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Bio-Inspired Routing in Wireless Sensor Networks
Abstract
In order to increase network life time scalable, efficient and adaptive routing protocols are need of current time. Many energy efficient protocols have been proposed, the Clustering algorithm is also a basic technique used for energy efficiency.In this paper we propose an energy efficient routing protocol that is based on Artificial Bee Colony (ABC) algorithm of Bio Inspired.The presented protocol efficiently utilized characteristics of ABC algorithm such as foraging principle and waggle dance of honey bees. Waggle dance technique is used to find Routing Node (RN) that has maximum energy.Simulation results proves increase network life time and high throughput with minimum delay.
... A wireless sensor network (WSN) comprises sensors that are spatially distributed [1] and deployed in an area called the sensor field [2]. Each sensor collects data and sends it to a central aggregation unit known as the base station (BS) or sink [3]. WSNs have a wide spectrum of applications, including environmental monitoring, target tracking, healthcare monitoring, and machine automation [1,4,5]. ...
... The basic components of a sensor node are shown in Figure 1. The sensors are limited in power, storage, and range [3]. They can perform: (1) computation, (2) communication, and (3) sensing [1]. ...
Wireless Sensor Networks (WSNs) draw the attention of researchers due to the diversity of applications that use them. Basically, a WSN comprises many sensor nodes that are supplied with power by means of a small battery installed in the node itself; the node can also be self-charged by a solar cell. Sometimes it is impossible to change the power supply of battery-operated nodes. This dictates that sensor nodes must utilize the energy they have in an optimal manner. Data communication is the main cause of energy dissipation. In this context, designing protocols for WSNs demands more attention to the design of energy-efficient routing protocols that allow communications between sensor nodes and their base station (BS) with the least cost. LEACH is a prominent hierarchical cluster-based routing protocol. It groups sensor nodes into clusters to reduce energy dissipation. On the other hand, LEACH-C is a protocol based on LEACH that claims to improve energy dissipation over LEACH. In this paper, a successful attempt was made to compare these two protocols using MATLAB. The results show that LEACH-C has better performance than LEACH in terms of power dissipation.
... A novel nature-inspired algorithm named the salp swarm algorithm (SSA) is presented in [15] for accurate node localization in WSNs and is compared with four other optimization algorithms, namely the firefly algorithm, butterfly optimization algorithm, particle swarm optimization, and grey wolf optimizer based on its performance and simulation results [16]. An energy-efficient routing technique based on the artificial cee colony (ABC) algorithm that mimics the foraging behavior and waggle dance of bees is proposed in [17]. ...
Energy efficiency and data security of smart grids are one of the major concerns in the context of implementing modern approaches in smart cities. For the intelligent management of energy systems, wireless sensor networks and advanced metering infrastructures have played an essential role in the transformation of traditional cities into smart communities. In this paper, a smart city energy model is proposed in which prosumer communities were built by interconnecting energy self-sufficient households to generate, consume and share clean energy on a decentralized trading platform by integrating blockchain technology with a smart microgrid. The efficiency and stability of the grid network were improved by using several wireless sensor nodes that manage a massive amount of data in the network. However, long communication distances between sensor nodes and the base station can greatly consume the energy of sensors and decrease the network lifespan. Therefore, bio-inspired algorithm approaches were proposed to improve routing by obtaining the shortest path for traversing the entire network and increasing the system performance in terms of the efficient selection of cluster heads, reduced energy consumption, and extended network lifetime. This was carried out by studying the properties and mechanisms of biological systems and applying them in the communication systems in order to obtain the best results for a specific problem. In this comprehensive model, particle swarm optimization and a genetic algorithm are used to search for the optimal solution in any problem space in less processing time.
... In the presented work, the proposed novel routing scheme explicitly concentrates on the challenges of IBNNs by taking into account the fundamental disparity between the communication load and limited available resources of NBSs. In line with the vast applicability of bio-inspired solutions such as swarm intelligence algorithms for low energy and computational devices [15]- [17], they have been used in various applications, including energy-efficient clustering [18]- [20], node localization [21], [22], and improved data collection [23] in wireless sensor networks. Moreover, in the context of the internet of things and vehicular ad-hoc networks, bio-inspired routing techniques efficiently handle the frequently changing topology issues and enable the design of low complexity routing protocols [24], [25]. ...
