Maximizing the Lifetime of Query-Based Wireless Sensor Networks
TL;DR: In this paper, the authors consider the problem of maximizing the lifetime of a query-based wireless sensor network in which all of the sensor nodes are both producers and consumers of network resources.
Abstract: We consider the problem of maximizing the lifetime of a query-based wireless sensor network in which all of the sensor nodes are both producers and consumers of network resources. Of particular concern is the problem of selecting a common transmission range for all of the sensor nodes, the resource replication level (or time-to-live counter), and the active/sleep schedule of nodes while satisfying connectivity and quality-of-service constraints. To this end, we first formulate a general, mixed-integer programming model that selects the optimal operating parameters in each period of a finite planning horizon. Subsequently, we examine in detail specific connectivity and quality-of-service constraints that can be considered within this framework. Due to the complexity of the model, we formulate an alternative linearized version that can be solved more efficiently. Additionally, we devise a simple algorithm to solve a special case of the problem when alive nodes are always active. Computational results indicate that the maximum attainable lifetime can be significantly improved by adjusting the key operating parameters as sensor nodes fail over time due to energy depletion.
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TL;DR: An energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query and the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time.
152 citations
Cites background from "Maximizing the Lifetime of Query-Ba..."
...minor modification of some of the results in [27] by enforcing a detection of the objects within the proximity of the tracked-object (o1) and properly updating the answer when needed....
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TL;DR: A compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit, and outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures.
Abstract: The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems.
54 citations
Cites methods from "Maximizing the Lifetime of Query-Ba..."
...Besides, scheduling the activation/sleeping periods has been also considered to maximize the lifetime of a query-based WSN in [21]....
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TL;DR: An alternative approach that iteratively solves energy balancing problems is proposed, and analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.
Abstract: Many physical systems, such as water/electricity distribution networks, are monitored by battery-powered wireless-sensor networks (WSNs). Since battery replacement of sensor nodes is generally difficult, long-term monitoring can be only achieved if the operation of the WSN nodes contributes to long WSN lifetime. Two prominent techniques to long WSN lifetime are 1) optimal sensor activation and 2) efficient data gathering and forwarding based on compressive sensing. These techniques are feasible only if the activated sensor nodes establish a connected communication network (connectivity constraint), and satisfy a compressive sensing decoding constraint (cardinality constraint). These two constraints make the problem of maximizing network lifetime via sensor node activation and compressive sensing NP-hard. To overcome this difficulty, an alternative approach that iteratively solves energy balancing problems is proposed. However, understanding whether maximizing network lifetime and energy balancing problems are aligned objectives is a fundamental open issue. The analysis reveals that the two optimization problems give different solutions, but the difference between the lifetime achieved by the energy balancing approach and the maximum lifetime is small when the initial energy at sensor nodes is significantly larger than the energy consumed for a single transmission. The lifetime achieved by energy balancing is asymptotically optimal, and that the achievable network lifetime is at least 50% of the optimum. Analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.
41 citations
Cites background from "Maximizing the Lifetime of Query-Ba..."
...In [9], the energy consumptions of the nodes in sleep mode are assumed to be 0, whereas that of the active nodes follows an independent and identical distribution....
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...The sensor nodes can also be put into sleep or idle mode to save energy [9], [10]....
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TL;DR: In this paper, a comprehensive review of the developed methods that exploit mobility of sensor nodes and/or sink(s) to effectively maximize the lifetime of an mobile wireless sensor networks (MWSNs) is presented.
39 citations
TL;DR: This paper addresses the core issue of component interaction and operation under the IoT umbrella, and presents a novel paradigm in the framework of wireless sensor networks (WSNs), to build a resilient architecture that decouples operational mandates from the nodes.
Abstract: As competing industries delve into the Internet of Things (IoT), a growing challenge of interoperability and redundant deployments is magnified Specifically, as we augment more “things” in the IoT fabric, how will these components interact across their heterogeneity, let alone collaborate In this paper, we address the core issue of component interaction and operation under the IoT umbrella We present our contribution in the framework of wireless sensor networks (WSNs), as a founding block in the IoT More importantly, we present a novel paradigm in the design of WSNs, to build a resilient architecture that decouples operational mandates from the nodes We abstract IoT things as wirelessly interfaced components, which introduce functionality physically decoupled from their devices; boosting resilience, dynamicity, and resource utilization This approach dissects the study of any IoT nodal capacity to its “connected” components, and empowers dynamic associativity between things to serve varying functional requirements and levels It also enables reintroducing only the components required to suffice for network operation, or only those needed to meet a new requirement More importantly, critical resources in the network will be shared within their neighborhoods Thus network lifetime will relate to functional cliques of dynamic IoT nodes, rather than individual networks We evaluate the cost effectiveness and resilience of our paradigm via simulations
38 citations
References
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TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
17,936 citations
TL;DR: A survey of state-of-the-art routing techniques in WSNs is presented and the design trade-offs between energy and communication overhead savings in every routing paradigm are studied.
