- [Show abstract] [Hide abstract] ABSTRACT: In this paper we develop a novel energy aware routing approach for mobile ad hoc network (MANET) problems. The approach is based on using Optimized Link State Routing Protocol. Our Energy Aware OLSR labeled as OLSR_EA measures and predicts per-interval energy consumptions using the well-known Auto-Regressive Integrated Moving Average time series method. We develop a composite energy cost, by considering transmission power consumption and residual energy of each node, and use this composite energy index as the routing metric. Our extensive ns2 simulation experiments show that OLSR_EA substantially prolongs the network lifetime and saves total energy used in MANET. In our experiments we considered different scenarios considering a variety of traffic loads, node mobilities, homogeneous power consumption, and heterogeneous power consumption. Simulation results also confirm that OLSR_EA improves the traffic balance between nodes, and packet delivery ratio in higher node speed. We further develop characteristics of OLSR_EA in power-wise heterogeneous MANET to achieve efficient energy preserving performance.
- [Show abstract] [Hide abstract] ABSTRACT: In this paper, we develop a multi-objective approach for proactive routing in a Mobile Ad Hoc Network (MANET). We consider three routing objectives: minimizing average end-to-end delay, maximizing network energy lifetime, and maximizing packet delivery ratio. Accordingly, we develop three routing metrics: mean queuing delay on each node, energy cost on each node, and link stability on each link. For the proposed multi-objective approach, we develop efficient prediction methods: (a) predicting queuing delay and energy consumption using double exponential smoothing, and (b) predicting residual link lifetime using a heuristic of the distributions of the link lifetimes in MANET. Extensive simulation (by using ns2) is performed for the comparison of this multi-objective OLSR with existing OLSRs. The results show that the multi-objective OLSR is effective in finding optimal routing by tradeoffs among proposed objectives.
- [Show abstract] [Hide abstract] ABSTRACT: Existing MANET routing protocols rely heavily on hop count evaluation. Although this is simple and efficient, it sacrifices the potential performance gains obtainable by considering other dynamic routing metrics. In this paper, we propose a delay prediction mechanism and its integration with a MANET proactive routing protocol. We demonstrate our approach of predicting mean queuing delay as a nonstationary time series using appropriate neural network models: Multi-Layer Perceptron or Radial Basis Function. To support MANET proactive routing, our delay prediction mechanism is devised as a distributed, independent, and continuous neural network training and prediction process conducted on individual nodes. We integrated our delay prediction mechanism with a well-known MANET proactive routing protocol—OLSR. The essential part of this integration is our TierUp algorithm, which is a novel node-state routing table computation algorithm. The structure and the key parameters of the resulting extended OLSR, called OLSR_NN, are also discussed. Our simulation shows that because of its capability of balancing the traffic, OLSR_NN is able to increase data packet delivery ratio and reduce average end-to-end delay in scenarios with complex traffic patterns and wide range of node mobility, compared to OLSR.
Conference Paper: Predictive Multiple Metrics in Proactive Mobile Ad Hoc Network Routing[Show abstract] [Hide abstract] ABSTRACT: In this paper we present a MANET proactive routing enhancement scheme by comprehensive evaluation of multiple dynamic routing metrics, including delay, energy cost, and link stability. We developed efficient routing metric prediction methods: predicting delay and energy using double exponential smoothing, and predicting link stability using a heuristic based on the normal-like distributions of the link lifetimes in typical MANET mobility scenarios. The routing metrics predictions are incorporated with regular routing information exchanges. On each node, routing table is constructed by a modified version of Dijkstra's algorithm, which evaluates the predicted metrics values compositively. We integrated such a multi-metric prediction/evaluation mechanism into OLSR and name it OLSR_MC. We show by simulation that OLSR_MC is more adaptive to the network dynamics and therefore is able to improve performance significantly on multiple routing objectives, including higher packet delivery ratio, shorter average end-to-end delay, and prolonged network energy lifetime.