An Intrabody Nanonetwork (IBNN) is constituted by nanoscale devices that are implanted inside the human body for monitoring of physiological parameters for disease diagnosis and treatment purposes. The extraordinary accuracy and precision of these nanoscale devices in cellular level disease diagnosis and drug delivery are envisioned to advance the traditional healthcare system.However, the feature constraints of these nanoscale devices, such as inadequate energy resources, topology-unawareness, and limited computational power, challenges the development of energy-efficient routing protocol for IBNNs. The presented work concentrates on the primary limitations and responsibilities of IBNNs and designs a routing protocol that incorporates characteristics of Exponential Weighted Moving Average (EWMA) Based Opportunistic Data Transmission (EWMA-ODT) and Artificial Colony Algorithm Based Query Response Transmission (ABC-QRT) approaches for efficiently handling the routing challenges of IBNNs. In EWMA-ODT, the moving Nano Biosensors (NBSs) employ the EWMA method attributes to aggregate detected data by assigning high weightage to the recent detected information. Later, the aggregated data is transmitted to the Nano Router (NR) when the direct data transmission opportunity is available, the reception of aggregated briefs NR about the condition of the network after the last successful interaction with minimum energy consumption. Whereas, the ABC-QRT approach introduces the ABC algorithm for the selection of those optimal NBSs that have maximum fitness value for satisfying the data transmission demand of the external healthcare system with minimal traffic overhead. The simulation results validate that the joint contribution of these approach enhances IBNNs life time and reduces end-to-end delay as compared to the flooding scheme.
... In the presented work, the proposed novel routing scheme explicitly concentrates on the challenges of IBNNs by taking into account the fundamental disparity between the communication load and limited available resources of NBSs. In line with the vast applicability of bio-inspired solutions such as swarm intelligence algorithms for low energy and computational devices [15]- [17], they have been used in various applications, including energy-efficient clustering [18]- [20], node localization [21], [22], and improved data collection [23] in wireless sensor networks. Moreover, in the context of the internet of things and vehicular ad-hoc networks, bio-inspired routing techniques efficiently handle the frequently changing topology issues and enable the design of low complexity routing protocols [24], [25]. ...
An Intrabody Nanonetwork (IBNN) is constituted by nanoscale devices that are implanted inside the human body for monitoring of physiological parameters for disease diagnosis and treatment purposes. The extraordinary accuracy and precision of these nanoscale devices in cellular level disease diagnosis and drug delivery are envisioned to advance the traditional healthcare system. However, the feature constraints of these nanoscale devices, such as inadequate energy resources, topology-unawareness, and limited computational power, challenges the development of energy-efficient routing protocol for IBNNs. The presented work concentrates on the primary limitations and responsibilities of IBNNs and designs a routing protocol that incorporates characteristics of Exponential Weighted Moving Average (EWMA) Based Opportunistic Data Transmission (EWMA-ODT) and Artificial Colony Algorithm Based Query Response Transmission (ABC-QRT) approaches for efficiently handling the routing challenges of IBNNs. In EWMA-ODT, the moving Nano Biosensors (NBSs) employ the EWMA method attributes to aggregate detected data by assigning high weightage to the recent detected information. Later, the aggregated data is transmitted to the Nano Router (NR) when the direct data transmission opportunity is available, the reception of aggregated briefs NR about the condition of the network after the last successful interaction with minimum energy consumption. Whereas, the ABC-QRT approach introduces the ABC algorithm for the selection of those optimal NBSs that have maximum fitness value for satisfying the data transmission demand of the external healthcare system with minimal traffic overhead. The simulation results validate that the joint contribution of these approaches enhances IBNNs lifetime and reduces end-to-end delay as compared to the flooding scheme.
