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.     

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.

     

Cluster Based Routing Protocol for Mobile Nodes in Wireless Sensor Network

Mobility of sensor nodes in wireless sensor network (WSN) has posed new challenges particularly in packet delivery ratio and energy consumption. Some real applications impose combined environments of fixed and mobile sensor nodes in the same network, while others demand a complete mobile sensors environment. Packet loss that occurs due to mobility of the sensor nodes is one of the main challenges which comes in parallel with energy consumption. In this paper, we use cross layer design between medium access control (MAC) and network layers to overcome these challenges. Thus, a cluster based routing protocol for mobile sensor nodes (CBR-Mobile) is proposed. The CBR-Mobile is mobility and traffic adaptive protocol. The timeslots assigned to the mobile sensor nodes that had moved out of the cluster or have not data to send will be reassigned to incoming sensor nodes within the cluster region. The protocol introduces two simple databases to achieve the mobility and traffic adaptively. The proposed protocol sends data to cluster heads in an efficient manner based on received signal strength. In CBR-Mobile protocol, cluster based routing collaborates with hybrid MAC protocol to support mobility of sensor nodes. Schedule timeslots are used to send the data message while the contention timeslots are used to send join registration messages. The performance of proposed CBR-Mobile protocol is evaluated using MATLAB and was observed that the proposed protocol improves the packet delivery ratio, energy consumption, delay and fairness in mobility environment compared to LEACH-Mobile and AODV protocols.     

基于传输距离和Sink移动的扩延网络寿命算法

在无线传感网络WSN(Wireless Sensor Network)中,传感节点通常以多跳方式向信宿Sink传输感测数据.由于邻近信宿Sink的传感节点需要承担数据转发的任务,比其他节点消耗更多的能量,缩短了网络寿命.为此,提出一种扩延网络寿命的新算法,记为NLTA(Network LifeTime Augmentation).NLTA算法采用了节点传输距离自适应调整和信宿Sink移动两个策略.节点依据能量情况,调整传输距离,减少能量消耗,然后根据路径容量值,调整Sink的位置,平衡网内的节点能量消耗,避免信宿Sink的周围节点能量过度消耗.仿真结果表明,提出的NLTA方案能够有效地提高网络寿命.     

WSN网络中一种新的关联规则数据挖掘算法

在无线传感器网络（WSN）中,传感器关联规则在很多方面有着重要作用。但是大部分关联规则的数据挖掘算法都要求获取传感器节点的行为数据。同时,将这些数据传输到sink节点,并且由sink节点创建传感器数据库,并应用不同的算法从传感器数据库来发现关联规则。本文则提出了一个网络内部机制来查找频繁项集。因此,节点只发送传感器频繁模式到sink节点,而不是节点整个活动集,从而节省sink节点开销。     

General Network Lifetime and Cost Models for Evaluating Sensor Network Deployment Strategies

In multihop wireless sensor networks that are often characterized by many-to-one (convergecast) traffic patterns, problems related to energy imbalance among sensors often appear. Sensors closer to a data sink are usually required to forward a large amount of traffic for sensors farther from the data sink. Therefore, these sensors tend to die early, leaving areas of the network completely unmonitored and reducing the functional network lifetime. In our study, we explore possible sensor network deployment strategies that maximize sensor network lifetime by mitigating the problem of the hot spot around the data sink. Strategies such as variable-range transmission power control with optimal traffic distribution, mobile-data-sink deployment, multiple-data-sink deployment, nonuniform initial energy assignment, and intelligent sensor/relay deployment are investigated. We suggest a general model to analyze and evaluate these strategies. In this model, we not only discover how to maximize the network lifetime given certain network constraints but also consider the factor of extra costs involved in more complex deployment strategies. This paper presents a comprehensive analysis on the maximum achievable sensor network lifetime for different deployment strategies, and it also provides practical cost-efficient sensor network deployment guidelines.     

