DR-NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks

Internet of Things (IoT) has got significant popularity among the researchers' community as they have been applied in numerous application domains. Most of the IoT applications are implemented with the help of wireless sensor networks (WSNs). These WSNs use different sensor nodes with a limited battery power supply. Hence, the energy of the sensor node is considered as one of the primary constraints of WSN. Besides, data communication in WSN dissipates more energy than processing the data. In most WSNs applications, the sensed data generated from the same location sensor nodes are identical or time-series/periodical data. This redundant data transmission leads to more energy consumption. To reduce the energy consumption, a data reduction strategy using neural adaptation phenomenon (DR-NAP) has been proposed to decrease the communication energy in routing data to the BS in WSN. The neural adaptation phenomenon has been utilized for designing a simple data reduction scheme to decrease the amount of data transmitted. In this way, the sensor node energy is saved and the lifetime of the network is enhanced. The proposed approach has been implanted in the existing gravitational search algorithm (GSA)-based clustered routing for WSN. The sensed data are transmitted to CH and BS using DR-NAP. Real sensor data from the Intel Berkeley Research lab have been used for conducting the experiments. The experiment results show 47.82% and 51.96% of improvement in network lifetime when compared with GSA-based clustered routing and clustering scheme using Canada Geese Migration Principle (CS-CGMP) for routing, respectively.     

P-LUET: A Prolong Lines of Uniformity Based Enhanced Threshold Algorithm for Heterogeneous Wireless Sensor Network Enabled Internet of Things Framework

With expanding realms of Internet of Things (IoT), researchers have started venturing into designing such algorithms for Wireless Sensor Networks (WSN) that support IoT network requirements. However, collaborating a sensor network into Internet of Things applicability faces surging data flow amidst which the senor network is expected to provide reliable data over extended span. Amidst variant techniques like routing, data aggregation, packet scheduling etc. which can be improvised upon, clustering has been a widespread energy efficient technique that primarily shortens distances in large-scale networks while also bring down commuting packets over improved connectivity. Therefore, utilizing clustering to incorporate WSN enabled IoT (WSN-IoT) standards becomes the primary focus of this paper. Heuristic based clustering algorithm termed as Prolong—Lines of Uniformity based Energy Threshold protocol (P-LUET) has been proposed that focusses on expanding the stable operating period of WSN-IoT. This algorithm is based on certain measures of WSN-IoT’s per unit, that is, residual energy, cartesian coordinate based location, shadow distance from the Sink Node, and density within the field. The parameters are employed such that they help combat hotspot issue, cluster overlapping, and network connectivity. A cluster-based conditional gridding Voronoi structure has also been consolidated that enables multi-hop communication. P-LUET algorithm is thoroughly analysed through comparisons with the existing approaches. Furthermore, analysis on P-LUET has also been carried on scenarios that are based on heterogeneity synthesis and b, c parameters.

     

Cluster based wireless sensor network routing using artificial bee colony algorithm

Due to recent advances in wireless communication technologies, there has been a rapid growth in wireless sensor networks research during the past few decades. Many novel architectures, protocols, algorithms, and applications have been proposed and implemented. The efficiency of these networks is highly dependent on routing protocols directly affecting the network life-time. Clustering is one of the most popular techniques preferred in routing operations. In this paper, a novel energy efficient clustering mechanism, based on artificial bee colony algorithm, is presented to prolong the network life-time. Artificial bee colony algorithm, simulating the intelligent foraging behavior of honey bee swarms, has been successfully used in clustering techniques. The performance of the proposed approach is compared with protocols based on LEACH and particle swarm optimization, which are studied in several routing applications. The results of the experiments show that the artificial bee colony algorithm based clustering can successfully be applied to WSN routing protocols.     

