Data Delivery Latency Research Articles

United nodes: cluster-based routing protocol for mobile cognitive radio networks

Advancement of cognitive radio (CR) technology can overcome the problems encountered from bandwidth and spectrum access limitations because of tremendous potential to improve the utilisation of the radio spectrum by efficiently reusing and sharing the licensed spectrum bands, as long as the interference power inflicted on the primary users of the band remains below a predefined threshold level. In mobile CR ad hoc networks, routing is one of the most important issues to be addressed and desires deep investigation. In this study, a distributed and efficient cluster-based spectrum and interference aware routing protocol is proposed. The protocol incorporates the spectrum availability cost and interference metrics into the routing algorithm to find better routes. A route preservation method is also implemented to repair the route when it is defective because of primary user activity. Extensive experimental evaluations are performed in the ns2 simulator. Results of the simulations illustrate that, the proposed algorithm can well fit into the mobile CR ad hoc networks and improve the network performance. The results indicate that the proposed protocol provides better adaptability to the environment than the existing ones. It also increases throughput and reduces data delivery latency in a number of realistic scenarios and outperforms recently proposed routing protocols for CR networks.

Green Wave Sleep Scheduling: Optimizing Latency and Throughput in Duty Cycling Wireless Networks

Duty cycling or periodic sleep scheduling of RF transceivers of nodes in a wireless ad hoc or sensor network can significantly reduce energy consumption. This paper sheds light on the fundamental limits of the end-to-end data delivery latency and the per-flow throughput in a wireless network with multiple interfering flows, in the presence of "coordinated" duty cycling. We propose green wave sleep scheduling (GWSS) - inspired by synchronized traffic lights - for scheduling sleep-wake slots and routing data in a duty cycling wireless network, whose performance can approach the aforementioned limits. Particularly, we derive a general latency lower bound and show that GWSS is latency optimal on various structured topologies, such as the line, grid and the tree, at low traffic load. For an arbitrary network, finding a solution to the delay-efficient sleep scheduling problem is NP-hard. But for the 2D grid topology, we show that a non-interfering construction of GWSS is optimal in the sense of scaling laws of latency and capacity. Finally, using results from percolation theory, we extend GWSS to random wireless networks, where nodes are placed in a square area according to the Poisson point process. Aided by strong numerical evidence for a new conjecture on percolation on a semi-directed lattice that we propose, we demonstrate the latency optimality of GWSS on a random extended network, i.e., for an area-n random network with unit-density-Poisson distributed nodes, and a node-active (duty-cycling) rate p, GWSS can achieve a per-flow throughput scaling of T(n, p) = Ω(p/√n) bits/sec and latency D(n, p) scaling of O(√n) + O(1/p) hops/packet/flow.

Wait, focus and spray: efficient data delivery in wireless sensor networks with ubiquitous mobile data collectors

With decrease in the cost and the size of sensor devices, we can envision a world of ubiquitous sensor networks. Usually, sensor data needs to be disseminated from the source to data collectors, making the spatially distributed sensor data available for applications. The widespread and ubiquitous nature of mobile devices, e.g., PDAs and cell phones around the world, makes them attractive to be used as mobile data collectors (MDCs) to collect and deliver the sensor data. The goal of this work is to design a dissemination protocol that leads to efficient data delivery from the source sensors to ubiquitous MDCs. We propose the Wait-Focus-Spray (WFS) data delivery scheme for wireless sensor networks with ubiquitous MDCs. The main objective of WFS is to balance the data delivery latency and transmission overhead when considering the existence of ubiquitous MDCs. In WFS, we also propose a corresponding mechanism-probabilistic scattered binary spraying (PSBS), to reduce the spatial redundancy when spraying data copies, which can increase the probability of meeting a MDC. We then present an analytical model based on the Markov chain model to analyze the trade-off between delivery latency and transmission cost in WFS. Through extensive simulations, we demonstrate that our proposed scheme reduces the transmission cost per message while provides comparable delivery delay compared with the alternative approach.

