Unreliable Nodes Research Articles

A performance modeling of wireless sensor networks as a queueing network with on and off servers

In this work, we consider performance modeling of a wireless sensor network with a time division multiple access (TDMA) media access protocol with slot reuse. It is assumed that all the nodes are peers of each other and they have two modes of operation, active and sleep modes. We model the sensor network as a Jackson network with unreliable nodes with on and off states. Active and sleep modes of sensor nodes are modeled with on and off states of unreliable nodes. We determine the joint distribution of the sensor node queue lengths in the network. From this result, we derive the probability distribution of the number of active nodes and blocking probability of node activation. Then, we present the mean packet delay, average sleep period of a node and the network throughput. We present numerical results as well as simulation results to verify the analysis. Finally, we discuss how the derived results may be used in the design of sensor networks.

On the behavior of stable subnetworks in non-ergodic networks with unreliable nodes

We consider Jackson networks with unreliable nodes, which randomly break down and are under repair for a random time. The network is described by a Markov process which encompasses the availability status and queue lengths vector. Ergodicity conditions for many related networks are available in the literature and can often be expressed as rate conditions. For (reliable) nodes in Jackson networks the overall arrival rate has to be strictly less than its service rate. If for some nodes this condition is violated, the network process is not ergodic. Nevertheless, it is known that in such a situation, especially in large networks, parts of the network (where the rate condition is fulfilled) in the long run stabilize. For standard Jackson networks without breakdown of nodes, the asymptotics of such stable subnetworks were derived by Goodman and Massey [J.B. Goodman, W.A. Massey, The non-ergodic Jackson network, Journal of Applied Probability 21 (1984) 860-869]. In this paper, we obtain the asymptotics of Jackson networks with unreliable nodes and show that the state distribution of the stable subnetworks converges to a Jackson-type product form distribution. In such networks with breakdown and repair of nodes, in general, the ergodicity condition is more involved. Because no stationary distribution for the network exists, steady-state availability and performance evaluation is not possible. We show that instead assessment of the quality of service in the long run for the stabilizing subnetwork can be done by using limiting distributions. Additionally, we prove that time averages of cumulative rewards can be approximated by state-space averages.

Lifetime-Aware Replication for Data Durability in P2P Storage Network

Many p2p based wide-area storage networks have been proposed to provide scalable storage services by combining the idle resources of many unreliable nodes. These storage networks can also provide highly available and reliable storage services, by replicating each data on several nodes. The popular approach is availability based replication which uses individual node availability. However, some replicas leave within a short time under high churn in p2p networks. This results in heavy and bursty data traffic, and sometimes some data are lost. This paper presents the lifetime-aware replication which uses the lifetime of each node to prevent the bursty failures and the data loss. It keeps a primary replica which has enough time to replace a lost redundancy. It also spreads replicas on the timeline to reduce the overlapped replicas as best as it can. Results from event-driven simulations show that the lifetime-aware replication keeps high data durability with less data traffic.

Study on multi-objective optimization of flow allocation in a multi-commodity stochastic-flow network with unreliable nodes

A long distance transportation problem was abstracted to a resource flow allocation problem upon a stochastic-flow network with unreliable nodes. The objectives were the probability that transmission was successful and transportation cost. In order to solve constructed model, a multi-objective genetic algorithm was propounded. Tested by examples, the algorithm well solved the flow allocation problem in a stochastic-flow network.

Coupons: A Multilevel Incentive Scheme for Information Dissemination in Mobile Networks

Integrating mobile computing and localized user interaction into the Internet requires more than simply overcoming pure routing challenges. Apart from issues such as intermittent connectivity and unreliable nodes, the human factor is becoming increasingly important. To date, user behavior has only been considered in terms of mobility patterns and incentives for encouraging cooperation. Moreover, the focus has always been on systems that aim to enforce fairness by either bringing an indirect equilibrium or by punishing "misbehaving" users. The goal of this paper is therefore to present Coupons. Coupons is an idea to define, evaluate, and implement algorithms for the emerging class of social network and information sharing applications. Coupons is a system designed to allow users to opportunistically interact and share data through a wireless medium, to overcome highly variable Internet access through adaptive localized interaction, and to incorporate an incentive scheme to make the system usable and encourage participation.

