Buildings are required to follow standard operational procedures during emergency evacuation. In addition to people evacuating the building, one of the recommended steps during a fire evacuation is to shut down the air handling units (AHUs) of the heating, ventilation, and air conditioning (HVAC) system to prevent smoke from spreading in the building via the air ducts. Shutting down the AHU will inevitably cut-off cooling, resulting in internal temperatures rising steeply particularly on hot days. This phenomenon imposes considerable power demand on the HVAC to rapidly cool the building down during reoccupation. In this article, we study the energy implications of post-evacuation scenarios. Our contributions are threefold: 1) we quantify power excursion caused in 43 evacuation events across 14 buildings of a university campus using a data-driven building thermal model. We show evacuations during summer season can result in power consumption up to 150% above the power demand threshold; 2) we develop a method to reschedule planned evacuations in order to eliminate the power excursions while adhering to building evacuation standards; and 3) we develop a formal optimization framework to minimize the energy costs during planned and emergency evacuations without compromising the desired thermal comfort temperatures by intelligently cooling the building post evacuation. This is the first study to understand and reduce the HVAC power consumption associated with building evacuation events.

It is well-known that the HVAC (heating, ventilation, and air conditioning) dominates electricity consumption in commercial buildings. In this paper, we focus on one of the core problems in building operation, namelychiller sequencingto reduce HVAC electricity consumption. Our contributions are threefold. First, we make a case for why it is important to quantify the performance profile of a chiller, namely coefficient of performance (COP), atrun-time, by developing a data-driven COP estimation methodology. Second, we show that predicting COP accurately is a non trivial problem, requiring considerable computation time. To overcome this barrier, we develop a data-driven COP prediction model and an edge-based chiller sequencing framework integrating the COP predictions, and show that they strike a good balance between electricity saving and ease of use for real-world deployment. Finally, we evaluate the performance of our scheme by applying it to real-world data, spanning four years, obtained from multiple chillers across three large commercial buildings in Hong Kong. The results show an electricity saving of over 30 percent compared to baselines. We offer our edge based data-driven chiller sequencing framework as a cost-effective and practical mechanism to reduce electricity consumption associated with HVAC operation in commercial buildings.

The buildings sector is one of the largest energy consuming entities today, accounting for about 40% of global energy consumption. Alongside, electricity prices are increasing in several nations worldwide, putting pressure on facility managers to reduce the expense incurred in operating their buildings. In this paper, we focus on building heating, ventilation, and air conditioning and make the following contributions. First, inspired by the deployment of Internet of Things in buildings, we propose a data driven gray box model for zone thermal dynamics and use it to develop an optimization framework for precooling a building. The goal is to determine a precooling strategy that not only shifts the peak air conditioning power to low electricity tariff regimes, but also reduces the peak power, energy consumption, and electricity costs associated with cooling a building. Second, the optimal precooling problem turns out to be nonlinear. To enable ease of use of our framework in the real-world without undermining its efficacy, we develop a linearized version of the problem and show that it has comparable performance to the original formulation. Third, we quantify the benefits of the precooling optimization framework by applying it to building management system data, across multiple days, obtained from a large office building in Australia. The evaluations reveal that optimal precooling lowers the peak power, energy consumption, and electricity costs for cooling the building by up to 30% while ensuring that the thermal comfort of the occupants is maintained. We conclude by describing two applications of the proposed optimization framework for use in developing countries. Our precooling solution incurs no capital expense and permits facility managers to take decisions from a data driven point of view for improving the energy efficiency of their buildings.

