Enhanced slotted aloha protocols for underwater sensor networks with large propagation delay

Recently underwater sensor networks (UWSN) attracted large research interests. Medium access control (MAC) is one of the major challenges faced by UWSN due to the large propagation delay and narrow channel bandwidth of acoustic communications used for UWSN. Widely used slotted aloha (S-Aloha) protocol suffers large performance loss in UWSNs, which can only achieve performance close to pure aloha (P-Aloha). In this paper we theoretically model the performances of S-Aloha and P-Aloha protocols and analyze the adverse impact of propagation delay. According to the observation on the performances of S-Aloha protocol we propose two enhanced S-Aloha protocols in order to minimize the adverse impact of propagation delay on S-Aloha protocol. The first enhancement is a synchronized arrival S-Aloha (SA-Aloha) protocol, in which frames are transmitted at carefully calculated time to align the frame arrival time with the start of time slots. Propagation delay is taken into consideration in the calculation of transmit time. As estimation error on propagation delay may exist and can affect network performance, an improved SA-Aloha (denoted by ISA-Aloha) is proposed, which adjusts the slot size according to the range of delay estimation errors. Simulation results show that both SA-Aloha and ISA-Aloha perform remarkably better than S-Aloha and P-Aloha for UWSN, and ISA-Aloha is more robust even when the propagation delay estimation error is large. © 2011 IEEE.

Modeling link adaptation algorithm for IEEE 802.11 wireless LAN networks

Link adaptation is a critical component of IEEE 802.11 systems. In this paper, we analytically model a retransmission based Auto Rate Fallback (ARF) link adaptation algorithm. Both packet collisions and packet corruptions are modeled with the algorithm. The models can provide insights into the dynamics of the link adaptation algorithms and configuration of algorithms parameters. It is also observed that when the competing number of stations is high, packet collisions can largely affected the performance of ARF and make ARF operate with the lowest date rate, even when no packet corruption occur. This is in contrast to the existing assumption that packet collision will not affect the correct operation of ARF and can be ignored in the evaluation of ARF. The work presented in this paper can provide guidelines on configuring the link adaptation algorithms and designing new link adaptation algorithms for future high speed 802.11 systems. © 2006 IEEE.

Wavelength-division and spatial multiplexing using tandem interferometers for Bragg grating sensor networks

We present a new method for the interrogation of large arrays of Bragg grating sensors. Eight gratings operating between the wavelengths of 1533 and 1555 nm have been demultiplexed. An unbalanced Mach—Zehnder interferometer illuminated by a single low-coherence source provides a high-phase-resolution output for each sensor, the outputs of which are sequentially selected in wavelength by a tunable Fabry-Perot interferometer. The minimum detectable strain measured was 90 ne-vHz at 7 Hz for a wavelength of 1535 nm.

Quality-driven resource utilization methods for video streaming in wireless communication networks

