Resource Management for Ad-Hoc Wireless Networks with Cluster Organization

Boosted by technology advancements, government and commercial interest, ad-hoc wireless networks are emerging as a serious platform for distributed mission-critical applications. Guaranteeing QoS in this environment is a hard problem because several applications may share the same resources in the network, and mobile ad-hoc wireless networks (MANETs) typically exhibit high variability in network topology and communication quality. In this paper we introduce DYNAMIQUE, a resource management infrastructure for MANETs. We present a resource model for multi-application admission control that optimizes the application admission utility, defined as a combination of the QoS satisfaction ratio. A method based on external adaptation (shrinking QoS for existing applications and later QoS expansion) is introduced as a way to reduce computation complexity by reducing the search space. We designed an application admission protocol that uses a greedy heuristic to improve application utility. For this, the admission control considers network topology information from the routing layer. Specifically, the admission protocol takes benefit from a cluster network organization, as defined by ad-hoc routing protocols such as CBRP and LANMAR. Information on cluster membership and cluster head elections allows the admission protocol to minimize control signaling and to improve application quality by localizing task mapping.     

Modeling and Assessing Variability in Energy Consumption During the Use Stage of Online Multimedia Services

In this study, we use an improved, more accurate model to analyze the energy footprint of content downloaded from a major online newspaper by means of various combinations of user devices and access networks. Our results indicate that previous analyses based on average figures for laptops or desktop personal computers predict national and global energy consumption values that are unrealistically high. Additionally, we identify the components that contribute most of the total energy consumption during the use stage of the life cycle of digital services. We find that, depending on the type of user device and access network employed, the data center where the news content originates consumes between 4% and 48% of the total energy consumption when news articles are read and between 2% and 11% when video content is viewed. Similarly, we find that user devices consume between 7% and 90% and 0.7% and 78% for articles and video content, respectively, depending on the type of user device and access network that is employed. Though increasing awareness of the energy consumption by data centers is justified, an analysis of our results shows that for individual users of the online newspaper we studied, energy use by user devices and the third‐generation (3G) mobile network are usually bigger contributors to the service footprint than the datacenters. Analysis of our results also shows that data transfer of video content has a significant energy use on the 3G mobile network, but less so elsewhere. Hence, a strategy of reducing the resolution of video would reduce the energy footprint for individual users who are using mobile devices to access content by the 3G network.     

On an improved clustering algorithm based on node density for WSN routing protocol

To better collect data in context to balance energy consumption, wireless sensor networks (WSN) need to be divided into clusters. The division of clusters makes the network become a hierarchical organizational structure, which plays the role of balancing the network load and prolonging the life cycle of the system. In clustering routing algorithm, the pros and cons of clustering algorithm directly affect the result of cluster division. In this paper, an algorithm for selecting cluster heads based on node distribution density and allocating remaining nodes is proposed for the defects of cluster head random election and uneven clustering in the traditional LEACH protocol clustering algorithm in WSN. Experiments show that the algorithm can realize the rapid selection of cluster heads and division of clusters, which is effective for node clustering and is conducive to equalizing energy consumption.

     

Lifetime extension based on residual energy for receiver-driven multi-hop wireless network

An important research topic in wireless sensor networking is the extension of operating time by controlling the power consumption of individual nodes. In a receiver-driven communication protocol, a receiver node periodically transmits its ID to the sender node, and in response the sender node sends an acknowledgment, after which data transmission starts. By applying such a receiver-driven protocol to wireless sensor networks, the average power consumption of the network can be controlled, but there still remains the problem of unbalanced load distribution among nodes. Therefore, part of the network shuts down when the battery of the node that consumes the most power is completely discharged. To extend the network lifetime, we propose a method where information about the residual energy level is exchanged through ID packets in order to balance power consumption. Simulation results show that the network lifetime can be extended by about 70–100 % while maintaining high network performance in terms of packet collection ratio and delay.     

Predator population size structure alters consumption of prey from epigeic and grazing food webs

Numerous studies have found that predators can suppress prey densities and thereby impact important ecosystem processes such as plant productivity and decomposition. However, prey suppression by spiders can be highly variable. Unlike predators that feed on prey within a single energy channel, spiders often consume prey from asynchronous energy channels, such as grazing (live plant) and epigeic (soil surface) channels. Spiders undergo few life cycle changes and thus appear to be ideally suited to link energy channels, but ontogenetic diet shifts in spiders have received little attention. For example, spider use of different food channels may be highly specialized in different life stages and thus a species may be a multichannel omnivore only when we consider all life stages. Using stable isotopes, we investigated whether wolf spider (Pardosa littoralis, henceforth Pardosa) prey consumption is driven by changes in spider size. Small spiders obtained > 80% of their prey from the epigeic channel, whereas larger spiders used grazing and epigeic prey almost equally. Changes in prey consumption were not driven by changes in prey density, but by changes in prey use by different spider size classes. Thus, because the population size structure of Pardosa changes dramatically over the growing season, changes in spider size may have important implications for the strength of trophic cascades. Our research demonstrates that life history can be an important component of predator diet, which may in turn affect community- and ecosystem-level processes.     

