238 research outputs found
Optimal Embedding of Functions for In-Network Computation: Complexity Analysis and Algorithms
We consider optimal distributed computation of a given function of
distributed data. The input (data) nodes and the sink node that receives the
function form a connected network that is described by an undirected weighted
network graph. The algorithm to compute the given function is described by a
weighted directed acyclic graph and is called the computation graph. An
embedding defines the computation communication sequence that obtains the
function at the sink. Two kinds of optimal embeddings are sought, the embedding
that---(1)~minimizes delay in obtaining function at sink, and (2)~minimizes
cost of one instance of computation of function. This abstraction is motivated
by three applications---in-network computation over sensor networks, operator
placement in distributed databases, and module placement in distributed
computing.
We first show that obtaining minimum-delay and minimum-cost embeddings are
both NP-complete problems and that cost minimization is actually MAX SNP-hard.
Next, we consider specific forms of the computation graph for which polynomial
time solutions are possible. When the computation graph is a tree, a polynomial
time algorithm to obtain the minimum delay embedding is described. Next, for
the case when the function is described by a layered graph we describe an
algorithm that obtains the minimum cost embedding in polynomial time. This
algorithm can also be used to obtain an approximation for delay minimization.
We then consider bounded treewidth computation graphs and give an algorithm to
obtain the minimum cost embedding in polynomial time
Achieving Energy Efficiency on Networking Systems with Optimization Algorithms and Compressed Data Structures
To cope with the increasing quantity, capacity and energy consumption of transmission and routing equipment in the Internet, energy efficiency of communication networks has attracted more and more attention from researchers around the world. In this dissertation, we proposed three methodologies to achieve energy efficiency on networking devices: the NP-complete problems and heuristics, the compressed data structures, and the combination of the first two methods.
We first consider the problem of achieving energy efficiency in Data Center Networks (DCN). We generalize the energy efficiency networking problem in data centers as optimal flow assignment problems, which is NP-complete, and then propose a heuristic called CARPO, a correlation-aware power optimization algorithm, that dynamically consolidate traffic flows onto a small set of links and switches in a DCN and then shut down unused network devices for power savings.
We then achieve energy efficiency on Internet routers by using the compressive data structure. A novel data structure called the Probabilistic Bloom Filter (PBF), which extends the classical bloom filter into the probabilistic direction, so that it can effectively identify heavy hitters with a small memory foot print to reduce energy consumption of network measurement.
To achieve energy efficiency on Wireless Sensor Networks (WSN), we developed one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. Based on the Open Vehicle Routing problem, EDAL exploits the topology requirements of Compressive Sensing (CS), then implement CS to save more energy on sensor nodes
Improving latency in Crankshaft - An energy-aware MAC protocol for Wireless Sensor Networks
Due to the dramatic growth in the use of Wireless Sensor Network (WSN) applications - ranging from environment and habitat monitoring to tracking and surveillance, network research in WSN protocols has been very active in the last decade. With battery-powered sensors operating in unattended environments, energy conservation becomes the key technique for improving WSN lifetimes. WSN Medium Access Control (MAC) protocols address energy awareness and reduced duty cycles since the radio is the component that consumes most of the energy. This thesis investigates the performance of two recently published energy-aware MAC protocols, Crankshaft and SCP-MAC. Crankshaft has been shown to be one of the best protocols in terms of energy consumption in dense WSNs while SCP-MAC has a dedicated low duty cycle and low average latencies. The focus of this investigation is to discover techniques for reducing the latency of Crankshaft. Using OMNeT++, an open source and component-based simulation framework, this study investigates possible modifications to Crankshaft to improve its latency. The potential improvements considered include modifications to Crankshaft’s retransmission contention scheme (Sift), adjustments to its inherent settings, and investigating the impact of ACKs. Since OMNeT++ readily provided only a variant of SCP-MAC identified as SCP-MAC*, the simulations results presented involve comparing variants of both protocols (Crankshaft and SCP-MAC*). The performance of these protocols is also analyzed using distinct sensor node communication patterns. It was determined that Crankshaft’s latency depends on its ACK/Retransmission settings. Specifically, Crankshaft has the best latency with No ACKs, without much loss in energy consumption. But the latency can also be improved when ACKs are enabled by reducing the number of retries. Furthermore, the latency and delivery ratio are also directly governed by the WSN traffic pattern and the congestion in the network, as there was a noticeable improvement for both parameters in one-hop traffic, compared to multi-hop convergecast traffic to the sink. Finally, it was observed that Crankshaft’s broadcast performance in flooding traffic can be improved by increasing the number of broadcast slots used, though this is detrimental to its performance in unicast traffic
