Multi-sink Wireless Sensor Networks Research Articles

Adaptive squirrel coyote optimization-based secured energy efficient routing technique for large scale WSN with multiple sink nodes

In general, Wireless Sensor Networks (WSNs) require secure routing approaches for delivering the data packets to their sinks or destinations. Most of the WSNs identify particular events in their explicit platforms. However, several WSNs may examine multiple events using numerous sensors in a similar place. Multi-sink and multi-hop WSNs include the ability to offer network efficiency by securing effective data exchanges. The group of nodes in the multi-sink scenario is described through a distance vector. Though, the efficiency of multi-sink WSNs is considerably impacted by the routing of data packets and sink node placement in the cluster. In addition, many WSNs for diverse reasons existed in the similar geographical region. Hence, in this task, a secured energy-efficient routing technique is designed for a Wireless sensor network with Large-scale and multiple sink nodes. Here, the concept of an improved meta-heuristic algorithm termed Adaptive Squirrel Coyote Search Optimization (ASCSO) is implemented for selecting the accurate selection of cluster head. The fitness function regarding residual distance, security risk, energy, delay, trust, and Quality of Service (QoS) is used for rating the optimal solutions. The consumption of energy can be reduced by measuring the mean length along with the cluster head and multiple sink nodes. The latest two heuristic algorithms such as Coyote Optimization Algorithm (COA) and Squirrel Search Algorithm (SSA) are integrated for suggesting a new hybrid heuristic technique. Finally, the offered work is validated and evaluated by comparing it with several optimization algorithms regarding different evaluation metrics between the sensor and sink node.

CFMS: A Cluster-based Convergecast Framework for Dense Multi-Sink Wireless Sensor Networks

Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data collection process must be conducted while copying with packet collisions, nodes’ congestion or data redundancy. These issues always result in energy waste which is detrimental to network efficiency and lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks’ duty cycling, data aggregation and transmission scheduling in order to minimise message overhead and packet losses. Simulation results show that this solution can mitigate delay while significantly increasing packet delivery and network lifetime.

Environment-Aware Energy Efficient and Reliable Routing in Real-Time Multi-Sink Wireless Sensor Networks for Smart Cities Applications

Internet of things (IoT) is one of the leading technologies that have been used in many fields, such as environmental monitoring, healthcare, and smart cities. The core of IoT technologies is sensors; sensors in IoT form an autonomous network that is able to route messages from one place to another to the base station or the sink. Recently, due to the rapid technological development of sensors, wireless sensor networks (WSNs) have become an important part of IoT. However, in applications such as smart cities, WSNs with one sink might not be suitable due to the limited communication range of sensors and the wide area to be covered. Therefore, multi-sink WSN solutions seem to be suitable for such applications. The multi-sink WSNs are gaining popularity because they increase network throughput, network lifetime, and energy usage. At the same time, multi-hop routing is essential for the WSNS to collect data from sensor nodes and route it to the sink node for decision-making. Many routing algorithms developed for multi-sink WSNs focus on being energy efficient to extend the network lifetime, but the delay was not the main concern. However, these algorithms are unable to deal with such applications in which the data packets have to reach sink nodes within predefined real-time information. On the other hand, in the most existing routing schemes, the effects of the external environmental factors such as temperature and humidity and the reliability of real-time data delivery have largely been ignored. These issues can dramatically influence the network performance. Therefore, this paper designs a routing algorithm that satisfies three critical conditions: energy-efficient, real-time, environment-aware, and reliable routing. Therefore, the routing decisions are made according to different parameters. Such parameters include environmental impact metrics, energy balance metrics to balance the energy consumption among sensor nodes and sink nodes, desired deadline time (required delivery time), and wireless link quality. The problem is formed in integer linear programming (ILP) for optimal solution. The problem formulation is designed to fully understand the problem with its major constraints by the sensor networks research community. In addition, the optimal solution for small-scale problems could be used to measure the quality of any given heuristic that might be used to solve the same problem. Then, the paper proposes swarm intelligence to solve the optimization problem for large-scale multi-sink WSNs as a heuristic algorithm. The proposed algorithm is evaluated and analyzed compared with two recent algorithms, which are the most related to our proposal, SMRP and EERP protocols using an extensive set of experiments. The obtained results prove the superiority of the proposed algorithm over the compared algorithms in terms of packet delivery ratio, deadline miss ratio, average end-to-end delay, network lifetime, and energy imbalance factor under different aspects. In particular, the proposed algorithm requires more computational energy compared to comparison algorithms.

