Dynamic clustering approach with ACO-based mobile sink for data collection in WSNs

Enhancing the network lifetime of wireless sensor networks is an essential task. It involves sensor deployment, cluster formation, routing, and effective utilization of battery units. Clustering and routing are important techniques for adequate enhancement of the network lifetime. Since the existing clustering and routing approaches have high message overhead due to forwarding collected data to sinks or the base station, it creates premature death of sensors and hot-spot issues. The objective of this study is to design a dynamic clustering and optimal routing mechanism for data collection in order to enhance the network lifetime. A new dynamic clustering approach is proposed to prevent premature sensor death and avoid the hot spot problem. In addition, an Ant Colony Optimization (ACO) technique is adopted for effective path selection of mobile sinks. The proposed algorithm is compared with existing routing methodologies, such as LEACH, GA, and PSO. The simulation results show that the proposed cluster head selection algorithm with ACO-based MDC enhances the sensor network lifetime significantly.

     

REACA: An Efficient Protocol Architecture for Large Scale Sensor Networks

The emergence of wireless sensor networks has imposed many challenges on network design such as severe energy constraints, limited bandwidth and computing capabilities. This kind of networks necessitates network protocol architectures that are robust, energy-efficient, scalable, and easy for deployment. This paper proposes a robust energy-aware clustering architecture (REACA) for large-scale wireless sensor networks. We analyze the performance of the REACA network in terms of quality-of-service, asymptotic throughput capacity, and power consumption. In particular, we study how the throughput capacity scales with the number of nodes and the number of clusters. We show that by exploiting traffic locality, clustering can achieve performance improvement both in capacity and in power consumption over general-purpose ad hoc networks. We also explore the fundamental trade-off between throughput capacity and power consumption for single-hop and multi-hop routing schemes in cluster-based networks. The protocol architecture and performance analysis developed in this paper provide useful insights for practical design and deployment of large-scale wireless sensor network.     

Development of Energy Efficient Image/Video Sensor Networks

Image/Video Sensor Networks are emerging applications for sensor network technologies. The relatively high energy consuming image capturing process and the large size of the data collected by image/video sensors presents new challenges for the sensor network in terms of energy consumption and network capacity. We propose to address these issues through the use of a high density network deployment. A high density network allows network nodes to conserve power by reducing their transmission power and simultaneously increases the potential for spatially concurrent transmissions within the network, resulting in improved network throughput. Furthermore, with the use of additional relay nodes, we allow a communication density that differs from the sensing density. A higher communication density has the potential to further increase the spatially concurrent transmission. Moreover, this reduces the relay burden of the sensor node, thus conserving sensor energy. In this work, we show analytically how a high density network design effectively improves energy consumption and network capacity. Furthermore, we discuss the constraints placed on a high density sensor network deployment due to application latency requirements, sensor coverage requirements, connectivity requirements, and node costs.     

Optimal load balanced clustering in homogeneous wireless sensor networks

Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.     

Combinatorial Design of Key Distribution Mechanisms for Wireless Sensor Networks

Secure communications in wireless sensor networks operating under adversarial conditions require providing pairwise (symmetric) keys to sensor nodes. In large scale deployment scenarios, there is no priory knowledge of post deployment network configuration since nodes may be randomly scattered over a hostile territory. Thus, shared keys must be distributed before deployment to provide each node a key-chain. For large sensor networks it is infeasible to store a unique key for all other nodes in the key-chain of a sensor node. Consequently, for secure communication either two nodes have a key in common in their key-chains and they have a wireless link between them, or there is a path, called key-path, among these two nodes where each pair of neighboring nodes on this path have a key in common. Length of the key-path is the key factor for efficiency of the design. This paper presents novel deterministic and hybrid approaches based on Combinatorial Design for deciding how many and which keys to assign to each key-chain before the sensor network deployment. In particular, Balanced Incomplete Block Designs (BIBD) and Generalized Quadrangles (GQ) are mapped to obtain efficient key distribution schemes. Performance and security properties of the proposed schemes are studied both analytically and computationally. Comparison to related work shows that the combinatorial approach produces better connectivity with smaller key-chain sizes     

A Tabu Search Algorithm for Cluster Building in Wireless Sensor Networks

The main challenge in wireless sensor network deployment pertains to optimizing energy consumption when collecting data from sensor nodes. This paper proposes a new centralized clustering method for a data collection mechanism in wireless sensor networks, which is based on network energy maps and Quality-of-Service (QoS) requirements. The clustering problem is modeled as a hypergraph partitioning and its resolution is based on a tabu search heuristic. Our approach defines moves using largest size cliques in a feasibility cluster graph. Compared to other methods (CPLEX-based method, distributed method, simulated annealing-based method), the results show that our tabu search-based approach returns high-quality solutions in terms of cluster cost and execution time. As a result, this approach is suitable for handling network extensibility in a satisfactory manner.     

