There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining Practical Machine Learning Tools and Techniques

Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions.

Energy Harvesting Sensor Nodes: Survey and Implications

Providing various wireless connectivities for vehicles enables the communication between vehicles and their internal and external environments. Such a connected vehicle solution is expected to be the next frontier for automotive revolution and the key to the evolution to next generation intelligent transportation systems (ITSs). Moreover, connected vehicles are also the building blocks of emerging Internet of Vehicles (IoV). Extensive research activities and numerous industrial initiatives have paved the way for the coming era of connected vehicles. In this paper, we focus on wireless technologies and potential challenges to provide vehicle-to-x connectivity. In particular, we discuss the challenges and review the state-of-the-art wireless solutions for vehicle-to-sensor, vehicle-to-vehicle, vehicle-to-Internet, and vehicle-to-road infrastructure connectivities. We also identify future research issues for building connected vehicles.

http://bbcr.uwaterloo.ca/~xshen/paper/2015/cvsac.pdf

Connected Vehicles: Solutions and Challenges

Codes are widely used in many engineering applications to offer robustness against noise . In large-scale systems, there are several types of noise that can affect the performance of distributed machine learning algorithms—straggler nodes, system failures, or communication bottlenecks—but there has been little interaction cutting across codes, machine learning, and distributed systems. In this paper, we provide theoretical insights on how coded solutions can achieve significant gains compared with uncoded ones. We focus on two of the most basic building blocks of distributed learning algorithms: matrix multiplication and data shuffling . For matrix multiplication, we use codes to alleviate the effect of stragglers and show that if the number of homogeneous workers is $n$ , and the runtime of each subtask has an exponential tail, coded computation can speed up distributed matrix multiplication by a factor of $\log n$ . For data shuffling, we use codes to reduce communication bottlenecks, exploiting the excess in storage. We show that when a constant fraction $\alpha $ of the data matrix can be cached at each worker, and $n$ is the number of workers, coded shuffling reduces the communication cost by a factor of $\left({\alpha + \frac {1}{n}}\right)\gamma (n)$ compared with uncoded shuffling, where $\gamma (n)$ is the ratio of the cost of unicasting $n$ messages to $n$ users to multicasting a common message (of the same size) to $n$ users. For instance, $\gamma (n) \simeq n$ if multicasting a message to $n$ users is as cheap as unicasting a message to one user. We also provide experimental results, corroborating our theoretical gains of the coded algorithms.

Speeding Up Distributed Machine Learning Using Codes

Renewable energy enables sensor networks with the capability to recharge and provide perpetual data services. Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing services without interruptions caused by battery runouts is non-trivial. Most environment monitoring applications require data collection from all nodes at a steady rate. The objective of this paper is to design a solution for fair and high throughput data extraction from all nodes in presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate for each node, such that no node will ever run out of energy. We propose a centralized algorithm and an asynchronous distributed algorithm that can compute the optimal lexicographic rate assignment for all nodes. The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, while the distributed algorithm computes the optimal rate when the routes are pre-determined. We prove the optimality for both the centralized and the distributed algorithms, and use a testbed with 155 sensor nodes to validate the distributed algorithm.

/pdf/steady-and-fair-rate-allocation-for-rechargeable-sensors-in-4wrls3jyhg.pdf

Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks

Renewable energy enables sensor networks with the capability to recharge and provide perpetual data services. Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing services without interruptions caused by battery runouts is nontrivial. Most environment monitoring applications require data collection from all nodes at a steady rate. The objective of this paper is to design a solution for fair and high throughput data extraction from all nodes in the presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate and routing paths for each node such that no node will ever run out of energy. We propose a centralized algorithm and two distributed algorithms. The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, the first distributed algorithm computes the optimal rate when the routing structure is a given tree, and the second distributed algorithm, although heuristic, jointly computes a routing structure and a high lexicographic rate assignment that is nearly optimum. We prove the optimality for the centralized and the first distributed algorithm, and use real test-bed experiments and extensive simulations to evaluate both of the distributed algorithms.

