This work is devoted to the study of application of new topologies in the design of networks-on-chip (NoCs). It is proposed to use two-dimensional optimal circulant topologies for NoC design, and it is developed an optimized routing algorithm with the decreased memory usage. The proposed routing algorithm was compared with Table routing, Clockwise routing, and Adaptive routing algorithms, previously developed for ring circulant topologies, and specialized routing algorithm for multiplicative circulants. The results of synthesis of routers implementing proposed routing algorithms are presented. The cost of ALM and register resources for the implementation of communication subsystems in NoCs with circulant topologies is estimated.

Introduction

Multiprocessor systems-on-chip (MPSoCs) [1] are currently one of the fastest growing areas in computing. An extensive increase in the size of the chip and the number of transistors per unit area allows developing chips with tens and hundreds of processor cores even now. At the same time, the problem of effective organization of a communication subsystem that would ensure quick data exchange between nodes of such NoCs is becoming urgent [2]. Since communication is carried out within the framework of a single chip, laws and principles that exist in telecommunication networks work differently in NoCs; therefore, the development of new solutions and methods for organizing data transfer in such networks is required. In particular, it is the topology that has a significant impact on NoC performance. The most common are classical regular topologies such as mesh [3] and torus [4], which do not always meet modern requirements for NoCs, especially with an increase in the number of nodes [5]. Different researchers suggest other topology options among which we can distinguish hypercube [6, 7, 8], chordal ring [9, 10], spidergon [11], L-networks [12]. Their feature is that they can be reduced to presentation in the form of ring-like graphs called circulant topologies in the general form. Optimal circulants have several significant advantages over mesh and torus topologies – better structural survivability, reliability, and connectivity [13, 14].

Software Defined Networking (SDN) has focused enormous attractiveness in changing conventional network by means of offering flexible and dynamic network management. It has drawn important concentration of the researchers from together academia and industries. Mainly, integrating SDN in Wireless Body Area Network (WBAN) applications specifies capable results in terms of handling with the issues like traffic management, security, energy efficiency etc. Recent improvements in miniaturization and energy efficient physiological sensor designs in SDN based Wireless Body Area Networks (WBANs) paved the way for health monitoring systems for collection and processing the real-time physiological data. The collection of signals from different sensor allows reliable diagnosis in heterogeneous than in homogeneous WBANs. Inspired by the evolutions of heterogeneous WBANs, a study on Wireless Electroencephalography Sensor Networks (WESNs) is carried out under distributed signal processing. The distributed WESNs are designed under two different hierarchy i.e. Hierarchical Fully-Connected Topology (HFCT) and Ad-Hoc Nearest-Neighbor Topology (ANNT) to improve the energy-efficiency using distributed Multi-channel Weighted Weiner Filter design (MW2F). Here, each module transmits linear combination of local channels with other modules. The power efficiency is improved Journal Pre-proof in MW2F signal processing algorithm by avoiding centralization of EEG data. A case study is carried out to test the reduced energy consumption after the removal of eye blink artifacts and it is tested with centralized counterparts. The MW2F is evaluated in both topologies against centralized environments and significant reduction of eye blink artifacts improves the energy efficiency in HFCT than other topologies.

Introduction

The energy efficient and miniaturization is the most hot research area in health monitoring systems, where the body is placed with several sensor nodes. These nodes can collect the EEG data and process it in real time and measures are considered to improve the energy efficiency. This system is called as Wireless Body Area Networks (WBANs) [1], and it offers higher demand in the area of physiological monitoring in hospital environment. The WBAN system is composed of several nodes with physiological sensors, wireless communication devices and a signal processing unit. These nodes can communicate with other sensor nodes placed over the body or it can be connected to a fusion center or a storage device. The signals collected from various resources are analyzed jointly in heterogeneous WBANs in real time environment for medical diagnosis.

