Fish bone structure based data aggregation and routing in wireless sensor network: multi-agent based approach

Wireless sensor networks (WSNs) are constrained by limited node (device) energy, low network bandwidth, high communication overhead and latency. Data aggregation alleviates the constraints of WSN. In this paper, we propose a multi-agent based homogeneous temporal data aggregation and routing scheme based on fish bone structure of WSN nodes by employing a set of static and mobile agents. The primary components of fishbone structure are backbone and ribs connected to both sides of a backbone. A backbone connects a sink node and one of the sensor nodes on the boundary of WSN through intermediate sensor nodes. Our aggregation scheme operates in the following steps. (1) Backbone creation and identifying master centers (or nodes) on it by using a mobile agent based on parameters such as Euclidean distance, residual energy, backbone angle and connectivity. (2) Selection of local centers (or nodes) along the rib of a backbone connecting a master center by using a mobile agent. (3) Local aggregation process at local centers by considering nodes along and besides the rib, and delivering to a connected master center. (4) Master aggregation process along the backbone from boundary sensor node to the sink node by using a mobile agent generated by a boundary sensor node. The mobile agent aggregates data at visited master centers and delivers to the sink node. (5) Maintenance of fish bone structure of WSN nodes. The performance of the scheme is simulated in various WSN scenarios to evaluate the effectiveness of the approach by analyzing the performance parameters such as master center selection time, local center selection time, aggregation time, aggregation ratio, number of local and master centers involved in the aggregation process, number of isolated nodes, network lifetime and aggregation energy. We observed that our scheme outperforms zonal based aggregation scheme.     

基于位置感知和代理的WSN多径路由方案

针对无线传感器网络(WSN)中多径路由的可靠性和能量效率问题,提出了一种基于代理和位置感知的多径路由发现方案(LABMR).事件节点根据位置信息,动态寻找其到Sink节点之间的特殊中间节点,来构建多径路由.利用移动代理来收集多径路由的局部拓扑结构信息,Sink节点根据代理收集的路由参数来计算路径权值,以此选择最优不相交路径.同时,对于信息的重要性差异,Sink节点选择单条或多条路径来传输数据,在保证传输可靠性的同时减少能耗.与现有的基于代理的多径路由(ABMR)方法相比,LABMP在数据包投递率、能量消耗、额外开销和延迟方面具有更好的性能.     

Clustering Based Two Dimensional Motion of Sink Node in Wireless Sensor Networks

The wireless sensor network (WSN) is always known for its limited-energy issues and finding a good solution for energy minimization in WSNs is still a concern for researchers. Implementing mobility to the sink node is used widely for energy conservation or minimization in WSNs which reduces the distance between sink and communicating nodes. In this paper, with the intention to conserve energy from the sensor nodes, we designed a clustering based routing protocol implementing a mobile sink called ‘two dimensional motion of sink node (TDMS)’. In TDMS, each normal sensor node collects data and send it to their respective leader node called cluster head (CH). The sink moves in the two dimensional direction to collect final data from all CH nodes, particularly it moves in the direction to that CH which has the minimum remaining energy. The proposed protocol is validated through rigorous simulation using MATLAB and comparisons have been made with WSN’s existing static sink and mobile sink routing protocols over two different geographical square dimensions of the network. Here, we found that TDMS model gives the optimal result on energy dissipation per round and increased network lifetime.

     

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.     

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.     

无线传感器网络动态分簇路由BWAS的算法研究

为加快无线传感器网络(WSN)路径搜索速度,减少了路径寻优能量消耗,提出了基于最优-最差蚂蚁系统(BWAS)算法的无线传感器网络动态分簇路由算法。该算法是基于WSN动态分簇能量管理模式,在簇头节点间运用BWAS算法搜寻从簇头节点到汇聚节点的多跳最优路径,以多跳接力方式将数据发送至汇聚节点。BWAS算法在路径搜寻过程中评价出最优-最差蚂蚁,引入奖惩机制,加强搜寻过程的指导性。结合动态分簇能量管理,避免网络连续过度使用某个节点,均衡了网络节点能量消耗。通过与基于蚁群算法(ACS)路由算法仿真比较,本算法减缓了网络节点的能量消耗,延长了网络寿命,在相同时间里具有较少的死亡节点,具有较强的鲁棒性。     

