Successive Nodes Research Articles

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

At present, the secure campus network strategy adopts technical means such as distinguishing applications and limiting them separately, but they have triggered other new problems, greatly affecting the unity of network resources and data. The relatively dispersed network architecture will inevitably limit the further development and expansion of the campus network. Therefore, when universities plan their networks, they must consider whether the network is safe, complete, smooth, and sustainable for smooth upgrading and development. In order to improve the effect of campus network security intrusion detection, this paper combines feature segmentation and deep learning technology to construct a campus network security intrusion detection model. To reduce the transmission time of query requirements in the grid, this paper improves the replica management mechanism and requires the information server to cache the Bloom Filter structure of nodes in its successor node list. Moreover, this paper uses the Compressed Bloom Filter algorithm to compress the Bloom Filter structure that needs to be transmitted, therefore reducing the network traffic generated during the update process of the Bloom Filter structure copy and avoiding network congestion. It also constructs a campus network security intrusion detection model based on feature segmentation and deep learning. Through experimental verification, the effectiveness of the system in intrusion detection, user evaluation, information processing, and other aspects is verified, and it has certain advantages compared to traditional algorithms. Through experimental research, it can be seen that the campus network security intrusion detection model based on feature segmentation and deep learning proposed in the paper can effectively improve the effect of campus network security monitoring. The method proposed in this article can not only be applied to campus network security, but also to the network security management of enterprises and other units, with certain scalability

An Energy-Efficient Learning Automata and Cluster-Based Routing Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs), which may be used for a broad variety of applications, have recently emerged as a prominent data collection paradigm. The fundamental concerns in wireless sensor networks are the efficient use of energy and the reliable delivery of data, both of which are largely determined by the rate at which packets are dropped. When developing an energy-efficient routing protocol, one of the most important steps is selecting a node to act as a successor node in a routing path. The application of learning automata theory to guide the routing decisions made by the sensors in a WSN has recently been the subject of research in the field of WSNs, where it has been shown to have several advantages. In this paper, a learning automata-based PSO relay selection scheme for energyefficient relay selection and reliable data delivery is proposed. The network is clustered using the LEACH protocol. The random number in the traditional LEACH protocol will be stabilized with the sensor node energy level for CH stability. Every sensor node in the network estimates the best possible routes to the sink node using the PSO algorithm. Instead of retransmissions, here we introduce learning automata for successor node selection during packet loss. The proposed learning automata calculate the next node’s selection probability in a routing path using multi-objective parameters like communication cost, residual energy, distance from BS, buffer size, and previous selection probability. Performance evaluation clearly showed that the proposed approach decreases energy usage, transmission delays, and data transfers while extending network lifetime. According to the experimental results, the proposed scheme can improve energy efficiency by 21.68%, delay by 31%, PDR by 87%, routing overhead by 0.5%, and throughput by 18.76% as compared to existing techniques like O-LEACH (Optimized Low Energy Adaptive Clustering Hierarchy Protocol) and EEPC (enhanced energy proficient clustering).

A Biologically Inspired Self-Organizing Underwater Sensor Network

Wireless underwater sensor networks have various applications—such as ocean exploration and deep-sea disaster monitoring—making them a hot topic in the research field. To cover a larger area and gather more-precise information, building large-scale underwater sensor networks has become a trend. In such networks, acoustic signals are used to transmit messages in an underwater environment. Their features of low speed and narrow bandwidth make media access control (MAC) protocols unsuitable for radio communications. Furthermore, a network consists of a large number of randomly deployed nodes, making it impossible to pre-define an optimized routing table or assign a central controller to coordinate the message propagation process. Thus, optimized routing should emerge via interaction among individual nodes in the network. To address these challenges, in this paper we propose a communication coordinator under the time division multiple access (TDMA) framework. Each node in the network is equipped with such a coordinator so that messages in the network can be sent following the shortest path in a self-organized way. The coordinator consists of a slot distributor and a forwarding guide. With the slot distributor, nodes in the sensor network occupy proper communication slots and the network finally converges to the state without communication collision. This is achieved with a set of ecological niche- and pheromone-inspired laws, which encourage nodes to occupy slots that can decrease the waiting time for a node to send a message packet while weakening the enthusiasm for a node to occupy the slots that it fails to occupy several times. With the forwarding guide, a node can send the message packet to the best successor node so that the message packet can be sent to the base station along the shortest path. It has been proven that the laws in the forwarding guide are equivalent to the Dijkstra Algorithm. Simulation experiment results indicate that with our coordinator, the network can converge to the state without collision using fewer coordination messages. In addition, the time needed to send a message to the destination is shorter than that of the classical Aloha protocol.