1- Introduction

2- Methods

3- Results

4- Discussion

5- Conclusions

References

Abstract

The Natura 2000 protected area network is the cornerstone of European Union’s biodiversity conservation strategy. These protected areas range across multiple biogeographic regions, and they include a diversity of species assemblages along with a diversity of managing organizations, altogether making difficult to pool relevant sites to facilitate the flow of knowledge significant to their management. Here we introduce an approach to navigating protected area networks that has the potential to foster systematic identification of key sites for facilitating the exchange of knowledge and diffusion of information within the network. To demonstrate our approach, we abstractly represented Romanian Natura 2000 network as a co-occurrence network, with individual sites as nodes and shared species as edges, further combining into our analysis network topology, community detection, and network reduction methods. We identified most representative Natura 2000 sites that may increase the transfer of information within the national network of protected areas, detected clusters of sites and key sites for maintaining network cohesiveness, and highlighted the subsample of sites that retain the characteristics of the entire network. Our analysis provides implications for protected area prioritization by proposing a network perspective approach to collaboration rooted in ecological principles.

Introduction

Protected areas are established to safeguard biodiversity in the longterm by implementing conservation measures on well-defined territories (Watson et al., 2014). Protected areas are considered as ‘networks’ when they are under the same jurisdictions and governed by similar principles and regulations (Evans, 2012; Lemos and Agrawal, 2006). The Natura 2000 protected areas network of the European Union (EU) is a cornerstone of the EU’s biodiversity conservation strategy. EU Member States are required to designate Special Protection Areas (SPAs, for species covered by Birds Directive) and Sites of Community Importance (SCIs, for habitat and species covered by Habitats Directive) as part of EUs “Natura 2000 network” (Evans, 2012). To fully implement the Birds and Habitats Directives, Member States must take appropriate conservation measures to ensure a Favorable Conservation Status of protected habitats and species at the national and EUs biogeographical region levels (European Commission, 2011). These measures require collaborative approaches to conservation of species and habitats in Natura 2000 sites and sharing of best management and conservation practices (European Commision, 2015). The high number of Natura 2000 sites (over 27000 in the EU), their distribution across multiple biogeographic regions, and the diversity of their species assemblages, coupled with diverse managing organizations, makes difficult the flow of knowledge and expertise relevant to species and habitat management across Natura 2000 sites (Battisti and Fanelli, 2015; Hoffmann et al., 2018; Rozylowicz et al., 2017).

Effective energy control while maintaining reliable monitoring performance becomes a key issue in wireless sensor networks (WSNs) based surveillance applications. While importance difference of surveillance zone, limited energy and dynamic network topology pose great challenges to surveillance performance. It is necessary to adjust sensor nodes awakening frequency dynamically for information fusion. Thus an energy-aware scheduling with quality guarantee method named ESQG is proposed in this paper which considers sensor nodes residual energy, different importance degrees of the surveillance zone and network topology comprehensively. It first uses a Voronoi diagram to determine the effective scope of each sensor node and then calculates node importance according to its residual energy and the importance degree of the effective scope. Then ESQG utilizes the importance of individual sensing scope and current forwarding costs to further compute node importance and awakening frequency for information fusion. In this way, ESQG can dynamically adapts each nodes awakening frequency to its dynamic network topology and importance degree of each individual sensing scope. The nodes are then turned on stochasticlly via the node awakening probability and node importance based information fusion is conducted for target detection. Besides, an adaptive process of perception factor C is proposed to match actual situation, and automatically change according to the detected data. Experiments results demonstrate that the proposed method ESQG can reduce the number of awakening nodes to a large extent while maintaining high reliability via information fusion.