Secured routing in wireless sensor networks using fault‐free and trusted nodes

Wireless sensor network (WSN) should be designed such that it is able to identify the faulty nodes, rectify the faults, identify compromised nodes from various security threats, and transmit the sensed data securely to the sink node under faulty conditions. In this paper, we propose an idea of integrating fault tolerance and secured routing mechanism in WSN named as fault tolerant secured routing: an integrated approach (FASRI) that establishes secured routes from source to sink node even under faulty node conditions. Faulty nodes are identified using battery power and interference models. Trustworthy nodes (non‐compromised) among fault‐free nodes are identified by using agent‐based trust model. Finally, the data are securely routed through fault‐free non‐compromised nodes to sink. Performance evaluation through simulation is carried out for packet delivery ratio, hit rate, computation overhead, communication overhead, compromised node detection ratio, end‐to‐end delay, memory overhead, and agent overhead. We compared simulation results of FASRI with three schemes, namely multi‐version multi‐path (MVMP), intrusion/fault tolerant routing protocol (IFRP) in WSN, and active node‐based fault tolerance using battery power and interference model (AFTBI) for various measures and found that there is a performance improvement in FASRI compared with MVMP, IFRP, and AFTBI. Copyright © 2014 John Wiley & Sons, Ltd.     

一种基于K-means的WSN移动汇聚路由算法

无线传感网络(Wireless Sensor Network,WSN)作为一种资源受限的网络,网络中节点的能耗直接影响了网络的性能。因此,均衡网络中的能耗,延长网络的生命周期,成为设计WSN路由算法的重要目标。于是,在LEACH-C协议的基础上提出了一种移动汇聚路由算法。分簇阶段由Sink节点计算最优簇首个数,通过K-means聚类将网络中的节点划分至不同的集群,选择通信成本最低的节点作为各集群的簇首。稳定传输阶段通过移动Sink进行数据采集,针对不同的延迟分别规划Sink节点的移动轨迹。MATLAB仿真结果表明,与LEACH和LEAHC-C算法相比簇首的分布更合理,结合Sink节点的移动策略能有效均衡网络能耗,延长网络的寿命。     

Trusted Neighbors Based Secured Routing Scheme in Wireless Sensor Networks Using Agents

In Wireless Sensor Network (WSN) applications, nodes are deployed in public domains that are prone to be attacked by various types of attacks wherein the data confidentiality, integrity and authentication are compromised. In this paper, we propose Agent based SEcured Routing using Trusted neighbors (ASERT) in WSN. ASERT selects trustworthy neighbors and establishes secured routes using software agents. ASERT operates in following phases. (1) Defining Safeguard Agency (SA) and Routing Agency (RA), both consisting of static and mobile agents and a knowledge base. (2) Identification of trustworthy neighbor nodes by SA with trust model that consists of probability and Message Authentication Codes (MAC) model. (3) Probability model estimates the trustworthy channel and trustworthy nodes whereas MAC model ensures them. (4) MAC’s are dynamically computed by agents by generating the keys with the help of Random Oracle Extractor (ROE). (5) RA establishes secured routes from source to the sink node using agents. (6) Agents effectively identify possible security threats on wireless channel and node. (7) An improvement in packet delivery ratio, packet delivery latency, route maintenance overhead, memory overhead and energy consumption is achieved in ASERT as compared to two protocols, namely, Bio-inspired Trust and Reputation model in WSN (BTRM-WSN) using ant colony based approach and Ambient Trust Sensor Routing that uses trust combined with location information for routing.     