UEEDA: Uniform and energy‐efficient deployment algorithm for wireless sensor networks

Efficient and accurate sensor deployment is a critical requirement for the development of wireless sensor networks. Recently, distributed energy‐efficient self‐deployment algorithms, such as the intelligent deployment and clustering algorithm (IDCA) and the distributed self‐spreading algorithm (DSSA), have been proposed to offer almost uniform distribution for sensor deployment by employing a synergistic combination of cluster structuring and a peer‐to‐peer deployment scheme. However, both DSSA and IDCA suffer from unnecessary movements that have arisen from an inappropriate design in partial force. To improve the performance of self‐deployment algorithms, a uniform and energy‐efficient deployment algorithm (UEEDA) is proposed in this paper. Simulation results demonstrate that the proposed UEEDA outperforms both DSSA and IDCA in terms of uniformity and algorithm convergence speed. Copyright © 2007 John Wiley & Sons, Ltd.     

Design of scalable and efficient multi-radio wireless networks

A proper design of Wireless Mesh Networks (WMNs) is a fundamental task that should be addressed carefully to allow the deployment of scalable and efficient networks. Specifically, choosing strategic locations to optimally place gateways prior to network deployment can alleviate a number of performance/scalability related problems. In this paper, we first, propose a novel clustering based gateway placement algorithm (CBGPA) to effectively select the locations of gateways. Existing solutions for optimal gateway placement using clustering approaches are tree-based and therefore are inherently less reliable since a tree topology uses a smaller number of links. Independently from the tree structure, CBGPA strategically places the gateways to serve as many routers as possible that are within a bounded number of hops. Next, we devise a new multi-objective optimization approach that models WMN topologies from scratch. The three objectives of deployment cost, network throughput and average congestion of gateways are simultaneously optimized using a nature inspired meta-heuristic algorithm coupled with CBGPA. This provides the network operator with a set of bounded-delay trade-off solutions. Comparative simulation studies with different key parameter settings are conducted to show the effectiveness of CBGPA and to evaluate the performance of the proposed model.     

Towards efficient dynamic surface gateway deployment for underwater network

In underwater acoustic sensor network, deploying multiple surface-level radio capable gateways is an efficient way to alleviate the burdens of high propagation delay and high error probability during transmission. However, the locations of gateways need to be carefully selected to maximize the benefit in a cost-effective way. In this paper, we present our formulation of the surface gateway deployment problem as an integer linear programming (ILP) and we solve the problem with heuristic approaches to provide a realtime solution for large scale deployment problems. By applying the proposed heuristic algorithms to a variety of deployment scenarios, we show that they are nearly optimal for practical cases, which opens the door for dynamic deployment. Therefore, we extend our solution to a dynamic case and propose a modified framework that integrates Aqua-sim, a NS2-based underwater wireless sensor network simulator. Our simulation result shows the benefits of dynamic gateway redeployment over static deployment.     

A Novel and Secure Secret Sharing Algorithm Applied to Insecure Networks

Being independent of any fixed equipment, Ad Hoc wireless sensor networks, a kind of acentric and self-organized wireless network, possesses some features such as easiness of deployment, strong invulnerability and flexibility of networking, which leads to a promising application prospect in terms of UAV military and civilian use. This paper proposes a new slot adaptive 4D network clustering algorithm based on UAV autonomous formation and reconfiguration to solve the problem of UAV Ad Hoc network such as networking confusion, poor network reconstruction performance, huge energy consumption and other issues. The algorithm can optimize the topology of UAVs network. We build the network topology and generate clustering network by the slot adaptive 4D network clustering algorithm in Matlab. According to the real combat of UAV, four states are simulated and analyzed. The simulation results validate the feasibility of the slot adaptive 4D network clustering algorithm. The clustering structure generated by the slot adaptive 4D network clustering algorithm is robust and the algorithm is suitable for the UAV group operation.

