Energy efficient Chessboard Clustering and routing in heterogeneous sensor networks

In recent years, a new wave of networks labelled Wireless Sensor Networks (WSNs) has attracted a lot of attention from researchers in both academic and industrial communities. A WSN consists of a collection of sensor nodes and a base station connected through wireless channels, and can be used for many applications such as military application, building distributed systems, physical environment monitoring, and security surveillance among others. A big advantage of sensor networks is represented by ease of deployment, reducing installation cost, possibility to distribute the tiny sensors over a wide region, and larger fault tolerance (V. Loscri et al., 2005). However, despite the infinite scopes of wireless sensor networks applications, they are limited by the node battery lifetime. Such constraints combined with a typical deployment of large number of sensor nodes have posed many challenges to the design and management of sensor networks and necessitate energyawareness at all layers of the networking protocol stack (Q. Xue & A. Ganz, 2004). Therefore, energy efficient algorithms have been one of the most challenging issues for WSNs. Sensor nodes can be in one of four states, namely transmit, receive, idle and sleep. The largest part of a node’s energy is consumed while transmitting and receiving. Minimizing the number of communications by eliminating or aggregating redundant sensed data saves much amount of energy (L. B. Ruiz et al., 2003). Among these clustering sensor networks are a very attractive approach because clustering allows for scalability, data aggregation, and energy efficiency. In a clustering network, nodes are grouped into clusters and there are special nodes called cluster head. They are responsible for an efficient way to lower energy consumption within a cluster by performing data aggregation. In a heterogeneous sensor network, two or more different types of nodes with different battery energy and functionality are used. On the other hand, in homogeneous networks all the sensor nodes are identical in terms of battery energy and hardware complexity. As a result, network performance decreases since the cluster head nodes goes down before other nodes do. Thus dynamic, energy efficient and adaptive cluster head selection algorithm is very important. Sensor networks can be divided in two classes as event-driven and continuous dissemination networks according to the periodicity of communication (L. B. Ruiz et al., 9

In Wireless Sensor Networks (WSNs), the reduction of energy consumption in the batteries of a sensor node is an important task. Sensor nodes of WSNs perform three significant functions such as data sensing, data transmitting and data relaying. Routing technique is one of the methods to enhance the sensor nodes battery lifetime. Energy optimization is done by using one of the heuristic routing methods for sensing and transmitting the data. To enhance the energy optimization mainly concentrated on data relaying. In this work stochastic hill climbing is adapted. The proposed solution for data relaying utilizes geographical routing and mobile sink technique. The sink collects the data from cluster heads and movement of the sink is routed by stochastic hill climbing. Network simulator 2 is used for experimentation purpose. This work also compares with the existing routing protocols like Energy-efficient Low Duty Cycle (ELDC), Threshold sensitive Energy Efficient sensor Network (TEEN) and Adaptive clustering protocol. The proposed work shows promising results with respect to lifetime, average energy of nodes and packet delivery ratio.

Research on sensor network routing focused on efficiency and effectiveness of data dissemination. Few of them considered security issues during the design time of a routing protocol. Furthermore, previous research on sensor networks mainly considered homogeneous sensor networks where all sensor nodes have the same capabilities. It has been shown that homogeneous ad hoc networks have poor fundamental performance limits and scalability. To achieve better performance, we adopt a heterogeneous sensor network (HSN) model. In this paper, we present a secure and efficient routing protocol for HSNs - two tier secure routing (TTSR). TTSR takes advantage of powerful high-end sensors in an HSN. Our security analysis demonstrates that TTSR can defend typical attacks on sensor routing. Our performance evaluation shows that TTSR has higher delivery ratio, lower end-to-end delay and energy consumption than a popular sensor network routing protocol.

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

Heterogeneous wireless sensor networks (HWSNs) provide realistic application design capabilities. The sensor nodes (SNs) are arranged in groups or clusters. Each cluster has a cluster head (CH). The CH accumulates the sensed data from its SNs to be sent to a base station (BS). SNs have been positioned with batteries, which cannot be replaced. Energy consumption is a major concern for WSNs. In order to achieve efficient mission-critical sensor networks, it is necessary to develop energy-efficient routing protocols. We suggest an innovative and inventive approach to design an enhanced energy-efficient clustering approach (EEECA) to increase the network lifetime and reduce the energy consumption in HWSNs. The proposed protocol is based on the three-tier nodes, namely, standard, intermediate, and advanced nodes. In the clustering procedure, we consider various parameters for selecting the CH, namely, the initial energy of the SNs, the number of alive/dead nodes, and the remaining energy. Extensive performance parameters show that the proposed EEECA-THWSN extends the network lifetime by 25% and decreases the network’s energy consumption. The proposed protocol improves the energy efficiency of EEECA-THWSN and significantly increases the network throughput, network lifetime, and network stability.