A distributed,multi‐hop,adaptive, tree‐based energy‐balanced routing approach

To accomplish the primary objective of data sensing and collection of wireless sensor networks (WSN), the design of an energy efficient routing algorithm is very important. However, the energy constrained sensing nodes along with the intrinsic properties of the (WSN) environment makes the routing a challenging task. To overcome this routing dilemma, an improved distributed, multi‐hop, adaptive, tree‐based energy‐balanced (DMATEB) routing scheme is proposed in this paper. In this scheme, a relay node is selected in view of minimum distance and high energy from a current sensing node. Further, the parent node is chosen among the selected relay nodes on the basis of high residual energy and less power consumption with due consideration of its associated child nodes. As each sensing node itself selects its parent among the available alternatives, the proposed scheme offers a distributive and adaptive approach. Moreover, the proposed system does not overload any selected parent of a particular branch as it starts acting as a child whenever its energy lowers among the other available relay nodes. This leads to uniform energy utilization of nodes that offers a better energy balance mechanism and improves the network lifespan by 20% to 30% as compared with its predecessors.     

Delay aware reliable transport in wireless sensor networks

Wireless sensor networks (WSN) are event‐based systems that rely on the collective effort of several sensor nodes. Reliable event detection at the sink is based on collective information provided by the sensor nodes and not on any individual sensor data. Hence, conventional end‐to‐end reliability definitions and solutions are inapplicable in the WSN regime and would only lead to a waste of scarce sensor resources. Moreover, the reliability objective of WSN must be achieved within a certain real‐time delay bound posed by the application. Therefore, the WSN paradigm necessitates a collective delay‐constrained event‐to‐sink reliability notion rather than the traditional end‐to‐end reliability approaches. To the best of our knowledge, there is no transport protocol solution which addresses both reliability and real‐time delay bound requirements of WSN simultaneously. In this paper, the delay aware reliable transport (DART) protocol is presented for WSN. The objective of the DART protocol is to timely and reliably transport event features from the sensor field to the sink with minimum energy consumption. In this regard, the DART protocol simultaneously addresses congestion control and timely event transport reliability objectives in WSN. In addition to its efficient congestion detection and control algorithms, it incorporates the time critical event first (TCEF) scheduling mechanism to meet the application‐specific delay bounds at the sink node. Importantly, the algorithms of the DART protocol mainly run on resource rich sink node, with minimal functionality required at resource constrained sensor nodes. Furthermore, the DART protocol can accommodate multiple concurrent event occurrences in a wireless sensor field. Performance evaluation via simulation experiments show that the DART protocol achieves high performance in terms of real‐time communication requirements, reliable event detection and energy consumption in WSN. Copyright © 2007 John Wiley & Sons, Ltd.     

Pipelined forwarding with energy balance in hexagonal wireless sensor networks

This paper presents a pipelined forwarding scheme with energy balance for hexagonal wireless sensor networks (H‐WSN). An H‐WSN consists of hexagonal clusters in which the distance between any two cluster heads is , where R is the radius of a hexagonal cluster. A trade‐off exists in pipelined forwarding for an H‐WSN; that is, reducing pipeline lengths can decrease data forwarding delay, but it will adversely increase the number of clusters, which consequently increases the total collecting time of a mobile sink. This paper therefore introduces a mobile sink data collecting and routing scheme to dynamically adjust the pipeline lengths and to periodically switch pipeline directions. With this mobile sink data collecting and routing, the overall system throughput is increased, and the energy consumptions among all cluster heads are balanced. Copyright © 2012 John Wiley & Sons, Ltd.     

