In-network Nodes Research Articles

WINNER: A network biology tool for biomolecular characterization and prioritization.

In network biology, molecular functions can be characterized by network-based inference, or "guilt-by-associations." PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process. We describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding "non-seed" molecules to the original biomolecular interaction network consisting of "seed" molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree-preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND. WINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.

Diffusion bias-compensated recursive maximum correntropy criterion algorithm with noisy input

In recent years, the distributed estimation problem based on the diffusion strategy has received more and more attention, where the node filters cooperate with other in-network node filters to estimate the unknown parameter vector. In this paper, we propose a Diffusion Bias Compensated Recursive Maximum Correntropy Criterion (DBCRMCC) algorithm based on the idea of distributed estimation for adaptive filtering in network containing input noise and non-Gaussian output noise. The Recursive Maximum Correntropy Criterion (RMCC) is an adaptive filtering algorithm with Maximum Correntropy Criterion (MCC) as the cost function which is robust to large outliers. Considering that the estimates directly obtained by the RMCC algorithm are biased when the input is disturbed by noise, this paper proposes the DBCRMCC algorithm under some reasonable assumptions by using the principle of unbiased estimation and combining it with a diffusion strategy. Through bias-compensation for biased estimates and cooperation among node filters, the asymptotic unbiased estimates of the unknown parameters can be obtained. The simulation results show that the proposed DBCRMCC algorithm has acceptable convergence speed and estimation accuracy in the environment where the input signal is noisy and the output noise is non-Gaussian.

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

With increasing numbers of computer vision and object detection application scenarios, those requiring ultra-low service latency times have become increasingly prominent; e.g., those for autonomous and connected vehicles or smart city applications. The incorporation of machine learning through the applications of trained models in these scenarios can pose a computational challenge. The softwarization of networks provides opportunities to incorporate computing into the network, increasing flexibility by distributing workloads through offloading from client and edge nodes over in-network nodes to servers. In this article, we present an example for splitting the inference component of the YOLOv2 trained machine learning model between client, network, and service side processing to reduce the overall service latency. Assuming a client has 20% of the server computational resources, we observe a more than 12-fold reduction of service latency when incorporating our service split compared to on-client processing and and an increase in speed of more than 25% compared to performing everything on the server. Our approach is not only applicable to object detection, but can also be applied in a broad variety of machine learning-based applications and services.

Scalable and efficient routing protocol for internet of things by clustering cache and diverse paths

This paper proposes SENR, a scalable and efficient named-based routing protocol for the internet of things (IoT). SENR explores semantic notification and opportunistic caching strategy to enhance the data retrieval efficiency for smart systems. First, SENR performs data aggregation according to the hierarchy of namespace, and stores data over in-network storage node. It also marks each route notification at the edge nodes, and allows the interest with the same name prefix to choose the same edge routing node towards to the storage node. Second, SENR adopts link-state routing protocols to collect network topology and compute the shortest path to the content source nodes, moreover, it uses distance-vector routing protocols to select multiple alternative paths to cope with node failures. Third, SENR employs partial routing update to explicit support of dynamic paths and caching capabilities. Simulation results demonstrate that SENR can significantly reduce the content retrieval delay and routing overheads.

Optimization-Based Offloading and Routing Strategies for Sensor-Enabled Video Surveillance Networks

For the Internet of things, having sensors in devices used for video surveillance services, such as cameras, is crucial. The advancement of edge computing technology has enabled high computing capacity and the handling of massive data sets. The concept of cloudlets is employed in edge computing for in-network processing, especially for large-size multimedia data processing. Cloudlets are essential for services with high computing costs. Contrary to traditional cloud computing, data can be offloaded to in-network devices and core clouds, thereby improving the quality of service and enhancing resource utilization. However, the trade-off between network transmissions and nodal processes with delay-aware multimedia traffic has been demonstrated to be an NP-complete problem. The problem is presented as a mathematical formula to maximize the minimal delay gap between the tolerable event delay, sending time, and processing time. The problem is subject to in-network processing node assignment, routing paths, transmission capacities, computing capacities, and the effective service period. The Lagrangian approach was employed to evaluate the method proposed in this study; a near-optimal solution was obtained, and several experiments were performed to demonstrate that the proposed method outperforms existing methods.