Perpetual and fair data collection for environmental energy harvesting sensor networks

Tracking of movements such as that of people, animals, vehicles, or of phenomena such as fire, can be achieved by deploying a wireless sensor network. So far only prototype systems have been deployed and hence the issue of scale has not become critical. Real-life deployments, however, will be at large scale and achieving this scale will become prohibitively expensive if we require every point in the region to be covered (i.e., full coverage), as has been the case in prototype deployments. In this paper we therefore propose a new model of coverage, called trap coverage, that scales well with large deployment regions. A sensor network providing trap coverage guarantees that any moving object or phenomena can move at most a (known) displacement before it is guaranteed to be detected by the network, for any trajectory and speed. Applications aside, trap coverage generalizes the de-facto model of full coverage by allowing holes of a given maximum diameter. From a probabilistic analysis perspective, the trap coverage model explains the continuum between percolation (when coverage holes become finite) and full coverage (when coverage holes cease to exist). We take first steps toward establishing a strong foundation for this new model of coverage. We derive reliable, explicit estimates for the density needed to achieve trap coverage with a given diameter when sensors are deployed randomly. Our density estimates are more accurate than those obtained using asymptotic critical conditions. We show by simulation that our analytical predictions of density are quite accurate even for small networks. We then propose polynomial-time algorithms to determine the level of trap coverage achieved once sensors are deployed on the ground. Finally, we point out several new research problems that arise by the introduction of the trap coverage model.

/pdf/trap-coverage-allowing-coverage-holes-of-bounded-diameter-in-4sim66g581.pdf

Trap Coverage: Allowing Coverage Holes of Bounded Diameter in Wireless Sensor Networks

With increasing popularity of media enabled hand-helds, the need for high data-rate services for mobile users is evident. Large-scale Wireless LANs (WLANs) can provide such a service, but they are expensive to deploy and maintain. Open WLAN access-points (APs), on the other hand, need no new deployments, but can offer only opportunistic services with no guarantees on short term throughput. In contrast, a carefully planned sparse deployment of roadside WiFi provides an economically scalable infrastructure with quality of service assurance to mobile users. In this paper, we propose to study the deployment techniques with respect to roadside WiFi. In particular, we present a new metric, called Contact Opportunity, as a characterization of a roadside WiFi network. Informally, the contact opportunity for a given deployment measures the fraction of distance or time that a mobile user is in contact with some AP when moving through a certain path. Such a metric is closely related to the quality of data service that a mobile user might experience while driving through the system. We then present an efficient deployment method that maximizes the worst case contact opportunity under a budget constraint. We further show how to extend this concept and the deployment techniques to a more intuitive metric -- the average throughput -- by taking various dynamic elements into account. Simulations over a real road network and experimental results show that our approach achieves more than 200% higher minimum contact opportunity, 30%-100% higher average contact opportunity and a significantly improved distribution of average throughput compared with two commonly used baseline algorithms.

Maximizing the Contact Opportunity for Vehicular Internet Access

Vehicular Internet access via open WLAN access points (APs) has been demonstrated to be a feasible solution to provide opportunistic data service to moving vehicles. Using an in situ deployment, however, such a solution does not provide worst-case performance guarantees due to unpredictable intermittent connectivity. On the other hand, a solution that tries to cover every point in an entire road network with APs (full coverage) is not very practical due to the prohibitive deployment and operational cost. In this paper, we introduce a new notion of intermittent coverage for mobile users, called a-coverage, which provides worst-case guarantees on the interconnection gap while using significantly fewer APs than needed for full coverage. We propose efficient algorithms to verify whether a given deployment provides alpha-coverage and approximation algorithms for determining a deployment of APs that will provide alpha-coverage. We compare alpha-coverage with opportunistic access of open WLAN APs (modeled as a random deployment) via simulations over a real-world road network and show that using the same number of APs as random deployment, alpha-coverage bounds the interconnection gap to a much smaller distance than that in a random deployment.

Zizhan Zheng

Papers

Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks

Perpetual and fair data collection for environmental energy harvesting sensor networks

Trap Coverage: Allowing Coverage Holes of Bounded Diameter in Wireless Sensor Networks

Maximizing the Contact Opportunity for Vehicular Internet Access

Alpha Coverage: Bounding the Interconnection Gap for Vehicular Internet Access