- [Show abstract] [Hide abstract] ABSTRACT: Energy-aware routing in mobile wireless ad hoc networks has been investigated extensively in the framework of reactive routing. In this paper we present an energy preserving mechanism suitable to be integrated with a proactive MANET routing scheme. We use a compositive energy cost, which considers both transmission power consumption and residual energy of the nodes, as the routing metric. Our energy cost calculation is based on prediction of node energy consumption using ARIMA model. The formulation of our energy cost is tailored to heterogeneous MANET in terms of power consumptions. We extended OLSR with our energy preserving mechanism. Our power-aware version of OLSR is proven by simulation to be able to prolong network lifetime significantly in both homogeneous and heterogeneous scenarios in terms of power consumptions.
- [Show abstract] [Hide abstract] ABSTRACT: In this paper, we propose an adaptability enhancement mechanism to be integrated with OLSR, and potentially any Mobile Ad Hoc Network (MANET) proactive routing protocol. The key of this mechanism is prediction and evaluation of the mean queuing delay as a routing metric. Neural network methods are used to predict delays. We investigated the pros and cons of using two types of neural networks, namely Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF), in predicting nonstationary time series (e.g., mean queuing delay time series). We present TierUp -- our novel node-state routing table calculation algorithm, which is developed for the integration of delay prediction and OLSR. We name the extended version of OLSR as OLSR_NN. We show through ns2 simulation that compared to OLSR, OLSR_NN is able to increase data packet delivery ratio and reduce average end-to-end delay in scenarios with complex traffic patterns and various node mobility. Our simulation also shows the advantage of using neural network for delay prediction compared to moving average and exponential smoothing. The enhanced adaptability of OLSR_NN is further verified by the more balanced traffic observed in our simulation.
Conference Paper: Intelligent Multiple-Criteria Broadcasting in Mobile Ad-hoc Networks[Show abstract] [Hide abstract] ABSTRACT: Networks often require a copy of a message to be delivered to every network node. The network-layer can provide this service, referred to as network-wide broadcast routing, or simply "broadcasting". Broadcasting has many applications, including its role as a building block in many routing protocols. In a MANET, simplistic broadcast schemes (such as flooding) inundate the network with redundancy, contention, collision, and energy-inefficiency. This can prevent broadcasts from achieving the objectives of optimal delivery ratio, energy balancing, and latency. As a solution, we propose multiple-criteria broadcasting (MCB). In MCB, the source of each broadcast specifies the importance assigned to broadcast objectives. Network nodes use this policy, along with local and neighbor knowledge, to broadcast in accordance with the objective priorities attributed to the message. Using ns2, the performance of MCB is evaluated and compared to other broadcast protocols. To present knowledge, MCB constitutes the first reconfigurable, multi-objective approach to broadcasting
- [Show abstract] [Hide abstract] ABSTRACT: Many network emulators have been developed for performance evaluation of network protocols and distributed applications. While most of them specialize on some features targeted for certain applications, few of them address the overall accuracy and efficiency of a general-purpose network emulator. In this paper, we present the key elements of constructing an accurate and efficient general-purpose network emulator. We focus on the architecture of an emulator and the packet capture methods. We analyze the typical approaches a network emulator takes with emphasis on two outstanding distributed network emulators: NETShaper and EMPOWER. We present the functions we have added to NETShaper, namely zero bandwidth emulation and bit error rate emulation. Application of distributed network emulation to protocol design for inter-planetary networks is also discussed
- [Show abstract] [Hide abstract] ABSTRACT: Research on wireless routing protocols have been striving to achieve two important but contradicting goals: adaptability to the dynamic network conditions and efficient routing information diffusion. A possible approach to solve this issue is through intelligent use of the nodes' past experiences of the network traffic conditions, and making predictions on the future network traffic conditions based on the experiences. Delay is a typical routing metric. In this paper, we present a scheme for predicting mean per-packet one-hop delays using neural network approaches. The predicted one-hop delays are then used by the nodes to participate in routing information diffusion. By experiments, we prove the feasibility of predicting mean delays as a time series using either tapped-delay-line multi-layer perceptron (MLP) network or tapped-delay-line radial basis function (RBF) network. Two types of inputs for prediction are used: a) the mean delay time series itself only, b) the mean delay time series together with the corresponding traffic loads. The advantages and limitations of these neural network approaches are discussed