Stephen J. Hartley Oxford University Press, New York, 1998, 260 pp. ISBN 0-19-511315-2, $45.00 Concurrent Programming  is a thorough treatment of Java multi-threaded programming for both a stand-alone and distributed environment. Designed mostly for students in concurrent or parallel programming classes, the text is also an excellent reference for the practicing professional developing multi-threaded programs or applets. Hartley first provides a complete explanation of the features of Java necessary to write concurrent programs, including topics such as exception handling, interfaces, and packages. He then gives the reader a solid background to write multi-threaded programs and also presents the problems introduced when writing concurrent programs—namely race conditions, mutual exclusion, and deadlock. Hartley also provides several software solutions that do not require the use of common process and thread mechanisms. Once the groundwork is laid for writing concurrent programs, Hartley then takes a different approach than most Java references. Rather than presenting how Java handles mutual exclusion with the  synchronized  keyword (although it is covered later), he first looks at semaphore-based solutions to classic concurrent problems such as bounded-buffer, readers-writers, and the dining philosophers. Hartley also uses the same approach to develop Java classes for monitors and message passing. This unique approach to introducing concurrency allows the readers to both understand how Java threads are synchronized and how the basic synchronization mechanism can be used to construct more abstract tools such as semaphores. If there is a shortcoming with the text it is with the lack of sufficient coverage of remote method invocation (RMI), although there is a section covering RMI. This is quite understandable as RMI is a fairly recent phenomenon with the Java community. Also, the classes that Hartley provides could easily implement RMI rather than sockets to handle communication. The strengths of the book include its ease in reading, several examples at the end of chapters, a package similar to Xtango that provides algorithm animation, and a supportive web site by the author (see  www.mcs.drexel.edu/~shartley/ConcProgJava/index.html ) including compressed source code. As Java becomes more dominant on the server side of multi-tier applications, writing thread-safe concurrent applications becomes even more important.  Concurrent Programming  is a strong step towards teaching students and professionals such skills. Greg Gagne, Westminster College of Salt Lake City Salt Lake City, Utah

Distributed Computing: Fundamentals, Simulations and Advanced Topics

It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

/pdf/computational-thinking-ji-suan-lun-de-si-kao-3qvv34ha3s.pdf

Computational Thinking 計算論的思考

We consider a class of sensor networks with two special characteristics. First, the nodes periodically generate data for transfer to a distinguished node called the access point. Second, the nodes are (transmit) power and energy limited, but the access point, which communicates with the 'outside world', is not so limited. Such networks might be used for instance when a geographically distributed physical process, such as traffic on a freeway or at an urban street intersection, is periodically sensed for purposes of process control. We propose a medium access control scheme, called PEDAMACS, for this special class of networks. PEDAMACS uses the high-powered access point to synchronize the nodes and to schedule their transmissions and receptions in a TDMA manner. The protocol first enables the access point to gather topology (connectivity) information. A scheduling algorithm then determines when each node should transmit its data, and the access point announces the transmission schedule to the other nodes. The scheduling algorithm ideally should minimize the delay-the time needed for data from all nodes to reach the access point. However, this optimization problem is NP-complete. PEDAMACS instead uses a polynomial-time scheduling algorithm which guarantees a delay proportional to the number of nodes in the sensor network. Because PEDAMACS schedules node transmissions, its performance is much better than that of protocols designed for more general contention (or random access) networks in terms of power consumption, delay, fairness, and congestion control. The comparison is based on simulations in TOSSIM, a simulation environment for TinyOS, the operating system for the Berkeley sensor nodes. For the traffic application we consider, the PEDAMACS network provides a lifetime of several years compared to several months and days based on random access schemes with and without sleep cycles respectively, making sensor network technology economically viable.

PEDAMACS: Power Efficient and Delay Aware Medium Access Protocol for Sensor Networks

PEDAMACS is a Time Division Multiple Access (TDMA) scheme that extends the common single hop TDMA to a multihop sensor network, using a high-powered access point to synchronize the nodes and to schedule their transmissions and receptions. The protocol first enables the access point to gather topology (connectivity) information. A scheduling algorithm then determines when each node should transmit and receive data, and the access point announces the transmission schedule to the other nodes. The performance of PEDAMACS is compared to existing protocols based on simulations in TOSSIM, a simulation environment for TinyOS, the operating system for the Berkeley sensor nodes. For the traffic application we consider, the PEDAMACS network provides a lifetime of several years compared to several months and days based on random access schemes with and without sleep cycles, respectively, making sensor network technology economically viable.

/pdf/pedamacs-power-efficient-and-delay-aware-medium-access-npdh6fr6dj.pdf

PEDAMACS: power efficient and delay aware medium access protocol for sensor networks

A machine equivalent to Turing machine, that is more intuitive in its working is defined. Three derivation rules are added to Elementary Arithmetic of Godel and his incompleteness theorems are proved without using any metalanguage. Two axioms are added to Zermelo-Fraenkel theory to derive the Continuum Hypothesis and to split the unit interval into infinitesimals.

Foundations of Computer Science

In the self-stabilizing algorithmic paradigm for distributed computation each node has only a local view of the system, yet in a finite amount of time, the system converges to a global state satisfying some desired property. In this paper we present polynomial time self-stabilizing algorithms for finding a dominating bipartition, a maximal independent set, and a minimal dominating set in any graph.

https://www.cs.clemson.edu/stabiliz/Papers/cam-2003.pdf

Self-stabilizing algorithms for minimal dominating sets and maximal independent sets

We propose two distributed algorithms to maintain, respectively, a maximal matching and a maximal independent set in a given ad hoc network; our algorithms are fault tolerant (reliable) in the sense that the algorithms can detect occasional link failures and/or new link creations in the network (due to mobility of the hosts) and can readjust the global predicates. We provide time complexity analysis of the algorithms in terms of the number of rounds needed for the algorithm to stabilize after a topology change, where a round is defined as a period of time in which each node in the system receives beacon messages from all its neighbors. In any ad hoc network, the participating nodes periodically transmit beacon messages for message transmission as well as to maintain the knowledge of the local topology at the node; as a result, the nodes get the information about their neighbor nodes synchronously (at specific time intervals). Thus, the paradigm to analyze the complexity of the self-stabilizing algorithms in the context of ad hoc networks is very different from the traditional concept of an adversary daemon used in proving the convergence and correctness of self-stabilizing distributed algorithms in general.

/pdf/self-stabilizing-protocols-for-maximal-matching-and-maximal-fnwmi4ge36.pdf

Self-stabilizing protocols for maximal matching and maximal independent sets for ad hoc networks

https://www.cs.clemson.edu/stabiliz/Papers/coloring-ipl-2003.pdf

Linear time self-stabilizing colorings

Eigenvalues and energy in threshold graphs

https://www.cs.clemson.edu/stabiliz/Papers/matching-ipl-2001.pdf

Maximal matching stabilizes in time O( m )

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