A New Heuristic Algorithm for Multi Vehicle Routing Problem with AND/OR-Type Precedence Constraints and Hard Time Windows

In this paper the classic known Multi Vehicle Routing Problem (VRP) is studied where classically several vehicles are set in a central depot, depending on the allocation strategy, each vehicle starts traveling to visit a set of nodes one after another to provide a service of gathering or delivering commodities with the aim of minimizing total traveling distances and costs. In the current paper, this classic problem is extended by new approach of AND/OR precedence constraints (PC) which have not been considered so far in the related literature. Traditionally, PC have been considered in VRPs as the 'AND' type, where the immediate successor of a node cannot be visited until its predecessor nodes have reached their end of services. However by additional OR-type precedence constraints, there are some interconnected nodes through the concept of OR, therein no mandatory need to visit all predecessors of a successor node is acquired before it can be met, and only finishing a part of them can let to that particular node to get visited. This fact is widely observed in pickup and routing and distribution real cases where requisites for some specific products can be fulfilled via various potential suppliers. Implementation of this type of PC graph in VRP is considered as the first introduction and application in the category of this problem. So, initially, the problem is mathematically formulated, then, due to problem’s NP-hard complexity and allocation-routing characteristics, a decomposition-based technique is utilized to solve the problem. The procedure is based on recently enhanced Benders’ decomposition approach named as Branch and Search Algorithm, partitioning the original main problem into allocation and routing parts. Unlike the previous version of Benders algorithm, logic based, this newly promoted method acts in a way that at the allocation level, it generates partition of nodes with feasible solutions in lower routing level. The routing part itself has been enhanced by heuristics to cover AND/OR PC graphs. Furthermore, the efficiency of the proposed solution procedure is tested and verified by running on several generated problems in different sizes and in the larger scale it is implemented on the real case of a distribution company in Iran.

Fuzzy hierarchical network embedding fusing structural and neighbor information

Many network-based tasks need powerful feature expression to capture the diversity of networks. It can be provided by network embedding learning of nodes. The related researches led to a significant progress so far. Nevertheless, suffering from paying unique attention on the structure or neighborhoods of nodes, most of methods cannot describe the vector representations of nodes well enough. In addition, random walk plays a key role during the learning procedure in many advanced methods. The successor node is selected according t the proximity of its direct prior node in each walk step. It is easy to lead a similarity drifting in a long walk sequence and influence the sequence quality. To address these issues, a fuzzy hierarchical network embedding approach (FHNE) is put forward to learn the vector expression fusing structure and neighbor information. Firstly, a multi-granular graph is constructed by a fuzzy k-core decomposition to encode the structural and neighborhood information of nodes. Then, inspired by the epidemic method, a biased random walk is designed to solve the similarity drifting. Many numerical experiments demonstrate that our method exhibits superior performance on various tasks in some real datasets. It verifies that FHNE can learn the efficient network representations.

Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing

A heterogeneous system can be portrayed as a variety of unlike resources that can be locally or geologically spread, which is exploited to implement data-intensive and computationally intensive applications. The competence of implementing the scientific workflow applications on heterogeneous systems is determined by the approaches utilized to allocate the tasks to the proper resources. Cost and time necessity are evolving as different vital concerns of cloud computing environments such as data centers. In the area of scientific workflows, the difficulties of increased cost and time are highly challenging, as they elicit rigorous computational tasks over the communication network. For example, it was discovered that the time to execute a task in an unsuited resource consumes more cost and time in the cloud data centers. In this paper, a new cost- and time-efficient planning algorithm for scientific workflow scheduling has been proposed for heterogeneous systems in the cloud based upon the Predict Optimistic Time and Cost (POTC). The proposed algorithm computes the rank based not only on the completion time of the current task but also on the successor node in the critical path. Under a tight deadline, the running time of the workflow and the transfer cost are reduced by using this technique. The proposed approach is evaluated using true cases of data-exhaustive workflows compared with other algorithms from written works. The test result shows that our proposed method can remarkably decrease the cost and time of the experimented workflows while ensuring a better mapping of the task to the resource. In terms of makespan, speedup, and efficiency, the proposed algorithm surpasses the current existing algorithms—such as Endpoint communication contention-aware List Scheduling Heuristic (ELSH)), Predict Earliest Finish Time (PEFT), Budget-and Deadline-constrained heuristic-based upon HEFT (BDHEFT), Minimal Optimistic Processing Time (MOPT) and Predict Earlier Finish Time (PEFT)—while holding the same time complexity.

