Effect of Co-channel Interference on Two-Way Untrustworthy Relay Selection

Wireless Personal Communications - In wireless sensor networks (WSNs), traffic flow congestion can reduce network performance. We propose a congestion control method for WSNs that use a cache state...     

Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

The Internet of Things (IoT) is the next big challenge for the research community where the IPv6 over low power wireless personal area network (6LoWPAN) protocol stack is a key part of the IoT. Recently, the IETF ROLL and 6LoWPAN working groups have developed new IP based protocols for 6LoWPAN networks to alleviate the challenges of connecting low memory, limited processing capability, and constrained power supply sensor nodes to the Internet. In 6LoWPAN networks, heavy network traffic causes congestion which significantly degrades network performance and impacts on quality of service aspects such as throughput, latency, energy consumption, reliability, and packet delivery. In this paper, we overview the protocol stack of 6LoWPAN networks and summarize a set of its protocols and standards. Also, we review and compare a number of popular congestion control mechanisms in wireless sensor networks (WSNs) and classify them into traffic control, resource control, and hybrid algorithms based on the congestion control strategy used. We present a comparative review of all existing congestion control approaches in 6LoWPAN networks. This paper highlights and discusses the differences between congestion control mechanisms for WSNs and 6LoWPAN networks as well as explaining the suitability and validity of WSN congestion control schemes for 6LoWPAN networks. Finally, this paper gives some potential directions for designing a novel congestion control protocol, which supports the IoT application requirements, in future work.

     

Adaptive Load-Aware Congestion Control Protocol for Wireless Sensor Networks

Congestion control in wireless sensor networks (WSNs) is crucial. In this article, we discuss congestion control and the adaptive load-aware problem for sensor nodes in WSNs. When the traffic load of a specific node exceeds its the available capacity of the node, a congestion problem occurs because of buffer memory overflow. Congestion may cause serious problems such as packet loss, the consumption of power, and low network throughput for sensor nodes. To address these problems, we propose a distributed congestion control protocol called adaptive load-aware congestion control protocol (ALACCP). The protocol can adaptively allocate the appropriate forwarding rate for jammed sensor nodes to mitigate the congestion load. Through the buffer management mechanism, the congestion index of neighboring sensor nodes, and an adjustment of the adaptive forwarding rate, the degree of congestion is alleviated markedly. The performance in allocating the forwarding rate effectively to neighboring sensor nodes also improves. The ALACCP can avoid packet loss because of traffic congestion, reduce the power consumption of nodes, and improve the network throughput. Simulation results revealed that the proposed ALACCP can effectively improve network performance and maintain the fairness of networks.     

Throughput Evaluation of a Novel Scheme to Mitigate the Congestion over WSNs

In recent years, due to fast development of wireless sensor networks (WSNs), the numbers of nodes are increasing, and their scope of applications is continuously expanding, including environmental monitoring, military and smart home applications. The power supply, memory and computing power of wireless sensor nodes are greatly hampered in WSNs so that the WSNs are classified as a task-oriented framework. This study focused on exploring problems caused by traffic congestion on the WSNs with a large amount of flow, such as packet loss, bandwidth reduction, and waste of energy on the sensor nodes. On the other hand, a cooperative strong node mechanism is presented and named as Cooperative Strong Node Mechanism, in which a threshold is set to determine whether the node traffic is over or not. When the load exceeds, the privilege of corresponding sensor nodes is upgraded so that it can command its child nodes to change the transmission path to distribute the traffic effectively. Furthermore, when the traffic exceeds preset overall network flow, new sensor nodes are added in the network to relieve the traffic. This novel proposed mechanism can not only increase network throughput and effectively prevent the occurrence from congestion problems, but is suitable for a variety of routing protocols.     

Efficient REBTA data reporting algorithm for object tracking in wireless sensor networks

Object tracking is widely referred as one of the most interesting applications of wireless sensor networks (WSNs). This application is able to detect and track objects and report information about these objects to a central base station. One of the major drawbacks in the current research in WSNs is the quality of the data reporting where the major research focus is dedicated to localization of objects; however, few of these works were concentrated on the data reporting. An efficient data reporting algorithm for object tracking in WSNs is proposed in this paper. The main objective of this paper is to enhance the WSN lifetime by achieving both minimum energy and balancing such consumption in sensor nodes during reporting operation. Furthermore, in our model, the enhancement of network reliability is considered. Finally, it reduces the effects of congestion by sufficiently utilizing the under loaded nodes to improve the network throughput. This paper formulates the object tracking problem in large‐scale WSN into 0/1 integer linear programming problem, and then proposes a reliable energy balance traffic aware approach to solve the optimization problem. From the obtained simulation results, the proposed solution has proved to be able to enhance the network performance in network lifetime, throughput, end‐to‐end delay, energy balance, and complexity for both homogeneous and heterogeneous networks.     