The major concern in design and operation of wireless sensor networks is the issue of prolonging network lifetime which is basically rely on the network connectivity. And connectivity of the networks are directly depends on battery life of sensor nodes. However, the nodes have inadequate energy supplies and making battery change/substitution is very hard or even impractical. As a result, energy should be efficiently conserved to elongate sensor networks lifetime by having an energy-efficient routing algorithms. Hierarchical cluster routing algorithms can reduce energy consumption requirement; but the main problem and challenge of cluster formation is to select proper and suitable cluster heads by taking into consideration various parameters like remaining energy of the nodes, number of neighbours, position of node, and distance from base station. Cluster formation is a Non Polynomial hard problem, and evolutionary based technique is proposed to design an energy-efficient cluster to prolong lifetime of sensor networks, while ensuring energy efficient routing path construction for sensor nodes. Moreover, the proposed protocol maintains centrality of the cluster heads and let balanced energy dissipation among clusters. The protocol is implemented using OMNeT++ and the result analysis shows the protocol has good network stability period and the energy depletion of every node is smooth.

An energy efficient and QoS aware routing protocol for wireless sensor and actuator networks

We present geometric broadcast for sensor and actor networks (GBSA), a novel broadcasting protocol for heterogeneous wireless sensor and actor networks. While broadcasting is a very energy-expensive protocol, it is also widely used as a building block for a variety of other network layer protocols. Therefore, reducing the energy consumption by optimizing broadcasting is a major improvement in heterogenous sensor networking. GBSA is a distributed algorithm where nodes make local decisions on whether to transmit based on a geometric approach. GBSA does not need any neighborhood information and imposes very low communication overhead. GBSA is scalable to the change in network size, node type, node density and topology. GBSA accommodates seamlessly such network changes, including the presence of actors in heterogeneous sensor networks. Indeed GBSA takes advantage of actor nodes, and uses their resources when possible, thus reducing the energy consumption by sensor nodes. Through simulation evaluations, we show that GBSA is very scalable and its performance is improved by the presence of actors. At best of our knowledge, GBSA is the first broadcast protocol designed specifically for heterogeneous sensor and actor networks.

In multicasting packets of data from a node will be sent to a group of receiver nodes at the same time. Multicasting lowers transmission costs. Energy conservation is critical to a sensor network’s long-term viability. Sensor networks have limited and non-replenishable energy supplies, maximizing network lifetime is crucial in sensor nodes. As a result, clustering has become one of the popular methods for extending the lifetime of an entire system by integrating information at the cluster head. Cluster head (CH) selection is the important serving node in each cluster in the Wireless sensor networks (WSN). This paper introduces a High Power Node (HPN) multicasting approach which embeds a cluster of sink node data in packet headers to allow receiver for utilizing a approach for transferring multicast packet data via the shortest paths. The proposed Energy efficient multicasting cluster based routing (EEMCR) protocol utilized high power nodes, which shall play a critical role in minimal energy usage. The implementation findings demonstrate that, when compared with the previous methodologies, the suggested algorithm has enhanced in terms of packet delivery ratio (PDR), End to end delivery rate, efficiency and achieves low energy consumption. The proposed EEMCR obtain 95% efficiency. The results are then compared to other existing algorithms to determine the superiority of the proposed methodology.

Wireless sensor networks contains small sensor node and processor which have several issues and challenges with regards to Limited Computational Capacity, Energy Efficiency, Path Planning Overhead and many more. Entire lifetime of network depends on efficient energy consumption in sensor network so it is a challenging task to design an Energy Efficient Routing Protocol for wireless sensor networks. Wireless sensor nodes dynamically self-organize themselves without utilizing preexisting infrastructure. Problem becomes more complex if some sensor node having mobility for some particular instance. In this paper we proposed an energy efficient routing protocol for WSN motivated by swarm intelligence of rummage behavior of ant and also covered mobility behavior of node. Furthermore we have combined the fuzzy logic with behavior of ant to take a better decision to find optimal path with less energy consumption. Our proposed algorithm utilize the principle of Fuzzy logic in addition of Ant Colony Optimization to develop energy efficient routing. The performance of our proposed work is evaluated on QualNet 7.4 Network Simulator. The simulation result shows that the proposed technique optimize the energy consumption, decrease RREQ packet and increase the lifetime of network as compare to existing routing protocol.

A large number of spatially dispersed nodes on the wireless network create Wireless Sensor Networks (WSNs) to collect and analyze the physical data from the environment. The issues that affected the network and had an impact on network energy consumption were cluster head random selection, working node redundancy, and cluster head transmission path construction. Consequently, this energy constraint also has an impact on the network lifetime and energy-efficient routing. Therefore, the primary goals of this research are to decrease energy consumption and lengthen the network’s lifespan. So, using improved optimization algorithms, this paper presents a dynamic cluster head-based energy-efficient routing system. The Improved Coyote Optimization Algorithm (ICOA), in this case, consists of three phases setup, transmission, and measurement phase. The Improved Jaya Optimization Algorithm with Levy Flight (IJO-LF) then determines the route between the BS and the CH. It selects the most effective course based on the distance, node degree, and remaining energy. The proposed approach is compared with traditional methods and the routing protocols Power-Efficient Gathering in Sensor Information Systems (PEGASIS) and Threshold sensitive Energy Efficient Sensor Network protocol (TEEN) during implementation on the MATLAB platform. Performance indicators for the suggested methodology are evaluated based on data packets collected by the BS, energy usage, alive nodes, and dead nodes. The outputs of the suggested methodology performed better than the conventional plans.