Securing skeletal systems with limited performance penalty: The muskel experience

Algorithmic skeletons have been exploited to implement several parallel programming environments, targeting workstation clusters as well as workstation networks and computational grids. When targeting non-dedicated clusters, workstation networks and grids, security has to be taken adequately into account in order to guarantee both code and data confidentiality and integrity. However, introducing security is usually an expensive activity, both in terms of the effort required to managed security mechanisms and in terms of the time spent performing security related activities at run time. We discuss the cost of security introduction as well as how some features typical of skeleton technology can be exploited to improve the efficiency code and data securing in a typical skeleton based parallel programming environment and we evaluate the performance cost of security mechanisms implemented exploiting state of the art tools. In particular, we take into account the cost of security introduction in muskel, a Java based skeletal system exploiting macro data flow implementation technology. We consider the adoption of mechanisms that allow securing all the communications involving remote, unreliable nodes and we evaluate the cost of such mechanisms. Also, we consider the implications on the computational grains needed to scale secure and insecure skeletal computations.

Combinatorial Approach to Reliability Evaluation of Network with Unreliable Nodes and Unreliable Edges

Combinatorial Approach to Reliability Evaluation of Network with Unreliable Nodes and Unreliable Edges

Generate upper boundary vectors meeting the demand and budget for a p-commodity network with unreliable nodes

The system capacity for a single-commodity flow network is the maximum flow from the source to the sink. This paper discusses the system capacity problem for a p-commodity limited-flow network with unreliable nodes. In such a network, arcs and nodes all have several possible capacities and may fail. Different types of commodity, which are transmitted through the same network simultaneously, competes the capacities of arcs and nodes. In particular, the consumed capacity by different types of commodity varies from arcs and nodes. We first define the system capacity as a vector and then a performance index, the probability that the upper bound of the system capacity is a given pattern subject to the budget constraint, is proposed. Such a performance index can be easily computed in terms of upper boundary vectors meeting the demand and budget. A simple algorithm based on minimal cuts is thus presented to generate all upper boundary vectors. The manager can apply this performance index to measure the system capacity level for a supply-demand system.

MC-based Algorithm for a telecommunication network under node and budget constraints

It is an important issue to design some performance indexes in order to measure the performance for a telecommunication network. Network analysis is an available approach to solve the performance problem for a real-life system. We construct a two-commodity stochastic-flow network with unreliable nodes (arcs and nodes all have several possible capacities and may fail) to model the telecommunication network. In which, all types of commodity are transmitted through the same network simultaneously and compete the capacities. This paper defines the system capacity as a 2-tuple vector, and then proposes a performance index, the probability that the upper bound of the system capacity equals a demand vector subject to the budget constraint. An upper boundary point is a vector representing the capacities of arcs and nodes, and is the maximal vector exactly meeting the demand vector. A simple algorithm based on minimal cuts (or named MC-based algorithm) is then presented to generate all upper boundary points in order to evaluate the performance index. The storage and computational time complexity of this algorithm are also analyzed. The performance evaluation for the multicommodity case can be extended easily.

System Reliability of a Limited-Flow Network in Multicommodity Case

Network analysis is an important approach to model real-world systems. System reliability, and system unreliability are two related performance indices useful to measure the quality level of a supply-demand system. For a binary-state network without flow, the system unreliability is the probability that the system can not connect the source and the sink. Extending to a limited-flow network in the single-commodity case, the arc capacity is stochastic, and the system capacity (i.e. the maximum flow) is not a fixed number. The system unreliability for (d+1), the probability that the upper bound of the system capacity equals d, can be computed in terms of upper boundary points. An upper boundary point is the maximal system state such that the system fulfills the demand. This paper concentrates on a multicommodity limited-flow network (MLFN) in which multicommodity are transmitted through unreliable nodes and arcs. Nevertheless, the system capacity is not suitable to be treated as the maximal sum of the commodity because each commodity consumes the capacity differently. We define the system capacity as a demand vector if the system fulfills at most such a demand vector. The main problem of this paper is to measure the quality level of a MLFN. We propose a new performance index, the probability that the upper bound of the system capacity equals the demand vector subject to the budget constraint, to evaluate the quality level of a MLFN. A branch-and-bound algorithm based on minimal cuts is presented to generate all upper boundary points in order to compute the performance index. The computational complexity of the proposed algorithm is analyzed

Asymptotic uniform data-rate guarantees in large wireless networks

In this paper, we study asymptotic uniform data-rate guarantees in large wireless networks from an information-theoretic viewpoint. We consider the following question: what is the maximum achievable data rate that such a network can support for communication from an arbitrary radio node to its destination under transmission power and network-topology constraints, as the network size goes to infinity? In other words, we study the data-rate guarantee for an arbitrarily chosen source–destination pair assuming all other nodes act as relays. We consider two types of network deployments: (1) a regular deployment with unreliable nodes; and (2) a random deployment. We provide upper and lower bounds on the asymptotic achievable data rate for both linear and planar topologies under the two deployment models.