Decentralized Convex Optimization for Wireless Sensor Networks
Many real-world applications arising in domains such as large-scale machine learning, wired and wireless networks can be formulated as distributed linear least-squares over a large network. These problems often have their data naturally distributed. For instance applications such as seismic imaging, smart grid have the sensors geographically distributed and the current algorithms to analyze these data rely on centralized approach. The data is either gathered manually, or relayed by expensive broadband stations, and then processed at a base station. This approach is time-consuming (weeks to months) and hazardous as the task involves manual data gathering in extreme conditions. To obtain the solution in real-time, we require decentralized algorithms that do not rely on a fusion center, cluster heads, or multi-hop communication. In this thesis, we propose several decentralized least squares optimization algorithm that are suitable for performing real-time seismic imaging in a sensor network. The algorithms are evaluated and tested using both synthetic and real-data traces. The results validate that our distributed algorithm is able to obtain a satisfactory image similar to centralized computation under constraints of network resources, while distributing the computational burden to sensor nodes
Generic sensor network architecture for wireless automation (GENSEN)
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ENERGY CONSERVATION FOR WIRELESS AD HOC ROUTING
Self-configuring wireless ad hoc networks have attracted considerable attention in the last few years due to their valuable civil and military applications. One aspect of such networks that has been studied insufficiently is the energy efficiency. Energy efficiency is crucial to prolong the network lifetime and thus make the network more survivable.Nodes in wireless ad hoc networks are most likely to be driven by battery and hence operate on an extremely frugal energy budget. Conventional ad hoc routing protocols are focused on handling the mobility instead of energy efficiency. Energy efficient routing strategies proposed in literature either do not take advantage of sleep modes to conserve energy more efficiently, or incur much overhead in terms of control message and computing complexity to schedule sleep modes and thus are not scalable.In this dissertation, a novel strategy is proposed to manage the sleep of the nodes in the network so that energy can be conserved and network connectivity can be kept. The novelty of the strategy is its extreme simplicity. The idea is derived from the results of the percolation theory, typically called gossiping. Gossiping is a convenient and effective approach and has been successfully applied to several areas of the networking. In the proposed work, we will developa sleep management protocol from gossiping for both static and mobile wireless ad hoc networks. Then the protocol will be extended to the asynchronous network, where nodes manage their own states independently. Analysis and simulations will be conducted to show thecorrectness, effectiveness and efficiency of the proposed work. The comparison between analytical and simulation results will justify them for each other. We will investigate the most important performance aspects concerning the proposed strategy, including the effect ofparameter tuning and the impacts of routing protocols. Furthermore, multiple extensions will be developed to improve the performance and make the proposed strategy apply to different network scenarios
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
GossiCrypt: Wireless Sensor Network Data Confidentiality Against Parasitic Adversaries
Resource and cost constraints remain a challenge for wireless sensor network
security. In this paper, we propose a new approach to protect confidentiality
against a parasitic adversary, which seeks to exploit sensor networks by
obtaining measurements in an unauthorized way. Our low-complexity solution,
GossiCrypt, leverages on the large scale of sensor networks to protect
confidentiality efficiently and effectively. GossiCrypt protects data by
symmetric key encryption at their source nodes and re-encryption at a randomly
chosen subset of nodes en route to the sink. Furthermore, it employs key
refreshing to mitigate the physical compromise of cryptographic keys. We
validate GossiCrypt analytically and with simulations, showing it protects data
confidentiality with probability almost one. Moreover, compared with a system
that uses public-key data encryption, the energy consumption of GossiCrypt is
one to three orders of magnitude lower
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