Using Direct Sequence Spread Spectrum to Determine the Responsiveness of a TCP Aggregate to Packet Drops

In this paper we introduce a test through which the responsiveness of a TCP aggregate can be measured. The first introduced test is based on dropping a few packets from the aggregate and measuring the resulting rate decrease of that aggregate. This kind of test is not robust to multiple simultaneous tests performed at different routers. Extensions are done to make the test robust to multiple simultaneous tests by inspiring from the CDMA approach in the literature of multiple access channels in communication theory. The measurements of responsiveness can be utilized for different purposes like congestion control or mitigating a Distributed Denial of Service Attack

Lifetime Maximizing Adaptive Traffic Distribution and Power Control in Wireless Sensor Networks

In this paper we study how to maximize the lifetime of randomly deployed wireless sensor networks by applying adaptive traffic distribution and power control. We model this problem as a linear program by abstracting the network into multiple layers. First we focus on the scenario where transmission energy consumption plays the dominant role in overall energy consumption. After ignoring the processing energy consumption, we observe that: in order to maximally extend the lifetime, each node should split its traffic into two portions, and send one portion directly to the sink, and the other one to its neighbor in the next inner layer. Next we consider the effect of incorporating the processing energy consumption. In this case, we have similar observation: for each packet to be sent, the sender should either transmit it using the transmission range with the highest energy efficiency per bit per meter, or transmit it directly to the sink. Besides studying the upper bound of maximum achievable lifetime extension, we discuss some practical issues, such as how to handle the signal interference caused by adaptive power control. Finally, we propose a fully distributed algorithm to adaptively split traffic and adjust transmission power for randomly deployed wireless sensor networks. We also provide extensive simulation results which demonstrat that the network lifetime can be dramatically extended by applying the proposed approach in various scenarios

Lifetime Maximizing Adaptive Power Control in Wireless Sensor Networks

Network lifetime is one of the most critical performance measures for wireless sensor networks. Various schemes have been proposed to maximize the network lifetime. In this paper we consider the lifetime maximization problem via a new approach: adaptive power control. We focus on the sensor networks that consists of a sink and a set of homogeneous wireless sensor nodes, which are randomly deployed according to a uniform distribution. Each node has the same initial energy and the same data generation rate. We formally analyze the lifetime maximizing adaptive power control problem by dividing the network into different layers and then modelling it as a linear programming problem, where the goal is to find an optimal way to adjust the transmission power and split the traffic such that the maximum energy consumption speed among all layers is minimized, and therefore the network lifetime is maximized. One surprising observation from the numerical results is that when every node can reach the sink directly, the optimal solution for each node is to send traffic either to its next inner layer or to the sink directly. This observation has also been justified by the theoretical analysis. The numerical results also show that the lifetime elongation can still be significant even when only those nodes in the innermost few layers are allowed to adaptively adjust their transmission power. We then propose a fully distributed algorithm, the Energy-Aware Push Algorithm (EAPA), and show through simulation that it can dramatically extend the network lifetime

Design Optimization of Multi-Sink Sensor Networks by Analogy to Electrostatic Theory

In this work we introduce a new mathematical tool for optimization of routes, and topology design in wireless sensor networks. We introduce a vector field formulation that models communication in the network, and routing is performed in the direction of this vector field at every location of the network. The magnitude of the vector field at every location represents the density of amount of data that is being transited through that location. We define the total communication cost in the network as the integral of a quadratic form of the vector field over the network area. Our mathematical machinery is based on partial differential equations analogous to the Maxwell equations in electrostatic theory. We use our vector field model to solve the optimization problem for the case in which there are multiple destinations (sinks) in the network. In order to optimally determine the destination for each sensor, we partition the network into areas, each corresponding to one of the destinations. We define a vector field, which is conservative, and hence it can be written as the gradient of a scalar function (also known as a potential function). Then we show that in the optimal assignment of the communication load of the network to the destinations, the value of that potential function should be equal at the locations of all the destinations. Also, we show that such an optimal partitioning of the network load among the destination is unique, and we give iterations to find the optimal solution

Interesting Examples of IBGP Configuration

In this paper we give examples to show that if an Internal Border Gateway Protocol (IBGP) configuration using route reflections violates even one of the four conditions mentioned in the theorem given in a previous work, then there may be persistent oscillations or forwarding loops

Utilizing Path Diversity via Asynchronous and Asymmetric Wakeups in Sensor Networks