Path Planning of Data Mules in Sensor Networks

We study the problem of planning the motion of “data mules” for collecting the data from stationary sensor nodes in wireless sensor networks. Use of data mules significantly reduces energy consumption at sensor nodes compared to commonly used multihop forwarding approaches, but has a drawback in that it increases the latency of data delivery. Optimizing the motion of data mules, including path and speed, is critical for improving the data delivery latency and making the data mule approach more useful in practice. In this article, we focus on the path selection problem: finding the optimal path of data mules so that the data delivery latency can be minimized. We formulate the path selection problem as a graph problem that is capable of expressing the benefit from larger communication range. The problem is NP-hard and we present approximation algorithms for both single-data mule case and multiple-data mules case. We further consider the case in which we have only partial knowledge of communication range, where we design semionline algorithms that improve the offline plan using online knowledge at runtime. Simulation experiments on Matlab and ns2 demonstrate that our offline and semionline algorithms produce significantly shorter path lengths and data delivery latency compared to previously proposed methods, suggesting that controlled mobility can be exploited much more effectively.

A scalable network-based mobility management framework in heterogeneous IP-based networks

This paper proposes a mobility management scheme to provide a mobile node with high-quality handovers among heterogeneous wireless access networks. The proposed scheme employs a signaling architecture to support fast and reliable delivery of control messages by separating a control plane from a data transport plane in the core network. The proposed scheme is based on the network-based mobility management framework which requires the minimum modifications on terminal devices. With interaction between Layers 2 and 3, the proposed scheme accelerates the handover control procedures. It also enables a mobile subscriber to select a target network for a vertical handover with consideration of not only wireless signal strength but also user preference and quality-of-service status. The proposed scheme addresses the well-known problems of the Mobile IP-based approaches, triangular routing and bottleneck at the home agent, since it establishes a data tunnel for a mobile node along the shortest path between two different access networks. The simulation and experimental results indicate that our scheme provisions more efficient performance than the existing approaches in terms of handover latency, data packet loss, data delivery latency and load balancing.

RAC: Range Adaptive Cognitive Radio Networks

In recent years, cognitive radio has received a great attention due to tremendous potential to improve the utilization of the radio spectrum by efficiently reusing and sharing the licensed spectrum bands, as long as the interference power inflicted on the primary users of the band remains below a predefined threshold level. Cognitive radio allows the secondary users in the cognitive radio network to access the licensed spectrum of the primary users opportunistically. In this paper, an autonomous distributed adaptive transmission range control scheme for cognitive radio networks which is called the RAC is proposed. The RAC considers the QoS requirements of both the primary and the secondary users simultaneously. The cognitive user's maximization of its achievable throughput without interfering the primary user by adapting transmission range of the secondary users dynamically is the key feature of the RAC. One of the advantages of using the proposed scheme is its implementation simplicity. The RAC is compared to other cognitive radio schemes in a simulation environment by using ns2. Simulations indicate that, the RAC can well fit into the mobile cognitive radio ad hoc networks and improve the network performance. Having compared to the other schemes utilizing contemporary cognitive radio technology, the RAC provides better adaptability to the environment and maximizes throughput and minimizes data delivery latency.

Distributed self-learning scheduling approach for wireless sensor network

Energy constraints pose great challenges to wireless sensor network (WSN) with battery-powered nodes. But the reduction of energy consumption often introduces additional latency of data delivery. In this paper, a new distributed scheduling approach, self-learning scheduling approach (SSA), is presented in order to reduce energy consumption and to achieve low latency for WSN. This approach, extending the Q-learning method, enables nodes to learn continuous transmission parameter and sleep parameter through interacting with the WSN. We compare SSA with S-MAC protocol and DW-MAC protocol using simulations. The results show that the SSA can make nodes to learn the optimal scheduling policy gradually. The results under different work loads also exhibit that SSA performs much better than S-MAC protocol and DW-MAC protocol in terms of energy consumption and throughput. With regard to latency and maximum queue length, SSA also outperforms the other two MAC protocols in the scenarios, where the collision is serious and the work load is heavy.

