A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks

In the last decade, numerous efforts have been devoted to design efficient algorithms for clustering the wireless mobile ad-hoc networks (MANET) considering the network mobility characteristics. However, in existing algorithms, it is assumed that the mobility parameters of the networks are fixed, while they are stochastic and vary with time indeed. Therefore, the proposed clustering algorithms do not scale well in realistic MANETs, where the mobility parameters of the hosts freely and randomly change at any time. Finding the optimal solution to the cluster formation problem is incredibly difficult, if we assume that the movement direction and mobility speed of the hosts are random variables. This becomes harder when the probability distribution function of these random variables is assumed to be unknown. In this paper, we propose a learning automata-based weighted cluster formation algorithm called MCFA in which the mobility parameters of the hosts are assumed to be random variables with unknown distributions. In the proposed clustering algorithm, the expected relative mobility of each host with respect to all its neighbors is estimated by sampling its mobility parameters in various epochs. MCFA is a fully distributed algorithm in which each mobile independently chooses the neighboring host with the minimum expected relative mobility as its cluster-head. This is done based solely on the local information each host receives from its neighbors and the hosts need not to be synchronized. The experimental results show the superiority of MCFA over the best existing mobility-based clustering algorithms in terms of the number of clusters, cluster lifetime, reaffiliation rate, and control message overhead.     

Enabling richer statistical MANET simulations through cluster computing

The wide-scale adoption of modern smart phones and other multi-radio mobile devices, has begun to provide pragmatic deployment environments for non-cellular mobile ad hoc network (MANET) services (i.e., for disaster recovery scenarios, peered mobile games, social networking applications, etc.). User perceptions of the quality of such MANET services will be driven, in part, by standard network-level quality of service (QoS) metrics such as delay, jitter, throughput, etc. Much of the existing MANET literature has explored these issues, as well as MANET routing protocol design, through single computer Monte Carlo simulations (e.g., via ns-2, ns-3, OMNeT++, or OpNet). Results are then reported as the averages of these Monte Carlo runs. As is well known from probability and statistics, such averaging is only meaningful when applied across statistically ergodic data (i.e., data drawn from the same underlying distribution). But, assessing the validity of this underlying ergodic assumption requires transitioning to more rigorous cluster-based MANET simulation frameworks. This work highlights the theoretical rationale for such ergodicity testing, the developments of a cluster-based framework, the STARs framework, to support such testing, and the results and insights obtained by using this framework to evaluate the popular DYMO and OLSR MANET routing protocols. This work also discusses why the insights ergodic testing provides are of interest to potential real-world MANET deployments.     

STACRP: a secure trusted auction oriented clustering based routing protocol for MANET

In mobile ad hoc network?(MANET) nodes have a tendency to drop others’ packet to conserve its own energy. If most of the nodes in a network start to behave in this way, either a portion of the network would be isolated or total network functionality would be hampered. This behavior is known as selfishness. Therefore, selfishness mitigation and enforcing cooperation between nodes is very important to increase the availability of nodes and overall throughput and to achieve the robustness of the network. Both credit and reputation based mechanisms are used to attract nodes to forward others’ packets. In light of this, we propose a game theoretic routing model, Secure Trusted Auction oriented Clustering based Routing Protocol (STACRP), to provide trusted framework for MANET. Two auction mechanisms procurement and Dutch are used to determine the forwarding cost-per-hop for intermediate nodes. Our model is lightweight in terms of computational and communication requirements, yet powerful in terms of flexibility in managing trust between nodes of heterogeneous deployments. It manages trust locally with minimal overhead in terms of extra messages. STACRP organizes the network into 1-hop disjoint clusters and elects the most qualified and trustworthy nodes as Clusterhead. The trust is quantified with carefully chosen parameters having deep impact on network functionality. The trust model is analyzed using Markov chain and is proven as continuous time Markov chain. The security analysis of the model is analyzed to guarantee that the proposed approach achieves a secure reliable routing solution for MANETs. The proposed model have been evaluated with a set of simulations that show STACRP detects selfish nodes and enforces cooperation between nodes and achieves better throughput and packet delivery ratio with lees routing overhead compare to AODV.     