Optimizing routing based on congestion control for wireless sensor networks

Along with the increasing demands for the applications running on the wireless sensor network (WSN), energy consumption and congestion become two main problems to be resolved urgently. However, in most scenes, these two problems aren’t considered simultaneously. To address this issue, in this paper a solution that sufficiently maintains energy efficiency and congestion control for energy-harvesting WSNs is presented. We first construct a queuing network model to detect the congestion degree of nodes. Then with the help of the principle of flow rate in hydraulics, an optimizing routing algorithm based on congestion control (CCOR) is proposed. The CCOR algorithm is designed by constructing two functions named link gradient and traffic radius based on node locations and service rate of packets. Finally, the route selection probabilities for each path are allocated according to the link flow rates. The simulation results show that the proposed solution significantly decreases the packet loss rate and maintains high energy efficiency under different traffic load.     

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion,aggregation, and routing scheme

Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.     

A Robust and Efficient Cross-Layer Optimal Design in Wireless Sensor Networks

When using wireless sensor networks (WSNs) for data transmission, some critical respects should be considered. These respects are limited computational power, storage capability and energy consumption. To save the energy in WSNs and prolong the network lifetime, we design for the signal control input, routing selection and capacity allocation by the optimization model based on compressed sensing (CS) framework. The reasonable optimization model is decomposed into three subsections for three layers in WSNs: congestion control in transport layer, scheduling in link layer and routing algorithm in network layer, respectively. These three functions interact and are regulated by congestion ratio so as to achieve a global optimality. Congestion control can be robust and stable by CS theory that a relatively small number of the projections for a sparse signal contain most of its salient information. Routing selection is abided by fair resource allocation principle. The resources can be allocated more and more to the channel in the case of not causing more severe congestion, which can avoid conservatively reducing resources allocation for eliminating congestion. Simulation results show the stability of our algorithm, the accurate ratio of CS, the throughput, as well as the necessity of considering congestion in WSNs.     

A Queuing Theory-Enabled Dynamic Bandwidth Allocation Algorithm for a Wired-Wireless Converged Network

Multi-sink wireless sensor networks (WSNs) are being increasingly deployed in an ever-widening range of application scenarios, especially as they are reliable and exhibit low power consumption. Providing a backhaul for WSN traffic has become an important issue because the sensor data must usually be sent to the Internet or the core network. Passive optical networks (PONs) represent one next-generation access network technology which is appropriate for such a backhaul, however existing research appears to have concentrated on either WSN performance or PON performance, without considering the overall performance of both networks converged together. This paper proposes a new architecture which converges multi-sink WSNs and PONs, then provides a novel queuing-theory analysis of the converged network performance. Results from this analytical model are then used to motivate a new a DBA algorithm which optimizes grant size allocation. Numerical results show that this algorithm outperforms existing proposals when minimizing the system queue length in the converged network, while providing shorter end-to-end packet delay and higher throughput. We believe that this first resource allocation algorithm which considers the performance of both networks as one converged unit.     

无线传感器网络突发感知的拥塞控制协议(英文)

In monitoring Wireless Sensor Networks(WSNs),the traffic usually has bursty characteristics when an event occurs.Transient congestion would increase delay and packet loss rate severely,which greatly reduces network performance.To solve this problem,we propose a Burstiness-aware Congestion Control Protocol(BCCP) for wireless sensor networks.In BCCP,the backoff delay is adopted as a congestion indication.Normally,sensor nodes work on contention-based MAC protocol(such as CSMA/CA).However,when congestion occur...     

Upstream congestion control in wireless sensor networks through cross-layer optimization

Congestion in wireless sensor networks not only causes packet loss, but also leads to excessive energy consumption. Therefore congestion in WSNs needs to be controlled in order to prolong system lifetime. In addition, this is also necessary to improve fairness and provide better quality of service (QoS), which is required by multimedia applications in wireless multimedia sensor networks. In this paper, we propose a novel upstream congestion control protocol for WSNs, called priority-based congestion control protocol (PCCP). Unlike existing work, PCCP innovatively measures congestion degree as the ratio of packet inter-arrival time along over packet service time. PCCP still introduced node priority index to reflect the importance of each sensor node. Based on the introduced congestion degree and node priority index, PCCP utilizes a cross-layer optimization and imposes a hop-by-hop approach to control congestion. We have demonstrated that PCCP achieves efficient congestion control and flexible weighted fairness for both single-path and multi-path routing, as a result this leads to higher energy efficiency and better QoS in terms of both packet loss rate and delay.     

