A Clustering Routing Algorithm for Heterogeneous Sensor Networks with Multilevel Energies

The main source of energy consumption in the wireless sensor network is due to transmission and receiving of data packets. However the cluster head (CH) near to the base station (BS) in cluster based wireless sensor network depletes energy faster and die quickly due to transmission of heavier data traffic of other CHs to the BS. In this paper we propose a particle swarm optimization (PSO) based approach which construct the routing path and distribute the routing load over the CHs near to the BS and maximize the lifetime of networks. We also consider the fault tolerance issue due to the sudden failure of CHs during data routing phase. The simulation results show the effectiveness of the proposed algorithm over the existing algorithms.

Energy balance is an important performance index in the design of routing algorithm for wireless sensor networks. A swarm intelligence optimization based routing algorithm for wireless sensor networks is proposed, whose kernel idea is taking less hop numbers into consideration and choosing the nodes with less pheromone as next hop to avoid some nodespsila prematurely exhausting their energy because of too concentrated routes through the nodes. The experiments show that the algorithm proposed in this paper is better than the directed diffusion routing protocol both in end-to-end delay and global energy balance and can effectively balance the global energy consumption and prolong the network lifetime.


 
 
 Cluditem is a routing algorithm for wireless sensor networks developed for applications of environmental monitoring with periodic measurement of variables. These applications tolerate a maximum phase shift of the clocks of nodes in the order of milliseconds. This paper proposes two centralized synchronization schemes that introduce a limited processing load. The objective of the work was to assess the impact on the energy consumption of the incorporation of synchronization techniques described. Simulations of the full communication protocol were conducted in the NS2 environment and a comparative study of results was performed. This study showed that the first proposed schema is more convenient with respect to energy consumption, when the area to monitor has reduced dimensions. On the other hand, the second scheme, although it permits to work in larger areas, introduces a decrease in the network lifetime in the order of 14%.
 
 

Cluster based routing approaches have been researched extensively for saving energy of wireless sensor networks (WSNs). In a cluster based routing mechanism, cluster heads (CHs) cooperate mutually to forward their data to the base station (BS) through multi-hop fashion. Due to this process, CHs near to the BS loaded with huge relay traffic and tend to die quickly, which causes partition of the network is popularly known as a hot-spot problem. To tackle the hot-spot problem, in this paper, competitive swarm optimization (CSO) based algorithms have been proposed, jointly call these algorithms as CSO-UCRA (CSO based Unequal Clustering and Routing Algorithms). First, the CH selection algorithm has been presented which is based on CSO based technique, next assign the non-CH sensors to CHs based on the derived CHproficiency function. Finally, a CSO based routing algorithm has been presented. Efficient particle encoding schemes and novel fitness functions have been developed for these algorithms. The CSO-UCRA is simulated extensively with varying number of sensor nodes and CHs for various WSN scenarios, and the obtained results are compared with some recent devised algorithms and standard meta-heuristic based algorithm called PSO-UCRA to show the efficiancy in terms of various performance metrics. CSO-UCRA shows decreased energy consumption of 28.48%, 22.55%, 12.92%, and 3.81%, increased network lifetime of 56.92%, 46.02%, 26.2%, and 8.04% and increased data packets received 73%, 52.5%, 20.8%, and 6.18% over EBUC, EAUCF, EPUC and PSO-UCRA respectively.

Recent years have witnessed a proliferation of routing algorithms for Wireless Sensor Networks (WSNs), hence complicating the choice of the proper algorithm to be used for a given application. Simulation frameworks represent a viable solution to anticipate crucial choices, however existing solutions do not encompass the impact of changes (e.g., route updates, node crashes) on the nodes behavior and vice-versa. This article proposes a novel adaptive modeling approach to master the complexity of the thorough simulation of routing algorithms for WSN. Experimental results are provided showing the effectiveness of the proposed approach at managing changes, and dealing with detailed aspects, during the simulation and comparison of several routing algorithms.