Using ant-based agents for congestion control in ad-hoc wireless sensor networks

Ad-hoc wireless sensor networks suffer from problems of congestion, which lead to packet loss and excessive energy consumption. In this paper, we address the issue of congestion in these networks. We propose a new routing protocol for wireless sensor networks namely Ant-based Routing with Congestion Control (ARCC), which takes into account the congestion of the network at a given instant and proposes to reduce it and then finds the optimum paths between the source and the sink nodes. Simulation results show that ARCC performs better with respect to the throughput, the number of packets lost and the priority performance.     

Dynamic Social Community Detection and Its Applications

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.     

Energy Reduction Through a Deeper Understanding of Household Consumption

This article proposes a multidisciplinary and systemic approach to sustainable consumption that combines environmental considerations of energy usage from a life cycle perspective with a social understanding of consumption grounded in economic anthropology. The goal is to understand both consumption patterns and drivers, with a focus on household energy used for cooling in the metropolitan region of Manila in the Philippines. For different socioeconomic groups, cooling devices also deliver social and cultural services, such as socializing or adhering to Western fashion trends. This article argues for the need to address these aspects if reductions in household energy usage are to become possible. The limits of individual‐choice theories are rendered apparent, with examples of how institutional and structural conditions lock in consumption patterns and restrict household choices. The notion that emerging economies might be able to “leapfrog” over the environmental errors of more industrialized countries is also raised and critiqued.     

Securing Mobile Ad Hoc Networks Using Danger Theory-Based Artificial Immune Algorithm

A mobile ad hoc network (MANET) is a set of mobile, decentralized, and self-organizing nodes that are used in special cases, such as in the military. MANET properties render the environment of this network vulnerable to different types of attacks, including black hole, wormhole and flooding-based attacks. Flooding-based attacks are one of the most dangerous attacks that aim to consume all network resources and thus paralyze the functionality of the whole network. Therefore, the objective of this paper is to investigate the capability of a danger theory-based artificial immune algorithm called the mobile dendritic cell algorithm (MDCA) to detect flooding-based attacks in MANETs. The MDCA applies the dendritic cell algorithm (DCA) to secure the MANET with additional improvements. The MDCA is tested and validated using Qualnet v7.1 simulation tool. This work also introduces a new simulation module for a flooding attack called the resource consumption attack (RCA) using Qualnet v7.1. The results highlight the high efficiency of the MDCA in detecting RCAs in MANETs.     

Novel trust evaluation using NSGA-III based adaptive neuro-fuzzy inference system

Cluster Computing - Recently Mobile adhoc networks (MANETs) have received the great attention of researchers as these models provide a wide range of applications. But MANET nodes are prone to...     

Dose calculation accuracy of different image value to density tables for cone-beam CT planning in head & neck and pelvic localizations

PurposeWe aimed to identify the most accurate combination of phantom and protocol for image value to density table (IVDT) on volume-modulated arc therapy (VMAT) dose calculation based on kV-Cone-beam CT imaging, for head and neck (H&N) and pelvic localizations.MethodsThree phantoms (Catphan^®600, CIRS^®062M (inner phantom for head and outer phantom for body), and TomoTherapy^® “Cheese” phantom) were used to create IVDT curves of CBCT systems with two different CBCT protocols (Standard-dose Head and Standard Pelvis). Hounsfield Unit (HU) time stability and repeatability for a single On-Board-Imager (OBI) and compatibility of two distinct devices were assessed with Catphan^®600. Images from the anthropomorphic phantom CIRS ATOM^® for both CT and CBCT modalities were used for VMAT dose calculation from different IVDT curves. Dosimetric indices from CT and CBCT imaging were compared.ResultsIVDT curves from CBCT images were highly different depending on phantom used (up to 1000 HU for high densities) and protocol applied (up to 200 HU for high densities). HU time stability was verified over seven weeks. A maximum difference of 3% on the dose calculation indices studied was found between CT and CBCT VMAT dose calculation across the two localizations using appropriate IVDT curves. One IVDT curve per localization can be established with a bi-monthly verification of IVDT-CBCT.ConclusionsThe IVDT-CBCT_CIRS-Head phantom with the Standard-dose Head protocol was the most accurate combination for dose calculation on H&N CBCT images. For pelvic localizations, the IVDT-CBCT_Cheese established with the Standard Pelvis protocol provided the best accuracy.     

