Navigating in Trees with Permanently Noisy Advice

We consider a search problem on trees in which an agent starts at the root of a tree and aims to locate an adversarially placed treasure, by moving along the edges, while relying on local, partial information. Specifically, each node in the tree holds a pointer to one of its neighbors, termed advice . A node is faulty with probability q . The advice at a non-faulty node points to the neighbor that is closer to the treasure, and the advice at a faulty node points to a uniformly random neighbor. Crucially, the advice is permanent , in the sense that querying the same node again would yield the same answer. Let Δ denote the maximum degree. For the expected number of moves (edge traversals) until finding the treasure, we show that a phase transition occurs when the noise parameter q is roughly 1 √Δ. Below the threshold, there exists an algorithm with expected number of moves O ( D √Δ), where D is the depth of the treasure, whereas above the threshold, every search algorithm has an expected number of moves, which is both exponential in D and polynomial in the number of nodes n . In contrast, if we require to find the treasure with probability at least 1 − δ, then for every fixed ɛ > 0, if q < 1/Δ ɛ , then there exists a search strategy that with probability 1 − δ finds the treasure using (Δ −1 D ) O (1/ε) moves. Moreover, we show that (Δ −1 D ) Ω(1/ε) moves are necessary.

Efficient Energy Conservation and Faulty Node Detection on Machine Learning-Based Wireless Sensor Networks

Wireless sensor networks are used in machine learning for data communication and classification. Sensor nodes in network suffer from low battery power, so it is necessary to reduce energy consumption. One way of decreasing energy utilization is reducing the information transmitted by an advanced machine learning process called support vector machine. Further, nodes in WSN malfunction upon the occurrence of malicious activities. To overcome these issues, energy conserving and faulty node detection WSN is proposed. SVM optimizes data to be transmitted via one-hop transmission. It sends only the extreme points of data instead of transmitting whole information. This will reduce transmitting energy and accumulate excess energy for future purpose. Moreover, malfunction nodes are identified to overcome difficulties on data processing. Since each node transmits data to nearby nodes, the misbehaving nodes are detected based on transmission speed. The experimental results show that proposed algorithm provides better results in terms of reduced energy consumption and faulty node detection.

The Component Diagnosability of Hypercubes with Large-Scale Faulty Nodes

The diagnosability is one of the most important measures of the reliability of networks. Consider the setting where there are large-scale failures that disconnect the network and result in many components. Then, the diagnosability is closely related to the number of components. In this paper, we define and study the $\boldsymbol{g}$-component diagnosability of network $\boldsymbol{G}$, which is denoted by $\boldsymbol{ct_g(G)}$ and has not been addressed before. $\boldsymbol{ct_g(G)}$ is the maximum number of nodes in the faulty node set $\boldsymbol{F}$ of $\boldsymbol{G}$ such that $\boldsymbol{G-F}$ has at least $\boldsymbol{g}$ components and diagnosis model can identify all nodes in $\boldsymbol{F}$. Under PMC and MM$^*$ diagnosis models, we show that, in the hypercube $\boldsymbol{Q_n\ (n\geq 7)}$, $\boldsymbol{ct_{g+1}(Q_n)=-(1/2)g^2+(n-3/2)g+n}$ when $\boldsymbol{g\leq n-1}$. Moreover, we determine the $\boldsymbol{(n+1)}$-component diagnosability $\boldsymbol{ct_{n+1}(Q_n)=n^2/2+n/2-2}$ for $\boldsymbol{n\geq 7}$.

Fuzzy Logic Based Hardware Faulty Node Detection And Redundancy Mechanism For Wireless Sensor Networks

In recent years, applications of wireless sensor network (WSN) is emerged as the revolutionary phase in many functional areas such as industrial, environmental, business, military and many need based self-intelligent real time systems. Some of the applications require data communication from harsh physical environment which poses great challenges to wireless sensor networks. The deployment of these sensor nodes in the hostile environment cause sensor nodes failure. This demands fast, redundant fault tolerant, energy saving approaches which meet the requirements of most recurring failures and path disruption scenarios in wireless sensor networks. Hence there is need for fuzzy knowledge based fault detection because traditional fault detection methods are endured by low detection accuracy. The proposed fuzzy knowledge based faulty node detection and redundancy approach (FNDRA) is presented to identify the faulty nodes and provide the management method for nodes reusability. The effectiveness of the proposed approach was implemented using Matlab and the results shows that the proposed approach meets the constraints and requirements of most common and predicated critical failure scenarios.

Cluster-Fault Tolerant Routing in a Torus

The number of Internet-connected devices grows very rapidly, with even fears of running out of available IP addresses. It is clear that the number of sensors follows this trend, thus inducing large sensor networks. It is insightful to make the comparison with the huge number of processors of modern supercomputers. In such large networks, the problem of node faults necessarily arises, with faults often happening in clusters. The tolerance to faults, and especially cluster faults, is thus critical. Furthermore, thanks to its advantageous topological properties, the torus interconnection network has been adopted by the major supercomputer manufacturers of the recent years, thus proving its applicability. Acknowledging and embracing these two technological and industrial aspects, we propose in this paper a node-to-node routing algorithm in an n -dimensional k -ary torus that is tolerant to faults. Not only is this algorithm tolerant to faulty nodes, it also tolerates faulty node clusters. The described algorithm selects a fault-free path of length at most n ( 2 k + ⌊ k / 2 ⌋ − 2 ) with an O ( n 2 k 2 | F | ) worst-case time complexity with F the set of faulty nodes induced by the faulty clusters.

Dynamic Reconfiguration of Network Partition Under the Constraints of Hostile Environment

Node failure may interrupt the smooth network operations of a sensor network. A faulty node may choke the entire network. Fault detection and recovery provisions may extend the network life span. In this paper, a recovery mechanism is proposed to handle the partitioned network using instant reconfiguration method based on minimum spanning tree. Performance of the proposed method is analyzed using different routing protocols (AODV/LEACH/DSDV) under the constraints of partitioned network.