Learning automata based energy efficient and reliable data delivery routing mechanism in wireless sensor networks

Wireless Sensor Networks (WSNs) emerged as major data gathering paradigm due to its wide variety of applications. Achieving energy efficient reliable data delivery is an important concern in WSNs. In a network, packet loss reduces the reliable data transmission rate. Retransmissions are required to increase the reliable data transmission rate. However, number of retransmissions also increase the network energy consumptions and packet delivery delay. Packet loss rate can be reduced by selecting a best successor node in a routing path. In this work, a Learning Automata based Routing mechanism is proposed for wireless sensor networks to achieve energy efficiency and reliable data delivery. In this paper, learning automata has been adopted to calculate next node’s selection probability in a routing path using node’s score, quality of link and previous selection probability. Further, an energy efficient reliable routing mechanism is proposed using combination of learning automata and A-star search algorithm. The proposed mechanism determines energy efficient and reliable routing paths in WSNs by favoring highest residual energy, good quality of link , more free buffer space and less distance. Finally, simulation results indicate that the proposed algorithm reduces the energy consumptions, data delivery delay, data transmissions and increases the network lifetime.

A Distributed Token Passing Protocol for Time Constrained Data Gathering in VANETs

This paper proposes a novel approach for time constrained information gathering in a typical Vehicular Ad Hoc Network (VANET), based on a token passing scheme, adapted to wireless communications by creating a virtual ring where nodes are connected to a predecessor and a successor node. To address the typical fast topology changes of VANETs, we proposed a specific approach, called Tom Thumb that is a distributed protocol that node-by-node circulates a special packet, called token, which collects the information stored in each vehicle until returning to the first unit within a specified time constraint. The protocol has been properly designed in terms of (i) the more effective hop-by-hop and distributed heuristic implementing the objective function (ii) the token packet format, i.e., the syntax and semantics of its fields. Finally, the performance of the proposed approach is validated for different time constraints and numbers of vehicles, always pointing out a remarkable gain, especially in the presence of severe constraints, i.e., in terms of time deadline, collected information amount and success probability.

Role of metabolic reprogramming toward fatty acid oxidation on tumor lymph node metastasis.

e14501 Background: Metastasis of tumors to lymph node (LN) is a strong predictor of cancer patient mortality and determinant of disease progression and treatment options. Despite the clinical significance of tumor LN metastasis, the mechanisms underlying this process remain largely unexplored. Methods: We used comparative transcriptomics and metabolomics analyses of the primary tumor and LN micro- and macro-metastatic tumors from B16F10 murine melanoma model. B16F10 melanoma footpad implantation model and MMTV-PyMT spontaneous breast cancer genetic mouse models were analyzed in vivo. We also investigated the LN-metastatic tumors from sentinel LNs in 21 patients with melanoma. Results: We found that tumor cells undergo dramatic metabolic reprogramming towards fatty acid oxidation (FAO) for successful LN metastasis. Mechanistically, transcription co-activator YAP is selectively activated in LN metastatic tumors, leading to stimulation of FAO gene programs. Pharmacological inhibition of FAO or genetic ablation of YAP in tumors markedly suppressed LN metastasis. Unexpectedly, metastatic tumors highly stimulated the bile acid synthesis pathway upon arrival at the LN, and these bile acids activated YAP of tumor cells via nuclear vitamin D receptor. Lastly, YAP activation in the metastatic LNs of melanoma patients was reversely correlated with distant metastasis–free survival. Conclusions: This study propose that inhibition of FAO or YAP signaling to prevent the metabolic reprogramming of metastatic tumors offers a potential therapeutic strategy for mitigating tumor LN metastasis.

