Radio link quality estimation in Wireless Sensor Networks (WSNs) has a fundamental impact on the network performance and also affects the design of higher-layer protocols. Therefore, for about a decade, it has been attracting a vast array of research works. Reported works on link quality estimation are typically based on different assumptions, consider different scenarios, and provide radically different (and sometimes contradictory) results. This article provides a comprehensive survey on related literature, covering the characteristics of low-power links, the fundamental concepts of link quality estimation in WSNs, a taxonomy of existing link quality estimators, and their performance analysis. To the best of our knowledge, this is the first survey tackling in detail link quality estimation in WSNs. We believe our efforts will serve as a reference to orient researchers and system designers in this area.

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Radio link quality estimation in wireless sensor networks: A survey

In one aspect there is provided a method. The method may include receiving a first analog radio frequency signal including a signal of interest and an interference signal caused by a second analog radio frequency signal transmitted in full duplex over a channel from which the first analog transmission is received; combining the first analog radio frequency signal and a portion of the second analog radio frequency signal to generate an output analog radio frequency signal characterized by at least a reduction or an elimination of the interference signal included in the output analog radio frequency signal; and providing the output analog radio frequency signal. Related apparatus, systems, methods, and articles are also described.

Single channel full duplex wireless communications

We present the Low-Power Wireless Bus (LWB), a communication protocol that supports several traffic patterns and mobile nodes immersed in static infrastructures. LWB turns a multi-hop low-power wireless network into an infrastructure similar to a shared bus, where all nodes are potential receivers of all data. It achieves this by mapping all traffic demands on fast network floods, and by globally scheduling every flood. As a result, LWB inherently supports one-to-many, many-to-one, and many-to-many traffic. LWB also keeps no topology-dependent state, making it more resilient to link changes due to interference, node failures, and mobility than prior approaches. We compare the same LWB prototype on four testbeds with seven state-of-the-art protocols and show that: (i) LWB performs comparably or significantly better in many-to-one scenarios, and adapts efficiently to varying traffic loads; (ii) LWB outperforms our baselines in many-to-many scenarios, at times by orders of magnitude; (iii) external interference and node failures affect LWB's performance only marginally; (iv) LWB supports mobile nodes acting as sources, sinks, or both without performance loss.

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Low-power wireless bus

In this paper, we propose a new data-gathering mechanism for large-scale wireless sensor networks by introducing mobility into the network. A mobile data collector, for convenience called an M-collector in this paper, could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, working like a mobile base station and gathering data while moving through the field. An M-collector starts the data-gathering tour periodically from the static data sink, polls each sensor while traversing its transmission range, then directly collects data from the sensor in single-hop communications, and finally transports the data to the static sink. Since data packets are directly gathered without relays and collisions, the lifetime of sensors is expected to be prolonged. In this paper, we mainly focus on the problem of minimizing the length of each data-gathering tour and refer to this as the single-hop data-gathering problem (SHDGP). We first formalize the SHDGP into a mixed-integer program and then present a heuristic tour-planning algorithm for the case where a single M-collector is employed. For the applications with strict distance/time constraints, we consider utilizing multiple M-collectors and propose a data-gathering algorithm where multiple M-collectors traverse through several shorter subtours concurrently to satisfy the distance/time constraints. Our single-hop mobile data-gathering scheme can improve the scalability and balance the energy consumption among sensors. It can be used in both connected and disconnected networks. Simulation results demonstrate that the proposed data-gathering algorithm can greatly shorten the moving distance of the collectors compared with the covering line approximation algorithm and is close to the optimal algorithm for small networks. In addition, the proposed data-gathering scheme can significantly prolong the network lifetime compared with a network with static data sink or a network in which the mobile collector can only move along straight lines.

Tour Planning for Mobile Data-Gathering Mechanisms in Wireless Sensor Networks

Traditionally, routing in wireless sensor networks consists of two steps: First, the routing protocol selects a next hop, and, second, the MAC protocol waits for the intended destination to wake up and receive the data. This design makes it difficult to adapt to link dynamics and introduces delays while waiting for the next hop to wake up. In this paper we introduce ORW, a practical opportunistic routing scheme for wireless sensor networks. In a duty-cycled setting, packets are addressed to sets of potential receivers and forwarded by the neighbor that wakes up first and successfully receives the packet. This reduces delay and energy consumption by utilizing all neighbors as potential forwarders. Furthermore, this increases resilience to wireless link dynamics by exploiting spatial diversity. Our results show that ORW reduces radio duty-cycles on average by 50% (up to 90% on individual nodes) and delays by 30% to 90% when compared to the state of the art.