We present an asynchronous wakeup policy for wireless sensor networks that exploits the available path diversity for maximizing the expected network lifetime. We assume a random traffic generation model such that the rate is constant in time. Each node is assumed to have a set of forwarding neighbors, any of which may be used for forwarding its traffic to the sink. A node having data packet to send, transmits the packet to the first available node in its forwarding set. In order to maximize the network lifetime, we balance the power dissipation at the network nodes by adjusting the wakeup parameters at various nodes. Allowing different nodes to wakeup with different rates makes the scheme asymmetric. For ease of analysis, we restrict ourselves to static, open-loop policies. We show that the optimization problem is a Signomial Program (SP), that can be well approximated as a Geometric Program (GP). By extensive simulations, we compare the asymmetric policy thus obtained to the best possible symmetric policy obtained from the same optimization setup but ensuring additionally that the wakeup rates at all the nodes are the same (in which case the optimization problem is shown to be exactly a GP). The simulations show that allowing asymmetry can extend the network lifetime by effectively exploiting the available path diversity. Moreover, we also prove that, in case of symmetric policies, no piecewise static policy can beat the simple static policy that we use for comparison in our results. This shows that in the space of open-loop, asynchronous wakeup policies, employing the static, asymmetric policy presented in this paper is much more profitable than even the best piecewise static, symmetric policy.Research partially supported by the NSF under grant CNS-051955

Using POMDP as Modeling Framework for Network Fault Management

For highדּpeed networks, it is important that fault management be proactive--i.e., detect, diagnose, and mitigate problems before they result in severe degradation of network performance. Proactive fault manageשּׂent depends on monitoring the network to obtain the data on which to base manager decisions. However, monitoring introduces additional overhead that may itself degrade network performance especially when the network is in a stressed state. Thus, a tradeoff must be made be﫠tween the amount of data collected and transferred on one hand, and the speed and accuracy of fault detection and diagnosis on the other hand. Such a tradeoff can be naturally formulated as a Partially Observable Markov decision process (POMDP).Since exact solution of POMDPs for a realistic number of states is computationally prohibitive, we develop a reinforcementשּׁearningﬢased fast algorithm which learns the decisionגּule in an approximate network simulator and makes it fast deployable to the real network. Simulation results are given to diagnose a switch fault in an ATM network. This approach can be applied to centralized fault management or to construct intelligent agents for distributed fault management

A Local Optimization Algorithm for Logical Topology Design and Traffic Grooming in IP over WDM Networks

In this paper we investigate logical topology design algorithms using local optimization technique. Since the problem of the optimal logical topology design for all traffic demands is NP-complete, we design a logical topology by sequentially constructing the shortest path for one source-destination pair at a time. The path is a locally optimized path in the sense that there are no other paths with less hop count that may be constructed from existing links and newly created links. For this we define an Estimated Logical Hop Count (ELH), which is the shortest logical hop count for a given source and destination when it is applied. Also, we propose two heuristic logical topology design algorithms making use of ELH: ELH with Maximum Traffic Demands (MTD) and with Resource Efficiency Factor (REF). Finally, we evaluate the performance of the proposed algorithms by GLASS/SSF simulator. The simulation results show that ELH with REF outperforms other well-known algorithms in terms of the weighted hop count and network throughput

Solving POMDP by On﬐olicy Linear Approximate Learning Algorithm

This paper presents a fast Reinforcement Learning (RL) algorithm to solve Partially Observable Markov Decision Processes (POMDP) prob﫠lem. The proposed algorithm is devised to provide a policyשּׂaking frame﫠work for Network Management Systems (NMS) which is in essence an engineering application without an exact model.The algorithm consists of two phases. Firstly, the model is estimated and policy is learned in a completely observable simulator. Secondly, the estimated model is brought into the partially observed real﬷orld where the learned policy is then fineהּuned.The learning algorithm is based on the onאּolicy linear gradientﬤescent learning algorithm with eligibility traces. This implies that the Qזּalue on belief space is linearly approximated by the Qזּalue at vertex over the belief space where onשּׁine TD method will be applied.The proposed algorithm is tested against the exact solutions to exten﫠sive small/middleדּize benchmark examples from POMDP literature and found near optimal in terms of averageﬤiscountedגּeward and stepהּo﫠goal. The proposed algorithm significantly reduces the convergence time and can easily be adapted to large stateאַumber problems