A Self-Heuristic Ant-Based Method for Path Planning of Unmanned Aerial Vehicle in Complex 3-D Space With Dense U-Type Obstacles

Optimal path planning is required in autonomous navigation and intelligent control of the unmanned aerial vehicle (UAV). However, as a kind of common obstacles in complex three-dimensional (3-D) spaces, U-type obstacles may cause UAV to be confused and even lead to a collision or out of control. Although most of the Ant Colony Optimization (ACO) algorithm can generate proper path, solutions to U-type obstacles based on the specific behaviors of each ant are investigated rarely. Hence, different search strategies are studied and a novel ACO-based method called Self-Heuristic Ant (SHA) is proposed in this paper. The whole space is constructed by grid workspace model firstly, and then a new optimal function for UAV path planning is built. To avoid ACO deadlock state (i.e., ants are trapped in U-type obstacles when there is no optional successor node), two different search strategies are designed for choosing the next path node. In addition, the SHA is utilized to improve the ability of the basic ACO-based method. Specifically, besides pheromone update, a new information communion mechanism is fused to deal with the special areas which contain dense obstacles or many concave blocks. Finally, several experiments are investigated deeply. The results show that the deadlock state can be reduced effectively by the designed two different search strategies of ants. More importantly, compared with the conventional fallback strategy, the average number of retreats and the average running time of ACO can be reduced when SHA is applied.

Cycles and Optimistic Stability in Graphs: The Role of Competition, Veto Players and Moderators

This article provides sufficient conditions for existence of a non-empty optimistic stable standard of behaviour (OSSB) in directed graphs. Graphs which are completely connected by single-player arcs for each player always admit a (non-unique) OSSB. More specifically, a loop is broken by adding a single-player move to some appropriately chosen default position. If there are nodes with a decision cycle over the move to the successor node, introducing veto players ensures that an OSSB exists which assigns a non-empty solution to every vertex along the equilibrium path.

Efficient Multipath Routing Protocol based on Path Survivability Factor

ad hoc network is a collection of mobile devices which can communicate through wireless links. The task of routing protocol is to send packets from source to destination. This is particularly hard in mobile ad hoc networks due to the mobility of the network elements and lack of centralized control, infrastructure, etc. In this paper, we propose a routing algorithm for the mobile ad hoc networks to discover an optimal route for transmitting data packets from source to destination. This protocol helps every node in MANET to choose next efficient successor node on the basis of channel parameters like noise, energy, bandwidth, number of hop, traffic load. The protocol improves the performance of a route by increasing network life time, reducing link failure and selecting best node for forwarding the data packet to next node. The protocol adapts quickly to routing changes when host movement is frequent, yet requires little or no overhead during periods in which hosts move less frequently.

Finding numerical solutions of diophantine equations using ant colony optimization

The paper attempts to find numerical solutions of Diophantine equations, a challenging problem as there are no general methods to find solutions of such equations. It uses the metaphor of foraging habits of real ants. The ant colony optimization based procedure starts with randomly assigned locations to a fixed number of artificial ants. Depending upon the quality of these positions, ants deposit pheromone at the nodes. A successor node is selected from the topological neighbourhood of each of the nodes based on this stochastic pheromone deposit. If an ant bumps into an already encountered node, the pheromone is updated correspondingly. A suitably defined pheromone evaporation strategy guarantees that premature convergence does not take place. The experimental results, which compares with those of other machine intelligence techniques, validate the effectiveness of the proposed method.

An Efficient Routing Protocol under Noisy Environment for Mobile Ad Hoc Networks using Fuzzy Logic

A MANET is a collection of mobile nodes communicating and cooperating with each other to route a packet from the source to their destinations. A MANET is used to support dynamic routing strategies in absence of wired infrastructure and centralized administration. In this paper, we propose a routing algorithm for the mobile ad hoc networks based on fuzzy logic to discover an optimal route for transmitting data packets to the destination. This protocol helps every node in MANET to choose next efficient successor node on the basis of channel parameters like environment noise and signal strength. The protocol improves the performance of a route by increasing network life time, reducing link failure and selecting best node for forwarding the data packet to next node.