Introduction

With the rapid development of integrated circuits, digital signal processing and low-range radio electronics, wireless sensor networks (WSNs) have received great attention for wide application potentials. WSN based applications enlarge 5 human capabilities to remotely interact with the physical world. These applications include battlefield surveillance [1], target tracking [2], home appliances and inventory tracking in which information fusion [3] is needed. For example, in the monitoring of Blue-green Algae Bloom on Lake Tai [4], its physical information (eg., water temperature, water color) can be obtained to predict biolog10 ical growth and potential ecological disasters via information fusion. However, WSN enjoys some unique characteristics such as limited energy supply. That is because sensor node has a finite energy reserve supplied by a battery and is often unfeasible to recharge the battery, effective energy control while maintaining reliable detection performance is key problem to wireless sensor networks 15 surveillance. Thus minimizing energy consumption while maintaining system performance via information fusion remains a high priority in designing wireless sensor networks. To prolong the lifespan of a network, sensor nodes are often scheduled to sleep dynamically. While adjacent nodes share common sensing tasks, which 20 implies that not all sensor nodes are required to perform the sensing task in the whole system lifetime. That is to say, as long as there are enough working nodes, the function of the whole system will not be affected by some sleeping nodes. Therefore, if the sensors can be well scheduled, the system lifetime can be prolonged correspondingly; i.e. the system lifetime is prolonged by exploiting 25 redundancy [5]. In order to increase the node energy utilization and lengthen the lifetime of the wireless sensor network (WSN), a novel clustering node sleep scheduling algorithm based on information fusion is proposed in [6]. But these works mentioned above does not consider providing differentiated surveillance services for different importance degree areas while maintaining high enough 30 detection probability to targets. All of them do not consider link fluctuations and other network dynamics. What is more, sleep scheduling mechanism is the most widely used technique for efficiently managing network energy consumption. The author of [7] provides a survey on energy-efficient scheduling mechanisms in wireless sensor networks that has different network architecture 35 than the traditional wireless sensor networks. However, although much more energy can be saved by reducing the working time of sensor module [8], duty cycling usually wastes energy due to unnecessary wakeups, and low-power radios are used to awake a node only when it needs to receive or transmit packets while a power-hungry radio is used for data transmission and information fusion.

Over the past decades, supply chains have grown longer and became interconnected as a result of globalisation and rising cost pressures (Christopher and Holweg, 2011). Interconnectedness implies that a failure in one supply chain entity can potentially cascade across the whole network (Schmitt and Singh, 2012), making risk monitoring and mitigation challenging. Suppliers of multiple tiers are tied together creating emergent, yet predictable connection patterns, described as “supply network topology” (Thadakamalla et al., 2004). Studies on network topology, conducted under the framework of network science aim to unveil the behavioural phenomena of interconnected systems, which cannot be well understood from the perspective of a single entity. Understanding how the decision-making of multiple interconnected entities influence overall network resilience is necessary to cope with disruptions effectively because failures are more likely to propagate in certain topologies (Watts, 2002). Supply Chain Risk Management (SCRM) methods rarely consider the impact of disruptions on the extended supply network, where the term extended refers to ties beyond a firm’s direct suppliers and customers. The relationship between supply network topology and the effectiveness of recovery from disruptions using risk management strategies has not yet been explored. We aim to address this gap as follows. After reviewing previous work done in the field of SCRM and complex supply networks, we employ a modelling approach, where several theoretical network topologies based on the extant empirical literature are generated. The generated topologies are used to configure a supply network, after which the networks are subjected to random disruptions. Two SCRM strategies, namely inventory mitigation and contingent rerouting, are applied and the extent to which these strategies are able to enhance network recovery is observed. Our results sound a cautionary note. We find that the effectiveness of the two SCRM strategies is moderated by the topology of the supply network and that an increased understanding of supply network topology is necessary to underpin the choice of an effective strategy. First, it is shown that inventory mitigation outperforms contingent rerouting in a complex supply network setting regardless of topology. A key lesson is that random topologies need significantly higher inventory levels to recover from disruptions than scale-free networks. It is also observed that contingent rerouting is not effective for scale-free networks due to low numbers of alternative suppliers, particularly for short-term disruptions. We then explore targeted risk management, where only suppliers which suffered the most from disruptions apply a risk management strategy. Targeting suppliers does not always result in cost reduction. On the contrary, targeted inventory mitigation might significantly increase costs when the network is exposed to rare disruptions due to excessive inventory being kept for long periods of time. Targeted contingent rerouting creates inventory oscillations when network is exposed to short-lasting disruptions, resulting in decreased fill-rates and increased costs. Our work motivates further studies on the relationship between the functionality and performance of supply networks and their topology.

