Wai-Leong Yeow

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%

Minimizing Delay for Multicast-Streaming in Wireless Networks with Network Coding

Network coding is a method that promises to achieve the min-cut capacity in multicasts. However, pushing towards this gain in throughput comes with two sacrifices. Delay suffers as the decoding procedure requires buffering and is performed in batches of coded packets, and unfairness prevails in terms of delay increases between receivers with worse channel conditions and those with better channel conditions. In this paper, we focus on optimizing the delay performance in reliably multicasting a data stream to a set of one-hop receivers from the receiver perspective. We analyze the system based on queueing theory using semi-Markov chains from both the system-wide and receiver perspectives. We find that the average delay per received packet at the receivers end can be minimized by appropriate scheduling of data packets and appropriate size of the coding buffer, which depends on the rate of incoming data stream and capacities of the receivers. To circumvent unduly computational complexities, we design a heuristic scheme which can achieve significant performance gain when compared to an existing method. Our scheme readily adapts the coding size to the dynamics of the system, and schedules data packets to be coded via some strict priority measure for optimized delay performance. We show through extensive simulations that our scheme gives low average delay at high streaming rates and narrows the performance gap between receivers with bad and good channel conditions.

Performance Comparison of Three Spectrum Admission Control Policies in Coordinated Dynamic Spectrum Sharing Systems

When spectrum is shared among multiple radio systems, spectrum admission control (SAC) can be performed via a centralized spectrum manager to meet their respective traffic demands. The adopted policy will determine the admission of service requests and, in turn, affect the overall spectrum utilization efficiency. The design of SAC policies becomes more challenging when each radio system provides a grade of service (GoS) in the form of a blocked service guarantee to its users. Given such constraints, we study the admission region, which indicates the maximum amount of supported traffic for the given resources. We first study the performance based on a simple first-come-first-serve (FCFS) policy. Our studies show that the admission region is, in general, limited by the radio system that first violates its prescribed GoS guarantee (i.e., the performance-limiting radio). We propose three SAC policies to enhance the total offered traffic and compare them against the FCFS policy. The first is a random discard (RD) policy, where requests from other systems are discarded with some predetermined probabilities so that a larger portion of the spectrum is made available to the performance-limiting radio. The second SAC uses a reservation (RES) policy, where a suitable amount of spectrum is reserved for the performance-limiting radio. In the third policy, SAC is formulated as a discrete-time constrained Markov decision process (MDP). We analyzed the performance of each policy at low and high request rates and show how the additional design parameters in each policy can be optimized to enhance the offered traffic. As admission decisions are jointly made with traffic predictions, the MDP admission policy shows superior performance over the other policies due to its flexibility to operate both radios at the bound of the respective GoS constraint.

A novel target movement model and energy efficient target tracking in 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. Whilst this is not new, the chief novelty comes in conserving energy through both dynamic spatial and temporal management of sensors and yet assuming minimal locality information. We present a novel target trajectory model adapted from the Gauss-Markov mobility model, formulate the tracking problem as a hierarchical Markov decision process (HMDP) and solve it through neuro-dynamic programming (NDP). Our HMTT (hierarchical MDP for target tracking) algorithm conserves energy by reducing the rate of sensing (temporal management) but maintains acceptable tracking accuracy through trajectory prediction (spatial management). Analysis and simulation results demonstrate its effectiveness in energy conservation and tracking accuracy against other known target tracking algorithms.

Simple Directional Antennas: Improving Performance in Wireless Multihop Networks

Directional antennas are a promising option for use in ad-hoc networks for a variety of reasons, such as increased spatial reuse, reduced interference and enabling more efficient MAC designs. The main difficulty in using directional antennas is the complexity of coordinating and managing users’ transmissions, which we collectively refer to as link scheduling. In this paper, we propose the concept of simple directional antennas (SDA), which combine several of the benefits of directional antennas while avoiding the aforementioned complexity. We investigate the use of SDA in the context of wireless multihop networks with contention-based MAC protocols such as ALOHA and CSMA. The metrics we consider are network connectivity, dilation, throughput, delay and energy consumption. We study their behavior extensively via analysis and simulation. In the case of slotted ALOHA, we obtain the optimum SDA parameters to maximize MAC throughput. In addition, SDA is shown to be a better strategy for reducing interference as compared to power-controlled omni-directional antennas. Our results indicate that SDA is a good way to exploit the benefits of directionality without incurring the high computational costs of other directional antenna approaches.