Harnessing battery recovery effect in wireless sensor networks: Experiments and analysis

Many applications of wireless sensor networks rely on batteries. But most batteries are not simple energy reservoirs, and can exhibit battery recovery effect. That is, the deliverable energy in a battery can be self-replenished, if left idling for sufficient time. As a viable approach for energy optimisation, we made several contributions towards harnessing battery recovery effect in sensor networks. 1) We empirically examine the gain of battery runtime of sensor devices due to battery recovery effect, and affirm its significant benefit in sensor networks. We also observe a saturation threshold, beyond which more idle time will contribute only little to battery recovery. 2) Based on our experiments, we propose a Markov chain model to capture battery recovery considering saturation threshold and random sensing activities, by which we can study the effectiveness of duty cycling and buffering. 3) We devise a simple distributed duty cycle scheme to take advantage of battery recovery using pseudo-random sequences, and analyse its trade-off between the induced latency of data delivery and duty cycle rates.

Speed control and scheduling of data mules in sensor networks

Unlike traditional multihop forwarding among stationary sensor nodes, use of mobile devices for data collection in wireless sensor networks has recently been gathering more attention. The use of mobility significantly reduces the energy consumption at sensor nodes, elongating the functional lifetime of the network. However, a drawback is an increased data delivery latency. Reducing the latency through optimizing the motion of data mules is critical for this approach to thrive. In this article, we focus on the problem of motion planning, specifically, determination of the speed of the data mule and the scheduling of the communication tasks with the sensors. We consider three models of mobility capability of the data mule to accommodate different types of vehicles. Under each mobility model, we design optimal and heuristic algorithms for different problems: single data mule case, single data mule with periodic data generation case, and multiple data mules case. We compare the performance of the heuristic algorithm with a naive algorithm and also with the multihop forwarding approach by numerical experiments. We also compare one of the optimal algorithms with a previously proposed method to see how our algorithm improves the performance and is also useful in practice. As far as we know, this study is the first of a kind that provides a systematic understanding of the motion planning problem of data mules.

Energy Efficient Path-constrained Sink Navigation in Delay-guaranteed Wireless Sensor Networks

Recent work shows sink mobility can improve the energy efficiency in wireless sensor networks (WSNs). However, data delivery latency often increases due to the speed limit of mobile sink. In practice, some application has strict requirements on delay. This paper focuses on the path selection problem in delay-guaranteed sensor networks with a path-constrained mobile sink. The optimal path is chosen to meet the requirement on delay as well as minimize the energy consumption of entire network. According to whether data aggregation is adopted, we formulate the respective optimization problems and present corresponding practical algorithms. Theoretical analysis and simulation experiments validate the effectiveness of the proposed formulations and algorithms by comparing the energy consumption with some baseline algorithms.

Optimal Speed Control of Mobile Node for Data Collection in Sensor Networks

A data mule represents a mobile device that collects data in a sensor field by physically visiting the nodes in a sensor network. The data mule collects data when it is in the proximity of a sensor node. This can be an alternative to multihop forwarding of data when we can utilize node mobility in a sensor network. To be useful, a data mule approach needs to minimize data delivery latency. In this paper, we first formulate the problem of minimizing the latency in the data mule approach. The data mule scheduling (DMS) problem is a scheduling problem that has both location and time constraints. Then, for the 1D case of the DMS problem, we design an efficient heuristic algorithm that incorporates constraints on the data mule motion dynamics. We provide lower bounds of solutions to evaluate the quality of heuristic solutions. Through numerical experiments, we show that the heuristic algorithm runs fast and yields good solutions that are within 10 percent of the optimal solutions.

Fast proxy delivery of multiple streaming sessions in shared running buffers

With the falling price of memory, an increasing number of multimedia servers and proxies are now equipped with a large memory space. Caching media objects in the memory of a proxy helps to reduce the network traffic, the disk I/O bandwidth requirement, and the data delivery latency. The running buffer approach and its alternatives are representative techniques to caching streaming data in the memory. There are two limits in the existing techniques. First, although multiple running buffers for the same media object co-exist in a given processing period, data sharing among multiple buffers is not considered. Second, user access patterns are not insightfully considered in the buffer management. In this paper, we propose two techniques based on shared running buffers in the proxy to address these limits. Considering user access patterns and characteristics of the requested media objects, our techniques adaptively allocate memory buffers to fully utilize the currently buffered data of streaming sessions, with the aim to reduce both the server load and the network traffic. Experimentally comparing with several existing techniques, we show that the proposed techniques achieve significant performance improvement by effectively using the shared running buffers.