Energy efficient path selection for mobile sink and data gathering in wireless sensor networks

Mobile sink (MS) has drawn significant attention for solving hot spot problem (also known as energy hole problem) that results from multihop data collection using static sink in wireless sensor networks (WSNs). MS is regarded as a potential solution towards this problem as it significantly reduces energy consumption of the sensor nodes and thus enhances network lifetime. In this paper, we first propose an algorithm for designing efficient trajectory for MS, based on rendezvous points (RPs). We next propose another algorithm for the same problem which considers delay bound path formation of the MS. Both the algorithms use k-means clustering and a weight function by considering several network parameters for efficient selection of the RPs by ensuring the coverage of the entire network. We also propose an MS scheduling technique for effective data gathering. The effectiveness of the proposed algorithms is demonstrated through rigorous simulations and comparisons with some of the existing algorithms over several performance metrics.     

Energy efficient dispatch strategy for the dual-functional mobile sink in wireless rechargeable sensor networks

Scavenging energy from radio-frequency (RF) signals has drawn significant attention in recent years. By introducing the technology of RF energy harvesting into wireless sensor networks, a new type of network named mobile data gathering based wireless rechargeable sensor network (MGWRSN) is considered in this paper. In the MGWRSN, a dual-functional mobile sink (MS) which has the abilities of data collecting and RF energy generating is employed. Data sensed by sensor nodes is gathered at several selected head nodes (HNs). Through using the RF energy supplied by the MS, the HNs deliver the gathered data to the MS arriving at the corresponding rendezvous points (RPs). In our works, the network energy consumption model of the MGWRSN is built, and the energy efficient dispatch strategy for the MS is studied, aiming at cutting down the total network energy consumption. For the simplest case, i.e., the one-HN MGWRSN, the optimal location of the RP is provided to minimize the total network energy consumption. After that, the researches are extended into the case of multi-HN MGWRSN and a heuristic dispatch strategy named HEEDS is proposed. Theoretical analysis and numerical results show that: (1) in the one-HN MGWRSN, the optimal location of the RP is close related to the data bulk to be transmitted, the unit mobility energy cost, the required bit error rate, the modulation scheme, and the departure position of the MS; (2) comparing with the existing algorithm WRP which directly dispatches the MS to the locations of HNs to collect data, the proposed strategy HEEDS is shown to be more energy efficient. Moreover, when a high energy transfer power is available at the MS, HEEDS renders shorter packet delay compared to WRP.     

Band-based geocasting for mobile sink groups in wireless sensor networks

This paper studies on delivery-guaranteed and effective data dissemination for mobile sink groups in wireless sensor networks. A mobile sink group denotes a set of tightly coupled mobile sinks for team collaborations such as a team of firefighters and a group of solders. The mobile sinks have a group movement feature. They thus randomly move in personal spaces as well as collectively move together as a single entity. To support such group mobility, previous studies provide circle-based protocols determining successive circular areas of a group continuously moving, and then propagate data in the areas by flooding. However, since a group is still moving during decision of each circle, they may cause asynchrony between circles and actual group positions. Eventually, it could harm reachability and energy-efficiency. We therefore propose a novel data dissemination protocol using motion properties of a mobile sink group: slowly varying and streamlike movement. By the slowly varying constraint, the protocol predictively and effectively delivers data to a group through a band of sensor nodes located in front of the streamlike trajectory of the group.     

An image processing inspired mobile sink solution for energy efficient data gathering in wireless sensor networks

Wireless Networks - This paper presents a gradient-based multi-hop clustering protocol combined with a mobile sink (MS) solution for efficient data gathering in wireless sensor networks. The main...     

EMS: Efficient mobile sink scheduling in wireless sensor networks

Sink scheduling, in the form of scheduling multiple sinks among the available sink sites to relieve the level of traffic burden, is shown to be a promising scheme in wireless sensor networks (WSNs). However, the problem of maximizing the network lifetime via sink scheduling remains quite a challenge since routing issues are tightly coupled. Previous approaches on this topic either suffer from poor performance due to a lack of joint considerations, or are based on relaxed constraints. Therefore, in this paper, we aim to fill in the research blanks. First, we develop a novel notation Placement Pattern (PP) to bound time-varying routes with the placement of sinks. This bounding technique transforms the problem from time domain into pattern domain, and thus, significantly decreases the problem complexity. Then, we formulate this optimization in a pattern-based way and create an efficient Column Generation (CG) based approach to solve it. Simulations not only demonstrate the efficiency of the proposed algorithm but also substantiate the importance of sink mobility for energy-constrained WSNs.     