Energy efficiency has been a major research topic in Wireless Sensor Networks (WSN). Because the sensor networks are powered by batteries, they will die after a given amount of time. As a result, extending the life of sensor equipment by optimizing data dissipation in a cost-effective manner remains a difficulty. Generally, Flat, Hierarchical and location-based routing are the three types of routing Protocol. Awareness of the network structure and Routing Protocol is essential, and it must be suitable for the user need. The survey presents Comparison of various LEACH Protocols in WSN
An Intrabody Area Nanonetwork (Intra-BANN) is a set of nanoscale devices, which have outstanding cellular level precision and accuracy for enabling non-invasive healthcare monitoring and disease diagnosis. In the proposed work, we design a novel Feedforward Neural Networks (FFNNs) based data aggregation scheme that integrates the attributes of artificial intelligence to boost the computational intelligence of Intra-BANNs for prolonged network lifetime. In the proposed scheme, data division and labelling are performed to transmit detected information using two different types of packets with different sizes to save energy resources and to avoid redundant data transmission. FFNN-based periodic data transmission exploits the fitness function approximation characteristics of FFNN to increase the transmission probability of critical information with minimum energy consumption and delay. Whereas our proposed event-driven data transmission also ensures the transmission of high priority data with minimal delay and storage overhead. The detailed evaluation and comparison of our proposed framework
with three existing schemes conducted using the Nano-Sim tool highlight that our proposed scheme performs 50-60% better than state-of-the-art schemes in terms of residual energy, delay, and packet loss.
My PhD Thesis final defence presentation.
The smart grid plays a vital role in decreasing electricity cost via Demand Side Management (DSM). Smart homes, being a part of the smart grid, contribute greatly for minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the Peak to Average Ratio (PAR) and electricity cost with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time where user waiting time is considered to be minimum for residential consumers with multiple homes. Hence, in contribution 1, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart electricity storage system is also taken into account for more efficient operation of the HEM System. Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is instigated in a smart building which is comprised of thirty smart homes (apartments). Critical Peak Pricing (CPP) and Real-Time Pricing (RTP) signals are examined in terms of electricity cost assessment for both a single smart home and a smart building. In addition, feasible regions are presented for multiple and single smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results prove the effectiveness of our proposed scheme for multiple and single smart homes concerning electricity cost and PAR minimization. Moreover, there subsists a tradeoff between electricity cost and user waiting.
With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, DSM is modeled as an optimization problem and the solution is obtained by applying metaheuristic techniques with different pricing schemes. In contribution 2, an optimization technique, the Hybrid Gray Wolf Differential Evolution (HGWDE) is proposed by merging the Enhanced Differential Evolution (EDE) and Gray Wolf Optimization (GWO) schemes using the same RTP and CPP tariffs. Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility.
However, there is a trade-off between User Comfort (UC) and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the PAR is reduced up to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced up to 12.81%, 12.012% and 12.95%, respectively, for 15-min, 30-min and 60-min operational time intervals (OTI). On the other hand, the PAR and electricity bill are reduced up to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.
Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources. Microgrid generates power for electricity consumers and operates in both islanded and grid-connected modes more efficiently and economically. In contribution 3, we propose optimization schemes for reducing electricity cost and minimizing PAR with maximum UC in a smart home. We consider a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through Multiple Knapsack (MKP) then it is solved by existing heuristic techniques: GWO, binary particle swarm optimization (BPSO), GA and Wind Driven Optimization (WDO). Furthermore, we also propose three hybrid schemes for electricity cost and PAR reduction: (1) hybrid of GA and WDO named as WDGA; (2) hybrid of WDO and GWO named as WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system has also integrated to make our proposed schemes more cost-efficient and reliable to ensure stable grid operations. Finally, simulations have been performed to verify our proposed schemes. Results show that our proposed schemes efficiently minimize the electricity cost and PAR. Moreover, our proposed techniques: WDGA, WDGWO and WBPSO outperform the existing heuristic techniques.
The advancements in smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In contribution 4, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a Home Energy Management Controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the electricity proving utility with the load profile of the home. The smart meter is connected to power grid having an advanced metering infrastructure which is responsible for two way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising UC. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and RTP information. Simulation results show that proposed algorithms reduce the PAR by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.
