocabulary. Three approaches with different configurations of the system are compared with Chordino, which is probably the only both MIREX evaluated and "SeventhsBass" acceptable ACE system. Evaluation results on "The Beatles" dataset show that the best approach outperforms Chordino in the most difficult "SeventhsBass" metric in a significant way.

Nonlinear model order reduction has always been a challenging but important task in various science and engineering fields. In this paper, a novel symmetric tensor-based order-reduction method (STORM) is presented for simulating large-scale nonlinear systems. The multidimensional data structure of symmetric tensors, as the higher order generalization of symmetric matrices, is utilized for the effective capture of high-order nonlinearities and efficient generation of compact models. Compared to the recent tensor-based nonlinear model order reduction (TNMOR) algorithm [1], STORM shows advantages in two aspects. First, STORM avoids the assumption of the existence of a low-rank tensor approximation. Second, with the use of the symmetric tensor decomposition, STORM allows significantly faster computation and less storage complexity than TNMOR. Numerical experiments demonstrate the superior computational efficiency and accuracy of STORM against existing nonlinear model order reduction methods.

Energy balancing is an effective technique in enhancing the lifetime of a wireless sensor network WSN. Specifically, balancing the energy consumption among sensors can prevent losing some critical sensors prematurely due to energy exhaustion so that the WSN's coverage can be maintained. However, the heterogeneous hostile operating conditions-different transmission distances, varying fading environments, and distinct residual energy levels-have made energy balancing a highly challenging task. A key issue in energy balancing is to maintain a certain level of energy fairness in the whole WSN. To achieve energy fairness, the transmission load should be allocated among sensors such that, regardless of a sensor's working conditions, no sensor node should be unfairly overburdened. In this paper, we model the transmission load assignment in WSN as a game. With our novel utility function that can capture realistic sensors' behaviors, we have derived the Nash equilibrium NE of the energy balancing game. Most importantly, under the NE, while each sensor can maximize its own payoff, the global objective of energy balancing can also be achieved. Moreover, by incorporating a penalty mechanism, the delivery rate and delay constraints imposed by the WSN application can be satisfied. Through extensive simulations, our game theoretic approach is shown to be effective in that adequate energy balancing is achieved and, consequently, network lifetime is significantly enhanced. Copyright © 2012 John Wiley & Sons, Ltd.

Peer-to-peer (P2P) media streaming quickly emerges as an important application over the Internet. A plethora of approaches have been suggested and implemented to support P2P media streaming. In our study, we first classified existing approaches and studied their characteristics by looking at three important quantities: number of upstream peers (parents), number of downstream peers (children), and average number of links per peer. In existing approaches, peers are assigned with a fixed number of parents without regard to their contributions, measured by the amount of outgoing bandwidths. Obviously, this is an undesirable arrangement as it leads to highly inefficient use of the P2P links. This observation motivates us to model the peer selection process as a cooperative game among peers. This results in a novel peer selection protocol such that the number of upstream peers of a peer is related to its outgoing bandwidth. Specifically, peers with larger outgoing bandwidth are given more parents, which make them less vulnerable to peer dynamics. Simulation results show that the proposed protocol improves delivery ratio using similar number of links per peer, comparing with existing approaches under a wide range of system parameters.

Orthogonal frequency-division multiplexing (OFDM) systems are the major cellular platforms for supporting ubiquitous high-speed mobile applications. However, a number of research challenges remain to be tackled. One of the most important challenges is the design of a judicious packet scheduler that will make efficient use of the spectrum bandwidth. Due to the multicarrier nature of the OFDM systems, the applicability and performance of traditional wireless packet-scheduling algorithms, which are usually designed for single-carrier systems, are largely unknown. In th.is paper, we propose a new quality-of-service (QoS)-aware proportional fairness (QPF) packet-scheduling policy with low complexity for the downlink of multiuser OFDM systems to allocate radio resources among users. Our proposed algorithm is based on a cross-layer design in that the scheduler is aware of both the channel (i.e., physical layer) and the queue state (i.e., data link layer) information to achieve proportional fairness while maximizing each user's packet-level QoS performance. The simulation results show that the proposed QPF algorithm is efficient in terms of average system throughput, packet-dropping probability, and packet delay, while maintaining adequate fairness among users with relatively low scheduling overhead.

Ultra wideband (UWB) systems are considered as the key wireless infrastructure platforms for efficient short-range communications. In particular, the UWB based mobile computing systems are envisioned to be attractive solutions to various ad hoc networking applications. However, due to UWB's unique physical characteristics, the traditional resource management schemes for ad hoc networks cannot be applied to UWB based systems directly. In this paper, we consider the bandwidth scheduling problem in a UWB based hierarchical wireless ad hoc network, which is typically used in an enterprise-scale mobile computing environment. Based on the mathematical analysis and the computer simulations, it is demonstrated that our proposed scheduling scheme exhibits close-to-optimal performance governed by the proportional fairness (PF) constraint. Moreover, a novel self-organized clustering method is designed to improve the system throughput while meeting the PF constraint. Simulation results suggest that the proposed clustering method is effective under various system configurations.