Energy-Efficient Routing Algorithm Based on Unequal Clustering and Connected Graph in Wireless Sensor Networks

Clustering and multi-hop routing algorithms substantially prolong the lifetime of wireless sensor networks (WSNs). However, they also result in the energy hole and network partition problems. In order to balance the load between multiple cluster heads, save the energy consumption of the inter-cluster routing, in this paper, we propose an energy-efficient routing algorithm based on Unequal Clustering Theory and Connected Graph Theory for WSN. The new algorithm optimizes and innovates in two aspects: cluster head election and clusters routing. In cluster head election, we take into consideration the vote-based measure and the transmission power of sensor nodes when to sectionalize these nodes into different unequal clusters. Then we introduce the connected graph theory for inter-cluster data communication in clusters routing. Eventually, a connected graph is constituted by the based station and all cluster heads. Simulation results show that, this new algorithm balances the energy consumption among sensor nodes, relieves the influence of energy-hole problem, improve the link quality, achieves a substantial improvement on reliability and efficiency of data transmission, and significantly prolongs the network lifetime.     

Network energy optimization and intelligent routing in WSN applicable for IoT using self-adaptive coyote optimization algorithm

The Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self-adaptive meta-heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self-adaptive coyote optimization algorithm (SA-COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA-COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA-COA. From the simulation findings, the best value of the designed SA-COA-LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self-adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C-ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods.     

Energy-balanced unequal clustering protocol for wireless sensor networks

Clustering provides an effective way to prolong the lifetime of wireless sensor networks.One of the major issues of a clustering protocol is selecting an optimal group of sensor nodes as the cluster heads to divide the network.Another is the mode of inter-cluster communication.In this paper,an energy-balanced unequal clustering(EBUC)protocol is proposed and evaluated.By using the particle swarm optimization(PSO)algorithm,EBUC partitions all nodes into clusters of unequal size,in which the clusters closer to the base station have smaller size.The cluster heads of these clusters can preserve some more energy for the inter-cluster relay traffic and the 'hot-spots' problem can be avoided.For inter-cluster communication,EBUC adopts an energy-aware multihop routing to reduce the energy consumption of the cluster heads.Simulation results demonstrate that the protocol can efficiently decrease the dead speed of the nodes and prolong the network lifetime.     

Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications

Energy is vital parameter for communication in Internet of Things (IoT) applications via Wireless Sensor Networks (WSN). Genetic algorithms with dynamic clustering approach are supposed to be very effective technique in conserving energy during the process of network planning and designing for IoT. Dynamic clustering recognizes the cluster head (CH) with higher energy for the data transmission in the network. In this paper, various applications, like smart transportation, smart grid, and smart cities, are discussed to establish that implementation of dynamic clustering computing-based IoT can support real-world applications in an efficient way. In the proposed approach, the dynamic clustering-based methodology and frame relay nodes (RN) are improved to elect the most preferred sensor node (SN) amidst the nodes in cluster. For this purpose, a Genetic Analysis approach is used. The simulations demonstrate that the proposed technique overcomes the dynamic clustering relay node (DCRN) clustering algorithm in terms of slot utilization, throughput and standard deviation in data transmission.

     

Virtual MIMO-based cross-layer design for wireless sensor networks

In this paper, a novel multihop virtual multiple-input-multiple-output (MIMO) communication protocol is proposed by the cross-layer design to jointly improve the energy efficiency, reliability, and end-to-end (ETE) QoS provisioning in wireless sensor network (WSN). In the protocol, the traditional low-energy adaptive clustering hierarchy protocol is extended by incorporating the cooperative MIMO communication, multihop routing, and hop-by-hop recovery schemes. Based on the protocol, the overall energy consumption per packet transmission is modeled and the optimal set of transmission parameters is found. Then, the issues of ETE QoS provisioning of the protocol are considered. The ETE latency and throughput of the protocol are modeled in terms of the bit-error-rate (BER) performance of each link. Then, a nonlinear constrained programming model is developed to find the optimal BER performance of each link to meet the ETE QoS requirements with a minimum energy consumption. The particle swarm optimization (PSO) algorithm is employed to solve the problem. Simulation results show the effectiveness of the proposed protocol in energy saving and QoS provisioning.     

A boolean spider monkey optimization based energy efficient clustering approach for WSNs

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.     