This paper reports on a project to investigate, build upon and test a relatively new routing algorithm for wireless sensor networks. The algorithm is based on the behaviour of ants in nature. It attempts to address the need for an energy-efficient routing algorithm for wireless sensor networks. This journal article briefly discusses the algorithm’s design, including the algorithm outline and relevant data structures and formulae. The article proceeds to discuss the testing procedure and test results for the algorithm. Finally, a conclusion summarising findings and recommendations for future work is included.

One of the main emphases of the prevalent clustering and routing algorithms in wireless sensor networks is efficient energy utilization aimed towards enhanced network lifetime. In this paper we are proposing a clustering approach where the sensor nodes are clustered together based on a defined spatial correlation and the geographical location of each sensor nodes. The Cluster Head or Cluster Leader along with routing path is decided by our proposed algorithm based on the estimated chance-value during each rounds of data transfer. Thus in our scheme a multi-hop routing is effected wherein either two-level or three-level hops are dynamically chosen based on the spatial correlation existing between the nodes for the data transfer from the sensing node to the sink. It has been observed that our proposed algorithm outperforms the standard algorithms namely Low Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP) and Distributed Energy Efficient Clustering (DEEC) Protocol with regards to system throughput and Network Lifespan of WSN.

Utilization of energy and the lifetime increment are the big issues in designing of routing algorithms for wireless sensor networks (WSNs). Many routing algorithms have been developed by various researchers to achieve energy efficiency and to improve the lifetime of the network. But, the way to route the information from the sensor node (SN) to the base station (BS) and vice versa is an important issue, because of resource constraints. In this paper, we have proposed a low energy consumed, cluster-based routing protocol named an energy-efficient and double cluster-head routing (EEDCR) protocol, to increase the network lifetime and minimize the end-to-end delay. Selection of the cluster head (CH) is depending on random selection method, energy method, the total number of nodes, and its energy level. The performances of developed protocol were assessed using simulations and found that it provides successful outcomes in terms of energy-efficient and lifetime increment in routing for WSNs.

In the routing protocol for wireless sensor network, the cluster size is generally fixed in clustering routing algorithm for wireless sensor network, which can easily lead to the “hot spot” problem. Furthermore, the majority of routing algorithms barely consider the problem of long distance communication between adjacent cluster heads that brings high energy consumption. Therefore, this paper proposes a new cross unequal clustering routing algorithm based on the EEUC algorithm. In order to solve the defects of EEUC algorithm, this algorithm calculating of competition radius takes the node’s position and node’s remaining energy into account to make the load of cluster heads more balanced. At the same time, cluster adjacent node is applied to transport data and reduce the energy-loss of cluster heads. Simulation experiments show that, compared with LEACH and EEUC, the proposed algorithm can effectively reduce the energy-loss of cluster heads and balance the energy consumption among all nodes in the network and improve the network lifetime

Popularity of Mobile Wireless Sensor Network (MWSN) has increased tremendously due to the ability to connect the physical world with the virtual world. In MWSN, Sensor nodes move freely in a target area without the need of any special infrastructure. MWSN has attracted many researchers as it has introduced unique challenges in aspects like resource management, coverage, routing protocols, security in many applications such as target tracking in military applications, detection of catastrophic events, environment monitoring, health applications etc. Due to mobility, routing process in MWSN has become more complicated as connections in the network can change dynamically. This paper proposes a Mobility-Aware Multi-Objective Hybrid Routing Algorithm (MMOHRA) for Wireless Sensor Network (WSN). MMOHRA selects the best route and forwards the data packets from source to sink using multiple metrics such as Average Energy consumption, Control Overhead, Reaction Time, LQI, and HOP Count. Performance of MMOHRA is compared with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-objective Routing Algorithm (DyMORA) using metrics such as Average Energy Consumption, Packet Delivery Ratio, and Jitter. MMOHRA improves the Average Energy Consumption (AEC) by a factor of 14.1% as compared with SHRP and 8.13 % as compared to DyMORA. MMOHRA outperformed SHRP and DyMORA in terms of packet Delivery Ratio (PDR) by 10.17 % and 9.83 % respectively.

