Optimal Data Gathering Paths and Energy Balance Mechanisms in Wireless Networks

Jarry, Aubin; Leone, Pierre; Nikoletseas, Sotiris; Rolim, José D. P.

doi:10.1007/978-3-642-13651-1_21

Cited by 17 publications

(14 citation statements)

References 19 publications

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“…The main objective (e.g. [2,3,9,10]) is to plan a proper order, usually with a total path length as short as possible, for the robot to visit waypoints of all clusters and gather all data from every sensor.…”

Section: B Problemmentioning

confidence: 99%

“…In unmanned planet exploration, sensors play an important role; a number of sensors are often distributed over a geographic area with the capacity to communicate with each other across limited distances. As a result, numerous studies focus on gathering data by sensor networks [1][2][3][4]. There are multiple ways to collect the data in a wireless sensor network.…”

mentioning

confidence: 99%

“…1). Recent studies [9][10][11] have shown that for nodes with different densities, the use of a data mule indeed significantly reduces data latency and energy consumption at sensor nodes compared with the commonly-used multi-hop forwarding approach, thus prolonging network lifetime [3]. However, a fatal drawback due to the use of a data mule is the increase of data delivery latency in WSN.…”

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confidence: 99%

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Robot Routing Using Clustering-Based Parallel Genetic Algorithm with Migration

Chiu¹,

Liu²,

Yang³

2014

JCEN

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Abstract-Wireless sensor network (WSN) technology is widely used in environment monitoring, health care, surveillance systems and unmanned space or planet exploration. To collect the exploration data, controllable mobility such as mobile robot called data mules can be used to help complete data gathering missions using the same sensors. This study focuses on data gathering by a mobile robot in a WSN, also referred to as a robot routing problem. It is formulated as a Traveling Salesman Problem with Neighborhoods (TSPN). Basing on the need of practical applications, a two-phase method, a clustering-based parallel genetic algorithm with migration (CBPGA), is proposed as a TSPN route computing and optimizing scheme that allows simultaneously searching of the order and locations of waypoints of clusters. The first phase of the method consists of a Euclidean-distance based clustering algorithm that assigns the nodes to clusters, and the second phase routes these clusters by planning the data collection points in the common intersection area of each cluster and the order to visit them using a genetic algorithm (GA). A travel cost reduction scheme by finding the closest waypoint in a cluster according to the visiting order is used to further shorten the path length. Simulation studies are conducted to evaluate the performance of CBPGA, by comparing it with other solutions to TSPN in WSN with identical or random sensing radii. To reveal the relative performance of each solution scheme, the effect of clustering, the role of GA and parallelism, and the integration of CBPGA in data-gathering route design are highlighted.

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Section: B Problemmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Robot Routing Using Clustering-Based Parallel Genetic Algorithm with Migration

Chiu¹,

Liu²,

Yang³

2014

JCEN

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“…Without a wake-up scheduling, choosing a forwarding node in this case turns out to be probabilistic and non-optimal. In [23], different network architectures were studied that support max-flow, and two distributed probabilistic on-line routing algorithms were proposed for energy balancing.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike the optimisation approaches in [30 -34], it looks for a joint optimality criteria from the protocol operation viewpoint by combining the factors of greedy geographic forwarding, transceiver energy consumption, nodal residual energy and link layer retransmission, in data forwarding decision at each hop, which is also different from the protocol-level solutions in [12,13,18,20,21,23]. …”

Section: Related Workmentioning

confidence: 99%

Network lifetime maximising distributed forwarding strategies in Ad Hoc wireless sensor networks

Panigrahi

Panda

et al. 2012

IET Commun.

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Abstract:The authors propose three variants of distributed and stateless forwarding strategies for wireless sensor networks, namely greedy minimum energy consumption forwarding protocol (GMFP), lifetime maximising GMFP (LM-GMFP) and variance minimising GMFP (VAR-GMFP), which aim at maximising the network lifetime while achieving a high forwarding success rate. GMFP selects a forwarding node that minimises per-packet energy consumption while maximising the forwarding progress. LM-GMFP extends the GMFP algorithm by also taking into account the remaining energy at the prospective one-hop forwarding nodes. In VAR-GMFP, on the other hand, the packet is forwarded to the next node that ensures a locally high mean and low variance of nodal remaining energy. Through simple probabilistic analysis the authors prove the intuition behind the optimum forwarding node selection for network lifetime maximisation. They then model the lifetime maximisation of a sensor network as an optimisation problem and compare the practical protocol-dependent network lifetime with the theoretical upper bound. Through extensive simulations the author demonstrate that the proposed protocols outperform the existing energy-aware protocols in terms of network lifetime and end-to-end delay.

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Energy‐efficient task allocation with quality of service provisioning for concurrent applications in multi‐functional wireless sensor network systems

Delicato

Pires

et al. 2013

Concurrency and Computation

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Multi-functional wireless sensor network (WSN) system is a new design trend of WSNs, which are evolving from dedicated application-specific systems to an integrated infrastructure that supports the execution of multiple concurrent applications. Such system offers inherent advantages in terms of cost and flexibility because it allows the effective utilization of available sensors and resource sharing among multiple applications. However, sensor nodes are very constrained in resources, mainly regarding their energy. Therefore, the usage of such resources needs to be carefully managed, and the sharing with several applications imposes new challenges in achieving energy efficiency in these networks. In order to exploit the full potential of multi-functional WSN systems, it is crucial to design mechanisms that effectively allocate tasks onto sensors so that the entire system lifetime is maximized while meeting various application requirements. However, it is likely that the requirements of different applications cannot be simultaneously met. In this paper, we present the Multi-Application Requirements Aware and Energy Efficiency algorithm as a new resource allocation heuristic for multi-functional WSN system to maximize system lifetime subject to various application requirements. The heuristic effectively deals with different quality of service parameters (possibly conflicting) trading those parameters and exploiting heterogeneity of multiple WSNs. tasks are very different from sensing tasks. For instance, the popular energy-efficient technique, Dynamic Voltage Scaling (DVS), used in distributed computing, although being potentially useful in WSNs (see for instance [16]), is not very commonly employed in real-world sensor hardware for energy conservation when performing tasks. Furthermore, the resource allocation problem addressed in other areas (e.g., grid, cluster, and wireless network) is fundamentally different from that the same problem when addressed in the WSNs context. For example, cluster and grid computing systems address issues like number of machines and task completion time, while the wireless network mostly addresses network-level issues like data rate, packet delay, throughput, and packet loss probability. Consequently, the algorithms developed for allocating multiple applications onto multi-clusters system cannot be directly applied to the multi-functional WSN system or even conventional WSNs.Several noticeable works for a single WSN with different goals have been proposed recently. In [17], Byers and Nasser introduced a framework in which the task-sensor assignment problem is modeled with notions of utility (accuracy of the collected data and its usefulness to a task) and cost (the energy consumption of activating and operating the sensors and other possible cost factors). They aimed to develop an algorithm that maximizes the utility while staying under a predefined budget (of energy). A market-based task-sensor assignment method is proposed in [18], where the tasks are allocated to those sensors that...

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Optimal Data Gathering Paths and Energy Balance Mechanisms in Wireless Networks

Cited by 17 publications

References 19 publications

Robot Routing Using Clustering-Based Parallel Genetic Algorithm with Migration

Robot Routing Using Clustering-Based Parallel Genetic Algorithm with Migration

Network lifetime maximising distributed forwarding strategies in Ad Hoc wireless sensor networks

Energy‐efficient task allocation with quality of service provisioning for concurrent applications in multi‐functional wireless sensor network systems

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