Towards Identifying Performance Anomalies

AbstractLarge-scale-software systems (LSSs) are composed of hundreds of subsystems that interact with each other in an unforeseen and complex ways. The operators of these LSSs strictly monitor thousands of metrics (performance counters) to quickly identify performance anomalies before a catastrophe. The existing monitoring tools and methodologies have not kept in pace with the rapid growth and inherit complexity of these LSSs; hence are ineffective in assisting practitioners to effectively pinpoint performance anomalies. We propose a methodology that uses entropy analysis to assist practitioners/operators of LSSs in quickly detecting underlying anomalies in the system. Our performance tests conducted on an open source benchmark system reveal that the proposed methodology is robust in pinpointing anomalies, do not require any domain knowledge to operate, and avoid information overload on practitioners

Challenges and trends in wireless ubiquitous computing systems

In the last decade, the Internet paradigm has been evolving toward a new frontier with the emergence of ubiquitous and pervasive systems, including wireless sensor networks, ad hoc networks, RFID systems, and wireless embedded systems. In fact, while the initial purpose of the Internet was to interconnect computers to share digital data at large scale, the current tendency is to enable ubiquitous and pervasive computing to control everything anytime and at a large scale. This new paradigm has given rise to a new generation of networked systems, commonly known as Internet-of-Things or Cyber-Physical Systems

An Adaptive User Interface in Healthcare

AbstractHealthcare is a broad subject with many different challenges, yet it is important and relatable to everyone. The aging Baby Boomer generation is an important healthcare issue today. In Canada, and many other developed nations, the number of citizens reaching the age of retirement and seniority is growing faster than the rate of citizens working and providing health related services. As people age they tend to require more frequent checkups and health services, ultimately putting a bigger resource drain on healthcare infrastructure. New advancements in Computer Science and Engineering are allowing the development of next generation applications with the purpose of providing healthcare services in a cost effective and efficient way. This paper proposes a multi-agent system for tracking and monitoring health data for patients. Furthermore, agents within the system use reinforcement learning techniques to build an adaptive user interface for each human user. The actions and behaviour of users are monitored and used to modify their respective user interface over time. To demonstrate the feasibility of the architecture, two scenarios are provided. We conclude with several possible future directions for this research

Dynamic Healthcare Interface for Patients

AbstractCanadian healthcare is a fundamental part of society. Challenges such as the aging baby boomer generation require the healthcare industry to meet higher demands while using fewer resources. Computer systems designed to record and report physical health properties of an individual personcan be used in part to accomplish this task. In this paper, we present the architecture of a hypothetical multi-agent system designed to provide healthcare information about specific patients through continuous monitoring. The resulting data from the system is accessible by the patient to whom it belongs as well as his or her healthcare professional. Furthermore, the proposed system utilizes an adaptive user interface for the purpose of improving the overall experience for users with poor vision or motor skills. Specifically, we focus on the implementation of several of the key components involved in the adaptive user interface: learning component and the user model. To demonstrate the feasibility of the implementation two scenarios are provided. We conclude with several possible future directions for this research

Comparative Study of Fingerprint and Centroid Localization Protocol Using COOJA

AbstractSensor networks are in a numerous number of applications. However, implementing wireless sensor networks present new challenges compared with theoretical networks. Cooja is the Contiki network simulator. It allows large and small networks of Contiki motes to be simulated; moreover, motes can be emulated at the hardware level. In this paper, we evaluate the accuracy performance of two very well-known localization protocols, namely: fingerprint and centroid protocols using Tmote sky in Cooja. It is worth mentioning that this the first time this study is conducted in Cooja. The results conform to the theory that fingerprint protocol has a better performance than centroid in terms of accuracy when accuracy is quantified

ConVeh: Driving Safely into a Connected Future

© 2017 The Authors. Published by Elsevier B.V. The loss of lives and damages to the property due to the vehicle crashes and road accidents have been an issue for long; a quarter of these accidents happen due to the adverse weather conditions. This paper presents the idea of cooperative driving technique for the drivers with the use of Connected Vehicles to minimize road accidents, traffic congestions, and to lessen, as far as possible, the effects of traffic on the environment and the loss of lives and economy. The frameworks for improving situational awareness and crash avoidance suggested hereby are vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) data transmission systems. The research will primarily focus on the feasibility of CVs as applicable to the contemporary physical and virtual infrastructure and suggest the required adaptations, while the technical needs for the effective and successful implementation of a robust communication framework through the use of dedicated short-range communications (DSRC) will be discussed thereafter. Peer-review under responsibility of the Conference Program Chairs

Specification and automatic verification of trust-based multi-agent systems

We present a new logic-based framework for modeling and automatically verifying trust in Multi-Agent Systems (MASs). We start by refining TCTL, a temporal logic of trust that extends the Computation Tree Logic (CTL) to enable reasoning about trust with preconditions. A new vector-based version of interpreted systems is defined to capture the trust relationship between the interacting parties. We introduce a set of reasoning postulates along with formal proofs to support our logic. Moreover, we present new symbolic model checking algorithms to formally and automatically verify the system under consideration against some desirable properties expressed using the proposed logic. We fully implemented our proposed algorithms as a model checker tool called MCMAS-T on top of the MCMAS model checker for MASs along with its new input language VISPL (Vector-extended ISPL). We evaluated the tool and reported experimental results using a real-life scenario in the healthcare field

Autonomous Driving and Connected Mobility Modeling: Smart Dynamic Traffic Monitoring and Enforcement System for Connected and Autonomous Mobility

In recent years, autonomous vehicles (AVs), connected vehicles (CVs) and all relative technology have been in the spotlight, being intensively researched and developed. There is high anticipation on the benefits of automation and the overall reform it will bring to the transport sector, with some optimistic estimates considering it as a reality within the next few years. Evidently, AVs and CVs are attracting considerable attention and are developed very rapidly, cultivating great expectations for traffic safety improvements. While their potential is enormous and undeniable, benefits are not automatically guaranteed as there are parameters that currently appear unforeseen. This paper investigates the ways that monitoring and enforcement of autonomous vehicles can be improved and serious problems such as tailgating and crashes can be mitigated. This paper\u27s result could provide useful conclusions about human factor, the effectiveness of existing monitoring and enforcing systems and possible future systems regarding enforcement and monitoring of autonomous vehicles (AVs)