Ultra wideband (UWB) systems are currently an important wireless infrastructure for high performance short-range communications and mobile applications. Indeed, forming ad-hoc networks among various UWB enabled devices is considered as an important mobile data exchange operating environment. In our study, we explore the problem of jointly optimizing the power level and data rate used in the devices in such a UWB based ad-hoc network. We propose a practical optimization algorithm based on judicious power control for real-time applications and opportunistic scheduling for non-real-time applications. Simulation results indicate that our proposed techniques are effective under various practical scenarios.

Attempts to satisfy the demand for ubiquitous communications have resulted in a proliferation of hand-held short range communication devices based on the ISM (Industrial, Scientific, Medical) band technologies, most notably Bluetooth and IEEE 802.11b. However, coexistence between Bluetooth and IEEE 802.11b has become a critical issue that could severely hinder the performance achieved by user devices. In this study we performed a detailed implementation of a Linux based network access point (NAP), in which Bluetooth and IEEE 802.11b interfaces are colocated. Such an NAP is crucial in supporting ''hot-spot'' systems targeted to serve nomadic users carrying either a Bluetooth or an IEEE 802.11b device. Specifically, the goal of our study is to investigate the efficacy of a software-based interference coordination approach, through a detailed actual implementation so as to identify system issues which are difficult to obtained by simulations. We considered a wide range of common scheduling algorithms as the possible solutions in a Linux environment to estimate the interference effects as viewed from the network layer perspective. Upon our investigation, two wireless scheduling algorithms based on Channel State Independent Fair Queueing (CIFQ) were implemented in Linux to test their empirical performance under this NAP application. Finally, guided by our practical findings, we proposed and implemented two new packet scheduling algorithms in Linux to provide the best trade-offs to colocated Bluetooth and IEEE 802.11b traffics, as well as QoS support for different applications. Our results show that dynamic priorities and cooperative transmissions between Bluetooth and IEEE 802.11b traffic can effectively protect both interfaces from interference. We also compared our proposed scheme with two MAC layer approaches.

To enhance the lifetime of a sensor network which consists of hundreds or even thousands of resource-limited devices, energy efficient communication is mandatory. Despite that a plethora of work has been done in designing energy efficient protocols for sensor networks, the time-varying nature of wireless channel is largely unexplored. Indeed, we believe that a cross-layer design on power aware communication is necessary to further optimize energy usage. In this paper, we propose a new channel adaptive power aware protocol, called CAEM, which works by dynamically adjusting the data throughput under different channel conditions with the help of an adaptive channel coding and modulation facility. Each sensor device judiciously accesses the wireless medium in that communication activity is reduced for devices under poor channel conditions. Simulation results indicate that the proposed CAEM protocol can lead to energy conservation by as much as 30 per cent. Furthermore, CAEM is also efficient in channel utilization as it generates a higher data throughput even under heavy traffic load. Copyright © 2007 John Wiley & Sons, Ltd.

CypherDB addresses the problem of protecting the confidentiality of database stored externally in a cloud and enabling efficient computation over it to thwart any curious-but-honest cloud computing service provider. It works by encrypting the entire outsourced database and executing queries over the encrypted data using our novel CypherDB secure processor architecture. To optimize computational efficiency, our proposed processor architecture provides tightly-coupled datapaths that avoid information leakage during database access and query execution. Our simulation using a well-known database benchmark TPC-H over a commercial grade Database Management System (SQLite) demonstrates that our proposed architecture incurs an average of about 10 percent overhead when compared with the same set of operations without secure database processing.

Road safety is a huge concern due to the large number of fatalities and injuries caused by road accidents. Research has shown that fatigue can adversely affect driving performance and increase risk of road accidents. It has been shown that driving performance is enhanced by stress-relieving music which thereby promotes safer driving. Context-aware music delivery systems promote safer driving through intelligent music recommendations based on contextual knowledge. Two key aspects of situation-aware music delivery are effectiveness and efficiency of music recommendation. Efficiency is a critical aspect in real-time context based music recommendation as the music delivery system should quickly sense any change in the situation and deliver suitable music before the sensed context-data becomes obsolete. We focus on the efficiency of situation-aware music delivery systems in this paper. Music mood-mapping is a process which helps in understanding the mood of a song and is hence used in situation-aware music recommendation systems. This process requires a large processing time due to the complex calculations and large sizes of music files involved. Hence, optimizing this process is the key to improving the efficiency of context-aware music delivery systems. Here, we propose a novel cloud and crowd-sensing based approach to considerably optimize the efficiency of situation-aware music delivery systems.