PSO-based sink placement and load-balanced anycast routing in multi-sink WSNs considering compressive sensing theory

This paper deals with the sink placement and anycast routing to increase the lifetime of multi-sink wireless sensor networks. Two algorithms are proposed, namely “Multi-sink Placement and Anycast Routing (MPAR)” and “Extended Multi-sink Placement and Anycast Routing (EMPAR)”, to jointly address the problems of clustering, multi-sink placement, and load-balanced anycast routing. MPAR and EMPAR rely on a two-level architecture in which sensors are clustered at the lower level. Each sensor transmits its data to the corresponding Cluster Head (CH) via a load-balanced data aggregation routing tree. At the upper level, both schemes use a modified particle swarm optimization algorithm to determine the best location of sinks. For each sink, a high-level anycast routing tree is developed using the ant colony optimization algorithm. Each anycast tree uses the hybrid Compressive Sensing (CS) method to forward the aggregated data from CHs to sinks. Extensive simulations are conducted to illustrate the efficiency of the proposed algorithms in terms of energy consumption, energy consumption variance, and network lifetime. The results show that EMPAR has a better performance than MPAR due to its CH selection strategy. As an advantage, EMPAR considers both remaining energy and distance criteria along with a rest factor to select the best CH for each cluster. For an average number of clusters, EMPAR reduces the energy consumption by 5.98% and 12.20%, respectively, compared to the MPAR algorithm and the energy-aware CS-based data aggregation algorithm. It also increases the network lifetime in comparison with the aforementioned algorithms by 12.26% and 30.38%, respectively.

The Impact of Deployment Pattern and Routing Scheme on the Lifetime in Multi-Sink Wireless Sensor Network

Extensive use of sensor and actuator networks in many real-life applications introduced several new performance metrics at the node and network level. Since wireless sensor nodes have significant battery constraints, therefore, energy efficiency, as well as network lifetime, are among the most significant performance metrics to measure the effectiveness of given network architecture. This work investigates the performance of an event-based data delivery model using a multipath routing scheme for a wireless sensor network with multiple sink nodes. This routing algorithm follows a sink initiated route discovery process with the location information of the source nodes already known to the sink nodes. It also considers communication link costs before making decisions for packet forwarding. Carried out simulation compares the network performance of a wireless sensor network with a single sink, dual sink, and multi sink networking approaches. Based on a series of simulation experiments, the lifetime aware multipath routing approach is found appropriate for increasing the lifetime of sensor nodes significantly when compared to other similar routing schemes. However, energy-efficient packet forwarding is a major concern of this work; other network performance metrics like delay, average packet latency, and packet delivery ratio are also taken into the account.

Modeling and Optimizing the Cascading Robustness of Multisink Wireless Sensor Networks

Current research on cascading failures of wireless sensor networks (WSNs) mainly focuses on single-sink networks and rarely involves multisink networks. To this end, this article proposes a realistic cascading model for multisink WSNs based on a new load metric “multioriented link betweenness.” On this basis, a memetic algorithm MA-MSP is proposed to help WSNs resist cascading failures via multisink placement optimization, in which the local search operator is designed based on a new network balancing metric “multioriented network entropy.” Extensive simulations have shown that the proposed cascading model can properly characterize the cascading process of multisink WSNs. Link capacity is a key factor in determining network robustness. MA-MSP can obtain a more robust placement scheme with less time compared to existing algorithms. The network communication efficiency is positively related to network robustness, and the average shortest path length is negatively related to network robustness.

Low latency and energy efficient routing-aware network coding-based data transmission in multi-hop and multi-sink WSN

Multi-hop and multi-sink wireless sensor networks have the potential to provide network performance through efficient data exchanges. In multi-sink phenomena, clusters of nodes are defined using distance vector and thereby specific node that lies at the center of the cluster is identified as a sink. The performance of multi-hop and multi-sink wireless networks is significantly affected by sink node placement and routing of data packets within the cluster. In this paper, the authors propose an application of three different algorithms to improve the performance of a sensor network in terms of sink node placement along with route construction and optimization using nature-inspired computational methods. Furthermore, at potential relays, opportunistic coding is used to reduce the number of transmissions. Hence, the proposed implementation integrates three algorithms, which combines the merits of each for significant enhancement in data transmission. First is the placement of sink node through particle swarm optimization, second is the route construction from sensors and sink of the particular cluster using minimum wiener spanning tree, which further optimized by artificial bee colony technique and third is opportunistic packet amalgamation before transmitting to neighbors. Finally, the proposed work is evaluated and validated for coded transmissions and non-coded transmissions through comparisons of evaluation metrics like throughput, energy conservation, packet delivery ratio and average hop-count between sensor and sink node.