The Internet of Things (IoT) is being hailed as the next wave revolutionizing our society, and smart homes, enterprises, and cities are increasingly being equipped with a plethora of IoT devices. Yet, operators of such smart environments may not even be fully aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. In this paper, we address this challenge by developing a robust framework for IoT device classification using traffic characteristics obtained at the network level. Our contributions are fourfold. First, we instrument a smart environment with 28 different IoT devices spanning cameras, lights, plugs, motion sensors, appliances, and health-monitors. We collect and synthesize traffic traces from this infrastructure for a period of six months, a subset of which we release as open data for the community to use. Second, we present insights into the underlying network traffic characteristics using statistical attributes such as activity cycles, port numbers, signalling patterns, and cipher suites. Third, we develop a multi-stage machine learning based classification algorithm and demonstrate its ability to identify specific IoT devices with over 99 percent accuracy based on their network activity. Finally, we discuss the trade-offs between cost, speed, and performance involved in deploying the classification framework in real-time. Our study paves the way for operators of smart environments to monitor their IoT assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols.

Realizing the many benefits from Facebook require users to share information reciprocally, which has overtime created trillions of bytes of information online—a treasure trove for cybercriminals. The sole protection for any user are three sets of privacy protections afforded by Facebook: settings that control information privacy (i.e., security of social media accounts and identity information), accessibility privacy or anonymity (i.e., manage who can connect with a user), and those that control expressive privacy (i.e., control who can see a user's posts and tag you). Using these settings, however, involves a trade‐off between making oneself accessible and thereby vulnerable to potential attacks, or enacting stringent protections that could potentially make someone inaccessible thereby reducing the benefits that are accruable through social media. Using two theoretical frameworks, Uses and Gratifications (U&G) and Protection Motivation Theory (PMT), the research examined how individuals congitvely juxtaposed the cost of maintaining privacy through the use of these settings against the benefits of openness. The application of the U&G framework revealed that social need fulfillment was the single most significant benefit driving privacy management. From the cost standpoint, the PMT framework pointed to perceived severity impacting expressive and information privacy, and perceived susceptability influencing accessibility privacy.

Residential broadband consumption is growing rapidly, increasing the gap between Internet service provider (ISP) costs and revenues. Meanwhile, proliferation of Internet-enabled devices is congesting access networks, degrading end-user experience, and affecting content provider monetization. In this paper, we propose a new model whereby the content provider explicitly signals fast- and slow-lane requirements to the ISP on a per-flow basis, using open APIs supported through software defined networking (SDN). Our first contribution is to develop an architecture that supports this model, presenting arguments on why this benefits consumers (better user experience), ISPs (two-sided revenue), and content providers (fine-grained control over peering arrangement). Our second contribution is to evaluate our proposal using a real trace of over 10 million flows to show that video flow quality degradation can be nearly eliminated by the use of dynamic fast-lanes, and web-page load times can be hugely improved by the use of slow-lanes for bulk transfers. Our third contribution is to develop a fully functional prototype of our system using open-source SDN components (Openflow switches and POX controller modules) and instrumented video/file-transfer servers to demonstrate the feasibility and performance benefits of our approach. Our proposal is a first step towards the long-term goal of realizing open and agile access network service quality management that is acceptable to users, ISPs, and content providers alike.

Continued scaling of switching capacity in the Internet core is threatened by power considerations. Internet service providers face increased carbon footprint and operational costs, while router manufacturers encounter upper limits on switching capacity per rack. This paper studies the role of packet buffer memory on the power consumption of backbone routers. Our first contribution is to estimate from published datasheets the energy costs of static RAM/dynamic RAM packet-buffer memory, showing that it accounts for over 10% of power consumption in a typical router line-card; we then show, using empirical data from core and enterprise networks, that much of this memory is used for only a small fraction of time. Our second contribution is to develop a simple yet practical algorithm for putting much of the memory components to sleep and waking them as needed, while being able to control resulting traffic performance degradation in the form of packet loss during transient congestion. Finally, we conduct a comprehensive evaluation of our scheme, via analytical models pertaining to long-range-dependent traffic, using simulations of offline traffic traces taken from carrier/enterprise networks as well as online Transmission Control Protocol flows in ns2, and by implementing our scheme on a programmable-router test bed. This paper is the first to show the feasibility of, and energy savings from, dynamic management of packet buffer memory in core routers in the market today.