Energy Efficient Energy Hole Repelling (EEEHR) Algorithm for Delay Tolerant Wireless Sensor Network

Reducing energy consumption and increasing network lifetime are the major concerns in Wireless Sensor Network (WSN). Increase in network lifetime reduces the frequency of recharging and replacing batteries of the sensor node. The key factors influencing energy consumption are distance and number of bits transmitted inside the network. The problem of energy hole and hotspot inside the network make neighbouring nodes unusable even if the node is efficient for data transmission. Energy Efficient Energy Hole Repelling (EEEHR) routing algorithm is developed to solve the problem. Smaller clusters are formed near the sink and clusters of larger size are made with nodes far from the sink. This methodology promotes equal sharing of load repelling energy hole and hotspot issues. The opportunity of being a Cluster Head (CH) is given to a node with high residual energy, very low intra cluster distance in case of nodes far away from the sink and very low CH to sink distance for the nodes one hop from the sink. The proposed algorithm is compared with LEACH, LEACH-C and SEP routing protocol to prove its novel working. The proposed EEEHR routing algorithm provides improved lifetime, throughput and less packet drop. The proposed algorithm also reduces energy hole and hotspot problem in the network.     

Connectivity Analysis for Wireless Sensor Networks with Antenna Array Integrated Central Node

In a wireless sensor network (WSN), after gathering information, tiny sensor nodes need to transmit data to a sink. It is important to guarantee that each node can communicate with a sink. Due to the multi-hop communication of WSNs, an essential condition for reliable transmission is completely connectivity of a network. Adaptive or smart antenna (SA) techniques in WSNs have been a topic of active research in recent years. These techniques have been shown to be effective with respect to decreasing energy consuming via specified regions which are formed by the SA beams. In this paper, we propose a probabilistic technique to determine the network connectivity probability of the SA integrated WSN. We employ the geometric shape model to evaluate the network connectivity probability of the WSN using the SA beam specifications. The sensor node density to satisfy the desired network connectivity is determined in terms of the beam-width of the antenna array and node transmission range. The analytical results agree with the simulation results by less than 4.7 % error in the average.     

基于移动Agent和WSN的突发事件场景数据收集算法研究

该文针对无线传感器网络应用于突发事件监测场景的能量消耗和网络延迟问题,提出了基于移动Agent的无线传感器网络簇式数据收集算法.动态成簇过程基于事件严重程度,并由其决定簇的生命周期和覆盖范围.Sink和簇头之间形成以Sink节点为簇头的虚拟簇.移动Agent迁移路径规划过程中下一跳节点的选取基于节点剩余能量、路径损耗及受刺激强度.移动Agent通过节点遍历的方式完成对所有簇内成员节点信息的收集.仿真结果表明,相对于C/S数据收集模型,基于移动Agent的模型具有更好的节能效果,并能一定程度地减少网络延迟,尤其适用于大规模无线传感器网络应用.     

Energy Efficient Clustering Scheme (EECS) for Wireless Sensor Network with Mobile Sink

The participants in the Wireless Sensor Network (WSN) are highly resource constraint in nature. The clustering approach in the WSN supports a large-scale monitoring with ease to the user. The node near the sink depletes the energy, forming energy holes in the network. The mobility of the sink creates a major challenge in reliable and energy efficient data communication towards the sink. Hence, a new energy efficient routing protocol is needed to serve the use of networks with a mobile sink. The primary objective of the proposed work is to enhance the lifetime of the network and to increase the packet delivered to mobile sink in the network. The residual energy of the node, distance, and the data overhead are taken into account for selection of cluster head in this proposed Energy Efficient Clustering Scheme (EECS). The waiting time of the mobile sink is estimated. Based on the mobility model, the role of the sensor node is realized as finite state machine and the state transition is realized through Markov model. The proposed EECS algorithm is also been compared with Modified-Low Energy Adaptive Clustering Hierarchy (MOD-LEACH) and Gateway-based Energy-Aware multi-hop Routing protocol algorithms (M-GEAR). The proposed EECS algorithm outperforms the MOD-LEACH algorithm by 1.78 times in terms of lifetime and 1.103 times in terms of throughput. The EECS algorithm promotes unequal clustering by avoiding the energy hole and the HOT SPOT issues.     

CH selection and compressive sensing-based data aggregation in WSN using hybrid Golden circle-inspired optimization

The arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS-based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network-long short term memory (CNN-LSTM) is employed for the data aggregation process. The proposed HGCIO-based CNN-LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%.     

