The recent development of 60GHz technology has made hybrid Data Center Networks (hybrid DCNs) possible, i.e., augmenting wired DCNs with highly directional 60GHz wireless links to provide flexible network connectivity. Although a few recent proposals have demonstrated the feasibility of this hybrid design, it still remains an open problem how to route DCN traffics with guaranteed performance under a hybrid DCN environment. In this paper, we make the first attempt to tackle this challenge, and propose the RUSH framework to minimize the network congestion in hybrid DCNs, by jointly routing flows and scheduling wireless (directional) antennas. Though the problem is shown to be NP-hard, the RUSH algorithms offer guaranteed performance bounds. Our algorithms are able to handle both batched arrivals and sequential arrivals of flow demands, and the theoretical analysis shows that they achieve competitive ratios of O(log n), where n is the number of switches in the network. We also conduct extensive simulations using ns-3 to verify the effectiveness of RUSH. The results demonstrate that RUSH produces nearly optimal performance and significantly outperforms the current practice and a simple greedy heuristics.

Because data dissemination is crucial to wireless sensor networks (WSNs), its energy efficiency and reliability are of paramount importance. While achieving these two goals together is highly nontrivial, the situation is exacerbated if WSN nodes are duty-cycled (DC) and their transmission power is adjustable. In this paper, we study the problem of minimizing the expected total transmission power for reliable data dissemination (multicast/broadcast) in DC-WSNs. Due to the NP-hardness of the problem, we design efficient approximation algorithms with provable performance bounds for it. To facilitate our algorithm design, we propose the novel concept of Time-Reliability-Power (TRP) space as a general data structure for designing data dissemination algorithms in WSNs, and the performance ratios of our algorithms based on the TRP space are proven to be$ {\cal O}(\log \Delta \log k)$for both multicast and broadcast, where$\Delta $is the maximum node degree in the network and$k$is the number of source/destination nodes involved in a data dissemination session. We also conduct extensive simulations to firmly demonstrate the efficiency of our algorithms.

Because data dissemination is crucial to Wireless Sensor Networks (WSNs), its energy-efficiency and reliability are of paramount importance. While achieving these two goals together is highly non-trivial, the situation is exacerbated if WSN nodes are duty-cycled (DC) and their transmission power is adjustable. In this paper, we study the problem of minimizing the expected total transmission power for reliable data dissemination (multicast/broadcast) in DC-WSNs. Due to the NP-hardness of the problem, we design efficient approximation algorithms with provable performance bounds for it.

Inspired by the recent invention of a high efficiency Wireless Power Transfer (WPT) technique, we propose in this paper the Perpetual Wireless Networks (PWNs) as a novel wireless networking paradigm. Similar to the conventional wireless (data) access point, a PWN has a power access point, from which electrical power is injected and distributed into the network in a form of multi-hop transfer. Consequently, we lay our focus on this new type of multi-hop flow problems concerning not data but power. We formulate and analyze a set of such power flow problems (some are joint with data flow), and we devise algorithms to solve them. The intriguing insights obtained from solving these optimization problems offer instructive guidance for future studies on real PWN constructions.

We focus on wireless sensor networks (WSNs) that perform data collection with the objective of obtaining the whole dataset at the sink (as opposed to a function of the dataset). In this case, energy-efficient data collection requires the use of data aggregation. Whereas many data aggregation schemes have been investigated, they either compromise the fidelity of the recovered data or require complicated in-network compressions. In this paper, we propose a novel data aggregation scheme that exploits compressed sensing (CS) to achieve both recovery fidelity and energy efficiency in WSNs with arbitrary topology. We make use of diffusion wavelets to find a sparse basis that characterizes the spatial (and temporal) correlations well on arbitrary WSNs, which enables straightforward CS-based data aggregation as well as high-fidelity data recovery at the sink. Based on this scheme, we investigate the minimum-energy compressed data aggregation problem. We first prove its NP-completeness, and then propose a mixed integer programming formulation along with a greedy heuristic to solve it. We evaluate our scheme by extensive simulations on both real datasets and synthetic datasets. We demonstrate that our compressed data aggregation scheme is capable of delivering data to the sink with high fidelity while achieving significant energy saving.

