We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Modern Graph Theory

Appearing in 1st IEEE International Workshop on Sensor Net Protocols and Applications (SNPA). Anchorage, Alaska, USA. May 11, 2003. RMST: Reliable Data Transport in Sensor Networks Fred Stann, John Heidemann Abstract – Reliable data transport in wireless sensor networks is a multifaceted problem influenced by the physical, MAC, network, and transport layers. Because sensor networks are subject to strict resource constraints and are deployed by single organizations, they encourage revisiting traditional layering and are less bound by standardized placement of services such as reliability. This paper presents analysis and experiments resulting in specific recommendations for implementing reliable data transport in sensor nets. To explore reliability at the transport layer, we present RMST (Reliable Multi- Segment Transport), a new transport layer for Directed Diffusion. RMST provides guaranteed delivery and fragmentation/reassembly for applications that require them. RMST is a selective NACK-based protocol that can be configured for in-network caching and repair. Second, these energy constraints, plus relatively low wireless bandwidths, make in-network processing both feasible and desirable [3]. Third, because nodes in sensor networks are usually collaborating towards a common task, rather than representing independent users, optimization of the shared network focuses on throughput rather than fairness. Finally, because sensor networks are often deployed by a single organization with inexpensive hardware, there is less need for interoperability with existing standards. For all of these reasons, sensor networks provide an environment that encourages rethinking the structure of traditional communications protocols. The main contribution is an evaluation of the placement of reliability for data transport at different levels of the protocol stack. We consider implementing reliability in the MAC, transport layer, application, and combinations of these. We conclude that reliability is important at the MAC layer and the transport layer. MAC-level reliability is important not just to provide hop-by-hop error recovery for the transport layer, but also because it is needed for route discovery and maintenance. (This conclusion differs from previous studies in reliability for sensor nets that did not simulate routing. [4]) Second, we have developed RMST (Reliable Multi-Segment Transport), a new transport layer, in order to understand the role of in- network processing for reliable data transfer. RMST benefits from diffusion routing, adding minimal additional control traffic. RMST guarantees delivery, even when multiple hops exhibit very high error rates. 1 Introduction Wireless sensor networks provide an economical, fully distributed, sensing and computing solution for environments where conventional networks are impractical. This paper explores the design decisions related to providing reliable data transport in sensor nets. The reliable data transport problem in sensor nets is multi-faceted. The emphasis on energy conservation in sensor nets implies that poor paths should not be artificially bolstered via mechanisms such as MAC layer ARQ during route discovery and path selection [1]. Path maintenance, on the other hand, benefits from well- engineered recovery either at the MAC layer or the transport layer, or both. Recovery should not be costly however, since many applications in sensor nets are impervious to occasional packet loss, relying on the regular delivery of coarse-grained event descriptions. Other applications require loss detection and repair. These aspects of reliable data transport include the provision of guaranteed delivery and fragmentation/ reassembly of data entities larger than the network MTU. Sensor networks have different constraints than traditional wired nets. First, energy constraints are paramount in sensor networks since nodes can often not be recharged, so any wasted energy shortens their useful lifetime [2]. This work was supported by DARPA under grant DABT63-99-1-0011 as part of the SCAADS project, and was also made possible in part due to support from Intel Corporation and Xerox Corporation. Fred Stann and John Heidemann are with USC/Information Sciences Institute, 4676 Admiralty Way, Marina Del Rey, CA, USA E-mail: fstann@usc.edu, johnh@isi.edu. 2 Architectural Choices There are a number of key areas to consider when engineering reliability for sensor nets. Many current sensor networks exhibit high loss rates compared to wired networks (2% to 30% to immediate neighbors)[1,5,6]. While error detection and correction at the physical layer are important, approaches at the MAC layer and higher adapt well to the very wide range of loss rates seen in sensor networks and are the focus of this paper. MAC layer protocols can ameliorate PHY layer unreliability, and transport layers can guarantee delivery. An important question for this paper is the trade off between implementation of reliability at the MAC layer (i.e. hop to hop) vs. the Transport layer, which has traditionally been concerned with end-to-end reliability. Because sensor net applications are distributed, we also considered implementing reliability at the application layer. Our goal is to minimize the cost of repair in terms of transmission.

