Reliable data delivery in low energy ad hoc sensor networks

Reliable delivery of data is a classical design goal for reliability-oriented collection routing protocols for ad hoc wireless sensor networks (WSNs). Guaranteed packet delivery performance can be ensured by careful selection of error free links, quick recovery from packet losses, and avoidance of overloaded relay sensor nodes. Due to limited resources of individual senor nodes, there is usually a trade-off between energy spending for packets transmissions and the appropriate level of reliability. Since link failures and packet losses are unavoidable, sensor networks may tolerate a certain level of reliability without significantly affecting packets delivery performance and data aggregation accuracy in favor of efficient energy consumption. However a certain degree of reliability is needed, especially when hop count increases between source sensor nodes and the base station as a single lost packet may result in loss of a large amount of aggregated data along longer hops. An effective solution is to jointly make a trade-off between energy, reliability, cost, and agility while improving packet delivery, maintaining low packet error ratio, minimizing unnecessary packets transmissions, and adaptively reducing control traffic in favor of high success reception ratios of representative data packets. Based on this approach, the proposed routing protocol can achieve moderate energy consumption and high packet delivery ratio even with high link failure rates. The proposed routing protocol was experimentally investigated on a testbed of Crossbow's TelosB motes and proven to be more robust and energy efficient than the current implementation of TinyOS2.x MultihopLQI

Traffic eavesdropping based scheme to deliver time-sensitive data in sensor networks

Due to the broadcast nature of wireless channels, neighbouring sensor nodes may overhear packets transmissions from each other even if they are not the intended recipients of these transmissions. This redundant packet reception leads to unnecessary expenditure of battery energy of the recipients. Particularly in highly dense sensor networks, overhearing or eavesdropping overheads can constitute a significant fraction of the total energy consumption. Since overhearing of wireless traffic is unavoidable and sometimes essential, a new distributed energy efficient scheme is proposed in this paper. This new scheme exploits the inevitable overhearing effect as an effective approach in order to collect the required information to perform energy efficient delivery for data aggregation. Based on this approach, the proposed scheme achieves moderate energy consumption and high packet delivery rate notwithstanding the occurrence of high link failure rates. The performance of the proposed scheme is experimentally investigated a testbed of TelosB motes in addition to ns-2 simulations to validate the performed experiments on large-scale network

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Smart Microgrids: Overview and Outlook

The idea of changing our energy system from a hierarchical design into a set of nearly independent microgrids becomes feasible with the availability of small renewable energy generators. The smart microgrid concept comes with several challenges in research and engineering targeting load balancing, pricing, consumer integration and home automation. In this paper we first provide an overview on these challenges and present approaches that target the problems identified. While there exist promising algorithms for the particular field, we see a missing integration which specifically targets smart microgrids. Therefore, we propose an architecture that integrates the presented approaches and defines interfaces between the identified components such as generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid Worksho

A parallel algorithm to calculate the costrank of a network

We developed analogous parallel algorithms to implement CostRank for distributed memory parallel computers using multi processors. Our intent is to make CostRank calculations for the growing number of hosts in a fast and a scalable way. In the same way we intent to secure large scale networks that require fast and reliable computing to calculate the ranking of enormous graphs with thousands of vertices (states) and millions or arcs (links). In our proposed approach we focus on a parallel CostRank computational architecture on a cluster of PCs networked via Gigabit Ethernet LAN to evaluate the performance and scalability of our implementation. In particular, a partitioning of input data, graph files, and ranking vectors with load balancing technique can improve the runtime and scalability of large-scale parallel computations. An application case study of analogous Cost Rank computation is presented. Applying parallel environment models for one-dimensional sparse matrix partitioning on a modified research page, results in a significant reduction in communication overhead and in per-iteration runtime. We provide an analytical discussion of analogous algorithms performance in terms of I/O and synchronization cost, as well as of memory usage