https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

http://www.123seminarsonly.com/Seminar-Reports/015/53603449-zigbee.pdf

Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards

In this paper, we describe LEAP (Localized Encryption and Authentication Protocol), a key management protocol for sensor networks that is designed to support in-network processing, while at the same time restricting the security impact of a node compromise to the immediate network neighborhood of the compromised node. The design of the protocol is motivated by the observation that different types of messages exchanged between sensor nodes have different security requirements, and that a single keying mechanism is not suitable for meeting these different security requirements. LEAP supports the establishment of four types of keys for each sensor node -- an individual key shared with the base station, a pairwise key shared with another sensor node, a cluster key shared with multiple neighboring nodes, and a group key that is shared by all the nodes in the network. The protocol used for establishing and updating these keys is communication- and energy-efficient, and minimizes the involvement of the base station. LEAP also includes an efficient protocol for inter-node traffic authentication based on the use of one-way key chains. A salient feature of the authentication protocol is that it supports source authentication without precluding in-network processing and passive participation. We analyze the performance and the security of our scheme under various attack models and show our schemes are very efficient in defending against many attacks.

LEAP: efficient security mechanisms for large-scale distributed sensor networks

Internet of Things (IoT) is playing a more and more important role after its showing up, it covers from traditional equipment to general household objects such as WSNs and RFID. With the great potential of IoT, there come all kinds of challenges. This paper focuses on the security problems among all other challenges. As IoT is built on the basis of the Internet, security problems of the Internet will also show up in IoT. And as IoT contains three layers: perception layer, transportation layer and application layer, this paper will analyze the security problems of each layer separately and try to find new problems and solutions. This paper also analyzes the cross-layer heterogeneous integration issues and security issues in detail and discusses the security issues of IoT as a whole and tries to find solutions to them. In the end, this paper compares security issues between IoT and traditional network, and discusses opening security issues of IoT.

https://csi.dgist.ac.kr/uploads/Seminar/1407_IoT_SSH.pdf

Security of the Internet of Things: perspectives and challenges

Exploiting idle workstations for hosting guest computation has been a popular research area [l, 2, 3, 4, 5, 61. With the current growth in the number and the size of data-intensive tasks, exploiting idle workstations for their memory is an attractive option. This would be particularly attractive for programs with transient peaks in their memory requirements or for data-intensive programs that are run infrequently. In the research described in this paper, we examine the utility of exploiting idle memory in workstation pools. We attempt to answer the following questions. First, given a workstation pool, what fraction of the memory can be expected to be idle? This provides an estimate of the opportunity for hosting guest data. Second, what fraction of a individual host’s memory can be expected to be idle? This helps determine the recruitment policy what is the maximum amount of memory that should be recruited on a single host? Third, what is the distribution of memory idletimes? This indicates how long guest data can be expected to survive; applications that access their data-sets frequently within the expected life-time of guest data are more likely to benefit from exploiting idle memory. Fourth, how much benefit can a user expect? We use two metrics for the benefit of exploiting idle memory: (1) if I have a pool with w workstations, how much memory should I expect to get for free by harvesting idle memory; (2) how much improvement can be achieved in end-to-end execution time? Finally, how long and how frequently might a user have to wait to reclaim her machine if she volunteers to host guest data on her machine? This helps answer the question of social acceptability whether users will feel comfortable enough with the performance of the system to agree to participate.

Using idle memory for data-intensive computations (extended abstract)

In this paper, we examine the utility of exploiting idle memory in workstation pools. We attempt to answer the following questions. First, given a workstation pool, what fraction of the memory can be expected to be idle? This provides an estimate of the opportunity for hosting guest data. Second, what fraction of a individual host''s memory can be expected to be idle? This helps determine the recruitment policy -- what is the maximum amount of memory that should be recruited on a single host? Third, what is the distribution of memory idle-times? That is, what is the probability that a chunk of memory that is currently idle will be idle for longer than time t? This information indicates how long guest data can be expected to survive; applications that access their data-sets frequently within the expected life-time of guest data are more likely to benefit from exploiting idle memory. Fourth, how much benefit can a user expect? We use two metrics for the benefit of exploiting idle memory: (1) if I have a pool with w workstations, how much memory should I expect to get for free by harvesting idle memory; (2) how much improvement can be achieved in end-to-end execution time? Finally, how long and how frequently might a user have to wait to reclaim her machine if she volunteers to host guest pages on her machine? This helps answer the question of social acceptability. To answer the questions relating to the availability of idle memory, we have analyzed two-week long traces from five workstation pools with different sizes, locations, and patterns of use. To evaluate the expected benefits and costs, we have simulated three data-intensive applications (0.5GB-5GB) on these workstation pools.

The Utility of Exploiting Idle Memory for Data-Intensive Computations

Wireless sensor networks (WSNs) have proven to be useful in many applications, such as military surveillance and environment monitoring. To meet the severe energy constraints in WSNs, several researchers have proposed to use the in-network data aggregation technique (i.e., combining partial results at intermediate nodes during message routing), which significantly reduces the communication overhead. Given the lack of hardware support for tamper-resistance and the unattended nature of sensor nodes, sensor network protocols need to be designed with security in mind. Recently, researchers proposed algorithms for securely computing a few aggregates, such as Sum (the sum of the sensed values), Count (number of nodes) and Average. However, to the best of our knowledge, there is no prior work which securely computes the Median, although the Median is considered to be an important aggregate. The contribution of this paper is twofold. We first propose a protocol to compute an approximate Median and verify if it has been falsified by an adversary. Then, we design an attack-resilient algorithm to compute the Median even in the presence of a few compromised nodes. We evaluate the performance and cost of our approach via both analysis and simulation. Our results show that our approach is scalable and efficient.

/pdf/secure-median-computation-in-wireless-sensor-networks-2t1eckfixt.pdf

Secure median computation in wireless sensor networks

Design and implementation of a parallel solution to the cutting stock problem

The Self-Paced Learning Increases Retention and Capacity (SPARC) project is responding to the well-documented surge in CS enrollment by creating a self-paced learning environment that blends online learning, automated assessment, collaborative practice, and peer-supported learning. SPARC delivers educational material online, encourages students to practice programming in groups, frees them to learn material at their own pace, and allows them to demonstrate proficiency at any time. This model contrasts with traditional course offerings, which impose a single schedule of due dates and exams for all students. SPARC allows students to complete courses faster or slower at a pace tailored to the individual, thereby allowing universities to teach more students with the same or fewer resources. This paper describes the goals and elements of the SPARC model as applied to CS1. We present results so far and discuss the future of the project.

Sanjeev Setia

Papers

Using idle memory for data-intensive computations (extended abstract)

The Utility of Exploiting Idle Memory for Data-Intensive Computations

Secure median computation in wireless sensor networks

Design and implementation of a parallel solution to the cutting stock problem

A Novel Self-Paced Model for Teaching Programming