Analysis of fair rate calculations in resilient packet ring aggressive mode

Resilient packet ring (RPR) standardized as IEEE 802.17 is a new medium access control (MAC) protocol for metro-ring networks. RPR supports spatial reuse which increases the achieved throughput but it can also result in congestion and starvation of nodes on the ring. Therefore, it is necessary to employ mechanisms to enforce a fair allocation of the ring bandwidth in RPR. In order to maintain fairness among nodes, a fairness algorithm is deployed at each RPR node. When a node detects congestion, it calculates a fair rate which is advertised to all upstream nodes contributing to congestion. Upon receiving the fair rate, the upstream nodes limit the rate of their injected traffic to the advertised fair rate. Consequently, the congested node can utilize the unused capacity and add its local traffic to the ring. In this paper, we develop an analytical model for fair rate calculation in the RPR aggressive mode fairness algorithm in the parking-lot scenario. This model provides an insight on dynamics of the RPR fairness algorithm and can be used to evaluate its performance. We investigate this problem in two cases. First, we assume that the link propagation delay is zero and derive the fair rate equations for this ideal case. We then consider the link propagation delay and develop a more realistic model. We verify the accuracy of our model by simulation results. Furthermore, we use the developed model to study the impact of various parameters on convergence of the fair rate.     

A novel residual energy‐based distributed clustering and routing approach for performance study of wireless sensor network

Non‐uniform energy consumption during operation of a cluster‐based routing protocol for large‐scale wireless sensor networks (WSN) is major area of concern. Unbalanced energy consumption in the wireless network results in early node death and reduces the network lifetime. This is because nodes near the sink are overloaded in terms of data traffic compared with the far away nodes resulting in node deaths. In this work, a novel residual energy–based distributed clustering and routing (REDCR) protocol has been proposed, which allows multi‐hop communication based on cuckoo‐search (CS) algorithm and low‐energy adaptive‐clustering–hierarchy (LEACH) protocol. LEACH protocol allows choice of possible cluster heads by rotation at every round of data transmission by a newly developed objective function based on residual energy of the nodes. The information about the location and energy of the nodes is forwarded to the sink node where CS algorithm is implemented to choose optimal number of cluster heads and their positions in the network. This approach helps in uniform distribution of the cluster heads throughout the network and enhances the network stability. Several case studies have been performed by varying the position of the base stations and by changing the number of nodes in the area of application. The proposed REDCR protocol shows significant improvement by an average of 15% for network throughput, 25% for network scalability, 30% for network stability, 33% for residual energy conservation, and 60% for network lifetime proving this approach to be more acceptable one in near future.     

Increasing network lifetime by balancing node energy consumption in heterogeneous sensor networks

Sensor nodes are powered by battery and have severe energy constraints. The typical many‐to‐one traffic pattern causes uneven energy consumption among sensor nodes, that is, sensor nodes near the base station or a cluster head have much heavier traffic burden and run out of power much faster than other nodes. The uneven node energy dissipation dramatically reduces sensor network lifetime. In a previous work, we presented the chessboard clustering scheme to increase network lifetime by balancing node energy consumption. To achieve good performance and scalability, we propose to form a heterogeneous sensor network by deploying a few powerful high‐end sensors in addition to a large number of low‐end sensors. In this paper, we design an efficient routing protocol based on the chessboard clustering scheme, and we compute the minimum node density for satisfying a given lifetime constraint. Simulation experiments show that the chessboard clustering‐based routing protocol balances node energy consumption very well and dramatically increases network lifetime, and it performs much better than two other clustering‐based schemes. Copyright © 2006 John Wiley & Sons, Ltd.     