Chain Based Fault Tolerant Routing Protocols

Sensor networks have emerged as a promising tool for monitoring (and possibly actuating) the physical world; utilizing self-organizing networks of battery-powered wireless sensors that can sense, process, and communicate. In wireless sensor networks (WSNs), energy is a critical resource; hence power efficient routing protocols is necessary for data transmission in order to extend the network lifetime. Recently number of efficient chain based protocols has been proposed for WSNs routing. These routing protocols have achieved lowest consumed energy and delay. However, fault-tolerance was not considered in these protocols. Since node failures are inevitable in WSNs due to the harsh deployment environment, nodes mobility, and nodes depleted of energy. Therefore, fault tolerance is a must for successful routing protocols. In this paper, fault tolerance is incorporated for chain based routing protocols. More specifically, two techniques for fault detection and fault recovery for chain based routing protocols are proposed. The two techniques employ the same strategy for fault detection; every node sends a notifying message to its successor neighboring node, if the successor node is alive it replies with a ready message else the node sending the notifying message deduces that its successor node is dead. However, the recovery strategy is different for the two techniques. The first technique overcomes the fault by having every predecessor node to a failed node instead of sending its data to the failed node forward it to the successor node of the failed node. This technique is used for both a single chain routing protocols as well as chain-chain routing protocols. The second technique gets around the fault by choosing a backup node for the faulty from the neighboring chain closest the sink which satisfies minimum energy consumption. However, the second technique is used only for chain-chain routing protocols. The fault detection phase of the two proposed protocols may be applied at each data round or at varying intervals as dedicated by the application and the environment in which the WSN is deployed. The simulation results indicate that the two proposed protocols achieved fault tolerance efficiently (energy and time wise) for a single node failure at each chain.

Understanding and Tackling the Root Causes of Instability in Wireless Mesh Networks

We investigate, both theoretically and experimentally, the stability of CSMA-based wireless mesh networks, where a network is said to be stable if and only if the queue of each relay node remains (almost surely) finite. We identify two key factors that impact stability: the network size and the so-called effect, a consequence of the hidden-node problem and nonzero transmission delays. We consider the case of a greedy source and prove, by using Foster's theorem, that three-hop networks are stable, but only if the stealing effect is accounted for. We also prove that four-hop networks are, on the contrary, always unstable (even with the stealing effect) and show by simulations that instability extends to more complex linear and nonlinear topologies. To tackle this instability problem, we propose and evaluate a novel, distributed flow-control mechanism called EZ-flow. EZ-flow is fully compatible with the IEEE 802.11 standard (i.e., it does not modify headers in packets), can be implemented using off-the-shelf hardware, and does not entail any communication overhead. EZ-flow operates by adapting the minimum congestion window parameter at each relay node, based on an estimation of the buffer occupancy at its successor node in the mesh. We show how such an estimation can be conducted passively by taking advantage of the broadcast nature of the wireless channel. Real experiments, run on a nine-node test-bed deployed over four different buildings, show that EZ-flow effectively smooths traffic and improves delay, throughput, and fairness performance.

Exact symbol error probability of m-psk for multihop transmission with regenerative relays

We determine the exact symbol error probability of M-ary phase shift keying (M-PSK) for multihop communication systems with regenerative relays, where the source terminal transmits data to the destination terminal via a set of intermediate relay stations, which perform hard decisions on the received symbols before forwarding them to their respective successor node. Both, time-invariant additive white Gaussian noise channels as well as frequency-flat fading channels are considered and we derive generic expressions, which might be easily evaluated numerically or even be given in closed-form for various cases.

Fault-tolerant wormhole routing using a variation of the distributed recovery block approach

A fault-tolerant wormhole routing technique that incorporates a variation of the distributed recovery block (DRB) approach is described. The section of a parallel system that spans between the source and destination nodes is dynamically partitioned into overlapping DRB groups. A DRB group consists of a current node, a primary and an alternate successor node. The message packets travel towards the destination from one DRB group to the next group. A prototype of the routing system is implemented for mesh and hypercube topologies; however, the method can be used for topologies with a minimum node connectivity of three. The simulation results indicate that the DRB approach based wormhole routing tolerates both node and link failures.

Infinite Deterministic Graphical Games

A deterministic graphical game is a two-person zero-sum game with perfect information played on a directed graph. Nodes with no successor are called terminal nodes and have a payoff associated with them. The other nodes are labelled to indicate which of the players chooses the successor node. Play starts at a specific node and only stops when a terminal node is reached at which point player 1 obtains the payoff corresponding to that node; if play never ends the payoff is zero. The paper shows that such games have a solution even when the graph has an infinite number of nodes.

Cycles in extensive form perfect information games

We define and analyse a new class of perfect information games. The nodes of a directed graph G are partitioned into n player sets. Starting at a fixed node of G an infinite path is created as follows: If the current node belongs to player k, then player k chooses any successor node. A local reward n-vector is assigned to every arc. The payoff corresponding to the infinite path is the long term average of the local reward vectors. Such games are called DGA games. Negative and positive results are obtained for the existence of Nash equilibria in certain types of pure strategies (e.g., stationary and automated strategies). Applications to duopoly pricing models and “surveillance games” on graphs are given.