Rami Abielmona

Bio: Rami Abielmona is an academic researcher from University of Ottawa. The author has contributed to research in topics: Wireless sensor network & Risk management. The author has an hindex of 11, co-authored 82 publications receiving 535 citations. Previous affiliations of Rami Abielmona include Ottawa University.

Robotic sensor agents: a new generation of intelligent agents for complex environment monitoring

Abstract: Monitoring environment parameters is a complex task of great importance in many areas, such as the natural living environment; homeland security; industrial or laboratory hazardous environments (biologically, radioactively, or chemically contaminated); polluted/toxic natural environments; water treatment plants; nuclear stations; war zones; or remote, difficult-to-reach environments, such as the deep space or underwater This article will discuss a new generation of intelligent, autonomous, wireless robotic sensor agents (RSAs) for complex environment monitoring Shown in this article is the architecture of an RSA system under development in our laboratory at the University of Ottawa (see Petriu et al, p14-19, May 2002) Monitoring is done by continuously collecting sensory data from stationary and mobile RSAs deployed in the field

A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator Networks

Abstract: Wireless sensor and actuator networks (WSANs) are heterogeneous networks composed of many different nodes that can cooperatively sense the environment, determine an appropriate action to take, then change the environment’s state after acting on it. As a natural extension of wireless sensor networks (WSNs), WSANs inherit from them a variety of research challenges and bring forth many new ones. These challenges are related to dealing with imprecise and vague information, solving complicated optimization problems or collecting and processing data from multiple sources. Computational intelligence (CI) is an overarching term denoting a conglomerate of biologically and linguistically inspired techniques that provide robust solutions to NP-hard problems, reason in imprecise terms and yield high-quality yet computationally tractable approximate solutions to real-world problems. Many researchers have consequently turned to CI in hope of finding answers to a plethora of WSAN-related challenges. This paper reviews the application of several methodologies under the CI umbrella to the WSAN field. We describe and categorize existing works leaning on fuzzy systems , neural networks , evolutionary computation , swarm intelligence , learning systems , and their hybridizations to well-known or emerging WSAN problems along five major axes: 1) actuation; 2) communication; 3) sink mobility; 4) topology control; and 5) localization. The survey offers informative discussions to help reason through all the studies under consideration. Finally, we point to future research avenues by: 1) suggesting suitable CI techniques to specific problems; 2) borrowing concepts from WSNs that have yet to be applied to WSANs; or 3) describing the shortcomings of current methods in order to spark interest on the development of more refined models.

An evolving risk management framework for wireless sensor networks

Abstract: Individual units in a wireless sensor network (WSN) are exposed to multiple risks, either during or after their deployment. The identification of the risk sources and their watchful monitoring in dynamic, unpredictable environments is pivotal to ensure a smooth, long-term functioning of the WSN. We introduce an evolving risk management framework for WSNs that captures multiple risk features and provides both a visual depiction of the corporate network threats at any time and a numerical assessment of any sensor's overall risk. The visualization module is embodied through an evolving clustering architecture which heavily relies on shadowed sets. The risk assessment module embraces fuzzy and shadowed evaluations of the risk sources and incorporates a simple adaptive learning process that weights the risk sources proportionally to their observed impact on failed sensors. A distinctive trait of the proposed framework is its highly automated yet still human-centric nature. Experiments utilizing different sensor models and deployment scenarios confirm the feasibility of the risk management platform under consideration.

Textual risk mining for maritime situational awareness

Abstract: In this paper, we propose an auxiliary Machine Learning (ML) and Natural Language Processing (NLP) integrated system for maritime situational awareness (MSA) operations. We bring into account a new and influential asset — human intuition and perception — to the existing semi-automated decision support systems that mostly rely on numerical data collected by electronic sensors or cameras located either directly on the vessels or in the maritime command-and-control centers. For our project, we gathered weekly textual reports spanning twelve months from the United States Worldwide Threats to Shipping Reports repository that belongs to the National Geospatial-Intelligence Agency (NGA), We considered the maritime incident reports written by human operators as a valuable and accessible unstructured textual input source in which a span of text1 is called “risk” if it expresses one of the following kinds of vessel incidents: fired, robbed, boarded, hijacked, attacked, chased, approached, kidnapped, boarding attempted, suspiciously approached or clashed with. Our approach benefits from probability distributions of some useful features annotated based on a list of lexicons that contain expressions denoting vessel types, risks types, risk associates, maritime geographical locations, dates and times. These distributions are captured and used to anchor the span of “risks” as they are described in the textual reports. After some preprocessing steps that include tokenization, named entity extraction and part-of-speech tagging, the textual risk mining system applies a variety of sequence classification algorithms, e.g., Conditional Random Fields, Conditional Markov Models and Hidden Markov Models in order to compare the risk classification performance. Empirical results show that our NLP/ML-based system can extract variable-length risk spans from the textual reports with about 90% correctness.

Method and system for downloading updates

Abstract: Embodiments of the present invention provide the ability for a software provider to distribute software updates to several different recipients utilizing a peer-to-peer environment. The invention described herein may be used to update any type of software, including, but not limited to, operating software, programming software, anti-virus software, database software, etc. The use of a peer-to-peer environment with added security provides the ability to minimize download time for each peer and also reduce the amount of egress bandwidth that must be provided by the software provider to enable recipients (peers) to obtain the update.

Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

Abstract: The automatic identification system (AIS) tracks vessel movement by means of electronic exchange of navigation data between vessels, with onboard transceiver, terrestrial, and/or satellite base stations. The gathered data contain a wealth of information useful for maritime safety, security, and efficiency. Because of the close relationship between data and methodology in marine data mining and the importance of both of them in marine intelligence research, this paper surveys AIS data sources and relevant aspects of navigation in which such data are or could be exploited for safety of seafaring, namely traffic anomaly detection, route estimation, collision prediction, and path planning.

Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

Abstract: This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless sensor networks (WSNs) are the main components of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in machine-to-machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.