Abstract: Wireless sensor networks consist of small nodes with sensing, computation, and wireless communications capabilities. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy awareness is an essential design issue. Routing protocols in WSNs might differ depending on the application and network architecture. In this article we present a survey of state-of-the-art routing techniques in WSNs. We first outline the design challenges for routing protocols in WSNs followed by a comprehensive survey of routing techniques. Overall, the routing techniques are classified into three categories based on the underlying network structure: flit, hierarchical, and location-based routing. Furthermore, these protocols can be classified into multipath-based, query-based, negotiation-based, QoS-based, and coherent-based depending on the protocol operation. We study the design trade-offs between energy and communication overhead savings in every routing paradigm. We also highlight the advantages and performance issues of each routing technique. The article concludes with possible future research areas.
4,701 citations
28 Sep 2002
TL;DR: An in-depth study of applying wireless sensor networks to real-world habitat monitoring and an instance of the architecture for monitoring seabird nesting environment and behavior is presented.
Abstract: We provide an in-depth study of applying wireless sensor networks to real-world habitat monitoring. A set of system design requirements are developed that cover the hardware design of the nodes, the design of the sensor network, and the capabilities for remote data access and management. A system architecture is proposed to address these requirements for habitat monitoring in general, and an instance of the architecture for monitoring seabird nesting environment and behavior is presented. The currently deployed network consists of 32 nodes on a small island off the coast of Maine streaming useful live data onto the web. The application-driven design exercise serves to identify important areas of further work in data sampling, communications, network retasking, and health monitoring.
4,623 citations
"Maximizing the Lifetime of Query-Ba..." refers background in this paper
...The emergence of WSNs is prevalent in such diverse applications as military, environmental, health care, industrial safety, infrastructure security and residential use (see [Schurgers and Srivastava 2001; Welsh 2004; Flammini et al. 2010; Mainwaring et al. 2002; Milenkovic et al. 2006; Gungor and Hancke 2009; Mishra et al. 2006; Herring and Kaplan 2000] for representative samples of applications)....
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Book•
01 Jan 1983
TL;DR: This book provides an introduction to statistical methods for analysing data in the form of spatial point distributions, described in intuitive terms and illustrated by many applications to real data drawn from the biological and biomedical sciences.
Abstract: A spatial point pattern is a set of data consisting of a map of points These points might represent, for example, cases of a disease in a human or animal population, or trees in a forest, or cells in a microscopic tissue section This book provides an introduction to statistical methods for analysing data in the form of spatial point distributions Theoretical results are described in intuitive terms and statistical methods are illustrated by many applications to real data drawn from the biological and biomedical sciences
2,911 citations
TL;DR: This paper proposes S-MAC, a medium access control (MAC) protocol designed for wireless sensor networks that enables low-duty-cycle operation in a multihop network and reveals fundamental tradeoffs on energy, latency and throughput.
Abstract: This paper proposes S-MAC, a medium access control (MAC) protocol designed for wireless sensor networks. Wireless sensor networks use battery-operated computing and sensing devices. A network of these devices will collaborate for a common application such as environmental monitoring. We expect sensor networks to be deployed in an ad hoc fashion, with nodes remaining largely inactive for long time, but becoming suddenly active when something is detected. These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 802.11 in several ways: energy conservation and self-configuration are primary goals, while per-node fairness and latency are less important. S-MAC uses a few novel techniques to reduce energy consumption and support self-configuration. It enables low-duty-cycle operation in a multihop network. Nodes form virtual clusters based on common sleep schedules to reduce control overhead and enable traffic-adaptive wake-up. S-MAC uses in-channel signaling to avoid overhearing unnecessary traffic. Finally, S-MAC applies message passing to reduce contention latency for applications that require in-network data processing. The paper presents measurement results of S-MAC performance on a sample sensor node, the UC Berkeley Mote, and reveals fundamental tradeoffs on energy, latency and throughput. Results show that S-MAC obtains significant energy savings compared with an 802.11-like MAC without sleeping.
2,843 citations
"Maximizing the Lifetime of Query-Ba..." refers background in this paper
...First, it is well known that remarkable energy conservation can be achieved by turning-off the communication capability (radio) of a node during idle time-slots (see [Akyildiz et al. 2002; Sinha and Chandrakasan 2001; Ye et al. 2004])....
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