In this work, we study algorithms for cooperative search and survey using a fleet of Autonomous Underwater Vehicles (AUVs). Due to the limited energy, communication range/bandwidth, and sensing range of the AUVs, underwater search and survey with multiple AUVs brings about several new challenges since a large amount of data needs to be collected by each AUV, and any AUV may fail unexpectedly. To address the challenges and meet our objectives of minimizing the total survey time and traveled distance of AUVs, we propose a cooperative rendezvous scheme called Synchronization-Based Survey (SBS) to facilitate cooperation among a large number of AUVs when surveying a large area. In SBS, AUVs form an intermittently connected network (ICN) in that they periodically meet each other for data aggregation, control signal dissemination, and AUV failure detection/recovery. Numerical analysis and simulations have been performed to compare the performance of three variants of SBS schemes, namely, Alternating Column Synchronization (ACS), Strict Line Synchronization (SLS), and X Synchronization (XS). The results show that XS can outperform other SBS schemes in terms of the survey time and the traveled distance of AUVs. We also compare XS with nonsynchronization-based survey and the lower bound on the survey time and traveled distance. The results show that XS achieves a close to optimal performance.

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Cooperative Search and Survey Using Autonomous Underwater Vehicles (AUVs)

Stationary wireless sensor networks (WSNs) fail to scale when the area to be monitored is unbounded and the physical phenomenon to be monitored may migrate through a large region. Deploying mobile sensor networks (MSNs) alleviates this problem, as the self-configuring MSN can relocate to follow the phenomenon of interest. However, a major challenge here is to maximize the sensing coverage in an unknown, noisy, and dynamically changing environment with nodes having limited sensing range and energy, and moving under distributed control. To address these challenges, we propose a new distributed algorithm, Causataxis, which enables the MSN to relocate toward the interesting regions and adjust its shape and position as the sensing environment changes. (In Latin, causa means motive/interest. A taxis (plural taxes) is an innate behavioral response by an organism to a directional stimulus. We use Causataxis to refer to an interest driven relocation behavior.) Unlike conventional cluster-based systems with backbone networks, a unique feature of our proposed approach is its biosystem inspired growing and rotting behaviors with coordinated locomotion. We compare Causataxis with a swarm-based algorithm, which uses the concept of virtual spring forces to relocate mobile nodes based on local neighborhood information. Our simulation results show that Causataxis outperforms the swarm-based algorithm in terms of the sensing coverage, the energy consumption, and the noise tolerance with a slightly high communication overhead.

Coordinated Locomotion and Monitoring Using Autonomous Mobile Sensor Nodes

In this paper, we propose a data-gathering scheme for hierarchical underwater sensor networks, where multiple Autonomous Underwater Vehicles (AUVs) are deployed over large-scale coverage areas. The deployed AUVs constitute an intermittently connected multihop network through inter-AUV synchronization (in this paper, synchronization means an interconnection between nodes for communication) for forwarding data to the designated sink. In such a scenario, the performance of the multihop communication depends upon the synchronization among the vehicles. The mobility parameters of the vehicles vary continuously because of the constantly changing underwater currents. The variations in the AUV mobility parameters reduce the inter-AUV synchronization frequency contributing to delays in the multihop communication. The proposed scheme improves the AUV synchronization frequency by permitting neighboring AUVs to share their status information via a pre-selected node called an agent-node at the static layer of the network. We evaluate the proposed scheme in terms of the AUV synchronization frequency, vertical delay (node→AUV), horizontal delay (AUV→AUV), end-to-end delay, and the packet loss ratio. Simulation results show that the proposed scheme significantly reduces the aforementioned delays without the synchronization time-out process employed in conventional works.

Data-Gathering Scheme Using AUVs in Large-Scale Underwater Sensor Networks: A Multihop Approach.

In information systems (IS) research, Information Technology Service Management (ITSM) has become a popular research area among IS and management researchers as a result of industry push and the development and advancement of research in service sciences. From academic perspective, a growing number of papers have been published addressing many aspects of ITSM issues. This paper presents the results based on a study of comprehensive review of publications in ITSM from 2000 to 2010. A total of 152 research papers from leading information systems (IS) journals and conference proceedings were identified, categorized and analyzed from the perspectives of reference discipline, theoretical foundation, research method, level of analysis, and research topic. The findings suggest five primary conclusions: 1) there is generally a lack of theoretically driven researches; 2) the field is still improving, with a growing number of published papers dealing with the development of concepts, constructs, models, methods and implementations for theory formalization; 3) ITSM performance issues, justifications, and IT Infrastructure Library (ITIL) topics are among the most popular topics of research; 4) ITSM researchers do not seem to consider research at an individual level; 5) the most popular research method was the conceptual orientation. Recommendations for future research in ITSM are presented and articulated.