A Coverage-Based Scheduling Algorithm for WSNs

Node scheduling in wireless sensor networks (WSNs) plays a vital role in conserving energy and lengthening the lifetime of networks, which are considered as prime design challenges. In large-scaled WSNs, especially where sensor nodes are deployed randomly, 100 % coverage is not possible all the times. Additionally, several types of applications of WSNs do not require 100 % coverage. Following these facts, in this paper, we propose a coverage based node scheduling algorithm. The algorithm shows that by sacrificing a little amount of coverage, a huge amount of energy can be saved. This, in turns, helps to increase the lifetime of the network. We provide mathematical analysis, which verifies the correctness of the proposed algorithm. The proposed algorithm ensures balanced energy consumption over the sensor networks. Moreover, simulation results demonstrate that the proposed algorithm almost doubles the lifetime of a wireless sensor network by sacrificing only 5–8 % of coverage.     

Load balancing techniques for lifetime maximizing in wireless sensor networks

Energy consumption has been the focus of many studies on Wireless Sensor Networks (WSN). It is well recognized that energy is a strictly limited resource in WSNs. This limitation constrains the operation of the sensor nodes and somehow compromises the long term network performance as well as network activities. Indeed, the purpose of all application scenarios is to have sensor nodes deployed, unattended, for several months or years.This paper presents the lifetime maximization problem in “many-to-one” and “mostly-off” wireless sensor networks. In such network pattern, all sensor nodes generate and send packets to a single sink via multi-hop transmissions. We noticed, in our previous experimental studies, that since the entire sensor data has to be forwarded to a base station via multi-hop routing, the traffic pattern is highly non-uniform, putting a high burden on the sensor nodes close to the base station.In this paper, we propose some strategies that balance the energy consumption of these nodes and ensure maximum network lifetime by balancing the traffic load as equally as possible. First, we formalize the network lifetime maximization problem then we derive an optimal load balancing solution. Subsequently, we propose a heuristic to approximate the optimal solution and we compare both optimal and heuristic solutions with most common strategies such as shortest-path and equiproportional routing. We conclude that through the results of this work, combining load balancing with transmission power control outperforms the traditional routing schemes in terms of network lifetime maximization.     

Localization Algorithm for Large Scale Wireless Sensor Networks Based on Fast-SVM

Sensor node localization is one of research hotspots in the applications of wireless sensor networks (WSNs) field. In recent years, many scholars proposed some localization algorithms based on machine learning, especially support vector machine (SVM). Localization algorithms based on SVM have good performance without pairwise distance measurements and special assisting devices. But if detection area is too wide and the scale of wireless sensor network is too large, the each sensor node needs to be classified many times to locate by SVMs, and the location time is too long. It is not suitable for the places of high real-time requirements. To solve this problem, a localization algorithm based on fast-SVM for large scale WSNs is proposed in this paper. The proposed fast-SVM constructs the minimum spanning by introducing the similarity measure and divided the support vectors into groups according to the maximum similarity in feature space. Each group support vectors is replaced by linear combination of “determinant factor” and “adjusting factor” which are decided by similarity. Because the support vectors are simplified by the fast-SVM, the speed of classification is evidently improved. Through the simulations, the performance of localization based on fast-SVM is evaluated. The results prove that the localization time is reduce about 48 % than existing localization algorithm based on SVM, and loss of the localization precision is very small. Moreover, fast-SVM localization algorithm also addresses the border problem and coverage hole problem effectively. Finally, the limitation of the proposed localization algorithm is discussed and future work is present.     

REBTAM: reliable energy balance traffic aware data reporting algorithm for object tracking in multi-sink wireless sensor networks

Recently, Multi-sink Wireless Sensor Networks (WSNs) have received more and more attention due to their significant advantages over the single sink WSNs such as improving network throughput, balancing energy consumption, and prolonging network lifetime. Object tracking is regarded as one of the key applications of WSNs due to its wide real-life applications such as wildlife animal monitoring and military area intrusion detection. However, many object tracking researches usually focus on how to track the location of objects accurately, while few researches focus on data reporting. In this work, we propose an efficient data reporting method for object tracking in multi-sink WSNs. Due to the limited energy resource of sensor nodes, it seems especially important to design an energy efficient data reporting algorithm for object tracking in WSNs. Moreover, the reliable data transmission is an essential aspect that should be considered when designing a WSN for object tracking application, where the loss of data packets will affect the accuracy of the tracking and location estimation of a mobile object. In addition, congestion in WSNs has negative impact on the performance, namely, decreased throughput, increased per-packet energy consumption and delay, thus congestion control is an important issue in WSNs. Consequentially, this paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. Furthermore, data reliability is considered in our model where the sensed data can reach the sink node in a more reliable way. Finally, this paper presents a solution that sufficiently exerts the underloaded nodes to alleviate congestion and improve the overall throughput in WSNs. This work first formulates the problem as 0/1 Integer Linear Programming problem, and proposes a Reliable Energy Balance Traffic Aware greedy Algorithm in multi-sink WSNs (REBTAM) to solve the optimization problem. Through simulation, the performance of the proposed approach is evaluated and analyzed compared with the previous work which is related to our topic such as DTAR, NBPR, and MSDDGR protocols.     