Wireless Sensor Networks, are made of low-cost, low
-power, small in
size, and multifunctional sensor
nodes. The efficient energy utilization is one of t
he important performance factors for wireless senso
r
networks survivability be-cause nodes operate with
limited battery power. In this paper we propose and
analyze a new approach of zone based clustering hea
d selection algorithm for wireless sensor network o
f
homogeneous nodes. Nodes in the network are uniform
ly distributed. In this clustering algorithm, netwo
rk
performance is improved by selecting cluster heads
on the basis of the residual energy of existing clu
ster
heads, and nearest hop distance of the node. In thi
s paper we evaluate various performance metrics lik
e
energy consumption, network life time, number of ch
annel heads metrics in each round and compare these
with respect to random algorithm i.e. LEACH. We con
clude that proposed protocol effectively extends th
e
network lifetime without degrading the other critic
al overheads and perform-ance metrics.
Random mobility and energy constraint are two main factors affecting system performance in mobile sensor networks, which cause many difficulties to system design. It is necessary to develop high-efficiency algorithms and protocols for mobile sensor networks to adapt to dynamic network environment and energy limitation. In this paper, a new clustering algorithm based on residual energy difference ratio is presented to improve system performance. Firstly, it is an energy-efficient algorithm. The residual energy of sensor nodes and average residual energy of system are considered in the residual energy difference ratio, which effectively avoid the nodes with low residual energy being selected as cluster heads. An energy-optimal scheme is used in cluster formation phase to minimize energy consumption. Secondly, it is a dynamic algorithm. The system dynamically clusters the sensor nodes according to the data transmission delays. It makes the whole system adapt to the random mobility of sensor nodes. The NS2 software is used to simulate the new clustering algorithm. The simulation experiments can verify the validity of the proposed theory.
Aiming at the problem of limited energy of sensors in Wireless Sensor Network, based on the classic clustering routing algorithm LEACH, a distance-energy cluster structure algorithm considering both the distance and residual energy of nodes is presented in the dissertation, which improves the process of cluster head election and the process of data transmission. It reduces the adverse effect on the energy consumption of the cluster head, resulting from the non-uniform distribution of nodes in network and avoids the direct communication between the base station and cluster head, which may has low energy and far away from base station The results of simulation indicate that the improved algorithm effectively balances the energy consumption, prolongs 31% of the lifetime, reduces 40% of the energy consumption and has a better performance than the original LEACH protocol. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology
The primary objectives of the wireless sensor network routing protocol design are balancing network energy consumption and extending the entire network lifetime. This paper analyses the effectiveness of LEACH protocol in cluster-head selection, and proposes an improved clustering algorithm. This new algorithm takes nodes residual energy and location information into account, optimizes the selection method of the threshold for electing cluster-head, improves optimal cluster-head selection strategy that is normal nodes select the optimal cluster-head based on the cost function. Simulation results show that the improved protocol is better than LEACH in balancing node energy consumption, improving the efficiency of data transmission and prolonging the network life.
Mobile devices have become a popular tool for ubiquitous learning in recent years. Multiple mobile users can be connected via ad hoc networks for the purpose of learning. In this context, due to limited battery capacity, energy efficiency of mobile devices becomes a very important factor that remarkably affects the user experience of mobile learning. Based on the artificial bee colony (ABC) algorithm, we propose a new clustering protocol, namely BeeCup, to save the energy of mobile devices while guaranteeing the quality of learning. The BeeCup protocol takes advantage of biologically-inspired computation, with focus on improving the energy efficiency of mobile devices. It first estimates the number of cluster heads (CHs) adaptively according to the network scale, and then selects the CHs by employing the ABC algorithm. In case some CHs consume energy excessively, clusters will be dynamically updated to keep energy consumption balanced within the whole network. Simulation results demonstrate the effectiveness and superiority of the proposed protocol.