This research is focused on the optimisation of resource utilisation in wireless mobile networks with the consideration of the users’ experienced quality of video streaming services. The study specifically considers the new generation of mobile communication networks, i.e. 4G-LTE, as the main research context. The background study provides an overview of the main properties of the relevant technologies investigated. These include video streaming protocols and networks, video service quality assessment methods, the infrastructure and related functionalities of LTE, and resource allocation algorithms in mobile communication systems. A mathematical model based on an objective and no-reference quality assessment metric for video streaming, namely Pause Intensity, is developed in this work for the evaluation of the continuity of streaming services. The analytical model is verified by extensive simulation and subjective testing on the joint impairment effects of the pause duration and pause frequency. Various types of the video contents and different levels of the impairments have been used in the process of validation tests. It has been shown that Pause Intensity is closely correlated with the subjective quality measurement in terms of the Mean Opinion Score and this correlation property is content independent. Based on the Pause Intensity metric, an optimised resource allocation approach is proposed for the given user requirements, communication system specifications and network performances. This approach concerns both system efficiency and fairness when establishing appropriate resource allocation algorithms, together with the consideration of the correlation between the required and allocated data rates per user. Pause Intensity plays a key role here, representing the required level of Quality of Experience (QoE) to ensure the best balance between system efficiency and fairness. The 3GPP Long Term Evolution (LTE) system is used as the main application environment where the proposed research framework is examined and the results are compared with existing scheduling methods on the achievable fairness, efficiency and correlation. Adaptive video streaming technologies are also investigated and combined with our initiatives on determining the distribution of QoE performance across the network. The resulting scheduling process is controlled through the prioritization of users by considering their perceived quality for the services received. Meanwhile, a trade-off between fairness and efficiency is maintained through an online adjustment of the scheduler’s parameters. Furthermore, Pause Intensity is applied to act as a regulator to realise the rate adaptation function during the end user’s playback of the adaptive streaming service. The adaptive rates under various channel conditions and the shape of the QoE distribution amongst the users for different scheduling policies have been demonstrated in the context of LTE. Finally, the work for interworking between mobile communication system at the macro-cell level and the different deployments of WiFi technologies throughout the macro-cell is presented. A QoEdriven approach is proposed to analyse the offloading mechanism of the user’s data (e.g. video traffic) while the new rate distribution algorithm reshapes the network capacity across the macrocell. The scheduling policy derived is used to regulate the performance of the resource allocation across the fair-efficient spectrum. The associated offloading mechanism can properly control the number of the users within the coverages of the macro-cell base station and each of the WiFi access points involved. The performance of the non-seamless and user-controlled mobile traffic offloading (through the mobile WiFi devices) has been evaluated and compared with that of the standard operator-controlled WiFi hotspots.

Self-organising zooms for decentralised redundancy management in visual sensor networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences.

Adaptive sending rate over wireless mesh networks using SNR

Wireless Mesh Networks (WMNs) have emerged as a key technology for the next generation of wireless networking. Instead of being another type of ad-hoc networking, WMNs diversify the capabilities of ad-hoc networks. Several protocols that work over WMNs include IEEE 802.11a/b/g, 802.15, 802.16 and LTE-Advanced. To bring about a high throughput under varying conditions, these protocols have to adapt their transmission rate. This paper proposes a scheme to improve channel conditions by performing rate adaptation along with multiple packet transmission using packet loss and physical layer condition. Dynamic monitoring, multiple packet transmission and adaptation to changes in channel quality by adjusting the packet transmission rates according to certain optimization criteria provided greater throughput. The key feature of the proposed method is the combination of the following two factors: 1) detection of intrinsic channel conditions by measuring the fluctuation of noise to signal ratio via the standard deviation, and 2) the detection of packet loss induced through congestion. The authors show that the use of such techniques in a WMN can significantly improve performance in terms of the packet sending rate. The effectiveness of the proposed method was demonstrated in a simulated wireless network testbed via packet-level simulation.

Exploiting heterogeneous scientific literature networks to combat ranking bias:evidence from the computational linguistics area

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.

An intelligent approach for deploying applications in WSN's

Wireless sensor networks have been identified as one of the key technologies for the 21st century. They consist of tiny devices with limited processing and power capabilities, called motes that can be deployed in large numbers of useful sensing capabilities. Even though, they are flexible and easy to deploy, there are a number of considerations when it comes to their fault tolerance, conserving energy and re-programmability that need to be addressed before we draw any substantial conclusions about the effectiveness of this technology. In order to overcome their limitations, we propose a middleware solution. The proposed scheme is composed based on two main methods. The first method involves the creation of a flexible communication protocol based on technologies such as Mobile Code/Agents and Linda-like tuple spaces. In this way, every node of the wireless sensor network will produce and process data based on what is the best for it but also for the group that it belongs too. The second method incorporates the above protocol in a middleware that will aim to bridge the gap between the application layer and low level constructs such as the physical layer of the wireless sensor network. A fault tolerant platform for deploying and monitoring applications in real time offers a number of possibilities for the end user giving him in parallel the freedom to experiment with various parameters, in an effort towards the deployed applications running in an energy efficient manner inside the network. The proposed scheme is evaluated through a number of trials aiming to test its merits under real time conditions and to identify its effectiveness against other similar approaches. Finally, parameters which determine the characteristics of the proposed scheme are also examined.

Wireless machine-to-machine networks 2013

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