Research in Information Technology Service Management (ITSM) (2000 — 2010): An Overview

Wireless sensor networks (WSN) are extensively applied in civil and military areas. Localization is an essential prerequisite for many WSN applications, and is often based on beacons that provide geographical information in real time. Mobile Beacons (MB) can be used to replace many static beacons with paths that can be controlled in real-time. Robotic and/or flight vehicles can work as MBs. In this paper we consider the use of reinforcement learning (RL) (a significant branch of machine learning) to control MBs. Usually, RL needs an infinite series of episodes to determine an optimal policy. We propose however a method of localization employing mobile beacon whose behavior will be controlled by an adapted RL algorithm. A MB learns and makes decisions based on weighted information collected from unknown sensors. Simulation results show that the adapted RL algorithm provides sufficient information to the MB to localise unknown sensors in a lightweight but effective way.

Dynamic Path Determination of Mobile Beacons Employing Reinforcement Learning for Wireless Sensor Localization

In harsh outdoor terrains like disaster areas and urban toxic regions, optimal placements of static sensor nodes might not be possible, leading to possible incomplete coverage of the region. In such situations, mobile sensor nodes (MSNs) might be a better option and a significantly fewer number of nodes are required than their static counterparts. A triangulation-based coverage where, groups of three MSNs position themselves to form equilateral triangles, has several applications in localization, 3D imaging and coordinated search operation. In this paper, we present several mobile traversal algorithms (MTA) based on triangulation coverage that employs N ≥ 3 MSNs such that, up to N - 3 node failures can be tolerated. MTA achieves three objectives: (a) as N increases, the total time to cover the field decreases in the absence of a failure; (b) each MSN travels a minimum distance; (c) upon a failure, the remaining MSNs efficiently complete the coverage of the field. The bounds on the total traveling distance and time are derived and the tightness of the bounds are proven through simulations. Furthermore, we also derive the bounds on the distance traveled by an MSN to complete the coverage process. The bounds on the individual traveling distances are useful in minimizing and bounding the energy consumption of the MSNs.

Mobile traversal schemes based on triangulation coverage

This work presents a novel algorithm to improve energy conservation in sensor networks. The algorithm is based upon selective activation of sensor nodes using thresholding (SAT). The sensor continuously monitors the received signal and makes a binary decision to join the network if the average received signal power falls within the specified minimum and maximum threshold range. Thus, SAT divides all the receivers within the coverage range of the transmitter into sets of active and inactive nodes realizing a saving in power consumption proportional to the number of inactive nodes. The sensor life-time is enhanced by allowing the node to transmit at a power that is a constant fraction of the total residual energy. For two cases of linear and hexagonal networks considered in this work, it is shown that for each transmission, the fraction of inactive nodes may exceed by over 30% of the total number of nodes present within maximum one-hop distance of the transmitter. The cost associated with energy conservation through SAT is (a) increased sensor node density or (b) increased transmission power requirement.

Energy conservation in sensor networks through selective node activation

A triangulation-based coverage scheme using mobile sensor nodes (MSNs) has several applications in localization, 3D imaging and coordinated search operation. In this work, we introduce an efficient failure-tolerant mobile traversal algorithm (FTMTA) that provides a triangulation-based coverage of a field. FTMTA employs N MSNs such that, upto N -- 3 node failures can be tolerated to complete the coverage. FTMTA achieves three objectives: (a) as N increases, the total time to cover the field decreases in the absence of a failure; (b) each MSN travels a minimum distance; (c) upon a failure, the remaining MSNs efficiently complete the coverage of the field.

A failure-tolerant mobile traversal scheme based on triangulation coverage

Framework for Mobile Assisted Localization in Wireless Sensor Networks

In many network and IT systems, users submit loosely-defined (or fuzzy) requests to obtain answers, solutions or resources. In this paper, we focus on trouble-shooting problem tickets in an IT service management system. In such a system, problems are typically reported using vague user-generated descriptions of the symptoms (e.g., 'my email is not working!') in a ticket. IT Specialists are then responsible for identifying the troubled component from the reported symptoms. Usually, the ticket is sent to one Specialist at a time until it is resolved hence, an efficient mechanism to select the (next) potential Specialist becomes critical towards a timely resolution of the ticket.

In this paper, we introduce a framework to deal with fuzzy requests in a distributed environment. We first show the importance of considering both the traditional graph-based network routing efficiency as well as social network theory to generate important ranking metrics. For each newly arrived ticket, we propose several selection and ranking policies to choose and rank IT Specialists who can potentially resolve the ticket. We evaluate the ranking system with synthetic tickets, generated by an in-depth simulated IT management model that closely mimics an operational system. Our preliminary results suggest the importance of considering both routing efficiency and social connectivity to minimize the resolution time of a ticket.

Asheq Khan

Papers

Mobile traversal schemes based on triangulation coverage

Energy conservation in sensor networks through selective node activation

A failure-tolerant mobile traversal scheme based on triangulation coverage

Framework for Mobile Assisted Localization in Wireless Sensor Networks

A Framework for Rank Computation and Aggregation in Fuzzy Environments