On a multicommodity stochastic-flow network with unreliable nodes subject to budget constraint

From the quality management and decision making view point, reliability and unreliability are important indices to measure the quality level for a stochastic-flow network. In a multicommodity stochastic-flow network with unreliable nodes, the branches and nodes all have several possible capacities and may fail. Different types of the commodity, which are transmitted through the same network simultaneously, compete the capacities of branches and nodes. In this paper we first define the system capacity as a vector for a multicommodity stochastic-flow network with unreliable nodes. Then we design a performance index which is the probability that the upper bound of the system capacity is a given pattern subject to the budget constraint. It can be applied to evaluate the quality level for such a network. A simple approach based on minimal cuts is thus presented to evaluate the performance index.

Probabilistic analysis on connectivity for sensor grids with unreliable nodes

This paper mainly investigates the connectivity of the unreliable sensor grid network. We consider an unreliable sensor grid network with mn nodes placed in a certain planar areaA, and we assume that each node has independent failure probabilityp and has the same transmission rangeR. This paper presents a new method for calculating the connectivity probability of the network, which uses thorough mathematical methods to derive the relationship among the network connectivity probability, the probability that a node is “failed” (not active), the numbers of node, and the node's transmission range in unreliable sensor networks. Our approach is more useful and efficient for given problem and conditions. Such as the numerical calculating results indicate that, for a 100×100 size sensor network, if node failure probability is bounded 0.5%, even if the transmission range is small (such asR=10), we can still maintain very high connectivity probability (reach 95.8%). On the other hand, the simulation results show that building high connectivity probability is entirely possible on unreliable sensor grid networks.

Improving the quality of patient care using reliability measures: a classification tree approach

This paper considers the application and interpretation of new reliability measures for a classification tree-based medical risk assessment tool. Following the construction of a classification tree reliability measures may then be used to provide an estimate of the precision of the classification and the probability in each terminal node of the classification tree. Identification of unreliable nodes (those that have low precision) in this application may indicate patient groups requiring closer monitoring or scenarios in which further information about the patient is required, thereby providing medical practitioners with an avenue for more informed decision making.

System capacity for a two-commodity multistate flow network with unreliable nodes and capacity weight

The system capacity of a single-commodity flow network is the maximum flow from the source to the destination. This paper discusses the system capacity problem for a two-commodity multistate flow network composed of multistate components (edges and nodes). In particular, each component has both capacity and cost attributes. Both types of commodity, which are transmitted through the same network simultaneously, consume the capacities of edges and nodes differently. That is, the capacity weight varies with types of commodity, edges and nodes. We first define the system capacity as a 2-tuple vector and then propose a performance index, the probability that the upper bound of the system capacity is a given pattern subject to the budget constraint. Such a performance index can be easily computed in terms of upper boundary vectors. An efficient algorithm based on minimal cuts is thus presented to generate all upper boundary vectors. The manager can apply this performance index to measure the quality level of supply-demand systems such as computer, logistics, power transmission, telecommunication and urban traffic systems.

A Scalable Peer-to-Peer Architecture for Distributed Information Monitoring Applications

We present PeerCQ, a decentralized architecture for Internet scale information monitoring using a network of heterogeneous peer nodes. PeerCQ uses continual queries (CQs) as its primitives to express information-monitoring requests. The PeerCQ development has three unique characteristics. First, we develop a systematic and serverless approach to large scale information monitoring, aiming at providing a fully distributed, highly scalable, and self-configurable architecture for scalable and reliable processing of a large number of CQs over a network of loosely coupled, heterogeneous, and possibly unreliable nodes (peers). Second, we introduce an effective service partitioning scheme at the P2P protocol layer to distribute the processing of CQs over a peer-to-peer information monitoring overlay network while maintaining a good balance between system utilization and load balance in the presence of peer joins, departures, and failures. A unique feature of our service partitioning scheme is its ability to incorporate strategies for handling hot spot monitoring requests and peer heterogeneity into the load balancing scheme in PeerCQ. Third, but not least, we develop a dynamic passive replication scheme to enable reliable processing of long-running information monitoring requests in an environment of inherently unreliable peers, including an analytical model to discuss its fault tolerance properties. We report a set of experiments demonstrating the feasibility and the effectiveness of the PeerCQ approach to large-scale peer-to-peer information monitoring.

