Chen-Khong Tham

Mobile agents based routing protocol for mobile ad hoc networks

A novel routing scheme for mobile ad hoc networks (MANETs), which combines the on-demand routing capability of Ad Hoc On-Demand Distance Vector (AODV) routing protocol with a distributed topology discovery mechanism using ant-like mobile agents is proposed in this paper. The proposed hybrid protocol reduces route discovery latency and the end-to-end delay by providing high connectivity without requiring much of the scarce network capacity. On the one side the proactive routing protocols in MANETs like Destination Sequenced Distance Vector (DSDV) require to know, the topology of the entire network. Hence they are not suitable for highly dynamic networks such as MANETs, since the topology update information needs to be propagated frequently throughout the network. These frequent broadcasts limit the available network capacity for actual data communication. On the other hand, on-demand, reactive routing schemes like AODV and Dynamic Source Routing (DSR), require the actual transmission of the data to be delayed until the route is discovered. Due to this long delay a pure reactive routing protocol may not be applicable for real-time data and multimedia communication. Through extensive simulations in this paper it is proved that the proposed Ant-AODV hybrid routing technique, is able to achieve reduced end-to-end delay compared to conventional ant-based and AODV routing protocols.

Interference-Minimized Multipath Routing with Congestion Control in Wireless Sensor Network for High-Rate Streaming

High-rate streaming in WSN is required for future applications to provide high-quality information of battlefield hot spots. Although recent advances have enabled large-scale WSN to be deployed supported by high-bandwidth backbone network for high-rate streaming, the WSN remains the bottleneck due to the low-rate radios used and the effects of wireless interferences. First, we propose a technique to evaluate the quality of a pathset for multipath load balancing, taking into consideration the effects of wireless interferences and that nodes may interfere beyond communication ranges. Second, we propose an interference- minimized multipath routing (I2MR) protocol that increases throughput by discovering zone-disjoint paths for load balancing, requiring minimal localization support. Third, we propose a congestion control scheme that further increases throughput by loading the paths for load balancing at the highest possible rate supportable. Finally, we validate thepath-set evaluation technique and also evaluate the I2MR protocol and congestion control scheme by comparing with AODV protocol and node-disjoint multipath routing (NDMR) protocol. Simulation results show that I2MR with congestion control achieves on average 230% and 150% gains in throughput over AODV and NDMR respectively, and consumes comparable or at most 24% more energy than AODV but up to 60% less energy than NDMR.

Integration of mobile IP and multi-protocol label switching

Multi-protocol label switching (MPLS) is a technology that combines the simplicity of IP routing with the high-speed switching of ATM. Mobile IP is a protocol that allows users to move around and yet maintain continuous IP network connectivity. We propose a scheme to integrate the mobile IP and MPLS protocols. The integration improves the scalability of the mobile IP data forwarding process by leveraging on the features of MPLS which are fast switching, small state maintenance and high scalability. In addition, we have removed the need for IP-in-IP tunneling from home agent (HA) to foreign agent (FA) under this scheme. This paper covers some issues regarding mobile IP scalability and also defines the signaling and control mechanisms required to integrate MPLS and mobile IP.

Energy Efficient Multiple Target Tracking in Wireless Sensor Networks

Energy awareness is a crucial component in the design of wireless sensor networks at all layers. This paper looks into efficient energy utilization of a target-tracking sensor network by predicting a targets trajectory through experience. While this is not new, the chief novelty comes in conserving energy through both dynamic spatial and temporal management of sensors while assuming minimal locality information. We adapted our target trajectory model from the Gauss-Markov mobility model, formulated the tracking problem as a hierarchical Markov decision process (HMDP), and solved it through neurodynamic programming. Our HMDP for target-tracking (HMTT) algorithm conserves energy by reducing the rate of sensing (temporal management) but maintains an acceptable tracking accuracy through trajectory prediction (spatial management) of multiple targets. We derived some theoretical bounds on accuracy and energy utilization of HMTT. Simulation results demonstrated the effectiveness of HMTT in energy conservation and tracking accuracy against two other predictive tracking algorithms, with accuracy of up to 47% higher and energy savings of up to 200%

Evolutionary fuzzy multi-objective routing for wireless mobile ad hoc networks

The complexity involved in implementing multi-objective routing in computer networks has led to many researchers exploring alternate solutions with the use of heuristic based techniques. The rationale underlying the use of heuristic based priorities in achieving multiple objectives appears to be ad hoc and unclear due to the complex interactions among the various objectives. However these uncertainties can be effectively modeled using fuzzy set theory. This work introduces the notion of multi-objective route selection in mobile ad hoc networks (MANET) using a evolutionary fuzzy cost function to deliberately calculate cost adaptively. The fuzzy cost function is a continuous function of the metrics describing the state of a route. Simulation results demonstrate the superiority of the proposed technique over conventional MANET routing schemes.

