Small cluster in cyber physical systems: network topology, interdependence and cascading failures

In cyber physical system (CPS), computational resources and physical resources are strongly correlated and mutually dependent. Cascading failures occur between coupled networks, cause the system more fragile than single network. Besides widely used metric giant component, we study small cluster (small component) in interdependent networks after cascading failures occur. We first introduce an overview on how small clusters distribute in various single networks. Then we propose a percolation theory based mathematical method to study how small clusters be affected by the interdependence between two coupled networks. We prove that the upper bounds exist for both the fraction and the number of operating small clusters. Without loss of generality, we apply both synthetic network and real network data in simulation to study small clusters under different interdependence models and network topologies. The extensive simulations highlight our findings: except the giant component, considerable proportion of small clusters exists, with the remaining part fragmenting to very tiny pieces or even massive isolated single vertex; no matter how the two networks are tightly coupled, an upper bound exists for the size of small clusters. We also discover that the interdependent small-world networks generally have the highest fractions of operating small clusters. Three attack strategies are compared: Inter Degree Priority Attack, Intra Degree Priority Attack and Random Attack. We observe that the fraction of functioning small clusters keeps stable and is independent from the attack strategies.

Network coding based converge-cast scheme in wireless sensor networks

The converge-cast in wireless sensor networks (WSNs) is widely applied in many fields such as medical applications and the environmental monitoring. WSNs expect not only providing routing with high throughput but also achieving efficient energy saving. Network coding is one of the most promising techniques to reduce the energy consumption. By maximizing the encoding number, the message capacity per package can be extended to the most efficient condition. Thus, many researchers have focused their work on this field. Nevertheless, the packages sent by the outer nodes need to be temporary stored and delayed in order to maximize the encoding number. To find out the balance between inserting the delay time and maximizing the encoding number, a Converge-cast Scheme based on data collection rate prediction (CSRP) is proposed in this paper. To avoid producing the outdated information, a prediction method based on Modifying Index Curve Model is presented to deal with the dynamic data collection rate of every sensor in WSNs. Furthermore, a novel coding conditions based on CDS is proposed to increase the coding opportunity and to solve the collision problems. The corresponding analysis and experimental results indicate that the feasibility and efficiency of the CSRP is better than normal conditions without the prediction.

A cross-layer key establishment scheme in wireless mesh networks

Cryptographic keys are necessary to secure communications among mesh clients in wireless mesh networks. Traditional key establishment schemes are implemented at higher layers, and the security of most such designs relies on the complexity of computational problems. Extracting cryptographic keys at the physical layer is a promising approach with information-theoretical security. But due to the nature of communications at the physical layer, none of the existing designs supports key establishment if communicating parties are out of each other's radio range, and all schemes are insecure against man-in-the-middle attacks. This paper presents a cross-layer key establishment scheme where the established key is determined by two partial keys: one extracted at the physical layer and the other generated at higher layers. The analysis shows that the proposed cross-layer key establishment scheme not only eliminates the aforementioned shortcomings of key establishment at each layer but also provides a flexible solution to the key generation rate problem. © 2014 Springer International Publishing Switzerland.

Understanding of hydrogel network formation and its application in the architecture of significantly enhanced hydrogel

An understanding of the physical hydrogel network formation has been obtained by dynamic rheological experiments. The evidence shows that the network formation turns out to be a nucleation-controlled process. It was found that there exists a critical temperature Tc; fiber branching is greatly enhanced when the network formation is performed in the regime of T<Tc (T, the final setting temperature). This finding enables the authors to build significantly enhanced gel networks. So far G′ (elastic modulus) of the hydrogel network has been enhanced by 187% while the formation period can be greatly shortened to only 1/20 of the previous process.

Adaptation of LEACH routing protocol to cognitive radio sensor networks

One of the drawbacks of LEACH protocol is the uncontrolled selection of cluster heads which, in some rounds, leads to the concentration of them in a limited area due to the randomness of the selection procedure. LEACH-C is a variant of LEACH that uses a centralized clustering algorithm and forms good clusters through sink control. According to experimental results, the IEEE 802.15.4 packets are damaged by WLAN interferences in ISM band. It seems that, sensor nodes equipped with cognitive radio capabilities can overcome this problem. In cognitive radio sensor networks (CRSN), routing must be accompanied by channel allocation. This requires spectrum management which can be devolved to cluster heads. For this networks, new duty cycle mechanisms must be designed that jointly consider neighbor discovery, and spectrum sensing/allocation. Cluster-based network architecture is a good choice for effective dynamic spectrum management. In such architecture, cluster heads have a proper spatial distribution and are optimally located all over the network. In this paper, using the physical layer information and preserving the feature of random cluster head selection in LEACH, it has been tried to both move the position of cluster heads to appropriate locations and make their quantity optimal. The simulation results show that the transferal of cluster heads to appropriate locations increases the network lifetime significantly though this comes at the price of early instability appearance. By considering the energy level in cluster head election algorithm, one can overcome the network stability issues too. However, this will move the cluster heads away from their appropriate locations. © 2012 IEEE.