Comparative Energy, Environmental, and Economic Analysis of Traditional and E-commerce DVD Rental Networks

This study is a comparative life-cycle assessment (LCA) of two competing digital video disc (DVD) rental networks: the e-commerce option, where the customer orders the movies online, and the traditional business option, where the customer goes to the rental store to rent a movie. The analytical framework proposed is for a customer living in the city of Ann Arbor, Michigan in the United States. The primary energy and environmental performance for both networks are presented using a multicriterion LCA. The package selected by the traditional network is responsible for 67% of the difference in total energy consumption of the two alternatives. Results show that the e-commerce alternative consumed 33% less energy and emitted 40% less CO2 than the traditional option. A set of sensitivity analyses test the influence of distance traveled, transportation mode, and reuse of DVD and DVD packaging on the final results. The mode of transportation used by the customer in the traditional business model also affects global emissions and energy consumption. The customer walking to the store is by far the best option in the traditional network; however, the e-commerce option performed comparatively better despite all transportation modes tested. A novel economic indicator, ESAL, is used to compare different transportation modes based on the level of stress exerted on the pavement. The two networks are compared on the basis of cost accounting; consistent with its energy and environmental advantages, the e-commerce network also exerts lesser economic impact, by $1.17, for the functional unit tested.     

Traffic-aware routing protocol for wireless sensor networks

In wireless sensor networks, when a sensor node detects events in the surrounding environment, the sensing period for learning detailed information is likely to be short. However, the short sensing cycle increases the data traffic of the sensor nodes in a routing path. Since the high traffic load causes a data queue overflow in the sensor nodes, important information about urgent events could be lost. In addition, since the battery energy of the sensor nodes is quickly exhausted, the entire lifetime of wireless sensor networks would be shortened. In this paper, to address these problem issues, a new routing protocol is proposed based on a lightweight genetic algorithm. In the proposed method, the sensor nodes are aware of the data traffic rate to monitor the network congestion. In addition, the fitness function is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets in a genetic algorithm, the proposed method selects suitable data forwarding sensor nodes to avoid heavy traffic congestion. In experiments, the proposed method demonstrates efficient data transmission due to much less queue overflow and supports fair data transmission for all sensor nodes. From the results, it is evident that the proposed method not only enhances the reliability of data transmission but also distributes the energy consumption across wireless sensor networks.     

Battery‐like Supercapacitors from Vertically Aligned Carbon Nanofiber Coated Diamond: Design and Demonstrator

To fabricate battery‐like supercapacitors with high power and energy densities, big capacitances, as well as long‐term capacitance retention, vertically aligned carbon nanofibers (CNFs) grown on boron doped diamond (BDD) films are employed as the capacitor electrodes. They possess large surface areas, high conductivity, high stability, and importantly are free of binder. The large surface areas result from their porous structures. The containment of graphene layers and copper metal catalysts inside CNFs leads to their high conductivity. Both electrical double layer capacitors (EDLCs) in inert solutions and pseudocapacitors (PCs) using Fe(CN)₆^3?/4? redox‐active electrolytes are constructed with three‐ and two‐electrode systems. The assembled two‐electrode symmetrical supercapacitor devices exhibit capacitances of 30 and 48 mF cm^?2 at 10 mV s^?1 for EDLC and PC devices, respectively. They remain constant even after 10 000 charging/discharging cycles. The power densities are 27.3 and 25.3 kW kg^?1 for EDLC and PC devices, together with their energy densities of 22.9 and 44.1 W h kg^?1, respectively. The performance of these devices is superior to most of the reported supercapacitors and batteries. Vertically aligned CNF/BDD hybrid films are thus useful to construct high‐performance battery‐like and industry‐orientated supercapacitors for future power devices.     

Fast energy-aware OLSR routing in VANETs by means of a parallel evolutionary algorithm

This work tackles the problem of reducing the power consumption of the OLSR routing protocol in vehicular networks. Nowadays, energy-aware and green communication protocols are important research topics, specially when deploying wireless mobile networks. This article introduces a fast automatic methodology to search for energy-efficient OLSR configurations by using a parallel evolutionary algorithm. The experimental analysis demonstrates that significant improvements over the standard configuration can be attained in terms of power consumption, with no noteworthy loss in the QoS.     

Functional response of early stages of the cuttlefish Sepia officinalis preying on the mysid Mesopodopsis slabberi

The functional response of Sepia officinalis early stages, preying on mysids of the species Mesopodopsis slabberi was investigated. The effects of five prey densities (12.5, 25, 37.5, 50 and 125 mysids l^-1) and two hatchling ages (1-day-old and 7-day-old) on consumption rate and the frequency of non-feeding animals were tested. Older animals were approximately 50% heavier than newly hatched ones. Hatchlings were individually assayed under 0.25 W m^-2 natural light, 37.8 psu in salinity and 19°C. The effect of prey density on consumption rate was highly significant and no effect of age was detected within the age range tested. Maximal values recorded for consumption rate were about 0.45 mysids h^-1. The frequency of non-feeding individuals was strongly reduced at saturating prey densities. The functional response curve showed an interval of prey densities for which density-dependent prey mortality is probable.     