Agriculture and energy are closely related since efficient use of energy is a key factor in sustainable agricultural production. Increasing requirement of higher food production has led to intensive use of agricultural and natural resources (Khoshroo, 2014). However, bio-energy has placed agriculture in the position of energy consumer and energy supplier (Esengun et al., 2007). Efficient energy use in agriculture is a pathway toward decreasing environmental hazards and improving agricultural sustainability (Izadikhah and Khoshroo, 2018). Energy demand in agriculture can be classified into direct and indirect energies or renewable and non-renewable energies (Ozkan et al., 2004a). Direct energy consists of human labor, diesel fuel, electricity and water for irrigation, while farmyard manure (FYM), chemicals and machinery are considered indirect energy. Renewable energy includes human labor, FYM and water for irrigation whereas machinery, diesel fuel and chemicals are considered non-renewable forms of energy (Demircan et al., 2006; Ozkan et al., 2004a). The established method to determine energy efficiency of production systems is the input-output analysis. Using this type of analysis, researchers have studied energy consumption in the production of fruits such as citrus (Ozkan et al., 2004), grape (Ozkan et al., 2007), apple (Gokdogan and Baran, 2017; Taghavifar and Mardani, 2015), prune (Tabatabaie et al., 2013), walnut (Khoshroo and Mulwa, 2014) and pomegranate (Houshyar et al., 2017). Modeling crop yield based on energy consumption is an interesting issue for researchers. Prediction of agricultural production is useful for farmers, governments, and agribusiness industries. It helps farmers to make marketing decision. Government requires forecasts of the crop yield to implement policies that provide technical and market support for the agricultural sector. Processors of food, and others in the marketing chain, need forecasts for their purchasing and storing decisions. Various approaches and methods have been used to model energy consumption (Arabi et al., 2017; Jebaraj and Iniyan, 2006; Laha and Chakraborty, 2017; Say and Yücel, 2006; Tso and Yau, 2007). Traditionally, econometric models, based on Cobb-Douglass production function were the most popular modeling technique for investigating functional relations between input energy and various crop yield (Hamedani et al., 2011; Hatirli et al., 2006; Houshyar et al., 2015).

A learning organization can be understood as a complex network in which individuals interact with each other, aiming at some global purpose. It can be considered as a peer-to-peer system (P2P) in which learning occurs during the interactions of individuals which are located in it. In a learning organization, collective knowledge of the individuals is needed in order for the organization to reach its over-all goals. When an organization becomes a learning organization [38], knowledge application is necessitated to help organizations to retain correct and valuable knowledge. If the workforces of an organization learn more quickly than the workforces of its competitors we can say that the company has a competitive advantage or edge over its rivals. Therefore, researchers and scientists try to understand the effective way of the knowledge collection and management in order to create value of its potentials and use it as a competitive advantage. Knowledge management in an organization refers to the process of creating value from the organization’s intangible assets [30]. In other words, it leverages the collective wisdom of the people by soliciting ideas and solutions for an organization. Organizational employees improve the chances of achieving organizational goals by utilization of all available resources as well as knowledge. Learn from the experiences of others who have been more effective in the similar or other areas, improve the quality and speed of problem solving. From an organizational perspective, the most important aspect of knowledge management is to transfer knowledge. Therefore, organizations must adopt structures that allow them to create and transfer more knowledge. In this article we focus on a popular set of network metrics, mainly measures of centrality of individuals. Centrality measures help us to identify positions within the network, from where people have a better chance of accessing worthwhile information. We argue that centrality measures can be considered as strategic or expert type of knowledge transfer in an organization where the collective knowledge of the individuals is needed to accomplish the organizational goal, which is of critical importance to the whole organization. Despite the creation of new knowledge and process of learning at an individual level, organizational informal communication structure affects the nature of human interactions and information flow. We investigate and emphasize the role of individual structural importance within the communication structure of individuals as a mechanism for knowledge flow and organizational learning. Structural position of individuals in a network can help us to have a better access to ongoing information exchange processes in the organization.