Finding scalable configurations for AEDB broadcasting protocol using multi-objective evolutionary algorithms

Energy consumption is one of the main concerns in mobile ad hoc networks (or MANETs). The lifetime of its devices highly depends on the energy consumption as they rely on batteries. The adaptive enhanced distance based broadcasting algorithm, AEDB, is a message dissemination protocol for MANETs that uses cross-layer technology to highly reduce the energy consumption of devices in the process, while still providing competitive performance in terms of coverage and time. We use two different multi-objective evolutionary algorithms to optimize the protocol on three network densities, and we evaluate the scalability of the best found AEDB configurations on larger networks and different densities.     

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...     

Distributed Clone Detection in Static Wireless Sensor Networks: Random Walk with Network Division

Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.     

Integrated control of water hyacinth with a mycoherbicide and a phenylpropanoid pathway inhibitor

The fungus Alternaria eichhorniae isolate 5 (Ae5) is being developed as an effective mycoherbicide against water hyacinth in Egypt. To improve its pathogenicity, integration with 3,4-methylenedioxy trans-cinnamic acid (MDCA), a phenylpropanoid pathway inhibitor that weakens the plant's defense system, was explored. The severity of the disease induced by Ae5 increased when applied to water hyacinth plants pretreated with MDCA. Infection with Ae5 amplified the total phenol concentration in diseased water hyacinth leaves, whereas MDCA reduced it. Plants treated with both Ae5 and MDCA contained a comparable level of total phenols to that in the untreated control plants. Phenol-storing cells were located at three sites in the leaf, within the adaxial palisade tissue, above the abaxial epidermis and in the vicinity of the vascular bundles. Dimensions of these three cell types were increased by infection with Ae5, decreased by MDCA treatment and, in the combined treatment, were similar to those in control leaves. Increased numbers of phenol-storing cells were found only in the region near vascular bundles of plants treated with either Ae5 or MDCA.     

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.     

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.     

Herbivore-induced plant vaccination. Part I. The orchestration of plant defenses in nature and their fitness consequences in the wild tobacco Nicotiana attenuata

A plant's responses to attack from particular pathogens and herbivores may result in resistance to subsequent attack from the same species, but may also affect different species. Such a cross-resistance, called immunization or vaccination, can benefit the plant, if the fitness consequences of attack from the initial attacker are less than those from subsequent attackers. Here, we report an example of naturally occurring vaccination of the native tobacco plant, Nicotiana attenuata, against Manduca hornworms by prior attack from the mirid bug, Tupiocoris notatus (Dicyphus minimus), which results from the elicitation of two categories of induced plant responses. First, attack from both herbivore species causes the plants in nature to release predator-attracting volatile organic compounds (VOCs), and the attracted generalist predator, Geocoris pallens, preferentially attacks the less mobile hornworm larvae. Second, attack from both mirids and hornworms increases the accumulation of secondary metabolites and proteinase inhibitors (PIs) in the leaf tissue, which is correlated with the slow growth of Manduca larvae. Mirid damage does not significantly reduce the fitness of the plant in nature, whereas attack from the hornworm reduces lifetime seed production. Consequently, plants that are attacked by mirids realize a significant fitness advantage in environments with both herbivores. The combination of growth-slowing direct defenses and predator-attracting indirect defenses results in greater hornworm mortality on mirid-attacked plants and provides the mechanism of the vaccination phenomenon.     

Robustness of the p53 network and biological hackers

The p53 protein interaction network is crucial in regulating the metazoan cell cycle and apoptosis. Here, the robustness of the p53 network is studied by analyzing its degeneration under two modes of attack. Linear Programming is used to calculate average path lengths among proteins and the network diameter as measures of functionality. The p53 network is found to be robust to random loss of nodes, but vulnerable to a targeted attack against its hubs, as a result of its architecture. The significance of the results is considered with respect to mutational knockouts of proteins and the directed attacks mounted by tumour inducing viruses.     