GRAdient Broadcast: A Robust Data Delivery Protocol for Large Scale Sensor Networks

Although data forwarding algorithms and protocols have been among the first set of issues explored in sensor networking, how to reliably deliver sensing data through a vast field of small, vulnerable sensors remains a research challenge. In this paper we present GRAdient Broadcast (GRAB), a new set of mechanisms and protocols which is designed specifically for robust data delivery in face of unreliable nodes and fallible wireless links. Similar to previous work [12,13], GRAB builds and maintains a cost field, providing each sensor the direction to forward sensing data. Different from all the previous approaches, however, GRAB forwards data along a band of interleaved mesh from each source to the receiver. GRAB controls the width of the band by the amount of credit carried in each data message, allowing the sender to adjust the robustness of data delivery. GRAB design harnesses the advantage of large scale and relies on the collective efforts of multiple nodes to deliver data, without dependency on any individual ones. We have evaluated the GRAB performance through both analysis and extensive simulation. Our analysis shows quantitatively the advantage of interleaved mesh over multiple parallel paths. Our simulation further confirms the analysis results and shows that GRAB can successfully deliver over 90% of packets with relatively low energy cost, even under the adverse conditions of 30% node failures compounded with 15% link message losses.

A minimum cost heterogeneous sensor network with a lifetime constraint

We consider a heterogeneous sensor network in which nodes are to be deployed over a unit area for the purpose of surveillance. An aircraft visits the area periodically and gathers data about the activity in the area from the sensor nodes. There are two types of nodes that are distributed over the area using two-dimensional homogeneous Poisson point processes; type 0 nodes with intensity (average number per unit area) /spl lambda//sub 0/ and battery energy E/sub 0/; and type 1 nodes with intensity /spl lambda//sub 1/ and battery energy E/sub 1/. Type 0 nodes do the sensing while type 1 nodes act as the cluster heads besides doing the sensing. Nodes use multihopping to communicate with their closest cluster heads. We determine them optimum node intensities (/spl lambda//sub 0/, /spl lambda//sub 1/) and node energies (E/sub 0/, E/sub 1/) that guarantee a lifetime of at least T units, while ensuring connectivity and coverage of the surveillance area with a high probability. We minimize the overall cost of the network under these constraints. Lifetime is defined as the number of successful data gathering trips (or cycles) that are possible until connectivity and/or coverage are lost. Conditions for a sharp cutoff are also taken into account, i.e., we ensure that almost all the nodes run out of energy at about the same time so that there is very little energy waste due to residual energy. We compare the results for random deployment with those of a grid deployment in which nodes are placed deterministically along grid points. We observe that in both cases /spl lambda//sub 1/ scales approximately as /spl radic/(/spl lambda//sub 0/). Our results can be directly extended to take into account unreliable nodes.

Bounds on the Reliability of Distributed Systems With Unreliable Nodes & Links

The reliability of distributed systems & computer networks in which computing nodes and/or communication links may fail with certain probabilities have been modeled by a probabilistic network. Computing the residual connectedness reliability (RCR) of probabilistic networks under the fault model with both node & link faults is very useful, but is an NP-hard problem. Up to now, there has been little research done under this fault model. There are neither accurate solutions nor heuristic algorithms for computing the RCR. In our recent research, we challenged the problem, and found efficient algorithms for the upper & lower bounds on RCR. We also demonstrated that the difference between our upper & lower bounds gradually tends to zero for large networks, and are very close to zero for small networks. These results were used in our dependable distributed system project to find a near-optimal subset of nodes to host the replicas of a critical task.

A simple MC-based algorithm for evaluating reliability of stochastic-flow network with unreliable nodes

A MP/minimal cutset (MC) is a path/cut set such that if any edge is removed from this path/cut set, then the remaining set is no longer a path/cut set. An intuitive method is proposed to evaluate the reliability in terms of MCs in a stochastic-flow network subject to both edge and node failures under the condition that all of the MCs are given in advance. This is an extension of the best of known algorithms for solving the d -MC (a special MC but formatted in a system-state vector, where d is the lower bound points of the system capacity level) problem from the stochastic-flow network without unreliable nodes to with unreliable nodes by introducing some simple concepts. These concepts were first developed in the literature to implement the proposed algorithm to reduce the number of d -MC candidates. This method is more efficient than the best of known existing algorithms regardless if the network has or does not have unreliable nodes. Two examples are illustrated to show how the reliability is determined using the proposed algorithm in the network with or without unreliable nodes. The computational complexity of the proposed algorithm is analyzed and compared with the existing methods.