The main problem for event gathering in wireless sensor networks (WSNs) is the restricted communication range for each node. Due to the restricted communication range and high network density, event forwarding in WSNs is very challenging and requires multihop data forwarding. Currently, the energy-efficient ant based routing (EEABR) algorithm, based on the ant colony optimization (ACO) metaheuristic, is one of the state-of-the-art energy-aware routing protocols. In this paper, we propose three improvements to the EEABR algorithm to further improve its energy efficiency. The improvements to the original EEABR are based on the following: (1) a new scheme to intelligently initialize the routing tables giving priority to neighboring nodes that simultaneously could be the destination, (2) intelligent update of routing tables in case of a node or link failure, and (3) reducing the flooding ability of ants for congestion control. The energy efficiency improvements are significant particularly for dynamic routing environments. Experimental results using the RMASE simulation environment show that the proposed method increases the energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR without incurring a significant increase in complexity. The method is also compared and found to also outperform other swarm-based routing protocols such as sensor-driven and cost-aware ant routing (SC) and Beesensor.
Optimal path selection in wireless sensor networks (WSNs) is one of the challenging tasks. Several efficient routing protocols are proposed for specific scenarios to achieve particular objectives in WSN. However, such networks have many limitations such as low data rates and security threats. In this paper, we propose an adaptive secure routing protocol which is based on bioinspired mechanism. It uses distributed ant-based methodology to select two optimal paths keeping in view route security. Simulation results show that our routing protocol can perform better in many scenarios.
Self-configuration and autonomy are key features required for the next generation of gadgets and networks, since regular users are willing to have computationally enabled devices pervasively spread into their environment. Sensors are among the most promising devices into this new scenario. However, their low battery and processing power raise several issues to these autonomy requirements. This paper presents BiO4SeL (Bio-Inspired Optimization for Sensor Network Lifetime), a swarm intelligence-based algorithm to perform self-organization and optimization of lifetime by means of routing into a Wireless Sensor Network. Results show that BiO4SeL achieves its objectives when compared to similar approaches: ARAMA (Ant-based Routing Algorithm for MANETs), EAR (Energy-Aware Routing) and AODV (Ad-hoc On-demand Distance Vector).
Abstract—In a Wireless Sensor Network (WSN), nodes have limited energy and low processing capabilities and the network as a whole should act with a strategy that minimizes its energy consumption to maximize its lifetime. This paper presents a research work that aims at introducing a biological inspiration, the Gene Regulatory Network (GRN), to address this problem. We propose a GRN-based controller in each node that is adapted by the Genetic Algorithm to build an auto-organized, robust and adaptive network similar to a biological system. The classical approach to ensure redundancy in the network and make it more robust to failures can be replaced by a simple distributed strategy inspired from robust biological systems. The performed simulation shows that the approach permits to increase the lifetime and the event detection rates of each node.
We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources—this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.
The twenty-first century has witnessed major technological changes that have transformed the way we live, work, and interact
with one another. One of the major technology enablers responsible for this remarkable transformation in our global society
is the deployment and use of Information and Communication Technology (ICT) equipment. In fact, today ICT has become highly
integrated in our society that includes the dependence on ICT of various sectors, such as business, transportation, education,
and the economy to the point that we now almost completely depend on it. Over the last few years, the energy consumption resulting
from the usage of ICT equipment and its impact on the environment have fueled a lot of interests among researchers, designers,
manufacturers, policy makers, and educators. We present some of the motivations driving the need for energy-efficient communications.
We describe and discuss some of the recent techniques and solutions that have been proposed to minimize energy consumption
by communication devices, protocols, networks, end-user systems, and data centers. In addition, we highlight a few emerging
trends and we also identify some challenges that need to be addressed to enable novel, scalable, cost-effective energy-efficient
communications in the future.
KeywordsCellular–Efficiency–Energy–Green networking–Performance–Protocols–Wireless
The advancement in wireless communications and electronics has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (e.g., health, military, home). For different application areas, there are different technical issues that researchers are currently resolving. The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections. This article also points out the open research issues and intends to spark new interests and developments in this field.
This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all...
This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance un...
In order to prolong the network lifetime, energy-efficient protocols should be designed to adapt the characteristic of wireless sensor networks. Clustering Algorithm is a kind of key technique used to reduce energy consumption, which can increase network scalability and lifetime. This paper studies the performance of clustering algorithm in saving energy for heterogeneous wireless sensor networks. A new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks is proposed and evaluated. In the new clustering scheme, cluster-heads are elected by a probability based on the ratio between residual energy of node and the average energy of network. The high initial and residual energy nodes will have more chances to be the cluster-heads than the low energy nodes. Simulational results show that the clustering scheme provides longer lifetime and higher throughput than the current important clustering protocols in heterogeneous environments.
