Analysis of Energy-Efficient Connected Target Coverage Algorithms for Industrial Wireless Sensor Networks
TL;DR: Characteristics of four recent energy-efficient coverage strategies are analyzed by carefully choosing four representative connected coverage algorithms to provide IWSNs designers with useful insights to choose an appropriate coverage strategy and achieve expected performance indicators in different industrial applications.
Abstract: Recent breakthroughs in wireless technologies have greatly spurred the emergence of industrial wireless sensor networks (IWSNs). To facilitate the adaptation of IWSNs to industrial applications, concerns about networks’ full coverage and connectivity must be addressed to fulfill reliability and real-time requirements. Although connected target coverage (CTC) algorithms in general sensor networks have been extensively studied, little attention has been paid to reveal both the applicability and limitations of different coverage strategies from an industrial viewpoint. In this paper, we analyze characteristics of four recent energy-efficient coverage strategies by carefully choosing four representative connected coverage algorithms: 1) communication weighted greedy cover; 2) optimized connected coverage heuristic; 3) overlapped target and connected coverage; and 4) adjustable range set covers. Through a detailed comparison in terms of network lifetime, coverage time, average energy consumption, ratio of dead nodes, etc., characteristics of basic design ideas used to optimize coverage and network connectivity of IWSNs are embodied. Various network parameters are simulated in a noisy environment to obtain the optimal network coverage. The most appropriate industrial field for each algorithm is also described based on coverage properties. Our study aims to provide IWSNs designers with useful insights to choose an appropriate coverage strategy and achieve expected performance indicators in different industrial applications.
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Cites background from "Analysis of Energy-Efficient Connec..."
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...Recent advances in ICT have fueled a plethora of innovations in various areas, including networking, caching, and computing [1, 3–5]....
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TL;DR: The intent of this work is to present the state-of-the-art of various aspects of wireless body area sensor network, its communication architectures, wireless body Area sensor network applications, programming frameworks, security issues, and energy-efficient routing protocols.
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TL;DR: The prime agenda of the presented paper is to categorize various coverage techniques into four major parts: computational geometry- based techniques, force-based techniques, grid-based Techniques, and metaheuristic-Based techniques.
Abstract: Wireless sensor networks (WSNs) have been considered as one of the fine research areas in recent years because of vital role in numerous applications. To process the extracted data and transmit it to the various location, a large number of nodes must be deployed in a proper way because deployment is one of the major issues in WSNs. Hence, the minimum number of node deployment to attain full coverage is of enormous significance for research. The prime agenda of the presented paper is to categorize various coverage techniques into four major parts: computational geometry-based techniques, force-based techniques, grid-based techniques, and metaheuristic-based techniques. Additionally, several comparisons amid these schemes are provided in view of their benefits and drawbacks. Our discussion weighs on the classification of coverage, practical challenges in the deployment of WSNs, sensing models, and research issues in WSNs. Moreover, a detailed analysis of performance metrics and comparison among various WSNs simulators is listed. In conclusion, standing research issues along with potential work guidelines are discussed.
120 citations
References
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TL;DR: The aim is to provide a contemporary look at the current state of the art in IWSNs and discuss the still-open research issues in this field and to make the decision-making process more effective and direct.
Abstract: In today's competitive industry marketplace, the companies face growing demands to improve process efficiencies, comply with environmental regulations, and meet corporate financial objectives. Given the increasing age of many industrial systems and the dynamic industrial manufacturing market, intelligent and low-cost industrial automation systems are required to improve the productivity and efficiency of such systems. The collaborative nature of industrial wireless sensor networks (IWSNs) brings several advantages over traditional wired industrial monitoring and control systems, including self-organization, rapid deployment, flexibility, and inherent intelligent-processing capability. In this regard, IWSN plays a vital role in creating a highly reliable and self-healing industrial system that rapidly responds to real-time events with appropriate actions. In this paper, first, technical challenges and design principles are introduced in terms of hardware development, system architectures and protocols, and software development. Specifically, radio technologies, energy-harvesting techniques, and cross-layer design for IWSNs have been discussed. In addition, IWSN standards are presented for the system owners, who plan to utilize new IWSN technologies for industrial automation applications. In this paper, our aim is to provide a contemporary look at the current state of the art in IWSNs and discuss the still-open research issues in this field and, hence, to make the decision-making process more effective and direct.
1,595 citations
"Analysis of Energy-Efficient Connec..." refers background in this paper
...I. INTRODUCTION R ECENTLY, industrial wireless sensor networks(IWSNs), which consist of many sensor nodes, have evolved as a powerful tool for an industrial automation system (IAS)....