A mobile sink path planning for wireless sensor networks based on priority‐ordered dependent nonparametric trees

The professional design of the routing protocols with mobile sink(s) in wireless sensor networks (WSNs) is important for many purposes such as maximizing energy efficiency, increasing network life, and evenly distributing load balance across the network. Moreover, mobile sinks ought to first collect data from nodes which have very important and dense data so that packet collision and loss can be prevented at an advanced level. For these purposes, the present paper proposes a new mobile path planning protocol by introducing priority‐ordered dependent nonparametric trees (PoDNTs) for WSNs. Unlike traditional clustered or swarm intelligence topology‐based routing methods, a topology which has hierarchical and dependent infinite tree structure provides a robust link connection between nodes, making it easier to reselect ancestor nodes (ANs). The proposed priority‐ordered infinite trees are sampled in the specific time frames by introducing new equations and hierarchically associated with their child nodes starting from the root node. Hence, the nodes with the highest priority and energy that belong to the constructed tree family are selected as ANs with an opportunistic approach. A mobile sink simply visits these ANs to acquire data from all nodes in the network and return to where it started. As a result, the route traveled is assigned as the mobile path for the current round. We have performed comprehensive performance analysis to illustrate the effectiveness of the present study using NS‐2 simulation environment. The present routing protocol has achieved better results than the other algorithms over various performance metrics.     

Virtual grid-based rendezvous point and sojourn location selection for energy and delay efficient data acquisition in wireless sensor networks with mobile sink

Wireless Networks - Rendezvous points (RPs) based data acquisition methods are widely accepted as the solution for data acquisition delay/latency problem. In these methods, RPs are a subset of...     

A myopic mobile sink migration strategy for maximizing lifetime of wireless sensor networks

Network lifetime maximization is challenging particularly for large-scale wireless sensor networks. The sensor nodes near the sink node tend to suffer high energy consumption due to heavy traffic relay operations, becoming vulnerable to energy depletion. The rationale of the sink mobility approach is that as the sink node moves around, such risk of energy depletion at some nodes can be alleviated. In this paper, we first obtain the optimal mobile sink sojourning pattern by solving a linear programming model and then we mathematically analyze why the optimal solution exhibits such sojourning pattern. We use the insights from this analysis to design a simple practical heuristic algorithm for sink mobility, which utilizes only local information. Our heuristic is very different from the existing algorithms which often use the traffic volume as the main decision factor, in that we consider the variance of residual energy of neighboring sensor nodes. The simulation results show that our scheme achieves near-optimal network lifetime even with the relatively low moving speed of the mobile sink.     

无线传感器网络中能量高效的基站位置优选算法

将基站位置选择及节能路由优化联合考虑,定义了最短路径树剖分,分析了二维空间中剖分单元的结构与相邻剖分单元搜索算法,并设计了3种启发式算法.通过仿真实验对算法性能进行了分析与对比,结果表明所提出的启发式算法的性能有效地接近或者收敛于全局最优解.     

Multihop data gathering in wireless sensor networks with a mobile sink

Network performance can be improved by using a mobile sink (MS) to collect sensed data in a wireless sensor network. In this paper, we design an efficient trajectory for MS, collecting data from sensor nodes in a multihop fashion, with the aim of prolonging the network lifetime. Considering event‐driven applications, we present an approach to jointly determine the optimal trajectory for MS and data paths and transmission rates from source nodes to MS, without considering any rendezvous points. In these applications, an MS is supposed to harvest the data from source nodes in a given time‐slot. We first show that this problem is in form of a mixed integer nonlinear programming model, which is NP‐hard. Then, to achieve an approximate solution, we divide the mentioned problem into 2 simple subproblems. In fact, after determining an approximate zone for the trajectory of MS, the optimal data paths and transmission rates from source nodes to the MS are obtained through a mathematical optimization model. Finally, to illustrate the efficiency of the proposed approach, we compare the performance of our algorithm to an rendezvous point–based and also the state‐of‐the‐art approach in different scenarios.     