Algorithm for fairness in schedule lengths of sink-rooted trees in multi-sink heterogeneous wireless sensor networks

Sensor networks are used for observing some region of interest. Sensors sense different physical quantities and send to base station or sink. The tree based networks with TDMA as MAC protocol are preferable because of simplicity of tree and guaranteed data access in TDMA. Often sensor networks are multi-sink and multi-attribute networks. Multi-sink network means more than one sinks are present and so multiple sink-rooted trees are formed. When more than one types of nodes are deployed in the network, the network is known as heterogeneous network or multi-attribute network. Sometimes node density or heterogeneity is not uniform across entire network. As a result of non-uniform node distribution or non-uniform heterogeneity distribution, schedule lengths of sink-rooted trees are very different. Nodes part of trees with small schedule length will get more frequent transmission turns compared to those which belong to trees with large schedule length. To ensure fairness in terms of transmission opportunities, it is desired that schedule lengths should be balanced. In this work, an algorithm named as Schedule Length Balancing for Multi-sink HeTerogeneous networks (SLBMHT) is presented to balance schedule lengths. The SLBMHT algorithm is evaluated through simulations. It is found that the SLBMHT algorithm results in 8–56% reduction in schedule length difference of trees. It also results in 2–17% energy savings during data transmission phase. Only demerit is increase in control overhead. But as resulting increase in energy consumption is not much, it is overcome by savings in data energy consumption. Thus network lifetime is likely to increase.

Fuzzy-based Clustering Scheme with Sink Selection Algorithm for Monitoring Applications of Wireless Sensor Networks

Wireless sensor networks (WSNs) are predominantly used for monitoring applications. The sensor nodes are resource-constrained devices, and hence efficient energy utilization of these nodes is one of the major challenges. The communication distances directly impact on the energy consumption of the sensor nodes. Clustering methods are popularly used to reduce communication distances and prolong the network lifetime. Multi-sink deployment is another method to reduce communication distances. It also resolves congestion and hotspot issues. In multi-sink WSNs, the number of sinks to be considered is a challenging task as it affects the network topology, lifetime and deployment cost. In this research work, multi-sink deployment and clustering scheme with sink selection algorithm are jointly proposed to maximize the network lifetime and minimize the deployment cost. An iterative filtering model is proposed to estimate optimal number of sinks, while sink positions are determined based on Fuzzy logic inference system (FLIS). Fuzzy-c-means algorithm is used to form balanced clusters in the network. Cluster representative and sink selection processes are based on FLIS. The proposed optimal multi-sink deployment scheme reduces the deployment cost and the propagation delay of the system, while enhancing the network lifetime. The proposed scheme is also energy efficient in the case of higher node density. Hence, the proposed scheme can be suitably implemented for large-scale monitoring applications of WSNs.

Sustainable Multipath Routing Protocol for Multi-Sink Wireless Sensor Networks in Harsh Environments

Existing routing protocols for multi-sink wireless sensor networks (WSNs) attempt to optimize energy efficiency and routing survivability from the perspective of the network itself, without considering the impact of external environment, making them unable to respond to environmental changes in a timely manner. Therefore, the routing survivability of these routing protocols in harsh environments is questionable. To solve this problem, we design a sustainable multipath routing protocol SMRP, in which the routing decisions are made according to a mixed potential field in terms of depth, residual energy and environment. The basic idea of SMRP is to instruct messages to select paths with a tradeoff among delivery latency, energy balance and routing survivability. As the environmental field is constructed and updated using the sensing capability of WSN itself, the constructed multipath can be secured by avoiding passing through the dangerous areas. We explore the impact of the number of sink nodes and the number of paths on routing performance, and compare SMRP with commonly used routing protocols (i.e., EDGR and IPF). Through extensive experiments, SMRP has proved to have better performance in terms of packet delivery ratio and network lifetime under harsh environments.