PUC: Packet Update Caching for energy efficient IoT-based Information-Centric Networking

The future Internet, known as Information-Centric Networking (ICN), is a realistic solution to content delivery between the content request generators (subscribers) and the server (publisher) in the Internet of Things (IoT) environment due to caching contents by in-network nodes. However, significant redundant copies of contents can be cached in this kind of network which, besides numerous advantages, introduces some undesirable features, such as security issues, content redundancy, access control, and cache overflow among others. ICN has different modules, such as mobility, routing, and caching, which are utmost important for the IoT network due to the nature of energy-constrained IoT devices. While numerous attempts are presently being made to institutionalize this emerging paradigm, careful considerations are needed to caching module at the early stage of this architecture. This is important instead of holding up until the innovation gets used and experienced. In this article, we first list some of the important features and limitations of the ICN-based IoT caching and then propose an ICN caching strategy that fits well in the energy efficient and secure IoT environment. The proposed strategy is simulated and compared with the ProbCache mechanism with regards to energy consumption and bandwidth utilization. Preliminary experimental analyses demonstrate that the proposed strategy produces better results than the ProbCache as long as the cache size of network nodes is increased.

A game theoretical distributed approach for opportunistic caching strategy

Wireless network virtualization (WNV) has been a hot topic recently, which can provide the customized service to construct virtual networks for various requirements quickly and flexibly by resource sharing. However, most of the traffic is generated by duplicate downloads for a few popular contents. Meanwhile, due to the isolation in the existing WNV, these popular contents cannot be shared among various requests, and thus the congestion and overhead would be caused in the remote data center. Therefore, in order to decline the congestion and overhead from the remote data center, the caching problem is addressed in this paper, which aims to overhear and cache content opportunistically to provide the quicker response for the future requests by in-network nodes. To well understand the impact of content caching, we formulate this issue as a Markov chain, and analyze the content response ratio according to the steady-state mathematically. Furthermore, we define the content caching strategy decision as an optimization problem, and use potential game to decide caching strategy based on Nash Equilibrium in a distributed manner, denoted as Game based Opportunistic Caching Strategy (GOCS). The experimental results show the effectiveness of GOCS in terms of average response hops, average cache hit ratio, data center load reduction ratio, and average resource consumption.

Content search and routing under custodian unavailability in information-centric networks

Dealing with the sudden unavailability of content custodians (origin servers) in a network has received limited attention in information-centric networks (ICN). We consider a scenario where the content custodians in an ICN are temporarily unavailable, possibly due to reasons such as device malfunctioning, power outage or Denial of Service (DoS) attacks. In contrast to the traditional host-centric Internet, users can still be served in an ICN as content is cached at in-network nodes. We begin by making the observation that if caches continue to operate using their native cache insertion and eviction policies after the content custodians become unavailable, over time the network content diversity decreases. Therefore, we recommend freezing network caches once the custodians become unavailable. We then propose a routing and search algorithm, Content Exploration under Server Unavailability (CESU), that uses breadcrumbs to effectively locate content cached at in-network nodes and serve user requests when the custodians are unavailable. We perform extensive simulations on real Internet topologies in the Icarus simulator and demonstrate that CESU can efficiently locate content cached in the network. CESU outperforms shortest path routing in terms of content requests served by 56% on average, and achieves up to 98.5% of the total request serving potential.

A Connectivity Enhancement Scheme Based on Link Transformation in IoT Sensing Networks

Large-scale and heterogeneity of the Internet of Things (IoT) sensing networks introduce a big challenge to device connectivity. There exist some isolated nodes in randomly deployed IoT sensing networks running on a tree-typed topology due to limitations of some network parameters, which reduces network connectivity. In this paper, a connectivity enhancement scheme for the sensing networks of the IoT is proposed based on link transformation. Under constraints of network depth and the number of child nodes, we boost capability of an in-network node to connect more isolated nodes by reducing its or ancestors’ depth. Furthermore, three-level node shifting is utilized to take full advantage of network locality, thus highly improving ability of a potential parent node to accept connection request of an isolated node. Finally, when failing to reduce depth of a node and disabling to shift out a child node of a parent node, the scheme exploits node swapping to improve present link status, thus enabling further some isolated nodes to join into the sensing networks. Our simulation results show that the proposed scheme can raise proportion of joined nodes and effectively enhance connectivity of the sensing networks in the IoT.

Measuring in-network node similarity based on neighborhoods: a unified parametric approach

In many applications, we need to measure similarity between nodes in a large network based on features of their neighborhoods. Although in-network node similarity based on proximity has been well investigated, surprisingly, measuring in-network node similarity based on neighborhoods remains a largely untouched problem in literature. One grand challenge is that in different applications we may need different measurements that manifest different meanings of similarity. In this paper, we investigate the problem in a principled and systematic manner. We develop a unified parametric model and a series of four instance measures. Those instance similarity measures not only address a spectrum of various meanings of similarity, but also present a series of tradeoffs between computational cost and strictness of matching between neighborhoods of nodes being compared. By extensive experiments and case studies, we demonstrate the effectiveness of the proposed model and its instances.

Value-Based Caching in Information-Centric Wireless Body Area Networks.