Access networks, largely based on DSL or cable links, continue to be the bandwidth bottleneck between device-rich households and high-speed core networks, causing frustration for both end-users and content service providers (CSPs). In this paper, we advocate that the scarce bandwidth resource on the access link be managed jointly, under software control, by the Internet service provider (ISP), consumer, and CSP. Our first contribution is to develop software defined networking (SDN) APIs for bandwidth control at fine-grain (per-flow) by the CSP and at coarse-grain (per-device) by the consumer, and highlight the benefits of such an architecture for all entities. Second, we develop an economic model to guide the ISP in determining bandwidth allocation that balances the needs of the CSP against those of the consumer, and demonstrate its utility via simulation of trace data comprising over 10 million flows. Finally, we prototype our system using commodity home routers and open-source SDN platforms, and conduct experiments in a campus-scale network to demonstrate how our scheme permits proactive and reactive improvement in end-user experience.

Enhancing quality-of-service (QoS) for specific traffic streams by assigning them to "fast-lanes" on the broadband Internet service is a subject of intense ongoing debate. While Internet Service Providers (ISPs) have clear economic imperatives for fast-lanes paid by content service providers (CSPs), proponents of net-neutrality argue that consumer interest will be ignored in the selection of traffic thus prioritized. In this paper we propose a new solution in which ISP fast-lanes have "two-sided" control, i.e. by both consumers and CSPs. Our contributions are two-fold: (1) We develop an architecture in which ISP-operated fast-lanes can be controlled at fine-grain (per-flow) by the CSP and at coarse-grain (per-device) by the consumer, and argue why we think such an architecture can meet the needs of all three parties; and (2) We develop an economic model to guide the ISP in determining fast-lane allocation that balances the needs of the CSP against those of the consumer, and evaluate our model via simulation of trace data comprising over 10 million flows.

The increasing uptake of smart home appliances, such as lights, smoke-alarms, power switches, baby monitors, and weighing scales, raises privacy and security concerns at unprecedented scale, allowing legitimate and illegitimate entities to snoop and intrude into the family's activities. In this paper we first illustrate these threats using real devices currently available in the market. We then argue that as more such devices emerge, the attack vectors increase, and ensuring privacy/security of the house becomes more challenging. We therefore advocate that device-level protections be augmented with network-level security solutions, that can monitor network activity to detect suspicious behavior. We further propose that software defined networking technology be used to dynamically block/quarantine devices, based on their network activity and on the context within the house such as time-of-day or occupancy-level. We believe our network-centric approach can augment device-centric security for the emerging smart-home.

Interactive cloud computing and cloud-based applications are a rapidly growing sector of the expanding digital economy because they provide access to advanced computing and storage services via simple, compact personal devices. Recent studies have suggested that processing a task in the cloud is more energy-efficient than processing the same task locally. However, these studies have generally ignored the power consumption of the network and end-user devices when accessing the cloud. In this paper, we develop a power consumption model for interactive cloud applications that includes the power consumption of end-user devices and the influence of the applications on the power consumption of the various network elements along the path between the user and the cloud data centre. As examples, we apply our model to Google Drive and Microsoft Skydrive's word processing, presentation and spreadsheet interactive applications. We demonstrate via extensive packet-level traffic measurements that the volume of traffic generated by a session of the application vastly exceeds the amount of data keyed in by the user. This has important implications on the overall power consumption of the service. We show that using the cloud to perform certain tasks consumes more power (by a watt to 10 watts depending on the scenario) than performing the same tasks locally on a low-power consuming computer and a tablet.