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Low power, low delay: opportunistic routing meets duty cycling

This paper explores metrics that capture to what degree packet reception on different links is correlated. Specifically, it explores metrics that shed light on when and why opportunistic routing and network coding protocols perform well (or badly). It presents a new metric, κ that, unlike existing widely used metrics, has no bias based on the packet reception ratios of links. This lack of bias makes κ a better predictor of performance of opportunistic routing and network coding protocols. Comparing Deluge and Rateless Deluge, Deluge's network coding counterpart, we find that κ can predict which of the two is best suited for a given environment. For example, irrespective of the packet reception ratios of the links, if the average κ of the link pairs is very high (close to 1.0), then using a protocol that does not code works better than using a network coding protocol. Measuring κ on several 802.15.4 and 802.11 testbeds, we find that it varies significantly across network topologies and link layers. κ can be a metric for quantifying what kind of a network is present and help decide which protocols to use for that network.

https://sing.stanford.edu/pubs/sing-09-02.pdf

The κ factor: inferring protocol performance using inter-link reception correlation

We present the Whirlpool Routing Protocol (WARP), which efficiently routes data to a node moving within a static mesh. The key insight in WARP's design is that data traffic can use an existing routing gradient to efficiently probe the topology, repair the routing gradient, and communicate these repairs to nearby nodes.Using simulation, controlled testbeds, and real mobility experiments, we find that using the data plane for topology maintenance is highly effective due to the incremental nature of mobility updates. WARP leverages the fact that converging flows at a destination make the destination have the region of highest traffic. We provide a theoretical basis for WARP's behavior, defining an "update area" in which the topology must adjust when a destination moves. As long as packets arrive at a destination before it moves outside of the update area, WARP can repair the topology using the data plane. Compared to existing protocols, such as DYMO and HYPER, WARP's packet drop rate is up to 90% lower while sending up to 90% fewer packets.

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Whirlpool routing for mobility

As data continues to be generated at exponentially growing rates in heterogeneous formats, fast analytics to extract meaningful information is becoming increasingly important. Systems widely use in-memory caching as one of their primary techniques to speed up data analytics. However, caches in data analytics systems cannot rely on simple caching policies and a fixed data layout to achieve good performance. Different datasets and workloads require different layouts and policies to achieve optimal performance.This paper presents ReCache, a cache-based performance accelerator that is reactive to the cost and heterogeneity of diverse raw data formats. Using timing measurements of caching operations and selection operators in a query plan, ReCache accounts for the widely varying costs of reading, parsing, and caching data in nested and tabular formats. Combining these measurements with information about frequently accessed data fields in the workload, ReCache automatically decides whether a nested or relational column-oriented layout would lead to better query performance. Furthermore, ReCache keeps track of commonly utilized operators to make informed cache admission and eviction decisions. Experiments on synthetic and real-world datasets show that our caching techniques decrease caching overhead for individual queries by an average of 59%. Furthermore, over the entire workload, ReCache reduces execution time by 19-75% compared to existing techniques.

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ReCache: reactive caching for fast analytics over heterogeneous data

The Grid Analysis Environment (GAE), which is a continuation of the CAIGEE project [5], is an effort to develop, integrate and deploy a system for distributed analysis. The current focus within the GAE is on the CMS experiment [1] however the GAE design abstracts from any specific scientific experiment and focuses on scientific analysis in general. The GAE project does not intend to reinvent services, but rather to integrate existing services into a collaborative system of web services.

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Grid Enabled Analysis : Architecture, prototype and status

Large scientific collaborations are moving towards service oriented architectures for implementation and deployment of globally distributed systems. Clarens is a high performance, easy to deploy Web service framework that supports the construction of such globally distributed systems. This paper discusses some of the core functionality of Clarens that the authors believe is important for building distributed systems based on Web services that support scientific analysis.

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Tahir Azim

Papers

The κ factor: inferring protocol performance using inter-link reception correlation

Whirlpool routing for mobility

ReCache: reactive caching for fast analytics over heterogeneous data

Grid Enabled Analysis : Architecture, prototype and status

The Clarens Web service framework for distributed scientific analysis in grid projects