Throughput performance of back-pressure scheduling in wireless cooperative networks

It is well-known that throughput of wired multi-hop, multi-commodity networks can be maximized by employing back-pressure scheduling. With this approach, packets belonging to different destinations are dynamically routed/scheduled in a network based on buffer occupancies and link quality. There has been a considerable amount of research on applying back-pressure scheduling in the wireless environment; usually by abstracting each wireless channel as a point-to-point link and therefore, ignoring the fundamental wireless broadcast property. In this paper, we consider a two-hop wireless cooperative network and characterize the minimum throughput gain obtained by exploiting broadcast property in back-pressure scheduling. Numerical results are provided to support our analysis.

On Average Packet Delay Bounds and Loss Rates of Network-Coded Multicasts over Wireless Downlinks

Latency is a critical concern in interactive or delay-sensitive services such as interactive IPTV and VoIP. This is especially so when using network coding as a means to conserve bandwidth in these bandwidth-hungry services. In practical network coding, packets are coded in batches and thus suffer a large average delay per packet when packets get decoded after the whole batch is received. A larger batch size, however, also gives the highest bandwidth savings. In this paper, we analyze the achievable upper and lower bounds of the average delay per packet as well as packet loss rates due to finite-sized queue in a multicast downlink transmission from the system and client perspectives. We validate our analysis with simulation results and characterize the queueing and transmission delays, and packet loss performance with respect to (i) the maximum size of a batch and (ii) packet arrival rates. We find that random linear coding is an upper bound in delay performance and other hybrid network coding method might achieve better delay gains.

Energy efficient multiple target tracking in sensor networks

Classical tracking methods are not concerned with energy efficiency and require precise localisation. We addressed these in our previous work through HMTT (hierarchical Markov decision process for target tracking) that tracks single targets at location granularity. HMTT conserves energy by reducing the rate of sensing but preserves acceptable tracking accuracy through trajectory prediction. In this paper, HMTT is extended for the multiple targets case where the state of clusters could be affected by multiple incoming targets and where multiple updates are required at the lower level. The theoretical performance of HMTT in the multiple targets case is derived and simulations demonstrate its effectiveness against 2 other predictive tracking algorithms with up to 200% improvement.

Optimizing Application Performance through Learning and Cooperation in a Wireless Sensor Network

A wireless sensor network performing surveillance in time-critical missions involving event or target tracking demands accurate ground information be delivered within a delay guarantee. Present methods solve this by using in-network fusion across all packets to reduce network load in the hope of achieving the delay guarantee. In this paper, we aim to maximize data quality from sensor fusion, while still respecting delay guarantees. The proposed method makes admission control and routing decisions using a fully distributed algorithm based on constrained Markov Decision Processes (MDPs). Cooperation is enforced through well-defined rewards and leading nodes. Assessment of data quality is derived from likelihood ratio, which is a commonly used metric in sensor fusion. We study the performance of the proposed algorithm through extensive simulations, and show that it can achieve soft delay guarantees and good data quality compared to other schemes.

Service differentiation and load balancing in grid architecture

The first part of this paper proposes an architecture to enable service differentiation of jobs on computational grids built using the Globus toolkit. This feature is absent in the toolkit, which is also unable to manage computation capacity planning among the distributed systems in the grid. The proposed architecture facilitates a front-end web service to accept service-differentiated jobs, allowing the grid to act as a backend application service provider. The second part of this paper proposes an algorithm to load balance the incoming jobs on the various autonomous systems within the grid, taking into consideration the requested QoS level of each job, amount of computation required on the grid, as well as both the existing load and its history to ensure fair and efficient use of computational resources. We conducted extensive experiments that demonstrate the correctness of the algorithm and its limitations.