Residential broadband gateways (comprising modem, router, and Wi-Fi access point), though individually consuming only 5-10 Watts of power, are significant contributors to overall network energy consumption due to large deployment numbers. Moreover, home gateways are typically always on, so as to provide continuous online presence to household devices for VoIP, smart metering, security surveillance, medical monitoring, etc. A natural solution for reducing the energy consumption of home gateways is to leverage the overlap of Wi-Fi networks common in urban environments and aggregate user traffic on to fewer gateways, thus putting the remaining to sleep. In this paper we propose, evaluate, and prototype an architecture that overcomes significant challenges in making this solution feasible at large-scale. We advocate a centralized approach, whereby a single authority co-ordinates the home gateways to maximize energy savings in a fair manner. Our solution can be implemented across heterogeneous ISPs, avoids client-side modifications (thus encompassing arbitrary user devices and operating systems), and permits explicit control of session migrations. We apply our solution to Wi-Fi traces collected in a building with 30 access points and 25,000 client connections, and evaluate via simulation the tradeoffs between energy savings, session disruptions, and fairness. We then prototype our system on commodity Wi-Fi access points, test it in a two-storey building emulating 6 residences, and demonstrate radio energy reduction of over 60 percent with little impact on user experience.

Online social networks (OSNs) with their huge number of active users consume significant amount energy both in the data centers and in the transport network. Existing studies focus mainly on the energy consumption in the data centers and do not take into account the energy consumption during the transport of data between end-users and data centers. To indicate the amount of the neglected energy, this paper provides a comprehensive framework and a set of measurements for understanding the energy consumption of cloud applications such as photo sharing in social networks. A new energy model is developed to estimate the energy consumption of cloud applications and applied to sharing photos on Facebook, as an example. Our results indicate that the energy consumption involved in the network and end-user devices for photo sharing is approximately equal to 60% of the energy consumption of all Facebook data enters. Therefore, achieving an energy-efficient cloud service requires energy efficiency improvement in the transport network and end-user devices along with the related data centers.

Routers and switches are major contributors to the energy consumption of modern networks. Today, many energy efficiency metrics for these high-capacity devices are coarse-grained, i.e., based upon a single energy per bit value given at peak load or averaged over several specific loads. In this paper, we develop a new power model and a vendor-agnostic methodology that permits quantifying the energy efficiency of Internet equipment at a more fundamental level, i.e., at the granularity of per-packet processing, and per-byte store and forward packet handling operations. We demonstrate the efficacy of the proposed technique by applying it to various types of routers and switches. We describe how our technique can be used to accurately estimate the network-wide energy footprint incurred when accessing different applications. We offer our method as a valuable framework against which the energy efficiency of current and future generation of load-proportional Internet equipment can be benchmarked.

In this paper, we study the efficacy of error control schemes for energy-efficient reliable delivery of large files (hundreds of GBs) over core optical networks. Specifically, we examine two schemes: automatic repeat request (ARQ), and hybrid ARQ (i.e. ARQ combined with forward error correction (FEC) capability). We focus on Reed-Solomon (RS) FEC codes (in hybrid ARQ) and propose a new model, incorporating different block sizes as well as code error-correction capability, to estimate the energy consumption for performing encoding and decoding operations in optical networks. The model considers the impact of varying pre-FEC bit-error rates (BER) of the optical channel, and the signal processing blocks used to implement RS codes. Our results show that when the pre-FEC channel BER is in excess of 10-5, hybrid ARQ offers better performance than ARQ in terms of energy efficiency. However, both hybrid ARQ and ARQ have similar performance under lower BER.

Cloud computing and cloud-based services are a rapidly growing sector of the expanding digital economy. Recent studies have suggested that processing a task in the cloud is more energy-efficient than processing the same task locally. However, these studies have generally ignored the network transport energy and the additional power consumed by end-user devices when accessing the cloud. In this paper, we develop a simple model to estimate the incremental power consumption involved in using interactive cloud services. We then apply our model to a representative cloud-based word processing application and observe from our measurements that the volume of traffic generated by a session of the application typically exceeds the amount of data keyed in by the user by more than a factor of 1000. This has important implications on the overall power consumption of the service. We provide insights into the reasons behind the observed traffic levels. Finally, we compare our estimates of the power consumption with performing the same task on a low-power consuming computer. Our study reveals that it is not always energy-wise to use the cloud. Performing certain tasks locally can be more energy-efficient than using the cloud.