基于感知数据分析的传感器网络覆盖控制

提出“决策覆盖”的覆盖控制理论和方法.以保证有效覆盖度为目标,通过对传感器网络局部感知数据的实时分析,计算节点的工作优先级并且动态地调整局部物理覆盖度,实现节点状态的反馈调度.“决策覆盖”克服了现有覆盖控制在灵活性和适应性方面的缺陷,突破了在节点发生故障和网络遭受攻击后服务质量难以保证的瓶颈.结合传统的覆盖区域计算及概率分析的结论,本文对现有覆盖控制算法进行了改进,相对于原始算法,改进后的算法在覆盖有效性和健壮性上均有明显提升.     

基于无线传感器网络的跨层拥塞控制协议

无线传感器网络(WSN)中由拥塞引起的大量分组重传以及重传多次失败后的分组丢弃会导致较长的时延、较高的分组丢失率和较多的能量消耗.为了准确探测和控制网络拥塞,提出了一种基于跨层设计的拥塞控制协议,即上行拥塞控制(UCC)协议.该协议利用节点在媒质接人控制(MAC)层中未占用的缓冲器区间大小和所预测的通信流量作为该节点的...     

An optimal resource control scheme under fidelity and energy constraints in sensor networks

To control congestion, either the traffic from sources should be “reduced” (traffic controlling) or the available resources should be “increased” (resource controlling). Compared to the wired and other wireless counterparts, wireless sensor networks usually have elastic resource availability, and the applications require a certain level of throughput called fidelity. As a result, resource control strategies cannot only alleviate congestion but also ensure the required fidelity level during congestion by accommodating higher incoming traffic. In this paper, we first attempt to formally define the resource control framework that adjusts the resource provisioning at the hotspot nodes during congestion. In an effort to find the optimal resource control under the fidelity and energy constraints, we present a resource increase and decrease algorithm called Early Increase/Early Decrease (EIED) that tries to adjust the effective channel capacity quickly to suit the incoming traffic volume in an energy-efficient manner, thereby increasing the fidelity level observed by the application. Under the energy-constrained optimization, we prove this algorithm incurs the lowest overhead of energy consumption for the given fidelity level that is required by the application. We also prove that the EIED algorithm performed in a distributed manner also lowers the energy consumption per packet at an end-to-end level. The effectiveness of the EIED algorithm is verified by simulations based on realistic sensor network configurations.

An Efficient Cross-Layer Optimization Algorithm for Data Transmission in Wireless Sensor Networks

In this paper, we propose a cross layer congestion optimization scheme for allocating the resources of wireless sensor networks to achieve maximization of network performance. The congestion control, routing selection, link capacity allocation, and power consumption are all taken account to yield an optimal scheme based on the Lagrangian optimization. The Lagrangian multiplier is adopted to adjust power consumption, congestion rate, routing selection and link capacity allocation, so that the network performance can be satisfied between the trade-off of efficiency and fairness of resource allocation. The proposed algorithm can significantly achieve the maximization of network performance in relieving the network congestion with less power consumption. Excellent simulation results are obtained to demonstrate our innovative idea, and show the efficiency of our proposed algorithm.     

A Tabu search based routing algorithm for wireless sensor networks

In this paper, a Tabu search based routing algorithm is proposed to efficiently determine an optimal path from a source to a destination in wireless sensor networks (WSNs). There have been several methods proposed for routing algorithms in wireless sensor networks. In this paper, the Tabu search method is exploited for routing in WSNs from a new point of view. In this algorithm (TSRA), a new move and neighborhood search method is designed to integrate energy consumption and hop counts into routing choice. The proposed algorithm is compared with some of the ant colony optimization based routing algorithms, such as traditional ant colony algorithm, ant colony optimization-based location-aware routing for wireless sensor networks, and energy and path aware ant colony algorithm for routing of wireless sensor networks, in term of routing cost, energy consumption and network lifetime. Simulation results, for various random generated networks, demonstrate that the TSRA, obtains more balanced transmission among the node, reduces the energy consumption and cost of the routing, and extends the network lifetime.