Regarding energy efficiency in Wireless Sensor Net-works (WSNs), routing protocols are engaged in a playful manner suggesting a consciousness of high value. In this research work, we present Away Cluster Heads with Adaptive Clustering Habit ((ACH) 2) scheme for WSNs. Our proposed scheme increases the stability period, network lifetime and throughput of the WSN. The beauty of our proposed scheme is its away Cluster Heads (CHs) formation, and free association mechanisms. The (ACH) 2 controls the CHs' election and selection in such a way that uniform load on CHs is ensured. On the other hand, free association mechanism removes back transmissions. Thus, the scheme operations minimize the over all energy consumption of the network. In subject to throughput maximization, a linear programming based mathematical formulation is carried out in which the induced subproblem of bandwidth allocation is solved by mixed-bias resource allocation scheme. We implement (ACH) 2 scheme, by varying node density and initial energy of nodes in homogeneous, heterogeneous, reactive and proactive simulation environments. Results justify its applicability.
The distributed nature and dynamic topology of Wireless Sensor Networks (WSNs) introduces very special requirements in routing protocols that should be met. The most important feature of a routing protocol, in order to be efficient for WSNs, is the energy consumption and the extension of the network's lifetime. During the recent years, many energy efficient routing protocols have been proposed for WSNs. In this paper, energy efficient routing protocols are classified into four main schemes: Network Structure, Communication Model, Topology Based and Reliable Routing. The routing protocols belonging to the first category can be further classified as flat or hierarchical. The routing protocols belonging to the second category can be further classified as Query-based or Coherent and non-coherent-based or Negotiation-based. The routing protocols belonging to the third category can be further classified as Location-based or Mobile Agent-based. The routing protocols belonging to the fourth category can be further classified as QoS-based or Multipath-based. Then, an analytical survey on energy efficient routing protocols for WSNs is provided. In this paper, the classification initially proposed by Al-Karaki, is expanded, in order to enhance all the proposed papers since 2004 and to better describe which issues/operations in each protocol illustrate/enhance the energy-efficiency issues.
In wireless sensor networks, nodes in the area of interest must report sensing readings to the sink, and this report always satisfies the report frequency required by the sink. This paper proposes a link-aware clustering mechanism, called LCM, to determine an energy-efficient and reliable routing path. The LCM primarily considers node status and link condition, and uses a novel clustering metric called the predicted transmission count (PTX), to evaluate the qualification of nodes for clusterheads and gateways to construct clusters. Each clusterhead or gateway candidate depends on the PTX to derive its priority, and the candidate with the highest priority becomes the clusterhead or gateway. Simulation results validate that the proposed LCM significantly outperforms the clustering mechanisms using random selection and by considering only link quality and residual energy in the packet delivery ratio, energy consumption, and delivery latency.
Due to the nonuniform node distribution, the energy consumption among nodes are more imbalanced in cluster-based wireless sensor networks. Based on this problem, in this paper, a cluster-based routing protocol for wireless sensor networks with nonuniform node distribution is proposed, which includes an energy-aware clustering algorithm EADC and a cluster-based routing algorithm. EADC uses competition range to construct clusters of even sizes. At the same time, the routing algorithm increases forwarding tasks of the nodes in scarcely covered areas by forcing cluster heads to choose nodes with higher energy and fewer member nodes as their next hops, and finally, achieves load balance among cluster heads. Theoretical analysis and simulation results show that our protocol can balance the energy consumption among nodes and increase the network lifetime significantly.
