RSEM: System-Entropy-Measure-Guided Routing Algorithm for Industrial Wireless Sensor Networks.

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.

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.

Routing algorithms for large-scale sensor networks should be capable of finding energy efficient paths to prolong the lifetime of the networks in a decentralized manner. With this respect, Ant System has several proper characteristics for routing algorithm in large-scale wireless sensor networks. First, its distributed mechanism enables routing algorithm to find a solution with only local information and be robust for uncertainties in wireless sensor networks. Second, the framework of the Ant System is proper to solve dynamic problems such as routing problem. Transition probability in Ant System can be used to estimate how good a given routing path is. Capturing these features, this work proposes two Ant Systems based routing algorithms, which are AS-RWSNs (Ant System for Routing in Wireless Sensor Networks) and SAAS-RWSN (Structure-Aware AS-RWSN). The AS-RWSN applies the original Ant System to routing algorithm for wireless sensor network and SAAS-RSN upgrades AS-RWSN with considering properties of network structure such as degree of node. In SAAS-RSN, sensors with high node degree have high data traffic since they have more routing paths. Consequently, SAAS-RSN achieves an energy balance over sensor network through this routing scheme. We demonstrate the effectiveness of the proposed algorithms by comparing three existing routing algorithms.

Wireless Sensor Networks (WSNs) are usually comprised of a large number of sensor nodes which have limited power resources and require energy efficient routing algorithms. We have presented an Adaptive Transmission Power - Geographical and Energy Aware Routing (ATP-GEAR) algorithm in this paper, which routes the packet towards the destination by calculating the cost of the neighbors of a node in terms of their location and current battery level and transmitting the packet to the lowest costing node with the minimum required transmission power. We analyze the performance of our algorithm based on total power consumption and end-to-end delay from source to destination. Simulation results show that ATP-GEAR yields almost 25% less power consumption and 17% less end-to-end delay when compared with traditional Cluster Based Routing (CBR).

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.


 
 
 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.

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.

Industrial Wireless Sensor Networks (IWSNs) have come to be widely used for collecting industrial information due to their low cost, rapid deployment, and high service quality. Clustering sensors together extends the lifetime of IWSNs is an essential part of improving their energy efficiency and network stability. In this paper, we propose a novel Multi-Objective Cluster Head Selection Optimization Model (MOCHSOM) to improve the selection of cluster heads by taking into account multiple factors critical to the IWSNs lifetime. Specifically, our model minimizes the total network energy consumption, balances the network cluster energy consumption, and maximizes the network node alive time. Moreover, we employ an Evolutionary Algorithm using Reference-point Based Non-dominated Sorting Approach (NSGA-III) to optimize the MOCHSOM model further. The experimental results show that our proposed algorithm significantly improves the network lifetime when compared with conventional clustering algorithms.

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.

The process of beaconing used to gather information about position of sensors consumes extra energy due to unnecessary transmissions. In this paper, we propose a position-based beaconless routing (PBR) algorithm for wireless sensor networks that finds the path from source sensor to destination sensor and reduces the unnecessary transmissions. The next forwarder selection and packet forwarding algorithms are presented. Mathematical analysis of distribution of sensors in forward area, expected distance between sender and next forwarder, and average number of successful hops is provided. PBR uses distance and angle based neighbor selection at each forwarding step. It should be noted that PBR does not require use of extensive broadcast to select next forwarder sensor. The proposed protocol is simulated on MATLAB platform. Simulation results show that PBR performs better than LAR1, MFR, and GEDIR in terms of number of transmissions, end-to-end delay and consequently the energy consumption.

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.

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

In this study, the impact of route length on the performance of a routing protocol and flow admission control is analysed. First, the authors present an end‐to‐end available‐bandwidth‐based proactive routing protocol for ad‐hoc wireless sensor networks. The routing protocol maintains the best data forwarding path in terms of the end‐to‐end available bandwidth. Second, to determine the impact of route length on a routing protocol's performance, they modify the routing protocol. The modified available‐bandwidth‐based protocol trades‐off the end‐to‐end available bandwidth against the route length. Third, they integrate a state‐of‐the‐art flow admission control algorithm with the proposed protocols and a shortest hop‐count‐based protocol. Through simulations they evaluate the following: (i) performance of the proposed protocols and a state‐of‐the‐art available‐bandwidth‐based opportunistic protocol and (ii) the effectiveness of a state‐of‐the‐art flow admission control algorithm over proposed protocols and a shortest hop‐count‐based protocol. The simulation results demonstrate the following drawbacks of not considering the hop‐count metric: longer data forwarding paths, higher number of retransmissions, and reduced effectiveness of the admission control algorithm. The modified available‐bandwidth‐based proactive protocol provides the best overall performance. Therefore, using their results they conclude that route length impacts the performance of routing and flow admission control algorithms, but is not a singularly decisive factor.

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.