On incorporating differentiated levels of network service into GridSim

Grid computing technologies are increasingly being used to aggregate computing resources that are geographically distributed. Commercial networks are being used to connect these resources, and thus serve as a fundamental component of Grid computing. Since these Grid resources are connected over a shared infrastructure, it is essential that we consider the effects of using this shared infrastructure during simulations. In this paper, we discuss how new additions to the GridSim simulation toolkit can be used to explore network effects in Grid computing. We also investigate techniques to incorporate differentiated levels of service, background traffic and the collection of information from the network during runtime in GridSim. As a result, these features enable GridSim to realistically model Grid computing experiments.

A novel routing protocol using mobile agents and reactive route discovery for ad hoc wireless networks

This paper proposes a novel routing scheme for mobile ad hoc networks (MANETs), which combines the on-demand routing capability of ad hoc on-demand distance vector (AODV) routing protocol with a distributed topology discovery mechanism using ant like mobile agents. AODV requires the actual communication to be delayed until the route is determined (found). This may not be suitable for real time data and multimedia communication applications. Ant-AODV provides high connectivity, reducing the amount of route discoveries before starting new connections. This eliminates the delay before starting actual communication for most new connections making ant-AODV routing protocol ideal for real time communication in highly dynamic networks such as MANETs. Simulation results show that the ant-AODV hybrid technique proposed is able to achieve reduced end-to-end delay as compared to conventional ant-based and AODV routing protocols. In addition, ant-AODV also provides high connectivity.

On the Design of Fault-Tolerant Scheduling Strategies Using Primary-Backup Approach for Computational Grids with Low Replication Costs

Fault-tolerant scheduling is an imperative step for large-scale computational grid systems, as often geographically distributed nodes co-operate to execute a task. By and large, primary-backup approach is a common methodology used for fault tolerance wherein each task has a primary copy and a backup copy on two different processors. In this paper, we identify two cases that may happen when scheduling dependent tasks with primary-backup approach. We derive two important constraints that must be satisfied. Further, we show that these two constraints play a crucial role in limiting the schedulability and overloading efficiency of backups of dependent tasks. We then propose two strategies to improve schedulability and overloading efficiency, respectively. We propose two algorithms (MRC-ECT and MCT-LRC), to schedule backups of independent jobs and dependent jobs, respectively. MRC-ECT is shown to guarantee an optimal backup schedule in terms of replication cost for an independent task, while MCT-LRC can schedule a backup of a dependent task with minimum completion time and less replication cost. We conduct extensive simulation experiments to quantify the performance of the proposed algorithms.

Information Quality Aware Routing in Event-Driven Sensor Networks

Upon the occurrence of a phenomenon of interest in a wireless sensor network, multiple sensors may be activated, leading to data implosion and redundancy. Data aggregation and/or fusion techniques exploit spatio-temporal correlation among sensory data to reduce traffic load and mitigate congestion. However, this is often at the expense of loss in Information Quality (IQ) of data that is collected at the fusion center. In this work, we address the problem of finding the least-cost routing tree that satisfies a given IQ constraint. We note that the optimal least-cost routing solution is a variation of the classical NP-hard Steiner tree problem in graphs, which incurs high overheads as it requires knowledge of the entire network topology and individual IQ contributions of each activated sensor node. We tackle these issues by proposing: (i) a topology-aware histogram-based aggregation structure that encapsulates the cost of including the IQ contribution of each activated node in a compact and efficient way; and (ii) a greedy heuristic to approximate and prune a least-cost aggregation routing path. We show that the performance of our IQ-aware routing protocol is: (i) bounded by a distance-based aggregation tree that collects data from all the activated nodes; and (ii) comparable to another IQ-aware routing protocol that uses an exhaustive brute-force search to approximate and prune the least-cost aggregation tree.

A calculus for stochastic QoS analysis

The issue of Quality of Service (QoS) performance analysis in packet-switched networks has drawn a lot of attention in the networking community. There is a lot of work including an elegant theory under the name of network calculus, which focuses on analysis of deterministic worst case QoS performance bounds. In the meantime, researchers have studied stochastic QoS performance for specific schedulers. However, most previous works on deterministic QoS analysis or stochastic QoS analysis have only considered a server that provides deterministic service, i.e. deterministically bounded rate service. Few have considered the behavior of a stochastic server that provides input flows with variable rate service, for example wireless links. In this paper, we propose a stochastic network calculus to analyze the end-to-end stochastic QoS performance of a system with stochastically bounded input traffic over a series of deterministic and stochastic servers. We also prove that a server serving an aggregate of flows can be regarded as a stochastic server for individual flows within the aggregate. Based on this, the proposed framework is further applied to analyze per-flow stochastic QoS performance under aggregate scheduling.