A Cooperative MIMO Framework for Wireless Sensor NetworksNguyen, Diep N.; Krunz, Marwan
doi: 10.1145/2499381pmid: N/A
We explore the use of cooperative multi-input multi-output (MIMO) communications to prolong the lifetime of a wireless sensor network (WSN). Single-antenna sensor nodes are clustered into virtual antenna arrays that can act as virtual MIMO (VMIMO) nodes. We design a distributed cooperative clustering protocol (CCP), which exploits VMIMO's diversity gain by optimally selecting the cooperating nodes (CNs) within each cluster and balancing their energy consumption. The problem of optimal CN selection at the transmit and receive clusters is formulated as a nonlinear binary program. Aiming at minimizing the imbalance in the residual energy at various nodes, we decompose this problem into two subproblems: finding the optimal number of CNs (ONC) in a cluster and the CN assignment problem. For the ONC problem, we first analyze the energy efficiency of two widely used VMIMO methods: distributed Space Time Block Code (DSTBC) and distributed Vertical-Bell Laboratories-Layered-Space-Time (DVBLAST). Our analysis provides an upper bound on the optimal number of CN nodes, which greatly reduces the computational complexity of the ONC problem. The second subproblem is addressed by assigning CNs based on the residual battery energy. To make CCP scalable to large WSNs, we propose a multihop energy-balanced routing mechanism for clustered WSNs (C-EBR) with a novel cost metric. Finally, we derive sufficient conditions on the intra- and intercluster ranges, under which CCP guarantees connectivity of the intercluster topology. Extensive simulations show that the proposed approach dramatically improves the network lifetime.
Temporal Adaptive Link Quality Prediction with Online LearningLiu, Tao; Cerpa, Alberto E.
doi: 10.1145/2594766pmid: N/A
Link quality estimation is a fundamental component of the low-power wireless network protocols and is essential for routing protocols in Wireless Sensor Networks (WSNs). However, accurate link quality estimation remains a challenging task due to the notoriously dynamic and unpredictable wireless environment. In this article we argue that, in addition to the estimation of current link quality, prediction of the future link quality is more important for the routing protocol to establish low-cost delivery paths. We propose to apply machine learning methods to predict the link quality in the near future to facilitate the utilization of intermediate links with frequent quality changes. Moreover, we show that, by using online learning methods, our adaptive link estimator (TALENT) adapts to network dynamics better than statically trained models without the need of a priori data collection for training the model before deployment. We implemented TALENT in TinyOS with Low-Power Listening (LPL) and conducted extensive experiments in three testbeds. Our experimental results show that the addition of TALENT increases the delivery efficiency 1.95 times on average compared with a 4B, state-of-the-art link quality estimator, as well as improves the end-to-end delivery rate when tested on three different wireless testbeds.
MC2Xia, Ming; Dong, Yabo; Xu, Wenyuan; Li, Xiangyang; Lu, Dongming
doi: 10.1145/2509856pmid: N/A
Real-world, long-running wireless sensor networks (WSNs) require intense user intervention in the development, hardware testing, deployment, and maintenance stages. A majority of network design is network centric and focuses primarily on network performance, for example, efficient sensing and reliable data delivery. Although several tools have been developed to assist debugging and fault diagnosis, it is yet to systematically examine the underlying heavy burden that users face throughout the lifetime of WSNs. In this article, we propose a general Multimode user-CentriC (MC2) framework that can, with simple user inputs, adjust itself to assist user operation and thus reduce the users' burden at various stages. In particular, we have identified utilities that are essential at each stage and grouped them into modes. In each mode, only the corresponding utilities will be loaded, and modes can be easily switched using the customized MC2 sensor platform. As such, we reduce the runtime interference between various utilities and simplify their development as well as their debugging. We validated our MC2 software and the sensor platform in a long-lived microclimate monitoring system deployed at a wildland heritage site, Mogao Grottoes. In our current system, 241 sensor nodes have been deployed in 57 caves, and the network has been running for over five years. Our experimental validation shows that the MC2 framework shortens the time for network deployment and maintenance, and makes network maintenance doable by field experts (in our case, historians).
Occupancy Modeling and Prediction for Building Energy ManagementErickson, Varick L.; Carreira-Perpiñán, Miguel Á.; Cerpa, Alberto E.
doi: 10.1145/2594771pmid: N/A
Heating, cooling and ventilation accounts for 35 energy usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Thus, in order to achieve efficient conditioning, we require knowledge of occupancy. This article shows how real time occupancy data from a wireless sensor network can be used to create occupancy models, which in turn can be integrated into building conditioning system for usage-based demand control conditioning strategies. Using strategies based on sensor network occupancy model predictions, we show that it is possible to achieve 42 annual energy savings while still maintaining American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) comfort standards.
Efficient Solutions Framework for Optimal Multitask Resource Assignments for Data Fusion in Wireless Sensor NetworksHariharan, Srikanth; Bisdikian, Chatschik; Kaplan, Lance M.; Pham, Tien
doi: 10.1145/2594768pmid: N/A
Motivated by the need to judiciously allocate scarce sensing resources to attain the highest benefit for the applications that sensor networks serve, in this article we develop a flexible solutions methodology for maximizing the overall reward attained, subject to constraints on the resource demands under fairly general reward or demand functions. We map a broad class of related problems for data fusion in wireless sensor networks into an integer programming problem and provide an iterative Lagrangian relaxation technique to solve it. Each iteration step involves solving for a maximum-weight independent set of an appropriately constructed graph, which, in many cases, can be obtained in polynomial time. We apply our methodology to the problem of tracking targets moving over a period of time through a nonhomogeneous, energy-constrained sensor field. With rewards represented by the quality of information attained in tracking, we study its trade-offs and relationship with energy consumption and periodic measurement taking. We finally illustrate other applications of our framework in sensor networks.