An improved stochastic modeling of opportunistic routing in vehicular CPS

Vehicular Cyber-Physical System (VCPS) provides CPS services via exploring the sensing, computing and communication capabilities on vehicles. VCPS is deeply influenced by the performance of the underlying vehicular network with intermittent connections, which make existing routing solutions hardly to be applied directly. Epidemic routing, especially the one using random linear network coding, has been studied and proved as an efficient way in the consideration of delivery performance. Much pioneering work has tried to figure out how epidemic routing using network coding (ERNC) performs in VCPS, either by simulation or by analysis. However, none of them has been able to expose the potential of ERNC accurately. In this paper, we present a stochastic analytical framework to study the performance of ERNC in VCPS with intermittent connections. By novelly modeling ERNC in VCPS using a token-bucket model, our framework can provide a much more accurate results than any existing work on the unicast delivery performance analysis of ERNC in VCPS. The correctness of our analytical results has also been confirmed by our extensive simulations.

Robust single-hidden layer feedforward network-based pattern classifier

In this paper, a new robust single-hidden layer feedforward network (SLFN)-based pattern classifier is developed. It is shown that the frequency spectrums of the desired feature vectors can be specified in terms of the discrete Fourier transform (DFT) technique. The input weights of the SLFN are then optimized with the regularization theory such that the error between the frequency components of the desired feature vectors and the ones of the feature vectors extracted from the outputs of the hidden layer is minimized. For the linearly separable input patterns, the hidden layer of the SLFN plays the role of removing the effects of the disturbance from the noisy input data and providing the linearly separable feature vectors for the accurate classification. However, for the nonlinearly separable input patterns, the hidden layer is capable of assigning the DFTs of all feature vectors to the desired positions in the frequencydomain such that the separability of all nonlinearly separable patterns are maximized. In addition, the output weights of the SLFN are also optimally designed so that both the empirical and the structural risks are well balanced and minimized in a noisy environment. Two simulation examples are presented to show the excellent performance and effectiveness of the proposed classification scheme.

Friendship network characteristics are associated with physical activity and sedentary behavior in early adolescence

INTRODUCTION: There is limited understanding of the association between peer social networks and physical activity (PA), sedentary and screen-related behaviors. This study reports on associations between personal network characteristics and these important health behaviors for early adolescents. METHODS: Participants were 310 students, aged 11-13 years, from fifteen randomly selected Victorian primary schools (43% response rate). PA and sedentary behaviors were collected via accelerometer and self-report questionnaire, and anthropometric measures via trained researchers. Participants nominated up to fifteen friends, and described the frequency of interaction and perceived activity intensity of these friends. Personal network predictors were examined using regression modelling for PA and sedentary/screen behavior. RESULTS: Perceived activity levels of friends, and friendships with very frequent interaction were associated with outside-of-school PA and/or sedentary/screen time. Differences according to sex were also observed in the association between network characteristics and PA and sedentary time. A higher number of friends and greater proportion of same sex friends were associated with boys engaging in more moderate-to-vigorous PA outside of school hours. PA intensity during school-day breaks was positively associated with having a greater proportion of friends who played sports for girls, and a greater proportion of male friends for boys. CONCLUSION: Friendship network characteristics are associated with PA and sedentary/screen time in late childhood/early adolescence, and these associations differ by sex. The positive influence of very active peers may be a promising avenue to strengthen traditional interventions for the promotion of PA and reduction in screen time.

Machine-to-machine communications with in-network data aggregation, processing, and actuation for large-scale cyber-physical systems

Machine-to-Machine (M2M) paradigm enables machines (sensors, actuators, robots, and smart meter readers) to communicate with each other with little or no human intervention. M2M is a key enabling technology for the cyber-physical systems (CPSs). This paper explores CPS beyond M2M concept and looks at futuristic applications. Our vision is CPS with distributed actuation and in-network processing. We describe few particular use cases that motivate the development of the M2M communication primitives tailored to large-scale CPS. M2M communications in literature were considered in limited extent so far. The existing work is based on small-scale M2M models and centralized solutions. Different sources discuss different primitives. Few existing decentralized solutions do not scale well. There is a need to design M2M communication primitives that will scale to thousands and trillions of M2M devices, without sacrificing solution quality. The main paradigm shift is to design localized algorithms, where CPS nodes make decisions based on local knowledge. Localized coordination and communication in networked robotics, for matching events and robots, were studied to illustrate new directions.