Attack resilience of the evolving scientific collaboration network

Stationary complex networks have been extensively studied in the last ten years. However, many natural systems are known to be continuously evolving at the local ("microscopic") level. Understanding the response to targeted attacks of an evolving network may shed light on both how to design robust systems and finding effective attack strategies. In this paper we study empirically the response to targeted attacks of the scientific collaboration networks. First we show that scientific collaboration network is a complex system which evolves intensively at the local level--fewer than 20% of scientific collaborations last more than one year. Then, we investigate the impact of the sudden death of eminent scientists on the evolution of the collaboration networks of their former collaborators. We observe in particular that the sudden death, which is equivalent to the removal of the center of the egocentric network of the eminent scientist, does not affect the topological evolution of the residual network. Nonetheless, removal of the eminent hub node is exactly the strategy one would adopt for an effective targeted attack on a stationary network. Hence, we use this evolving collaboration network as an experimental model for attack on an evolving complex network. We find that such attacks are ineffectual, and infer that the scientific collaboration network is the trace of knowledge propagation on a larger underlying social network. The redundancy of the underlying structure in fact acts as a protection mechanism against such network attacks.     

Novel Duplicate Address Detection with Hash Function

Duplicate address detection (DAD) is an important component of the address resolution protocol (ARP) and the neighbor discovery protocol (NDP). DAD determines whether an IP address is in conflict with other nodes. In traditional DAD, the target address to be detected is broadcast through the network, which provides convenience for malicious nodes to attack. A malicious node can send a spoofing reply to prevent the address configuration of a normal node, and thus, a denial-of-service attack is launched. This study proposes a hash method to hide the target address in DAD, which prevents an attack node from launching destination attacks. If the address of a normal node is identical to the detection address, then its hash value should be the same as the “Hash_64” field in the neighboring solicitation message. Consequently, DAD can be successfully completed. This process is called DAD-h. Simulation results indicate that address configuration using DAD-h has a considerably higher success rate when under attack compared with traditional DAD. Comparative analysis shows that DAD-h does not require third-party devices and considerable computing resources; it also provides a lightweight security resolution.     

Interleukin-4 acts at the locus of the antigen-presenting dendritic cell to counter-regulate cytotoxic CD8+ T-cell responses

The mechanism underlying suppression of immune responses by interleukin-4 (IL-4) has remained unexplained. Here we show that the antigen-presenting dendritic cell is central to counter-regulation of autoimmune disease by IL-4. IL-4 acts at the locus of the dendritic cell to decrease the cytolytic T-cell response, preventing autoimmunity. Stimulation of cytotoxic precursors by antigen pulsed dendritic cells induces their differentiation but the process is blocked by IL-4. IL-4-influenced DC produce distinct effects on CD8+ T cells depending on their state of activation. The molecular basis for this regulation is the alteration of the expression ratio of the costimulatory ligands B7.1/B7.2 on dendritic cells. Our findings demonstrate that B7.2 induces expansion of CD8+ T cells and B7.1 governs their acquisition of cytolytic activity. IL-4 influences the dendritic cell to elicit qualitative differences in T-cell responses, providing the basis for counter-regulation mediated by IL-4.     

Modeling the electrical behavior of anatomically complex neurons using a network analysis program: Passive membrane

We describe the application of a popular and widely available electrical circuit simulation program called SPICE to modeling the electrical behavior of neurons with passive membrane properties and arbitrarily complex dendritic trees. Transient responses may be calculated at any location in the cell model following current, voltage or conductance perturbations at any point. A numbering method is described for binary trees which is helpful in transforming complex dendritic structures into a coded list of short cylindrical dendritic segments suitable for input to SPICE. Individual segments are modeled as isopotential compartments comprised of a parallel resistor and capacitor, representing the transmembrane impedance, in series with one or two core resistors. Synaptic current is modeled by a current source controlled by the local membrane potential and an alpha-shaped voltage, thus simulating a conductance change in series with a driving potential. Extensively branched test cell circuits were constructed which satisfied the equivalent cylinder constraints (Rall 1959). These model neurons were perturbed by independent current sources and by synaptic currents. Responses calculated by SPICE are compared with analytical results. With appropriately chosen model parameters, extremely accurate transient calculations may be obtained. Details of the SPICE circuit elements are presented, along with illustrative examples sufficient to allow implementation of passive nerve cell models on a number of common computers. Methods for modeling excitable membrane are presented in the companion paper (Bunow et al. 1985).     