Embedded Real-time Battery State-of-Charge Forecasting in Micro-Grid Systems

Micro-grid systems (MGS) are increasingly investigated for green and energy efficient buildings in order to reduce energy consumption while maintaining occupants’ comfort. It includes renewable energy sources for power production, storage devices for storing power excess, and control strategies for orchestrating all components and improving the system's efficiency. In fact, MGS can be seen as complex systems composed of different heterogeneous entities that interact dynamically and in collective manner in order to balance between energy efficiency and occupants’ comfort. However, the uncertainty and intermittency of energy production and consumption requires the development of real-time forecasting methods and predictive control strategies. The State-of-Charge (SoC) of batteries is one of the main parameters used in MGS predictive control algorithms. It indicates how much energy is stored and how long MGS can be relying on deployed storage devices. Several methods have been developed for SoC estimation, but little work, however, has been dedicated for SoC forecasting in MGS. In this paper, we focus on advancing MGS predictive control through near real-time embedded forecasting of batteries SoC. In fact, we have deployed, on two platforms, two forecasting methods, Long Short-Term Memory (LSTM) and Auto Regressive Integrated Moving Average (ARIMA). Their accuracy and performance have been evaluated in both classical batch mode and streaming mode. Extensive experiments have been conducted for different forecasting horizons and results are presented using two main metrics, the accuracy and the computational time. Obtained results show that LSTM outperforms ARIMA for real-time forecasting, it has the better tradeoff in terms of forecasting accuracy and performance.     

Indoor Thin‐Film Photovoltaics: Progress and Challenges

Energy generation and consumption have always been an important component of social development. Interests in this field are beginning to shift to indoor photovoltaics (IPV) which can serve as power sources under low light conditions to meet the energy needs of rapidly growing fields, such as intelligence gathering and information processing which usually operate via the Internet‐of‐things (IoT). Since the power requirements for this purpose continue to decrease, IPV systems under low light may facilitate the realization of self‐powered high‐tech electronic devices connected through the IoT. This review discusses and compares the characteristics of different types of IPV devices such as those based on silicon, dye, III‐V semiconductors, organic compounds, and halide perovskites. Among them, specific attention is paid to perovskite photovoltaics which may potentially become a high performing IPV system due to the fascinating photophysics of the halide perovskite active layer. The limitations of such indoor application as they relate to the toxicity, stability, and electronic structure of halide perovskites are also discussed. Finally, strategies which could produce highly functional, nontoxic, and stable perovskite photovoltaics devices for indoor applications are proposed.     

Low density cell culture of locust neurons in closed-channel microfluidic devices

Microfluidic channel systems were fabricated out of polydimethylsiloxane (PDMS) and used as culture vessels for primary culture of neurons from locust thoracic ganglia. In a biocompatibility study it was shown that cell adhesion and neuronal cell growth of locust neurons on uncoated PDMS was restricted. Coating with concanavalin A improved cell adhesion. In closed-channel microfluidic devices neurons were grown in static-bath culture conditions for more than 15 days. Cell densities of up to 20 cells/channel were not exceeded in low-density cultures but we also found optimal cell growth of single neurons inside individual channels. The first successful cultivation of insect neurons in closed-channel microfluidic devices provides a prerequisite for the development of low density neuronal networks on multi electrode arrays combined with microfluidic devices.     

Challenging the Woodfuel Crisis in West African Woodlands

Fear of an upcoming woodfuel crisis caused by increasing woodfuel consumption in Bamako has had great influence on forestry policies aiming to reduce the impacts of urban woodfuel consumption. During the last 20 years, energy gap analyses—the relationship between supply and demand of woodfuels—have been produced by the government of Mali to prove the impacts of woodfuel consumption in Bamako on surrounding woodlands. This study evaluates the methodology and data used to describe this woodfuel crisis through a comparison with regional and historical data. The results of the energy gap analyses are challenged by using different estimates of woody resource availability and woodfuel consumption to create best and worst case scenarios. These show either high surpluses or high deficits with a difference of 2.7 million tons/yr. The woodfuel system of Bamako is highly dynamic and it is very difficult to evaluate its sustainability using a simple methodology such as the energy gap analysis. Trends over the last 20 years show a highly efficient woodfuel system that has adapted to changing circumstances, ensuring a continued affordable woodfuel supply for the urban residents. Better data on the productivity of West African woodlands and urban consumption are needed to avoid misinterpretations of the impacts of woodfuel harvesting on woody resources.