A scalable joint routing and scheduling scheme for large-scale wireless sensor networks

The multihop configuration of a large-scale wireless sensor network enables multiple simultaneous transmissions without interference within the network. Existing time division multiple access (TDMA) scheduling schemes exploit gain based on the assumption that the path is optimally determined by a routing protocol. In contrast, our scheme jointly considers routing and scheduling and introduces several new concepts. We model a large-scale wireless sensor network as a tiered graph relative to its distance from the sink, and introduce the notion of relay graph and relay factor to direct the next-hop candidates toward the sink fairly and efficiently. The sink develops a transmission and reception schedule for the sensor nodes based on the tiered graph search for a set of nodes that can simultaneously transmit and receive. The resulting schedule eventually allows data from each sensor node to be delivered to the sink. We analyze our scheduling algorithm both numerically and by simulation, and we discuss the impact of protocol parameters. Further, we prove that our scheme is scalable to the number of nodes, from the perspectives of mean channel capacity and maximum number of concurrent transmission nodes. Compared with the existing TDMA scheduling schemes, our scheme shows better performance in network throughput, path length, end-to-end delay, and fairness index.     

Traffic-Aware Density-Based Sleep Scheduling and Energy Modeling for Two Dimensional Gaussian Distributed Wireless Sensor Network

Sleep scheduling of sensors in network domain is considered to be the most fundamental way of achieving higher life expectancy of wireless sensor networks. In this paper we have proposed density-based sleep scheduling strategy with traffic awareness in Gaussian distributed sensor network for minimizing energy consumption. In uniform distributed sensor network, it has been found that nodes in the nearest belt around the sink consume more energy. The reason behind is that the nodes near the sink involve more packet relaying load than the distant nodes. Consequently, the energy of these sensors get exhausted rapidly, thereby creating connectivity breaks known as energy hole. For this purpose, Gaussian distribution is used by densely deploying nodes around the sink which well-balances the relaying load. In addition, we have developed the analytical model for computing the energy consumption and coverage analysis in the sensor network. The performance of our sleep scheduling method is evaluated with respect to the Randomized Scheduling and Linear Distance-based Scheduling protocols. The simulation results of our proposed work show commendable improvement in network lifetime.     

Optimized Congestion Management Protocol for Healthcare Wireless Sensor Networks

The application of Wireless Sensor Networks (WSNs) in healthcare is dominant and fast growing. In healthcare WSN applications (HWSNs) such as medical emergencies, the network may encounter an unpredictable load which leads to congestion. Congestion problem which is common in any data network including WSN, leads to packet loss, increasing end-to-end delay and excessive energy consumption due to retransmission. In modern wireless biomedical sensor networks, increasing these two parameters for the packets that carry EKG signals may even result in the death of the patient. Furthermore, when congestion occurs, because of the packet loss, packet retransmission increases accordingly. The retransmission directly affects the lifetime of the nodes. In this paper, an Optimized Congestion management protocol is proposed for HWSNs when the patients are stationary. This protocol consists of two stages. In the first stage, a novel Active Queue Management (AQM) scheme is proposed to avoid congestion and provide quality of service (QoS). This scheme uses separate virtual queues on a single physical queue to store the input packets from each child node based on importance and priority of the source’s traffic. If the incoming packet is accepted, in the second stage, three mechanisms are used to control congestion. The proposed protocol detects congestion by a three-state machine and virtual queue status; it adjusts the child’s sending rate by an optimization function. We compare our proposed protocol with CCF, PCCP and backpressure algorithms using the OPNET simulator. Simulation results show that the proposed protocol is more efficient than CCF, PCCP and backpressure algorithms in terms of packet loss, energy efficiency, end-to-end delay and fairness.     