Modeling and analysis of cascading node-link failures in multi-sink wireless sensor networks

Due to the prominent advantages in network lifetime and energy balance, the application of multi-sink wireless sensor networks (WSNs) is becoming more and more widespread. However, their cascading robustness is still rarely studied. Therefore, in this paper, a realistic cascading model for multi-sink WSNs is proposed. In this model, two load metrics are proposed to characterize the load distributions of sensor nodes and wireless links, and the cascading process of the network is jointly promoted by node overload events and link overload events, which can better reflect the cascading characteristics of multi-sink WSNs in practical scenarios. In addition, we focus on the cascading robustness of the network in the face of node attacks and link attacks. Through extensive experiments, we found that there are critical thresholds for both node capacity and link capacity, which can determine whether capacity expansion is helpful; there is a critical threshold for network load distribution, which can determine whether cascading failures occur; node attacks are more likely to trigger cascading failures than link attacks; increasing node capacity can more effectively reduce the damage of cascading failures to the network. The discovery of the above results can provide theoretical guidance for users to build a more robust multi-sink WSN against cascading failures.

Simulated annealing and genetic algorithm-based hybrid approach for energy-aware clustered routing in large-range multi-sink wireless sensor networks

In recent years, the researchers have got progressively more interest in wireless sensor networks (WSNs) as it plays a vital role in numerous applications. WSNs have strict resource constraints such as limited bandwidth, limited energy, limited processing capability, and limited memory. Among them, the most critical one is limited energy. So, energy efficiency has got significant importance in the WSNs. Energy consumption is mainly due to computation and communication between sensor nodes and the base station. Energy consumption for communication is more when compared to computation. Hence it is indispensable to focus on designing an energy-efficient routing for WSN. In this paper, a cluster-based routing using simulated annealing and genetic algorithm-based hybrid (SAGA-H) approach has been proposed. The proposed approach is explained and simulated using MATLAB. Further, the results observed have been compared with existing GA-based approach with respect to network lifetime, the number of packets sent to BS and sink, and average residual energy. From the simulation results, it is observed that the proposed approach outperforms the existing GA-based approach.

Simulated annealing and genetic algorithm-based hybrid approach for energy-aware clustered routing in large-range multi-sink wireless sensor networks

Simulated annealing and genetic algorithm-based hybrid approach for energy-aware clustered routing in large-range multi-sink wireless sensor networks

Delay minimising depth-based routing for multi-sink underwater wireless sensor networks

Routing in underwater acoustic network is challenging because of longer delays and mobility caused on account of ocean currents and tides. Existing algorithms focus only on delivery of packets with very little consideration to minimising packet delay. In this paper, we propose a new beacon-based routing algorithm for underwater sensor networks using multiple sink architecture. Delay minimising depth-based routing (DMDBR) attempts to minimise delay and uses depth, hop count and residual energy for making routing decisions. Novelty of our scheme is in a node accepting packets for forwarding only if it has sufficient energy for forwarding all the packets in the packet queue. A node declines acting as a forwarder once the estimated residual energy is insufficient to forward the new packets. Simulation results show that DMDBR improves packet delivery ratio and lowers end-to-end delay in comparison to depth-based routing.

Bidirectional multi-optical line terminals incorporated converged WSN-PON network using M/M/1 queuing

Wireless Sensor Networks (WSNs) have an assortment of application areas, for instance, civil, military, and video surveillance with restricted power resources and transmission link. To accommodate the massive traffic load in hefty sensor networks is another key issue. Subsequently, there is a necessity to backhaul the sensed information of such networks and prolong the transmission link to access the distinct receivers. Passive Optical Network (PON), a next-generation access technology, comes out as a suitable candidate for the convergence of the sensed data to the core system. The earlier demonstrated work with single-OLT-PON introduces an overloaded buffer akin to video surveillance scenarios. In this paper, to combine the bandwidth potential of PONs with the mobility capability of WSNs, the viability for the convergence of PONs and WSNs incorporating multi-optical line terminals is demonstrated to handle the overloaded OLTs. The existing M/M/1 queue theory with interleaving polling with adaptive cycle time as dynamic bandwidth algorithm is used to shun the probability of packets clash. Further, the proposed multi-sink WSN and multi-OLT PON converged structure is investigated in bidirectional mode analytically and through computer simulations. The observations establish the proposed structure competent to accommodate the colossal data traffic through less time consumption.