Evidence for lipopolysaccharideinduced differentiation of RAW264.7 murine macrophage cell line into dendritic like cells

Effect of lipopolysaccharide (LPS) on RAW264.7 macrophage cell line was studied. LPS-treated RAW264.7 cells increased in cell size and acquired distinct dendritic morphology. At the optimal dose of LPS (1 mg/ml), almost 70% RAW264.7 cells acquired dendritic morphology. Flow cytometric studies indicate that the cell surface markers known to be expressed on dendritic cells and involved in antigen presentation and T cell activation (B 7.1, B 7.2, CD40, MHC class II antigens and CD1d) were also markedly upregulated on LPS-treated RAW 264.7 cells. Our results suggest the possibility that LPS by itself could constitute a sufficient signal for differentiation of macrophages into DC-like cells.     

The novel costimulatory programmed death ligand 1/B7.1 pathway is functional in inhibiting alloimmune responses in vivo

The programmed death ligand 1 (PDL1)/programmed death 1 (PD1) costimulatory pathway plays an important role in the inhibition of alloimmune responses as well as in the induction and maintenance of peripheral tolerance. It has been demonstrated recently that PDL1 also can bind B7.1 to inhibit T cell responses in vitro. Using the bm12 into B6 heart transplant model, we investigated the functional significance of this interaction in alloimmune responses in vivo. PD1 blockade unlike PDL1 blockade failed to accelerate bm12 allograft rejection, suggesting a role for an additional binding partner for PDL1 other than PD1 in transplant rejection. PDL1 blockade was able to accelerate allograft rejection in B7.2-deficient recipients but not B7.1-deficient recipients, indicating that PDL1 interaction with B7.1 was important in inhibiting rejection. Administration of the novel 2H11 anti-PDL1 mAb, which only blocks the PDL1-B7.1 interaction, aggravated chronic injury of bm12 allografts in B6 recipients. Aggravated chronic injury was associated with an increased frequency of alloreactive IFN-γ-, IL-4-, and IL-6-producing splenocytes and a decreased percentage of regulatory T cells in the recipients. Using an in vitro cell culture assay, blockade of the interaction of PDL1 on dendritic cells with B7.1 on T cells increased IFN-γ production from alloreactive CD4(+) T cells, whereas blockade of dendritic cell B7.1 interaction with T cell PDL1 did not. These data indicate that PDL1 interaction with B7.1 plays an important role in the inhibition of alloimmune responses in vivo and suggests a dominant direction for PDL1 and B7.1 interaction.     