GMCAR: Grid-based multipath with congestion avoidance routing protocol in wireless sensor networks

Recently, the interest in wireless sensor networks has been magnetized in the delay sensitive applications such as real-time applications. These time critical applications crave certain QoS requirements as though end-to-end delay guarantee and network bandwidth reservation. However, the severe resource constraints of the wireless sensor networks pose great challenges that hinder supporting these requirements. In this paper, we propose a Grid-based Multipath with Congestion Avoidance Routing protocol (GMCAR) as an efficient QoS routing protocol that is suited for grided sensor networks. We employ the idea of dividing the sensor network field into grids. Inside each grid, one of the sensor nodes is selected as a master node which is responsible for delivering the data generated by any node in that grid and for routing the data received from other master nodes in the neighbor grids. For each master node, multiple diagonal paths that connect the master node to the sink are stored as routing entries in the routing table of that node. The novelty of the proposed protocol lies behind the idea of incorporating the grids densities along with the hop count into the routing decisions. A congestion control mechanism is proposed in order to relieve the congested areas in case of congestion occurrence. Simulation results show that our proposed protocol has the potential to achieve up to 19.5% energy saving, 24.7% reduction in the delay and up to 8.5% enhancement in the network throughput when compared to another QoS routing protocol. However, when compared to the basic grid-based coordinated routing protocol, it achieves 23% energy saving. In addition, the proposed protocol shows its superiority in achieving better utilization to the available storage.     

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.     

A Novel Energy Efficient and Lifetime Maximization Routing Protocol in Wireless Sensor Networks

The energy consumption is a key design criterion for the routing protocols in wireless sensor networks (WSN). Some of the conventional single path routing schemes may not be optimal to maximize the network lifetime and connectivity. Thus, multipath routing schemes is an optimal alternative to extend the lifetime of WSN. Multipath routing schemes distribute the traffic across multiple paths instead of routing all the traffic along a single path. In this paper, we propose a multipath Energy-Efficient data Routing Protocol for wireless sensor networks (EERP). The latter keeps a set of good paths and chooses one based on the node state and the cost function of this path. In EERP, each node has a number of neighbours through which it can route packets to the base station. A node bases its routing decision on two metrics: state and cost function. It searches its Neighbours Information Table for all its neighbours concerned with minimum cost function. Simulation results show that our EERP protocol minimizes and balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime.     

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.     

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.     

基于ERPMT改进启发式方法的WSN寿命最大化算法

针对传感器节点的电池容量限制导致无线传感网络寿命低的问题,基于容量最大化(CMAX)、线上最大化寿命(OML)两种启发式方法以及高效路由能量管理技术(ERPMT),提出了基于ERPMT改进启发式方法的无线传感网络寿命最大化算法。首先,通过启发式方法初始化每个传感器节点,将节点能量划分为传感器节点起源数据和其它节点数据延迟;然后利用加入的一种优先度量延迟一跳节点的能量消耗;最后,根据路径平均能量为每个路由分配一个优先级,并通过ERPMT实现最终的无线传感网络优化。针对不同分布类型网络寿命的实验验证了本文算法的有效性及可靠性,实验结果表明,相比较为先进的启发式方法CMAX及OML,本文算法明显增大了无线传感网络的覆盖范围,并且大大地延长了网络的寿命。     

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.     

Sink selection and clustering using fuzzy‐based controller for wireless sensor networks

Applying multiple sink nodes in a large‐scale wireless sensor networks (WSN) can increase the scalability and lifetime of the network. The current sink selection mechanisms assume an unlimited amount of buffer and bandwidth for the sink nodes. This can be problematic in real‐world applications, especially when many cluster heads select a specific sink node and send their data to the sink at the same time. In this situation, the sink node may not have enough buffer to receive and process data; consequently, some packets are dropped. To mitigate these occasions, a fuzzy‐based controller with reduced rules is proposed for sink selection by considering the capacity of the sink nodes. The capacity of the sink nodes is estimated using the long short‐term memory (LSTM) technique. Then another fuzzy‐based controller with reduced rules is designed to select the cluster head. The fuzzy rules are reduced by employing R‐implications method. Reducing the number of fuzzy rules decreases the complexity of the fuzzy controllers. The results show the efficiency of the proposed sink selection and clustering techniques in terms of consumed energy, remaining energy, first node dead (FND), half nodes dead (HND), last node dead (LND), packet loss, and delay.