     

基于CPK的安全分簇方案

传感器网络中,基于分簇的拓扑结构可以均衡网络中的能量消耗、延长网络的寿命、提高管理效能、增强可量测性,适合于大规模部署应用。CPK(Combined Public Key)算法密钥后台脱线产生、不需要第三方的验证、存储要求低、通信开销小。通过将CPK算法引入分簇过程,安全技术前移,实现安全分簇可以有效增强网络安全性并且减少不必要的能量消耗,对于传感器网络的实际应用有着重要意义。     

Secure coverage-preserving node scheduling scheme using energy prediction for wireless sensor networks

With the fast development of the micro-electro-mechanical systems(MEMS),wireless sensor networks(WSNs)have been extensively studied.Most of the studies focus on saving energy consumption because of restricted energy supply in WSNs.Cluster-based node scheduling scheme is commonly considered as one of the most energy-efficient approaches.However,it is not always so efficient especially when there exist hot spot and network attacks in WSNs.In this article,a secure coverage-preserved node scheduling scheme for WSNs based on energy prediction is proposed in an uneven deployment environment.The scheme is comprised of an uneven clustering algorithm based on arithmetic progression,a cover set partition algorithm based on trust and a node scheduling algorithm based on energy prediction.Simulation results show that network lifetime of the scheme is 350 rounds longer than that of other scheduling algorithms.Furthermore,the scheme can keep a high network coverage ratio during the network lifetime and achieve the designed objective which makes energy dissipation of most nodes in WSNs balanced.     

Relay Node Deployment Strategies in Heterogeneous Wireless Sensor Networks

In a heterogeneous wireless sensor network (WSN), relay nodes (RNs) are adopted to relay data packets from sensor nodes (SNs) to the base station (BS). The deployment of the RNs can have a significant impact on connectivity and lifetime of a WSN system. This paper studies the effects of random deployment strategies. We first discuss the biased energy consumption rate problem associated with uniform random deployment. This problem leads to insufficient energy utilization and shortened network lifetime. To overcome this problem, we propose two new random deployment strategies, namely, the lifetime-oriented deployment and hybrid deployment. The former solely aims at balancing the energy consumption rates of RNs across the network, thus extending the system lifetime. However, this deployment scheme may not provide sufficient connectivity to SNs when the given number of RNs is relatively small. The latter reconciles the concerns of connectivity and lifetime extension. Both single-hop and multihop communication models are considered in this paper. With a combination of theoretical analysis and simulated evaluation, this study explores the trade-off between connectivity and lifetime extension in the problem of RN deployment. It also provides a guideline for efficient deployment of RNs in a large-scale heterogeneous WSN.     

An energy‐efficient node‐scheduling scheme for wireless sensor networks based on minimum dominating sets

One of the major requirements for new wireless sensor networks is to extend the lifetime of the network. Node‐scheduling techniques have been used extensively for this purpose. Some existing approaches rely mainly on location information through global positioning system (GPS) devices for designing efficient scheduling strategies. However, integration of GPS devices with sensor nodes is expensive and increases the cost of deployment dramatically. In this paper we present a location‐free solution for node scheduling. Our scheme is based on a graph theoretical approach using minimum dominating sets. We propose a heuristic to extract a collection of dominating sets. Each set consists of a group of working nodes which ensures a high level of network coverage. At each round, one set is responsible for covering the sensor field while the nodes in other sets are in sleep mode. We evaluate our solution through simulations and discuss our future research directions. Copyright © 2008 John Wiley & Sons, Ltd.     

General Network Lifetime and Cost Models for Evaluating Sensor Network Deployment Strategies

In multihop wireless sensor networks that are often characterized by many-to-one (convergecast) traffic patterns, problems related to energy imbalance among sensors often appear. Sensors closer to a data sink are usually required to forward a large amount of traffic for sensors farther from the data sink. Therefore, these sensors tend to die early, leaving areas of the network completely unmonitored and reducing the functional network lifetime. In our study, we explore possible sensor network deployment strategies that maximize sensor network lifetime by mitigating the problem of the hot spot around the data sink. Strategies such as variable-range transmission power control with optimal traffic distribution, mobile-data-sink deployment, multiple-data-sink deployment, nonuniform initial energy assignment, and intelligent sensor/relay deployment are investigated. We suggest a general model to analyze and evaluate these strategies. In this model, we not only discover how to maximize the network lifetime given certain network constraints but also consider the factor of extra costs involved in more complex deployment strategies. This paper presents a comprehensive analysis on the maximum achievable sensor network lifetime for different deployment strategies, and it also provides practical cost-efficient sensor network deployment guidelines.     