A wireless sensor network (WSN) is a large collection of sensor nodes with limited power supply, constrained memory capacity, processing capability, and available bandwidth. The main problem in event gathering in wireless sensor networks is the formation of energy-holes or hot spots near the sink. Due to the restricted communication range and high network density, events forwarding in sensor networks is very challenging, and require multi-hop data forwarding. Improving network lifetime and network reliability are the main factors to consider in the research associated with WSN. In static wireless sensor networks, sensors nodes close to the sink node run out of energy much faster than nodes in other parts of the monitored area. The nodes near the sink are more likely to use up their energy because they have to forward all the traffic generated by the nodes farther away to the sink. The uneven energy consumption results in network partitioning and limit the network lifetime. To this end, we propose an on-demand and multipath routing algorithm that utilizes the behavior of real termites on hill building termed Termite-hill which support sink mobility. The main objective of our proposed algorithm is to efficiently relay all the traffic destined for the sink, and also balance the network energy. The performance of our proposed algorithm was tested on static, dynamic and mobile sink scenarios with varying speed, and compared with other state-of-the-art routing algorithms in WSN. The results of our extensive experiments on Routing Modeling Application Simulation Environment (RMASE) demonstrated that our proposed routing algorithm was able to balance the network traffic load, and prolong the network lifetime.
Design and development of power-aware, scalable and performance-efficient routing protocols for wireless sensor networks (WSNs) is an active area of research. In this paper, we show that insect-colonies-based-intelligence – commonly referred to as Swarm Intelligence (SI) – serves as an ideal model for developing routing protocols for WSNs because they consist of minimalist, autonomous individuals that through local interactions self-organize to produce system-level behaviors that show life-long adaptivity to changes and perturbations in an external environment. In this paper, we propose bee-inspired BeeSensor protocol that is energy-aware, scalable and efficient. The important contribution of this work is a three phase protocol design strategy: (1) we first take inspiration from biological systems to develop a distributed, decentralized and simple routing protocol, (2) we formally model important performance metrics of our protocol to get an analytic insight into its behavior, and (3) we improve our protocol on the basis of our analysis in phase 2. We then evaluate its performance in a sensor network simulator. The results of our experiments demonstrate the utility of this three phase protocol engineering, which helped BeeSensor in achieving the best performance with the least communication and processing costs – two main sources of energy consumption in sensor networks – as compared to other SI based WSN routing protocols.
SUMMARY In this paper, we describe AntHocNet, an algorithm for routing in mobile ad hoc networks. It is a hybrid algorithm, which combines reactive path setup with proactive path probing, maintenance and improvement. The algorithm is based on the nature-inspired ant colony optimisation framework. Paths are learned by guided Monte Carlo sampling using ant-like agents communicating in a stigmergic way. In an extensive set of simulation experiments, we compare AntHocNet with AODV, a reference algorithm in the field. We show that our algorithm can outperform AODV on different evaluation criteria. AntHocNet's performance advantage is visible over a broad range of possible network scenarios, and increases for larger, sparser and more mobile networks. Copyright # 2005 AEIT.
Energy efficient routing protocol for Wireless Sensor Networks (WSNs) is one
of the most challenging task for researcher. Hierarchical routing protocols
have been proved more energy efficient routing protocols, as compare to flat
and location based routing protocols. Heterogeneity of nodes with respect to
their energy level, has also added extra lifespan for sensor network. In this
paper, we propose a Centralized Energy Efficient Clustering (CEEC) routing
protocol. We design the CEEC for three level heterogeneous network. CEEC can
also be implemented in multi-level heterogeneity of networks. For initial
practical, we design and analyze CEEC for three level advance heterogeneous
network. In CEEC, whole network area is divided into three equal regions, in
which nodes with same energy are spread in same region.
Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
Data aggregation is important in energy constraint wireless sensor networks which exploits correlated sensing data and aggregates at the intermediate nodes to reduce the number of messages exchanged network. This paper considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. The ant colony system provides a natural and intrinsic way of exploring search space in determining data aggregation. Moreover, we propose an ant colony algorithm for data aggregation in wireless sensor networks. Every ant will explore all possible paths from the source node to the sink node. The data aggregation tree is constructed by the accumulated pheromone. Simulations have shown that our algorithm can reduce significant energy costs.