Clustering with Load Balancing-Based Routing Protocol for Wireless Sensor Networks

In this paper we propose a routing protocol based on clustering (IGP-C Protocol) to extend the lifetime in the context of wireless sensor networks while optimizing other resources (memory and processor). Firstly, a clustering algorithm and a load balancing technique are used together in order to reap the benefits of both approaches. The proposed clustering algorithm with load balancing (CALB Algorithm) is a fully distributed algorithm performed by each sensor and requires only communication with its immediate neighbors. Secondly, an Improved Gossiping Protocol (IGP) is proposed to extend the CALB algorithm to the data routing. The simulation results demonstrate the better and promising performances of the IGP-C protocol compared with the other protocols proposed in the literature. The IGP-C protocol allows a better distribution of energy, memory and processing capabilities of cluster-heads and reduces the number of clusters consisting of a single sensor along with the number of iterations. This demonstrates the effectiveness of the cluster-heads election process which improves the load balancing in the wireless sensors network in terms of cluster-heads load and clusters size. Furthermore, the proposed routing strategy builds around the clustering algorithm, is effective since it reduces the data transmission delay and prolongs the network lifetime.     

基于遗传聚类的无线传感器负载均衡路由算法

通常的无线传感器分簇网络存在节点负载不均衡的问题。为均衡各节点能量消耗,延长网络生存周期,将K均值算法与遗传算法相结合,提出一种负载均衡的无线传感器网络路由算法,算法利用遗传算法的全局寻优能力以克服传统K均值算法的局部性和对初始中心的敏感性,实现了传感器网络节点自适应成簇与各节点负载均衡。仿真实验表明,该算法显著延长了网络寿命,相对于其他分簇路由算法,其网络生存时间延长了约43%。     

Sling-shot spider optimization algorithm based packet length control in wireless sensor network and Internet of Things-based networks

Wireless sensor network (WSN) is a well-developed domain suitable in the optimal collection and processing of information needed for the present world. For processing and transmitting the correct data by the network, it is affected by many disrupting factors like interference, battery life, distance between the nodes, and redundant data. There have been many methods proposed in the past. Among all the disrupting factors, routing has been the most studied problem in the WSN literature, whereas packet length sizing has been the most untouched topic compared to routing. Sizing the packet lengths to transmit it through the network is important to get error-free data and run an energy-efficient network. In this paper, we propose a sling-shot spider optimization (S²SO) algorithm for packet length optimization in WSN and Internet of Things (IoT)-based networks. The S²SO algorithm is developed based on the spider's unique behavior of catching prey. The proposed algorithm is implemented in three types of networks: 1-hop, 2-hop, and multi-hop networks. A mathematical model for the communication channel, energy efficiency, and energy consumption is developed for all three types of networks. The proposed packet length optimization model is simulated in MATLAB software and compared with conventional bio-inspired algorithms. The results show that the proposed algorithm is very fast in finding the optimal results and transmits optimal packet size with low error rate of 0.2 p.u. and a high efficiency of 98%.     

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.     

基于遗传聚类的无线传感器负载均衡路由算法

通常的无线传感器分簇网络存在节点负载不均衡的问题。为均衡各节点能量消耗,延长网络生存周期,将K均值算法与遗传算法相结合,提出一种负载均衡的无线传感器网络路由算法,算法利用遗传算法的全局寻优能力以克服传统K均值算法的局部性和对初始中心的敏感性,实现了传感器网络节点自适应成簇与各节点负载均衡。仿真实验表明,该算法显著延长了网络寿命,相对于其他分簇路由算法,其网络生存时间延长了约43%。     

Cross-Layer QoS Enabled SDN-Like Publish/Subscribe Communication Infrastructure for IoT

Publish/subscribe paradigm is often adopted to create the communication infrastructure of the Internet of Things(IoT)for many clients to access enormous real-time sensor data.However,most current publish/subscribe middlewares are based on traditional ossified IP networks,which are difficult to enable Quality of Service(QoS).How to design the next generation publish/subscribe middleware has become an urgent problem.The emerging Software Defined Networking(SDN)provides new opportunities to improve the QoS of publish/subscribe facilities for delivering events in IoT owing to its customized programmability and centralized control.We can encode event topics,priorities and security policies into flow entries of SDN-enabled switches to satisfy personalized QoS needs.In this paper,we propose a cross-layer QoS enabled SDN-like publish/subscribe communication infrastructure,aiming at building an IoT platform to seamlessly connect IoT services with SDN networks and improving the QoS of delivering events.We first present an SDN-like topic-oriented publish/subscribe middleware architecture with a cross-layer QoS control framework.Then we discuss prototype implementation,including topic management,topology maintenance,event routing and policy management.In the end,we use differentiated services and cross-layer access control as cross-layer QoS scenarios to verify the prototype.Experimental results show that our middleware is effective.     