Millions of people are severely injured or killed in road accidents every year and most of these accidents are caused by human error. Fatigue and negative emotions such as anger adversely affect driver performance, thereby increasing the risk involved in driving. Research has shown that listening to the right kind of music in these situations can ameliorate driver performance and improve road safety. Context-aware music delivery systems succeed in delivering suitable music according to the situation through the process of music mood-mapping which identifies the mood of a song. Additionally, we can leverage the power of the cloud to enable crowd sensing of the mood-mapping of various songs and enhance the effectiveness of situation-aware music delivery for drivers. The cloud can be used to aggregate the crowd sensed music mood-mapping data and improve the effectiveness of music delivery by providing accurate mood-mappings from the aggregated data. Currently, context-aware music delivery systems consider only features from the song for music mood-mapping. In this paper, we propose a novel approach to music mood-mapping for drivers which also incorporates the social context of a driver including age, gender and cultural background to enhance the effectiveness of music delivery in context-aware music recommendation systems for drivers.

With the recent emergence of the cloud market, cloud resource pricing fundamentally determines cloud revenue, cloud resource allocation and tenant demand dynamics. However, strategic interactions between cloud providers and tenant users are largely unknown. In this chapter, we consider a monopoly cloud market by formulating a competitive market among tenants. A novel Stackelberg game is proposed to tractably analyze such strategic interactions for optimal cloud resource pricing. To empirically evaluate our analyses, we conduct extensive simulations driven by 40 GB of realistic workload traces from Google.

Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this article, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which is designed to turn driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the drivers' social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driver's specific situations in an automated and intelligent manner. Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and mood-fatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease fatigue degree and negative mood degree of drivers by 49.09% and 36.35%, respectively, compared to traditional smartphone-based music player under similar driving situations.

We consider a basic scenario in wireless data access: a number of mobile clients are interested in a set of data items kept at a common server. Each client independently sends requests to inform the server of its desired data items and the server replies with a broadcast channel. We are interested in studying the energy consumption characteristics in such a scenario. First, we define a utility function for quantifying performance. Based on the utility function, we formulate the wireless data access scenario as a noncooperative game - wireless data access (WDA) game. Although our proposed probabilistic data access scheme does not rely on client caching, game theoretical analysis shows that clients do not always need to send requests to the server. Simulation results also indicate that our proposed scheme, compared with a simple always-request one, increases the utility and lifetime of every client while reducing the number of requests sent, with a cost of slightly larger average query delay. We also compare the performance of our proposed scheme with two popular schemes that employ client caching. Our results show that caching-only benefits clients with high query rates at the expense of both shorter lifetime and smaller utility in other clients

In scheduling a large number of user jobs for parallel execution on an open-resource grid system, the jobs are subject to system failures or delays caused by infected hardware, software vulnerability, and distrusted security policy. This paper models the risk and insecure conditions in grid job scheduling. Three risk-resilient strategies, preemptive, replication, and delay-tolerant, are developed to provide security assurance. We propose six risk-resilient scheduling algorithms to assure secure grid job execution under different risky conditions. We report the simulated grid performances of these new grid job scheduling algorithms under the NAS and PSA workloads. The relative performance is measured by the total job makespan, grid resource utilization, job failure rate, slowdown ratio, replication overhead, etc. In addition to extending from known scheduling heuristics, we developed a new space-time genetic algorithm (STGA) based on faster searching and protected chromosome formation. Our simulation results suggest that, in a wide-area grid environment, it is more resilient for the global job scheduler to tolerate some job delays instead of resorting to preemption or replication or taking a risk on unreliable resources allocated. We find that delay-tolerant min-min and STGA job scheduling have 13-23 percent higher performance than using risky or preemptive or replicated algorithms. The resource overheads for replicated job scheduling are kept at a low 15 percent. The delayed job execution is optimized with a delay factor, which is 20 percent of the total makespan. A Kiviat graph is proposed for demonstrating the quality of grid computing services. These risk-resilient job scheduling schemes can upgrade grid performance significantly at only a moderate increase in extra resources or scheduling delays in a risky grid computing environment

In our study, we investigate a packet-level protocol parallelization approach, which works by parallel multithreading the protocol execution such that packets within and among connections are processed in parallel using distinct processors/threads. The major advantage of this approach is its high scalability--with proper scheduling, more protocol connections, and hence more requests, can be supported by using more threads. In this paper, we present results of our detailed simulations using the NS-2 platform for reliably transferring video stream data in a client-server system. Different types of parameters are used to measure the performance. The parameters include available network bandwidth, different number of TCP connections, and different video sources. Our results show that a parallel approach can indeed significantly enhance the playback quality.