Wireless Sensor Networks (WSNs) have acquired new features recently, i.e., both the sensor and the antenna of a node can be directional. This brings new challenges to the Connected Coverage (CoCo) problem, where a finite set of targets needs to be monitored by some active sensor nodes, and the connectivity of these active nodes with the sink must be retained at the same time. In this paper, we study the Minimum-Energy Connected Coverage (MeCoCo) problem in WSNs with Omni-directional (O) and Directional (D) features, aiming at minimizing the total energy cost of both sensing and connectivity. Considering different combinations of O and D features, we study the MeCoCo problem under four cases, namely: O-Antenna and O-Sensor (OAOS), O-Antenna and D-Sensor (OADS), D-Antenna and D-Sensor (DADS), as well as D-Antenna and O-Sensor (DAOS). We prove that the MeCoCo problem is NP-hard under all these cases, and present approximation algorithms with provable approximation ratios. In particular, we propose a constant-approximation for OAOS, and polylogarithmic approximations for all other cases. Finally, we conduct extensive simulations and the results strongly confirm the effectiveness of our approach.

Although wireless sensor networks (WSNs) are powerful in monitoring physical events, the data collected from a WSN are almost always incomplete if the surveyed physical event spreads over a wide area. The reason for this incompleteness is twofold: i) insufficient network coverage and ii) data aggregation for energy saving. Whereas the existing recovery schemes only tackle the second aspect, we develop Dual-lEvel Compressed Aggregation (DECA) as a novel framework to address both aspects. Specifically, DECA allows a high fidelity recovery of a widespread event, under the situations that the WSN only sparsely covers the event area and that an in-network data aggregation is applied for traffic reduction. Exploiting both the low-rank nature of real-world events and the redundancy in sensory data, DECA combines matrix completion with a fine-tuned compressed sensing technique to conduct a dual-level reconstruction process. We demonstrate that DECA can recover a widespread event with less than 5% of the data (with respect to the dimension of the event) being collected. Performance evaluation based on both synthetic and real data sets confirms the recovery fidelity and energy efficiency of our DECA framework.

With the development of ever-expanding wireless sensor networks (WSNs) that are meant to connect physical worlds with human societies, gathering sensory data at a single point is becoming less and less practical. Unfortunately, the alternative in-network data management schemes may fail to operate in the face of communication voids (or holes) in WSNs (especially 3D WSNs). In response to this challenge, we propose harmonic quorum systems (HQSs) as a lightweight data management system for 2D/3D WSNs. HQSs innovate in exploiting a few scalar fields (constructed using pure localized algorithms) to guide data accesses. This liberates HQSs from depending on any routing mechanisms or location services, hence making HQSs efficient and robust against anomalies in WSN topologies. We implement HQSs in TinyOS, and we perform intensive simulations using TOSSIM to validate the performance of HQSs.

As a burgeoning technique for signal processing, compressed sensing (CS) is being increasingly applied to wireless communications. However, little work is done to apply CS to multihop networking scenarios. In this paper, we investigate the application of CS to data collection in wireless sensor networks, and we aim at minimizing the network energy consumption through joint routing and compressed aggregation. We first characterize the optimal solution to this optimization problem, then we prove its NP-completeness. We further propose a mixed-integer programming formulation along with a greedy heuristic, from which both the optimal (for small scale problems) and the near-optimal (for large scale problems) aggregation trees are obtained. Our results validate the efficacy of the greedy heuristics, as well as the great improvement in energy efficiency through our joint routing and aggregation scheme.

Although compressed sensing (CS) has been envisioned as a useful technique to improve the performance of wireless sensor networks (WSNs), it is still not very clear how exactly it will be applied and how big the improvements will be. In this paper, we propose two different ways (plain-CS and hybrid-CS) of applying CS to WSNs at the networking layer, in the form of a particular data aggregation mechanism. We formulate three flow-based optimization problems to compute the throughput of the non-CS, plain-CS, and hybrid-CS schemes. We provide the exact solution to the first problem corresponding to the non-CS case and lower bounds for the cases with CS. Our preliminary numerical results are only for a low-power regime. They illustrate two crucial insights: first, applying CS naively may not bring any improvement, and secondly, our hybrid-CS can achieve significant improvement in throughput.

This paper proposes a context aware and fuzzy rule data mining model for enterprise supply chain finance cooperative system that interacts with bank, buyer and supplier. It consists of three main components. The first component is the model for enterprise supply chain finance cooperative systems with cooperative agents serving as the simulation representatives of bank, buyer and supplier communicating with each other over the company's network. In the second component, context aware and fuzzy rule data mining model is proposed based on the simulation model for the enterprise supply chain finance cooperative systems. The third component is a simulation system which has been designed and implemented for the context aware and fuzzy rule data mining methodology.