We propose a resilient cache replacement approach based on a Value of sensed Information (VoI) policy. To resolve and fetch content when the origin is not available due to isolated in-network nodes (fragmentation) and harsh operational conditions, we exploit a content caching approach. Our approach depends on four functional parameters in sensory Wireless Body Area Networks (WBANs). These four parameters are: age of data based on periodic request, popularity of on-demand requests, communication interference cost, and the duration for which the sensor node is required to operate in active mode to capture the sensed readings. These parameters are considered together to assign a value to the cached data to retain the most valuable information in the cache for prolonged time periods. The higher the value, the longer the duration for which the data will be retained in the cache. This caching strategy provides significant availability for most valuable and difficult to retrieve data in the WBANs. Extensive simulations are performed to compare the proposed scheme against other significant caching schemes in the literature while varying critical aspects in WBANs (e.g., data popularity, cache size, publisher load, connectivity-degree, and severe probabilities of node failures). These simulation results indicate that the proposed VoI-based approach is a valid tool for the retrieval of cached content in disruptive and challenging scenarios, such as the one experienced in WBANs, since it allows the retrieval of content for a long period even while experiencing severe in-network node failures.

Data Transfer Security solution for Wireless Sensor Network

WSN is a wide growth area for specific resource limited application. Factor associated with technology like, the encryption security, operating speed and power consumption for network. Here, we introduce a mechanism for secure transferring of data is WSN and various security related issues. This energy-efficient encryption is a secure communication framework in which an algorithm is used to encode the sensed data using like, RC5, AES and CAST Algorithm. The proposed scheme is most suitable for wireless sensor networks that incorporate data centric routing protocols. An algorithm in sensor network is help to designers predict security performance under a set of constraints for WSNs. This symmetric key function is used to guarantee secure communications between in-network nodes and reliable operation cost. RC5 is good on the code point of view, but the key schedule consumes more resource time for efficient security aspects.

An Efficient Key Management Scheme for Data-Centric Storage Wireless Sensor Networks

Data-centric storage wireless sensor networks in general provide in-network node repositories to keep sensory data for later use. However, most of previous studies pay less attention to data security issues, so that the sensory data could be easily explored to the intruders. The pDCS scheme although has implemented with data protection mechanism, it incurs a lot of rekeying message traffics while a node was compromised, and thus greatly consumes network energy. In this paper, based on the Exclusion Basis System (EBS), we propose an efficient and distributed key management protocol, termed as ERP-DCS, to improve pDCS's deficiency. Through extensive simulations, we conclude that: at a slight increase on the number of keys (about one more key) stored in each node, the proposed scheme can achieve about 70% reduction on rekeying message overheads, and thus elongates the network lifetime.

PEJA: Progressive Energy-Efficient Join Processing for Sensor Networks

Sensor networks are widely used in many applications to collaboratively collect information from the physical environment. In these applications, the exploration of the relationship and linkage of sensing data within multiple regions can be naturally expressed by joining tuples in these regions. However, the highly distributed and resource-constraint nature of the network makes join a challenging query. In this paper, we address the problem of processing join query among different regions progressively and energy-efficiently in sensor networks. The proposed algorithm PEJA (Progressive Energy-efficient Join Algorithm) adopts an event-driven strategy to output the joining results as soon as possible, and alleviates the storage shortage problem in the in-network nodes. It also installs filters in the joining regions to prune unmatchable tuples in the early processing phase, saving lots of unnecessary transmissions. Extensive experiments on both synthetic and real world data sets indicate that the PEJA scheme outperforms other join algorithms, and it is effective in reducing the number of transmissions and the delay of query results during the join processing.

Stepwise Creation of Multimedia Services for Broadband Intelligent Networks

Intelligent Network is one of the most successful communication concepts world-wide. The survival of this concept into the next century largely depends on the deployment of interactive multimedia services which in turn must be supported by an adequate network infrastructure. In this paper a staged service specification and development philosophy is proposed which blends a multimedia oriented IN service architecture with B-ISDN transport capabilities. The outstanding feature regarding this approach is that, while it originates from the existing narrowband single media reality, it additionally provides detailed evolutionary steps towards future services running over the emerging high speed public networks. An enhanced IN/B-ISDN architecture is used as the starting point for the provision of core multimedia characteristics such as call/connection separation and co-ordination of multiple calls within a session. Consequently, the way by which the service prose description can serve as a means for the decomposition of a multimedia service into discrete functional stages is presented. The available options regarding the distribution of the identified functionality among the IN network nodes are examined next, followed by the definition of service specific information flows across the various interfaces. The results from this specification work are used for the production of a formal service description in SDL. This description is mapped to a library of object oriented service building blocks utilised by a service creation environment for the production of the actual service logic program. Selected parts of new multimedia services (Video on Demand) as well as existing multimedia services, originally targeted at TCP/IP environments (Video Conference), are presented in order to demonstrate an implementation viewpoint with respect to the proposed methodology. Finally, the advantages of the specification and design approach discussed in this paper are justified through comparison to other existing approaches in the same field.