Recently, network localization systems that are based on inter-node ranges have received significant attention. Geographic routing has been considered an application which can utilize the location information from these localization systems. In this paper, we firstly recognize that sensor network localization algorithms generate positioning data with different error patterns compared to those networks where node positions are determined directly from GNSS measurements. Secondly, by simulating practical sensor network scenarios using data from our localization algorithm, we observe that existing geographic routing algorithms in wireless sensor networks (WSNs) adopt very simplistic methods in the treatment of position error, without due consideration of error distribution. Additionally, an insight is given into localization algorithms for WSNs with inhomogeneous error environments. Our observations represent an initial step toward a detailed understanding and design of efficient geographic routing algorithms in location aware WSNs.

Energy conservation is the most significant issue in wireless sensor networks due to limited power sources of sensor nodes that cannot be recharged or replaced. Clustering is considered to be energy-efficient in WSN as it reduces the number of sensor nodes taking part in long range communication. During clustering, each sensor node selects a cluster head (CH) to join a cluster among several candidate CHs. The selection of cluster head uses different parameters such as residual energy of cluster head and distance between node and cluster head. A poor selection of cluster head will lead to increased energy consumption in network that will degrade the network lifetime. Furthermore, sensor nodes are likely to fail due to different factors such as limited energy and environment hazards. So, the fault tolerance is another challenge for long run of the WSNs. In this paper, we propose a distributed fault-tolerant multi-objective clustering algorithm (DFMCA). In this algorithm, each sensor takes its decision using local information only. We also propose a recovery algorithm in case of sudden death of the cluster heads in the network. A simulation result demonstrates that our algorithm outperforms the existing state-of-the-art algorithms, namely FTCA and LBCA, in terms of network lifetime.

Clustering is an effective method for reducing energy consumption in wireless sensor networks (WSNs). In a multihop clustered network scenario, each sensor node transmits data to its own cluster head (CH), and the CH aggregates the data from its member nodes and forwards it to the base station (BS) via other CHs. However, the “hot spot” problem is prone to occur in clustered WSNs because CHs closer to the BS have heavier intercluster forwarding tasks. To address this problem, this paper proposes an unequal clustering algorithm based on interval type-2 TSK fuzzy logic theory (UCT2TSK). The relative distance to the BS (RDB), residual energy (RE), and node density (ND) are considered as the inputs of an interval type-2 fuzzy logic system (FLS). Through fuzzy reasoning, outputs are acquired that can be used to optimize the CHs and determine the cluster sizes. Simulation results verify that UCT2TSK can effectively balance energy consumption and enhance energy efficiency because it has better performance in network lifetime and network throughput than other classical and recent clustering algorithms.

Wireless sensor networks area collection of a large number of unattended sensor nodes. The sensor nodes are dynamically deployed in the region. In wireless sensor networks, the unlicensed sensor nodes use their temporarily available vacant licensed channels or spectrum band in a telecommunication network. The many open challenges with issues such as adaptive in self-organisation, fault-tolerance and network scalability with cooperative spectrum sensing and sharing are identifying new challenges in wireless sensor networks. In this paper, we proposed the bio-inspired adaptive routing algorithm for cognitive radio wireless sensor networks. The utilisation of the foraging approach of ANTs and BEEs for making the decision of using the available spectrum sensing and sharing of channels over multi-hops communication, provides the method to utilise idle channel and decrease the delay for communication in wireless sensor network. In this paper, we design the algorithm to solve the issue of wireless sensor network and performance of classical routing protocol LEACH and cognitive radio sensor networks (CRSN) evaluates the parameters which proof the self-organisation routing algorithm.

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.