Energy efficient rendezvous points based routing technique using multiple mobile sink in heterogeneous wireless sensor networks

Wireless Networks - In recent decades, Sensor nodes (SNs) are used in numerous uses of heterogeneous wireless sensor networks (HWSNs) to obtain a variety of sensing data sources. Sink mobility...     

A novel approach for void avoidance in wireless sensor networks

We propose a novel approach to treat the problem of void avoidance in sensor networks. In our approach, the choice of the forwarding region around a discovered void is oriented by the locations of (i) the sender node, (ii) the void center and (iii) the destination node. Three void‐avoidance schemes are described which use the same void‐discovery algorithm. Based on an oriented face routing, the first scheme is used by the boundary nodes to prevent packets from dropping. The second scheme is used by sender nodes located n‐hops away from the void boundary to launch a preventive rerouting. The third scheme tends to protect packets from both the concave zones and the boundaries nodes of voids. Proposed schemes are simple to implement. They save network resources and could be easily associated with existing geographic routing protocols. Simulation results show the efficiency of the proposed approach. Copyright © 2009 John Wiley & Sons, Ltd.     

无线传感器网络中基于链路质量的路径延时分析

基于链路质量给出了路径满足实时性概率的上界,并证明了计算其上界的时间复杂度为指数级。另外在考虑链路质量的基础上,提出了一种在给定的延迟阈值下最大化端到端数据分组发送成功概率的贪心算法(RROP)。根据给定的延迟阈值和链路质量,RROP算法通过设置每跳链路的最大重传次数来优化端到端数据分组发送成功的概率。证明该算法能够在多项式时间内找到最优解并且通过该最优解获得路径满足实时性概率的一个近似最优的下界。实验结果表明给出的路径延迟分析上界和下界是准确的,并且提出的RROP算法在节省能量和满足实时性上比传统的方法能够获得高出10%以上的性能。     

Distributed trajectory design for data gathering using mobile sink in wireless sensor networks

Several studies have demonstrated the benefits of using a mobile sink (MS) to reduce energy consumption resulting from multi-hop data collection using a static sink in wireless sensor networks (WSNs). However, using MS may increase data delivery latency as it needs to visit each sensor node in the network to collect data. This is a critical issue in delay-sensitive applications where all sensed data must be gathered within a given time constraint. In this paper, we propose a distributed data gathering protocol utilizing MS for WSNs. The proposed protocol designs a trajectory for the MS, which minimizes energy consumption and delay. Our protocol operates in four main phases: data sensing, rendezvous point (RP) selection, trajectory design, and data gathering. In data sensing, a number of deployed sensor nodes keep sensing the target field for a specific period of time to capture events. Then, using a cluster-based RP selection algorithm, some sensor nodes are selected to become RPs based on local information. The selected RPs are then used to determine a trajectory for the MS. To do so, we propose three trajectory design algorithms that support different types of applications, namely reduced energy path (REP), reduced delay path (RDP), and delay bound path (DBP). The MS moves through the constructed path to accomplish its data gathering according to an effective scheduling technique that is introduced in this work. We validate the proposed protocol via extensive simulations over several metrics such as energy, delay, and time complexity.     

A novel approach based on bio-inspired efficient clustering algorithm for large-scale heterogeneous wireless sensor networks

In large-scale heterogeneous wireless sensor networks (WSNs), clustering is particularly significant for lowering sensor nodes (SNs) energy consumption and creating algorithm more energy efficient. The selection of cluster heads (CHs) is a crucial task in the clustering method. In this paper, optimised K-means clustering algorithm and optimised K-means based modified intelligent CH selection based on BFOA for large-scale network (lar-OK-MICHB) is hybridised for CH selection process. Here, we utilised the extended capabilities of OK-MICHB algorithm for large-scale network. Furthermore, in many applications where energy is a primary constraint, such as military surveillance and natural disaster prediction, the stability region is also a significant factor, with a longer network lifespan being a primary requirement. In the proposed approach, only the CH selection is made after every round in place of cluster and CH change as done in conventional hierarchical algorithm. The simulation results reveal that, while keeping the distributive structure of WSNs, suggested lar-OKMIDEEC can locate real greater leftover energy nodes for selection of CH without utilising randomise or estimated procedures. Furthermore, as compared with the multi-level MIDEEC protocol, this offers a larger stability region with 68.96% increment, more consistent selection of CH in every round, and greater packets (i.e., in numbers) received at the base station (BS) with a longer network lifetime with 327% increment.     