RMST: Reliable Data Transport in Sensor Networks

Demand response programs offer efficient solutions for many power system problems, such as high generation cost, high demand’s peak to average ratio, high emissions, reliability issues and congestion in generation, transmission and distribution systems. Their main function is to assist power systems during peak demand hours and also during contingencies. They are a subcategory of the family of demand side management (DSM) strategies. DR programs are classified into two broad categories; price-based DR programs and incentive-based DR programs. In order to exploit their full potential, DR programs must be implemented optimally. Such a problem, which here is referred to as “DR optimisation problem”, is a hot research topic and has been frequently researched in the literature. This paper aims to review different research works on DR optimisation problems. Based on the conducted review, some directions for future research are proposed.

Optimisation of demand response in electric power systems, a review

Thank you for downloading computers and intractability a guide to the theory of np completeness. As you may know, people have search numerous times for their favorite books like this computers and intractability a guide to the theory of np completeness, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their desktop computer.

Computers And Intractability A Guide To The Theory Of Np Completeness

Developers can use cross-platform mobile development tools to create smartphone apps that meet user expectations, but existing XMTs need improvement.

Cross-Platform Development Tools for Smartphone Applications

Linear self-stabilizing algorithms for the independent and dominating set problems using an unfair distributed scheduler

This paper introduces BGR, a new beacon-less geographic routing algorithm for wireless sensor networks. Data packets are forwarded toward the destination, and nodes, which hear the packet compete for becoming the next hop. A recovery strategy is provided for the case of empty forwarding areas. The main innovation is a strategy to avoid simultaneous forwarding situations, which would otherwise cause packet failures. It is confirmed by simulation that BGR sends very few packets and is reliable infields with sufficiently high node density. Furthermore, BGR is compared with similar algorithms after developing a general framework for beacon-less algorithms with an integrative terminology. Different kinds of forwarding areas are compared.

/pdf/bgr-blind-geographic-routing-for-sensor-networks-4dxtp7jiy3.pdf

BGR: blind geographic routing for sensor networks

Smart meters are increasingly penetrating the market, resulting in enormous data volumes to be communicated. In many cases, embedded devices collect the metering data and transmit them wirelessly to achieve cheap and facile deployment. Bandwidth is yet scarce and transmission occupies the spectrum. Smart meter data should hence be compressed prior to transmission. Here, solutions for personal computers are not applicable, as they are too resource-demanding. In this paper, we propose four lossless compression algorithms for smart meters. We analyze processing time and compression gains and compare the results with five off-the-shelf compression algorithms. We show that excellent compression gains can be achieved when investing a moderate amount of memory. A discussion of the suitability of the algorithms for different kinds of metering data is presented.

The Hitchhiker's guide to choosing the compression algorithm for your smart meter data

The accuracy of link-quality estimators (LQE) is missioncritical in many application scenarios in wireless sensor networks (WSN), since the link-quality metric is used for routing decisions or neighborhood formation. Link-quality estimation must offer validity for different timescales. Existing LQEs describe and approximate the current quality in a single value only. This method leads to a limited accuracy and expressiveness about the presumed future behavior of a link. The LQE developed in this paper incorporates four quality metrics that give a holistic assessment of the link and its dynamic behavior; therefore, this research is an important step to achieving a higher prediction accuracy including knowledge about the short- and long-term behavior.

/pdf/prediction-accuracy-of-link-quality-estimators-2utm5xls67.pdf

Volker Turau

Papers

Cross-Platform Development Tools for Smartphone Applications

Linear self-stabilizing algorithms for the independent and dominating set problems using an unfair distributed scheduler

BGR: blind geographic routing for sensor networks

The Hitchhiker's guide to choosing the compression algorithm for your smart meter data

Prediction accuracy of link-quality estimators