3D node deployment strategies prediction in wireless sensors network

ABSTRACT

3D Deployment plays a fundamental role in setting up efficient wireless sensor networks (WSNs) and IoT networks. In general, WSN are widely utilised in a set of real contexts such as monitoring smart houses and forest fires with parachuted sensors. This study focus on planned 3D deployment in which the sensor nodes must be accurately positioned at predetermined locations to optimise one or more design objectives under some given constraints. The purpose of planned deployment is to identify the type, the number, and the locations of nodes to optimise the coverage, the connectivity and the network lifetime. There have been a large number of studies that proposed algorithms resolving the premeditated problem of 3D deployment. The objective of this study is twofold. The first one is to present the complexity of 3D deployment and then detail the types of sensors, objectives, applications and recent research that concerns the strategy used to solve this problem. The second one is to present a comparative survey between the recent optimisation strategies solving the problem of 3D deployment in WSN. Based on our extensive review, we discuss the strengths and limitations of each proposed solution and compare them in terms of WSN design factors.     

无线传感器网络可靠性建模方法

无线传感器网络部署设计的基础就是可靠性,因此,对无线传感器网络可靠性研究已经成为全世界在这个领域中研究的一个重点内容。无线传感器网络可靠性研究的一个有效方法就是建立数学模型。为了能够更好地进行无线传感器网络可靠性的建模,本文先介绍了无线传感器网络结构和节点模型,然后主要对无线传感器网络可靠性建模方法进行分析,提出了K-可靠性模型,并对无线传感器网络系统设计时参考依据进行简单说明。经过实践证明,无线传感器网络的可靠性完全能够通过K-可靠性模型进行测量。     

Efficient Sensing Topology Management for Spatial Monitoring with Sensor Networks

We focus on exploiting redundancy for sensor networks in the context of spatial interpolation. The network acts as a distributed sampling system, where sensors periodically sample a physical phenomenon of interest, e.g. temperature. Samples are then used to construct a continuous spatial estimate of the phenomenon over time through interpolation. In this regime, the notion of sensing range typically utilized to characterize redundancy in event detection applications is meaningless and sensor selection schemes based on it become unsuitable. Instead, this paper presents pragmatic approaches for exploiting redundancy in such applications. Their underlying characteristic is that no a-priori assumptions need to be made on the statistical properties of the physical phenomenon. These are instead learned by the network after deployment. Our approaches are evaluated through real as well as synthetic sensor network data showing that significant reductions in the number of active sensors are indeed possible.     

Static wireless sensor networks deployment using an improved binary PSO

A major issue in designing wireless sensor networks is the deployment problem. Indeed, many performances of the sensor network, such as coverage, are determined by the number and locations of deployed sensors. This paper reviews existing deterministic deployment strategies and devises a modified binary particle swarm optimization, which adopts a new position updating procedure for a faster convergence and exploits the abandonment concept to avoid some drawbacks such as premature convergence. The devised approach combines, in a meaningful way, the characteristics of the binary particle swarm optimization with the wireless sensor networks deployment requirements in order to devise a lightweight and efficient sensor placement algorithm. The effectiveness and efficiency of the proposed approach are evaluated through extensive simulations. The obtained results show that the proposed algorithm outperforms the state‐of‐the‐art approaches, especially in the case of preferential coverage. Copyright © 2015 John Wiley & Sons, Ltd.     

Hybrid Grasshopper and Improved Cat Swarm Optimization Algorithm-based clustering for guaranteeing energy stability and network lifetime in WSN

Wireless sensor networks (WSNs) plays an indispensable role in the human life by supporting a diversified number of applications that includes military, environment monitoring, manufacturing, education, agriculture, etc. However, the sensor node batteries cannot be replaced under its deployment in an unattended or remote area due to their wireless existence. Cluster-based routing is significant in handling the issue of energy stability and network lifetime. The meta-heuristic algorithms-based cluster head (CH) selection is determined to be highly promising for attaining the objective of CH selection that results in acquiring an optimal network performance. In this paper, a Hybrid Grasshopper and Improved Cat Swarm Optimization Algorithm (HGICSOA)-based clustering scheme is proposed for attaining potential CH selection and guarantee significant sink mobility-based data transmission. The capability of GHOA that controls the rate of exploitation and exploration degree is utilized for CH selection. It specifically adopted OBL-based GHOA for optimal CH selection based on the objective function, which is formulated using node density, residual energy, and distance between sensor node and sink. It incorporated new CSOA for mobility-based data transmission for increasing population diversity. It also utilized the benefits of ICSOA with a predominant local search strategy for achieving better sink mobility-based data transmission. Simulation and statistical results confirmed that the proposed HGICSOA is better in attaining maximum energy stability by 17.21% and improved network lifetime by 23.82%, compared to the benchmarked schemes used for investigation. Moreover, the prevention rate of worst sensor nodes selected as CH is improved by 21.38%, better than baseline approaches.