In recent years there are many extensive applications of wireless sensor networks such as environment monitoring, surveillance, enemy tracking, etc. Since the available energy of sensor nodes are limited and hard to renew, energy supervision is critical for nodes in wireless sensor networks. In this paper, we propose a dynamic multi hop routing technique using residual energy based clustering algorithm to prolong node as well as network lifetime. Here, clusters are constructed using certain suitable parameters such as remaining energy of the nodes, its centrality, energy efficiency and cluster heads selection frequency Furthermore, after collecting all data within a cluster, the cluster heads gradually enhance their transmission range till finding a high residual energy based adjacent cluster heads to forward the data to the base station. In this way the proposed method generates an energy efficient routing path from each sensor to base station to send the data. Simulation results show that our approach effectively conserves energy for cluster heads as well as cluster members and prolong their life time effectively. This proposed method also reduces number of clusters and thus improve the nodes life time significantly.

Due to the restricted hardware resources of the sensor nodes, modelling and designing energy efficient routing methods to increase the overall network lifetime have become one of the most significant strategies in wireless sensor networks (WSNs). Cluster‐based heterogeneous routing protocols, a popular part of routing technology, have proven effective in management of topology, energy consumption, data collection or fusion, reliability, or stability in a distributed sensor network. In this article, an energy efficient three‐level heterogeneous clustering method (DEEC) based distributed energy efficient clustering protocol named TBSDEEC (Threshold balanced sampled DEEC) is proposed. Contrary to most other studies, this study considers the effect of the threshold balanced sampled in the energy consumption model. Our model is compared with the DEEC, EDEEC (Enhanced Distributed Energy Efficient Clustering Protocol), and EDDEEC (Enhanced Developed Distributed Energy Efficient Clustering Protocol) using MATLAB as two different scenarios based on quality metrics, including living nodes on the network, network efficiency, energy consumption, number of packets received by base station (BS), and average latency. After, our new method is compared with artificial bee colony optimization (ABCO) algorithm and energy harvesting WSN (EH‐WSN) clustering method. Simulation results demonstrate that the proposed model is more efficient than the other protocols and significantly increases the sensor network lifetime.

In this study, a triangular fuzzy-based spectral cluster routing (TF-SCR) mechanism is proposed in order to improve network lifetime and reliability of data packet transmission in WSN with minimum routing overhead. The designed TF-SCR mechanism contains three steps, namely preprocessing, clustering and post-processing. TF-SCR mechanism initially carried out preprocessing where the residual energy and received signal strength of all sensor nodes in WSN is determined. After preprocessing, TF-SCR mechanism performs clustering process with the application of spectral clustering in which the sensor nodes in WSN are grouped based on residual energy level. After completing clustering process, TF-SCR mechanism implements post-processing step where triangular fuzzy membership function is applied to select the sensor node with higher residual energy and signal strength as cluster head for routing data packets to sink nodes in WSN. This helps for TF-SCR mechanism to obtain energy-efficient routing in WSN. Thus, TF-SCR mechanism enhances the network lifetime and reliability of data broadcasting in WSN as compared to state-of-the-art works. The simulation of TF-SCR mechanism is conducted on factors such as energy consumption, network lifetime, routing overhead and reliability with respect to a number of sensor nodes and data packets. The simulation results demonstrate that the TF-SCR mechanism is able to improve the network lifetime and also minimize the overhead of routing with minimal energy consumption as compared to state-of-the-art works.

Sensor devices have been widely applied to variety applications, including internet of things (IoT), cyber physical systems (CPS) and environmental monitoring systems. Because all sensing data in such applications are very important for data analysis and mining in a host machine, sensor devices are equipped with non-volatile memory (NVM) [1] and nonvolatile processor [2], [3] to prevent sensing data in sensor nodes from data loss. As a result, sensing data can be permanently stored in a sensor device without the risk of data loss even if the battery of the sensor device is run out. Based on such observations, the lifetime of a sensor network should be redefined due to non-volatile technologies, such as NVM and NV processor. Conventionally, the lifetime of a sensor network is the time at which the first sensor node uses up its battery due to the consideration of sensor network QoS [4], [5]. However, since sensing data can be permanently stored in a local drive, the lifetime of a sensor network should be redefined as the time at which the first out-of-date data is produced in a sensor node. If the battery of a sensor node is run out but the sensor node does not contain any out-of-date data, the sensor node will be regarded as a moribund node. If all moribund nodes can be rescued by a rechargeable technology, the sensor network will be immortal. Such observations motivate us to design an energy-efficient solution to achieve the goal of immortal sensor network by exploiting the benefits of wireless charging vehicle.