To cope with the phenomenal growth of the Internet over the next decade, core networks are expected to scale to capacities of terabits-per-second and beyond. Increasing the role of optics for switching and transmission inside the core network seems to be the most promising way forward to accomplish this capacity scaling. Unfortunately, unlike electronic memory, it remains a formidable challenge to build even a few packets of integrated all-optical buffers. In this context, we envision a bufferless (or near-zero buffer) core optical network and make three contributions: First, we propose a novel edge-to-edge based packet-level forward error correction (FEC) scheme that combats packet loss in the bufferless core, and characterise the impact of FEC strength on loss at a single link. Second, we develop a global optimisation framework for multi-hop networks, and propose a heuristic algorithm that adjusts FEC strength to achieve fairness amongst the different single- and multi-hop flows. Finally, we evaluate the performance of our FEC scheme for realistic mixes of short- and long-lived TCP flows, and show that edge-to-edge packet-level FEC can be tuned to effectively mitigate contention losses in the core, thus opening the doors to bufferless optical networks in the near future.

Reducing the power consumption of core Internet routers is important for both Internet Service Providers (ISPs) and router vendors. ISPs can reduce their Carbon footprint and operational costs, while router manufacturers can achieve higher switching capacity per rack space. In this work, we examine the impact of packet buffers on the power consumption of backbone router line-cards. We argue that Gigabytes of always-on SRAM and DRAM buffers account for around 10% of the power, but are actively used only during transient periods of congestion. We propose a simple and practical algorithm for activating buffers incrementally as needed and putting them to sleep when not in use. We evaluate our algorithm on traffic traces from carrier and enterprise networks, via simulations in ns2, and by implementing it on a programmable-router test-bed. Our study shows that much of the energy associated with off-chip packet buffers can be eliminated with negligible impact on traffic performance. Dynamic adjustment of active router buffer size provides a low-complexity low-risk mechanism of saving energy that is amenable for incremental deployment in networks today.

In the past few years there has been vigorous debate regarding the size of buffers required at core Internet routers. Recent arguments supported by theory and experimentation show that under certain conditions, core router buffer sizes of a few tens of packets suffice for realizing acceptable end-to-end TCP throughputs. This is a significant step toward the realization of optical packet switched (OPS) networks, which are inherently limited in their ability to buffer optical signals. However, prior studies have largely ignored the presence of real-time traffic, which is increasing in importance as a source of revenue for Internet service providers. In this paper, we study the interaction that happens between real-time (open-loop) and TCP (closed-loop) traffic when they multiplex at buffers of very small size (few tens of packets) and make a significant discovery - namely that in a specific range of buffer size, real-time traffic losses increase as buffer size becomes larger. Our contributions pertaining to this anomalous behavior are threefold. First, we exhibit this anomalous loss performance for real-time traffic via extensive simulations using synthetic traffic and real video traces. Second, we develop quantitative models that reveal the dynamics of buffer sharing between real-time and TCP traffic that lead to this behavior. Third, we show how various factors such as the nature of real-time traffic, mixture of long-lived and short-lived TCP flows, and packet sizes impact the severity of the anomaly. Our study is the first to consider interactions between real-time and TCP traffic in very small (potentially all-optical) buffers and informs router manufacturers and network operators of the factors to consider when dimensioning such small buffer sizes for desired performance balance between real-time and TCP traffic.