Energy-Aware Data Aggregation for Grid-Based Wireless Sensor Networks with a Mobile Sink

Wireless sensor networks (WSNs) are made up of many small and highly sensitive nodes that have the ability to react quickly. In WSNs, sink mobility brings new challenges to large-scale sensor networks. Almost all of the energy-aware routing protocols that have been proposed for WSNs aim at optimizing network performance while relaying data to a stationary gateway (sink). However, through such contemporary protocols, mobility of the sink can make established routes unstable and non-optimal. The use of mobile sinks introduces a trade-off between the need for frequent rerouting to ensure optimal network operation and the desire to minimize the overhead of topology management. In this paper, in order to reduce energy consumption and minimize the overhead of rerouting frequency, we propose an energy-aware data aggregation scheme (EADA) for grid-based wireless sensor networks with a mobile sink. In the proposed scheme, each sensor node with location information and limited energy is considered. Our approach utilizes location information and selects a special gateway in each area of a grid responsible for forwarding messages. We restrict the flooding region to decrease the overhead for route decision by utilizing local information. We conducted simulations to show that the proposed routing scheme outperforms the coordination-based data dissemination scheme (CODE) (Xuan, H. L., & Lee, S. Proceedings of the Sensor Networks and Information Processing Conference, pp. 13–18, 2004).     

DABPR: a large-scale internet of things-based data aggregation back pressure routing for disaster management

In this paper, we propose a data aggregation back pressure routing (DABPR) scheme, which aims to simultaneously aggregate overlapping routes for efficient data transmission and prolong the lifetime of the network. The DABPR routing algorithm is structured into five phases in which event data is sent from the event areas to the sink nodes. These include cluster-head selection, maximization of event detection reliability, data aggregation, scheduling, and route selection with multi attributes decision making metrics phases. The scheme performs data aggregation on redundant data at relay nodes in order to decrease both the size and rate of message exchanges to minimize communication overhead and energy consumption. The proposed scheme is assessed in terms of packet delivery, network lifetime, ratio, energy consumption, and throughput, and compared with two other well-known protocols, namely “information-fusion-based role assignment (InFRA)” and “data routing for in-network aggregation (DRINA)”, which intrinsically are cluster and tree-based routing schemes designed to improve data aggregation efficiency by maximizing the overlapping routes. Meticulous analysis of the simulated data showed that DABPR achieved overall superior proficiency and more reliable performance in all the evaluated performance metrics, above the others. The proposed DABPR routing scheme outperformed its counterparts in the average energy consumption metric by 64.78% and 51.41%, packet delivery ratio by 28.76% and 16.89% and network lifetime by 42.72% and 20.76% compared with InFRA and DRINA, respectively.

     

Distributed audio sensing with homeostasis-inspired autonomous communication

Emerging applications of wireless sensor networks (WSN) requiring wide-band event signal communication such as multimedia surveillance sensor networks impose additional challenges including high communication bandwidth requirement and energy cost. Besides their partially or fully dependency on feedback messages from sink node, the existing protocols designed for WSN do not address the communication of wide-band event signals. Furthermore, the feedback messages may not reach in time to provide reliable communication of event information and save scarce network resources. Therefore, an autonomous communication protocol is imperative in order to provide wide-band event signal communication without any feedback from the sink. In nature, biological systems have self-organization capability, i.e., homeostasis, as they autonomously maintain a relatively stable equilibrium state for operation of vital functions. Hence, this natural phenomenon clearly gives promising inspirations in order to develop autonomous and efficient communication models and protocols for WSN domain. In this paper, the homeostasis-inspired autonomous communication (HAC) protocol is introduced for wireless audio sensor networks (WASN). Using the spectral properties of the wide-band event signal, i.e., audio signal, HAC enables WASN to maintain a relatively stable state in which sensor nodes reliably and energy-efficiently communicate the event signal to the sink node. Furthermore, with its self-organization capability, HAC does not rely on any feedback message from the sink node. Performance evaluations reveal that HAC successfully communicates wide-band event signal with minimum energy expenditure.