The clustering Algorithm is a kind of key technique used to reduce energy consumption. It can increase the scalability and lifetime of the network. Energy-efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks. We propose and evaluate a new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks, which is called DEEC. In DEEC, the cluster-heads are elected by a probability based on the ratio between residual energy of each node and the average energy of the network. The epochs of being cluster-heads for nodes are different according to their initial and residual energy. The nodes with high initial and residual energy will have more chances to be the cluster-heads than the nodes with low energy. Finally, the simulation results show that DEEC achieves longer lifetime and more effective messages than current important clustering protocols in heterogeneous environments.
In recent years, there has been a growing interest in wireless sensor networks. One of the major issues in wireless sensor network is developing an energy-efficient clustering protocol. Hierarchical clustering algorithms are very important in increasing the network’s life time. Each clustering algorithm is composed of two phases, the setup phase and steady state phase. The hot point in these algorithms is the cluster head selection. In this paper, we study the impact of heterogeneity of nodes in terms of their energy in wireless sensor networks that are hierarchically clustered. We assume that a percentage of the population of sensor nodes is equipped with the additional energy resources. We also assume that the sensor nodes are randomly distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. Homogeneous clustering protocols assume that all the sensor nodes are equipped with the same amount of energy and as a result, they cannot take the advantage of the presence of node heterogeneity. Adapting this approach, we introduce an energy efficient heterogeneous clustered scheme for wireless sensor networks based on weighted election probabilities of each node to become a cluster head according to the residual energy in each node. Finally, the simulation results demonstrate that our proposed heterogeneous clustering approach is more effective in prolonging the network lifetime compared with LEACH.
In this paper, we propose a novel hierarchical clustering approach for wireless sensor networks to maintain energy depletion of the network in minimum using Artificial Bee Colony Algorithm which is a new swarm based heuristic algorithm. We present a protocol using Artificial Bee Colony Algorithm, which tries to provide optimum cluster organization in order to minimize energy consumption. In cluster based networks, the selection of cluster heads and its members is an essential process which affects energy consumption. Simulation results demonstrate that the proposed approach provides promising solutions for the wireless sensor networks.
In this paper, a family of ant colony algorithms called DAACA for data
aggregation has been presented which contains three phases: the initialization,
packet transmission and operations on pheromones. After initialization, each
node estimates the remaining energy and the amount of pheromones to compute the
probabilities used for dynamically selecting the next hop. After certain rounds
of transmissions, the pheromones adjustment is performed periodically, which
combines the advantages of both global and local pheromones adjustment for
evaporating or depositing pheromones. Four different pheromones adjustment
strategies are designed to achieve the global optimal network lifetime, namely
Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data
aggregation algorithms, DAACA shows higher superiority on average degree of
nodes, energy efficiency, prolonging the network lifetime, computation
complexity and success ratio of one hop transmission. At last we analyze the
characteristic of DAACA in the aspects of robustness, fault tolerance and
scalability.
Hexagonally sectored routing protocol for wireless sensor networks
- A J Joseph
- U Hari
A. J. Joseph and U. Hari, "Hexagonally sectored routing protocol
for wireless sensor networks," in International Journal of Engineering
Research and Technology, vol. 2, ESRSA Publications, 2013.
An idea based on honey bee swarm for numerical optimization, " tech. rep., Technical report-tr06, Erciyes university, en-gineering faculty, computer engineering department
- D Karaboga
D. Karaboga, " An idea based on honey bee swarm for numerical optimization, " tech. rep., Technical report-tr06, Erciyes university, en-gineering faculty, computer engineering department, 2005.
Energyefficient communication protocol for wireless microsensor networks
- W R Heinzelman
- A Chandrakasan
- H Balakrishnan
W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energyefficient communication protocol for wireless microsensor networks,"
in System Sciences, 2000. Proceedings of the 33rd Annual Hawaii
International Conference on, pp. 10-pp, IEEE, 2000.
Cluster based wireless sensor network routing using artificial bee colony algorithm
- D Karaboga
- S Okdem
- C Ozturk
D. Karaboga, S. Okdem, and C. Ozturk, "Cluster based wireless
sensor network routing using artificial bee colony algorithm," Wireless
Networks, vol. 18, no. 7, pp. 847-860, 2012.