Internet traffic is expected to grow phenomenally over the next five to ten years, and to cope with such large traffic volumes, core networks are expected to scale to capacities of terabits-per-second and beyond. Increasing the role of optics for switching and transmission inside the core network seems to be the most promising way forward to accomplish this capacity scaling. Unfortunately, unlike electronic memory, it remains a formidable challenge to build even a few packets of integrated all-optical buffers. In the context of envisioning a bufferless (or near-zero buffer) core network, our contributions are threefold: First, we propose a novel edge-to-edge based packet-level forward error correction (FEC) framework as a means of combating high core losses, and investigate via analysis and simulation the appropriate FEC strength for a single core link. Second, we consider a realistic multi-hop network and develop an optimisation framework that adjusts the FEC strength on a per-flow basis to ensure fairness between single-and multi-hop flows. Third, we study the efficacy of FEC for various system parameters such as relative mixes of short-lived and long-lived TCP flows, and average offered link loads. Our study is the first to show that packet-level FEC, when tuned properly, can be very effective in mitigating high core losses, thus opening the doors to a bufferless core network in the future.

Optical packet switches of the foreseeable future are expected to have severely limited buffering capability, since storage of optical signals remains a difficult and expensive operation. Our observations in simulation of TCP and real-time traffic in networks with such small buffers have revealed regions of anomalous performance in which losses for real-time traffic become higher as buffers get larger. The detrimental impact of larger optical buffers is studied in this paper and three new contributions are made. First, we develop a Markov chain model that allows analytical computation of loss. Our model validates observations from simulation, and opens the doors to an analytical understanding of how various factors affect the anomaly. Second, we study the anomaly under realistic traffic mixes containing persistent and non-persistent TCP flows, and show that the traffic mix does not significantly alter the anomaly. Third, we show that larger diversity in packet size between TCP and real-time traffic increases the severity of the anomaly, and is an important consideration when sizing optical switch buffers, particularly since real-time and TCP ACK packets are significantly smaller than the TCP data packets. Our study informs switch manufacturers and network operators of factors to consider when selecting optical buffer sizes in order to achieve desired performance balance between TCP and real-time traffic.

In this paper, we propose an edge reconfigurable optical network as an intermediate step toward fully dynamic optical core network. ERON is an overlay-control network created by installing GMPLS-enabled MEMs optical switches at the edge of a core optical network composed of static lightpaths. We propose a network design algorithm that takes as input the key resource locations dispersed globally and a realistic traffic matrix from end-users and outputs a virtual topology comprised of the minimum required static lightpaths interconnected via ERON switches. This paper describes the key concepts involved in the design of an efficient ERON network combined with routing and reservation algorithms to assure low blocking probabilities. We also describe the management and control architecture and protocols required to provide end-user/application control of the dynamic lightpaths.

The past few years have seen researchers debate the size of buffers required at core Internet routers. Much of this debate has focused on TCP throughput, and recent arguments supported by theory and experimentation suggest that few tens of packets of buffering suffice at bottleneck routers for TCP traffic to realise acceptable link utilisation. This paper introduces a small fraction of real-time (i.e. open-loop) traffic into the mix, and discovers an anomalous behaviour: In this specific regime of very small buffers, losses for real-time traffic do not fall monotonically with buffer size, but instead exhibit a region where larger buffers cause higher losses. Our contributions pertaining to this phenomenon are threefold: First, we demonstrate this anomalous loss performance for real-time traffic via extensive simulations including real video traces. Second, we provide qualitative explanations for the anomaly and develop a simple analytical model that reveals the dynamics of buffer sharing between TCP and real-time traffic leading to this behaviour. Third, we show how various factors such as traffic characteristics and link rates impact the severity of this anomaly. Our study particularly informs all-optical packet router designs (envisaged to have buffer sizes in the few tens of packets) and network service providers who operate their buffer sizes in this regime, of the negative impact investment in larger buffers can have on the quality of service performance.

Abstract Increasingly, individuals use communication technologies such as e‐mail, IMs, blogs, and cell phones to locate, learn about, and communicate with one another. Not much, however, is known about how individuals relate to various personal technologies, their preferences for each, or their extensional associations with them. Even less is known about the cultural differences in these preferences. The current study used the Galileo system of multidimensional scaling to systematically map the extensional associations with nine personal communication technologies across three cultures: U.S., Germany, and Singapore. Across the three cultures, the technologies closest to the self were similar, suggesting a universality of associations with certain technologies. In contrast, the technologies farther from the self were significantly different across cultures. Moreover, the magnitude of associations with each technology differed based on the extensional association or distance from the self. Also, and more importantly, the antecedents to these associations differed significantly across cultures, suggesting a stronger influence of cultural norms on personal‐technology choice.