Minimization of the execution time of an iterative application in a heterogeneous parallel computing environment requires an appropriate mapping scheme for matching and scheduling the subtasks of a given application onto the processors. Often, some of the characteristics of the application subtasks are unknown a priori or change from iteration to iteration during execution-time based on the inputs being processed. In such a scenario, it may not be feasible to use the same off-line-derived mapping for each iteration of the application. One possibility is to employ a semi-static methodology that starts with an initial mapping but dynamically performs remapping between application iterations by observing the effects of the changing characteristics of the application's input data, called dynamic parameters, on the application's execution time. A contribution in this paper is to implement and evaluate a semi-static methodology involving the on-line use of off-line-derived mappings. The off-line phase is based on a genetic algorithm (GA) to generate high-quality mappings for a range of values for the dynamic parameters. A dynamic parameter space partitioning and sampling scheme is proposed that partitions the parameter space into a number of hyper-rectangles, within which the ''best'' mapping for each hyper-rectangle is stored in a mapping table. During the on-line phase, the actual dynamic parameters are observed and the off-line-derived mapping table is referenced to choose the most suitable mapping. Experimental results indicate that the semi-static approach outperforms a dynamic on-line approach and performs reasonably close to an infeasible on-line GA approach. Furthermore, the semi-static approach considerably outperforms the method of using the same mapping for all iterations.

Internet commerce and online commodity exchanges suffer from distrust among sellers and buyers, who are often strangers to each other. The authors present a new P2P reputation system based on fuzzy logic inferences, which can better handle uncertainty, fuzziness, and incomplete information in peer trust reports. This system aggregates peer reputations with affordable message overhead. By testing the system using eBay transaction data in the public domain, the authors seek to demonstrate the efficacy and robustness of two P2P reputation systems - fuzzytrust and eigentrust - at establishing trust among the peers in P2P applications.

Trusted Grid computing demands robust resource allocation with security assurance at all resource sites. Largescale Grid applications are being hindered by lack of security assurance from remote resource sites. We developed a security-binding scheme through site reputation assessment and trust integration across Grid sites. We do not treat the trust factor deterministically. Instead, we apply fuzzy theory to handle the fuzziness or uncertainties behind all trust attributes. The binding is achieved by periodic exchange of site security information and matchmaking to satisfy user job demands. PKI-based trust model supports Grids in multi-site authentication and single sign-on operations. However, cross certificates are inadequate to assess local security conditions at Grid sites. We propose a new fuzzy-logic trust model for distributed trust aggregation through fuzzification and integration of security attributes. We introduce the trust index of a Grid site, which is determined by site reputation from its track record and self-defense capability attributed to the risk conditions and hardware and software defenses deployed at a Grid site. A Secure Grid Outsourcing (SeGO) system is designed for secure scheduling a large number of autonomous and indivisible jobs to Grid sites. Significant performance gains are observed after trust aggregation, which is evaluated by running scalable NAS and PSA workloads over simulated Grids. Our security-binding scheme scales well with increasing user jobs and Grid sites. The new scheme can guide the security upgrade of Grid sites and predict the Grid performance of large workloads under risky conditions. Nomenclature: Si ‐t heith Grid site among m sites; Ji ‐t hej th job in a stream of n user jobs; m ‐ number of Grid sites; n ‐ number of jobs submitted to the system; tij ‐ trust index of site Si assessed by site Sj ; Vi ‐ trust vector maintained at site Si; M ‐ trust matrix for the entire Grid (dimension m × m); � ‐ site reputation or job success rate at a site; � ‐ self-defense capability of a Grid site; SD ‐ security demand of a user job; TI ‐ site trust index for secure job mapping, same as tij .

Large-scale worm outbreaks that lead to distributed denial-of-service attacks pose a major threat to Internet infrastructure security. Fast worm containment is crucial for minimizing damage and preventing flooding attacks against network hosts.

To efficiently support tetherless applications in ad hoc wireless mobile computing networks, a judicious ad hoc routing protocol is needed. Much research has been done on designing ad hoc routing protocols and some well-known protocols are also being implemented in practical situations. However; one major imperfection in existing protocols is that the time-varying nature of the wireless channels among the mobile-terminals is ignored; let alone exploited. This could be a severe design drawback because the varying channel quality can lead to very poor overall route quality in turn, resulting in low data throughput. Indeed, better performance could be achieved if a routing protocol dynamically changes the routes according to the channel conditions. In this paper, we first propose two channel adaptive routing protocols which work by using an adaptive channel coding and modulation scheme that allows a mobile terminal to dynamically adjust the data throughput via changing the amount of error protection incorporated. We then present a qualitative and quantitative comparison of the two classes of ad hoc routing protocols. Extensive simulation results indicate that channel adaptive ad hoc routing protocols are more efficient in that shorter delays and higher rates are achieved, at the expense of a higher overhead in route set-up and maintenance.