Playing their part: the role of physical activity and sport in sustaining the health and well being of small rural communities

It is widely recognised that the health of rural Australians is poor in comparison with their urban counterparts. Similarly, the role played by physical activity in maintaining health has been well researched and is well documented. However, little appears to have been published in recent years about the links between physical activity and health in rural communities. The objective of this article was to begin to address that gap. To achieve this, the article drew on research conducted in two small rural communities in Victoria Australia, and highlighted the role that physical activity and sport played in sustaining the health and wellbeing of individuals and communities in rural areas. Taking the World Health Organisation's definition of health (a state of complete physical, mental and social well-being and not merely the absence of disease) as its measure, the paper highlighted the many ways in which physical activity and sport in rural communities contribute to physical health, mental wellbeing and social cohesiveness. Based this finding, the authors suggest that physical activity and sport make a significant contribution to the health and wellbeing of rural people and their communities and suggest that further research is necessary to better define this apparent contribution.

Online algorithm for application layer anycast

Application layer anycast possesses the property of flexibility, however, all the proposed application layer anycast routing algorithms are based on probing so far. One disadvantage of probing algorithms is that there are too many probing packets, which degrade the network performance, wast network bandwidth. In this paper, we propose an online algorithm, balance algorithm, for application layer anycast. Compared with the probing algorithms, the proposed algorithm has no probing cost at all. We model the two kinds of algorithms, and analyse the performance of the two algorithms. The results show that the online balance algorithm is better than the probing algorithms in terms of performance. A simulation is conducting as future work.

Research on network anycast

Anycast is defined as a service in IPv6, which provides stateless best effort delivery of an anycast datagram to at least one, and preferably only one host. It is a topic of increasing interest. This paper is an attempt to gather and report on the work done on anycast. There are two main categories at present: network-layer anycast and application-layer anycast. Both involve anycast architectures, routing algorithms, metrics, applications, etc. We also present an efficient algorithm for application-layer anycast, and point out possible research directions based on our research.

Location based power control for energy critical sensors in a disconnected network

This paper provides a location based power control strategy for disconnected sensory nodes deployed for long term service. Power conservation is of importance particularly when sensors communicate with a mobile robot used for data collection. The proposed algorithm uses estimations from a Robust Extended Kalman Filter (REKF) with RSSI measurements, in implementing a sigmoid function based power control algorithm which essentially approaches a desired power emission trajectory based on carrier-to-interference ratios(CIR) to ensure interferenceless reception. The more realistic modelling we use incorporates physical dynamics between the mobile robot and the sensors together with the wireless propagation parameters between the transmitter and receiver to formulate a sophisticated and effective power control strategy for the exclusive usage of energy critical disconnected nodes in a sensory network increasing their life span.

Mitofusions1/2 and ERRα expression are increased in human skeletal muscle after physical exercise.

Mitochondrial impairment is hypothesized to contribute to the pathogenesis of insulin resistance. Mitofusin (Mfn) proteins regulate the biogenesis and maintenance of the mitochondrial network, and when inactivated, cause a failure in the mitochondrial architecture and decreases in oxidative capacity and glucose oxidation. Exercise increases muscle mitochondrial content, size, oxidative capacity and aerobic glucose oxidation. To address if Mfn proteins are implicated in these exercise-induced responses, we measured Mfn1 and Mfn2 mRNA levels, pre-, post-, 2 and 24 h post-exercise. Additionally, we measured the expression levels of transcriptional regulators that control mitochondrial biogenesis and functions, including PGC-1α, NRF-1, NRF-2 and the recently implicated ERRα. We show that Mfn1, Mfn2, NRF-2 and COX IV mRNA were increased 24 h post-exercise, while PGC-1α and ERRα mRNA increased 2 h post-exercise. Finally, using in vitro cellular assays, we demonstrate that Mfn2 gene expression is driven by a PGC-1α programme dependent on ERRα. The PGC-1α/ERRα-mediated induction of Mfn2 suggests a role of these two factors in mitochondrial fusion. Our results provide evidence that PGC-1α not only mediates the increased expression of oxidative phosphorylation genes but also mediates alterations in mitochondrial architecture in response to aerobic exercise in humans.