An efficient reconfigurable ad-hoc algorithm for multi-sink wireless sensor networks

This article presents a novel distributed protocol for wireless sensor network formation and operation in multi-sink environments. It consists of three stages: the first one performs network formation and maintenance under a policy of low power consumption based on a local strategy in which every sensor node in the environment interacts only with the neighbour nodes. The second one uses local information to build trees from the sink nodes using a load balance strategy. In the third stage, the sinks collect sensed data through the trees. The protocol has been modelled by a timed Petri net, which is used first for a qualitative validation in which deadlocks, operational functionality, overflows, bottlenecks, and delays were checked, and later for network performance analysis.

Intrusion Detection at International Borders and Large Military Barracks with Multi-sink Wireless Sensor Networks: An Energy Efficient Solution

Wireless Sensor Networks have profound applications in military systems. Intrusion at unmanned borders and at other sensitive places may be tracked using sensor networks. However, the domain of military applications could imply hostile environment and thus monitoring of the nodes of the deployed WSN could be practically impossible. It is thus required that the deployed WSN ensures low energy consumption to give a high network life such that the cost of deployment may be suitably amortized. In this paper we propose an energy efficient solution for detecting intrusions through unmanned borders and other sensitive places with prolonged network lifetime using two routing schemes: KPS and Loop Free (LF)-KPS. We have compared these two schemes with LEACH and TEEN, and have shown how data transfer through KPS and LF-KPS protocols would ensure an enhanced lifetime for the deployed network. At the end we have also shown the effect of looping on the lifetime of the deployed network.

A load balanced location service for location information management of multi-sink Wireless Sensor Networks

In many applications of Wireless Sensor Networks (WSNs), the network has a few mobile sinks in addition to a large number of static sensor nodes that gather information from their surrounding environment. The static sensor nodes in such applications use a location service to find the location of a mobile sink. Most of the proposed location services are useful in mobile ad hoc networks and are not efficient for WSNs. Furthermore, new protocols specifically designed for WSNs are not load balanced. Some sensor nodes relay more packets and deplete their energy quickly, hence reducing the network lifetime dramatically. In this paper, we propose a location service called Load Balanced Location Service (LBLS) for WSNs containing a few mobile sinks. LBLS selects four nodes in the network outline and only uses the location of these nodes. LBLS do not deplete the energy of specific nodes comparing to other mechanisms that forward location information messages toward selected nodes. We formally prove that in LBLS every sensor node can always locate any mobile node in the network. By using exhaustive simulation, we show that LBLS is a load balanced location service. Simulation results demonstrate the superiority of LBLS regarding the load balancing factor compared to most available related location services.

Fuzzy Logic-Based Sink Selection and Load Balancing in Multi-Sink Wireless Sensor Networks

Using multiple sink nodes in wireless sensor networks can greatly improve the lifetime and throughput of the network. One of the important issues in multi-sink wireless sensor networks is the congestion problem in sink nodes which reduces the effectiveness of data processing in sink nodes and increases the energy consumption of the sensor nodes. To prevent congestion in the sink nodes, in this paper, a distributed fuzzy logic-based sink selection algorithm is presented for one-hop sensor networks which makes each sensor node able to independently select a sink node based on the congestion situations in all the sink nodes. This scheme can effectively prevent congestion in the sink nodes and provide load balancing among them. Moreover, it reduces the delay of successful data transmissions to the sink nodes and improves the lifetime of wireless sensor networks by reducing the number of packet retransmissions. Simulation results indicate the effectiveness of our proposed solution.

A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels.

Traditional underground coalmine monitoring systems are mainly based on the use of wired transmission. However, when cables are damaged during an accident, it is difficult to obtain relevant data on environmental parameters and the emergency situation underground. To address this problem, the use of wireless sensor networks (WSNs) has been proposed. However, the shape of coalmine tunnels is not conducive to the deployment of WSNs as they are long and narrow. Therefore, issues with the network arise, such as extremely large energy consumption, very weak connectivity, long time delays, and a short lifetime. To solve these problems, in this study, a new routing protocol algorithm for multisink WSNs based on transmission power control is proposed. First, a transmission power control algorithm is used to negotiate the optimal communication radius and transmission power of each sink. Second, the non-uniform clustering idea is adopted to optimize the cluster head selection. Simulation results are subsequently compared to the Centroid of the Nodes in a Partition (CNP) strategy and show that the new algorithm delivers a good performance: power efficiency is increased by approximately 70%, connectivity is increased by approximately 15%, the cluster interference is diminished by approximately 50%, the network lifetime is increased by approximately 6%, and the delay is reduced with an increase in the number of sinks.