Energy-efficient routing protocol and optimized passive clustering in WSN for SMART grid applications

Due to low cost, ease of implementation and flexibility of wireless sensor networks (WSNs), WSNs are considered to be an essential technology to support the smart grid (SG) application. The prime concern is to increase the lifetime in order to find the active sensor node and thereby to find once the sensor node (SN) dies in any region. For this reason, an energy-efficient Dynamic Source Routing (DSR) protocol needs to provide the right stability region with a prolonged network lifetime. This work is an effort to extend the network's existence by finding and correcting the considerable energy leveraging behaviors of WSN. We build a comprehensive model based on real measures of SG path loss for different conditions by using the characteristics of WSN nodes and channel characteristics. This method also establishes a hierarchical network structure of balanced clusters and an energy-harvesting SN. The cluster heads (CHs) are chosen by these SN using a low overhead passive clustering strategy. The cluster formation method is focused on the use of passive clustering of the particle swarm optimization (PSO). For the sake of eliminating delayed output in the WSN, energy competent dynamic source routing protocol (EC-DSR) is used. Chicken swarm optimization (CSO) in which optimum cluster path calculation shall be done where distance and residual energy should be regarded as limitation. Finally, the results are carried out with regard to the packet distribution ratio, throughput, overhead management, and average end-to-end delay to demonstrate the efficiency of the proposed system.     

Zone-based routing protocol with mobility consideration for wireless sensor networks

Hierarchical routing and clustering mechanisms in Wireless Sensor Networks (WSN) help to reduce the energy consumptions and the overhead created when all the sensor nodes in the network are sending information to the central data collection point. Most of the routing and clustering protocols proposed for WSN assume that the nodes are stationary. However, in applications like habitat monitoring or search and rescue, that assumption makes those clustering mechanisms invalid, since the static nature of sensors is not real. In this paper, we propose Zone-based Routing Protocol for Mobile Sensor Networks (ZoroMSN) that considers the design aspects such as mobility of sensors, zones and routes maintenance, information update and communication between sensor nodes. Simulation results show the effectiveness and strengths of the ZoroMSN protocol such as a low routing and mobility overhead, while achieving a good performance in WSN using small zone sizes and sensors with low speed. Simulation results also show that ZoroMSN outperforms existing LEACH-ME and LEACH-M protocols in terms of network lifetime and energy consumptions.     

A Three Layered Peer-to-Peer Energy Efficient Protocol for Reliable and Secure Data Transmission in EAACK MANETs

The wireless body sensor network (WBSN) an extensive of WSN is in charge for the detection of patient’s health concerned data. This monitored health data are essential to be routed to the sink (base station) in an effective way by approaching the routing technique. Routing of tremendous sensed data to the base station minimizes the life time of the network due to heavy traffic occurrence. The major concern of this work is to increase the lifespan of the network which is considered as a serious problem in the wireless network functionalities. In order to recover this issue, we propose an optimal trust aware cluster based routing technique in WBSN. The human body enforced for the detection of health status is assembled with sensor nodes. In this paper, three novel schemes namely, improved evolutionary particle swarm optimization (IEPSO), fuzzy based trust inference model, and self-adaptive greedy buffer allocation and scheduling algorithm (SGBAS) are proposed for the secured transmission of data. The sensor nodes are gathered to form a cluster and from the cluster, it is necessary to select the cluster head (CH) for the effective transmission of data to nearby nodes without accumulation. The CH is chosen by considering IEPSO algorithm. For securable routing, we exhibit fuzzy based trust inference model to select the trusted path. Finally, to reduce traffic occurrence in the network, we introduce SGBAS algorithm. Experimental results demonstrate that our proposed method attains better results when compared with conventional clustering protocols and in terms of some distinctive QoS determinant parameters.     

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。