The Allelochemical MDCA Inhibits Lignification and Affects Auxin Homeostasis

The phenylpropanoid 3,4-(methylenedioxy)cinnamic acid (MDCA) is a plant-derived compound first extracted from roots of Asparagus officinalis and further characterized as an allelochemical. Later on, MDCA was identified as an efficient inhibitor of 4-COUMARATE-CoA LIGASE (4CL), a key enzyme of the general phenylpropanoid pathway. By blocking 4CL, MDCA affects the biosynthesis of many important metabolites, which might explain its phytotoxicity. To decipher the molecular basis of the allelochemical activity of MDCA, we evaluated the effect of this compound on Arabidopsis thaliana seedlings. Metabolic profiling revealed that MDCA is converted in planta into piperonylic acid (PA), an inhibitor of CINNAMATE-4-HYDROXYLASE (C4H), the enzyme directly upstream of 4CL. The inhibition of C4H was also reflected in the phenolic profile of MDCA-treated plants. Treatment of in vitro grown plants resulted in an inhibition of primary root growth and a proliferation of lateral and adventitious roots. These observed growth defects were not the consequence of lignin perturbation, but rather the result of disturbing auxin homeostasis. Based on DII-VENUS quantification and direct measurement of cellular auxin transport, we concluded that MDCA disturbs auxin gradients by interfering with auxin efflux. In addition, mass spectrometry was used to show that MDCA triggers auxin biosynthesis, conjugation, and catabolism. A similar shift in auxin homeostasis was found in the c4h mutant ref3-2, indicating that MDCA triggers a cross talk between the phenylpropanoid and auxin biosynthetic pathways independent from the observed auxin efflux inhibition. Altogether, our data provide, to our knowledge, a novel molecular explanation for the phytotoxic properties of MDCA.Plants growing in a tight community are in continuous competition for space, light, water, and nutrients. Potential survival strategies include optimizing plant architecture and maximizing growth rate, allowing the plant to capture light and receive nutrients and water more efficiently, while placing neighboring plants in an unfavorable position (Einhellig, 1995; Weir et al., 2004). Besides developmental shifts, plants release an array of secondary metabolites (allelochemicals) into the rhizosphere to negatively affect the growth and reproduction of neighboring, competitor plants (Putnam, 1988; Bertin et al., 2003). Despite a lot of research effort having been devoted to allelopathic chemical warfare over the past decades, it remains a difficult study object due to the complexity of plant-plant interactions (Zeng, 2014). Nevertheless, the significance of allelochemicals in structuring plant communities and preserving biodiversity has been fully recognized by the scientific community. Moreover, allelochemicals show the potential to be used as an environmentally friendly alternative for weed control to improve agricultural productivity (Zeng, 2014).Strictly speaking, the term “allelochemical” refers to a compound produced and released by one organism to affect the growth and development of susceptible species (Weir et al., 2004). In practice, compounds derived from plant extracts or exudates are often cataloged as allelochemicals based on their inhibitory effect on seed germination and/or growth of other plant species in an artificial setup. Despite their importance, the molecular mode of action of a given allelochemical compound has rarely been studied in detail; however, toxicity is relatively easily demonstrated, identifying its molecular target is far more challenging. An interesting example is the phenylpropanoid 3,4-(methylenedioxy)cinnamic acid (MDCA), which was isolated from lyophilized root tissues of Asparagus [Asparagus officinalis L.; Hartung et al. (1990)]. It was suggested to be an allelochemical based on its inhibitory effect on root and shoot growth of Lepidium sativum (Hartung et al., 1990). Independent studies revealed that MDCA acts as an efficient competitive inhibitor of 4-COUMARATE-CoA LIGASE (4CL), the enzyme converting hydroxycinnamates to their corresponding CoA-esters (Knobloch and Hahlbrock, 1977; Chakraborty et al., 2009). This conversion is an early step in the general phenylpropanoid pathway leading to a wide array of metabolites, including coumarins, stilbenes, salicylic acid, flavonoids, and monolignols (Vogt, 2010). Given that inhibition of 4CL in this metabolic pathway will have far-reaching effects on plant growth and development (Voelker et al., 2010), it is tempting to link the proposed phytotoxicity of MDCA to this metabolic block.Here, we evaluate whether the phytotoxicity of MDCA is a direct consequence of the inhibition of 4CL or if MDCA targets also other biological processes in Arabidopsis (Arabidopsis thaliana). We found that MDCA causes strong developmental defects in Arabidopsis seedlings at early developmental stages. Convincing evidence was found that MDCA affects the homeostasis of the plant signaling compound auxin. Our results provide an alternative explanation for the molecular mechanism underlying the phytotoxic properties of MDCA, and suggest that these multiple modes of action make it an attractive candidate as an environmental agrochemical or synergist.     

An empirical study of web browsers’ resistance to traffic analysis and website fingerprinting attacks

Anonymity protocols are employed to establish encrypted tunnels to protect the privacy of Internet users from traffic analysis attacks. However, the attackers strive to infer some traffic patterns’ characteristics (e.g. packet directions, packet sizes, inter-packet timing, etc.) in order to expose the identities of Internet users and their activities. A recent and popular traffic analysis attack is called website fingerprinting which reveals the identity of websites visited by target users. Existing work in the literature studied the website fingerprinting attack using a single web browser, namely Firefox. In this paper we propose a unified traffic analysis attack model composed of a sequence of phases that demonstrate the efficiency of website fingerprinting attack using popular web browsers under Tor (The Onion Router). In addition, we reveal the main factors that affect the accuracy of website fingerprinting attack over Tor anonymous system and using different browsers. To the best of our knowledge, no previous study uncovered such factors by deploying real-world traffic analysis attack utilizing the top five web browsers. The outcomes of the research are very relevant to Internet users (individuals/companies/governments) since they allow to assess to which extent their privacy is preserved in presence of traffic analysis attacks, in particular, website fingerprinting over different browsers. A recommendation for future research direction regarding the investigation of website fingerprinting over different scenarios is also provided.