In this paper we study the end-to-end delay constrained traffic grooming problem in WDM ring networks. Our aim is to incorporate quality of service (QoS) routing constraints into traffic grooming and address them jointly with the objective of maximizing the network throughput. It is well known that many real-time multimedia traffic not only make use of a fraction of the total wavelength capacity, but also have stringent end-to-end delay requirements. Consequently, while provisioning delay-bounded sub-wavelength traffic, it is of paramount importance to take traffic grooming and QoS routing constraints into consideration simultaneously to reduce the total network cost and improve the overall network performance. In this paper we first present an integer linear program (ILP) formulation for the problem, which is applicable when the problem size is small. We then propose three scalable heuristic algorithms. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithms. The experimental results show that, among the three proposed heuristics, the one based on ILP relaxation offers the best performance

Abstract Empirical evidence suggests that the ownership of related products that form a technology cluster is significantly better than the attributes of an innovation at predicting adoption. The treatment of technology clusters, however, has been ad hoc and study specific: Researchers often make a priori assumptions about the relationships between technologies and measure ownership using lists of functionally related technology, without any systematic reasoning. Hence, the authors set out to examine empirically the composition of technology clusters and the differences, if any, in clusters of technologies formed by adopters and nonadopters. Using the Galileo system of multidimensional scaling and the associational diffusion framework, the dissimilarities between 30 technology concepts were scored by adopters and nonadopters. Results indicate clear differences in conceptualization of clusters: Adopters tend to relate technologies based on their functional similarity; here, innovations are perceived to be complementary, and hence, adoption of one technology spurs the adoption of related technologies. On the other hand, nonadopters tend to relate technologies using a stricter ascendancy of association where the adoption of an innovation makes subsequent innovations redundant. The results question the measurement approaches and present an alternative methodology.

This paper considers the problem of on-line multicast routing in WDM grooming optical mesh networks without wavelength conversion capability. In such networks, provisioning of connection requests with fractional wavelength capacity requirements is achieved by dividing a wavelength into multiple time slots and multiplexing traffic on the wavelength. We present an on-line multicast traffic grooming algorithm for the concerned problem. The objective is to efficiently route multicast requests with sub-wavelength capacity requirements onto high-capacity wavelengths, and balance the load on the links in the network at the same time. To do so, we propose a cost function, which not only encourages grooming new requests onto the wavelengths that are being used by existing traffic, but also performs load balancing by intelligently increasing the cost of using wavelengths on links. The performance results obtained by experiments on a representative sized mesh network show that the proposed algorithm outperforms the other existing algorithms.

This paper considers the on-line traffic grooming problem in WDM-TDM switched optical mesh networks without wavelength conversion capability. The objective is to efficiently route connection requests with fractional wavelength capacity requirements onto high-capacity wavelengths and balance the load on the links in the network at the same time. To do so, we propose a cost function, which not only encourages grooming new connection requests onto the wavelengths that are being used by existing traffic, but also performs load balancing by intelligently increasing the cost of using wavelengths on links. The performance results obtained by experiments on a representative sized mesh network show that the proposed algorithm outperforms the existing algorithms.

Abstract An innovator's personality along with the perceived attributes of an innovation predicts the rate of diffusion. The current study focuses on the personality factors that determine the likelihood of adoption of a technological innovation. To that end, the study distinguishes between global innovativeness and context‐specific innovativeness. An information processing model was tested where technological innovativeness was purported to be indirectly influenced by an individual's global innovativeness, through its impact on communication and media use behaviors. The structural model was tested on two separate technology clusters, and partial support was found for linking sophistication in information search, and prior technology ownership to technological innovativeness.