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...IWSNs also need to be resistant to noisy environments [2], a requirement not commonly considered in consumer network design....
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...Different from traditional IAS realized through wired communications, sensors of an IWSN can be installed on industrial equipment and monitor critical parameters to ensure normal operations [1]....
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...Due to the absence of cables, the use of inexpensive and tiny sensor nodes contributes to the flexibility and energy efficiency of IAS [2]....
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...As a leading application of IAS, supervisory control and data acquisition require a high level of reliability in terms of data integrity and timely reporting [3]....
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13 Mar 2005
TL;DR: An efficient method to extend the sensor network life time by organizing the sensors into a maximal number of set covers that are activated successively, and designing two heuristics that efficiently compute the sets, using linear programming and a greedy approach are proposed.
Abstract: A critical aspect of applications with wireless sensor networks is network lifetime. Power-constrained wireless sensor networks are usable as long as they can communicate sensed data to a processing node. Sensing and communications consume energy, therefore judicious power management and sensor scheduling can effectively extend network lifetime. To cover a set of targets with known locations when ground access in the remote area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The lack of precise sensor placement is compensated by a large sensor population deployed in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node (e.g. cluster head) for processing. In this paper we propose un efficient method to extend the sensor network life time by organizing the sensors into a maximal number of set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while all other nodes are in a low-energy sleep mode. By allowing sensors to participate in multiple sets, our problem formulation increases the network lifetime compared with related work [M. Cardei et al], that has the additional requirements of sensor sets being disjoint and operating equal time intervals. In this paper we model the solution as the maximum set covers problem and design two heuristics that efficiently compute the sets, using linear programming and a greedy approach. Simulation results are presented to verify our approaches.
1,046 citations
"Analysis of Energy-Efficient Connec..." refers methods in this paper
...In [16], a maximum covers algorithm using mixed integer programming (MC-MIP) was proposed....
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TL;DR: This paper proposes an efficient method to extend the sensor network operational time by organizing the sensors into a maximal number of disjoint set covers that are activated successively, and designs a heuristic that computes the sets.
Abstract: A critical aspect of applications with wireless sensor networks is network lifetime. Battery-powered sensors are usable as long as they can communicate captured data to a processing node. Sensing and communications consume energy, therefore judicious power management and scheduling can effectively extend operational time. To monitor a set of targets with known locations when ground access in the monitored area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The loss of precise sensor placement would then be compensated by a large sensor population density in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node for processing. In this paper we propose an efficient method to extend the sensor network operational time by organizing the sensors into a maximal number of disjoint set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while nodes from all other sets are in a low-energy sleep mode. In this paper we address the maximum disjoint set covers problem and we design a heuristic that computes the sets. Theoretical analysis and performance evaluation results are presented to verify our approach.
918 citations
"Analysis of Energy-Efficient Connec..." refers methods in this paper
...In [17], a greedy algorithm designed for maximum set covers (MSC-Greedy) was...
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TL;DR: The coverage problem is classified from different angles, the evaluation metrics of coverage control algorithms are described, the relationship between coverage and connectivity is analyzed, typical simulation tools are compared, and research challenges and existing problems in this area are discussed.
523 citations
"Analysis of Energy-Efficient Connec..." refers background in this paper
...This is often referred to as the connected target coverage (CTC) problem, where each discrete target in the network must be within the sensing range of at least one sensor node, and where at least one routing path must be found to connect any source node to the sink node [5]....
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TL;DR: Experimental results show that FAF-EBRM outperforms LEACH and EEUC, which balances the energy consumption, prolongs the function lifetime and guarantees high QoS of WSN.
Abstract: As an important part of industrial application (IA), the wireless sensor network (WSN) has been an active research area over the past few years. Due to the limited energy and communication ability of sensor nodes, it seems especially important to design a routing protocol for WSNs so that sensing data can be transmitted to the receiver effectively. An energy-balanced routing method based on forward-aware factor (FAF-EBRM) is proposed in this paper. In FAF-EBRM, the next-hop node is selected according to the awareness of link weight and forward energy density. Furthermore, a spontaneous reconstruction mechanism for local topology is designed additionally. In the experiments, FAF-EBRM is compared with LEACH and EEUC, experimental results show that FAF-EBRM outperforms LEACH and EEUC, which balances the energy consumption, prolongs the function lifetime and guarantees high QoS of WSN.
436 citations
"Analysis of Energy-Efficient Connec..." refers background in this paper
...arguably the main constraint of wireless sensor nodes [6]–[8]....
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