多基站无线传感器网络中能量高效的基站位置优选算法

结合节能路由算法,研究以最小化网络总能耗为目标的基站位置选择问题.将基站位置候选集为有限集的情形化归为整数线性规划问题,并将候选集为全空间的情形化归为非线性规划问题.由于问题的NP-完全性质,分别针对2种情形设计了相应的启发式算法.通过仿真实验对所提出算法的性能进行验证,结果表明所提算法性能接近最优解.     

New path planning model for mobile anchor-assisted localization in wireless sensor networks

As event detection is one of the main purposes of using wireless sensor networks (WSNs), the nodes location is essential to determine the location of that event when it occurs. Many localization models have been proposed in the literature. One of the solutions is to deploy a set of location-aware nodes, called anchors, to exchange information with the other nodes in order to help estimate their own location. Another promising proposal involves replacing these sets of anchors with only one mobile anchor. While this proposal seems to provide favorable results, it brings new challenges. The main challenge is to find an optimal path for the mobile anchor to follow while taking into account the need to provide highly accurate data and more localizable nodes in less time and with less energy. In this paper, we introduced a new static path planning model for mobile anchor-assisted localization in WSNs. Our proposed model guarantees that all nodes are able to receive the localization information, thus, estimate their own location with higher localization accuracy in comparison to similar static models. Moreover, this model overcomes the problem of collinearity and takes into account the metrics of precision and energy consumption as well as accuracy, localization ratio and the path length of the mobile anchor.     

Reinforcement learning movement path for multiple mobile sinks in wireless sensor networks

Mobile sink nodes play a very active role in wireless sensor network (WSN) routing. Because hiring these nodes can decrease the energy consumption of each node, end-to-end delay, and network latency significantly. Therefore, mobile sinks can soar the network lifetime dramatically. Generally, there are three movement paths for a mobile sink, which are as follows: (1) Random/stochastic, (2) controlled, and (3) fixed/ predictable/predefined paths. In this paper, a novel movement path is introduced as a fourth category of movement paths for mobile sinks. This path is based on deep learning, so a mobile sink node can go to the appropriate region that has more data at a suitable time. Thereupon, WSN routing can improve very much in terms of end-to-end delay, network latency, network lifetime, delivery ratio, and energy efficiency. The new proposed routing suggests a reinforcement learning movement path (RLMP) for multiple mobile sinks. The network in the proposed work consists of a couple of regions; each region can be employed for a special purpose, so this method is hired for any application and any size of the network. All simulations in this paper are done by network simulator 3 (NS-3). The experimental results clearly show that the RLMP overcomes other approaches by at least 32.48% in the network lifetime benchmark.     

A novel efficient clustering protocol for energy harvesting in wireless sensor networks

Wireless Networks - The utilization of wireless sensor networks (WSNs) is proliferating in our daily life. It depends on the environmental monitoring such as weather tracking, battled field, etc....     

On reducing delay in mobile data collection based wireless sensor networks

In a wireless sensor network, battery power is a limited resource on the sensor nodes. Hence, the amount of power consumption by the nodes determines the node and network lifetime. This in turn has an impact on the connectivity and coverage of the network. One way to reduce power consumed is to use a special mobile data collector (MDC) for data gathering, instead of multi-hop data transmission to the sink. The MDC collects the data from the nodes and transfers it to the sink. Various kinds of MDC approaches have been explored for different assumptions and constraints. But in all the models proposed, the data latency is usually high, due to the slow speed of the mobile nodes. In this paper, we propose a new model of mobile data collection that reduces the data latency significantly. Using a combination of a new touring strategy based on clustering and a data collection mechanism based on wireless communication, we show that the delay can be reduced significantly without compromising on the advantages of MDC based approach. Using extensive simulation studies, we analyze the performance of the proposed approach and show that the packet delay reduces by more than half when compared to other existing approaches.